diff --git a/.buildinfo b/.buildinfo index 4a3f81307..29194b900 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d65f1dafa7b72460ef2a1d4b173d4c7d +config: 826481e42566ded7f032c01892b9785c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.doctrees/arguments.doctree b/.doctrees/arguments.doctree index eb5c00996..512ccc6bd 100644 Binary files a/.doctrees/arguments.doctree and b/.doctrees/arguments.doctree differ diff --git a/.doctrees/contributing.doctree b/.doctrees/contributing.doctree index 07ad0f21b..ffe95ef2f 100644 Binary files a/.doctrees/contributing.doctree and b/.doctrees/contributing.doctree differ diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index c5a69987a..2f3aba241 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/flying.doctree b/.doctrees/flying.doctree index dfbb3d260..3e324cea7 100644 Binary files a/.doctrees/flying.doctree and b/.doctrees/flying.doctree differ diff --git a/.doctrees/gcp.doctree b/.doctrees/gcp.doctree index effbaa7b0..16c590ac8 100644 Binary files a/.doctrees/gcp.doctree and b/.doctrees/gcp.doctree differ diff --git a/.doctrees/geo.doctree b/.doctrees/geo.doctree index fea014e76..cf351b815 100644 Binary files a/.doctrees/geo.doctree and b/.doctrees/geo.doctree differ diff --git a/.doctrees/index.doctree b/.doctrees/index.doctree index 01ee10de8..e63681550 100644 Binary files a/.doctrees/index.doctree and b/.doctrees/index.doctree differ diff --git a/.doctrees/installation.doctree b/.doctrees/installation.doctree index 588b5c1a3..18863d3d4 100644 Binary files a/.doctrees/installation.doctree and b/.doctrees/installation.doctree differ diff --git a/.doctrees/large.doctree b/.doctrees/large.doctree index 8f421f2fe..8d0838adc 100644 Binary files a/.doctrees/large.doctree and b/.doctrees/large.doctree differ diff --git a/.doctrees/map-accuracy.doctree b/.doctrees/map-accuracy.doctree index 626f85757..a09695ad4 100644 Binary files a/.doctrees/map-accuracy.doctree and b/.doctrees/map-accuracy.doctree differ diff --git a/.doctrees/masks.doctree b/.doctrees/masks.doctree index e321c13f5..87a3a68cf 100644 Binary files a/.doctrees/masks.doctree and b/.doctrees/masks.doctree differ diff --git a/.doctrees/multispectral.doctree b/.doctrees/multispectral.doctree index 6ecb94d4f..d7822cc16 100644 Binary files a/.doctrees/multispectral.doctree and b/.doctrees/multispectral.doctree differ diff --git a/.doctrees/outputs.doctree b/.doctrees/outputs.doctree index 99598a05a..6ab072478 100644 Binary files a/.doctrees/outputs.doctree and b/.doctrees/outputs.doctree differ diff --git a/.doctrees/requesting-features.doctree b/.doctrees/requesting-features.doctree index 8f8c033ea..abab9eda0 100644 Binary files a/.doctrees/requesting-features.doctree and b/.doctrees/requesting-features.doctree differ diff --git a/.doctrees/resources.doctree b/.doctrees/resources.doctree index 8f17d4bde..93c367336 100644 Binary files a/.doctrees/resources.doctree and b/.doctrees/resources.doctree differ diff --git a/.doctrees/tutorials.doctree b/.doctrees/tutorials.doctree index db5cd96a2..9dc5935c9 100644 Binary files a/.doctrees/tutorials.doctree and b/.doctrees/tutorials.doctree differ diff --git a/CNAME b/CNAME deleted file mode 100644 index 96ffc860e..000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -docs.opendronemap.org diff --git a/_sources/arguments.rst.txt b/_sources/arguments.rst.txt index 84ae87529..ce49943dd 100644 --- a/_sources/arguments.rst.txt +++ b/_sources/arguments.rst.txt @@ -15,6 +15,12 @@ Arguments ``--cameras`` Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/_static/documentation_options.js b/_static/documentation_options.js index c91cd7789..f592c063b 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/ar/.buildinfo b/ar/.buildinfo index 4a3f81307..29194b900 100644 --- a/ar/.buildinfo +++ b/ar/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d65f1dafa7b72460ef2a1d4b173d4c7d +config: 826481e42566ded7f032c01892b9785c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/ar/.doctrees/arguments.doctree b/ar/.doctrees/arguments.doctree index 238b4af60..06c5131e4 100644 Binary files a/ar/.doctrees/arguments.doctree and b/ar/.doctrees/arguments.doctree differ diff --git a/ar/.doctrees/contributing.doctree b/ar/.doctrees/contributing.doctree index 91354a941..81ddb9cc7 100644 Binary files a/ar/.doctrees/contributing.doctree and b/ar/.doctrees/contributing.doctree differ diff --git a/ar/.doctrees/environment.pickle b/ar/.doctrees/environment.pickle index e7be9c5de..ec43f4af1 100644 Binary files a/ar/.doctrees/environment.pickle and b/ar/.doctrees/environment.pickle differ diff --git a/ar/.doctrees/flying.doctree b/ar/.doctrees/flying.doctree index c719a8120..5fab9a518 100644 Binary files a/ar/.doctrees/flying.doctree and b/ar/.doctrees/flying.doctree differ diff --git a/ar/.doctrees/gcp.doctree b/ar/.doctrees/gcp.doctree index 193896d0e..959944371 100644 Binary files a/ar/.doctrees/gcp.doctree and b/ar/.doctrees/gcp.doctree differ diff --git a/ar/.doctrees/geo.doctree b/ar/.doctrees/geo.doctree index df86198fd..dcb6e8eaf 100644 Binary files a/ar/.doctrees/geo.doctree and b/ar/.doctrees/geo.doctree differ diff --git a/ar/.doctrees/index.doctree b/ar/.doctrees/index.doctree index e5803f678..3d01add8b 100644 Binary files a/ar/.doctrees/index.doctree and b/ar/.doctrees/index.doctree differ diff --git a/ar/.doctrees/installation.doctree b/ar/.doctrees/installation.doctree index 1adb065bf..e38916557 100644 Binary files a/ar/.doctrees/installation.doctree and b/ar/.doctrees/installation.doctree differ diff --git a/ar/.doctrees/large.doctree b/ar/.doctrees/large.doctree index d991b2b50..b448e305d 100644 Binary files a/ar/.doctrees/large.doctree and b/ar/.doctrees/large.doctree differ diff --git a/ar/.doctrees/map-accuracy.doctree b/ar/.doctrees/map-accuracy.doctree index 760b84e71..208a51b93 100644 Binary files a/ar/.doctrees/map-accuracy.doctree and b/ar/.doctrees/map-accuracy.doctree differ diff --git a/ar/.doctrees/masks.doctree b/ar/.doctrees/masks.doctree index 321fe5a05..6e3acd175 100644 Binary files a/ar/.doctrees/masks.doctree and b/ar/.doctrees/masks.doctree differ diff --git a/ar/.doctrees/multispectral.doctree b/ar/.doctrees/multispectral.doctree index fb52c2179..6908dd0ca 100644 Binary files a/ar/.doctrees/multispectral.doctree and b/ar/.doctrees/multispectral.doctree differ diff --git a/ar/.doctrees/outputs.doctree b/ar/.doctrees/outputs.doctree index 4b5d22bf2..32a1fba0a 100644 Binary files a/ar/.doctrees/outputs.doctree and b/ar/.doctrees/outputs.doctree differ diff --git a/ar/.doctrees/requesting-features.doctree b/ar/.doctrees/requesting-features.doctree index 810d3f441..0b5f2514f 100644 Binary files a/ar/.doctrees/requesting-features.doctree and b/ar/.doctrees/requesting-features.doctree differ diff --git a/ar/.doctrees/resources.doctree b/ar/.doctrees/resources.doctree index a64e78daf..048356976 100644 Binary files a/ar/.doctrees/resources.doctree and b/ar/.doctrees/resources.doctree differ diff --git a/ar/.doctrees/tutorials.doctree b/ar/.doctrees/tutorials.doctree index 99705e27f..4f8fc266c 100644 Binary files a/ar/.doctrees/tutorials.doctree and b/ar/.doctrees/tutorials.doctree differ diff --git a/ar/_sources/arguments.rst.txt b/ar/_sources/arguments.rst.txt index 84ae87529..ce49943dd 100644 --- a/ar/_sources/arguments.rst.txt +++ b/ar/_sources/arguments.rst.txt @@ -15,6 +15,12 @@ Arguments ``--cameras`` Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/ar/_static/documentation_options.js b/ar/_static/documentation_options.js index d534de0e6..b0e057efc 100644 --- a/ar/_static/documentation_options.js +++ b/ar/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'ar', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/ar/arguments.html b/ar/arguments.html index 8fa2b4521..2a89bff63 100644 --- a/ar/arguments.html +++ b/ar/arguments.html @@ -4,10 +4,11 @@ - + + - الخيارات والإعلام — OpenDroneMap 2.4.7 documentation + الخيارات والإعلام — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
- 2.4.7 + 2.5.5
@@ -292,9 +293,9 @@
-
+

الخيارات والإعلام

-
+

Arguments

--build-overviews

Build orthophoto overviews for faster display in programs such as QGIS. Default: False

@@ -303,6 +304,10 @@
--cameras <json>

Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: ``

+
--cog

Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

+
+
--copy-to <path>

Copy output results to this folder after processing.

+
--crop <positive float>

Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: 3

--debug

Print debug messages. Default: False

@@ -357,7 +362,7 @@
--min-num-features <integer>

Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

-
name <project name>

Name of dataset (i.e subfolder name within project folder). Default: code

+
name <dataset name>

Name of dataset (i.e subfolder name within project folder). Default: code

--optimize-disk-space

Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don't have sufficient disk space available. Default: False

@@ -365,6 +370,8 @@
--orthophoto-cutline

Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

+
--orthophoto-kmz

Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

+
--orthophoto-no-tiled

Set this parameter if you want a striped GeoTIFF. Default: False

--orthophoto-png

Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

@@ -393,7 +400,7 @@
--project-path <path>

Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder.

-
--radiometric-calibration none | camera | camera+sun

Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

+
--radiometric-calibration none | camera | camera+sun

Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

--rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

Rerun this stage only and stop. . Default: ``

@@ -427,6 +434,8 @@
--texturing-data-term gmi | area

When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

+
--texturing-keep-unseen-faces

Keep faces in the mesh that are not seen in any camera. Default: False

+
--texturing-outlier-removal-type none | gauss_clamping | gauss_damping

Type of photometric outlier removal method. . Default: gauss_clamping

--texturing-skip-global-seam-leveling

Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

@@ -453,8 +462,8 @@

Learn to edit and help improve this page!

-
-
+ +
diff --git a/ar/contributing.html b/ar/contributing.html index 7dc1e5e61..fb52d4aa4 100644 --- a/ar/contributing.html +++ b/ar/contributing.html @@ -4,10 +4,11 @@ - + + - .كيفية المشاركة — OpenDroneMap 2.4.7 documentation + .كيفية المشاركة — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
- 2.4.7 + 2.5.5
@@ -291,14 +292,14 @@
-
+

.كيفية المشاركة

OpenDroneMap يحتمل مشا ركةالإجتماعية.قد تشارك بطرق كثيرة وإلم تكن مرتب .

-
+

جو الإجتماعي

If you are looking to get involved, are stuck on a problem, or want to reach out, the forum is a great place to start. You may find your questions already answered or else you can find other useful tips and resources. You can also contribute your open access datasets for others to explore. It is a good place go before submitting bug reports or getting in touch with developers before writing a new feature.

-
-
+
+

البق للتقرير

يتم تتبع الأخطاء على أنها مشكلات Github. يرجى إنشاء مشكلة في المستودع ووضع علامة عليها علامة خطأ.

شرح المشكلة وقم بتضمين تفاصيل إضافية لمساعدة المشرفين على إعادة إنتاج المشكلة:

@@ -318,7 +319,7 @@

  • ** ما اسم وإصدار نظام التشغيل الذي تستخدمه؟ **

  • Are you running ODM in a virtual machine or Docker? If so, which VM software are you using and which operating systems and versions are used for the host and the guest?

    • -
    +

    نموذج لتقديم تقارير الأخطاء

    [Short description of problem here]
 
@@ -350,9 +351,9 @@
 * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
    -
    -
    -
    + + +

    سحب الطلبات

    • قم بتضمين لقطات شاشة وصور GIF متحركة في طلب السحب كلما أمكن ذلك.

      • @@ -376,8 +377,8 @@

    Learn to edit and help improve this page!

    -
    -
    + +
    diff --git a/ar/flying.html b/ar/flying.html index abfb00b1d..451be2d40 100644 --- a/ar/flying.html +++ b/ar/flying.html @@ -4,10 +4,11 @@ - + + - نصائح طيران — OpenDroneMap 2.4.7 documentation + نصائح طيران — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,7 +293,7 @@
    -
    +

    نصائح طيران

    فريق OpenStreetMap الإنساني   لديها إرشادات حول` الطيران لرسم خرائط الطائرات بدون طيار <https://uav-guidelines.openaerialmap.org/>`_:

    +
    diff --git a/ar/gcp.html b/ar/gcp.html index 5e2c632eb..6861d0f24 100644 --- a/ar/gcp.html +++ b/ar/gcp.html @@ -4,10 +4,11 @@ - + + - نقاط التحكم الأرضية — OpenDroneMap 2.4.7 documentation + نقاط التحكم الأرضية — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,19 +293,19 @@
    -
    +

    نقاط التحكم الأرضية

    نقاط التحكم الأرضية مفيدة لتصحيح التشوهات في البيانات والإشارة إلى البيانات لمعرفة أنظمة الإحداثيات.

    A Ground Control Point (GCP) is a position measurement made on the ground, typically using a high precision GPS. (Toffanin 2019)

    Ground control points can be set existing structures like pavement corners, lines on a parking lot or contrasting color floor tiles, otherwise can be set using targets placed on the ground.

    Targets can be purchased or build with an ample variety of materials ranging from bucket lids to floor tiles.

    - -
    +
    +

    GCP file format

    تنسيق ملف GCP بسيط

    @@ -333,8 +334,8 @@

    تعد الزوايا الحادة اختيارات جيدة لـ GCP. يجب عليك أيضًا وضع / العثور على نقاط المراقبة الحرجة بشكل متساوٍ حول منطقة المسح.

    يجب إنشاء ملف `` gcp_list.txt '' في قاعدة مجلد المشروع.

    للحصول على نتائج جيدة ، يجب أن يحتوي ملفك على 15 سطرًا على الأقل بعد الرأس (5 نقاط مع 3 صور لكل نقطة).

    -
    -
    + +

    واجهات المستخدم

    يمكنك استخدام واحدة من واجهات المستخدم لإنشاء ملفات GCP:

    @@ -343,7 +344,7 @@

  • GCP Editor Pro

    • -
    +

    POSM GCPi

    يتم تحميل POSM GCPi بشكل افتراضي على WebODM. يتوفر مثال في WebODM Demo. استخدام هذا مع قيم XYZ المعروفة للتحكم الأرضي ، يمكن للمرء القيام بما يلي:

    إنشاء قائمة GCP تتضمن فقط اسم gcp (هذا هو الملصق الذي سيظهر في واجهة GCP) و x و y و z ، مع رأس بسلسلة proj4 من GCP (تأكد من وجودها في إحداثيات مستوية النظام ، مثل UTM. يجب أن يبدو شيء مثل هذا:

    @@ -356,8 +357,8 @@

    ثم يمكن للمرء تحميل قائمة GCP هذه في الواجهة ، وتحميل الصور ، ووضع كل من GCP في الصورة.

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    GCP Editor Pro

    يجب تثبيت هذا التطبيق بشكل منفصل أو يمكن تحميله كبرنامج مساعد WebODM من https://github.com/uav4geo/GCPEditorPro

    أنشئ ملف CSV يتضمن اسم gcp والشمال والشرق والارتفاع.

    @@ -369,14 +370,14 @@

    م قم باستيراد ملف CSV من الشاشة الرئيسية واكتب +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs` in the EPSG/PROJ صندوق.

    سيتم عرض الشاشة التالية خريطة من أين تختار GCP وضع علامة على الصور ذات الصلة واستيرادها.

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    References

    Toffanin, Piero. Open Drone Map: The Missing Guide. MasseranoLabs LLC, 2019.

    Learn to edit and help improve this page!

    -
    - + + diff --git a/ar/genindex.html b/ar/genindex.html index da1a5c791..9c3244276 100644 --- a/ar/genindex.html +++ b/ar/genindex.html @@ -7,7 +7,7 @@ - Index — OpenDroneMap 2.4.7 documentation + Index — OpenDroneMap 2.5.5 documentation @@ -62,7 +62,7 @@
    - 2.4.7 + 2.5.5
    diff --git a/ar/geo.html b/ar/geo.html index 2db196b4c..76ff92bdf 100644 --- a/ar/geo.html +++ b/ar/geo.html @@ -4,10 +4,11 @@ - + + - Image Geolocation Files — OpenDroneMap 2.4.7 documentation + Image Geolocation Files — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,7 +293,7 @@
    -
    +

    Image Geolocation Files

    By default ODM will use the GPS information embedded in the images, if it is available. Sometimes images do not contain GPS information, or a user wishes to override the information with more accurate data (such as RTK).

    Starting from ODM 2.0 people can supply an image geolocation file (geo) for this purpose.

    @@ -320,7 +321,7 @@

    If you supply a file called geo.txt then ODM will automatically detect it. If it has another name you can specify using --geo <path>.

    The geo.txt file must be created in the base of your project folder.

    Learn to edit and help improve this page!

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    diff --git a/ar/index.html b/ar/index.html index 5c1d994b2..8fcf5344a 100644 --- a/ar/index.html +++ b/ar/index.html @@ -4,10 +4,11 @@ - + + - مرحبًا بك في وثائق OpenDroneMap — OpenDroneMap 2.4.7 documentation + مرحبًا بك في وثائق OpenDroneMap — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
    - 2.4.7 + 2.5.5
    @@ -291,10 +292,10 @@
    -
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    OpenDroneMap Logo -
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    مرحبًا بك في وثائق OpenDroneMap


    @@ -314,9 +315,9 @@


    -
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    image of seedling -
    +
    -
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    image of pencil -
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    -
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    diff --git a/ar/installation.html b/ar/installation.html index dfa0f3b01..5e248b458 100644 --- a/ar/installation.html +++ b/ar/installation.html @@ -4,10 +4,11 @@ - + + - التثبيت والبدء — OpenDroneMap 2.4.7 documentation + التثبيت والبدء — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,50 +293,50 @@
    -
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    التثبيت والبدء

    تم اقتباس هذا القسم وتعديله بإذن من OpenDroneMap: الدليل المفقود, by Piero Toffanin.

    حتى وقت قريب كان OpenDroneMap هو المصطلح المستخدم للإشارة إلى تطبيق سطر أوامر واحد (ما يعرف الآن بمشروع ODM). مع التطور الأخير لواجهة الويب وواجهة برمجة التطبيقات والأدوات الأخرى ، أصبحت OpenDroneMap نظامًا إيكولوجيًا من التطبيقات المختلفة لمعالجة البيانات الجوية وتحليلها وعرضها. يتكون هذا النظام البيئي من عدة مكونات:

    -
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    ODM Logo -
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    • ** ODM ** هو محرك المعالجة ، والذي يمكن استخدامه من سطر الأوامر. يأخذ الصور كمدخلات وينتج مجموعة متنوعة من المخرجات ، بما في ذلك الغيوم النقطية ، والنماذج ثلاثية الأبعاد والصور التقويمية

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    NodeODM Logo -
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    • ** NodeODM ** عبارة عن واجهة برمجة تطبيقات خفيفة الوزن مبنية على أعلى ODM. يسمح للمستخدمين والتطبيقات للوصول إلى وظائف ODM عبر شبكة الكمبيوتر

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    WebODM Logo -
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    • ** WebODM ** عبارة عن واجهة مستخدم سهلة الاستخدام تتضمن عارض خرائط وعارض ثلاثي الأبعاد وتسجيلات دخول المستخدمين ونظامًا إضافيًا والعديد من الميزات الأخرى المتوقعة من منصات رسم الخرائط الحديثة بطائرات بدون طيار

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    CloudODM Logo -
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    • ** CloudODM ** هو عميل سطر أوامر صغير للتواصل مع ODM عبر واجهة برمجة تطبيقات NodeODM

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    PyODM Logo -
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    • ** PyODM ** هي Python SDK لإنشاء المهام عبر واجهة برمجة تطبيقات NodeODM. نغطيها بمزيد من التفصيل في فصل "المعالجة الآلية باستخدام Python"

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    ClusterODM Logo -
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    • ** ClusterODM ** هو موازن تحميل لربط أمثلة NodeODM المتعددة معًا

    تتوفر ODM و NodeODM و WebODM على جميع المنصات الرئيسية (Windows و macOS و Linux) عبر برنامج يسمى docker ، وهو مطلوب لتشغيل البرنامج. تقدم شركة Docker طريقة لتشغيل "الحاويات". الحاويات عبارة عن نُسخ مُعبّأة من نظام برمته وبرامجه وتبعياته. تعمل هذه الحاويات في بيئة افتراضية. تتوفر بيئة افتراضية على Linux من نظام التشغيل وهي فعالة للغاية. في نظامي التشغيل macOS و Windows ، يتم تشغيل الحاويات داخل جهاز ظاهري ، لذلك هناك القليل من النفقات العامة. لكنها لا تزال مناسبة جدًا لتشغيل البرنامج. بمجرد تثبيت المستخدمين ، لا داعي للقلق كثيرًا بشأن عامل الميناء ، لأنه يعمل (تقريبًا) بشفافية.

    بدون عامل إرساء ، لن يكون من الممكن تشغيل ODM على Windows أو macOS. لا يمكن تشغيل ODM على هذه الأنظمة الأساسية بشكل أصلي. يتم تركيز جهود التطوير المستقبلية على الاستفادة من نظام Windows الفرعي الجديد لنظام Linux (WSL) وإمكانية جعل المنفذ الأصلي لجميع التبعيات إلى macOS ، الأمر الذي سيجعل التثبيت أسهل بكثير.

    في Ubuntu Linux 18.04 ، من الممكن تشغيل جميع برامج OpenDroneMap أصلاً. ومع ذلك ، نظرًا لوجود القليل جدًا من عقوبة الأداء لتشغيل عامل الميناء على Linux ووصلة عامل الميناء مباشرة على الإعداد على هذا النظام الأساسي ، فإننا لا نوصي بذلك. في لينكس ، تفوق مزايا الحاويات بشكل كبير عقوبة الأداء الصغيرة. مع مستخدمي docker ، يحصلون أيضًا على تحديثات سهلة لخطوة واحدة للبرنامج ، وهذا أمر جيد.

    -
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    توصيات الأجهزة

    الحد الأدنى من المتطلبات لتشغيل البرنامج هي:

      @@ -350,28 +351,28 @@

    • ذاكرة رام 16 جيجا

    سيسمح ما سبق بمعالجة بضع مئات من الصور دون مشاكل كثيرة. ستسمح وحدة المعالجة المركزية (CPU) المزودة بمزيد من النوى بمعالجة أسرع ، بينما لا تؤثر بطاقة الرسومات (GPU) حاليًا على الأداء. لمعالجة المزيد من الصور ، أضف المزيد من مساحة القرص وذاكرة الوصول العشوائي خطيًا إلى عدد الصور التي تحتاج إلى معالجتها.

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    التركيب

    نوصي باستخدام الناس docker أو تشغيل ODM ، سواء كنت تستخدم نظام التشغيل Windows أو macOS أو Linux.

    -
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    Windows

    لتشغيل OpenDroneMap ، تحتاج إلى Windows 7. على الأقل. الإصدارات السابقة من Windows غير مدعومة.

    -
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    الخطوة 1. تحقق من دعم المحاكاة الافتراضية

    يتطلب Docker ميزة من وحدة المعالجة المركزية الخاصة بك تسمى المحاكاة الافتراضية ، والتي تسمح لها بتشغيل الأجهزة الافتراضية (VMs). تأكد من تمكينه! في بعض الأحيان يتم تعطيل هذا. للتحقق ، في Windows 8 أو أعلى ، يمكنك فتح Task Manager (اضغط CTRL + SHIFT + ESC) والتبديل إلى علامة التبويب علامة التبويب الأداء.

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    Image of checking virtualization in Windows 8 or higher -
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    Virtualization should be enabled

    في نظام التشغيل Windows 7 لمعرفة ما إذا تم تمكين المحاكاة الافتراضية ، يمكنك استخدام أداة الكشف عن المحاكاة الافتراضية المدعومة بالأجهزة من Microsoft® <http:// www.microsoft.com/en-us/download/details.aspx?id=592>`_ في حين أن.

    إذا تم تعطيل المحاكاة الافتراضية ، فستحتاج إلى تمكينها. للأسف ، يختلف الإجراء قليلاً بالنسبة لكل طراز كمبيوتر ، لذا فإن أفضل طريقة للقيام بذلك هي البحث في محرك بحث "كيفية تمكين vtx لـ <اكتب طراز الكمبيوتر الخاص بك هنا>". غالبًا ما يكون الأمر يتعلق بإعادة تشغيل الكمبيوتر ، والضغط على الفور على F2 أو F12 أثناء بدء التشغيل ، والتنقل في قائمة التمهيد وتغيير الإعدادات لتمكين التمثيل الافتراضي (غالبًا ما يسمى "VT-X")

    -
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    Table of different bios keys -
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    Common keys to press at computer startup to access the boot menu for various PC vendors

    -
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    الخطوة 2. تثبيت المتطلبات

    أولاً ، ستحتاج إلى تثبيت:

    بالنسبة إلى Python 3 ، تأكد من تحديد ** إضافة Python 3.x إلى PATH ** أثناء التثبيت.

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    Screenshot of Python3 installation process -
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    Don’t forget to add the Python executable to your PATH (so that you can run commands with it)

    بعد ذلك ، فقط إذا كنت تستخدم Windows 10 Home أو Windows 8 (أي إصدار) أو Windows 7 (أي إصدار) ، قم بتثبيت:

      @@ -393,8 +394,8 @@

    أرجوك** لا** تثبيت كل من برامج عامل الميناء. إنهما مختلفان وسيحدثان فوضى إذا تم تثبيتهما

    بعد تثبيت docker ، قم بتشغيله من أيقونة سطح المكتب التي تم إنشاؤها من التثبيت (** Docker Quickstart ** في حالة Docker Toolbox ، ** Docker for Windows ** for Docker for Windows). هذا مهم ، لا تتخطى هذه الخطوة. إذا كانت هناك أخطاء ، فاتبع المطالبات التي تظهر على الشاشة لإصلاحها.

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    الخطوة 3. تحقق من تخصيص الذاكرة ووحدة المعالجة المركزية

    يعمل Docker على Windows عن طريق تشغيل VM في الخلفية (فكر في VM كـ "محاكي كمبيوتر"). يحتوي هذا الجهاز الظاهري على مقدار معين من الذاكرة المخصصة ولا يمكن أن يستخدم WebODM سوى قدر الذاكرة المخصصة.

    إذا قمت بتثبيت Docker Toolbox (انظر أدناه إذا قمت بتثبيت Docker لنظام Windows بدلاً من ذلك):

    @@ -404,9 +405,9 @@

  • انقر بزر الماوس الأيمن على ** افتراضي ** VM واضغط على ** الإعدادات ... **

  • Move the Base Memory slider from the System panel and allocate 60-70% of all available memory, optionally adding 50% of the available processors from the Processor tab also

    • -
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    Screenshot of VirtualBox Settings -
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    إعدادات VirtualBox الافتراضية VM

    ثم اضغط على OK ، انقر بزر الماوس الأيمن على default **VM واضغط على **Start.

    إذا قمت بتثبيت Docker لنظام Windows بدلاً من ذلك:

    @@ -416,16 +417,16 @@

  • من اللوحة ، انقر فوق متقدم واستخدم أشرطة التمرير لتخصيص 60-70٪ من الذاكرة المتوفرة واستخدام نصف جميع وحدات المعالجة المركزية المتاحة.

  • اضغط تطبيق.

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    Step 1 Docker icon

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    Step 3 & 4 Docker settings

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    الخطوة 4. قم بتنزيل WebODM

    افتح برنامج ** Git Gui ** الذي يأتي مثبتًا مع Git. من هناك:

      @@ -434,27 +435,27 @@

    • في ** Target Directory ** انقر فوق تصفح وانتقل إلى مجلد من اختيارك (قم بإنشاء مجلد إذا لزم الأمر)

    • اضغط ** استنساخ **

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    Git Gui

    إذا نجح التنزيل ، فمن المفترض أن ترى هذه النافذة الآن:

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    Git Gui after successful download (clone)

    انتقل إلى قائمة Repository ، ثم انقر فوق Create Desktop Icon. هذا سيسمح لك بالعودة إلى هذا التطبيق بسهولة في المستقبل.

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    الخطوة 4. قم بتشغيل WebODM

    من Git Gui ، انتقل إلى قائمة Repository ، ثم انقر فوق Git Bash. من نوع سطر الأوامر:

    $ ./webodm.sh start&

    سيتم تنزيل العديد من المكونات على جهازك في هذه المرحلة ، بما في ذلك WebODM و NodeODM و ODM. بعد التنزيل ، يجب أن تستقبل الشاشة التالية:

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    إخراج وحدة التحكم بعد بدء WebODM لأول مرة

    • إذا كنت تستخدم Docker لنظام التشغيل Windows ، فافتح متصفح الويب على http: // localhost: 8000

      • @@ -468,12 +469,12 @@

    ثم اتصل بـ http://192.168.1.100:8000 (استبدال عنوان IP بالعنوان المناسب).

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    macOS

    يمكن لمعظم أجهزة الكمبيوتر الحديثة Mac (التي تعمل بعد عام 2010) التي تعمل بنظام macOS Sierra 10.12 أو أعلى تشغيل OpenDroneMap باستخدام عامل الإرساء ، طالما أن دعم الأجهزة الافتراضية (انظر أدناه).

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    الخطوة 1. تحقق من دعم المحاكاة الافتراضية

    افتح نافذة طرفية واكتب:

    $ sysctl kern.hv_support
@@ -485,8 +486,8 @@

    إذا كانت النتيجة kern.hv_support: 1 ، فهذا يعني أن جهاز Mac الخاص بك مدعوم! تابع مع الخطوة 2.

    إذا كانت النتيجة هي kern.hv_support: 0 ، فهذا يعني للأسف أن جهاز Mac قديم جدًا بحيث لا يمكنه تشغيل OpenDroneMap. :(

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    الخطوة 2. تثبيت المتطلبات

    يوجد برنامجان فقط للتثبيت:

      @@ -494,9 +495,9 @@

    1. Git: https://sourceforge.net/projects/git-osx-installer/files/

    بعد تثبيت عامل الميناء ، يجب أن تجد رمزًا يشبه الحوت في شريط المهام.

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    تطبيق Docker قيد التشغيل

    يمكنك التحقق من أن عامل الميناء يعمل بشكل صحيح من خلال فتح تطبيق Terminal وكتابة:

    $ docker run hello-world
@@ -515,8 +516,8 @@

    إذا حصلت على "bash: git: command not found" ، فحاول إعادة تشغيل تطبيق Terminal وتحقق جيدًا من أي أخطاء أثناء عملية التثبيت.

    -
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    الخطوة 3. تحقق من تخصيص الذاكرة ووحدة المعالجة المركزية

    عمل Docker على macOS عن طريق تشغيل VM في الخلفية (فكر في الأمر على أنه "محاكي كمبيوتر"). يحتوي هذا الجهاز الظاهري على مقدار معين من الذاكرة المخصصة ولا يمكن أن يستخدم WebODM سوى قدر الذاكرة المخصصة.

      @@ -525,12 +526,12 @@

    1. اضبط شريط تمرير وحدات المعالجة المركزية لاستخدام نصف جميع وحدات المعالجة المركزية المتاحة والذاكرة لاستخدام 60-70٪ من جميع الذاكرة المتاحة

    2. اضغط التقديم وإعادة التشغيل

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    إعدادات Docker المتقدمة

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    الخطوة 4. قم بتحميل وتشغيل WebODM

    من نوع Terminal:

    $ git clone https://github.com/OpenDroneMap/WebODM
@@ -539,12 +540,12 @@ $ ./webodm.sh start

    ثم افتح متصفح الويب على http://localhost:8000.

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    Linux

    يمكن تشغيل OpenDroneMap على أي توزيع Linux يدعم عامل الإرساء. وفقًا لموقع وثائق docker على الويب التوزيعات المدعومة رسميًا هي CentOS و Debian و Ubuntu و Fedora ، مع ثنائيات ثابتة متاحة للآخرين. إذا كان عليك اختيار توزيع فقط لتشغيل OpenDroneMap ، فإن Ubuntu هو الطريقة الموصى بها للذهاب.

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    الخطوة 1. تثبيت المتطلبات

    هناك أربعة برامج تحتاج للتثبيت:

      @@ -554,7 +555,7 @@ $ ./webodm.sh start

    1. Pip

    لا يمكننا تغطية عملية التثبيت لكل توزيع Linux هناك ، لذلك سنقصر التعليمات على التوزيعات التي تدعمها شركة docker رسميًا. في جميع الحالات ، يكون الأمر مجرد فتح موجه طرفية وكتابة بعض الأوامر.

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    التثبيت على Ubuntu / Debian

    أوامر للكتابة:

    $ sudo apt update
@@ -563,8 +564,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
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    التثبيت على CentOS / RHEL

    أوامر للكتابة:

    $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -572,8 +573,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
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    التثبيت على Fedora

    أوامر للكتابة:

    $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -581,16 +582,16 @@ $ sh get-docker.sh
 $ sudo dnf install git python python-pip
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    التثبيت على Arch

    أوامر للكتابة:

    $ sudo pacman -Sy docker git python python-pip
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    الخطوة 2. تحقق من المتطلبات الإضافية

    بالإضافة إلى البرامج الثلاثة أعلاه ، هناك حاجة أيضًا إلى البرنامج النصي المكون من docker. في بعض الأحيان يتم تثبيته بالفعل مع عامل الميناء ، ولكن في بعض الأحيان لم يتم تثبيته. للتحقق مما إذا كان مثبتًا ، حاول الكتابة:

    $ docker-compose --version
@@ -608,8 +609,8 @@ $ sudo dnf install git python python-pip
 
    $ sudo pip install docker-compose
 
    
 
    
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    الخطوة 3. قم بتحميل وتشغيل WebODM

    من نوع المحطة الطرفية:

    $ git clone https://github.com/OpenDroneMap/WebODM
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    ثم افتح متصفح الويب على http://localhost:8000.

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    الأوامر الأساسية واستكشاف الأخطاء وإصلاحها

    الشيء الرائع في استخدام عامل الإرساء هو أن 99٪ من المهام التي ستحتاج إلى تنفيذها أثناء استخدام WebODM يمكن إجراؤها عبر النص البرمجي .webodm.sh/. لقد واجهت بالفعل أحدهم:

    $ ./webodm.sh start
@@ -653,20 +654,20 @@ $ ./webodm.sh --help

    The community forum is a great place to ask for help if you get stuck during any of the installation steps and for general questions on using the ./webodm.sh script.

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    مرحبًا WebODM!

    بعد تشغيل .webodm.sh/ بدء وفتح WebODM في المتصفح ، سيتم الترحيب بك برسالة ترحيب وسيطلب منك إنشاء المستخدم الأول. خصص بعض الوقت للتعرف على واجهة الويب واستكشاف قوائمها المختلفة.

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    لوحة تحكم WebODM

    لاحظ أنه تحت قائمة Processing Nodes ، تم بالفعل تكوين عقدة "node-odm-1" لتتمكن من استخدامها. هذه عقدة NodeODM وتم إنشاؤها تلقائيًا بواسطة WebODM. تعمل هذه العقدة على نفس جهاز WebODM.

    إذا وصلت إلى هذا الحد ، تهانينا! حان الوقت الآن لبدء معالجة بعض البيانات.

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    يعمل على أكثر من جهاز

    اختياريًا: إذا كان لديك جهاز كمبيوتر آخر ، يمكنك تكرار عملية التثبيت (تثبيت docker و git و python وما إلى ذلك) وتشغيل عقدة NodeODM جديدة من خلال الكتابة من نافذة Terminal / Git Bash:

    docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -676,9 +677,9 @@ $ ./webodm.sh --help
 
    من WebODM يمكنك بعد ذلك الضغط على زر Add New تحت Nodes العقد. بالنسبة للحقل hostname / IP ، اكتب عنوان IP للكمبيوتر الثاني. بالنسبة للحقل المنفذ ، اكتب "3000". بالنسبة إلى الحقل الرمز المميز ، اكتب "سري". يمكنك أيضًا إضافة تسمية اختيارية للعقدة ، مثل "الكمبيوتر الثاني". ثم اضغط حفظ.
    
 
    إذا سار كل شيء على ما يرام ، يجب أن يكون لديك الآن عقدتان للمعالجة! ستتمكن من معالجة مهام متعددة بالتوازي باستخدام جهازين مختلفين.
    
 
    Learn to edit and help improve this page!
    
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    diff --git a/ar/large.html b/ar/large.html index 6e3a445d5..a139a99dd 100644 --- a/ar/large.html +++ b/ar/large.html @@ -4,10 +4,11 @@ - + + - تقسيم مجموعات البيانات الكبيرة — OpenDroneMap 2.4.7 documentation + تقسيم مجموعات البيانات الكبيرة — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    تقسيم مجموعات البيانات الكبيرة

    بدءًا من إصدار ODM `` 0.6.0 ، يمكنك تقسيم مجموعات البيانات الكبيرة جدًا إلى قطع قابلة للإدارة (تسمى النماذج الفرعية) ، وتشغيل خط الأنابيب على كل قطعة ، ثم إنتاج DEMs مدمجة ، وتقويم الصور ، وسحب النقاط. يشار إلى العملية باسم "الدمج المقسم".

    لماذا قد تستخدم خط أنابيب الدمج المقسم؟ إذا كان لديك عدد كبير جدًا من الصور في مجموعة البيانات الخاصة بك ، سيساعد الدمج المقسم على جعل المعالجة أكثر قابلية للإدارة على جهاز كبير (سيتطلب ذاكرة أقل). إذا كان لديك العديد من الأجهزة المتصلة جميعها بالشبكة نفسها ، فيمكنك أيضًا معالجة النماذج الفرعية بشكل متوازٍ ، مما يسمح بالقياس الأفقي ومعالجة آلاف الصور بسرعة أكبر.

    تعمل ميزة Split-merge في WebODM من خارج الصندوق طالما أن عقد المعالجة تدعم الدمج المقسم ، من خلال تمكين خيار `` -split '' عند إنشاء مهمة جديدة.

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    معايرة الصور

    يوصى بمعايرة الصورة (ولكن ليس مطلوبًا) لمجموعات البيانات الكبيرة لأن انتشار الخطأ بسبب تشوه الصورة يمكن أن يسبب تأثير الوعاء على النماذج. يمكن العثور على تعليمات المعايرة في Calibrate Images.

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    تأثير البولينج على السحابة النقطية أكثر من 13000 مجموعة بيانات للصور تم جمعها من قبل البنك الدولي في تنزانيا حول حوض الفيضانات المعرض للفيضانات ، دار السلام ، تنزانيا.

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    دمج التجزئة المحلية

    إن تقسيم مجموعة البيانات إلى نماذج فرعية أكثر قابلية للإدارة ومعالجة جميع النماذج الفرعية على نفس الجهاز بالتتابع أمر سهل! ما عليك سوى استخدام "--split" و "--split-interap" لتحديد متوسط ​​عدد الصور لكل نماذج فرعية والتداخل (بالأمتار) بين النماذج الفرعية على التوالي

    docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
@@ -321,14 +322,14 @@

    سيتم إنشاء 3 نماذج فرعية. تأكد من تمرير "--split-overap 0" إذا قدمت يدويًا ملف `` image_groups.txt ''.

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    دمج سبليت الموزعة

    يمكن لـ ODM أيضًا توزيع معالجة كل نموذج فرعي تلقائيًا على أجهزة متعددة عبر NodeODM nodes, مدبرة عبر ClusterODM.

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    الشروع في البدء في عملية دمج التجزئة الموزعة

    الخطوة الأولى هي بدء ClusterODM

    docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -356,8 +357,8 @@ Escape character is '^]'.
 
    docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
    
 
    
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    فهم الكتلة

    ند الاتصال عبر telnet ، من الممكن استجواب ما يحدث على الكتلة. على سبيل المثال ، يمكننا استخدام الأمر HELP لمعرفة الأوامر المتاحة

    # HELP
@@ -392,16 +393,16 @@ OK
 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
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    الوصول إلى السجلات

    أثناء تشغيل العملية ، من الممكن أيضًا سرد المهام ، وعرض إخراج المهمة

    # TASK LIST
 # TASK OUTPUT <taskId> [lines]
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    أوتوسكالينج ClusterODM

    يتضمن ClusterODM أيضًا خيار القياس التلقائي على منصات متعددة ، بما في ذلك حتى الآن ، أمازون والمحيط الرقمي. يسمح ذلك للمستخدمين بتقليل التكاليف المرتبطة بالمثيلات التي تعمل دائمًا بالإضافة إلى القدرة على توسيع نطاق المعالجة بناءً على الطلب.

    لإعداد القياس التلقائي ، يجب عليك:

    @@ -437,19 +438,19 @@ info: Found docker-machine executable

    بهذه الطريقة ستتم إعادة توجيه جميع المهام تلقائيًا إلى أوتوسكالر.

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    محددات

    لا يتم دمج الشبكات ثلاثية الأبعاد في الوقت الحالي كجزء من سير العمل (فقط الغيوم النقطية ، DEMs و orthophotos هي).

    يتم دعم GCP بشكل كامل ، ومع ذلك ، يجب أن يكون هناك 3 نقاط GCP على الأقل في كل نموذج فرعي حتى يتم إجراء الإشارة الجغرافية. إذا كان النموذج الفرعي يحتوي على أقل من 3 من نقاط Google Cloud Platform ، فسيتم استخدام مجموعة من بيانات GCPs + EXIF ​​المتبقية بدلاً من ذلك (والتي ستكون أقل دقة). نوصي باستخدام ملف `` image_groups.txt '' للتحكم بدقة في تقسيم النموذج الفرعي عند استخدام GCP.

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    شكر وتقدير

    الدعائم الضخمة لباو والناس في Mapillary لمساهماتهم المذهلة في OpenDroneMap من خلال كود OpenSfM الخاص بهم ، والذي يعد مكونًا رئيسيًا لخط الأنابيب المدمج. نحن نتطلع إلى زيادة حدود OpenDroneMap ومعرفة مدى ضخامة مجموعة البيانات التي يمكننا معالجتها.

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    Map accuracy

    Accuracy can be defined as the degree or closeness to which the information on a map matches the values in the real world. Therefore, when we refer to accuracy, we are talking about quality of data and about number of errors contained in a certain dataset (Pascual 2011).

    Relative or Local accuracy

    Local or relative accuracy can be defined as the degree to which de distances between two points on a map correspond to the actually distances between those points in the real world.

    Relative accuracy is independent of the location of the map in the world, so a map can have a high relative accuracy (in size and shape) but its position in the world can be shifted (Figure 1).

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    Model showing high relative accuracy -
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    Figure 1. Model showing high relative accuracy but misplaced according to its real world position

    Absolute or global Accuracy

    Absolute accuracy is the accuracy of the reconstruction in relation to its true position on the planet (Pix4D 2019). Figure 2 shows a relative and absolute accurate model, as the points are correctly placed according to its real world position.

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    Figure 2. Model showing high relative and absolute accuracy. Placed correctly according to its real world position

    An Accuracy level for each project

    Each project has specific accuracy needs to be met. For instance assessing the progress in a construction site or measuring an area affected by a fire does not require the use of GCP, since absolute accuracy will not impact the decision making process. In the other hand, there are tasks on which accuracy is critical, for example project compliance evaluations and land title surveying, which require a higher relative and absolute accuracy.

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    What to expect

    In general terms, one can expect the relative accuracy to be in the order of 1 to 3 times the average GSD for the dataset. And as for the absolute accuracy, one must consider that it is dependent of the GPS unit mounted in the UAV but the horizontal accuracy of a standard GPS is usually in the range of 2 to 6 meters and the vertical accuracy between 3 to 4 times the horizontal accuracy.

    When using GCP, absolute accuracy can be improved to 2.5 times GSD for the horizontal accuracy and 4 times the GSD for the vertical accuracy (Madawalagama 2016).

    At a GSD of 1cm, the accuracy is to that of the RTK GNSS, and is within 1:200 scales according to NSDI & FGDC mapping accuracy standards during sub-optimal conditions (Barry 2013).

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    Aspects impacting map accuracy

    Weather

    Weather conditions have direct impact in the photogrammetry results, so it is important to consider cloud coverage, wind speed, humidity, sun’s altitude and other factors influencing the UAV stability and terrain illumination.

    @@ -325,9 +326,9 @@

    The higher the flight altitude, the larger the image footprint and GSD. The resulting larger GSD the accuracy will be decreased as there will be less detail in the recognizable features. When a smaller GSD is required an altitude of 3 to 4 times the height of the highest point is recommended.

    Flight speed

    Flight speed have special effect in cameras equipped with rolling shutter, while those equipped with global or mechanical shutter tends to reduce this effect. UAV equipped with RTK positioning systems are also affected with the speed, but with hover at each photo taken, you can get very good accuracy. If instead you are moving during each photo take, the accuracy is going to be limited by two factors: the speed at which you are moving multiplied by the 1 second increments of RTK (Mather 2020).

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    References

    Barry, P., & Coakley, R. «Accuracy of UAV photogrammetry compared with Network RTK GPS.» Baseline Surveys. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (accessed 10 13, 2020).

    Drone Deploy. How Do I Use Ground Control Points?: A guide to using ground control points with drone mapping software. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (accessed 7 9, 2020).

    @@ -336,8 +337,8 @@

    Pascual, Manuel S. GIS Lounge: GIS Data: A Look at Accuracy, Precision, and Types of Errors. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (accessed 07 09, 2020).

    Pix4D. «What is accuracy in an aerial mapping project?» Pix4D. 25 de 05 de 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (accessed 10 13, 2020).

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    diff --git a/ar/masks.html b/ar/masks.html index 31647e5d9..f22d07690 100644 --- a/ar/masks.html +++ b/ar/masks.html @@ -4,10 +4,11 @@ - + + - Using Image Masks — OpenDroneMap 2.4.7 documentation + Using Image Masks — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    Using Image Masks

    Starting from ODM 2.0 people can supply image masks to inform the software to skip reconstruction over certain areas. This is useful for cases where the sky was accidentally included in the input photos from oblique shots, or simply to limit the reconstruction of a single subject.

    To add a mask, simply create a new black and white image of the same dimension as the target image you want to mask (you can use a program such as GIMP to do this). Color in black the areas to exclude from the reconstruction.

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    Target image -
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    Image mask -
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    3D result (building is masked) -
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    Name your file:

    <filename>_mask.JPG

    For example, DJI_0018.JPG can have a mask by creating a DJI_0018_mask.JPG file and include that in the list of images. You can use .JPG, .PNG, .BMP and .TIF formats for image masks.

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    diff --git a/ar/multispectral.html b/ar/multispectral.html index 0522b9b5e..f2efb4ed3 100644 --- a/ar/multispectral.html +++ b/ar/multispectral.html @@ -4,10 +4,11 @@ - + + - دعم متعدد الأطياف — OpenDroneMap 2.4.7 documentation + دعم متعدد الأطياف — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    دعم متعدد الأطياف

    منذ الإصدار 0.9.9 ODM لديه دعم أساسي للتطبيع الإشعاعي ، وهو قادر على توليد صور انعكاسية من الكاميرات متعددة الأطياف. تلتقط الكاميرات متعددة الأطياف لقطات متعددة للمشهد باستخدام مستشعرات نطاق مختلفة.

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    المعدات

    بينما نهدف إلى دعم أكبر عدد ممكن من الكاميرات ، فقد تم تطوير دعم متعدد الأطياف باستخدام الكاميرات التالية ، لذا ستعمل بشكل أفضل:

    @@ -305,13 +306,13 @@

    قد تعمل الكاميرات الأخرى أيضًا. يمكنك مساعدتنا في توسيع هذه القائمة من خلال مشاركة مجموعات البيانات  تم التقاطها بكاميرات أخرى.

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    الاستخدام

    قم بمعالجة جميع الصور من جميع النطاقات دفعة واحدة (لا تفصل النطاقات إلى مجلدات متعددة) واجتاز المعلمة "- معايرة قياس الإشعاع" لتمكين التطبيع الإشعاعي. إذا كانت الصور جزءًا من إعداد متعدد الكاميرات ، فستحتوي الصورة المنتخبة الناتجة على نطاقات N ، واحدة لكل كاميرا (+ ألفا).

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    diff --git a/ar/objects.inv b/ar/objects.inv index 140bca134..e5f9d3d4d 100644 Binary files a/ar/objects.inv and b/ar/objects.inv differ diff --git a/ar/outputs.html b/ar/outputs.html index 9718a780e..a19b58aec 100644 --- a/ar/outputs.html +++ b/ar/outputs.html @@ -4,10 +4,11 @@ - + + - مخرجات OpenDroneMap — OpenDroneMap 2.4.7 documentation + مخرجات OpenDroneMap — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    مخرجات OpenDroneMap

    فيما يلي بعض المخرجات المفيدة التي تنتجها ODM.

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    سحابة نقطة

    odm_georeferencing/odm_georeferenced_model.ply/laz/csv`` -- سحابة مرجعية جغرافية في تنسيقات ملفات مختلفة

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    Point cloud over State University Zanzibar, courtesy of Khadija Abdullah Ali

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    نموذج محكم ثلاثي الأبعاد

    odm_texturing/odm_textured_model.obj -- شبكة سطح محكم odm_texturing/odm_textured_model_geo.obj -- شبكة السطح ذات المرجعية الجغرافية والمحكم

    يمكنك الوصول إلى سحابة النقاط والشبكات المزخرفة باستخدام MeshLab. افتح MeshLab واختر File: Import Mesh واختر شبكتك المزخرفة من موقع مشابه لما يلي: odm_texturing\odm_textured_model.obj

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    Textured mesh courtesy of OpenDroneMap

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    Orthophoto

    odm_orthophoto/odm_orthphoto.png -- orthophoto ، ولكن هذا هو png بسيط ، والذي لا يحتوي على أي معلومات عن المراجع الجغرافية

    odm_orthophoto/odm_orthphoto.tif -- GeoTIFF Orthophoto. يمكنك استخدامه في QGIS كطبقة نقطية.

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    Orthophoto على جامعة ولاية زنجبار ، بإذن من * Khadija Abdullah Ali

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    DTM/DSM

    DTM/DSM سيتم إنشاؤه فقط في حالة استخدام خيارات "--dtm" أو "--dsm". انظر البرنامج التعليمي حول نماذج الارتفاع لمزيد من الخيارات في الإنشاء.

    سيتم تخزين البيانات في:

    @@ -331,12 +332,12 @@

  • odm_dem/dsm.tif

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    قائمة بجميع المخرجات

    project/
 ├── images/
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    كيفية طلب الميزات

    تحتاج جميع البرامج إلى تعليقات المستخدمين وطلبات الميزات ، لزيادة النمو والحفاظ على التوافق مع احتياجات المستخدمين.

    OpenDroneMap هو برنامج FOSS. تعتبر المشاريع المجانية والمفتوحة المصدر (FOSS) مثيرة للاهتمام من الداخل والخارج: من الخارج ، يشعر الناجحون أنه يجب أن يكونوا قادرين على القيام بأي شيء ، ومن الصعب معرفة الطلب المعقول. من داخل المشروع ، يمكن أن يشعروا بالقيود الكبيرة على الموارد: إلى حد كبير بالوقت والمال والفرص الزائدة.

    @@ -307,7 +308,7 @@

    يسعدنا أنك متحمس لرؤية الميزات الجديدة المضافة إلى المشروع. تحتاج بعض الميزات الجديدة إلى الدعم ، وبعضها أسهل في التنفيذ. سنبذل قصارى جهدنا لمساعدتك على فهم مكان سقوط طلبك ، ونقدر أي دعم يمكنك تقديمه.

    Learn to edit and help improve this page!

    -
    +
    diff --git a/ar/resources.html b/ar/resources.html index f1ede1497..f6b9ddb08 100644 --- a/ar/resources.html +++ b/ar/resources.html @@ -4,10 +4,11 @@ - + + - مراجع إضافية — OpenDroneMap 2.4.7 documentation + مراجع إضافية — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,9 +293,9 @@
    -
    +

    مراجع إضافية

    -
    +

    للمستخدمين

    الموارد التالية هي مكان جيد للبدء:

    -
    -
    +
    +

    للمطورين

    بالإضافة إلى موارد المستخدم ، نوصي أيضًا بقراءة ما يلي:

      @@ -315,8 +316,8 @@

    • نحتفظ بقسم في منتدانا مخصص للأوراق البحثية. ذا مكان قيم لقراءة المزيد عن أحدث الأبحاث المتعلقة بالهيكل من الحركة ، ستيريو متعدد الرؤية ، الشبكات ، التركيب ، إلخ. والتي يمكن استخدامها لتحسين البرنامج.

    Learn to edit and help improve this page!

    -
    -
    + +
    diff --git a/ar/search.html b/ar/search.html index 949019cf8..51de975de 100644 --- a/ar/search.html +++ b/ar/search.html @@ -7,7 +7,7 @@ - Search — OpenDroneMap 2.4.7 documentation + Search — OpenDroneMap 2.5.5 documentation @@ -64,7 +64,7 @@
    - 2.4.7 + 2.5.5
    diff --git a/ar/searchindex.js b/ar/searchindex.js index 8e13f89d0..b4643bfb2 100644 --- a/ar/searchindex.js +++ b/ar/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/ar/tutorials.html b/ar/tutorials.html index 918759957..508caf606 100644 --- a/ar/tutorials.html +++ b/ar/tutorials.html @@ -4,10 +4,11 @@ - + + - دروس — OpenDroneMap 2.4.7 documentation + دروس — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,14 +293,14 @@
    -
    +

    دروس

    ستجد أدناه تعليمات لبعض حالات الاستخدام الشائعة.

    -
    +

    إنشاء Orthophotos عالية الجودة

    -
    +
    image of OpenDroneMap orthophoto -
    +

    بدون أي تعديلات في المعلمات ، يختار ODM حل وسط جيد بين الجودة والسرعة واستخدام الذاكرة. إذا كنت ترغب في الحصول على نتائج عالية الجودة ، فأنت بحاجة إلى تعديل بعض المعلمات:

      @@ -310,39 +311,39 @@

    • يجب زيادة mesh-size-- إلى 300000-600000 و --mesh-octree-depth يجب زيادتها إلى 10-11 في المناطق الحضرية لإعادة إنشاء مباني / أسقف أفضل.

    -
    -
    +
    +

    معايرة الكاميرا

    تعد معايرة الأميرة تحديًا خاصًا مع كاميرات السلع. يمكن أن تؤثر التغيرات في درجات الحرارة والاهتزازات والتركيز والعوامل الأخرى على المعلمات المشتقة مع تأثيرات كبيرة على البيانات الناتجة. المعايرة التلقائية أو الذاتية ممكنة ومرغوب فيها مع رحلات الطائرات بدون طيار ، ولكن اعتمادًا على نمط الرحلة ، قد لا تؤدي المعايرة التلقائية إلى إزالة كل التشويه من المنتجات الناتجة. جيمس وروبسون (2014) في ورقتهما بعنوان `` التخفيف من الخطأ المنهجي في النماذج الطبوغرافية المشتقة من الطائرات بدون طيار وشبكات الصور الأرضية <https://onlinelibrary.wiley.com/doi/full/10.1002/esp.3609> "_ معالجة كيف لتقليل التشويه من المعايرة الذاتية.

    -
    +
    image of lens distortion effect on bowling of data -
    +

    Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

    للتخفيف من هذا التأثير ، هناك عدد قليل من الخيارات ولكن أبسطها على النحو التالي: حلّق بنمطين مفصولين بـ 20 درجة ، وبدلاً من أن يكون لديك كاميرا (إشارة مستقيمة لأسفل) ، استخدم واحدًا يميل إلى الأمام بمقدار 5 درجات.

    -
    +
    animation showing optimum -
    +

    نظرًا لأن هذا النهج في الطيران يمكن أن يستغرق وقتًا أطول من الرحلات النموذجية ، يمكن للطيار أو الفريق أن يطير في منطقة صغيرة باستخدام النهج أعلاه. ستقوم OpenDroneMap بإنشاء ملف معايرة يسمى camera.json يمكن استيراده بعد ذلك لاستخدامه لمعايرة رحلة أخرى يتم نقلها بكفاءة أكبر.

    بدلاً من ذلك ، يمكن تطبيق الطريقة التجريبية التالية: حلّق بتداخل أقل بكثير ، لكن رحلتين * متصالبتين * (تسمى أحيانًا التقاطع المائل) مفصولتين بزاوية 20 درجة مع كاميرا أمامية 5 درجات.

    • مكن أن تكون نسب التداخل بين خطوط الشبكة أقل من الرحلات المتوازية. للحصول على نتائج جيدة ثلاثية الأبعاد ، ستحتاج إلى تداخل 68٪ و sidelap لما يعادل 83٪ تداخل و sidelap.

    • للحصول على نتائج جيدة ثنائية الأبعاد و 2.5 D (نموذج الارتفاع الرقمي) ، ستحتاج إلى 42٪ تداخل و sidelap للحصول على 70٪ تراكب و sidelap مكافئ.

    -
    +
    animation showing experimental optimum -
    +

    كما تحسن خطوط الطيران المنفصلة عموديًا الدقة ، ولكنها أقل دقة من الكاميرا التي تواجه الأمام بمقدار 5 درجات.

    -
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    figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
    +

    من James and Robson (2014), CC BY 4.0

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    إنشاء نماذج الارتفاعات الرقمية

    بشكل افتراضي لا يُنشئ ODM DEMs. لإنشاء نموذج تضاريس رقمي ، تأكد من تمرير علامة "--dtm". لإنشاء نموذج سطح رقمي ، تأكد من تمرير علامة "--dsm".

    -
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    image of OpenDroneMap derived digital surface model -
    +

    بالنسبة لتوليد DTM ، يتم استخدام مرشح مورفولوجي بسيط (smrf) لتصنيف النقاط في الأرض مقابل غير الأرض ويتم استخدام نقاط الأرض فقط. يمكن التحكم في مرشح `` smrf '' من خلال عدة معلمات:

      @@ -355,9 +356,9 @@

      يمكن أن يؤثر تغيير هذه الخيارات على نتيجة DTM بشكل كبير. أفضل مصدر للقراءة لفهم كيفية تأثير المعلمات على الإخراج هو قراءة الورقة الأصلية مرشح مورفولوجي بسيط محسن لتصنيف التضاريس لبيانات LIDAR المحمولة جواً (PDF متاح مجانًا).

      بشكل عام ، فإن خيار "- smrf-threshold" له أكبر الأثر على النتائج.

      SMRF جيد في تجنب أخطاء النوع الأول (عدد صغير من نقاط الأرض المصنفة خطأً على أنها غير أرضية) ولكنها "مقبولة" فقط في تجنب أخطاء النوع الثاني (عدد كبير من النقاط غير الأرضية مصنفة خطأً كأرضية). يجب أن يؤخذ هذا في الاعتبار عند إنشاء DTMs التي من المفترض أن يتم استخدامها بصريًا ، لأن الكائنات التي تم الخلط بينها وبين الأرض تبدو وكأنها قطع أثرية في DTM النهائية.

      -
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      image of lens distortion effect on bowling of data -
      +

      هناك معلمتان مهمتان أخريان تؤثران على توليد DEM:

        @@ -369,36 +370,36 @@ 
        docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
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      -
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    +

    Measuring stockpile volume

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    Fieldwork planning

    Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

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    Flight pattern

    Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

    In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

    -
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    a simple lawnmower flight pattern can produce accurate results -
    +

    Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

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    Flight height

    Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

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    GCPs

    To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

    For further information on the use of GCPs, please refer to the Ground Control Points section.

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    Processing parameters

    A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

    If using ODM, these this reference values can help you configure the process settings.

    @@ -407,69 +408,69 @@ When placing or measuring GCP, equipment accuracy should be greater than the GSD

    --orthophoto-resolution 1.0

    --feature-quality high

    --pc-quality high

    -
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    + +

    Measuring

    As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

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    almost 50% of the material will be found in the first 20% of the stockpile height -
    +

    In WebODM Dashboard, clic on "view map" to start a 2D view of your project.

    Once in the 2D map view, clic on the "Measure volume, area and length" button.

    -
    +
    clic on the "Measure volume, area and length" button -
    +

    then clic on "Create a new measurement"

    -
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    clic on "Create a new measurement" -
    +

    Start placing the points to define the stockpile base plane

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    Define the stockpile base plane -
    +

    Clic on "Finish measurement" to finish the process.

    -
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    Clic on "Finish measurement" to finish the process -
    +

    Dialog box will show the message "Computing ..." for a few seconds, and after the computing is finished the volume measurement value will be displayed.

    -
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    Clic on "Finish measurement" to finish the process -
    +

    If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

    Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

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    Expected accuracy

    For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

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    باستخدام Docker

    نظرًا لأن العديد من المستخدمين يستخدمون عامل إرساء لنشر OpenDroneMap ، فقد يكون من المفيد فهم بعض الأوامر الأساسية من أجل استجواب حالات عامل الميناء عندما تسوء الأمور ، أو نشعر بالفضول حيال ما يحدث. Docker هي بيئة حاويات تهدف ، من بين أمور أخرى ، إلى تسهيل نشر البرامج بشكل مستقل عن البيئة المحلية. بهذه الطريقة ، فهي تشبه الأجهزة الافتراضية.

    يمكن لبعض الأوامر البسيطة أن تجعل تجربة docker أفضل بكثير.

    -
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    ادراج Docker ماكينات

    يمكننا البدء بإدراج آلات الرصيف المتوفرة على الجهاز الحالي الذي نقوم بتشغيله على النحو التالي:

    > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

    إذا أردنا رؤية الأجهزة التي قد لا تعمل ولكنها لا تزال موجودة ، فيمكننا إضافة علامة "-a":

    > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
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    +
    +

    الوصول إلى السجلات على سبيل المثال

    باستخدام " CONTAINER ID" أو الاسم ، يمكننا الوصول إلى أي سجلات متاحة على الجهاز على النحو التالي:

    > docker logs 2518817537ce
@@ -500,8 +501,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

    تخبر القيمة -5 الأمر tail بإعطائنا الأسطر الخمسة الأخيرة فقط من السجلات.

    -
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    وصول سطر الأوامر إلى المثيلات

    في بعض الأحيان نحتاج إلى التعمق أكثر في استكشافنا لعملية OpenDroneMap. لهذا ، يمكننا الحصول على وصول مباشر لسطر الأوامر إلى الأجهزة. لهذا ، يمكننا استخدام docker exec لتنفيذ قذيفة سطر الأوامر` bash` في الجهاز المعني على النحو التالي:

    > docker exec -ti 2518817537ce bash
@@ -509,24 +510,24 @@ The resulting accuracy is comparable to the commercially available photogrammetr

    الآن تم تسجيل دخولنا إلى مثيل عامل الميناء لدينا ويمكننا استكشاف الجهاز.

    -
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    التنظيف بعد Docker

    تمتع Docker باستخدام مؤسف للمساحة ولا يقوم افتراضيًا بتنظيف البيانات والآلات الزائدة عند اكتمال العمليات. يمكن أن يكون ذلك مفيدًا إذا احتجنا إلى الوصول إلى عملية تم إنهاؤها منذ ذلك الحين ، ولكنها تحمل عبء استخدام كميات متزايدة من التخزين بمرور الوقت. يقدم Maciej Łebkowski نظرة عامة ممتازة على كيفية إدارة الاستخدام الزائد للقرص في عامل الميناء <https://lebkowski.name/docker-volumes/> _.

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    + + +

    استخدام ODM من موقع النطاق الترددي المنخفض

    -
    +

    ما هذا ومن هو؟

    عبر Ivan Gayton's repo

    <https://www.opendronemap.org/>" __ لا يمكن دائمًا إعدادها بشكل فعال محليًا - يتطلب الأمر جهازًا قويًا إلى حد ما لمعالجة مجموعات البيانات الكبيرة - لذا يمكن أن تكون الآلة السحابية أحيانًا هي الحل للأشخاص في حقل. ومع ذلك ، فإن عرض النطاق الترددي يمثل مشكلة في العديد من الإعدادات منخفضة الدخل. لا يمكن حل هذا القيد تمامًا ، ولكن الطريقة التالية تؤدي مهمة معقولة لتقليل النطاق الترددي اللازم لمعالجة مجموعات بيانات صور الطائرات بدون طيار على السحابة من المواقع الأفريقية.

    نقدم هنا عملية صعبة ولكنها عملية لإنشاء جهاز سحابة OpenDroneMap (* لا * CloudODM ، ضع في اعتبارك أنه مجرد نسخة مستندة إلى السحابة من ODM تقوم بتشغيلها من سطر الأوامر) واستخدامها لمعالجة مجموعات الصور الكبيرة عن بُعد. إنه يتطلب معرفة باستخدام سطر أوامر Unix و ssh وحساب Digital Ocean (قد يعمل Amazon AWS أيضًا ، ربما مع اختلافات طفيفة في الإعداد) ، ومستوى معتدل من معرفة الكمبيوتر العامة. إذا لم تكن على دراية بالكمبيوتر إلى حد ما وترغب في إثارة ضجة مع إعداد صعب قليلاً ، فإن "CloudODM <https://www.opendronemap.org/cloudodm/>" __ هو ما يجب أن تنظر إليه.

    تستهدف العملية برمتها في الغالب شخصًا يقوم بمهام كبيرة في موقع أفريقي أو موقع مماثل يتطلع إلى معالجة البيانات في أسرع وقت ممكن بينما لا يزال في بيئة ميدانية. ولذلك ، فإنه يؤكد على سير العمل الذي يهدف إلى تقليل نقل البيانات / النطاق الترددي ، بدلاً من أبسط طريقة لتشغيل ODM.

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    خطوات

    -
    +

    تثبيت

    • قم بإنشاء قطرة محيط رقمية مع ذاكرة وصول عشوائي بسعة 4 جيجابايت على الأقل. سيكلفك ذلك حوالي 20 دولارًا / شهريًا. من المحتمل أن يفشل التثبيت أقل من 4 غيغابايت من ذاكرة الوصول العشوائي. عندما نقوم بتشغيل عملية ODM فعليًا ، سنقوم بتغيير حجمها إلى جهاز سحابي أكبر بكثير وأكثر تكلفة ، ولكن بين الجري يمكنك تقليصها بين الجري إلى ثاني أرخص قطيرة والتي تكلف 10 دولارات فقط في الشهر (أرخص قطرة ، بسعر 5 دولارات شهريًا ، ويأتي مع محرك أقراص صغير لا يمكنك تقليصه).

      @@ -573,8 +574,8 @@ export PATH=$PATH:/home/odm/ODM/

      • ستحتاج الآن إلى محرك أقراص ثابت سحابي ثانٍ ("حجم" في المصطلح الرقمي للمحيط) كبير بما يكفي لإدارة مشروعك. يبدو أن القاعدة الأساسية هي 10 أضعاف حجم مجموعة صورك الخام ؛ لدينا مجموعة من الصور بحجم 100 جيجابايت وقمنا بإعداد وحدة تخزين بحجم 1000 جيجابايت (بمجرد الانتهاء من التشغيل ، يجب أن تكون قادرًا على التخلص من معظم سعة محرك الأقراص الباهظة الثمن هذه ، ولكن يلزم إكمال العملية). قم بإعداد الصوت وإرفاقه بالقطرة الخاصة بك ، و تكوين نقطة التحميل الخاصة به (في هذا المثال ، نقوم بتعيينه إلى /mnt/odmdata/).

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    إعداد البيانات والمشروع

    • الآن ادفع صورك إلى الخادم. يمكنك استخدام Secure Copy (scp) مثل ذلك: scp -r /path/to/my/imagefolder odm@xxx.xxx.xxx.xxx:/mnt/odmdata/.

      @@ -584,7 +585,7 @@ export PATH=$PATH:/home/odm/ODM/
    -
    +
    بنية الدليل

    يتطلب ODM إعداد الدلائل الموجودة على الجهاز فقط. البتات الحرجة هي مجلد التثبيت (إذا قمت بتثبيته كما هو موضح أعلاه ، فهو /home/odm/ODM/ ومجلد المشروع (i.e. /mnt/odmdata/myproject/))

      @@ -594,9 +595,9 @@ export PATH=$PATH:/home/odm/ODM/

    • قم بتعديل settings.yaml لتحديد الدليل الأصلي لمجلد المشروع (في هذه الحالة المجلد الذي أنشأناه ، `` / mnt / odmdata / ). تأكد من أن الصور في الموضع الصحيح ، مثل  ``/mnt/odmdata/myproject/images والملفات المساعدة الأخرى (gcp_list.txt و image_groups.txt) في المجلد الجذر /mnt/odmdata/myproject/

    • إذا كانت لديك الصور في مجلدات منفصلة لكتل ​​أو رحلات طيران فردية (والتي ستفعلها إذا تم تنظيم إدارة رحلتك) ، فيمكنك إنشاء ملف image_groups.txt مع التعويذات لـ i in * ؛ هل cd $ i ؛ for j in *؛ do echo "$ j $ i" >> ../$i.txt؛ فعله؛ مؤتمر نزع السلاح ../ ؛ فعل ؛ و cd ../ ، لأني في myproject / *. txt ؛ do cat $ i >> image_groups.txt؛ فعل ؛. من المفترض أن يؤدي ذلك إلى إنشاء ملف بهيكل صحيح: قائمة بجميع ملفات الصور و "اسم المجموعة" بعد كل ملف (والذي سيكون في هذه الحالة ببساطة اسم المجلد الذي جاء منه). ثم انقل جميع ملفات الصور إلى دليل واحد يسمى الصور في دليل جذر المشروع (لذا /mnt/odmdata/myproject/images/). سيسمح ملف image_groups.txt لـ ODM بتتبع الصور التي تنتمي إلى نفس الدفعة ، على الرغم من أنها كلها في دليل واحد.

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    -
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    + + +

    تغيير حجم القطرة ، وسحب دبوس ، والهروب

    • قم بإيقاف تشغيل جهازك وتغيير حجمه إلى عدد هائل من وحدات المعالجة المركزية وكمية الذاكرة المناسبة. أستخدم الجهاز المُحسّن للذاكرة مع 24 وحدة vCPU مخصصة و 192 غيغابايت من ذاكرة الوصول العشوائي (والتي تكلف حوالي 1.60 دولارًا في الساعة - والتي تضيف بسرعة ، تزيد عن 1000 دولار / شهر). إعادة التشغيل ، والبدء في العمل بسرعة حتى لا تضيع وقتًا كبيرًا.

      • @@ -621,8 +622,8 @@ export PATH=$PATH:/home/odm/ODM/

      • قد ترغب في مراقبة htop (للحصول على فكرة عن استخدام الموارد حتى تتمكن في المستقبل من تدوير آلة كبيرة بقدر الضرورة)

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    بعد الانتهاء (بافتراض أنك تعيش طويلاً)

    • بمجرد الانتهاء من المعالجة ، أغلق الماكينة وقم بتغيير حجمها مرة أخرى إلى الحد الأدنى للسعة غير المكلفة.

      • @@ -641,10 +642,10 @@ export PATH=$PATH:/home/odm/ODM/

    Learn to edit and help improve this page!

    - - - - + + + + diff --git a/arguments.html b/arguments.html index a7978cd91..b976ed920 100644 --- a/arguments.html +++ b/arguments.html @@ -4,10 +4,11 @@ - + + - Options and Flags — OpenDroneMap 2.4.7 documentation + Options and Flags — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
    - 2.4.7 + 2.5.5
    @@ -291,9 +292,9 @@
    -
    +

    Options and Flags

    -
    +

    Arguments

    --build-overviews

    Build orthophoto overviews for faster display in programs such as QGIS. Default: False

    @@ -302,6 +303,10 @@
    --cameras <json>

    Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: ``

    +
    --cog

    Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

    +
    +
    --copy-to <path>

    Copy output results to this folder after processing.

    +
    --crop <positive float>

    Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: 3

    --debug

    Print debug messages. Default: False

    @@ -356,7 +361,7 @@
    --min-num-features <integer>

    Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

    -
    name <project name>

    Name of dataset (i.e subfolder name within project folder). Default: code

    +
    name <dataset name>

    Name of dataset (i.e subfolder name within project folder). Default: code

    --optimize-disk-space

    Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don’t have sufficient disk space available. Default: False

    @@ -364,6 +369,8 @@
    --orthophoto-cutline

    Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

    +
    --orthophoto-kmz

    Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

    +
    --orthophoto-no-tiled

    Set this parameter if you want a striped GeoTIFF. Default: False

    --orthophoto-png

    Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

    @@ -392,7 +399,7 @@
    --project-path <path>

    Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an “images” folder.

    -
    --radiometric-calibration none | camera | camera+sun

    Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

    +
    --radiometric-calibration none | camera | camera+sun

    Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

    --rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

    Rerun this stage only and stop. . Default: ``

    @@ -426,6 +433,8 @@
    --texturing-data-term gmi | area

    When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

    +
    --texturing-keep-unseen-faces

    Keep faces in the mesh that are not seen in any camera. Default: False

    +
    --texturing-outlier-removal-type none | gauss_clamping | gauss_damping

    Type of photometric outlier removal method. . Default: gauss_clamping

    --texturing-skip-global-seam-leveling

    Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

    @@ -452,8 +461,8 @@

    Learn to edit and help improve this page!

    -
    -
    + +
    diff --git a/contributing.html b/contributing.html index 29caf788a..3ba70c2d7 100644 --- a/contributing.html +++ b/contributing.html @@ -4,10 +4,11 @@ - + + - How To Contribute — OpenDroneMap 2.4.7 documentation + How To Contribute — OpenDroneMap 2.5.5 documentation @@ -62,7 +63,7 @@
    - 2.4.7 + 2.5.5
    @@ -290,14 +291,14 @@
    -
    +

    How To Contribute

    OpenDroneMap relies on community contributions. You can contribute in many ways, even if you are not a programmer.

    -
    +

    Community Forum

    If you are looking to get involved, are stuck on a problem, or want to reach out, the forum is a great place to start. You may find your questions already answered or else you can find other useful tips and resources. You can also contribute your open access datasets for others to explore. It is a good place go before submitting bug reports or getting in touch with developers before writing a new feature.

    -
    -
    +
    +

    Reporting Bugs

    Bugs are tracked as Github issues. Please create an issue in the repository and tag it with the Bug tag.

    Explain the problem and include additional details to help maintainers reproduce the problem:

    @@ -317,7 +318,7 @@

  • What’s the name and version of the OS you’re using?

  • Are you running ODM in a virtual machine or Docker? If so, which VM software are you using and which operating systems and versions are used for the host and the guest?

    • -
    +

    Template For Submitting Bug Reports

    [Short description of problem here]
 
@@ -349,9 +350,9 @@
 * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
    -
    -
    -
    + + +

    Pull Requests

    • Include screenshots and animated GIFs in your pull request whenever possible.

      • @@ -375,8 +376,8 @@

    Learn to edit and help improve this page!

    -
    -
    + +
    diff --git a/es/.buildinfo b/es/.buildinfo index 4a5a97f8a..1696d04ce 100644 --- a/es/.buildinfo +++ b/es/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 7b6c18e412e5c7be98cffa2106b021cc +config: 739a32178999b6687e6ad0c86b5f3ab8 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/es/.doctrees/arguments.doctree b/es/.doctrees/arguments.doctree index 2381d2528..298bfd38f 100644 Binary files a/es/.doctrees/arguments.doctree and b/es/.doctrees/arguments.doctree differ diff --git a/es/.doctrees/contributing.doctree b/es/.doctrees/contributing.doctree index 2d39d22a6..d38caedd5 100644 Binary files a/es/.doctrees/contributing.doctree and b/es/.doctrees/contributing.doctree differ diff --git a/es/.doctrees/environment.pickle b/es/.doctrees/environment.pickle index 51582fe72..ecfe0ae2a 100644 Binary files a/es/.doctrees/environment.pickle and b/es/.doctrees/environment.pickle differ diff --git a/es/.doctrees/flying.doctree b/es/.doctrees/flying.doctree index ae095c2a5..511ee0493 100644 Binary files a/es/.doctrees/flying.doctree and b/es/.doctrees/flying.doctree differ diff --git a/es/.doctrees/gcp.doctree b/es/.doctrees/gcp.doctree index 9be3c01a5..0048c5313 100644 Binary files a/es/.doctrees/gcp.doctree and b/es/.doctrees/gcp.doctree differ diff --git a/es/.doctrees/geo.doctree b/es/.doctrees/geo.doctree index 31ee50867..bcb6d6bff 100644 Binary files a/es/.doctrees/geo.doctree and b/es/.doctrees/geo.doctree differ diff --git a/es/.doctrees/index.doctree b/es/.doctrees/index.doctree index 4ed46b3ae..941186a13 100644 Binary files a/es/.doctrees/index.doctree and b/es/.doctrees/index.doctree differ diff --git a/es/.doctrees/installation.doctree b/es/.doctrees/installation.doctree index 113ccd485..aaf6d835a 100644 Binary files a/es/.doctrees/installation.doctree and b/es/.doctrees/installation.doctree differ diff --git a/es/.doctrees/large.doctree b/es/.doctrees/large.doctree index 6e28d66b0..96ccdb716 100644 Binary files a/es/.doctrees/large.doctree and b/es/.doctrees/large.doctree differ diff --git a/es/.doctrees/map-accuracy.doctree b/es/.doctrees/map-accuracy.doctree index 164bc8fa8..79afcaccd 100644 Binary files a/es/.doctrees/map-accuracy.doctree and b/es/.doctrees/map-accuracy.doctree differ diff --git a/es/.doctrees/masks.doctree b/es/.doctrees/masks.doctree index fc22e3ba6..36eb7551a 100644 Binary files a/es/.doctrees/masks.doctree and b/es/.doctrees/masks.doctree differ diff --git a/es/.doctrees/multispectral.doctree b/es/.doctrees/multispectral.doctree index ce4b43b1b..9a7f4e2b7 100644 Binary files a/es/.doctrees/multispectral.doctree and b/es/.doctrees/multispectral.doctree differ diff --git a/es/.doctrees/outputs.doctree b/es/.doctrees/outputs.doctree index ac71bf15c..39d998e56 100644 Binary files a/es/.doctrees/outputs.doctree and b/es/.doctrees/outputs.doctree differ diff --git a/es/.doctrees/requesting-features.doctree b/es/.doctrees/requesting-features.doctree index 1a267ce98..fd3fbce73 100644 Binary files a/es/.doctrees/requesting-features.doctree and b/es/.doctrees/requesting-features.doctree differ diff --git a/es/.doctrees/resources.doctree b/es/.doctrees/resources.doctree index 765cf0703..f2e584d16 100644 Binary files a/es/.doctrees/resources.doctree and b/es/.doctrees/resources.doctree differ diff --git a/es/.doctrees/tutorials.doctree b/es/.doctrees/tutorials.doctree index e75b8a1a3..83e8e618c 100644 Binary files a/es/.doctrees/tutorials.doctree and b/es/.doctrees/tutorials.doctree differ diff --git a/es/_sources/arguments.rst.txt b/es/_sources/arguments.rst.txt index 84ae87529..ce49943dd 100644 --- a/es/_sources/arguments.rst.txt +++ b/es/_sources/arguments.rst.txt @@ -15,6 +15,12 @@ Arguments ``--cameras`` Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/es/_static/documentation_options.js b/es/_static/documentation_options.js index 3e2e8743e..b4f4f0181 100644 --- a/es/_static/documentation_options.js +++ b/es/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'es', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/es/arguments.html b/es/arguments.html index 5e0b09399..1157a0ebb 100644 --- a/es/arguments.html +++ b/es/arguments.html @@ -4,10 +4,11 @@ - + + - Opciones e indicadores — documentación de OpenDroneMap - 2.4.7 + Opciones e indicadores — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -129,14 +130,14 @@
  • Creación de Ortofotos de Alta Calidad
  • Calibrar la cámara
  • Creación de modelos de elevación digitales
    • -
  • Measuring stockpile volume
      -
    • Fieldwork planning
      • -
    • Flight pattern
      • -
    • Flight height
      • +
    • Medir el volumen de pilas de materiales a granel
    • Usando Docker
        @@ -292,9 +293,9 @@
        -
        +

        Opciones e indicadores

        -
        +

        Argumentos

        --build-overviews

        Crea vistas generales de la ortofoto para una visualización mas rápida en programas como QGIS. Predeterminado: False

        @@ -303,6 +304,10 @@
        --cameras <json>

        Utilice los parámetros de cámara calculados a partir de otro conjunto de datos en lugar de calcularlos. Se puede especificar como una ruta a un archivo camera.json o como una cadena JSON que representa el contenido de un archivo camera.json. Default: ``

        +
        --cog

        Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

        +
        +
        --copy-to <path>

        Copy output results to this folder after processing.

        +
        --crop <positive float>

        Recorte automáticamente los resultados de una imagen creando una zona de amortiguación suave alrededor de los límites del conjunto de datos, reducido en N metros. Utilice 0 para deshabilitar el recorte. Predeterminado: 3

        --debug

        Imprime mensajes de depuración. Predeterminado: False

        @@ -311,39 +316,40 @@
        --dem-euclidean-map

        Calcula un mapa ráster euclidiano para cada DEM. El mapa informa la distancia desde cada celda al valor NODATA más cercano (antes de que se rellene cualquier agujero). Esto puede resultar útil para aislar las áreas que se han rellenado.

        -
        --dem-gapfill-steps <positive integer>

        Number of steps used to fill areas with gaps. Set to 0 to disable gap filling. Starting with a radius equal to the output resolution, N different DEMs are generated with progressively bigger radius using the inverse distance weighted (IDW) algorithm and merged together. Remaining gaps are then merged using nearest neighbor interpolation. Default: 3

        +
        --dem-gapfill-steps <positive integer>

        Número de pasos utilizados para rellenar áreas con huecos. Establezca en 0 para deshabilitar el llenado de espacios. Comenzando con un radio igual a la resolución de salida, se generan N diferentes DEM con un radio progresivamente mayor utilizando el algoritmo de distancia inversa ponderada (IDW) y se fusionan. Luego, los espacios restantes se fusionan utilizando la interpolación de vecino más cercano. Predeterminado:3

        -
        --dem-resolution <float>

        DSM/DTM resolution in cm / pixel. Note that this value is capped by a ground sampling distance (GSD) estimate. To remove the cap, check –ignore-gsd also. Default: 5

        +
        --dem-resolution <float>

        Resolución de DSM/DTM en cm/pixel. Note que este valor esta limitado por la distancia de muestreo del terreno (GSD) estimada. Para remover este límite, active tambien –ignore-gsd. Predeterminado: 5

        -
        --depthmap-resolution <positive float>

        Legacy option (use –pc-quality instead). Controls the density of the point cloud by setting the resolution of the depthmap images. Higher values take longer to compute but produce denser point clouds. Default: 640

        +
        --depthmap-resolution <positive float>

        Opción heredada (use –pc-quality en su lugar). Controla la densidad de la nube de puntos estableciendo la resolución de las imágenes del mapa de profundidad. Los valores más altos tardan más en calcularse, pero producen nubes de puntos más densas. Predeterminado: 640

        -
        --dsm

        Use this tag to build a DSM (Digital Surface Model, ground + objects) using a progressive morphological filter. Check the –dem* parameters for finer tuning. Default: False

        +
        --dsm

        Utilice esta etiqueta para construir un DSM (modelo digital de superficie, suelo + objetos) utilizando un filtro morfológico progresivo. Verifique los parámetros –dem* para un ajuste más fino. Predeterminado: False

        -
        --dtm

        Use this tag to build a DTM (Digital Terrain Model, ground only) using a simple morphological filter. Check the –dem* and –smrf* parameters for finer tuning. Default: False

        +
        --dtm

        Utilice esta etiqueta para construir un DTM (modelo digital de terreno, solo suelo) utilizando un filtro morfológico simple. Compruebe los parámetros –dem* y –smrf* para un ajuste más preciso. Predeterminado: False

        -
        --end-with,-e dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

        End processing at this stage. . Default: odm_report

        +
        --end-with,-e dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

        Finalizar procesamiento en esta etapa. Default: odm_report

        -
        --fast-orthophoto

        Skips dense reconstruction and 3D model generation. It generates an orthophoto directly from the sparse reconstruction. If you just need an orthophoto and do not need a full 3D model, turn on this option. Default: False

        +
        --fast-orthophoto

        Omite la reconstrucción densa y la generación de modelos 3D. Genera una ortofoto directamente a partir de la reconstrucción escasa. Active esta opción, si solo necesita una ortofoto y no necesita un modelo 3D completo. Predeterminado: False

        -
        --feature-quality ultra | high | medium | low | lowest

        Set feature extraction quality. Higher quality generates better features, but requires more memory and takes longer. . Default: high

        +
        --feature-quality ultra | high | medium | low | lowest

        Establezca la calidad de extracción de características. Una calidad más alta genera mejores características, pero requiere más memoria y lleva más tiempo. Predeterminado: high

        -
        --feature-type sift | hahog

        Choose the algorithm for extracting keypoints and computing descriptors. . Default: sift

        +
        --feature-type sift | hahog

        Elija el algoritmo para extraer puntos clave y calcular descriptores. Predeterminado: sift

        -
        --force-gps

        Use images” GPS exif data for reconstruction, even if there are GCPs present.This flag is useful if you have high precision GPS measurements. If there are no GCPs, this flag does nothing. Default: False

        +
        --force-gps

        Utilice los datos exif de GPS de las imágenes para la reconstrucción, incluso si hay GCP presentes. Esta bandera es útil si tiene mediciones de GPS de alta precisión. Si no hay GCP, esta bandera no hace nada. Predeterminado: False

        -
        --gcp <path string>

        Path to the file containing the ground control points used for georeferencing. The file needs to use the following format: EPSG:<code> or <+proj definition>geo_x geo_y geo_z im_x im_y image_name [gcp_name] [extra1] [extra2]Default: None

        +
        --gcp <path string>

        Ruta al archivo que contiene los puntos de control terrestre usados para georreferenciar. El archivo debe tener el siguiente formato: EPSG:<code> or <+proj definition>geo_x geo_y geo_z im_x im_y image_name [gcp_name] [extra1] [extra2] Predeterminado: None

        -
        --geo <path string>

        Path to the image geolocation file containing the camera center coordinates used for georeferencing. Note that omega/phi/kappa are currently not supported (you can set them to 0). The file needs to use the following format: EPSG:<code> or <+proj definition>image_name geo_x geo_y geo_z [omega (degrees)] [phi (degrees)] [kappa (degrees)] [horz accuracy (meters)] [vert accuracy (meters)]Default: None

        +
        --geo <path string>

        Ruta al archivo de geolocalización de las imágenes que contiene las coordenadas de los centros de las imágenes usadas para la georreferenciación. Note que actualmente no esta soportado omega/phi/kappa (los puede configurar en 0). El archivo debe tener el siguiente formato: EPSG:<code> or <+proj definition>image_name geo_x geo_y geo_z [omega (degrees)] [phi (degrees)] [kappa (degrees)] [horz accuracy (meters)] [vert accuracy (meters)] Predeterminado: None

        -
        --gps-accuracy <positive float>

        Set a value in meters for the GPS Dilution of Precision (DOP) information for all images. If your images are tagged with high precision GPS information (RTK), this value will be automatically set accordingly. You can use this option to manually set it in case the reconstruction fails. Lowering this option can sometimes help control bowling-effects over large areas. Default: 10

        +
        --gps-accuracy <positive float>

        Establezca un valor en metros para la información de Dilución de precisión de GPS (DOP) para todas las imágenes. Si sus imágenes están etiquetadas con información GPS de alta precisión (RTK), este valor se establecerá automáticamente en consecuencia. Puede utilizar esta opción para configurarla manualmente en caso de que falle la reconstrucción. Reducir esta opción a veces puede ayudar a controlar los efectos de combadura en áreas extensas. Predeterminado: 10

        -
        -h,--help

        show this help message and exit

        +
        -h,--help

        muestra este mensaje de ayuda y sale

        -
        --ignore-gsd

        Ignore Ground Sampling Distance (GSD). GSD caps the maximum resolution of image outputs and resizes images when necessary, resulting in faster processing and lower memory usage. Since GSD is an estimate, sometimes ignoring it can result in slightly better image output quality. Default: False

        +
        --ignore-gsd

        Ignore la distancia de muestreo del suelo (GSD). La GSD limita la resolución máxima de las salidas de imagen y cambia el tamaño de las imágenes cuando es necesario, lo que resulta en un procesamiento más rápido y un menor uso de memoria. Dado que la GSD es una estimación, a veces ignorarlo puede resultar en una calidad de salida de imagen ligeramente mejor. Predeterminado: False

        -
        --matcher-distance <integer>

        Distance threshold in meters to find pre-matching images based on GPS exif data. Set both matcher-neighbors and this to 0 to skip pre-matching. Default: 0

        +
        --matcher-distance <integer>

        92%match +Umbral de distancia en metros, para encontrar imágenes pre-coincidentes basado en la información exif de GPS. Establezca ambos matcher-neighbors y este en 0 para saltar la comparación previa. Predeterminado: 0

        -
        --matcher-neighbors <integer>

        Number of nearest images to pre-match based on GPS exif data. Set to 0 to skip pre-matching. Neighbors works together with Distance parameter, set both to 0 to not use pre-matching. Default: 8

        +
        --matcher-neighbors <integer>

        Número de imágenes cercanas a la comparación previa basado en la información exif de GPS. Establezca en 0 para omitir la comparación previa. El parámetro Neighbors trabaja junto con el parámetro Distance, establezca ambos en 0 para no utilizar la comparación previa. Predeterminado: 8

        --matcher-type flann | bow

        Matcher algorithm, Fast Library for Approximate Nearest Neighbors or Bag of Words. FLANN is slower, but more stable. BOW is faster, but can sometimes miss valid matches. . Default: flann

        @@ -357,7 +363,7 @@
        --min-num-features <integer>

        Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

        -
        name <project name>

        Name of dataset (i.e subfolder name within project folder). Default: code

        +
        name <dataset name>

        Name of dataset (i.e subfolder name within project folder). Default: code

        --optimize-disk-space

        Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don’t have sufficient disk space available. Default: False

        @@ -365,6 +371,8 @@
        --orthophoto-cutline

        Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

        +
        --orthophoto-kmz

        Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

        +
        --orthophoto-no-tiled

        Set this parameter if you want a striped GeoTIFF. Default: False

        --orthophoto-png

        Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

        @@ -393,7 +401,7 @@
        --project-path <path>

        Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an «images» folder.

        -
        --radiometric-calibration none | camera | camera+sun

        Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

        +
        --radiometric-calibration none | camera | camera+sun

        Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

        --rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

        Rerun this stage only and stop. . Default: ``

        @@ -427,6 +435,8 @@
        --texturing-data-term gmi | area

        When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

        +
        --texturing-keep-unseen-faces

        Keep faces in the mesh that are not seen in any camera. Default: False

        +
        --texturing-outlier-removal-type none | gauss_clamping | gauss_damping

        Type of photometric outlier removal method. . Default: gauss_clamping

        --texturing-skip-global-seam-leveling

        Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

        @@ -453,8 +463,8 @@

        Aprende a editar y ayuda a mejorar esta página!

        -
        -
        + +
        diff --git a/es/contributing.html b/es/contributing.html index d3a1e3e7a..d14d75179 100644 --- a/es/contributing.html +++ b/es/contributing.html @@ -4,10 +4,11 @@ - + + - Como contribuir — documentación de OpenDroneMap - 2.4.7 + Como contribuir — documentación de OpenDroneMap - 2.5.5 @@ -63,7 +64,7 @@
        - 2.4.7 + 2.5.5
        @@ -128,14 +129,14 @@
      • Creación de Ortofotos de Alta Calidad
      • Calibrar la cámara
      • Creación de modelos de elevación digitales
        • -
      • Measuring stockpile volume
          -
        • Fieldwork planning
          • -
        • Flight pattern
          • -
        • Flight height
          • +
        • Medir el volumen de pilas de materiales a granel
        • Usando Docker
            @@ -291,14 +292,14 @@
            -
            +

            Como contribuir

            OpenDroneMap depende de las contribuciones de la comunidad. Puedes contribuir de muchas formas, aunque no seas programador.

            -
            +

            Foro comunitario

            Si quieres involucrarte, tienes un porblema o quieres conectarte, el foro es un excelente lugar para comenzar. Es posible que que encuentres tus preguntas ya respondidas o puedes encontrar consejos utiles y recursos. Tambien puedes contribuir con tus conjuntos de datos de acceso abierto para que otros los puedan explorar. Es un buen lugar para revisar antes de enviar informes de errores oponerse ne contacto con los desarrolladores antes de escribir una nueva caracterisitca.

            -
            -
            +
            +

            Reportando Errores

            Errores son rastreados como problemas de Github. Por favor crea un porblema en el repositorio y etiquetalo con la etiqueta del error.

            Explica el problema e incluye detalles adicionales para ayudar a los mantenedores a reproducir el problema:

            @@ -319,7 +320,7 @@ Un lanzamiento estable? un clon de un master?

          • Cual es el nombre y la version del sistema operativo que estas usando?

          • Estas usando ODM en una maquina virtual o Docker? Si es asi, que software de maquina virtualestas usando y que sistema operativo y versiones estas usando para el host y el guest?

          -
          +

          Plantilla Para Enviar Informes de Errores

          [Short description of problem here]
 
@@ -351,9 +352,9 @@ Un lanzamiento estable? un clon de un master?
          • * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
    • - - -
    + + +

    Solicitudes de Extraxion

    • Incluye capturas de pantalla y GIFs animados en tus solicitudes de extraccion siempre que sea posible.

      • @@ -377,8 +378,8 @@ Un lanzamiento estable? un clon de un master?

    Aprende a editar y ayuda a mejorar esta página!

    -
    - + + diff --git a/es/flying.html b/es/flying.html index 13c8c0dd0..1c9b21546 100644 --- a/es/flying.html +++ b/es/flying.html @@ -4,10 +4,11 @@ - + + - Consejos de vuelo — documentación de OpenDroneMap - 2.4.7 + Consejos de vuelo — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -129,14 +130,14 @@
  • Creación de Ortofotos de Alta Calidad
  • Calibrar la cámara
  • Creación de modelos de elevación digitales
    • -
  • Measuring stockpile volume
      -
    • Fieldwork planning
      • -
    • Flight pattern
      • -
    • Flight height
      • +
    • Medir el volumen de pilas de materiales a granel
    • Usando Docker
        @@ -292,7 +293,7 @@
        -
        +

        Consejos de vuelo

        El Equipo humanitario de OpenStreetMap tiene pautas para volar para UAV mapping:

        +
        diff --git a/es/gcp.html b/es/gcp.html index 6ca144332..6809eb63e 100644 --- a/es/gcp.html +++ b/es/gcp.html @@ -4,10 +4,11 @@ - + + - Puntos de Control Terrestre — documentación de OpenDroneMap - 2.4.7 + Puntos de Control Terrestre — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
        - 2.4.7 + 2.5.5
        @@ -129,14 +130,14 @@
      • Creación de Ortofotos de Alta Calidad
      • Calibrar la cámara
      • Creación de modelos de elevación digitales
        • -
      • Measuring stockpile volume
          -
        • Fieldwork planning
          • -
        • Flight pattern
          • -
        • Flight height
          • +
        • Medir el volumen de pilas de materiales a granel
        • Usando Docker
            @@ -292,19 +293,19 @@
            -
            +

            Puntos de Control Terrestre

            Los puntos de control terrestre son útiles para corregir distorsiones en los datos y referenciarlos en sistemas de coordenadas conocidos.

            Un Punto de Control Terrestre (GCP por sus siglas en inglés) es una medición de la posición hecha sobre el terreno, típicamente usando un GPS de alta precisión (Toffanin 2019).

            Los puntos de control terrestres se pueden colocar en estructuras existentes como esquinas de pavimento, líneas en un estacionamiento o baldosas de colores contrastantes; de otra manera, se pueden configurar utilizando objetivos colocados en el suelo.

            Estos objetivos pueden comprarse o construirse con una amplia variedad de materiales que van desde tapas de cubetas hasta baldosas.

            - -
            +
            +

            Formato del archivo GCP

            El formato del archivo GCP es simple.

            @@ -334,8 +335,8 @@

            Las esquinas definidas son buenas elecciones para GCPs. También debes colocar los GCP de manera uniforme alrededor del área de la medición.

            El archivo gcp_list.txt debe ser creado en la carpeta base de tu proyecto.

            Para buenos resultados, tu archivo debe tener un mínimo de 15 líneas después del encabezado (5 puntos con 3 imágenes para cada punto).

            -
            -
            + +

            Interfaces de Usuario

            Puedes usar una de las dos interfaces de usuario para crear archivos de GCP:

            @@ -344,7 +345,7 @@

          • GCP Editor Pro <https://github.com/uav4geo/GCPEditorPro>

        • -
        +

        POSM GCPi

        POSM GCPi esta cargado automaticamente en WebODM. Un ejemplo esta disponible en the WebODM Demo. Para usarlo con valores XYZ de control terrestre conocidos, se haría lo siguiente:

        Crear una lista GCP, que solo incluya el nombre gcp (esta es la etiqueta que se verá en la interfaz GCP), x, y, y z, con un título con una cadena de caracteres proj4 de sus GCPs (asegurese de que están es un sistema de coordenadas plano, como UTM. Debería verse algo así:

        @@ -357,8 +358,8 @@

        Después se puede cargar la lista GCP en la interfaz, cargar las imágenes y colocar cada uno de los GCPs en la imagen.

        - -
        + +

        GCP Editor Pro

        Esta aplicación necesita ser instalada separadamente o puede ser cargada como un plugin WebODM desde https://github.com/uav4geo/GCPEditorPro

        Crear un archivo csv que incluya el nombre gcp, el norte, el este y la elevación.

        @@ -370,14 +371,14 @@

        Luego importar el archivo CSV desde la pantalla principal y escribir +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs en el cuadro EPSG/PROJ .

        La siguiente pantalla va a mostrar un mapa desde donde se puede seleccionar los GCPs a etiquetar e importar las respectivas imágenes.

        - - -
        + + +

        Referencias

        Toffanin, Piero. Open Drone Map: The Missing Guide. MasseranoLabs LLC, 2019.

        Aprende a editar y ayuda a mejorar esta página!

        -
        - + + diff --git a/es/genindex.html b/es/genindex.html index 14dc9c04a..15b15ef57 100644 --- a/es/genindex.html +++ b/es/genindex.html @@ -7,7 +7,7 @@ - Índice — documentación de OpenDroneMap - 2.4.7 + Índice — documentación de OpenDroneMap - 2.5.5 @@ -62,7 +62,7 @@
        - 2.4.7 + 2.5.5
        @@ -127,14 +127,14 @@
      • Creación de Ortofotos de Alta Calidad
      • Calibrar la cámara
      • Creación de modelos de elevación digitales
        • -
      • Measuring stockpile volume
          -
        • Fieldwork planning
          • -
        • Flight pattern
          • -
        • Flight height
          • +
        • Medir el volumen de pilas de materiales a granel
        • Usando Docker
            diff --git a/es/geo.html b/es/geo.html index c28f75e4c..42f315aa4 100644 --- a/es/geo.html +++ b/es/geo.html @@ -4,10 +4,11 @@ - + + - Archivos de geolocalización de imagen — documentación de OpenDroneMap - 2.4.7 + Archivos de geolocalización de imagen — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
            - 2.4.7 + 2.5.5
            @@ -129,14 +130,14 @@
          • Creación de Ortofotos de Alta Calidad
          • Calibrar la cámara
          • Creación de modelos de elevación digitales
            • -
          • Measuring stockpile volume
              -
            • Fieldwork planning
              • -
            • Flight pattern
              • -
            • Flight height
              • +
            • Medir el volumen de pilas de materiales a granel
            • Usando Docker
                @@ -292,7 +293,7 @@
                -
                +

                Archivos de geolocalización de imagen

                Por defecto, ODM utilizará la información GPS incrustada en las imágenes, si está disponible. A veces, las imágenes no contienen información GPS o un usuario desea anular la información con datos más precisos (como RTK).

                A partir de ODM `` 2.0 “”, las personas pueden proporcionar un archivo de geolocalización de imagen (geo) para este propósito.

                @@ -320,7 +321,7 @@

                Si suministras un archivo llamado geo.txt ODM lo detectara automáticamente. Si el archivo tiene otro nombre, puedes especificar usando --geo 1.

                El archivo geo.txt debe ser creado en la carpeta base de tu proyecto.

                Aprende a editar y ayuda a mejorar esta página!

                -
                +
                diff --git a/es/index.html b/es/index.html index 6cb4cafa2..10193824d 100644 --- a/es/index.html +++ b/es/index.html @@ -4,10 +4,11 @@ - + + - Bienvenido a la documentación de OpenDroneMap — documentación de OpenDroneMap - 2.4.7 + Bienvenido a la documentación de OpenDroneMap — documentación de OpenDroneMap - 2.5.5 @@ -63,7 +64,7 @@
                - 2.4.7 + 2.5.5
                @@ -128,14 +129,14 @@
              • Creación de Ortofotos de Alta Calidad
              • Calibrar la cámara
              • Creación de modelos de elevación digitales
                • -
              • Measuring stockpile volume
                  -
                • Fieldwork planning
                  • -
                • Flight pattern
                  • -
                • Flight height
                  • +
                • Medir el volumen de pilas de materiales a granel
                • Usando Docker
                    @@ -291,10 +292,10 @@
                    diff --git a/es/installation.html b/es/installation.html index 3c7151196..271c78be5 100644 --- a/es/installation.html +++ b/es/installation.html @@ -4,10 +4,11 @@ - + + - Instalación y Guía de Introducción — documentación de OpenDroneMap - 2.4.7 + Instalación y Guía de Introducción — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                    - 2.4.7 + 2.5.5
                    @@ -129,14 +130,14 @@
                  • Creación de Ortofotos de Alta Calidad
                  • Calibrar la cámara
                  • Creación de modelos de elevación digitales
                    • -
                  • Measuring stockpile volume
                      -
                    • Fieldwork planning
                      • -
                    • Flight pattern
                      • -
                    • Flight height
                      • +
                    • Medir el volumen de pilas de materiales a granel
                    • Usando Docker
                        @@ -292,50 +293,50 @@
                        -
                        +

                        Instalación y Guía de Introducción

                        Esta sección es extraída y modificada con el permiso de OpenDroneMap: The Missing Guide, realizada por Piero Toffanin.

                        Hasta hace poco OpenDroneMap era el término usado para referirse a una aplicación de comando único (que ahora es conocido como el proyecto ODM). Con el reciente desarrollo de una interfaz web, una API y otras herramientas, OpenDroneMap se ha convertido en un ecosistema de varias aplicaciones para procesar, analizar y mostrar datos aéreos. Este ecosistema es hecho de varios componentes:

                        -
                        +
                        ODM Logo -
                        +

                        • ODM es un motor de procesamiento, que puede ser usado desde la línea de comando. Este toma imágenes como datos de entrada y produce una variedad de datos de salida, incluyendo nubes de puntos, modelos 3D y ortofotografías

                        -
                        +
                        NodeODM Logo -
                        +

                        • NodeODM es una aplicación ligera construida sobre ODM. Esta permite a usuarios y aplicaciones tener acceso a las funciones de ODM en una red de computadores

                        -
                        +
                        WebODM Logo -
                        +

                        • WebODM es una interfaz fácil de usar que incluye un visualizador de mapas, un visualizador 3D, registros de los usuarios, un sistema de complementos y muchos más elementos que son esperados de las plataformas de cartografía con dron modernas

                        -
                        +
                        CloudODM Logo -
                        +

                        • CloudODM es un pequeño cliente de la línea de comandos para comunicarse con ODM vía la API NodeODM

                        -
                        +
                        PyODM Logo -
                        +

                        • PyODM es un Python SDK para crear tareas vía la API NodeODM. Cubrimos este aspecto en mayor detalle en el capítulo “Procesamiento Automático con Python”

                        -
                        +
                        ClusterODM Logo -
                        +

                        • ClusterODM es un equilibrador de carga para conectar varias instancias de NodeODM

                        ODM, NodeODM y WebODM están disponibles en todas las plataformas más importantes (Windows, macOS y Linux) vía un programa llamado docker, el cuál es requerido para hacer funcionar el software. Docker ofrece una manera de correr «contenedores». Los contenedores son copias empaquetadas de un sistema completo, su software y sus dependencias. Estos contenedores funcionan dentro de un ambiente virtual. En Linux este ambiente virtual está disponible desde el sistema operativo y es muy eficiente. En macOS y Windows, estos contenedores funcionan dentro de una máquina virtual, así que hay un poco de sobrecarga, sin embargo, es todavía muy adecuado para ejecutar el software. Una vez instalado los usuarios no deben preocuparse mucho acerca del docker, ya que este opera (casi) transparentemente.

                        Sin Docker, no sería posible ejecutar ODM en Windows o macOS. En estas plataformas, ODM no se puede ejecutar de forma nativa. Los esfuerzos de desarrollo futuros se están centrando en aprovechar el nuevo Sub sistema de Windows para Linux (WSL) y la posibilidad de hacer un puerto nativo de todas las dependencias para macOS, lo que facilitará mucho la instalación.

                        En Ubuntu Linux 18.04 es posible ejecutar todo el software OpenDroneMap de forma nativa. Sin embargo, como hay muy poca penalización de rendimiento por ejecutar Docker en Linux y Docker es fácil de configurar en esta plataforma, no lo recomendamos. En Linux, las ventajas de usar contenedores superan con creces la pequeña penalización de rendimiento. Con Docker los usuarios también obtienen fáciles actualizaciones del software, lo que está muy bien.

                        -
                        +

                        Recomendaciones de Hardware

                        Los requisitos mínimos para ejecutar el software son:

                          @@ -350,28 +351,28 @@

                        • 16GB de RAM

                        Las especificaciones de arriba permitirán que unos cuantos cientos de imágenes sean procesados sin muchos problemas. Una CPU con más cores permitirán un procesamiento más rápido, mientras que una tarjeta gráfica (GPU) en el momento no tiene impacto alguno en el desempeño. Para procesar más imágenes, agregue más espacio en el disco y agregue más RAM de manera lineal con respecto al número de imágenes que necesita procesar

                        -
                        -
                        +
                        +

                        Instalación

                        Recomendamos utilizar docker para ejecutar ODM, ya sea si está utilizando Windows, macOS o Linux.

                        -
                        +

                        Windows

                        Para ejecutar OpenDroneMap necesita al menos Windows 7. Versiones anteriores de Windows no están soportadas.

                        -
                        +

                        Paso 1. Verificar el Soporte de Virtualización

                        Docker requiere una función de la CPU llamada virtualización, la que permite ejecutar máquinas virtuales (VMs). Asegúrese de que la tiene activada! Ya que algunas veces está desactivada. En Windows 8 o versiones más actuales, esto se puede verificar abriendo el Administrador de Tareas (presionar CTRL+SHIFT+ESC) y cambiando a la ventana de Rendimiento.

                        -
                        +
                        Image of checking virtualization in Windows 8 or higher -
                        +

                        Virtualización debería estar activada

                        En Windows 7 para ver si la opción de virtualización está activada, se puede usar la herramienta Microsoft® Hardware-Assisted Virtualization Detection Tool <http:// www.microsoft.com/en-us/download/details.aspx?id=592>.

                        Si Ia opción de virtualización está desactivada, necesitará activarla. El procedimiento desafortunamente es un poco diferente para cada modelo de computador, así que la mejor manera de hacerlo es averiguando en un motor de búsqueda “cómo activar vtx para <type your computer model here>». Muchas veces es una cuestión de reiniciar el computador, inmediatamente presionando F2 o F12 durante el arranque, navegando por el menú de arranque y cambiando las configuraciones para activar la opción de virtualización (frecuentemente llamada “VT-X”).

                        -
                        +
                        Table of different bios keys -
                        +

                        Teclas comunes para presionar en el momento de arranque del computador, para acceder al menú de arranque para varios vendedores de PCs

                        -
                        -
                        +
                        +

                        Paso 2. Requisitos de instalación

                        Primero, deberá instalar:

                        Para Phyton 3, asegurese de marcar Agregar Phyton 3 a la ruta durante la instalación.

                        -
                        +
                        Screenshot of Python3 installation process -
                        +

                        No olvides agregar el ejecutable de Phyton 3 a la ruta (para que puedas correr los comandos con el)

                        Luego, solo si estas en Windows 10 Home, Windows 8 (cualquier versión) o Windows 7 (cualquier versión), instalar:

                          @@ -393,8 +394,8 @@

                        Por favor NO instalar ambos programas de Docker. Ellos son diferentes y crearán conflictos si están ambos instalados.

                        Después de instalar Docker, ábrelo desde el ícono en el Escritorio que es creado desde la instalación (Inicio Rápido de Docker en el caso de la caja de herramientas Docker, Docker para Windows en el caso de Docker para Windows).

                        -
                        -
                        +
                        +

                        Paso 3. Revisa la memoria y la asignación del procesador

                        Docker en Windows funciona ejecutando una VM en segundo plano (piensa en una VM como un «emulador de computador»). Esta VM tiene una cierta cantidad de memoria asignada y WebODM sólo puede usar tanta memoria como esté asignada.

                        Si instalaste la Caja de Herramientas Docker (observa abajo si instalaste Docker para Windows):

                        @@ -404,9 +405,9 @@

                      • Haga clic con el botón derecho en la ** VM predeterminada ** y presione ** Configuración…**

                      • Mueva el control deslizante de Base Memory desde el panel System y asigne el 60-70% de toda la memoria disponible, agregando opcionalmente el 50% de los procesadores disponibles desde la pestaña Processor también

                        • -
                        +
                        Screenshot of VirtualBox Settings -
                        +

                        Configuración predeterminada de la VM de VirtualBox

                        Luego presione OK, haga clic derecho en la VM predeterminada y presione Iniciar.

                        Si instalaste Docker para Windows en su lugar:

                        @@ -416,16 +417,16 @@

                      • Desde el panel, haga clic en Avanzado y use los controles deslizantes para asignar el 60-70% de la memoria disponible y utilice la mitad de los CPUs disponibles.

                      • Clic en Aplicar

                        • -
                        +
                        Screenshot of Docker Icon -
                        +

                        Paso 1 Ícono de Docker

                        -
                        +
                        Screenshot of Docker Settings -
                        +

                        Paso 3 y 4 Configuración de Docker

                        -
                        -
                        + +

                        Paso 4. Descarga WebODM

                        Abra el programa Git GUI que viene instalado con Git. Desde ahí:

                          @@ -434,27 +435,27 @@

                        • En Target Directory haga clic en Browse y navegue a la carpeta que elija (cree una si es necesario)

                        • Presione Clone

                        -
                        +
                        Screenshot of Git Gui -
                        +

                        Git Gui

                        Si la descarga se realizó correctamente, ahora debería ver esta ventana:

                        -
                        +
                        Screenshot of Git Gui after successful download -
                        +

                        Git Gui después de una descarga exitosa (clone)

                        Vaya al menú Repository, luego haga clic en Create Desktop Icon. Esto le permitirá volver fácilmente a esta aplicación en el futuro.

                        -
                        -
                        + +

                        Paso 4. Inicie WebODM

                        Desde Git Gui, vaya al menú Repository, luego haga clic en Git Bash. Desde la terminal de línea de comando teclee:

                        $ ./webodm.sh start&

                        En este punto, se descargarán varios componentes en su máquina, incluidos WebODM, NodeODM y ODM. Después de la descarga, debería aparecer la siguiente pantalla:

                        -
                        +
                        Screenshot of after successfully downloading WebODM -
                        +

                        Salida de consola después de iniciar WebODM por primera vez

                        • Si está utilizando Docker para Windows, abra un navegador web en http://localhost:8000

                          • @@ -468,12 +469,12 @@

                        Luego conéctese a http://192.168.1.100:8000 (reemplazando la dirección IP por la correcta).

                        -
                        -
                    • -
                    + + +

                    macOS

                    La mayoría de las computadoras Mac modernas (posteriores a 2010) que ejecutan macOS Sierra 10.12 o superior pueden ejecutar OpenDroneMap mediante la ventana acoplable, siempre que se admita la virtualización de hardware (ver más abajo).

                    -
                    +

                    Paso 1. Verificar el Soporte de Virtualización

                    Abre una ventana de Terminal y teclea:

                    $ sysctl kern.hv_support
@@ -485,8 +486,8 @@

                    Si el resultado es kern.hv_support: 1, ¡tu Mac es compatible! Continúa con el paso 2.

                    Si el resultado es kern.hv_support: 0, desafortunadamente significa que tu Mac es demasiado vieja para ejecutar OpenDroneMap. :(

                    -
                    -
                    +
                    +

                    Paso 2. Requisitos de instalación

                    Solo hay dos programas para instalar:

                      @@ -494,9 +495,9 @@

                    1. Git: https://sourceforge.net/projects/git-osx-installer/files/

                    Después de instalar Docker, deberías encontrar un icono que parece una ballena en la barra de tareas.

                    -
                    +
                    Screenshot of Docker whale -
                    +

                    Aplicación Docker en ejecución

                    Puedes verificar que la ventana acoplable se esté ejecutando correctamente abriendo la aplicación Terminal y escribiendo:

                    $ docker run hello-world
@@ -515,8 +516,8 @@

                    Si obtiene un «bash: git: comando no encontrado», intente reiniciar su aplicación Terminal y verifique si hay errores durante el proceso de instalación.

                    -
                    -
                    +
                    +

                    Paso 3. Revisa la memoria y la asignación del procesador

                    Docker en macOS funciona ejecutando una VM en segundo plano (considérelo como un «emulador de computadora»). Esta VM tiene una cierta cantidad de memoria asignada y WebODM solo puede usar la cantidad de memoria asignada.

                      @@ -525,12 +526,12 @@

                    1. Ajuste el control deslizante de las CPU para usar la mitad de todas las CPU disponibles y la memoria para usar el 60-70% de toda la memoria disponible

                    2. Presione Aplicar y reiniciar

                    -
                    +
                    Screenshot of Docker advanced settings -
                    +

                    Configuración avanzada de Docker

                    -
                    -
                    + +

                    Paso 4. Descargue y ejecute WebODM

                    Desde la Terminal teclee:

                    $ git clone https://github.com/OpenDroneMap/WebODM
@@ -539,12 +540,12 @@ $ ./webodm.sh start

                    Luego abra un navegador web en http://localhost:8000.

                    -
                    -
                • -
                + + +

                Linux

                OpenDroneMap puede ejecutarse en cualquier distribución de Linux que admita Docker. Según el sitio web de documentación de docker <https://docs.docker.com/install/> _ las distribuciones oficialmente soportadas son CentOS, Debian, Ubuntu y Fedora, con binarios estáticos disponibles para otros. Si tiene que elegir una distribución únicamente para ejecutar OpenDroneMap, Ubuntu es la distribución recomendada.

                -
                +

                Paso 1. Requerimientos de instalación

                Hay cuatro programas que deben instalarse:

                  @@ -554,7 +555,7 @@ $ ./webodm.sh start

                1. Pip

                No podemos cubrir el proceso de instalación de todas las distribuciones de Linux que existen, por lo que limitaremos las instrucciones a aquellas que son distribuciones oficialmente compatibles con Docker. En todos los casos, solo es cuestión de abrir un indicador de terminal y escribir algunos comandos.

                -
                +
                Instalar en Ubuntu / Debian

                Comandos para escribir:

                $ sudo apt update
@@ -563,8 +564,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
                -
                -
                +
                +
                Instalar en CentOS / RHEL

                Comandos para escribir:

                $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -572,8 +573,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
                -
                -
                +
                +
                Instalar en Fedora

                Comandos para escribir:

                $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -581,16 +582,16 @@ $ sh get-docker.sh
 $ sudo dnf install git python python-pip
                -
                -
                + +
                Instalar en Arch

                Comandos para escribir:

                $ sudo pacman -Sy docker git python python-pip
                -
                - -
                + + +

                Paso 2. Verifique los requisitos adicionales

                Además de los tres programas anteriores, también se necesita el script de Dockercompose. A veces, ya está instalado con Docker, pero a veces no. Para verificar si está instalado, intente escribir:

                $ docker-compose --version
@@ -608,8 +609,8 @@ $ sudo dnf install git python python-pip
 
                $ sudo pip install docker-compose
 
                
 
                
-
                -
                +
                +

                Paso 3. Descargue y ejecute WebODM

                Desde la Terminal escriba:

                $ git clone https://github.com/OpenDroneMap/WebODM
@@ -618,10 +619,10 @@ $ ./webodm.sh start

                Luego abra un navegador web en http://localhost:8000.

                -
                - - -
                + + + +

                Comandos básicos y resolución de problemas

                Lo bueno de usar Docker es que el 99% de las tareas que necesitará realizar mientras usa WebODM se pueden realizar a través del script ./webodm.sh. Ya te has encontrado con uno de ellos:

                $ ./webodm.sh start
@@ -631,7 +632,7 @@ $ ./webodm.sh start
 
                $ ./webodm.sh stop
 
                
 
                
-
                Hay otros varios comandos que puede usar, junto con diferentes banderas. Los indicadores son parámetros que se pasan al comando ./webodm.sh y suelen tener el prefijo «-«. El indicador port, por ejemplo, indica a WebODM que utilice un puerto de red diferente:
                
+
                Hay otros varios comandos que puede usar, junto con diferentes banderas. Los indicadores son parámetros que se pasan al comando ./webodm.sh y suelen tener el prefijo «-». El indicador port, por ejemplo, indica a WebODM que utilice un puerto de red diferente:
                
 
                $ ./webodm.sh start --port 80
 
                
 
                
@@ -653,20 +654,20 @@ $ ./webodm.sh --help

                El foro de la comunidad The community forum <https://community.opendronemap.org> _ es un gran lugar para pedir ayuda si tiene dificultades durante cualquiera de los pasos de instalación y si tiene preguntas generales sobre el uso del script ./webodm.sh.

                -
                -
                + +

                ¡Hola WebODM!

                Después de ejecutar ./webodm.sh start y abrir WebODM en el navegador, se le dará la bienvenida con un mensaje de bienvenida y se le pedirá que cree el primer usuario. Tómese un tiempo para familiarizarse con la interfaz web y explorar sus diversos menús.

                -
                +
                Screenshot of WebODM Dashboard -
                +

                WebODM Dashboard

                Tenga en cuenta que en el menú Processing Nodes hay un nodo «node-odm-1» ya configurado para su uso. Este es un nodo NodeODM y ha sido creado automáticamente por WebODM. Este nodo se ejecuta en la misma máquina que WebODM.

                Si has llegado hasta aquí, ¡felicitaciones! Ahora es el momento de comenzar a procesar algunos datos.

                -
                +
                Image of celebratory dance -
                -
                + +

                Ejecutando en más de una máquina

                Opcionalmente: Si tiene otra computadora, puede repetir el proceso de instalación (instalar docker, git, python, etc.) y lanzar un nuevo nodo NodeODM escribiendo desde una ventana Terminal / Git Bash:

                docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -676,9 +677,9 @@ $ ./webodm.sh --help
 
                Desde WebODM, puede presionar el botón Add new debajo de Processing Nodes. En el campo hostname/IP **, escriba la IP de la segunda computadora. Para el campo **port, escriba «3000». Para el campo token, escriba «secret». También puede agregar una etiqueta label opcional para su nodo, como «segunda computadora». Luego presione Save para guardar.
                
 
                Si todo salió bien, ¡ahora debería tener dos nodos de procesamiento! Podrá procesar múltiples tareas en paralelo utilizando dos máquinas diferentes.
                
 
                Aprende a editar y ayuda a mejorar esta página!
                
-
                -
                -
                +
                + +
                diff --git a/es/large.html b/es/large.html index d6d32403f..dd41038fc 100644 --- a/es/large.html +++ b/es/large.html @@ -4,10 +4,11 @@ - + + - División de grandes conjuntos de datos — documentación de OpenDroneMap - 2.4.7 + División de grandes conjuntos de datos — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                - 2.4.7 + 2.5.5
                @@ -129,14 +130,14 @@
              • Creación de Ortofotos de Alta Calidad
              • Calibrar la cámara
              • Creación de modelos de elevación digitales
                • -
              • Measuring stockpile volume
                  -
                • Fieldwork planning
                  • -
                • Flight pattern
                  • -
                • Flight height
                  • +
                • Medir el volumen de pilas de materiales a granel
                • Usando Docker
                    @@ -292,20 +293,20 @@
                    -
                    +

                    División de grandes conjuntos de datos

                    A partir de la versión 0.6.0 de ODM, puede dividir conjuntos de datos muy grandes en fragmentos manejables (llamados submodelos), ejecutar la canalización en cada fragmento y luego producir DEM, ortofotos y nubes de puntos fusionados. El proceso se conoce como «split-merge».

                    ¿Por qué debería utilizar la canalización split-merge? Si tiene una gran cantidad de imágenes en su conjunto de datos, split-merge ayudará a que el procesamiento sea más manejable en una máquina grande (requerirá menos memoria). Si tiene muchas máquinas conectadas a la misma red, también puede procesar los submodelos en paralelo, lo que permite el escalado horizontal y el procesamiento de miles de imágenes más rápidamente.

                    Split-merge funciona en WebODM de forma inmediata siempre que los nodos de procesamiento admitan split-merge, al habilitar la opción --split al crear una nueva tarea.

                    -
                    +

                    Calibrar imágenes

                    Se recomienda (pero no es obligatorio) la calibración de imagen para grandes conjuntos de datos porque la propagación del error debido a la distorsión de la imagen podría causar un efecto de tazón en los modelos. Las instrucciones de calibración se pueden encontrar en calibración de imágenes Calibrate Images.

                    -
                    +
                    image of lens distortion effect on bowling of data -
                    +

                    Efecto de tazón en la nube de puntos sobre más de 13,000 conjuntos de datos de imágenes recopilados por el Banco Mundial de Tanzania sobre la cuenca de Msimbasi, propensa a inundaciones, Dar es Salaam, Tanzania

                    -
                    -
                    +
                    +

                    Split-merge local

                    ¡Es fácil dividir un conjunto de datos en submodelos más manejables y procesar secuencialmente todos los submodelos en la misma máquina! Simplemente use --split y --split-overlap para decidir el número promedio de imágenes por submodelos y la superposición (en metros) entre submodelos respectivamente

                    docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
@@ -321,14 +322,14 @@

                    creará 3 submodelos. Asegúrese de pasar --split-overlay 0 si proporciona manualmente un archivo image_groups.txt.

                    -
                    -
                    + +

                    Split-Merge distribuido

                    ODM también puede distribuir automáticamente el procesamiento de cada submodelo a varias máquinas a través de los nodos NodeODM <https://github.com/OpenDroneMap/NodeODM> _, orquestados a través de ClusterODM.

                    -
                    +
                    image of lens distortion effect on bowling of data -
                    -
                    + +

                    Introducción a split-merge distribuido

                    El primer paso es iniciar ClusterODM

                    docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -356,8 +357,8 @@ Escape character is '^]'.
 
                    docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
                    
 
                    
-
                    -
                    +
                    +

                    Entendiendo el Cluster

                    Cuando se conecta a través de telnet, es posible interrogar qué está sucediendo en el clúster. Por ejemplo, podemos usar el comando HELP para averiguar los comandos disponibles.

                    # HELP
@@ -392,16 +393,16 @@ OK
 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
                    -
                    -
                    +
                    +

                    Acceder a los registros

                    Mientras se ejecuta un proceso, también es posible enumerar las tareas y ver el resultado de la tarea

                    # TASK LIST
 # TASK OUTPUT <taskId> [lines]
                    -
                    -
                    + +

                    Ajuste de escala automático de ClusterODM

                    ClusterODM también incluye la opción de escalar automáticamente en múltiples plataformas, incluidas, hasta la fecha, Amazon y Digital Ocean. Esto permite a los usuarios reducir los costos asociados con las instancias siempre activas, además de poder escalar el procesamiento en función de la demanda.

                    Para configurar el ajuste de escala automático, debe:

                    @@ -437,19 +438,19 @@ info: Found docker-machine executable

                    De esta forma, todas las tareas se reenviarán automáticamente al escalador automático.

                    -
                    - -
                    + + +

                    Limitaciones

                    Las mallas texturizados 3D actualmente no son fusionadas como parte del flujo de trabajo (solo las nubes de puntos, DEMs y las ortofotos lo son)

                    Los GCP son totalmente compatibles, sin embargo, debe haber al menos 3 puntos de GCP en cada submodelo para que se lleve a cabo la georreferenciación. Si un submodelo tiene menos de 3 GCP, en su lugar se usará una combinación de los GCP restantes + datos EXIF (que será menos precisa). Recomendamos utilizar el archivo ʻimage_groups.txt` para controlar con precisión la división del submodelo cuando se utilizan GCP.

                    -
                    -
                    + +

                    Agradecimientos

                    Felicitaciones para Pau y la gente de Mapillary por sus increíbles contribuciones a OpenDroneMap a través de su código OpenSfM, que es un componente clave del proceso de split-merge. Esperamos ampliar aún más los límites de OpenDroneMap y ver qué tan grande es el conjunto de datos que podemos procesar.

                    Aprende a editar y ayuda a mejorar esta página!

                    -
                    - + + diff --git a/es/map-accuracy.html b/es/map-accuracy.html index fd3368605..f227f79ab 100644 --- a/es/map-accuracy.html +++ b/es/map-accuracy.html @@ -4,10 +4,11 @@ - + + - Precisión del Mapa — documentación de OpenDroneMap - 2.4.7 + Precisión del Mapa — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                    - 2.4.7 + 2.5.5
                    @@ -129,14 +130,14 @@
                  • Creación de Ortofotos de Alta Calidad
                  • Calibrar la cámara
                  • Creación de modelos de elevación digitales
                    • -
                  • Measuring stockpile volume
                      -
                    • Fieldwork planning
                      • -
                    • Flight pattern
                      • -
                    • Flight height
                      • +
                    • Medir el volumen de pilas de materiales a granel
                    • Usando Docker
                        @@ -292,30 +293,30 @@
                        -
                        +

                        Precisión del Mapa

                        La precisión se puede definir como el grado o la cercanía con la que la información de un mapa coincide con los valores del mundo real. Por tanto, cuando nos referimos a la precisión, nos referimos a la calidad de los datos y al número de errores contenidos en un determinado conjunto de datos (Pascual 2011).

                        Precisión Relativa o Local

                        La precisión local o relativa se puede definir como el grado en que las distancias entre dos puntos en un mapa corresponden a las distancias reales entre esos puntos en el mundo real.

                        La precisión relativa es independiente de la ubicación del mapa en el mundo, por lo que un mapa puede tener una precisión relativa alta (en tamaño y forma), pero su posición en el mundo se puede cambiar (Figura 1).

                        -
                        +
                        Model showing high relative accuracy -
                        +

                        Figura 1. Modelo que muestra una alta precisión relativa pero está fuera de lugar según su posición en el mundo real

                        Precisión absoluta o global

                        La precisión absoluta es la precisión de la reconstrucción en relación con su posición real en el planeta (Pix4D 2019). La Figura 2 muestra un modelo de precisión relativa y absoluta, ya que los puntos están colocados correctamente de acuerdo con su posición en el mundo real.

                        -
                        +
                        Model showing high absolute accuracy -
                        +

                        Figura 2. Modelo que muestra una alta precisión relativa y absoluta. Colocado correctamente de acuerdo con su posición en el mundo real

                        ** Un nivel de precisión para cada proyecto**

                        Cada proyecto tiene necesidades específicas de precisión que se deben cumplir. Por ejemplo, evaluar el progreso en un sitio de construcción o medir un área afectada por un incendio no requiere el uso de GCP, ya que la precisión absoluta no afectará el proceso de toma de decisiones. Por otro lado, hay tareas en las que la precisión es fundamental, por ejemplo, evaluaciones de cumplimiento de proyectos y levantamiento de títulos de propiedad, que requieren una mayor precisión relativa y absoluta.

                        -
                        +

                        Que esperar

                        En términos generales, se puede esperar que la precisión relativa sea del orden de 1 a 3 veces el GSD promedio para el conjunto de datos. Y en cuanto a la precisión absoluta, se debe considerar que depende de la unidad GPS montada en el UAV pero la precisión horizontal de un GPS estándar suele estar en el rango de 2 a 6 metros y la precisión vertical entre 3 y 4 veces la precisión horizontal.

                        Cuando se utiliza GCP, la precisión absoluta se puede mejorar a 2,5 veces GSD para la precisión horizontal y 4 veces la GSD para la precisión vertical (Madawalagama 2016).

                        A un GSD de 1 cm, la precisión es igual a la del RTK GNSS, y está dentro de las escalas de 1:200 según los estándares de precisión de mapeo NSDI y FGDC durante condiciones subóptimas (Barry 2013).

                        -
                        +

                        Aspectos que impactan la precisión del mapa

                        Clima

                        Las condiciones climáticas tienen un impacto directo en los resultados de la fotogrametría, por lo que es importante considerar la cobertura de nubes, la velocidad del viento, la humedad, la altitud del sol y otros factores que influyen en la estabilidad del UAV y la iluminación del terreno.

                        @@ -325,9 +326,9 @@

                        Cuanto mayor sea la altitud de vuelo, mayor será la huella de la imagen y el GSD. Con el GSD más grande resultante, la precisión disminuirá ya que habrá menos detalles en las características reconocibles. Cuando se requiere un GSD más pequeño, se recomienda una altitud de 3 a 4 veces la altura del punto más alto.

                        Velocidad del vuelo

                        La velocidad de vuelo tiene un efecto especial en las cámaras equipadas con obturador «rolling shutter», mientras que las equipadas con obturador global o mecánico tienden a reducir este efecto. Los UAV equipados con sistemas de posicionamiento RTK también se ven afectados por la velocidad, pero deteniendo la UAV en cada foto tomada, puede obtener una muy buena precisión. Si, en cambio, se está moviendo durante cada toma de fotos, la precisión estará limitada por dos factores: la velocidad a la que se está moviendo multiplicada por los incrementos de 1 segundo de RTK (Mather 2020).

                        -
                        -
                        -
                        +
                        + +

                        Referencias

                        Barry, P., & Coakley, R. «Accuracy of UAV photogrammetry compared with Network RTK GPS.» Baseline Surveys. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (accessed 10 13, 2020).

                        Drone Deploy. How Do I Use Ground Control Points?: A guide to using ground control points with drone mapping software. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (último acceso el 9 del 7, 2020).

                        @@ -336,8 +337,8 @@

                        Pascual, Manuel S. GIS Lounge: GIS Data: A Look at Accuracy, Precision, and Types of Errors. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (último acceso el 09 del 07, 2020).

                        Pix4D. «What is accuracy in an aerial mapping project?» Pix4D. 25 de 05 de 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (último acceso el 13 del 10, 2020).

                        Aprende a editar y ayuda a mejorar esta página!

                        -
                        -
                        + +
                        diff --git a/es/masks.html b/es/masks.html index 75c1e134b..b7c73e64b 100644 --- a/es/masks.html +++ b/es/masks.html @@ -4,10 +4,11 @@ - + + - Usar máscaras de imagen — documentación de OpenDroneMap - 2.4.7 + Usar máscaras de imagen — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                        - 2.4.7 + 2.5.5
                        @@ -129,14 +130,14 @@
                      • Creación de Ortofotos de Alta Calidad
                      • Calibrar la cámara
                      • Creación de modelos de elevación digitales
                        • -
                      • Measuring stockpile volume
                          -
                        • Fieldwork planning
                          • -
                        • Flight pattern
                          • -
                        • Flight height
                          • +
                        • Medir el volumen de pilas de materiales a granel
                        • Usando Docker
                            @@ -292,24 +293,24 @@
                            -
                            +

                            Usar máscaras de imagen

                            A partir de ODM `` 2.0 “”, las personas pueden proporcionar máscaras de imagen para informar al software de que omita la reconstrucción en ciertas áreas. Esto es útil para los casos en los que el cielo se incluyó accidentalmente en las fotos de entrada de tomas oblicuas, o simplemente para limitar la reconstrucción de un solo sujeto.

                            Para agregar una máscara, simplemente cree una nueva imagen en blanco y negro de la misma dimensión que la imagen de destino que desea enmascarar (puede usar un programa como GIMP para hacer esto). Colorea de negro las zonas a excluir de la reconstrucción.

                            -
                            +
                            Target image -
                            -
                            + +
                            Image mask -
                            -
                            + +
                            3D result (building is masked) -
                            +

                            Nombre su archivo:

                            <filename>_mask.JPG

                            Por ejemplo, `` DJI_0018.JPG “” puede tener una máscara creando un archivo `` DJI_0018_mask.JPG “” e incluirlo en la lista de imágenes. Puede utilizar los formatos `` .JPG “”, `` .PNG “”, `` .BMP “” y `` .TIF “” para las máscaras de imagen.

                            Aprende a editar y ayuda a mejorar esta página!

                            -
                            +
                            diff --git a/es/multispectral.html b/es/multispectral.html index 08cceef10..f9041a237 100644 --- a/es/multispectral.html +++ b/es/multispectral.html @@ -4,10 +4,11 @@ - + + - Soporte multiespectral — documentación de OpenDroneMap - 2.4.7 + Soporte multiespectral — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                            - 2.4.7 + 2.5.5
                            @@ -129,14 +130,14 @@
                          • Creación de Ortofotos de Alta Calidad
                          • Calibrar la cámara
                          • Creación de modelos de elevación digitales
                            • -
                          • Measuring stockpile volume

                            • También pueden funcionar otras cámaras. Puede ayudarnos a ampliar esta lista compartiendo conjuntos de datos capturados con otras cámaras.

                            - -
                            + +

                            Uso

                            Procese todas las imágenes de todas las bandas a la vez (no separe las bandas en varias carpetas) y pase el parámetro –radiometric-calibración para permitir la normalización radiométrica. Si las imágenes son parte de una configuración multicámara, la ortofoto resultante tendrá N bandas, una para cada cámara (+ alfa).

                            Aprende a editar y ayuda a mejorar esta página!

                            -
                            - + + diff --git a/es/objects.inv b/es/objects.inv index 93356fae2..4bdd9ad97 100644 Binary files a/es/objects.inv and b/es/objects.inv differ diff --git a/es/outputs.html b/es/outputs.html index efaa1748b..50d01f9b3 100644 --- a/es/outputs.html +++ b/es/outputs.html @@ -4,10 +4,11 @@ - + + - Salidas de OpenDroneMap — documentación de OpenDroneMap - 2.4.7 + Salidas de OpenDroneMap — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                            - 2.4.7 + 2.5.5
                            @@ -129,14 +130,14 @@
                          • Creación de Ortofotos de Alta Calidad
                          • Calibrar la cámara
                          • Creación de modelos de elevación digitales
                            • -
                          • Measuring stockpile volume
                              -
                            • Fieldwork planning
                              • -
                            • Flight pattern
                              • -
                            • Flight height
                              • +
                            • Medir el volumen de pilas de materiales a granel
                            • Usando Docker
                                @@ -292,36 +293,36 @@
                                -
                                +

                                Salidas de OpenDroneMap

                                A continuación se enumeran algunos de los resultados útiles que produce ODM.

                                -
                                +

                                Nube de puntos

                                odm_georeferencing/odm_georeferenced_model.ply/laz/csv – La nube de puntos georeferenciada en diferentes formatos de archivo

                                -
                                +
                                image of OpenDroneMap derived point cloud -
                                +

                                Nube de puntos sobre la Universidad Estatal Zanzibar, cortesía de Khadija Abdullah Ali

                                -
                                -
                                +
                                +

                                Modelo texturizado 3D

                                odm_texturing/odm_textured_model.obj – La malla de superficie texturizada odm_texturing/odm_textured_model_geo.obj – La malla de superficie texturizada y georeferenciada

                                Puede acceder a la nube de puntos y las mallas texturizadas usando MeshLab. Abra MeshLab, y vaya a File:Import Mesh y seleccione su malla texturizada de una ubicación similar a la siguiente: odm_texturing\odm_textured_model.obj

                                -
                                +
                                image of OpenDroneMap derived textured mesh -
                                +

                                Malla texturizada cortesia de OpenDroneMap

                                -
                                -
                                + +

                                Ortofoto

                                odm_orthophoto/odm_orthphoto.png – La ortofoto, pero este es un simple png, que no tiene información de georreferenciación

                                odm_orthophoto/odm_orthophoto.tif – Ortofoto GeoTIFF. Puede usarla en QGIS como una capa raster.

                                -
                                +
                                image of OpenDroneMap orthophoto -
                                +

                                Ortofoto sobre la Universidad Estatal de Zanzibar, cortesía de Khadija Abdullah Ali

                                -
                                -
                                + +

                                DTM/DSM

                                Los DTM/DSM solo se crearán si se usan las opciones --dtm o --dsm. Vea el tutorial de modelos de elevación para mas opciones sobre su creación.

                                Los datos se almacenarán en:

                                @@ -331,12 +332,12 @@

                              • odm_dem/dsm.tif

                              -
                              +
                              image of OpenDroneMap derived digital surface model -
                              +

                              Modelo digital de superficie sobre la Universidad Estatal de Zanzibar, cortesía de Khadija Abdullah Ali

                              - -
                              + +

                              Lista de todas las salidas

                              project/
 ├── images/
@@ -382,8 +383,8 @@

                              Aprende a editar y ayuda a mejorar esta página!

                              -
                              - + + diff --git a/es/requesting-features.html b/es/requesting-features.html index 5bbd286cc..4245e25a4 100644 --- a/es/requesting-features.html +++ b/es/requesting-features.html @@ -4,10 +4,11 @@ - + + - Cómo solicitar funciones — documentación de OpenDroneMap - 2.4.7 + Cómo solicitar funciones — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                              - 2.4.7 + 2.5.5
                              @@ -129,14 +130,14 @@
                            • Creación de Ortofotos de Alta Calidad
                            • Calibrar la cámara
                            • Creación de modelos de elevación digitales
                              • -
                            • Measuring stockpile volume
                                -
                              • Fieldwork planning
                                • -
                              • Flight pattern
                                • -
                              • Flight height
                                • +
                              • Medir el volumen de pilas de materiales a granel
                              • Usando Docker
                                  @@ -292,7 +293,7 @@
                                  -
                                  +

                                  Cómo solicitar funciones

                                  Todo el software necesita comentarios de los usuarios y solicitudes de funciones para crecer y mantener la alineación con las necesidades de sus usuarios.

                                  OpenDroneMap es un software FOSS. Los proyectos gratuitos y de código abierto (FOSS) son interesantes por dentro y por fuera: desde fuera, los que tienen éxito sienten que deberían poder hacer cualquier cosa y es difícil saber qué es una solicitud razonable. Desde el interior de un proyecto, pueden sentirse muy limitados en recursos: principalmente por tiempo, dinero y sobrecarga de oportunidades.

                                  @@ -307,7 +308,7 @@

                                  Nos alegra que esté emocionado de ver nuevas funciones agregadas al proyecto. Algunas funciones nuevas necesitan soporte y otras son más fáciles de implementar. Haremos todo lo posible para ayudarlo a comprender dónde se encuentra su solicitud y agradecemos cualquier apoyo que pueda brindar.

                                  Aprende a editar y ayuda a mejorar esta página!

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                                  diff --git a/es/resources.html b/es/resources.html index c793a7035..c84121880 100644 --- a/es/resources.html +++ b/es/resources.html @@ -4,10 +4,11 @@ - + + - Referencias adicionales — documentación de OpenDroneMap - 2.4.7 + Referencias adicionales — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                                  - 2.4.7 + 2.5.5
                                  @@ -129,14 +130,14 @@
                                • Creación de Ortofotos de Alta Calidad
                                • Calibrar la cámara
                                • Creación de modelos de elevación digitales
                                  • -
                                • Measuring stockpile volume
                                    -
                                  • Fieldwork planning
                                    • -
                                  • Flight pattern
                                    • -
                                  • Flight height
                                    • +
                                  • Medir el volumen de pilas de materiales a granel
                                  • Usando Docker
                                      @@ -292,9 +293,9 @@
                                      -
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                                      Referencias adicionales

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                                      +

                                      Para usuarios

                                      Los siguientes recursos son un buen lugar para comenzar:

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                                      Para desarrolladores

                                      Además de los recursos del usuario, recomendamos leer también lo siguiente:

                                        @@ -315,8 +316,8 @@

                                      • Mantenemos una sección en nuestro foro dedicada a artículos de investigación. Este es un lugar valioso donde leer más sobre la investigación de vanguardia relacionada con la estructura del movimiento, estéreo de múltiples vistas, mallado, texturizado, etc. que se puede utilizar para mejorar el software.

                                      Aprende a editar y ayuda a mejorar esta página!

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                                      diff --git a/es/search.html b/es/search.html index 06b2e73b4..158244228 100644 --- a/es/search.html +++ b/es/search.html @@ -7,7 +7,7 @@ - Búsqueda — documentación de OpenDroneMap - 2.4.7 + Búsqueda — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +64,7 @@
                                      - 2.4.7 + 2.5.5
                                      @@ -129,14 +129,14 @@
                                    • Creación de Ortofotos de Alta Calidad
                                    • Calibrar la cámara
                                    • Creación de modelos de elevación digitales
                                      • -
                                    • Measuring stockpile volume
                                        -
                                      • Fieldwork planning
                                        • -
                                      • Flight pattern
                                        • -
                                      • Flight height
                                        • +
                                      • Medir el volumen de pilas de materiales a granel
                                      • Usando Docker
                                          diff --git a/es/searchindex.js b/es/searchindex.js index 7fdfb00af..5a1a54cdc 100644 --- a/es/searchindex.js +++ b/es/searchindex.js @@ -1 +1 @@ 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e indicadores","Como contribuir","Consejos de vuelo","Puntos de Control Terrestre","Archivos de geolocalizaci\u00f3n de imagen","Bienvenido a la documentaci\u00f3n de OpenDroneMap","Instalaci\u00f3n y Gu\u00eda de Introducci\u00f3n","Divisi\u00f3n de grandes conjuntos de datos","Precisi\u00f3n del Mapa","Usar m\u00e1scaras de imagen","Soporte multiespectral","Salidas de OpenDroneMap","C\u00f3mo solicitar funciones","Referencias 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e indicadores","Como contribuir","Consejos de vuelo","Puntos de Control Terrestre","Archivos de geolocalizaci\u00f3n de imagen","Bienvenido a la documentaci\u00f3n de OpenDroneMap","Instalaci\u00f3n y Gu\u00eda de Introducci\u00f3n","Divisi\u00f3n de grandes conjuntos de datos","Precisi\u00f3n del Mapa","Usar m\u00e1scaras de imagen","Soporte multiespectral","Salidas de OpenDroneMap","C\u00f3mo solicitar funciones","Referencias 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\ No newline at end of file diff --git a/es/tutorials.html b/es/tutorials.html index 33be1c804..98a11a6cc 100644 --- a/es/tutorials.html +++ b/es/tutorials.html @@ -4,10 +4,11 @@ - + + - Tutoriales — documentación de OpenDroneMap - 2.4.7 + Tutoriales — documentación de OpenDroneMap - 2.5.5 @@ -64,7 +65,7 @@
                                          - 2.4.7 + 2.5.5
                                          @@ -129,14 +130,14 @@
                                        • Creación de Ortofotos de Alta Calidad
                                        • Calibrar la cámara
                                        • Creación de modelos de elevación digitales
                                          • -
                                        • Measuring stockpile volume
                                            -
                                          • Fieldwork planning
                                            • -
                                          • Flight pattern
                                            • -
                                          • Flight height
                                            • +
                                          • Medir el volumen de pilas de materiales a granel
                                          • Usando Docker
                                              @@ -292,14 +293,14 @@
                                              -
                                              +

                                              Tutoriales

                                              A continuación, encontrará instrucciones para algunos casos de uso comunes.

                                              -
                                              +

                                              Creación de Ortofotos de Alta Calidad

                                              -
                                              +
                                              image of OpenDroneMap orthophoto -
                                              +

                                              Sin ningún ajuste de parámetros, ODM elije un buen balance entre calidad, velocidad y uso de memoria. Si quiere obtener resultados de mayor calidad, debe modificar algunos parámetros:

                                                @@ -310,39 +311,39 @@

                                              • --mesh-size debe incrementarse a 300000-600000 y --mesh-octree-depth debe incrementarse a 10-11 en áreas urbanas para recrear mejor los edificios/techos.

                                              -
                                              -
                                              +
                                              +

                                              Calibrar la cámara

                                              La calibración de la cámara es un desafío especial con las cámaras básicas. Los cambios de temperatura, las vibraciones, el enfoque y otros factores pueden afectar los parámetros derivados con efectos sustanciales en los datos resultantes. La calibración automática o la autocalibración es posible y deseable para los vuelos de drones, pero dependiendo del patrón de vuelo, la calibración automática puede no eliminar toda la distorsión de los productos resultantes. James y Robson (2014) en su artículo Mitigating systematic error in topographic models derived from UAV and ground‐based image networks abordan cómo para minimizar la distorsión de la autocalibración.

                                              -
                                              +
                                              image of lens distortion effect on bowling of data -
                                              +

                                              Efecto de tazón en la nube de puntos de un juego de datos de mas de 13,000 imágenes colectadas por el Banco mundial de Tanzania sobre la cuenca propensa a inundaciones de Msimbasi, Dar es Salaam, Tanzania.

                                              Para mitigar este efecto, hay algunas opciones, pero las más simples son las siguientes: vuele dos patrones separados por 20 °, y en lugar de tener una cámara nadir (apuntando hacia abajo), use una que se incline hacia adelante en 5 °.

                                              -
                                              +
                                              animation showing optimum -
                                              +

                                              Como este enfoque de vuelo puede llevar más tiempo que los vuelos típicos, un piloto o un equipo pueden volar en un área pequeña utilizando el enfoque anterior. OpenDroneMap generará un archivo de calibración llamado camera.json que luego se puede importar para usarlo para calibrar otro vuelo que se vuela de manera más eficiente.

                                              Alternativamente, se puede aplicar el siguiente método experimental: volar con una superposición mucho menor, pero dos vuelos de rejilla cruzada (a veces llamados rayados cruzados) separados por 20 ° con una cámara de 5 ° orientada hacia adelante.

                                              • Los porcentajes de superposición de rejillas cruzadas pueden ser más bajos que los vuelos paralelos. Para obtener buenos resultados en 3D, necesitará un 68% de superposición y superposición lateral para una superposición y superposición lateral equivalente del 83%.

                                              • Para obtener buenos resultados en 2D y 2.5D (modelo de elevación digital), necesitará un 42% de superposición y superposición lateral para una superposición y superposición lateral equivalente del 70%.

                                              -
                                              +
                                              animation showing experimental optimum -
                                              +

                                              Las líneas de vuelo separadas verticalmente también mejoran la precisión, pero menos que una cámara que mira hacia adelante en 5 °.

                                              -
                                              +
                                              figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
                                              +

                                              De James and Robson (2014), CC BY 4.0

                                              -
                                              -
                                              + +

                                              Creación de modelos de elevación digitales

                                              Por defecto, ODM no crea DEMs. Para crear un modelo digital de terreno, asegúrese de utilizar la marca --dtm. Para crear un modelo digital de superficie, asegúrese de pasar la marca --dsm.

                                              -
                                              +
                                              image of OpenDroneMap derived digital surface model -
                                              +

                                              Para la generación del DTM, se utiliza un filtro morfológico simple (smrf) para clasificar los puntos terrestres frente a los no terrestres y solo se utilizan los puntos terrestres. El filtro smrf se puede controlar mediante varios parámetros:

                                                @@ -355,9 +356,9 @@

                                                Cambiar estas opciones puede afectar significativamente el resultado de los DTM. La mejor fuente de lectura para comprender cómo los parámetros afectan la salida es leer el artículo original An improved simple morphological filter for the terrain classification of airborne LIDAR data (PDF disponible gratuitamente).

                                                En general, la opción --smrf-threshold tiene el mayor impacto en los resultados.

                                                SMRF es bueno para evitar errores de Tipo I (un pequeño número de puntos de tierra clasificados erróneamente como no terrestres) pero solo es «aceptable» para evitar errores de Tipo II (gran número de puntos no terrestres clasificados erróneamente como terrestres). Esto debe tenerse en cuenta al generar DTM para ser usados visualmente, ya que los objetos que se confunden con el suelo parecen artefactos en el DTM final.

                                                -
                                                +
                                                image of lens distortion effect on bowling of data -
                                                +

                                                Otros dos parámetros importantes afectan la generación de DEM:

                                                  @@ -369,107 +370,101 @@ 
                                                  docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
                                                  -
                                                -
                                                -

                                                Measuring stockpile volume

                                                -
                                                -

                                                Fieldwork planning

                                                -

                                                Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. -In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. -Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

                                                -
                                                -
                                                -

                                                Flight pattern

                                                -

                                                Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

                                                -

                                                In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

                                                -
                                                +
                                              +
                                              +

                                              Medir el volumen de pilas de materiales a granel

                                              +
                                              +

                                              Planificación del trabajo de campo

                                              +

                                              Las condiciones climáticas modifican la iluminación y, por lo tanto, afectan los resultados de la fotografía. Los mejores resultados se obtienen con cielos nublados uniformemente o despejados. También busque velocidades de viento bajas que permitan que la cámara permanezca estable durante el proceso de recopilación de datos. Para evitar sombras que puedan obstruir la detección de características en un lado de la pila y disminuir el número de puntos resultantes, prefiera siempre los vuelos durante el mediodía, cuando el sol está en el nadir para que todo esté iluminado consistentemente. También asegúrese de que la distancia de visibilidad horizontal a simple vista sea congruente con las distancias de vuelo planificadas para el proyecto específico, de modo que la calidad de la imagen no se vea afectada negativamente por el polvo, niebla, humo, cenizas volcánicas o la contaminación.

                                              +
                                              +
                                              +

                                              Patrón de vuelo

                                              +

                                              La mayoría de los trabajos de medición de pilas no requieren un patrón de vuelo cruzado o la cámara en ángulo, ya que el ángulo de reposo de los materiales de las pilas permite que la cámara capture todos los lados. Solo algunos casos especiales en los que la erosión o las operaciones de maquinaria causan ángulos pronunciados en las caras de la pila se verán beneficiados del patrón de vuelo cruzado y la cámara en ángulo, pero considere que estas características adicionales tienen un costo (en mano de obra de campo y tiempo de procesamiento) y las mejoras resultantes son a veces insignificantes.

                                              +

                                              En la mayoría de los casos, un patrón de vuelo paralelo como una podadora, es capaz de producir modelos de pilas de alta precisión.

                                              +
                                              a simple lawnmower flight pattern can produce accurate results -
                                              -

                                              Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

                                              -
                                              -
                                              -

                                              Flight height

                                              -

                                              Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. -As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

                                              -
                                              -
                                              + +

                                              El traslape frontal recomendado debe estar entre 75% y 80%, con un traslape lateral en el orden de 65% y 70%. También se recomienda incrementar ligeramente el traslape lateral y frontal conforme se incremente la altura del vuelo.

                                              + +
                                              +

                                              Altura de vuelo

                                              +

                                              La altura de vuelo puede verse influenciada por diferentes modelos de cámara, pero de manera general y con el fin de garantizar un equilibrio entre la calidad de imagen y la optimización del vuelo, se recomienda ejecutarlo a alturas de 3 a 4 veces la altura de la pila más alta. Entonces, para una pila de 10 metros, las imágenes se pueden capturar a una altura de 40 metros. A medida que aumenta la altura de vuelo, también se recomienda aumentar la superposición, por lo que para un vuelo de 40 metros de altura puede usar una superposición lateral del 65% y una superposición frontal del 75%, pero para una altura planificada de 80 metros, una superposición lateral del 70% y una superposición del 80% permitiendo que las estructuras sean reconocidas y procesadas correctamente.

                                              +
                                              +

                                              GCPs

                                              -

                                              To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. -When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

                                              -

                                              For further information on the use of GCPs, please refer to the Ground Control Points section.

                                              -
                                              -
                                              -

                                              Processing parameters

                                              -

                                              A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

                                              -

                                              If using ODM, these this reference values can help you configure the process settings.

                                              +

                                              Para lograr niveles de precisión superiores al 3%, se recomienda el uso de GCP. Normalmente, 5 GCP distribuidos son suficientes para garantizar resultados precisos. Al colocar o medir los GCP, la precisión del equipo debe ser mayor que el GSD. Las estaciones totales y los GNSS de grado topográfico están diseñadas para proporcionar la precisión milimétrica requerida.

                                              +

                                              Para mayor información sobre el uso de GCPs, por favor consulte la sección de Puntos de Control Terrestre.

                                              + +
                                              +

                                              Parámetros de procesamiento

                                              +

                                              Se puede lograr un modelo de alta precisión usando los parámetros predefinidos para alta resolución en WebODM. Luego puede ajustar aún más algunos parámetros según sea necesario.

                                              +

                                              Si utiliza ODM, estos valores de referencia pueden ayudarle a configurar los parámetros de proceso.

                                              –dsm: true

                                              –dem-resolution 2.0

                                              –orthophoto-resolution 1.0

                                              –feature-quality high

                                              –pc-quality high

                                              -
                                              -
                                              -

                                              Measuring

                                              -

                                              As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

                                              -
                                              + +
                                              +

                                              Medición

                                              +

                                              Como casi el 50% del material se encuentra en el primer 20% de la altura de la pila, se debe tener especial cuidado en definir adecuadamente el plano de la base.

                                              +
                                              almost 50% of the material will be found in the first 20% of the stockpile height -
                                              -

                                              In WebODM Dashboard, clic on «view map» to start a 2D view of your project.

                                              -

                                              Once in the 2D map view, clic on the «Measure volume, area and length» button.

                                              -
                                              + +

                                              En el tablero de WebODM, haga clic en «ver mapa» para iniciar una vista 2D de su proyecto.

                                              +

                                              Una vez en la vista de mapa 2D, haga clic en el botón «Medir volumen, área y distancia».

                                              +
                                              clic on the "Measure volume, area and length" button -
                                              -

                                              then clic on «Create a new measurement»

                                              -
                                              + +

                                              luego haga clic en «Crear una nueva medición»

                                              +
                                              clic on "Create a new measurement" -
                                              -

                                              Start placing the points to define the stockpile base plane

                                              -
                                              + +

                                              Empiece a colocar los puntos para definir el plano de la base de la pila

                                              +
                                              Define the stockpile base plane -
                                              -

                                              Clic on «Finish measurement» to finish the process.

                                              -
                                              + +

                                              Haga clic en «Finalizar medición» para finalizar el proceso.

                                              +
                                              Clic on "Finish measurement" to finish the process -
                                              -

                                              Dialog box will show the message «Computing …» for a few seconds, and after the computing is finished the volume measurement value will be displayed.

                                              -
                                              + +

                                              El cuadro de diálogo muestra el mensaje «Calculando…» por algunos segundos, y después de que el cálculo esté terminado la medición de volumen será desplegada.

                                              +
                                              Clic on "Finish measurement" to finish the process -
                                              -

                                              If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

                                              -

                                              Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. -Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

                                              -
                                              -
                                              -

                                              Expected accuracy

                                              -

                                              For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. -The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

                                              -
                                              -
                                              -
                                              + +

                                              Si utiliza OpenDroneMap en la línea de comandos, puede usar los archivos DSM para medir el volumen de las pilas usando otros programas.

                                              +

                                              También considere que una vez que los límites de la pila están definidos en un software como QGis, encontrará que existen varias maneras de determinar el plano de la base. Así que para pilas aisladas, cuyos límites son mayormente visibles, se puede usar un enfoque linear. Mientras que para pilas ubicadas en pendientes o en contenedores, el plano de la base es mejor definido el punto mas bajo. Se recomienda la creación de una superficie tridimensional triangulada para definir el plano de la base de pilas grandes. Esto también es válido para pilas sobre superficies irregulares.

                                              + +
                                              +

                                              Precisión esperada

                                              +

                                              Para proyectos cuidadosamente planificados y ejecutados, y especialmente cuando el GSD es menor de 1 cm, la precisión esperada debe estar en el rango de 1% a 2%. La precisión resultante es comparable a la del software de fotogrametría disponible comercialmente y la obtenida utilizando equipo GNSS de grado topográfico.

                                              +
                                              + +

                                              Usando Docker

                                              Dado que muchos usuarios emplean Docker para implementar OpenDroneMap, puede ser útil comprender algunos comandos básicos para consultar a las instancias de Docker cuando las cosas van mal o si tenemos curiosidad por ver lo que está sucediendo. Docker es un entorno en contenedores destinado, entre otras cosas, a facilitar la implementación de software independientemente del entorno local. De esta forma, es similar a las máquinas virtuales.

                                              Unos pocos comandos de Docker pueden mejorar mucho nuestra experiencia.

                                              -
                                              +

                                              Listado de máquinas Docker

                                              Podemos comenzar enumerando las máquinas Docker disponibles en la máquina actual que estamos ejecutando de la siguiente manera:

                                              > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

                                              Si queremos ver máquinas que pueden no estar ejecutándose pero que aún existen, podemos agregar la bandera -a:

                                              > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
                                              -
                                              -
                                              +
                                              +

                                              Accediendo a los registros en la instancia

                                              Usando el CONTAINER ID o el nombre, podemos acceder a cualquier registro disponible en la máquina de la siguiente manera:

                                              > docker logs 2518817537ce
@@ -500,8 +495,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

                                              El valor -5 le dice al comando tail que nos dé solo las últimas 5 líneas de los registros.

                                              -
                                              -
                                              + +

                                              Acceso de línea de comando a las instancias

                                              A veces necesitamos profundizar un poco más en nuestra exploración del proceso de OpenDroneMap. Para ello, podemos obtener acceso directo a la línea de comandos de las máquinas. Para esto, podemos usar docker exec para ejecutar un shell de línea de comando` bash` en la máquina de interés de la siguiente manera:

                                              > docker exec -ti 2518817537ce bash
@@ -509,24 +504,24 @@ The resulting accuracy is comparable to the commercially available photogrammetr

                                              Ahora estamos conectados a nuestra instancia de Docker y podemos explorar la máquina.

                                              -
                                              -
                                              + +

                                              Limpieza de Docker

                                              Docker tiene un uso lamentable del espacio y, de forma predeterminada, no limpia el exceso de datos y máquinas cuando los procesos están completos. Esto puede ser ventajoso si necesitamos acceder a un proceso que ya ha terminado, pero que lleva la carga de usar cantidades crecientes de almacenamiento a lo largo del tiempo. Maciej Łebkowski tiene una ʻexcelente descripción general de cómo administrar el uso excesivo del disco en la ventana acoplable <https://lebkowski.name/docker-volumes/>`_.

                                              -
                                              - -
                                              + + +

                                              Usando ODM desde una ubicación de ancho de banda bajo

                                              -
                                              +

                                              ¿Qué es esto y para quién es?

                                              A través del repositorio de ʻIvan Gayton <https://github.com/ivangayton/GDAL_scripts/>`_.

                                              ʻOpenDroneMap <https://www.opendronemap.org/>`__ no siempre se puede configurar localmente de forma efectiva; se necesita una máquina bastante poderosa para procesar conjuntos de datos grandes, por lo que una máquina en la nube a veces puede ser la respuesta para las personas en el campo. Sin embargo, el ancho de banda es un problema en muchos entornos de bajos ingresos. Esta restricción no se puede resolver por completo, pero el siguiente método hace un trabajo razonable al reducir el ancho de banda necesario para procesar conjuntos de datos de imágenes de drones en la nube desde ubicaciones africanas.

                                              Aquí presentamos un proceso complicado pero factible para crear una máquina en la nube OpenDroneMap (* no * CloudODM, eso sí, solo una instancia de ODM basada en la nube que se ejecuta desde la línea de comandos) y usarla para procesar de forma remota conjuntos de fotos grandes. Requiere familiaridad con el uso de la línea de comandos de Unix, ssh, una cuenta de Digital Ocean (Amazon AWS también funcionaría, posiblemente con ligeras diferencias en la configuración) y un nivel moderado de conocimientos informáticos generales. Si no es un experto en informática y está dispuesto a preocuparse por una configuración un poco complicada, CloudODM is what you should be looking at.

                                              Todo el proceso está dirigido principalmente a alguien que vuela misiones sustanciales en África o un lugar similar, y que busca procesar datos lo antes posible mientras aún se encuentra en un entorno de campo. Por lo tanto, enfatiza un flujo de trabajo destinado a reducir el ancho de banda/transferencia de datos, en lugar de solo la forma más simple de ejecutar ODM.

                                              -
                                              -
                                              +
                                              +

                                              Pasos

                                              -
                                              +

                                              Instalación

                                              • Cree una Droplet de Digital Ocean con al menos 4 GB de RAM. Eso costará alrededor de $ 20 USD al mes. Menos de 4 GB de RAM y la instalación probablemente fallará. Cuando realmente ejecutemos el proceso ODM, lo redimensionaremos a una máquina en la nube mucho más grande y más cara, pero entre ejecuciones puede reducirlo al segundo Droplet más barato que cuesta solo $10 USD (el Droplet mas barato a $5 USD por mes, viene con una unidad tan pequeña que no puede volver a reducirla).

                                                @@ -563,8 +558,8 @@ export PATH=$PATH:/home/odm/ODM/

                                                • Ahora necesitará un segundo disco duro en la nube (un «Volumen» en la jerga de Digital Ocean) lo suficientemente grande para administrar su proyecto. La regla de oro parece ser 10 veces el tamaño de su conjunto de imágenes sin procesar; tenemos un conjunto de imágenes de 100 GB y configuramos un volumen de 1000 GB (una vez que la ejecución haya terminado, debería poder deshacerse de la mayor parte de esta costosa capacidad de disco, pero es necesaria para completar el proceso). Configure el volumen, adjúntelo a su Droplet y configure su punto de montaje (en este ejemplo lo estamos configurando a /mnt/odmdata/).

                                                -
                                              -
                                              +
                                              +

                                              Datos de preparación y proyecto

                                              • Ahora envíe sus imágenes al servidor. Puede usar Secure Copy (scp) así: scp -r /path/to/my/imagefolder odm@xxx.xxx.xxx. xxx: /mnt/odmdata/.

                                                @@ -574,7 +569,7 @@ export PATH=$PATH:/home/odm/ODM/
                                            -
                                            +
                                            Estructura del directorio

                                            ODM requiere que los directorios de la máquina se configuren exactamente así. Los bits críticos son la carpeta de instalación (si instaló como antes, es /home/odm/ODM/) y la carpeta del proyecto (es decir, /mnt/odmdata/myproject/)

                                              @@ -584,9 +579,9 @@ export PATH=$PATH:/home/odm/ODM/

                                            • Modifique settings.yaml para especificar el directorio principal de la carpeta del proyecto (en este caso, el Volumen que creamos, /mnt/odmdata/). Asegúrese de que las imágenes estén en el lugar correcto, es decir, /mnt/odmdata/myproject/images y los otros archivos auxiliares (gcp_list.txt e image_groups.txt) están en la carpeta raíz /mnt/odmdata/myproject/

                                            • Si tiene las imágenes en carpetas separadas para bloques AOI o vuelos individuales (lo que hará si su gestión de vuelos fue organizada), puede crear un archivo image_groups.txt con los encantamientos for i in *; do cd $i; for j in *; do echo "$j $i" >> ../$i.txt; done; cd ../; done; y cd ../, for i in myproject/*.txt; do cat $i >> image_groups.txt; done;. Eso debería crear un archivo con la estructura correcta: una lista de todos los archivos de imagen y un «nombre de grupo» después de cada uno (que en este caso será simplemente el nombre de la carpeta de la que proviene). Luego mueva todos los archivos de imagen a un solo directorio llamado imágenes en el directorio raíz del proyecto (por lo tanto, /mnt/odmdata/myproject/images/). El archivo image_groups.txt permitirá que ODM realice un seguimiento de las imágenes que pertenecen al mismo lote, aunque estén todas en un solo directorio.

                                            -
                                            - -
                                            + + +

                                            Cambie el tamaño del Droplet, quite el seguro, huya

                                            • Apague y cambie el tamaño de su máquina a una cantidad monstruosa de CPU y memoria. Utilizo la máquina con memoria optimizada con 24 vCPU dedicadas y 192 GB de RAM (que cuesta alrededor de $ 1,60 / hora, lo que se suma rápido y supera los $ 1000/mes). Reinicie y comience a trabajar rápidamente para no perder el costoso tiempo de los Droplets grandes.

                                              • @@ -611,8 +606,8 @@ export PATH=$PATH:/home/odm/ODM/

                                              • Es posible que desee estar atento a htop (para tener una idea del uso de recursos para que en el futuro solo pueda hacer girar una máquina tan grande como sea necesario)

                                              -
                                            -
                                            + +

                                            Después de que termine (suponiendo que sobrevivas tanto tiempo)

                                            • Tan pronto como termine el procesamiento, apague la máquina y cambie su tamaño a la capacidad mínima económica.

                                              • @@ -631,10 +626,10 @@ export PATH=$PATH:/home/odm/ODM/

                                            Aprende a editar y ayuda a mejorar esta página!

                                            - - - - + + + + diff --git a/fil/.buildinfo b/fil/.buildinfo index 4a3f81307..29194b900 100644 --- a/fil/.buildinfo +++ b/fil/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d65f1dafa7b72460ef2a1d4b173d4c7d +config: 826481e42566ded7f032c01892b9785c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/fil/.doctrees/arguments.doctree b/fil/.doctrees/arguments.doctree index 0438769d8..37ac611b0 100644 Binary files a/fil/.doctrees/arguments.doctree and b/fil/.doctrees/arguments.doctree differ diff --git a/fil/.doctrees/contributing.doctree b/fil/.doctrees/contributing.doctree index f1f7bff8c..575e9ae69 100644 Binary files a/fil/.doctrees/contributing.doctree and b/fil/.doctrees/contributing.doctree differ diff --git a/fil/.doctrees/environment.pickle b/fil/.doctrees/environment.pickle index 79887811a..1bc307c17 100644 Binary files a/fil/.doctrees/environment.pickle and b/fil/.doctrees/environment.pickle differ diff --git a/fil/.doctrees/flying.doctree b/fil/.doctrees/flying.doctree index 3f5572640..61254c43b 100644 Binary files a/fil/.doctrees/flying.doctree and b/fil/.doctrees/flying.doctree differ diff --git a/fil/.doctrees/gcp.doctree b/fil/.doctrees/gcp.doctree index d7eb98267..86a546b3b 100644 Binary files a/fil/.doctrees/gcp.doctree and b/fil/.doctrees/gcp.doctree differ diff --git a/fil/.doctrees/geo.doctree b/fil/.doctrees/geo.doctree index 06e988f6c..0994c2f33 100644 Binary files a/fil/.doctrees/geo.doctree and b/fil/.doctrees/geo.doctree differ diff --git a/fil/.doctrees/index.doctree b/fil/.doctrees/index.doctree index ec4a1ef42..a6e89cfc6 100644 Binary files a/fil/.doctrees/index.doctree and b/fil/.doctrees/index.doctree differ diff --git a/fil/.doctrees/installation.doctree b/fil/.doctrees/installation.doctree index 65529df81..da65eae15 100644 Binary files a/fil/.doctrees/installation.doctree and b/fil/.doctrees/installation.doctree differ diff --git a/fil/.doctrees/large.doctree b/fil/.doctrees/large.doctree index 870562d2d..ef7553e3a 100644 Binary files a/fil/.doctrees/large.doctree and b/fil/.doctrees/large.doctree differ diff --git a/fil/.doctrees/map-accuracy.doctree b/fil/.doctrees/map-accuracy.doctree index cfd807a49..eeaf77a10 100644 Binary files a/fil/.doctrees/map-accuracy.doctree and b/fil/.doctrees/map-accuracy.doctree differ diff --git a/fil/.doctrees/masks.doctree b/fil/.doctrees/masks.doctree index cf1a188cc..a4d0270b9 100644 Binary files a/fil/.doctrees/masks.doctree and b/fil/.doctrees/masks.doctree differ diff --git a/fil/.doctrees/multispectral.doctree b/fil/.doctrees/multispectral.doctree index a5e6ca2d2..a6568c3c3 100644 Binary files a/fil/.doctrees/multispectral.doctree and b/fil/.doctrees/multispectral.doctree differ diff --git a/fil/.doctrees/outputs.doctree b/fil/.doctrees/outputs.doctree index 7607b6a61..963521abf 100644 Binary files a/fil/.doctrees/outputs.doctree and b/fil/.doctrees/outputs.doctree differ diff --git a/fil/.doctrees/requesting-features.doctree b/fil/.doctrees/requesting-features.doctree index 1f1785c4f..3f5f688a1 100644 Binary files a/fil/.doctrees/requesting-features.doctree and b/fil/.doctrees/requesting-features.doctree differ diff --git a/fil/.doctrees/resources.doctree b/fil/.doctrees/resources.doctree index 01c8861ae..5d4aa61af 100644 Binary files a/fil/.doctrees/resources.doctree and b/fil/.doctrees/resources.doctree differ diff --git a/fil/.doctrees/tutorials.doctree b/fil/.doctrees/tutorials.doctree index bef8b5e9c..22ea27e96 100644 Binary files a/fil/.doctrees/tutorials.doctree and b/fil/.doctrees/tutorials.doctree differ diff --git a/fil/_sources/arguments.rst.txt b/fil/_sources/arguments.rst.txt index 84ae87529..ce49943dd 100644 --- a/fil/_sources/arguments.rst.txt +++ b/fil/_sources/arguments.rst.txt @@ -15,6 +15,12 @@ Arguments ``--cameras`` Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/fil/_static/documentation_options.js b/fil/_static/documentation_options.js index 20a352878..61adf3d59 100644 --- a/fil/_static/documentation_options.js +++ b/fil/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'fil', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/fil/arguments.html b/fil/arguments.html index a0d690dc5..d88ee9414 100644 --- a/fil/arguments.html +++ b/fil/arguments.html @@ -4,10 +4,11 @@ - + + - Mga Opsyon at Tanda — OpenDroneMap 2.4.7 documentation + Mga Opsyon at Tanda — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                            - 2.4.7 + 2.5.5
                                            @@ -291,9 +292,9 @@
                                            -
                                            +

                                            Mga Opsyon at Tanda

                                            -
                                            +

                                            Mga Argumento

                                            --build-overviews

                                            Build orthophoto overviews for faster display in programs such as QGIS. Default: False

                                            @@ -302,6 +303,10 @@
                                            --cameras <json>

                                            Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: ``

                                            +
                                            --cog

                                            Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

                                            +
                                            +
                                            --copy-to <path>

                                            Copy output results to this folder after processing.

                                            +
                                            --crop <positive float>

                                            Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: 3

                                            --debug

                                            Print debug messages. Default: False

                                            @@ -356,7 +361,7 @@
                                            --min-num-features <integer>

                                            Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

                                            -
                                            name <project name>

                                            Name of dataset (i.e subfolder name within project folder). Default: code

                                            +
                                            name <dataset name>

                                            Name of dataset (i.e subfolder name within project folder). Default: code

                                            --optimize-disk-space

                                            Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don't have sufficient disk space available. Default: False

                                            @@ -364,6 +369,8 @@
                                            --orthophoto-cutline

                                            Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

                                            +
                                            --orthophoto-kmz

                                            Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

                                            +
                                            --orthophoto-no-tiled

                                            Set this parameter if you want a striped GeoTIFF. Default: False

                                            --orthophoto-png

                                            Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

                                            @@ -392,7 +399,7 @@
                                            --project-path <path>

                                            Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder.

                                            -
                                            --radiometric-calibration none | camera | camera+sun

                                            Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

                                            +
                                            --radiometric-calibration none | camera | camera+sun

                                            Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

                                            --rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

                                            Rerun this stage only and stop. . Default: ``

                                            @@ -426,6 +433,8 @@
                                            --texturing-data-term gmi | area

                                            When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

                                            +
                                            --texturing-keep-unseen-faces

                                            Keep faces in the mesh that are not seen in any camera. Default: False

                                            +
                                            --texturing-outlier-removal-type none | gauss_clamping | gauss_damping

                                            Type of photometric outlier removal method. . Default: gauss_clamping

                                            --texturing-skip-global-seam-leveling

                                            Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

                                            @@ -452,8 +461,8 @@

                                            Learn to edit and help improve this page!

                                            -
                                            -
                                            + +
                                            diff --git a/fil/contributing.html b/fil/contributing.html index 04592799e..bdfeb4624 100644 --- a/fil/contributing.html +++ b/fil/contributing.html @@ -4,10 +4,11 @@ - + + - Paano makadaragdag — OpenDroneMap 2.4.7 documentation + Paano makadaragdag — OpenDroneMap 2.5.5 documentation @@ -62,7 +63,7 @@
                                            - 2.4.7 + 2.5.5
                                            @@ -290,14 +291,14 @@
                                            -
                                            +

                                            Paano makadaragdag

                                            Ang OpenDroneMap ay produkto ng kontribusyon ng isang grupo na binubuo ng mga tao. Maari kang makaragdag ng kontribusiyon kahit na ikaw ay hindi isang ekspertong programmer.

                                            -
                                            +

                                            Pagtitipon ng Komunidad

                                            Kung ikaw ay isang indibidwal na gustong makatulong, nagkaroon ng problema at nangangailangan ng tulong, 'ang tugunan na ito <https://community.opendronemap.org/>`_ ay mabisang sanggunian. Maaaring mahanap ang sagot sa tanong at anumang karagdagan na impormasyon gamit ang tugunan na ito. Kung ikaw naman ay may nais ibahagi na datos, hinihikayat namin na gamitin ang platapormang ito sa pagbahagi ng mga karagdagang kaalaman upang makatulong sa iba. Nawa ay masulit ang tugunan na ito bago magsumite ng problema sa ating mga ekspertong.

                                            -
                                            -
                                            +
                                            +

                                            Pagsumite ng mga Sira

                                            Ang mga sira ang itinuturing na isyu ng Github. Maaari lamang na magbigay-ulat sa ating imbakan at markahan ito ng Bug.

                                            Ilahad ang kompleto na detalye ng problema upang masolusyonan ng eksperto ang problema:

                                            @@ -317,7 +318,7 @@

                                          • Ano ang pangalan at bersiyon ng OS ang iyong ginagamit?

                                          • ikaw ba ang gumagamit ng ODM sa pamamagitan ng isang virtual machine o ng isang Docker? Kung ang sagot ay oo, ano ang VM software at bersiyon ng OS ang iyong ginagamit bilang may-ari o bisita?

                                          -
                                          +

                                          Template para sa pagpapasa ng Bug Reports

                                          [Short description of problem here]
 
@@ -349,9 +350,9 @@
 * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
                                          -
                                          - -
                                          + + +

                                          Ipakita ang mga Request

                                          • Magpasa ng larawan ng animated GIF sa iyong pull request hanggat maaari.

                                            • @@ -375,8 +376,8 @@

                                          Learn to edit and help improve this page!

                                          -
                                          - + + diff --git a/fil/flying.html b/fil/flying.html index 0b743f775..af6696285 100644 --- a/fil/flying.html +++ b/fil/flying.html @@ -4,10 +4,11 @@ - + + - Mga Paalala sa Pagpapalipad — OpenDroneMap 2.4.7 documentation + Mga Paalala sa Pagpapalipad — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                          - 2.4.7 + 2.5.5
                                          @@ -291,7 +292,7 @@
                                          -
                                          +

                                          Mga Paalala sa Pagpapalipad

                                          Ang Humanitarian OpenStreetMap na pangkat ay may mga patnubay tungkol sa 'pagpapalipad ng UAV mapping <https://uav-guidelines.openaerialmap.org/>`_:

                                            @@ -303,7 +304,7 @@

                                            Maaari lamang na tingnan din and patnubay ng DroneDeploy sa Paggawa ng matagumpay na mapa, ito ay nagbibigay ng mga karagdagang kaalaman tungkol sa pagpaplano ng nga misyon.

                                            Ang pagbaluktot dahil sa lente ay isang hamon sa mga proyekto na nangangailangan ng tumpak na datos ng 3D. Tingnan ang aming seksyon sa mga dokumentong ito sa Camera Calibration <tutorials.html # calibrating-the-camera> _.

                                            Learn to edit and help improve this page!

                                            -
                                          +
                                          diff --git a/fil/gcp.html b/fil/gcp.html index 3f4f470b1..6670a1de4 100644 --- a/fil/gcp.html +++ b/fil/gcp.html @@ -4,10 +4,11 @@ - + + - Ground Control Points — OpenDroneMap 2.4.7 documentation + Ground Control Points — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                          - 2.4.7 + 2.5.5
                                          @@ -291,19 +292,19 @@
                                          -
                                          +

                                          Ground Control Points

                                          Ang mga GCP ay kapaki-pakinabang para sa pagwawasto ng mga pagbaluktot sa datos at pagtukoy sa datos upang malaman ang mga coordinate system.

                                          Ang Ground Control Point (GCP) ay isang pagsukat sa posisyon na ginawa sa lupa, karaniwang ginagamitan ng isang mataas na katumpakan na GPS. (Toffanin 2019)

                                          Ang mga GCP ay maaaring itakda sa mga istraktura tulad ng mga sulok ng simento, mga linya sa isang paradahan o mga tile sa sahig na nagsasalitan ang kulay, kung hindi man ay maitatakda gamit ang mga target na nakalagay sa lupa.

                                          Maaaring mabili o gumawa ang mga target gamit ang samut-saring mga materyales tuland ng mga takip ng timba at ng mga tile sa sahig.

                                          - -
                                          +
                                          +

                                          GCP file format

                                          Ang format ng GCP file ay simple lamang.

                                          @@ -332,8 +333,8 @@

                                          Ang sharp corners ay magandang option para sa GCPs. DApat ilagay/hanapin ang GCPs ng pantay pantay sa paligid ng isu-survey na area.

                                          Ang gcp_list.txt file ay dapat gawin sa base ng project folder.

                                          Para sa mas magandang resulta, ang iyong file ay dapat na magkaroon ng minimum na 15 na linya pagkatapos ng header (5 points na may 3 imahe kada point).

                                          -
                                          -
                                          + +

                                          User Interfaces

                                          Maaaring gumamit ng isa or dalawang user interfaces para sa paggawa ng GCP files:

                                          @@ -342,7 +343,7 @@

                                        • GCP Editor Pro

                                        -
                                        +

                                        POSM GCPi

                                        Ang POSM GCPi ay loaded by default sa WebODM. Isang halimbawa dito ay mahahanap sa the WebODM Demo. Para gamitin ito sa ground control XYZ values, dapat gawin ang mga sumusunod:

                                        Gumawa ng GCP na listahan na may kasamang gcp names (ito ang label na makikita sa GCP interface), x, y, and z, na may header na proj4 string sa GCPs (siguraduhin na may planar coordinate system tulad ng UTM. Ganito ang magiging itsura dapat:

                                        @@ -355,8 +356,8 @@

                                        Pwedeng i-load ang GCP list sa interface, i-load ang imahe, at ilagay ang mga GPCs sa imahe.

                                        - -
                                        + +

                                        GCP Editor Pro

                                        Ang aplikasyon na ito ay dapat i-install ng hiwalay o di kaya naman ay pwedeng i-load bilang WebODM plugin mula sa https://github.com/uav4geo/GCPEditorPro

                                        Gumawa ng CSV file na kasama ang GCP name, northing, easting and elevation.

                                        @@ -368,14 +369,14 @@

                                        I-import ang CSV mula sa main screen at i-type ang +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs mula sa EPSG/PROJ box.

                                        Ang susunod na screen ay magdi-display ng map kung saan pipiliin ang GCPs para ma-tag at ma-import ang mga imahe.

                                        - - -
                                        + + +

                                        References

                                        Toffanin, Piero. Open Drone Map: The Missing Guide. MasseranoLabs LLC, 2019.

                                        Learn to edit and help improve this page!

                                        -
                                        - + + diff --git a/fil/genindex.html b/fil/genindex.html index 0651c4d0c..00520e1dc 100644 --- a/fil/genindex.html +++ b/fil/genindex.html @@ -7,7 +7,7 @@ - Index — OpenDroneMap 2.4.7 documentation + Index — OpenDroneMap 2.5.5 documentation @@ -61,7 +61,7 @@
                                        - 2.4.7 + 2.5.5
                                        diff --git a/fil/geo.html b/fil/geo.html index 8a390c4bb..8d2a23b61 100644 --- a/fil/geo.html +++ b/fil/geo.html @@ -4,10 +4,11 @@ - + + - Image Geolocation Files — OpenDroneMap 2.4.7 documentation + Image Geolocation Files — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -291,7 +292,7 @@
                                        -
                                        +

                                        Image Geolocation Files

                                        By default, ginagamit ng ODM ang impormasyon na kasasam sa mga mahe kung ito ay available. Minsan ang mga imahe ay walang kasama na GPS na impormasyon o di kaya naman ay mas pinipili ng gagamit na palitan ang impormasyon ng mas sigurado na data (tulad ng RTK)

                                        Simula sa ODM 2.0 pwedeng magsupply ng image geolocation file (geo) para sa purpose.

                                        @@ -319,7 +320,7 @@

                                        Kung ikaw ay nagsupply ng imahe na tinatawag na geo.txt awtomatikong madedetect ito ng ODM. KUng ito ay may ibang pangalan, maaaring specify ito gamit ang --geo 1.

                                        Sa base ng project folder dapat gawin ang geo.txt.

                                        Learn to edit and help improve this page!

                                        -
                                        +
                                        diff --git a/fil/index.html b/fil/index.html index d3c9d15d1..365a36f52 100644 --- a/fil/index.html +++ b/fil/index.html @@ -4,10 +4,11 @@ - + + - Malugod na pagtanggap mula sa OpenDroneMap's Documentation — OpenDroneMap 2.4.7 documentation + Malugod na pagtanggap mula sa OpenDroneMap's Documentation — OpenDroneMap 2.5.5 documentation @@ -62,7 +63,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -290,10 +291,10 @@
                                        -
                                        +
                                        OpenDroneMap Logo -
                                        -
                                        + +

                                        Malugod na pagtanggap mula sa OpenDroneMap's Documentation


                                        @@ -313,9 +314,9 @@


                                        -
                                        +
                                        image of seedling -
                                        +
                                        -
                                        +
                                        image of pencil -
                                        +
                                        -
                                        +
                                        diff --git a/fil/installation.html b/fil/installation.html index 2eb8c05ed..fa51b4141 100644 --- a/fil/installation.html +++ b/fil/installation.html @@ -4,10 +4,11 @@ - + + - Instalasyon at Paano Simulan — OpenDroneMap 2.4.7 documentation + Instalasyon at Paano Simulan — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -291,50 +292,50 @@
                                        -
                                        +

                                        Instalasyon at Paano Simulan

                                        Ang seksiyon na ito ay binago ng bahagya ng may permisyon galing sa OpenDroneMap: The Missing Guide, na gawa ni Piero Toffanin.

                                        Ang OpenDroneMap ay isang salita na ginagamit patukoy sa isang single command line na programa (na kilala rin bilang ODM na proyekto). Sa kamakailan lamang na progreso ng web interface, ang API ay ibang tools, pati na rin ang OpenDroneMap ang naging pinagmulan ng iba't-ibang programa upang ma-proseso, ma-suri at makita ang aerial data. ang ekosistem na ito ay ginawa para sa iilang component:

                                        -
                                        +
                                        ODM Logo -
                                        +

                                        • Ang ODM ay ang processing engine, na pwedeng gamitin sa command line. Tinatanggap nito ang mga imahe bilang input at nagpo-produce ng iba't-ibang outputs katulad ng point clouds, 3D models at orthophotos.

                                        -
                                        +
                                        NodeODM Logo -
                                        +

                                        • Ang NodeODM ay isang light-weight API na nasa taas ng ODM. Ina-allow nito ang gumagamit ng aplikasyon na ma-access ang mga functions ng ODM sa computer network.

                                        -
                                        +
                                        WebODM Logo -
                                        +

                                        • Ang WebODM ay isang friendly user na interface na may kasamang map viewer, 3D viewer, user logins, plugin system at marami pang iba na ine-expect sa modern drone mapping platforms.

                                        -
                                        +
                                        CloudODM Logo -
                                        +

                                        • Ang CloudODM ay isang maliit na command line client na nagcocommunicate sa ODM via NodeODM API

                                        -
                                        +
                                        PyODM Logo -
                                        +

                                        • Ang PyODM ay Python SDK na gumagawa ng tasks via NodeODM API. Mababasa ang detalye sa “Automated Processing With Python” na chapter

                                        -
                                        +
                                        ClusterODM Logo -
                                        +

                                        • Ang ClusterODM ay isang load balancer na pang connect ng multiple NodeODM instances

                                        Ang ODM, NoteODM at WebODM ay mayroon sa lahat ng pangunahin na plataporma (Windows, macOS at Linux) sa pamamagitan ng programang docker, na kailangan para patakbuhin ang software. Ang docker ay naghahandog ng paraan kung paano patatakbuhin ang “containers”. Ang containers ay kaha ng mga kopya ng kabuoan na sisyema, ang software nito at ang dependencies. Ang mga container na ito ay tumatakbo sa loob ng virtual environment. Sa linux, mayroong virtual environment sa operating system at ito ay mahusay na klase. Sa macOS at Windows naman, ang container ay napapatakbo sa pamamagitan ng VM, so may kaunting oberhed ngunit naaangkop pa rin sa pagpapatakbo ng software. Kapag na-install, ang gagamit ay hindi na kelangan magalala sa docker dahil ito ay gumagana (madalas) ng malinaw.

                                        Kung walang docker, hindi posible na magpatakbo ng ODM sa macOS at Windows. Sa mga plataporma na ito, ang ODM ay hindi tatakbo ng kusa. Ang paghahanada para sa susunod na debelopment ay nakatuon sa pagkilos sa makabagong Windows Subsystem para sa Linux (WSL) at ang posibilidad na gawing native port ng lahat ng dependencies sa macOS na makakapagpadali sa pagi-install.

                                        Sa Ubuntu Linux 18.04 kaya patakbuhin ang lahat ng OpenDroneMap software. Ngutin, sa kadahilanan na may maliit na performance penalty sa pagpapatakbo ng docker sa Linux, and docker ay diretso sa pagset-up sa plataporma nito. Ito ay hindi inirerekomenda. Sa Linux, ang kalamangan ng containerization ay higit na malaki sa maliit na performance penalty. Sa docker, ang gagamit ay makakakuha ng mas madali na one-step updates ng software so ito ay maganda.

                                        -
                                        +

                                        Hardware Recommendations

                                        Ang pinakakonti na bilang ng requirements para patakbuhin ang software ay:

                                          @@ -349,28 +350,28 @@

                                        • 16 GB RAM

                                        Ang nasa taas ay papayagan ang ilang daan na imahe na maprocess ng walang masyadong issue. Ang CPU na maraming core ay papahintulutan ang mas mabilis na pagproseso, samantalang ang graphics card (GPU) naman ay kasalukuyan na walang epekto sa performance. para sa pagprocess ng mas maraming imahe, magdagdag ng mas maraming disk space sa RAM linearly para sa bilang ng imahe na kailangan iprocess.

                                        -
                                        -
                                        +
                                        +

                                        Installation

                                        Nirerekomenda na gamitin ang docker para patakbuhin ang ODM, ikaw man ay gumagamit ng Windows, macOS o Linux.

                                        -
                                        +

                                        Windows

                                        Para patakbuhin ang OpenDroneMap, kailangan mo ng kahit man lang Windows 7. Ang mga mas luma na bersiyon ng Windows ay hindi suportado.

                                        -
                                        +

                                        Step 1. I-check Virtualization Support

                                        Ang docker ay nangangailangan ng feature sa CPU na tinatawag na virtualization, ina-allow nito na patakbuhin ang virtual machines (VMs). Siguraduhin na ito ay enabled! Minsan ito ay disabled. Para i-check sa Windows 8 o mas mataas na bersiyon, buksan ang Task Manager (press CTRL+SHIFT+ESC) at i-switch sa Performance tab.

                                        -
                                        +
                                        Image of checking virtualization in Windows 8 or higher -
                                        +

                                        Virtualization should be enabled

                                        Sa Windows 7, para makita kung naka-enable ang virtualization,

                                        Kung ang virtualization ay hindi gumagana, kailangan paganahin ito. Bagamat ang proseso ay higit na iba kada modelo ng komputer. Ang pinakamainam na paraan para gawin ito ay ang hanapin sa search engine ang “how to enable vtx for 1”. Kadalasan, kailangan ire-start ang kompyuter, mabilis na pagpindot ng F2 o F12 habang start-up, ang pag-navigate ng boot menu at pagpalit ng settings para mapahintulot ang virtualization (madals na tinatawag na “VT-X”).

                                        -
                                        +
                                        Table of different bios keys -
                                        +

                                        Common keys to press at computer startup to access the boot menu for various PC vendors

                                        -
                                        -
                                        +
                                        +

                                        Step 2. Install Requirements

                                        Una, kailangan mong ma-install:

                                        Para sa Python 3, siguraduhin na i-check ang Add Python 3.x to PATH habang iniinstall.

                                        -
                                        +
                                        Screenshot of Python3 installation process -
                                        +

                                        Don’t forget to add the Python executable to your PATH (so that you can run commands with it)

                                        Kapag ikaw ay nasa Windows 10 Home, Windows 8 (kahit anong bersiyon) o WIndows 7 (kahit anong bersiyon), i-install:

                                          @@ -392,8 +393,8 @@

                                        HUWAG i-install ng sabay ang dalawang docker na programa. Sila ay magkaiba at maaring mahdulot ng gulo sa sistema.

                                        Matapos i-install ang docker, simulan ito mula sa Desktop icon na ginawa mula sa installation (Docker Quickstart sa kaso ng Docker Toolbox, Docker for Windows para sa Docker na pang-Windows). Ito ay importante, wag lagpasan ang hakbang na ito. Kung may kamalian, sundan ang mga nakadikta sa screen para ayusin ito.

                                        -
                                        -
                                        +
                                        +

                                        Step 3. I-check ang Memory at CPU Allocation

                                        Ang docker sa Windows ay tumatakbo sa pamamagitan ng VM sa likod (isipin ang VM bilang “computer emulator”). Ang VM na ito ay may sapat ng laki ng memory na nakasubi at ang WebODM ay makakagamit lamang ng sapat na memorya na nakatabi para sa kanya.

                                        Kung Docker Toolbox ang naka-install (tignan ang nasa ibaba kung Docker ang naka-install sa Windows):

                                        @@ -403,9 +404,9 @@

                                      • i-right click ang default ang VM at pindutin ang Settings...

                                      • Move the Base Memory slider from the System panel and allocate 60-70% of all available memory, optionally adding 50% of the available processors from the Processor tab also

                                        • -
                                        +
                                        Screenshot of VirtualBox Settings -
                                        +

                                        VirtualBox default VM settings

                                        Pindutin ang OK, i-right click ang default VM at pindutin ang Start.

                                        Kung Docker para sa Windows ang naka-install:

                                        @@ -415,16 +416,16 @@

                                      • Mula sa panel, i-click ang Advanced at gamitin ang sliders para maisubi ang 60-70% na memory at gamitin ang kalahati ng available sa CPUs.

                                      • Pindutin ang Apply.

                                        • -
                                        +
                                        Screenshot of Docker Icon -
                                        +

                                        Step 1 Docker icon

                                        -
                                        +
                                        Screenshot of Docker Settings -
                                        +

                                        Step 3 & 4 Docker settings

                                        -
                                        -
                                        + +

                                        Step 4. Download WebODM

                                        Buksan ang Git Gui na programa na kasama Git. galing dito:

                                          @@ -433,27 +434,27 @@

                                        • Mula sa Target Directory i-click ang browse at magnavigate sa folder na gusto mo (gumawa ng folder kung kinakailangan)

                                        • Pindutin ang Clone

                                        -
                                        +
                                        Screenshot of Git Gui -
                                        +

                                        Git Gui

                                        Kung ang pagda-download ay successful, makikita ang window na ito:

                                        -
                                        +
                                        Screenshot of Git Gui after successful download -
                                        +

                                        Git Gui after successful download (clone)

                                        Pumunta sa Repository menu at i-click ang Create Desktop Icon. Matutulungan ka nito na bumalik sa madaling makapunta sa aplikasyon na ito balang araw.

                                        -
                                        -
                                        + +

                                        Step 4. Launch WebODM

                                        Mula sa Git Gui, pumunta sa Repository menu at i-click ang Git Bash. Mula sa command line terminal i-type ang:

                                        $ ./webodm.sh start&

                                        Iba't ibang components na ang nadownload dapat ng iyong computer sa point na ito. Kasama na rito ang WebODM, NodeODM at ODM. Matapos ang pagda-download, ikaw ay babatiin ng mga sumusunod na mensahe sa screen:

                                        -
                                        +
                                        Screenshot of after successfully downloading WebODM -
                                        +

                                        Console output after starting WebODM for the first time

                                        • Kung ikaw ay gumagamit ng Docker for Windows, buksan ang web browser sa http://localhost:8000

                                          • @@ -467,12 +468,12 @@

                                        Tapos, magconnect sa http://192.168.1.100:8000 (replacing the IP address with the proper one).

                                        -
                                        -
                                        -
                                        + + +

                                        macOS

                                        Karamihan ng modernong bersiyon ng Mac computers (post 2010) na may macOS Sierra 10.12 o mas mataas pa ay kayang gumamit ng OpenDroneMap gamit ang docker, hanggat ang hardware virtualization ay suportado (tignan ang nasa ibaba).

                                        -
                                        +

                                        Step 1. I-check Virtualization Support

                                        Buksan ang Terminal na window at i-type ang:

                                        $ sysctl kern.hv_support
@@ -484,8 +485,8 @@

                                        Ang iyong Mac ay suportado kung ang resulta na lumalabas ay kern.hv_support: 1. Sundan ang hakbang bilang 2.

                                        Ang sistema ng iyong Mac ay luma na hindi na kakayanin ang OpenDroneMap kapag ang resulta na lumabas ay kern.hv_support: 0. :(

                                        -
                                        -
                                        +
                                        +

                                        Step 2. Install Requirements

                                        Mayroon lamang dalawang programa na dapat i-install:

                                          @@ -493,9 +494,9 @@

                                        1. At ang Git: https://sourceforge.net/projects/git-osx-installer/files/

                                        Matapoa i-install ang docker, makikita ang simbolo na mukang balyena sa task bar.

                                        -
                                        +
                                        Screenshot of Docker whale -
                                        +

                                        Ang Docker app ay tumatakbo

                                        Upang masigurado na tumatakbo ng matiwasay ang docker, buksan ang Terminal na app at i-type ang mga sumusunod:

                                        $ docker run hello-world
@@ -514,8 +515,8 @@

                                        Kapag nakatanggap ng “bash: git: command not found”, i-restart ang Terminal app at suriing mabuti kung may mga error habang inagi-install.

                                        -
                                        -
                                        +
                                        +

                                        Step 3. I-check ang Memory at CPU Allocation

                                        Ang docker ay tumatakbo lamang sa macOS sa pamamagitan ng paggamit ng VM sa background (isipin na ito ay parang "computer emulator"). Ang VM na ito ay may limitadong alokasyon lamang ng memorya para sa WebODM.

                                          @@ -524,12 +525,12 @@

                                        1. I-adjust ang CPU slider para magamit ang kalahato ng kabuoang CPU at ang memoryang magagamit ay 60-70% ng kabuoang memorya.

                                        2. Pindutin ang Apply & Restart*

                                        -
                                        +
                                        Screenshot of Docker advanced settings -
                                        +

                                        Docker advanced settings

                                        -
                                        -
                                        + +

                                        Step 4. I-download at i-launch ang WebODM

                                        Mula sa Terminal i-type ang:

                                        $ git clone https://github.com/OpenDroneMap/WebODM
@@ -538,12 +539,12 @@ $ ./webodm.sh start

                                        Tapos buksan ang web browser sa http://localhost:8000.

                                        -
                                        -
                                        -
                                        + + +

                                        Linux

                                        Ang OpenDroneMap ay makakatakbo lamang sa kahit anong Linux na distribusyon na nagsusuporta sa docker. Ayon sa docker’s documentation website ang opisyal nasuportadong distribusyon ay ang CentOS, Debian, Ubuntu at Fedora, na may static binaries na mayroon para sa iba. Kung ikaw ay pipili ng distribusyon para sa pagpapatakbo ng OpenDroneMap, Ubunto ang rekomendado.

                                        -
                                        +

                                        Step 1. Mga kailangan bago maginstall.

                                        May apat na programa na dapat ma-install:

                                          @@ -553,7 +554,7 @@ $ ./webodm.sh start

                                        1. Pip

                                        Hindi pwedeng takpan ang process ng installation kada Linux na distribusyon, kaya lilimitahan na lamang ang instruksiyon para sa distribusyon na opisyal na pang docker. Sa lahat ng kaso, ito ay isang bagay na pangbukas o terminal prompt at ang pag-type ng ilan na commands.

                                        -
                                        +
                                        Maginstall sa Ubuntu / Debian

                                        Mga command na ita-type:

                                        $ sudo apt update
@@ -562,8 +563,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
                                        -
                                        -
                                        +
                                        +
                                        Maginstall sa CentOS / RHEL

                                        Mga command na ita-type:

                                        $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -571,8 +572,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
                                        -
                                        -
                                        +
                                        +
                                        Maginstall sa Fedora

                                        Mga command na ita-type:

                                        $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -580,16 +581,16 @@ $ sh get-docker.sh
 $ sudo dnf install git python python-pip
                                        -
                                        -
                                        + +
                                        Maginstall sa Arch

                                        Mga command na ita-type:

                                        $ sudo pacman -Sy docker git python python-pip
                                        -
                                        - -
                                        + + +

                                        Step 2. I-check ang mga karagdagan na requirements

                                        Bukod sa mga tatlong programa sa taas, ang dockercompose script ay kailangan din. Minsan, ito ay installed na kasama ng docker at minsan naman ay hindi. Para ma-verify kung ito ay nakainstall na, itype ang:

                                        $ docker-compose --version
@@ -607,8 +608,8 @@ $ sudo dnf install git python python-pip
 
                                        $ sudo pip install docker-compose
 
                                        
 
                                        
-
                                        -
                                        +
                                        +

                                        Step 3. Download and Launch WebODM

                                        Mula sa terminal i-type ang:

                                        $ git clone https://github.com/OpenDroneMap/WebODM
@@ -617,10 +618,10 @@ $ ./webodm.sh start

                                        Tapos buksan ang web browser sa http://localhost:8000.

                                        -
                                        - - -
                                        + + + +

                                        Basic na mga cCommands at Troubleshooting

                                        Ang mainam na bagy sa paggamit ng dockey ay 99% ng mmga task na kailangan gamitin habang gamit ang WebODM ay magagawa sa pamamagitan ng ./webodm.sh script. Ikaw ay naka-encounter na ng isa sa mga ito:

                                        $ ./webodm.sh start
@@ -652,20 +653,20 @@ $ ./webodm.sh --help

                                        Ang community forum na <https://community.opendronemap.org>`_ ay magandang lugar na pagtanungan kapag ikaw ay na-stuck sa kahit anong mga hakbang pangi-installasyon at para sa mga general na katanungan kung paano gumamit ng ./webodm.sh script.

                                        -
                                        -
                                        + +

                                        Hello, WebODM!

                                        Matapos patakbuhin ang ./webodm.sh simulan at buksan ang WebODM sa browser, ikaw ay makatatanggap ng pangbungad na mensahe at tatanungin na gawin ang first user. Bigyang oras at pagsanayan ang web interface at i-explore ang various menus.

                                        -
                                        +
                                        Screenshot of WebODM Dashboard -
                                        +

                                        WebODM Dashboard

                                        Pansinin na sa ilalim ng Processing Nodes menu ay may "node-odm-1" node ay na-configure para sa iyo. Ito ay NodeODM node at nagawa ng awtomatik ng WebODM. Ang node na ito ay gumagana sa parehong machine tulad ng WebODM.

                                        Kung umabot ka na sa ganito kalayo, congratulations! Oras na para simulan ang pagprocess ng mga data.

                                        -
                                        +
                                        Image of celebratory dance -
                                        -
                                        + +

                                        Pagpapatakbo sa dalawa o mas marami pang machine

                                        Optionally: Kung ikaw ay may isa pang computer, mauulit mo ang installation process (install docker, git, python, etc.) at i-launch ang bagong NodeODM node sa pagta-type mula sa Terminal/Git Bash window:

                                        docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -675,9 +676,9 @@ $ ./webodm.sh --help
 
                                        Mula sa WebODM pwede mong piliin ang Add New button under Processing Nodes. Para sa hostname/IP field i-type ang IP ng pangalawang computer. Para sa port field type “3000”. Para sa token field type “secret”. Pwede mo rin dagdagan ng optional na label para sa iyong node, katulad ng “second computer”. Then press Save.
                                        
 
                                        Kung ang lahat ay nagawa ng maayos, dapat ay mayroon ka nang dalawang processing nodes! Kaya ng magprocess ng multiple tasks in parallel gamit ang dalawang magkaiba na machines.
                                        
 
                                        Learn to edit and help improve this page!
                                        
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                                        diff --git a/fil/large.html b/fil/large.html index ddbbe7667..2a656b6f5 100644 --- a/fil/large.html +++ b/fil/large.html @@ -4,10 +4,11 @@ - + + - Splitting Large Datasets — OpenDroneMap 2.4.7 documentation + Splitting Large Datasets — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -291,20 +292,20 @@
                                        -
                                        +

                                        Splitting Large Datasets

                                        sa ODM bersiyon 0.6.0, maaaring hatiin ang malalaking datasets sa mas maliliit na grupo na tinatawag na submodels

                                        Bakit gagamitin ang split-merge pipeline? Kung may napakalaking bilang ng imahe sa dataset, split-merge ang makaktulong sa pagpaparocess na maging mas maayos sa malaking machine (mangangailangan ito ng mas maliit na memory). Kung maraming machine ang nakakonekta sa iisang network, pwede i-proseso ang submodels ng parallel, papayagan nito ang horizontal scaling at pagproseso ng libong imahe ng mabilis.

                                        Ang split-merge ay gumagana sa WebODM sa labas ng box basta ang process nodes ay sumosoporta sa slit-merge, sa pagenable ng --split option kapag gumagawa ng bagong task.

                                        -
                                        +

                                        Calibrate images

                                        Ang image calibration ay rekomendado (pero hindi required) para sa large datasets dahil ang error propagation dahilan ng image distortion ay maaaring magsanhi ng bowl effect sa mga models. Ang image calibration instruction ay makikita sa Calibrate Images.

                                        -
                                        +
                                        image of lens distortion effect on bowling of data -
                                        +

                                        Bowling effect sa point cloud over 13,000+ na image dataset na nakolekta ng World Bank Tanzania sa flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

                                        -
                                        -
                                        +
                                        +

                                        Local Split-Merge

                                        Ang pagsplit ng dataset sa mas maayos na submodels at sunod-sunod na pagproseso n lahat ng submodels sa iisang machine ay madali lamang! Gamitin lang ang --split at --split-overlap para mapagdesisyunan ang karaniwan na bilang ng mga imahe kada submodel at i-overlap (sa metro) sa pagitan ng submodels ayon sa pagkakabanggit.

                                        docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
@@ -320,14 +321,14 @@

                                        gagawa ng 3 submodels. Siguraduhin na daanan ang --split-overlap 0 kung mano-mano na magbibigay ng image_groups.txt file.

                                        -
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                                        + +

                                        Distributed Split-Merge

                                        Ang ODM ay awtomatikong namamahagi ng proseso kada submodel sa maramihan na machines sa pamamagitan ng NodeODM nodes, na inayos sa pamamagitan ng ClusterODM.

                                        -
                                        +
                                        image of lens distortion effect on bowling of data -
                                        -
                                        + +

                                        Paano simulan ang Distributed Split-Merge

                                        Ang unang hakbang ay ang simulan ang ClusterODM

                                        docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -355,8 +356,8 @@ Escape character is '^]'.
 
                                        docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
                                        
 
                                        
-
                                        -
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                                        +

                                        Pagkaunawa sa Cluster

                                        Kapag konektado via telnet, posible na magtanong sa kung ano na ang kalagayan ng cluster. Halimbawa, pwedeng gamitin ang command na HELP para hanapin ang mga available na commands.

                                        # HELP
@@ -391,16 +392,16 @@ OK
 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
                                        -
                                        -
                                        +
                                        +

                                        Pag-access sa Logs

                                        Habang ang proseso ay umaandar, posible rin na ilista ang mga task at tignan ang mga task output.

                                        # TASK LIST
 # TASK OUTPUT <taskId> [lines]
                                        -
                                        -
                                        + +

                                        Autoscaling ClusterODM

                                        Ang ClusterODM ay mayroon din opsiyon na autoscalesa maramihan na plataporma kasama ng, hanggang ngayon, Amazon at Digital Ocean. Tinutulungan nito ang user na mababaan ang presyo na nauugnay sa always-on na mga pagkakataon pati na rin ang kakayanan na scale ang proseso base sa demand.

                                        Para i-set up ang autoscaling, dapat na:

                                        @@ -436,19 +437,19 @@ info: Found docker-machine executable

                                        Sa paraan na ito, ang lahat ng tasks ay awtomatikong napapadala sa autoscaler.

                                        -
                                        - -
                                        + + +

                                        Mga limitasyon

                                        Ang 3D textured meshes ay kasalukuyan na hindi name-merge bilang parte ng workflow (pino-point out lamang ang clouds, DEMS at orthophotos are).

                                        Ang GCPs ay suportado ng buo pero kailangan ng kahit man lang 3 GCP points kada submodel para sa georeferencing na magsimula. Kung ang submodel ay may mas kaunti sa 3 GCPs, ang kombinasyon ng natitirang GCPs + EXIF data ang gagamitin (na medyo hindi kasing eksakto). Nirerekomenda na gamitin ang image_groups.txt file para eksaktong macontrol ang submodel split habang ginagamit ang GCPs.

                                        -
                                        -
                                        + +

                                        Acknowledgments

                                        Malaking pagpugay kay Pau at sa mga tao ng Mapillary para sa kanilang kamangha-mangha na kontribusyon sa OpenDroneMap sa pamamagitan ng kanilang OpenSfM code, na isang pangunahing sangkap ng split-merge pipeline. Inaabangan namin ang kaunlaran at pagtulak sa mga limitasyon ng OpenDronMap at makita kung gaano kalaki ang dataset na mapa-process.

                                        Learn to edit and help improve this page!

                                        -
                                        - + + diff --git a/fil/map-accuracy.html b/fil/map-accuracy.html index 8d69a5cca..914412aa4 100644 --- a/fil/map-accuracy.html +++ b/fil/map-accuracy.html @@ -4,10 +4,11 @@ - + + - Kawastuan ng Mapa — OpenDroneMap 2.4.7 documentation + Kawastuan ng Mapa — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -291,30 +292,30 @@
                                        -
                                        +

                                        Kawastuan ng Mapa

                                        Ang accuracy ay matutukoy sa digri o lapit kung saan ang impormasyon ng mapa ay kapareho ng halaga sa totoong buhay. Sa madalit sabi, kapag accuracy ang pinaguusapan, kalidad ng datos at bilang ng mga mali na naipon sa isang dataset ang tinutukoy (Pascual 2011).

                                        Relative or Local accuracy

                                        Ang lokal o relative accuracy ay matutukoy bilang digri kung saan ang distansiya sa pagitan ng dalawang puntos sa mapa ay katumbas ng aktuwal na distansiya sa pagitan ng mga puntos sa totoong buhay.

                                        Ang relative accuracy ay independiente sa lokasyon sa mapa ng mundo, ang mapa ay maaaring may mataas na relative accuracy (sa laki at korte) ngunit ang posisyon sa mundo ay pwedeng baguhin (Figure1).

                                        -
                                        +
                                        Model showing high relative accuracy -
                                        +

                                        Figure 1. Ang modelo ay pinapakita ang mataas na relative accuracy ngunit hindi wasto ang lokasyon ayon sa totoong posisyon nito sa mundo.

                                        Absolute or global Accuracy

                                        Ang absolute accuracy ay ang kawastuan ng reconstruction kaugnay ng totoong posisyon sa planeta (Pix4D 2019). ANg Figure 2 ay pinapakita ang relative at absolute accurate na modelo, ang mga puntos ay tama ang posisyon ayon sa totoong buhay.

                                        -
                                        +
                                        Model showing high absolute accuracy -
                                        +

                                        Figure 2. Ang modelo ay pinapakita ang mataas na relative at absolute accuracy. Nakalagay ayon sa totoong posisyon nito sa totoong buhay

                                        An Accuracy level for each project

                                        Ang bawat proyekto ay may partikular na accuracy na pangangailangan. Halimbawa ang pagtukoy ng progresyon sa construction site o pagsukat ng apektadong lugar ng sunog ay hindi nangangailangan ng GCP, bilang ang absolute accuracy ang hindi makakaapekto sa proseso ng pagdedesisyon.

                                        -
                                        +

                                        Mga Dapat I-expect

                                        sa pangkalahatan na palatuntunin, asahan ang relative accuracy ay nasa order na 1 to 3 times ng average ng GSD para sa dataset. At para naman sa absolute accuracy, kaiilangan asahan na ito ay dumedepende sa GPS units na nakakabit sa UAV pero ang horizontal accuracy ng standard GPS ay madalas nasa 2 to 6 meters at ang vertical accuracy sa pagitan naman ng 3 to 4 times ng horizontal accuracy.

                                        Kapag ginagamit ang GCP, ang absolute accuracy ay mas mas mas mapapahusay ng 2.5 times GSD para sa horizontal accuracy at 4 times na GSD para sa vertical accuracy (Madawalagama 2016).

                                        Sa GSD na 1cm, ang accuracy ay katulad ng sa RTK GNSS, at nasa loob ng 1:200 na scale ayon sa NSDI & FGDC mapping accuracy standards habang nasa sub-optimal na kondisyon (Barry 2013).

                                        -
                                        +

                                        Mga aspeto na may impact sa map accuracy

                                        Weather

                                        Ang kondisyon ng panahon ay may direk na epekto sa resulta ng photogrammetry kaya importante na ipagsaalang-alang ang cloud coverage, wind speed, humidity, sun's altitude at iba pang factor na nagiimpluwensiya sa UAV stability at terrain illumination.

                                        @@ -324,9 +325,9 @@

                                        Mas mataas na flight altitude, mas malaki ang image footprint at GSD. Mas malaki na GSD, mas nababawasan ang linaw at kawastuan ng imahe gawa ng mas kaunti ang detalye na makikita. Kapag ang mas maliit na GSD ay kailangan ng altitude na 3 to 4 times ng taas ng pinaka mataas na point ang rekomendado.

                                        Flight speed

                                        ANg flight speed ay may ispesyal na epekto sa cameras na nilagyan ng rolling shutter, habang ang may global o mechanical shutter naman ay mababa ang epekto. Ang UAV na nilagyan ng RTK positioning system ay apektado rin ng bilis ngunit ang paghover sa kada imahe na kinukunan ay maaaring makakuha ng maayos na accuracy. Kung gumagalaw kada kuha ng photo, ang accuracy ay malilimitahan ng dalawang factor: ang bilis kung saan ikaw ay gumagalaw multiplied sa 1 second increments ng RTK (Mather 2020).

                                        -
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                                        Mga Sanggunian

                                        Barry, P., & Coakley, R. «Accuracy of UAV photogrammetry compared with Network RTK GPS.» Baseline Surveys. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (accessed 10 13, 2020).

                                        Drone Deploy. How Do I Use Ground Control Points?: A guide to using ground control points with drone mapping software. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (accessed 7 9, 2020).

                                        @@ -335,8 +336,8 @@

                                        Pascual, Manuel S. GIS Lounge: GIS Data: A Look at Accuracy, Precision, and Types of Errors. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (accessed 07 09, 2020).

                                        Pix4D. «What is accuracy in an aerial mapping project?» Pix4D. 25 de 05 de 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (accessed 10 13, 2020).

                                        Learn to edit and help improve this page!

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                                        diff --git a/fil/masks.html b/fil/masks.html index b2d0a8ebe..b7a492b5f 100644 --- a/fil/masks.html +++ b/fil/masks.html @@ -4,10 +4,11 @@ - + + - Gamit ang Image Masks — OpenDroneMap 2.4.7 documentation + Gamit ang Image Masks — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -291,24 +292,24 @@
                                        -
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                                        Gamit ang Image Masks

                                        Simula sa ODM 2.0 maaaring magsupply ng image masks para mainform ang software na lagpasan ang reconstruction sa ilang areas. Ito ay magagamit sa pagkakataon na ang langit ay aksidente na nasama sa litrato mula sa oblique shots or limitahan an reconstruction ng isang subject.

                                        Para magdagdag ng mask, gumawa ng bagong balck and white na imahe na may parehong dimension tulad ng target na imahe na gutong i-mask. (maaaring gumamit ng program na ulad ng GIMP para gawin ito) . Kulayan ng black ang mga areas na hindi isasama sa reconstruction.

                                        -
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                                        Target image -
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                                        Image mask -
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                                        3D result (building is masked) -
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                                        Titulo ng proyekto

                                        1_mask.JPG

                                        Halimbawa, ang DJI_0018.JPG ay pwedeng magkamask sa pamamagitan ng paggawa ng DJI_0018_mask.JPG file at isama ito sa listahan ng mga imahe. Pwede rin gamitin ang .JPG, .PNG, .BMP at .TIF formats para sa image masks.

                                        Learn to edit and help improve this page!

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                                        diff --git a/fil/multispectral.html b/fil/multispectral.html index 01725542e..d8c401a47 100644 --- a/fil/multispectral.html +++ b/fil/multispectral.html @@ -4,10 +4,11 @@ - + + - Multispectral Support — OpenDroneMap 2.4.7 documentation + Multispectral Support — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                        - 2.4.7 + 2.5.5
                                        @@ -291,10 +292,10 @@
                                        -
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                                        Multispectral Support

                                        Bilang ang cersion 0.9.9 ODM ay may basic support para sa radiometric normalization, na nagge-generate ng reflectance orthophotos mula sa multispectral cameras. Ang Multispectral cameras ay nagca-capture ng multiple shots ng isang scene gamit ang iba't-ibang band sensor.

                                        -
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                                        Hardware

                                        Habang ating gusto na suportahan ang lahat ng camera, ang multispectral support ay dinevelop para magamit ang mga sumusunod na camera para ito ay gumana ng mas maayos:

                                        @@ -304,13 +305,13 @@

                                      Ang ibang camera ay maaari din gumana. Matutulungan mo kami na palawakin ang listahan sa pamamagitan ng sharing datasets na nakunan ng ibang camera.

                                      - -
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                                      Paggamit

                                      Pinaprocess ang lahat ng imahe galing sa lahat ng bands ng sabay-sabay (wag paghiwalayin ang bands sa iba't-ibang folder) at ipasa ang --radiometric-calibration na parameter para ma-enable ang radiometric normalization. Kung ang imahe ay parte ng multi-camera na set-up, ang resulta ng orthophoto ay magkakaroon ng H bands, tig-isa ang bawat camera (+ alpha).

                                      Learn to edit and help improve this page!

                                      -
                                      - + + diff --git a/fil/objects.inv b/fil/objects.inv index 90abad2a8..dfa27dc5e 100644 Binary files a/fil/objects.inv and b/fil/objects.inv differ diff --git a/fil/outputs.html b/fil/outputs.html index 9767cf0b7..4ed0fd67c 100644 --- a/fil/outputs.html +++ b/fil/outputs.html @@ -4,10 +4,11 @@ - + + - OpenDroneMap Outputs — OpenDroneMap 2.4.7 documentation + OpenDroneMap Outputs — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                      - 2.4.7 + 2.5.5
                                      @@ -291,37 +292,37 @@
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                                      OpenDroneMap Outputs

                                      Ang mga nakalista sa ibaba ay ang mga makatutulong na outputs sa ODM procedure.

                                      -
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                                      Point Cloud

                                      odm_georeferencing/odm_georeferenced_model.ply/laz/csv` -- Ang georeferenced point cloud sa iba't-ibang file format

                                      -
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                                      image of OpenDroneMap derived point cloud -
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                                      Point cloud over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                                      3D Textured Model

                                      odm_texturing/odm_textured_model.obj -- The textured surface mesh odm_texturing/odm_textured_model_geo.obj -- The georeferenced and textured surface mesh

                                      Maaaccess ang point cloud at textured meshes gamit ang Meshlab. Buksan ang Meshlab at piliin ang File:Import Mesh at piliin ang textured mesh mula sa lokasyon tulad ng mga sumusunod: odm_texturing\odm_textured_model.obj

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                                      image of OpenDroneMap derived textured mesh -
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                                      Textured mesh courtesy of OpenDroneMap

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                                      Orthophoto

                                      odm_orthophoto/odm_orthphoto.png -- The orthophoto, pero eto ay simpleng png na walang georeferencing information.

                                      odm_orthophoto/odm_orthphoto.tif -- GeoTIFF Orthophoto. Maaaring gamitin ito sa QGIS bilang raster layer.

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                                      image of OpenDroneMap orthophoto -
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                                      Orthophoto over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                                      DTM/DSM

                                      Ang DTM/DSM ay magagawa lamang kapag ang --dtm o --dsm na opsiyon ang ginamit. Tignan ang tutorial on elevation models para sa mas maraming opsiyon ng paggawa.

                                      Ang data ay maitatago sa:

                                      @@ -331,12 +332,12 @@

                                    • odm_dem/dsm.tif

                                    -
                                    +
                                    image of OpenDroneMap derived digital surface model -
                                    +

                                    Digital surface model over State University Zanzibar, courtesy of Khadija Abdullah Ali

                                    - -
                                    + +

                                    Listahan ng mga output

                                    project/
 ├── images/
@@ -382,8 +383,8 @@

                                    Learn to edit and help improve this page!

                                    -
                                    - + + diff --git a/fil/requesting-features.html b/fil/requesting-features.html index 68b75326d..de6a9f10b 100644 --- a/fil/requesting-features.html +++ b/fil/requesting-features.html @@ -4,10 +4,11 @@ - + + - Paano Magrequest ng Features — OpenDroneMap 2.4.7 documentation + Paano Magrequest ng Features — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                    - 2.4.7 + 2.5.5
                                    @@ -291,7 +292,7 @@
                                    -
                                    +

                                    Paano Magrequest ng Features

                                    Lahat ng software ay nangangailangan ng user feedback at feature requests para mapalaki at mapanatili ang pangangailangan ng mga gagamit.

                                    Ang OpenDroneMap ay FOSS na software. Ang nga Free and open sourse (FOSS) na proyekto ay nakakainteres mula sa loob palabas: mula sa labas, ang pakiramdam ng mga matagumpay ay dapat kaya na nilang gawin ang kahit ano, at mahirap alamin kung ano ang risonable na request. Mula naman sa loob ng proyekto, mararamdaman nila na kulang ang kanilang pinagkukunan: higit sa lahat aa oras, pera at oportunitad.

                                    @@ -306,7 +307,7 @@

                                    Kami ay nagagalak ikaw ay nasasabik na makita ang makabagong features na nadagdag sa proyekto na ito. MAy iilan na features na kailangan ng suporta at ang iba ay madali lang na ipatupad. gagawin namin ang lahat ng aming makakaya para matulungan ka na maintindihan kung saan naaangkop ang iyong request at aming pasasalamatan ang anumang tulong na iyong maibibigay.

                                    Learn to edit and help improve this page!

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                                    diff --git a/fil/resources.html b/fil/resources.html index f921152af..09c3457f2 100644 --- a/fil/resources.html +++ b/fil/resources.html @@ -4,10 +4,11 @@ - + + - Karagdagang Sanggunian — OpenDroneMap 2.4.7 documentation + Karagdagang Sanggunian — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                    - 2.4.7 + 2.5.5
                                    @@ -291,9 +292,9 @@
                                    -
                                    +

                                    Karagdagang Sanggunian

                                    -
                                    +

                                    Para sa gagamit

                                    Ang mga sumusunod na impormasyon ay makakatulong sa pagsisimula.

                                    -
                                    -
                                    +
                                    +

                                    Para sa mga taga-buo.

                                    Bukod sa mga nailahad na impormasyon, inirerekomenda namin na basahin ang mga sumusunod:

                                      @@ -318,8 +319,8 @@

                                    • Pinananatili ang 'section sa porum na dedicated sa research papers <http://community.opendronemap.org/c/ideas-proposals/research-papers>`_. Ito ay importanteng lugar kung saan makakahanap ng state of the art research na related sa structure from motion, multi-view stereo, meshing, texturing, etc., na magagamit para mas mapaganda ang software

                                    Learn to edit and help improve this page!

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                                    diff --git a/fil/search.html b/fil/search.html index 53c9aef74..ecd369672 100644 --- a/fil/search.html +++ b/fil/search.html @@ -7,7 +7,7 @@ - Search — OpenDroneMap 2.4.7 documentation + Search — OpenDroneMap 2.5.5 documentation @@ -63,7 +63,7 @@
                                    - 2.4.7 + 2.5.5
                                    diff --git a/fil/searchindex.js b/fil/searchindex.js index 67c9333d3..72d38a802 100644 --- a/fil/searchindex.js +++ b/fil/searchindex.js @@ -1 +1 @@ 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Opsyon at Tanda","Paano makadaragdag","Mga Paalala sa Pagpapalipad","Ground Control Points","Image Geolocation Files","Malugod na pagtanggap mula sa OpenDroneMap's Documentation","Instalasyon at Paano Simulan","Splitting Large Datasets","Kawastuan ng Mapa","Gamit ang Image Masks","Multispectral Support","OpenDroneMap Outputs","Paano Magrequest ng Features","Karagdagang Sanggunian","Mga Tutoryal"],titleterms:{Using:14,access:[7,14],accuraci:[8,14],acknowledg:7,after:14,alloc:6,ang:[1,6,7,9,14],ano:14,arch:6,argumento:0,aspeto:8,assum:14,autosc:7,bago:6,bandwidth:14,basic:6,bug:1,buo:13,calibr:7,camera:14,ccommand:6,cento:6,check:6,clean:14,cloud:11,cluster:7,clusterodm:7,command:14,control:3,cpu:6,dalawa:6,dapat:8,data:14,dataset:7,debian:6,digit:14,direktoryo:14,distribut:7,docker:14,document:5,download:6,droplet:14,dsm:11,dtm:11,editor:3,elev:14,expect:[8,14],featur:12,fedora:6,fieldwork:14,file:[3,4],flight:14,format:3,gagamit:13,gamit:9,ganoon:14,gawi:3,gcp:[3,14],gcpi:3,geoloc:4,ground:3,hardwar:[6,10],hatakin:14,height:14,hello:6,ikaw:14,imag:[4,7,9],impact:8,instal:[6,14],instalasyon:6,instanc:14,interfac:3,ipakita:1,istraktura:14,ito:14,kailangan:6,kalibr:14,kalidad:14,kanino:14,karagdagan:6,karagdagang:13,katag:14,kawastuan:8,komunidad:1,lahat:14,larg:7,launch:6,limitasyon:7,line:14,linux:6,list:14,listahan:11,local:7,log:[7,14],lokasyon:14,low:14,machin:[6,14],maco:6,maginstal:6,magrequest:12,mai:8,makadaragdag:1,malugod:5,map:8,mapa:8,marami:6,mas:6,mask:9,mataa:14,matapo:14,measur:14,memori:6,merg:7,mga:[0,1,2,3,6,7,8,11,13,14],model:[11,14],mula:5,multispectr:10,nagsurv:14,nirerekumendang:3,odm:14,opendronemap:[5,11],opsyon:0,orthophoto:[11,14],output:11,paalala:2,paano:[1,6,7,12],pag:[7,14],paggamit:[10,14],pagkaunawa:7,paglagai:3,paglikha:14,pagpapalipad:2,pagpapasa:1,pagpapatakbo:6,pagsumit:1,pagtanggap:5,pagtitipon:1,pang:6,para:[1,3,13,14],paramet:14,pattern:14,pin:14,plan:14,point:[3,11],posm:3,prep:14,pro:3,process:14,project:14,proseso:14,recommend:6,refer:3,report:1,request:1,requir:6,resiz:14,rhel:6,sanggunian:[8,13],simulan:[6,7],sira:1,split:7,step:[6,14],stockpil:14,support:[6,10],taga:13,tanda:0,templat:1,textur:11,troubleshoot:6,tutory:14,ubuntu:6,user:3,virtual:6,volum:14,webodm:6,window:6}}) \ No newline at end of file diff --git a/fil/tutorials.html b/fil/tutorials.html index 1d8d37eb6..c81dd33bf 100644 --- a/fil/tutorials.html +++ b/fil/tutorials.html @@ -4,10 +4,11 @@ - + + - Mga Tutoryal — OpenDroneMap 2.4.7 documentation + Mga Tutoryal — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                    - 2.4.7 + 2.5.5
                                    @@ -291,14 +292,14 @@
                                    -
                                    +

                                    Mga Tutoryal

                                    Sa ibaba makikita ang ilang tagubilin para sa ilang mga pang-karaniwang paggamit.

                                    -
                                    +

                                    Paglikha ng Mataas na Kalidad ng Orthophotos

                                    -
                                    +
                                    image of OpenDroneMap orthophoto -
                                    +

                                    Nang walang anumang mga pagpapalit ng parameter, ang ODM ay pumipili ng isang mahusay na kompromiso sa pagitan ng kalidad, bilis at paggamit ng memorya. Kung nais mong makakuha ng mas mataas na kalidad ng mga resulta, kailangan mong magpalit ng ilang mga parameter:

                                      @@ -309,39 +310,39 @@

                                    • --mesh-size ay dapat itaas sa 300000-6000 at --mesh-octree-depth ay dapat itaas ng 10-11 sa urban areas para maka-create ng magandang building / bubong.

                                    -
                                    -
                                    +
                                    +

                                    Pag-kalibrate ng Camera

                                    Ang pag-calibrate ng camera ay isang malaking hamon sa mga pangkaraniwang camera. Ang mga pagbabago sa temperatura, mga vibrations, pokus, at iba pang mga kadahilanan ay maaaring makaapekto sa mga hango na mga parameter na may malaking epekto sa resultang data. Ang awtomatikong o pag-calibrate sa sarili ay posible at mas mainam sa mga drone flights, ngunit depende sa pattern ng flight, ang awtomatikong pagkakalibrate ay maaaring hindi mag-alis ng lahat ng pagbaluktot sa mga nagresultang produkto. Sa papel nina James at Robson (2014) na "Ang pagpapalit ng sistematikong error sa mga topographic models na nagmula sa UAV at ground ‐ based image networks <https://onlinelibrary.wiley.com/doi/full/10.1002/esp.3609> ay nagpapakita kung paano mabawasan ang pagkabaluktot.

                                    -
                                    +
                                    image of lens distortion effect on bowling of data -
                                    +

                                    Ang epekto ng "Bowling" sa point cloud ng mahigit "13,000+ imahe na nakalap ng World Bank Tanzania sa bahaing lugar ng Msimbasi, Basin, Dar es Salaam, Tanzania.

                                    Upang pagaanin ang epekto na ito, may ilang mga pagpipilian ngunit ang pinakasimple ay ang mga sumusunod: lumipad ng dalawang pattern na pinaghihiwalay ng 20°, at sa halip na magkaroon ng isang nadir (tuwid at pababang pagturo) camera, gumamit ng isa na nakakiling sa harap ng 5°.

                                    -
                                    +
                                    animation showing optimum -
                                    +

                                    Dahil ang pamamaraang ito sa paglipad ay maaaring mas matagal kaysa sa mga karaniwang lipad, ang isa o ilang mga piloto ay maaaring lumipad ng isang maliit na lugar gamit ang pamamaraan sa itaas. Ang OpenDroneMap ay bubuo ng isang pangkalibrate file na tinatawag na mga cameras.json na pagkatapos ay mai-import upang magamit na pang calibrate ang isa pang flight na mas mahusay na napaliparan.

                                    Bilang kahalili, ang sumusunod na pamamaraan ng eksperimento ay maaaring gawin: lumipad na may mas mababang overlap, ngunit dalawang flight ng crossgrid (kung minsan ay tinatawag na crosshatch) na pinaghiwalay ng 20° na may 5° pasulong na nakaharap na camera.

                                    • Ang mga porsyento na overlap ng crossgrid ay maaaring mas mababa kaysa sa mga kahilera na flight. Upang makakuha ng mahusay na mga resulta ng 3D, kakailanganin ang 68% na magkakapatong at sidelap para sa katumbas na 83% na magkakapatong at sidelap.

                                    • Upang makakuha ng magandang resulta ng 2D at 2.5D (digital elevation model), kakailanganin ang 42% na magkakapatong at sidelap para sa isang katumbas na 70% na magkakapatong at sidelap.

                                    -
                                    +
                                    animation showing experimental optimum -
                                    +

                                    Ang mga magkakahiwalay na linya ng flight ay nagpapabuti din sa kawastuhan, ngunit mas kaunti kaysa sa isang kamera na pasulong na hinaharap ng 5°.

                                    -
                                    +
                                    figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
                                    +

                                    Mula kay James at Robson (2014), 'CC BY 4.0 <https://creativecommons.org/licenses/by/4.0>_

                                    -
                                    -
                                    + +

                                    Paglikha ng Digital Elevation Models

                                    Bilang default, ang ODM ay hindi lumilikha ng DEM. Upang lumikha ng isang digital na modelo ng terrain, siguraduhing ipasa ang watawat --dtm. Upang lumikha ng isang digital na modelo ng ibabaw, siguraduhing ipasa ang watawat --dsm.

                                    -
                                    +
                                    image of OpenDroneMap derived digital surface model -
                                    +

                                    Para sa pag gawa ng DTM, ginagamit ang isang Simple Morphological Filter (smrf) upang mauri ang mga puntos sa nasa lupa kumpara sa wala sa lupa, kung saan ang mga nasa lupa na puntos lamang ang ginagamit. Ang filter na "smrf" ay maaaring kontrolin sa pamamagitan ng maraming mga parameter:

                                      @@ -354,9 +355,9 @@

                                      Ang pagpapalit ng mga opsiyon na ito ay nagreresulta ng malaking epekto sa DTMs. Ang pinakamahusay na basahin para mapagkunan ng pinakamahusay na impormasyon ukol sa parametro ng epekto ng output ay ang An improved simple morphological filter for the terrain classification of airborne LIDAR data (Ang PDF ay libre lamang)

                                      Ang --smrf-threshold ang may pinakamalaking impact sa resulta.

                                      Ang SMRF ay mahusay sa pagiwas ng Type I na pagkakamali (maliliit na numero ng ground poins na napagkakamalan bilang non-ground) pero "acceptable" sa pagiwas sa Type II na pagkakamali (malaking numero na non-ground points na napagkakamalan na ground). Ito ay dapat na bigyan importansiya kapag bumubuo ng DTMs na hangarin na gamitin ng biswal, bilang ang mga baga na napagkakamalan na ground ay mukhang artifacts sa resulta ng DTM.

                                      -
                                      +
                                      image of lens distortion effect on bowling of data -
                                      +

                                      May dalawa pang importanteng parameters na nakakaapekto sa DEM generation:

                                        @@ -368,36 +369,36 @@ 
                                        docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
                                        -
                                      -
                                      +
                                    +

                                    Measuring stockpile volume

                                    -
                                    +

                                    Fieldwork planning

                                    Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

                                    -
                                    -
                                    +
                                    +

                                    Flight pattern

                                    Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

                                    In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

                                    -
                                    +
                                    a simple lawnmower flight pattern can produce accurate results -
                                    +

                                    Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

                                    -
                                    -
                                    + +

                                    Flight height

                                    Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

                                    -
                                    -
                                    + +

                                    GCPs

                                    To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

                                    For further information on the use of GCPs, please refer to the Ground Control Points section.

                                    -
                                    -
                                    + +

                                    Processing parameters

                                    A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

                                    If using ODM, these this reference values can help you configure the process settings.

                                    @@ -406,69 +407,69 @@ When placing or measuring GCP, equipment accuracy should be greater than the GSD

                                    --orthophoto-resolution 1.0

                                    --feature-quality high

                                    --pc-quality high

                                    -
                                    -
                                    + +

                                    Measuring

                                    As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

                                    -
                                    +
                                    almost 50% of the material will be found in the first 20% of the stockpile height -
                                    +

                                    In WebODM Dashboard, clic on "view map" to start a 2D view of your project.

                                    Once in the 2D map view, clic on the "Measure volume, area and length" button.

                                    -
                                    +
                                    clic on the "Measure volume, area and length" button -
                                    +

                                    then clic on "Create a new measurement"

                                    -
                                    +
                                    clic on "Create a new measurement" -
                                    +

                                    Start placing the points to define the stockpile base plane

                                    -
                                    +
                                    Define the stockpile base plane -
                                    +

                                    Clic on "Finish measurement" to finish the process.

                                    -
                                    +
                                    Clic on "Finish measurement" to finish the process -
                                    +

                                    Dialog box will show the message "Computing ..." for a few seconds, and after the computing is finished the volume measurement value will be displayed.

                                    -
                                    +
                                    Clic on "Finish measurement" to finish the process -
                                    +

                                    If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

                                    Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

                                    -
                                    -
                                    + +

                                    Expected accuracy

                                    For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

                                    -
                                    -
                                    -
                                    + + +

                                    Using Docker

                                    Bilang maraming gumagamit ang nageemply ng docker para madeply ang OpenDroneMap, makakatulong kapag naiintindihan ang mga basic commands para matanong ang mga pangayayari na ang docker ay nagkakamali. Ang Docker ay isang contained na aplikasyon na ang intensiyon ay mapadali ang pagdeploy ng software independent sa lokal na kapaligiran. Sa lagay na ito, ito ay masasabing pareho sa virtual machines

                                    Ang ilan na simpleng commands ay makakatulong na mapaayos ang experience.

                                    -
                                    +

                                    Listing Docker Machines

                                    Masisimulan ito sa paglista ng magagamit na docker machines sa kasalukuyang machine na pinapatakbo:

                                    > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

                                    Kung nais na makakita ng machines na hindi na tumatakbo ngunit mayroon pa rin, maaaring i-add ang -a flag:

                                    > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
                                    -
                                    -
                                    +
                                    +

                                    Accessing logs on the instance

                                    Sa paggamit ng CONTAINER ID o ng pangalan, maaaring mapuntahan ang mga logs sa machine na sumusunod:

                                    > docker logs 2518817537ce
@@ -499,8 +500,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

                                    Ang value na -5 ay sinasabihan ang tail command na ibigay ang limang 5 huling linya ng logs.

                                    -
                                    -
                                    + +

                                    Command line access to instances

                                    Minsan, kailangan natin na laliman ang pagsusuri sa proseso ng OpenDroneMap. Makakakuha dito ng diretsong command line na daan sa machine. dahil dito, pwede gamitin ang docker exec ng mapasagawa ang bash` command line shell sa machine of interest na sumusunod:

                                    > docker exec -ti 2518817537ce bash
@@ -508,24 +509,24 @@ The resulting accuracy is comparable to the commercially available photogrammetr

                                    Ngayon ay naka log-in na tayo sa docker instance at maaari ng suriin ang machine.

                                    -
                                    -
                                    + +

                                    Cleaning up after Docker

                                    Ang docker ay may kahina-hinayang na gamit ng space at by default ay hindi naglilinis ng sobrang laman na data at machine kapag ang proseso ay kumpleto na. Ito ay may benepisyo kapag kailangan na puntahan ang proseso na matagal ng terminated pero kinakarga pa rin ang bigat ng nagamit na storage space overtime. Si Maciej Łebkowski ay may 'napakahusay na pangkahalatang-ideya kung papaano aayusin ang sobrang disk usage sa docker <https://lebkowski.name/docker-volumes/>`_.

                                    -
                                    -
                                    -
                                    + + +

                                    Paggamit ng ODM para sa low-bandwidth na lokasyon.

                                    -
                                    +

                                    Ano ito at para kanino ito?

                                    Via Ivan Gayton's repo.

                                    Ang OpenDroneMap  ay hindi naman laging epektibo sa pagset-up locally-ito ay makapagyarihan na machine sa pagproseso ng malalaking datasets ng sa gayon ang cloud machine ay maaaring masagot ang mga tao sa field. Ngunit, bandwidth ang problema sa maraming low-income na setting. Ang kakulangan na ito ay hindi mareresolusyonan agag-agad pero ang mga sumusunod na hakbang ay makatutulong sa pagpapababa ng kailangan na bandwidth para maproseso ang drone imagery datasets sa cloud mula sa African na lokasyon.

                                    Dito ay nagsasaad kami ng nakakalito ngunit maisasagawa na proseso para makagawa ng OPenDronMap cloud machine (hindi CloudODM ito, isa lamang cloud-based instance ng ODM na pwedeng patakbuhin mula sa command line) at gamitin ito para maproseso ang malalaking photo sets. Ito ay nangangailangan nga kaalaman sa Unix command line use, ssh, Digital Ocean account (ang Amazon AWS ay gagana ngunit mga kaunting kaibahan sa set-up), at katamtaman na kaalaman sa general computer literacy. Kung ikaw ay hindi masyadong eksperto at handang pagdaanan ang nakakalito na pagset-up, ang 'CloudODM <https://www.opendronemap.org/cloudodm/>`__ ang dapat pagbasehan.

                                    Ang kabuoan na proseso ay nakatarget sa pagpapalipad ng mga importanteng misyon sa African o kapareho na lokasyon na maghahanap ng ipoproseso na data habang naghihintay ng field setting. Sa madalit sabi, ito ay naglalayon ng trabaho na makababawas sa bandwidth/data transfer, kesa sa simlpeng pagpapatakbo lamang ng ODM.

                                    -
                                    -
                                    +
                                    +

                                    Steps

                                    -
                                    +

                                    Install

                                    • Gumawa ng Digital Ocean droplet na may 4GB na RAM. Ito ay magkakapresyo ng $20/month. Hindi sapat ang 4GB na RAM at ang installation ay siguradong hindi matutuloy. Kapag ang ODM na proseso ay pinapatakbo, ito ay nire-resize sa mas malaki-at mas mahal-na cloud machine pero habang ito ay pinapatakbo, maaari na pababain sa pangalawang pinakamura na drooplet na nagkakahalaga ng $10/month (pinakamura na droplet na nasa $5/month, may kasama na maliit na drive na hindi na pwedeng magdownsize pabalik dito).

                                      @@ -562,8 +563,8 @@ export PATH=$PATH:/home/odm/ODM/

                                      • Ngayon, kakailanganin mo ng pangalawang cloud hard drive (“Volume” in Digital Ocean jargon) na may sapat na laki para sa iyong proyekto. Ang pamantayan ay 10 beses ng laki iyong raw image set; ay mayroon 100GB na image set at set-up na 1000GB volume (kapag ito ay napaandar na, dapat ay makapagbawas na ng mga mamahalin na drive capacity, ito ay kailangan para matapos ang proseso). Iset-up ang volume at i-attach ito sa droplet at 'ayusin ang mount point <https://www.digitalocean.com/docs/volumes/how-to/mount/>`__ (sa halimbawa na ito, ise-set natin sa /mnt/odmdata/).

                                      -
                                    -
                                    +
                                    +

                                    Prep data and project

                                    • Ngayon, i-push ang mga imahe sa server. Maaaring gamitin ang Now push your images onto the server. You can use Secure Copy (scp) like so: scp -r /path/to/my/imagefolder odm@xxx.xxx.xxx.xxx:/mnt/odmdata/.

                                      @@ -573,7 +574,7 @@ export PATH=$PATH:/home/odm/ODM/
                                  -
                                  +
                                  Istraktura ng direktoryo

                                  Nirerequire ng ODM na iset-up ang direktoryo sa machine. ANg kritikal ay ang install folder (kung ininstall mo katulad ng nasa taas, /home/odm/ODM/) at ang project folder (i.e. /mnt/odmdata/myproject/)

                                    @@ -583,9 +584,9 @@ export PATH=$PATH:/home/odm/ODM/

                                  • I-modify ang settings.yaml para matukoy ang parent directory ng project folder (sa kaso na ito ang Volume na nilikha, /mnt/odmdata/). Siguraduhin na ang mga imahe ay nasa tamang lugaw, i.e. /mnt/odmdata/myproject/images at ang ibang ancillary files /mnt/odmdata/myproject/images ay nasa root folder /mnt/odmdata/myproject/

                                  • Kung ang mga imahe mo ay nasa hiwalay na folder para sa indibidwal na AOI blocks or flights (na kadalasan ay nangyayari kapag ang iyong pamamahala ay organisado), pwedeng gumawa ng image_groups.txt file na may incantation na for i in *; do cd $i; for j in *; do echo "$j $i" >> ../$i.txt; done; cd ../; done; at cd ../, for i in myproject/*.txt; do cat $i >> image_groups.txt; done;. Ito ay makakabuo ng file na may tamang istraktura: listahan ng mga imahe at "group name" pagkatapos ng bawat isa (na sa kaso na ito madalas ay pangalan ng folder na pinanggalingan nito). Ilagay ang lahat ng image files sa isang direktoryo na tatawagin na images sa project root dir (so /mnt/odmdata/myproject/images/). Ang image_groups.txt file ay papayagan ng ODM na bantayan ang mga imahe na na kasapi ng kaparehong batch, kahit na sila ay nasa iisang direktoryo lamang.

                                  -
                                  - -
                                  + + +

                                  I-resize ang droplet, hatakin ang pin at i-proseso ito.

                                  • Patayin at ire-size ang machine sa tamang bilang ng CPUs at laki ng memory. Ginagamit namin ang memory-optimized machine na may 24 na dedicated vCPUs at 192GB na RAM (na nagkakahalaga ng $1.60/hr-na dumaragdag ng mabilis, ito ay nasa $1000/month). I-restart at magsimula agad para hindi masayang nag mahal na big-droplet time.

                                    • @@ -610,8 +611,8 @@ export PATH=$PATH:/home/odm/ODM/

                                    • Kailangan bantayan ang htop (para mas matantsa ang konsumo ng sa susunod, ikaw ay magspin-up lamang ng machine base sa pangangalangan)

                                    -
                                  -
                                  + +

                                  Matapos ang lahat (assuming na ikaw ay nagsurvive ng ganoon katagal)

                                  • Kapag tapos na ang proseso, patayin ang machine at ire-size ito pabalik sa inexpensive minimum capacity.

                                    • @@ -630,10 +631,10 @@ export PATH=$PATH:/home/odm/ODM/

                                  Learn to edit and help improve this page!

                                  - - - - + + + + diff --git a/flying.html b/flying.html index 403881a5a..4810c9ce4 100644 --- a/flying.html +++ b/flying.html @@ -4,10 +4,11 @@ - + + - Flying Tips — OpenDroneMap 2.4.7 documentation + Flying Tips — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                                  - 2.4.7 + 2.5.5
                                  @@ -291,7 +292,7 @@
                                  -
                                  +

                                  Flying Tips

                                  The Humanitarian OpenStreetMap team has guidelines on flying for UAV mapping:

                                  +
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Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/fr/_static/documentation_options.js b/fr/_static/documentation_options.js index caf0c94b4..6558ff1b8 100644 --- a/fr/_static/documentation_options.js +++ b/fr/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'fr', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/fr/arguments.html b/fr/arguments.html index 91d309359..e34a63775 100644 --- a/fr/arguments.html +++ b/fr/arguments.html @@ -4,10 +4,11 @@ - + + - Options et indicateurs — Documentation OpenDroneMap 2.4.7 + Options et indicateurs — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                                  - 2.4.7 + 2.5.5
                                  @@ -160,7 +161,7 @@
                              • Options et indicateurs
                              • OpenDroneMap Outputs
                                  @@ -292,32 +293,36 @@
                                  -
                                  +

                                  Options et indicateurs

                                  -
                                  -

                                  Arguments

                                  +
                                  +

                                  Arguments : :

                                  -
                                  --build-overviews

                                  Build orthophoto overviews for faster display in programs such as QGIS. Default: False

                                  +
                                  --build-overviews

                                  Créez des aperçus d’orthophoto pour un affichage plus rapide dans des programmes tels que QGIS. Par défaut: False

                                  -
                                  --camera-lens auto | perspective | brown | fisheye | spherical

                                  Set a camera projection type. Manually setting a value can help improve geometric undistortion. By default the application tries to determine a lens type from the images metadata. . Default: auto

                                  +
                                  --camera-lens auto | perspective | brown | fisheye | spherical

                                  Définissez un type de projection de caméra. La définition manuelle d’une valeur peut aider à améliorer la non-distorsion géométrique. Par défaut, l’application tente de déterminer un type d’objectif à partir des métadonnées des images. . Par défaut: auto

                                  -
                                  --cameras <json>

                                  Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: ``

                                  +
                                  --cameras <json>

                                  Utilisez les paramètres de la caméra calculés à partir d’un autre jeu de données au lieu de les calculer. Peut être spécifié soit en tant que chemin d’accès à un fichier cameras.json, soit en tant que chaîne JSON représentant le contenu d’un fichier cameras.json. Par défaut: ``

                                  -
                                  --crop <positive float>

                                  Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: 3

                                  +
                                  --cog

                                  Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

                                  -
                                  --debug

                                  Print debug messages. Default: False

                                  +
                                  --copy-to <path>

                                  Copy output results to this folder after processing.

                                  -
                                  --dem-decimation <positive integer>

                                  Decimate the points before generating the DEM. 1 is no decimation (full quality). 100 decimates ~99%% of the points. Useful for speeding up generation of DEM results in very large datasets. Default: 1

                                  +
                                  --crop <positive float>

                                  Recadrez automatiquement les sorties d’image en créant un tampon lisse autour des limites du jeu de données, réduit de N mètres. Utilisez 0 pour désactiver le recadrage. Par défaut: 3

                                  -
                                  --dem-euclidean-map

                                  Computes an euclidean raster map for each DEM. The map reports the distance from each cell to the nearest NODATA value (before any hole filling takes place). This can be useful to isolate the areas that have been filled. Default: False

                                  +
                                  --debug

                                  Imprimer les messages de débogage. Par défaut: False

                                  -
                                  --dem-gapfill-steps <positive integer>

                                  Number of steps used to fill areas with gaps. Set to 0 to disable gap filling. Starting with a radius equal to the output resolution, N different DEMs are generated with progressively bigger radius using the inverse distance weighted (IDW) algorithm and merged together. Remaining gaps are then merged using nearest neighbor interpolation. Default: 3

                                  +
                                  --dem-decimation <positive integer>

                                  Décimez les points avant de générer le DEM. 1 indique sans décimation (qualité totale). 100 décime ~99%% des points. Utile pour accélérer la génération de résultats DEM dans des ensembles de données très volumineux. Par défaut: 1

                                  -
                                  --dem-resolution <float>

                                  DSM/DTM resolution in cm / pixel. Note that this value is capped by a ground sampling distance (GSD) estimate. To remove the cap, check –ignore-gsd also. Default: 5

                                  +
                                  --dem-euclidean-map

                                  Calcule une carte raster euclidienne pour chaque DEM. La carte indique la distance entre chaque cellule et la valeur NODATA la plus proche (avant tout remplissage de trou). Cela peut être utile pour isoler les zones qui ont été remplies. Par défaut: False

                                  -
                                  --depthmap-resolution <positive float>

                                  Legacy option (use –pc-quality instead). Controls the density of the point cloud by setting the resolution of the depthmap images. Higher values take longer to compute but produce denser point clouds. Default: 640

                                  +
                                  --dem-gapfill-steps <positive integer>

                                  Nombre d’étapes utilisées pour remplir les zones avec des vides. Réglez sur 0 pour désactiver le remplissage des espaces. En commençant par un rayon égal à la résolution de sortie, N différents DEM sont générés avec un rayon progressivement plus grand en utilisant l’algorithme de pondération de la distance inverse (IDW) et fusionnés ensemble. Les espaces restants sont ensuite fusionnés à l’aide de l’interpolation du plus proche voisin. Par défaut: 3

                                  -
                                  --dsm

                                  Use this tag to build a DSM (Digital Surface Model, ground + objects) using a progressive morphological filter. Check the –dem* parameters for finer tuning. Default: False

                                  +
                                  --dem-resolution <float>

                                  Résolution DSM / DTM en cm / pixel. Notez que cette valeur est plafonnée par une estimation de la distance d’échantillonnage au sol (GSD). Pour retirer le capuchon, cochez également –ignore-gsd. Par défaut: 5

                                  +
                                  +
                                  --depthmap-resolution <positive float>

                                  Option héritée (utilisez à la place –pc-quality). Contrôle la densité du nuage de points en définissant la résolution de la profondeur des images de la carte. Les valeurs plus élevées prennent plus de temps à calculer mais produisent des nuages de points plus denses. Par défaut: 640

                                  +
                                  +
                                  --dsm

                                  Utilisez cette balise pour construire un DSM (Digital Surface Model, sol + objets) à l’aide d’un filtre morphologique progressif. Vérifiez les paramètres –dem* pour un réglage plus fin. Par défaut: False

                                  --dtm

                                  Use this tag to build a DTM (Digital Terrain Model, ground only) using a simple morphological filter. Check the –dem* and –smrf* parameters for finer tuning. Default: False

                                  @@ -337,7 +342,7 @@
                                  --gps-accuracy <positive float>

                                  Set a value in meters for the GPS Dilution of Precision (DOP) information for all images. If your images are tagged with high precision GPS information (RTK), this value will be automatically set accordingly. You can use this option to manually set it in case the reconstruction fails. Lowering this option can sometimes help control bowling-effects over large areas. Default: 10

                                  -
                                  -h,--help

                                  show this help message and exit

                                  +
                                  -h,--help

                                  Montrer le message d’aide et sortir

                                  --ignore-gsd

                                  Ignore Ground Sampling Distance (GSD). GSD caps the maximum resolution of image outputs and resizes images when necessary, resulting in faster processing and lower memory usage. Since GSD is an estimate, sometimes ignoring it can result in slightly better image output quality. Default: False

                                  @@ -357,14 +362,16 @@
                                  --min-num-features <integer>

                                  Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

                                  -
                                  name <project name>

                                  Name of dataset (i.e subfolder name within project folder). Default: code

                                  +
                                  name <dataset name>

                                  Name of dataset (i.e subfolder name within project folder). Default: code

                                  -
                                  --optimize-disk-space

                                  Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don’t have sufficient disk space available. Default: False

                                  +
                                  --optimize-disk-space

                                  Supprimez les fichiers intermédiaires lourds pour optimiser l’utilisation de l’espace disque. Cela affecte la possibilité de redémarrer la file d’attente à partir d’une étape intermédiaire, mais permet de traiter les ensembles de données sur des machines qui ne disposent pas de suffisamment d’espace disque disponible. Par défaut: False

                                  --orthophoto-compression JPEG | LZW | PACKBITS | DEFLATE | LZMA | NONE

                                  Set the compression to use for orthophotos. . Default: DEFLATE

                                  --orthophoto-cutline

                                  Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

                                  +
                                  --orthophoto-kmz

                                  Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

                                  +
                                  --orthophoto-no-tiled

                                  Set this parameter if you want a striped GeoTIFF. Default: False

                                  --orthophoto-png

                                  Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

                                  @@ -393,7 +400,7 @@
                                  --project-path <path>

                                  Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an « images » folder.

                                  -
                                  --radiometric-calibration none | camera | camera+sun

                                  Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

                                  +
                                  --radiometric-calibration none | camera | camera+sun

                                  Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

                                  --rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

                                  Rerun this stage only and stop. . Default: ``

                                  @@ -427,6 +434,8 @@
                                  --texturing-data-term gmi | area

                                  When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

                                  +
                                  --texturing-keep-unseen-faces

                                  Keep faces in the mesh that are not seen in any camera. Default: False

                                  +
                                  --texturing-outlier-removal-type none | gauss_clamping | gauss_damping

                                  Type of photometric outlier removal method. . Default: gauss_clamping

                                  --texturing-skip-global-seam-leveling

                                  Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

                                  @@ -453,8 +462,8 @@

                                  Learn to edit and help improve this page!

                                  -
                                  -
                                  + +
                                  diff --git a/fr/contributing.html b/fr/contributing.html index 990b326cd..16a6e963a 100644 --- a/fr/contributing.html +++ b/fr/contributing.html @@ -4,10 +4,11 @@ - + + - How To Contribute — Documentation OpenDroneMap 2.4.7 + How To Contribute — Documentation OpenDroneMap 2.5.5 @@ -63,7 +64,7 @@
                                  - 2.4.7 + 2.5.5
                                  @@ -159,7 +160,7 @@
                              • Options et indicateurs
                              • OpenDroneMap Outputs
                                  @@ -291,14 +292,14 @@
                                  -
                                  +

                                  How To Contribute

                                  OpenDroneMap est dépendant des contributions de sa communauté. Vous pouvez contribuer de nombreuses façons, même si vous n’êtes pas programmeur.

                                  -
                                  +

                                  Forum de la communauté

                                  Si vous voulez vous impliquer, rencontrez un soucis ou voulez contacter quelqu’un, le forum <https://community.opendronemap.org/>`_  est l’endroit par lequel commencer. Vous pourriez y trouver vos questions déjà répondues, ou des astuces et ressources utiles. Vous pouvez aussi y partager vos données libres pour que les autres les utilise. C’est aussi le meilleur endroit pour y partager vos bugs ou partager vos suggestions d’amélioration avec les développeurs.

                                  -
                                  -
                                  +
                                  +

                                  Reporting Bugs

                                  Bugs are tracked as Github issues. Please create an issue in the repository and tag it with the Bug tag.

                                  Explain the problem and include additional details to help maintainers reproduce the problem:

                                  @@ -318,7 +319,7 @@

                                • What’s the name and version of the OS you’re using?

                                • Are you running ODM in a virtual machine or Docker? If so, which VM software are you using and which operating systems and versions are used for the host and the guest?

                                -
                                +

                                Template For Submitting Bug Reports

                                [Short description of problem here]
 
@@ -350,9 +351,9 @@
 * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
                                -
                                - -
                                + + +

                                Pull Requests

                                • Include screenshots and animated GIFs in your pull request whenever possible.

                                  • @@ -376,8 +377,8 @@

                                Learn to edit and help improve this page!

                                -
                                - + + diff --git a/fr/flying.html b/fr/flying.html index b910e8047..f4f099e3a 100644 --- a/fr/flying.html +++ b/fr/flying.html @@ -4,10 +4,11 @@ - + + - Conseils de vol — Documentation OpenDroneMap 2.4.7 + Conseils de vol — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                                - 2.4.7 + 2.5.5
                                @@ -160,7 +161,7 @@
                            • Options et indicateurs
                            • OpenDroneMap Outputs
                            • Options et indicateurs
                            • OpenDroneMap Outputs
                                @@ -292,19 +293,19 @@
                                -
                                +

                                Ground Control Points

                                Ground control points are useful for correcting distortions in the data and referencing the data to know coordinate systems.

                                A Ground Control Point (GCP) is a position measurement made on the ground, typically using a high precision GPS. (Toffanin 2019)

                                Ground control points can be set existing structures like pavement corners, lines on a parking lot or contrasting color floor tiles, otherwise can be set using targets placed on the ground.

                                Targets can be purchased or build with an ample variety of materials ranging from bucket lids to floor tiles.

                                - -
                                +
                                +

                                GCP file format

                                The format of the GCP file is simple.

                                @@ -333,8 +334,8 @@

                                Sharp corners are good picks for GCPs. You should also place/find the GCPs evenly around your survey area.

                                The gcp_list.txt file must be created in the base of your project folder.

                                For good results your file should have a minimum of 15 lines after the header (5 points with 3 images to each point).

                                -
                                -
                                + +

                                User Interfaces

                                You can use one of two user interfaces for creating GCP files:

                                @@ -343,7 +344,7 @@

                              • GCP Editor Pro

                              -
                              +

                              POSM GCPi

                              The POSM GCPi is loaded by default on WebODM. An example is available at the WebODM Demo. To use this with known ground control XYZ values, one would do the following:

                              Create a GCP list that only includes gcp name (this is the label that will be seen in the GCP interface), x, y, and z, with a header with a proj4 string of your GCPs (make sure they are in a planar coordinate system, such as UTM. It should look something like this:

                              @@ -356,8 +357,8 @@

                              Then one can load this GCP list into the interface, load the images, and place each of the GCPs in the image.

                              - -
                              + +

                              GCP Editor Pro

                              This app needs to be installed separately or can be loaded as a WebODM plugin from https://github.com/uav4geo/GCPEditorPro

                              Create a CSV file that includes the gcp name, northing, easting and elevation.

                              @@ -369,14 +370,14 @@

                              Then import the CSV from the main screen and type +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs in the EPSG/PROJ box.

                              The following screen will display a map from where to select the GCPs to tag and import the respective images.

                              - - -
                              + + +

                              References

                              Toffanin, Piero. Open Drone Map: The Missing Guide. MasseranoLabs LLC, 2019.

                              Learn to edit and help improve this page!

                              -
                              - + + diff --git a/fr/genindex.html b/fr/genindex.html index 38cd2c251..f8fe39700 100644 --- a/fr/genindex.html +++ b/fr/genindex.html @@ -7,7 +7,7 @@ - Index — Documentation OpenDroneMap 2.4.7 + Index — Documentation OpenDroneMap 2.5.5 @@ -62,7 +62,7 @@
                              - 2.4.7 + 2.5.5
                              @@ -158,7 +158,7 @@
                          • Options et indicateurs
                          • OpenDroneMap Outputs
                              diff --git a/fr/geo.html b/fr/geo.html index 6b059916f..a8a85c39a 100644 --- a/fr/geo.html +++ b/fr/geo.html @@ -4,10 +4,11 @@ - + + - Image Geolocation Files — Documentation OpenDroneMap 2.4.7 + Image Geolocation Files — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                              - 2.4.7 + 2.5.5
                              @@ -160,7 +161,7 @@
                          • Options et indicateurs
                          • OpenDroneMap Outputs
                              @@ -292,7 +293,7 @@
                              -
                              +

                              Image Geolocation Files

                              By default ODM will use the GPS information embedded in the images, if it is available. Sometimes images do not contain GPS information, or a user wishes to override the information with more accurate data (such as RTK).

                              Starting from ODM 2.0 people can supply an image geolocation file (geo) for this purpose.

                              @@ -320,7 +321,7 @@

                              If you supply a file called geo.txt then ODM will automatically detect it. If it has another name you can specify using --geo <path>.

                              The geo.txt file must be created in the base of your project folder.

                              Learn to edit and help improve this page!

                              -
                              +
                              diff --git a/fr/index.html b/fr/index.html index bbb5b61a5..36aefb6ae 100644 --- a/fr/index.html +++ b/fr/index.html @@ -4,10 +4,11 @@ - + + - Bienvenue dans la documentation d’OpenDroneMap — Documentation OpenDroneMap 2.4.7 + Bienvenue dans la documentation d’OpenDroneMap — Documentation OpenDroneMap 2.5.5 @@ -63,7 +64,7 @@
                              - 2.4.7 + 2.5.5
                              @@ -159,7 +160,7 @@
                          • Options et indicateurs
                          • OpenDroneMap Outputs
                              @@ -291,10 +292,10 @@
                              -
                              +
                              OpenDroneMap Logo -
                              -
                              + +

                              Bienvenue dans la documentation d’OpenDroneMap


                              @@ -314,9 +315,9 @@


                              -
                              +
                              image of seedling -
                              +
                              -
                              +
                              image of pencil -
                              +
                              -
                              +
                              diff --git a/fr/installation.html b/fr/installation.html index b17f3549c..0c869f0e2 100644 --- a/fr/installation.html +++ b/fr/installation.html @@ -4,10 +4,11 @@ - + + - Installation et Préambule — Documentation OpenDroneMap 2.4.7 + Installation et Préambule — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                              - 2.4.7 + 2.5.5
                              @@ -160,7 +161,7 @@
                          • Options et indicateurs
                          • OpenDroneMap Outputs
                              @@ -292,50 +293,50 @@
                              -
                              +

                              Installation et Préambule

                              Cette section est extraite et modifiée avec la permission de OpenDroneMap: The Missing Guide, par Piero Toffanin.

                              Jusqu’à récemment, OpenDroneMap était le terme utilisé pour désigner une application à unique ligne de commande (ce qui est maintenant connu sous le nom de projet ODM). Avec le développement récent d’une interface web, d’une API et d’autres outils, OpenDroneMap est devenu un écosystème de diverses applications pour traiter, analyser et afficher des données aériennes. Cet écosystème est composé de plusieurs éléments :

                              -
                              +
                              ODM Logo -
                              +

                              • ODM est le moteur de traitement, qui peut être utilisé à partir de la ligne de commande. Il prend des images en entrée et produit une variété de résultats, y compris des nuages de points, des modèles 3D et des orthophotos

                              -
                              +
                              NodeODM Logo -
                              +

                              • NodeODM est une API légère construite sur l’ODM. Elle permet aux utilisateurs et aux applications d’accéder aux fonctions de l’ODM via un réseau informatique

                              -
                              +
                              WebODM Logo -
                              +

                              • WebODM est une interface utilisateur ergonomique qui comprend un visualiseur de carte, un visualiseur 3D, des identifiants utilisateur, un système de plugins et de nombreuses autres fonctionnalités qui sont attendues des plateformes modernes de cartographie par drones

                              -
                              +
                              CloudODM Logo -
                              +

                              • CloudODM est un petit client sur ligne commande pour communiquer avec ODM via l’API NodeODM

                              -
                              +
                              PyODM Logo -
                              +

                              • PyODM est un SDK Python permettant de créer des tâches via l’API NodeODM. Nous l’abordons plus en détail dans le chapitre « Traitement automatisé avec Python »

                              -
                              +
                              ClusterODM Logo -
                              +

                              • ClusterODM est un équilibreur de charge permettant de connecter ensemble plusieurs instances de NodeODM

                              ODM, NodeODM et WebODM sont disponibles sur toutes les grandes plateformes (Windows, macOS et Linux) via un programme appelé docker, qui est nécessaire pour faire fonctionner le logiciel. Docker offre un moyen de faire fonctionner des « conteneurs ». Les conteneurs sont des copies empaqueté d’un système entier, de ses logiciels et de ses dépendances. Ces conteneurs fonctionnent dans un environnement virtuel. Sous Linux, cet environnement virtuel est disponible à partir du système d’exploitation et est très efficace. Sous MacOS et Windows, les conteneurs s’exécutent dans une VM, ce qui entraîne une certaine surcharge, mais cela reste très adapté à l’exécution du logiciel. Une fois installé, les utilisateurs n’ont plus à se soucier du docker, car il fonctionne (presque) en toute transparence.

                              Sans Docker, il ne serait pas possible de faire fonctionner ODM sous Windows ou MacOS. Sur ces plates-formes, ODM ne peut pas fonctionner en natif. Les futurs efforts de développement se concentrent sur l’exploitation du nouveau sous-système Windows pour Linux (WSL) et sur la possibilité de faire un portage natif de toutes les dépendances sur macOS, ce qui va rendre l’installation beaucoup plus facile.

                              On Ubuntu Linux 18.04 it’s feasible to run all OpenDroneMap software natively. However, because there’s very little performance penalty for running docker on Linux and docker is straightforward to setup on this platform, we don’t recommend it. On Linux the advantages of containerization far outweigh a tiny performance penalty. With docker users also get easy one-step updates of the software, so that’s nice.

                              -
                              +

                              Recommendations en matériel

                              La configuration minimale requise pour exécuter le logiciel est la suivante:

                                @@ -350,28 +351,28 @@

                              • 16 Go de RAM

                              Ce qui précède permettra de traiter quelques centaines d’images sans trop de problèmes. Un CPU avec plus de cœurs permettra un traitement plus rapide, tandis qu’une carte graphique (GPU) n’a actuellement aucun impact sur la performance. Pour traiter davantage d’images, ajoutez de l’espace disque et de la RAM de manière linéaire au nombre d’images à traiter.

                              -
                              -
                              +
                              +

                              Installation

                              Nous recommandons aux gens d’utiliser docker pour exécuter ODM, que vous soyez sous Windows, macOS ou Linux.

                              -
                              +

                              Windows

                              Pour exécuter OpenDroneMap, vous devez disposer au minimum de Windows 7. Les versions précédentes de Windows ne sont pas supportées.

                              -
                              +

                              Étape 1. Vérifiez si la virtualisation est supportée

                              Docker nécessite une fonctionnalité de votre CPU appelée virtualisation, qui lui permet d’exécuter des machines virtuelles (VM). Assurez-vous que cette fonction est activée ! Parfois, cette fonction est désactivée. Pour vérifier, sous Windows 8 ou supérieur, vous pouvez ouvrir le Gestionnaire de tâches (appuyez sur CTRL+Maj+ESC) et passer à l’onglet Performance.

                              -
                              +
                              Image of checking virtualization in Windows 8 or higher -
                              +

                              Virtualisation devrait être activé

                              Sous Windows 7, pour savoir si la virtualisation est activée, vous pouvez utiliser l’outil de détection de la virtualisation assistée par le matériel de Microsoft® à la place <http:// www.microsoft.com/en-us/download/details.aspx?id=592>`_.

                              Si la virtualisation est désactivée, vous devrez l’activer. La procédure est malheureusement un peu différente pour chaque modèle d’ordinateur, donc la meilleure façon de procéder est de chercher sur un moteur de recherche « comment activer la vtx pour<type your computer model here> ». Souvent, il s’agit de redémarrer l’ordinateur, d’appuyer immédiatement sur F2 ou F12 au démarrage, de naviguer dans le menu de démarrage et de modifier les paramètres pour activer la virtualisation (souvent appelée « VT-X »).

                              -
                              +
                              Table of different bios keys -
                              +

                              Touches spéciales à appuyer au démarrage de l’ordinateur pour accéder au menu de démarrage des différents fournisseurs de PC

                              -
                              -
                              +
                              +

                              Étape 2. Exigences d’installation

                              Tout d’abord, vous devrez installer:

                              Pour Python 3, assurez-vous de vérifier Add Python 3.x to PATH lors de l’installation.

                              -
                              +
                              Screenshot of Python3 installation process -
                              +

                              N’oubliez pas d’ajouter l’exécutable Python à votre PATH (pour que vous puissiez exécuter des commandes avec lui)

                              Si vous êtes sur Windows 10 Home, Windows 8 (n’importe quelle version) ou Windows 7 (n’importe quelle version), installez :

                                @@ -393,8 +394,8 @@

                              Veuillez NE PAS installer les deux programmes dockers. Ils sont différents et créeront un conflit s’ils sont tous les deux installés.

                              Après avoir installé le docker, lancez-le à partir de l’icône du bureau qui est créée à partir de l’installation (Docker Quickstart dans le cas de Docker Toolbox, Docker for Windows pour Docker for Windows). Ceci est important, ne sautez pas cette étape. S’il y a des erreurs, suivez les invites à l’écran pour les corriger.

                              -
                              -
                              +
                              +

                              Étape 3. Vérifier l’allocation de la mémoire et du processeur

                              Docker sous Windows fonctionne en faisant tourner une Machine virtuelle (VM) en arrière-plan (considérez une VM comme un « émulateur d’ordinateur »). Cette VM dispose d’une certaine quantité de mémoire assignée et WebODM ne peut utiliser que la quantité de mémoire qui lui est allouée.

                              Si vous avez installé Docker Toolbox (voir ci-dessous si vous avez installé Docker pour Windows à la place) :

                              @@ -404,9 +405,9 @@

                            • Faites un clic droit sur la VM par défaut et appuyez sur Réglages…

                            • Move the Base Memory slider from the System panel and allocate 60-70% of all available memory, optionally adding 50% of the available processors from the Processor tab also

                              • -
                              +
                              Screenshot of VirtualBox Settings -
                              +

                              Paramètres VM par défaut de VirtualBox

                              Ensuite, appuyez sur OK, faites un clic droit sur la VM par défaut et appuyez sur Start.

                              Si vous avez installé Docker pour Windows à la place :

                              @@ -416,16 +417,16 @@

                            • Dans le panneau, cliquez sur Avancé et glissez les boutons pour allouer 60-70% de toute la mémoire disponible et utiliser la moitié de tous les processeurs disponibles.

                            • Appuyez sur Appliquer.

                              • -
                              +
                              Screenshot of Docker Icon -
                              +

                              Étape 1 Icône du Docker

                              -
                              +
                              Screenshot of Docker Settings -
                              +

                              Étape 3 & 4 Paramètres du Docker

                              -
                              -
                              + +

                              Étape 4. Télécharger WebODM

                              Ouvrez le programme Git Gui qui est installé avec Git. A partir de là :

                                @@ -434,27 +435,27 @@

                              • Dans Répertoire cible, cliquez sur « Parcourir » et naviguez jusqu’à un dossier de votre choix (créez-en un si nécessaire)

                              • Appuyez Cloner

                              -
                              +
                              Screenshot of Git Gui -
                              +

                              Git Gui

                              Si le téléchargement a réussi, vous devriez voir cette fenêtre :

                              -
                              +
                              Screenshot of Git Gui after successful download -
                              +

                              Git Gui après un téléchargement réussi (clone)

                              Allez dans le menu Répertoire, puis cliquez sur Créer une icône de bureau. Cela vous permettra de revenir plus facilement à cette application à l’avenir.

                              -
                              -
                              + +

                              Étape 4. Lancer WebODM

                              Depuis l’interface GUI de Git, allez dans le menu Répertoire, puis cliquez sur Git Bash. Depuis le terminal en ligne de commande, tapez:

                              $ ./webodm.sh start&

                              Plusieurs composants seront téléchargés sur votre machine à ce stade, notamment WebODM, NodeODM et ODM. Après le téléchargement, vous devriez être accueilli par l’écran suivant :

                              -
                              +
                              Screenshot of after successfully downloading WebODM -
                              +

                              Résultats de la console après le premier démarrage de WebODM.

                              • Si vous utilisez Docker pour Windows, ouvrez http://localhost:8000 sur un navigateur web

                                • @@ -468,12 +469,12 @@

                              Ensuite, connectez-vous à http://192.168.1.100:8000 (en remplaçant l’adresse IP par la bonne).

                              -
                              - -
                              + + +

                              macOS

                              La plupart des ordinateurs Mac modernes (après 2010) fonctionnant sous MacOS Sierra 10.12 ou supérieur peuvent exécuter OpenDroneMap en utilisant le docker, à condition que la virtualisation matérielle soit prise en charge (voir ci-dessous).

                              -
                              +

                              Étape 1. Vérifiez si la virtualisation est supportée

                              Ouvrez la fenêtre du Terminal et tapez :

                              $ sysctl kern.hv_support
@@ -485,8 +486,8 @@

                              Si le résultat est kern.hv_support : 1, alors votre Mac est supporté ! Procédez à l’étape 2.

                              Si le résultat est kern.hv_support : 0, cela signifie malheureusement que votre Mac est trop vieux pour exécuter OpenDroneMap. :(

                              -
                              -
                              +
                              +

                              Étape 2. Exigences d’installation

                              Il n’y a que deux programmes à installer :

                                @@ -494,9 +495,9 @@

                              1. Git: https://sourceforge.net/projects/git-osx-installer/files/

                              Après avoir installé le docker, vous devriez trouver une icône qui ressemble à une baleine dans la barre des tâches.

                              -
                              +
                              Screenshot of Docker whale -
                              +

                              Docker app en cours

                              Vous pouvez vérifier que le docker fonctionne correctement en ouvrant l’application Terminal et en tapant :

                              $ docker run hello-world
@@ -515,8 +516,8 @@

                              Si vous obtenez un « bash : git : command not found », essayez de redémarrer votre application Terminal et vérifiez les erreurs éventuelles pendant le processus d’installation.

                              -
                              -
                              +
                              +

                              Étape 3. Vérifier l’allocation de la mémoire et du processeur

                              Docker sur macOS fonctionne en faisant tourner une Machine Virtuelle (VM) en arrière-plan (voyez ça comme un « émulateur d’ordinateur »). Cette VM dispose d’une certaine quantité de mémoire allouée et WebODM ne peut utiliser que la quantité de mémoire qui lui est allouée.

                                @@ -525,12 +526,12 @@

                              1. Ajustez le curseur pour utiliser la moitié de tous les processeurs et 60-70% de toute la mémoire disponible

                              2. Appuyez Appliquer et Redémarrer

                              -
                              +
                              Screenshot of Docker advanced settings -
                              +

                              Paramètres avancés du Docker

                              -
                              -
                              + +

                              Étape 4. Téléchargez et lancez WebODM

                              A partir du Terminal tapez:

                              $ git clone https://github.com/OpenDroneMap/WebODM
@@ -539,12 +540,12 @@ $ ./webodm.sh start

                              Ouvrez ensuite http://localhost:8000 sur votre Navigateur web

                              -
                              - -
                              + + +

                              Linux

                              OpenDroneMap peut fonctionner sur toute distribution Linux qui supporte le docker. Selon le site web de documentation de docker les distributions officiellement supportées sont CentOS, Debian, Ubuntu et Fedora, avec des binaires statiques disponibles pour les autres. Si vous devez choisir une distribution uniquement pour exécuter OpenDroneMap, Ubuntu est la solution recommandée.

                              -
                              +

                              Étape 1. Exigences d’installation

                              Il y a quatre programmes qui doivent être installés :

                                @@ -554,7 +555,7 @@ $ ./webodm.sh start

                              1. Pip

                              Nous ne pouvons pas couvrir le processus d’installation pour toutes les distributions Linux existantes, nous limiterons donc les instructions aux seules distributions officiellement prises en charge par Docker. Dans tous les cas, il suffit d’ouvrir une invite de terminal et de taper quelques commandes.

                              -
                              +
                              Installer sur Ubuntu / Debian

                              Commandes à taper:

                              $ sudo apt update
@@ -563,8 +564,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
                              -
                              -
                              +
                              +
                              Installer sur CentOS / RHEL

                              Commandes à taper:

                              $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -572,8 +573,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
                              -
                              -
                              +
                              +
                              Installer sur Fedora

                              Commandes à taper:

                              $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -581,16 +582,16 @@ $ sh get-docker.sh
 $ sudo dnf install git python python-pip
                              -
                              -
                              + +
                              Installer sur Arch

                              Commandes à taper:

                              $ sudo pacman -Sy docker git python python-pip
                              -
                              - -
                              + + +

                              Étape 2. Vérifier les exigences supplémentaires

                              En plus des trois programmes ci-dessus, le script dockercompose est également nécessaire. Parfois, il est déjà installé avec le docker, mais parfois il ne l’est pas. Pour vérifier s’il est installé, essayez de taper :

                              $ docker-compose --version
@@ -608,8 +609,8 @@ $ sudo dnf install git python python-pip
 
                              $ sudo pip install docker-compose
 
                              
 
                              
-
                              -
                              +
                              +

                              Étape 3. Télécharger et lancer WebODM

                              A partir du terminal, taper:

                              $ git clone https://github.com/OpenDroneMap/WebODM
@@ -618,10 +619,10 @@ $ ./webodm.sh start

                              Ouvrez ensuite http://localhost:8000 sur votre Navigateur web

                              -
                              - - -
                              + + + +

                              Commandes de base et dépannage

                              L’avantage de l’utilisation de docker est que 99% des tâches que vous devrez effectuer en utilisant WebODM peuvent être réalisées via le script ./webodm.sh. Vous avez déjà rencontré l’un d’entre eux :

                              $ ./webodm.sh start
@@ -631,7 +632,7 @@ $ ./webodm.sh start
 
                              $ ./webodm.sh stop
 
                              
 
                              
-
                              Il existe plusieurs autres commandes que vous pouvez utiliser, ainsi que différents indicateurs. Les indicateurs sont des paramètres passés à la commande ./webodm.sh et sont généralement préfixés par un « -« . L’indicateur port, par exemple, indique à WebODM d’utiliser un port réseau différent :
                              
+
                              Il existe plusieurs autres commandes que vous pouvez utiliser, ainsi que différents indicateurs. Les indicateurs sont des paramètres passés à la commande ./webodm.sh et sont généralement préfixés par un « - ». L’indicateur port, par exemple, indique à WebODM d’utiliser un port réseau différent :
                              
 
                              $ ./webodm.sh start --port 80
 
                              
 
                              
@@ -653,20 +654,20 @@ $ ./webodm.sh --help

                              Le forum communautaire est l’endroit idéal pour demander de l’aide si vous êtes bloqué pendant l’une des étapes de l’installation et pour des questions générales sur l’utilisation du script ./webodm.sh.

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                              Salut, WebODM!

                              Après avoir lancé ./webodm.sh et ouvert WebODM dans le navigateur, vous serez accueilli par un message de bienvenue et serez invité à créer le premier utilisateur. Prenez un peu de temps pour vous familiariser avec l’interface web et explorer ses différents menus.

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                              Screenshot of WebODM Dashboard -
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                              Tableau de bord WebODM

                              Notez que dans le menu Nœuds de traitement, il y a un nœud « node-odm-1 » déjà configuré pour que vous l’utilisiez. Il s’agit d’un nœud NodeODM et il a été créé automatiquement par WebODM. Ce nœud fonctionne sur la même machine que WebODM.

                              Si vous êtes arrivé jusqu’ici, félicitations ! Il est maintenant temps de commencer à traiter certaines données.

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                              Image of celebratory dance -
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                              En cours d’exécution sur plus d’une machine

                              Optionnellement: Si vous avez un autre ordinateur, vous pouvez répéter le processus d’installation (installer docker, git, python, etc.) et lancer un nouveau noeud NodeODM en tapant depuis une fenêtre Terminal/Git Bash:

                              docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -676,9 +677,9 @@ $ ./webodm.sh --help
 
                              À partir de WebODM, vous pouvez ensuite appuyer sur le bouton Ajouter nouveau sous Nœuds de traitement. Pour le champ hostname/IP, tapez l’IP du deuxième ordinateur. Pour le champ port, tapez « 3000 ». Pour le champ token, tapez « secret ». Vous pouvez également ajouter une étiquette facultative pour votre nœud, par exemple « second ordinateur ». Appuyez ensuite sur Enregistrer.
                              
 
                              Si tout s’est bien passé, vous devriez maintenant avoir deux nœuds de traitement ! Vous pourrez désormais traiter plusieurs tâches en parallèle en utilisant deux machines différentes
                              
 
                              Learn to edit and help improve this page!
                              
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                              diff --git a/fr/large.html b/fr/large.html index f4fe4ec2c..459e8bb3c 100644 --- a/fr/large.html +++ b/fr/large.html @@ -4,10 +4,11 @@ - + + - Splitting Large Datasets — Documentation OpenDroneMap 2.4.7 + Splitting Large Datasets — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                              - 2.4.7 + 2.5.5
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                          • Options et indicateurs
                          • OpenDroneMap Outputs
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                              Splitting Large Datasets

                              Starting with ODM version 0.6.0 you can split up very large datasets into manageable chunks (called submodels), running the pipeline on each chunk, and then producing merged DEMs, orthophotos and point clouds. The process is referred to as « split-merge ».

                              Why might you use the split-merge pipeline? If you have a very large number of images in your dataset, split-merge will help make the processing more manageable on a large machine (it will require less memory). If you have many machines all connected to the same network you can also process the submodels in parallel, thus allowing for horizontal scaling and processing thousands of images more quickly.

                              Split-merge works in WebODM out of the box as long as the processing nodes support split-merge, by enabling the --split option when creating a new task.

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                              Calibrate images

                              Image calibration is recommended (but not required) for large datasets because error propagation due to image distortion could cause a bowl effect on the models. Calibration instructions can be found at Calibrate Images.

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                              image of lens distortion effect on bowling of data -
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                              Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

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                              Local Split-Merge

                              Splitting a dataset into more manageable submodels and sequentially processing all submodels on the same machine is easy! Just use --split and --split-overlap to decide the the average number of images per submodels and the overlap (in meters) between submodels respectively

                              docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
@@ -321,14 +322,14 @@

                              will create 3 submodels. Make sure to pass --split-overlap 0 if you manually provide a image_groups.txt file.

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                              Distributed Split-Merge

                              ODM can also automatically distribute the processing of each submodel to multiple machines via NodeODM nodes, orchestrated via ClusterODM.

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                              image of lens distortion effect on bowling of data -
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                              Getting Started with Distributed Split-Merge

                              The first step is start ClusterODM

                              docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -356,8 +357,8 @@ Escape character is '^]'.
 
                              docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
                              
 
                              
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                              Understanding the Cluster

                              When connected via telnet, it is possible to interrogate what is happening on the cluster. For example, we can use the command HELP to find out available commands

                              # HELP
@@ -392,16 +393,16 @@ OK
 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
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                              Accessing the Logs

                              While a process is running, it is also possible to list the tasks, and view the task output

                              # TASK LIST
 # TASK OUTPUT <taskId> [lines]
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                              Autoscaling ClusterODM

                              ClusterODM also includes the option to autoscale on multiple platforms, including, to date, Amazon and Digital Ocean. This allows users to reduce costs associated with always-on instances as well as being able to scale processing based on demand.

                              To setup autoscaling you must:

                              @@ -437,19 +438,19 @@ info: Found docker-machine executable

                              This way all tasks will be automatically forwarded to the autoscaler.

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                              Limitations

                              The 3D textured meshes are currently not being merged as part of the workflow (only point clouds, DEMs and orthophotos are).

                              GCPs are fully supported, however, there needs to be at least 3 GCP points on each submodel for the georeferencing to take place. If a submodel has fewer than 3 GCPs, a combination of the remaining GCPs + EXIF data will be used instead (which is going to be less accurate). We recommend using the image_groups.txt file to accurately control the submodel split when using GCPs.

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                              Acknowledgments

                              Huge props to Pau and the folks at Mapillary for their amazing contributions to OpenDroneMap through their OpenSfM code, which is a key component of the split-merge pipeline. We look forward to further pushing the limits of OpenDroneMap and seeing how big a dataset we can process.

                              Learn to edit and help improve this page!

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                              - 2.4.7 + 2.5.5
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                          • Options et indicateurs
                          • OpenDroneMap Outputs
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                              Map accuracy

                              Accuracy can be defined as the degree or closeness to which the information on a map matches the values in the real world. Therefore, when we refer to accuracy, we are talking about quality of data and about number of errors contained in a certain dataset (Pascual 2011).

                              Relative or Local accuracy

                              Local or relative accuracy can be defined as the degree to which de distances between two points on a map correspond to the actually distances between those points in the real world.

                              Relative accuracy is independent of the location of the map in the world, so a map can have a high relative accuracy (in size and shape) but its position in the world can be shifted (Figure 1).

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                              Model showing high relative accuracy -
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                              Figure 1. Model showing high relative accuracy but misplaced according to its real world position

                              Absolute or global Accuracy

                              Absolute accuracy is the accuracy of the reconstruction in relation to its true position on the planet (Pix4D 2019). Figure 2 shows a relative and absolute accurate model, as the points are correctly placed according to its real world position.

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                              Model showing high absolute accuracy -
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                              Figure 2. Model showing high relative and absolute accuracy. Placed correctly according to its real world position

                              An Accuracy level for each project

                              Each project has specific accuracy needs to be met. For instance assessing the progress in a construction site or measuring an area affected by a fire does not require the use of GCP, since absolute accuracy will not impact the decision making process. In the other hand, there are tasks on which accuracy is critical, for example project compliance evaluations and land title surveying, which require a higher relative and absolute accuracy.

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                              What to expect

                              In general terms, one can expect the relative accuracy to be in the order of 1 to 3 times the average GSD for the dataset. And as for the absolute accuracy, one must consider that it is dependent of the GPS unit mounted in the UAV but the horizontal accuracy of a standard GPS is usually in the range of 2 to 6 meters and the vertical accuracy between 3 to 4 times the horizontal accuracy.

                              When using GCP, absolute accuracy can be improved to 2.5 times GSD for the horizontal accuracy and 4 times the GSD for the vertical accuracy (Madawalagama 2016).

                              At a GSD of 1cm, the accuracy is to that of the RTK GNSS, and is within 1:200 scales according to NSDI & FGDC mapping accuracy standards during sub-optimal conditions (Barry 2013).

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                              Aspects impacting map accuracy

                              Weather

                              Weather conditions have direct impact in the photogrammetry results, so it is important to consider cloud coverage, wind speed, humidity, sun’s altitude and other factors influencing the UAV stability and terrain illumination.

                              @@ -325,9 +326,9 @@

                              The higher the flight altitude, the larger the image footprint and GSD. The resulting larger GSD the accuracy will be decreased as there will be less detail in the recognizable features. When a smaller GSD is required an altitude of 3 to 4 times the height of the highest point is recommended.

                              Flight speed

                              Flight speed have special effect in cameras equipped with rolling shutter, while those equipped with global or mechanical shutter tends to reduce this effect. UAV equipped with RTK positioning systems are also affected with the speed, but with hover at each photo taken, you can get very good accuracy. If instead you are moving during each photo take, the accuracy is going to be limited by two factors: the speed at which you are moving multiplied by the 1 second increments of RTK (Mather 2020).

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                              References

                              Barry, P., & Coakley, R. «Accuracy of UAV photogrammetry compared with Network RTK GPS.» Baseline Surveys. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (accessed 10 13, 2020).

                              Drone Deploy. How Do I Use Ground Control Points?: A guide to using ground control points with drone mapping software. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (accessed 7 9, 2020).

                              @@ -336,8 +337,8 @@

                              Pascual, Manuel S. GIS Lounge: GIS Data: A Look at Accuracy, Precision, and Types of Errors. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (accessed 07 09, 2020).

                              Pix4D. «What is accuracy in an aerial mapping project?» Pix4D. 25 de 05 de 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (accessed 10 13, 2020).

                              Learn to edit and help improve this page!

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                              diff --git a/fr/masks.html b/fr/masks.html index c8a903741..4c6a1d3ba 100644 --- a/fr/masks.html +++ b/fr/masks.html @@ -4,10 +4,11 @@ - + + - Using Image Masks — Documentation OpenDroneMap 2.4.7 + Using Image Masks — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
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                          • Options et indicateurs
                          • OpenDroneMap Outputs
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                              Using Image Masks

                              Starting from ODM 2.0 people can supply image masks to inform the software to skip reconstruction over certain areas. This is useful for cases where the sky was accidentally included in the input photos from oblique shots, or simply to limit the reconstruction of a single subject.

                              To add a mask, simply create a new black and white image of the same dimension as the target image you want to mask (you can use a program such as GIMP to do this). Color in black the areas to exclude from the reconstruction.

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                              Target image -
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                              Image mask -
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                              3D result (building is masked) -
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                              Name your file:

                              <filename>_mask.JPG

                              For example, DJI_0018.JPG can have a mask by creating a DJI_0018_mask.JPG file and include that in the list of images. You can use .JPG, .PNG, .BMP and .TIF formats for image masks.

                              Learn to edit and help improve this page!

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                              diff --git a/fr/multispectral.html b/fr/multispectral.html index 8fb77b789..e273c2464 100644 --- a/fr/multispectral.html +++ b/fr/multispectral.html @@ -4,10 +4,11 @@ - + + - Multispectral Support — Documentation OpenDroneMap 2.4.7 + Multispectral Support — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                              - 2.4.7 + 2.5.5
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                          • Options et indicateurs
                          • OpenDroneMap Outputs
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                              Multispectral Support

                              Since version 0.9.9 ODM has basic support for radiometric normalization, which is able to generate reflectance orthophotos from multispectral cameras. Multispectral cameras capture multiple shots of the scene using different band sensors.

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                              Hardware

                              While we aim to support as many cameras as possible, multispectral support has been developed using the following cameras, so they will work better:

                              @@ -305,13 +306,13 @@

                            Other cameras might also work. You can help us expand this list by sharing datasets captured with other cameras.

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                            Usage

                            Process all the images from all bands at once (do not separate the bands into multiple folders) and pass the –radiometric-calibration parameter to enable radiometric normalization. If the images are part of a multi-camera setup, the resulting orthophoto will have N bands, one for each camera (+ alpha).

                            Learn to edit and help improve this page!

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                            - 2.4.7 + 2.5.5
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                        • Options et indicateurs
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                            OpenDroneMap Outputs

                            Listed below are some of the useful outputs ODM produces.

                            -
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                            Point Cloud

                            odm_georeferencing/odm_georeferenced_model.ply/laz/csv – The georeferenced point cloud in different file formats

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                            image of OpenDroneMap derived point cloud -
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                            Point cloud over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                            3D Textured Model

                            odm_texturing/odm_textured_model.obj – The textured surface mesh odm_texturing/odm_textured_model_geo.obj – The georeferenced and textured surface mesh

                            You can access the point cloud and textured meshes using MeshLab. Open MeshLab, and choose File:Import Mesh and choose your textured mesh from a location similar to the following: odm_texturing\odm_textured_model.obj

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                            image of OpenDroneMap derived textured mesh -
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                            Textured mesh courtesy of OpenDroneMap

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                            Orthophoto

                            odm_orthophoto/odm_orthphoto.png – The orthophoto, but this is a simple png, which doesn’t have any georeferencing information

                            odm_orthophoto/odm_orthphoto.tif – GeoTIFF Orthophoto. You can use it in QGIS as a raster layer.

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                            image of OpenDroneMap orthophoto -
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                            Orthophoto over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                            DTM/DSM

                            DTM/DSM will only be created if the --dtm or --dsm options are used. See tutorial on elevation models for more options in creating.

                            Data will be stored in:

                            @@ -332,12 +333,12 @@

                          • odm_dem/dsm.tif

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                          image of OpenDroneMap derived digital surface model -
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                          Digital surface model over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                          List of all outputs

                          project/
 ├── images/
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                          Learn to edit and help improve this page!

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                      • Options et indicateurs
                      • OpenDroneMap Outputs
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                          How To Request Features

                          All software needs user feedback and feature requests, to grow and maintain alignment with the needs of its users.

                          @@ -320,7 +321,7 @@ then there are two answers that work really well in response:

                          and some are easier to implement. We’ll do our best to help you understand where your request falls, and we appreciate any support you can provide.

                          Learn to edit and help improve this page!

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                          diff --git a/fr/resources.html b/fr/resources.html index 2450850a9..4362bcf79 100644 --- a/fr/resources.html +++ b/fr/resources.html @@ -4,10 +4,11 @@ - + + - Additional References — Documentation OpenDroneMap 2.4.7 + Additional References — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                          - 2.4.7 + 2.5.5
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                      • Options et indicateurs
                      • OpenDroneMap Outputs
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                          Additional References

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                          For Users

                          The following resources are a good place to start:

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                          For Developers

                          In addition to user resources, we recommend to also read the following:

                            @@ -315,8 +316,8 @@

                          • We keep a section in our forum dedicated to research papers. This is a valuable place where to read more about state of the art research related to structure from motion, multi-view stereo, meshing, texturing, etc. which can be used to improve the software.

                          Learn to edit and help improve this page!

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                          diff --git a/fr/search.html b/fr/search.html index 19864ba3d..5846b5173 100644 --- a/fr/search.html +++ b/fr/search.html @@ -7,7 +7,7 @@ - Recherche — Documentation OpenDroneMap 2.4.7 + Recherche — Documentation OpenDroneMap 2.5.5 @@ -64,7 +64,7 @@
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                      • Options et indicateurs
                      • OpenDroneMap Outputs
                          diff --git a/fr/searchindex.js b/fr/searchindex.js index 5576fe299..69758211c 100644 --- a/fr/searchindex.js +++ b/fr/searchindex.js @@ -1 +1 @@ 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4,5,7,8,10,11,14],willing:14,win:6,wind:[8,14],window:[0,1,5,14],wish:4,within:[0,3,8,12,14],without:14,won:14,wonderful_burnel:14,word:[0,7],work:[0,3,7,10,12,14],workabl:14,workflow:[0,7,14],world:[6,7,8,14],would:[3,12,14],writ:7,wrong:14,wrongly:0,wsl:6,www:[6,8],xxx:14,xyz:[3,11],yaml:[11,14],ycbcr:14,you:[0,1,3,4,5,6,7,8,9,10,11,12],your:[0,1,3,4,6,7,9,11,12,14],yum:6,zanzibar:11,zcvf:14,zen_wright:14,zero:7,zon:[0,3,4]},titles:["Options et indicateurs","How To Contribute","Conseils de vol","Ground Control Points","Image Geolocation Files","Bienvenue dans la documentation d\u2019OpenDroneMap","Installation et Pr\u00e9ambule","Splitting Large Datasets","Map accuracy","Using Image Masks","Multispectral Support","OpenDroneMap Outputs","How To Request Features","Additional References","Tutorials"],titleterms:{"\u00e9tap":6,"d\u00e9pannag":6,"ex\u00e9cu":6,"for":[1,3,13,14],"long":14,"m\u00e9moir":6,"mat\u00e9riel":6,"pr\u00e9ambul":6,"suppl\u00e9mentair":6,"t\u00e9l\u00e9charg":6,"this":14,"v\u00e9rif":6,"v\u00e9rifi":6,"with":7,access:14,accessing:[7,14],accuracy:[8,14],acknowledgment:7,additional:13,after:14,all:11,alloc:6,and:14,arch:6,argument:0,aspect:8,assuming:14,autoscaling:7,away:14,bandwidth:14,bas:6,bienvenu:5,bug:1,calibrat:7,calibrating:14,cam:14,centos:6,cleaning:14,cloud:11,clust:7,clusterodm:7,command:[6,14],communaut:1,conseil:2,contribut:1,control:3,cour:6,creating:14,dan:5,dat:14,dataset:7,debian:6,developer:13,digital:14,directory:14,distributed:7,dock:14,document:5,droplet:14,dsm:11,dtm:11,editor:3,elev:14,exigent:6,expect:8,expected:14,featur:12,fedor:6,fieldwork:14,fil:[3,4],finish:14,flight:14,format:3,forum:1,from:14,gcp:[3,14],gcpi:3,geoloc:4,getting:7,ground:3,hardwar:10,height:14,high:14,how:[1,12],imag:[4,7,9],impacting:8,indiqu:0,install:[6,14],instanc:14,interfac:3,lanc:6,larg:7,limit:7,lin:14,linux:6,list:11,listing:14,local:7,locat:14,log:[7,14],low:14,machin:[6,14],macos:6,map:8,mask:9,measuring:14,merg:7,model:[11,14],multispectral:10,odm:14,opendronemap:[5,11],option:0,orthophoto:11,orthophotos:14,output:11,parameter:14,pattern:14,pin:14,planning:14,plus:6,point:[3,11],posm:3,practic:3,prep:14,pro:3,processeur:6,processing:14,project:14,pull:[1,14],quality:14,recommend:6,recommended:3,referent:[3,8,13],report:1,reporting:1,request:[1,12],resiz:14,rhel:6,run:14,salut:6,setting:3,split:7,splitting:7,started:7,step:14,stockpil:14,structur:14,submitting:1,support:[6,10],surviv:14,templat:1,textured:11,that:14,the:[7,14],tutorial:14,ubuntu:6,understanding:7,usag:10,user:[3,13],using:[9,14],virtualis:6,vol:2,volum:14,webodm:6,what:[8,14],who:14,window:6,you:14}}) \ No newline at end of file diff --git a/fr/tutorials.html b/fr/tutorials.html index 00243872e..d344ef045 100644 --- a/fr/tutorials.html +++ b/fr/tutorials.html @@ -4,10 +4,11 @@ - + + - Tutorials — Documentation OpenDroneMap 2.4.7 + Tutorials — Documentation OpenDroneMap 2.5.5 @@ -64,7 +65,7 @@
                          - 2.4.7 + 2.5.5
                          @@ -160,7 +161,7 @@
                      • Options et indicateurs
                      • OpenDroneMap Outputs
                          @@ -292,14 +293,14 @@
                          -
                          +

                          Tutorials

                          Below you will find instructions for some common use cases.

                          -
                          +

                          Creating High Quality Orthophotos

                          -
                          +
                          image of OpenDroneMap orthophoto -
                          +

                          Without any parameter tweaks, ODM chooses a good compromise between quality, speed and memory usage. If you want to get higher quality results, you need to tweak some parameters:

                            @@ -310,39 +311,39 @@

                          • --mesh-size should be increased to 300000-600000 and --mesh-octree-depth should be increased to 10-11 in urban areas to recreate better buildings / roofs.

                          -
                          -
                          +
                          +

                          Calibrating the Camera

                          Camera calibration is a special challenge with commodity cameras. Temperature changes, vibrations, focus, and other factors can affect the derived parameters with substantial effects on resulting data. Automatic or self calibration is possible and desirable with drone flights, but depending on the flight pattern, automatic calibration may not remove all distortion from the resulting products. James and Robson (2014) in their paper Mitigating systematic error in topographic models derived from UAV and ground‐based image networks address how to minimize the distortion from self-calibration.

                          -
                          +
                          image of lens distortion effect on bowling of data -
                          +

                          Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

                          To mitigate this effect, there are a few options but the simplest are as follows: fly two patterns separated by 20°, and rather than having a nadir (straight down pointing) camera, use one that tilts forward by 5°.

                          -
                          +
                          animation showing optimum -
                          +

                          As this approach to flying can be take longer than typical flights, a pilot or team can fly a small area using the above approach. OpenDroneMap will generate a calibration file called cameras.json that then can be imported to be used to calibrate another flight that is more efficiently flown.

                          Alternatively, the following experimental method can be applied: fly with much lower overlap, but two crossgrid flights (sometimes called crosshatch) separated by 20° with a 5° forward facing camera.

                          • Crossgrid overlap percentages can be lower than parallel flights. To get good 3D results, you will require 68% overlap and sidelap for an equivalent 83% overlap and sidelap.

                          • To get good 2D and 2.5D (digital elevation model) results, you will require 42% overlap and sidelap for an equivalent 70% overlap and sidelap.

                          -
                          +
                          animation showing experimental optimum -
                          +

                          Vertically separated flight lines also improve accuracy, but less so than a camera that is forward facing by 5°.

                          -
                          +
                          figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
                          +

                          From James and Robson (2014), CC BY 4.0

                          -
                          -
                          + +

                          Creating Digital Elevation Models

                          By default ODM does not create DEMs. To create a digital terrain model, make sure to pass the --dtm flag. To create a digital surface model, be sure to pass the --dsm flag.

                          -
                          +
                          image of OpenDroneMap derived digital surface model -
                          +

                          For DTM generation, a Simple Morphological Filter (smrf) is used to classify points in ground vs. non-ground and only the ground points are used. The smrf filter can be controlled via several parameters:

                            @@ -355,9 +356,9 @@

                            Changing these options can affect the result of DTMs significantly. The best source to read to understand how the parameters affect the output is to read the original paper An improved simple morphological filter for the terrain classification of airborne LIDAR data (PDF freely available).

                            Overall the --smrf-threshold option has the biggest impact on results.

                            SMRF is good at avoiding Type I errors (small number of ground points mistakenly classified as non-ground) but only « acceptable » at avoiding Type II errors (large number non-ground points mistakenly classified as ground). This needs to be taken in consideration when generating DTMs that are meant to be used visually, since objects mistaken for ground look like artifacts in the final DTM.

                            -
                            +
                            image of lens distortion effect on bowling of data -
                            +

                            Two other important parameters affect DEM generation:

                              @@ -369,36 +370,36 @@ 
                              docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
                              -
                            -
                            +
                          +

                          Measuring stockpile volume

                          -
                          +

                          Fieldwork planning

                          Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

                          -
                          -
                          +
                          +

                          Flight pattern

                          Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

                          In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

                          -
                          +
                          a simple lawnmower flight pattern can produce accurate results -
                          +

                          Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

                          -
                          -
                          + +

                          Flight height

                          Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

                          -
                          -
                          + +

                          GCPs

                          To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

                          For further information on the use of GCPs, please refer to the Ground Control Points section.

                          -
                          -
                          + +

                          Processing parameters

                          A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

                          If using ODM, these this reference values can help you configure the process settings.

                          @@ -407,69 +408,69 @@ When placing or measuring GCP, equipment accuracy should be greater than the GSD

                          –orthophoto-resolution 1.0

                          –feature-quality high

                          –pc-quality high

                          -
                          -
                          + +

                          Measuring

                          As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

                          -
                          +
                          almost 50% of the material will be found in the first 20% of the stockpile height -
                          +

                          In WebODM Dashboard, clic on « view map » to start a 2D view of your project.

                          Once in the 2D map view, clic on the « Measure volume, area and length » button.

                          -
                          +
                          clic on the "Measure volume, area and length" button -
                          +

                          then clic on « Create a new measurement »

                          -
                          +
                          clic on "Create a new measurement" -
                          +

                          Start placing the points to define the stockpile base plane

                          -
                          +
                          Define the stockpile base plane -
                          +

                          Clic on « Finish measurement » to finish the process.

                          -
                          +
                          Clic on "Finish measurement" to finish the process -
                          +

                          Dialog box will show the message « Computing … » for a few seconds, and after the computing is finished the volume measurement value will be displayed.

                          -
                          +
                          Clic on "Finish measurement" to finish the process -
                          +

                          If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

                          Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

                          -
                          -
                          + +

                          Expected accuracy

                          For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

                          -
                          -
                          -
                          + + +

                          Using Docker

                          Since many users employ docker to deploy OpenDroneMap, it can be useful to understand some basic commands in order to interrogate the docker instances when things go wrong, or we are curious about what is happening. Docker is a containerized environment intended, among other things, to make it easier to deploy software independent of the local environment. In this way, it is similar to virtual machines.

                          A few simple commands can make our docker experience much better.

                          -
                          +

                          Listing Docker Machines

                          We can start by listing available docker machines on the current machine we are running as follows:

                          > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

                          If we want to see machines that may not be running but still exist, we can add the -a flag:

                          > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
                          -
                          -
                          +
                          +

                          Accessing logs on the instance

                          Using either the CONTAINER ID or the name, we can access any logs available on the machine as follows:

                          > docker logs 2518817537ce
@@ -500,8 +501,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

                          The value -5 tells the tail command to give us just the last 5 lines of the logs.

                          -
                          -
                          + +

                          Command line access to instances

                          Sometimes we need to go a little deeper in our exploration of the process for OpenDroneMap. For this, we can get direct command line access to the machines. For this, we can use docker exec to execute a bash command line shell in the machine of interest as follows:

                          > docker exec -ti 2518817537ce bash
@@ -509,15 +510,15 @@ The resulting accuracy is comparable to the commercially available photogrammetr

                          Now we are logged into our docker instance and can explore the machine.

                          -
                          -
                          + +

                          Cleaning up after Docker

                          Docker has a lamentable use of space and by default does not clean up excess data and machines when processes are complete. This can be advantageous if we need to access a process that has since terminated, but carries the burden of using increasing amounts of storage over time. Maciej Łebkowski has an excellent overview of how to manage excess disk usage in docker.

                          -
                          -
                          -
                          + + +

                          Using ODM from low-bandwidth location

                          -
                          +

                          What is this and who is it for?

                          Via Ivan Gayton’s repo.

                          OpenDroneMap can’t always be @@ -542,10 +543,10 @@ missions in an African or similar location looking to process data ASAP while still in a field setting. Therefore it emphasizes a workflow intended to reduce bandwidth/data transfer, rather than just the simplest way of running ODM.

                          -
                          -
                          +
                          +

                          Steps

                          -
                          +

                          Install

                          • Create a Digital Ocean droplet with at least 4GB of RAM. That’ll cost @@ -623,8 +624,8 @@ droplet, and /mnt/odmdata/).

                          -
                          -
                          +
                          +

                          Prep data and project

                          • Now push your images onto the server. You can use Secure Copy @@ -640,7 +641,7 @@ files.1, +

                            Directory structure

                            ODM requires the directories on the machine to be set up just so. The critical bits are the install folder (if you installed as above, it’s @@ -684,9 +685,9 @@ image_groups.txt file will allow ODM to keep track of which images belong to the same batch, even though they’re all in a single directory.

                          -
                          - -
                          + + +

                          Resize droplet, pull pin, run away

                          • Shut down and resize your machine to an appropriately monstrous @@ -741,8 +742,8 @@ a large dataset (>1000 images), omit the +

                          +

                          After it finishes (assuming you survive that long)

                          • As soon as processing is done, shut down the machine and resize it @@ -770,10 +771,10 @@ maybe smaller).

                          Learn to edit and help improve this page!

                          - - - - + + + + diff --git a/gcp.html b/gcp.html index 772915364..23dc27cba 100644 --- a/gcp.html +++ b/gcp.html @@ -4,10 +4,11 @@ - + + - Ground Control Points — OpenDroneMap 2.4.7 documentation + Ground Control Points — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                          - 2.4.7 + 2.5.5
                          @@ -291,19 +292,19 @@
                          -
                          +

                          Ground Control Points

                          Ground control points are useful for correcting distortions in the data and referencing the data to know coordinate systems.

                          A Ground Control Point (GCP) is a position measurement made on the ground, typically using a high precision GPS. (Toffanin 2019)

                          Ground control points can be set existing structures like pavement corners, lines on a parking lot or contrasting color floor tiles, otherwise can be set using targets placed on the ground.

                          Targets can be purchased or build with an ample variety of materials ranging from bucket lids to floor tiles.

                          - -
                          +
                          +

                          GCP file format

                          The format of the GCP file is simple.

                          @@ -332,8 +333,8 @@

                          Sharp corners are good picks for GCPs. You should also place/find the GCPs evenly around your survey area.

                          The gcp_list.txt file must be created in the base of your project folder.

                          For good results your file should have a minimum of 15 lines after the header (5 points with 3 images to each point).

                          -
                          -
                          + +

                          User Interfaces

                          You can use one of two user interfaces for creating GCP files:

                          @@ -342,7 +343,7 @@

                        • GCP Editor Pro

                        -
                        +

                        POSM GCPi

                        The POSM GCPi is loaded by default on WebODM. An example is available at the WebODM Demo. To use this with known ground control XYZ values, one would do the following:

                        Create a GCP list that only includes gcp name (this is the label that will be seen in the GCP interface), x, y, and z, with a header with a proj4 string of your GCPs (make sure they are in a planar coordinate system, such as UTM. It should look something like this:

                        @@ -355,8 +356,8 @@

                        Then one can load this GCP list into the interface, load the images, and place each of the GCPs in the image.

                        - -
                        + +

                        GCP Editor Pro

                        This app needs to be installed separately or can be loaded as a WebODM plugin from https://github.com/uav4geo/GCPEditorPro

                        Create a CSV file that includes the gcp name, northing, easting and elevation.

                        @@ -368,14 +369,14 @@

                        Then import the CSV from the main screen and type +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs in the EPSG/PROJ box.

                        The following screen will display a map from where to select the GCPs to tag and import the respective images.

                        - - -
                        + + +

                        References

                        Toffanin, Piero. Open Drone Map: The Missing Guide. MasseranoLabs LLC, 2019.

                        Learn to edit and help improve this page!

                        -
                        - + + diff --git a/genindex.html b/genindex.html index d803074cf..3d5a376eb 100644 --- a/genindex.html +++ b/genindex.html @@ -7,7 +7,7 @@ - Index — OpenDroneMap 2.4.7 documentation + Index — OpenDroneMap 2.5.5 documentation @@ -61,7 +61,7 @@
                        - 2.4.7 + 2.5.5
                        diff --git a/geo.html b/geo.html index 0c17f2d46..15d62a07f 100644 --- a/geo.html +++ b/geo.html @@ -4,10 +4,11 @@ - + + - Image Geolocation Files — OpenDroneMap 2.4.7 documentation + Image Geolocation Files — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                        - 2.4.7 + 2.5.5
                        @@ -291,7 +292,7 @@
                        -
                        +

                        Image Geolocation Files

                        By default ODM will use the GPS information embedded in the images, if it is available. Sometimes images do not contain GPS information, or a user wishes to override the information with more accurate data (such as RTK).

                        Starting from ODM 2.0 people can supply an image geolocation file (geo) for this purpose.

                        @@ -319,7 +320,7 @@

                        If you supply a file called geo.txt then ODM will automatically detect it. If it has another name you can specify using --geo <path>.

                        The geo.txt file must be created in the base of your project folder.

                        Learn to edit and help improve this page!

                        -
                        +
                        diff --git a/index.html b/index.html index 45c6df9ae..d589b230f 100644 --- a/index.html +++ b/index.html @@ -4,10 +4,11 @@ - + + - Welcome to OpenDroneMap’s documentation — OpenDroneMap 2.4.7 documentation + Welcome to OpenDroneMap’s documentation — OpenDroneMap 2.5.5 documentation @@ -62,7 +63,7 @@
                        - 2.4.7 + 2.5.5
                        @@ -290,10 +291,10 @@
                        -
                        +
                        OpenDroneMap Logo -
                        -
                        + +

                        Welcome to OpenDroneMap’s documentation


                        @@ -313,9 +314,9 @@


                        -
                        +
                        image of seedling -
                        +
                        -
                        +
                        image of pencil -
                        +
                        -
                        +
                        diff --git a/installation.html b/installation.html index dbd5f561c..9649c3643 100644 --- a/installation.html +++ b/installation.html @@ -4,10 +4,11 @@ - + + - Installation and Getting Started — OpenDroneMap 2.4.7 documentation + Installation and Getting Started — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                        - 2.4.7 + 2.5.5
                        @@ -291,50 +292,50 @@
                        -
                        +

                        Installation and Getting Started

                        This section is excerpted and modified with permission from OpenDroneMap: The Missing Guide, by Piero Toffanin.

                        Until recently OpenDroneMap was the term used to refer to a single command line application (what is now known as the ODM project). With the recent development of a web interface, an API and other tools, OpenDroneMap has become an ecosystem of various applications to process, analyze and display aerial data. This ecosystem is made of several components:

                        -
                        +
                        ODM Logo -
                        +

                        • ODM is the processing engine, which can be used from the command line. It takes images as input and produces a variety of outputs, including point clouds, 3D models and orthophotos

                        -
                        +
                        NodeODM Logo -
                        +

                        • NodeODM is a light-weight API built on top of ODM. It allows users and applications to access the functions of ODM over a computer network

                        -
                        +
                        WebODM Logo -
                        +

                        • WebODM is a friendly user interface that includes a map viewer, a 3D viewer, user logins, a plugin system and many other features that are expected of modern drone mapping platforms

                        -
                        +
                        CloudODM Logo -
                        +

                        • CloudODM is a small command line client to communicate with ODM via the NodeODM API

                        -
                        +
                        PyODM Logo -
                        +

                        • PyODM is a Python SDK for creating tasks via the NodeODM API. We cover it in more detail in the “Automated Processing With Python” chapter

                        -
                        +
                        ClusterODM Logo -
                        +

                        • ClusterODM is a load balancer for connecting together multiple NodeODM instances

                        ODM, NodeODM and WebODM are available on all major platforms (Windows, macOS and Linux) via a program called docker, which is required to run the software. Docker offers a way to run “containers”. Containers are packaged copies of an entire system, its software and its dependencies. These containers run within a virtual environment. On Linux this virtual environment is available from the operating system and is very efficient. On macOS and Windows the containers run within a VM, so there’s a bit of a overhead. but it’s still very suitable for running the software. Once installed users do not have to worry much about docker, as it operates (almost) transparently.

                        Without docker it would not be possible to run ODM on Windows or macOS. On these platforms ODM cannot run natively. Future development efforts are being focused on leveraging the new Windows Subsystem for Linux (WSL) and the possibility to make a native port of all dependencies to macOS, which is going to make the installation much easier.

                        On Ubuntu Linux 18.04 it’s feasible to run all OpenDroneMap software natively. However, because there’s very little performance penalty for running docker on Linux and docker is straightforward to setup on this platform, we don’t recommend it. On Linux the advantages of containerization far outweigh a tiny performance penalty. With docker users also get easy one-step updates of the software, so that’s nice.

                        -
                        +

                        Hardware Recommendations

                        The bare minimum requirements for running the software are:

                        @@ -350,29 +351,29 @@ are:

                      • 16 GB RAM

                      The above will allow for a few hundred images to be processed without too many issues. A CPU with more cores will allow for faster processing, while a graphics card (GPU) currently has no impact on performance. For processing more images, add more disk space and RAM linearly to the number of images you need to process.

                      - -
                      + +

                      Installation

                      We recommend people use docker for running ODM, whether you are on Windows, macOS or Linux.

                      -
                      +

                      Windows

                      To run OpenDroneMap you need at least Windows 7. Previous versions of Windows are not supported.

                      -
                      +

                      Step 1. Check Virtualization Support

                      Docker requires a feature from your CPU called virtualization, which allows it to run virtual machines (VMs). Make sure you have it enabled! Sometimes this is disabled. To check, on Windows 8 or higher you can open the Task Manager (press CTRL+SHIFT+ESC) and switch to the Performance tab.

                      -
                      +
                      Image of checking virtualization in Windows 8 or higher -
                      +

                      Virtualization should be enabled

                      On Windows 7 to see if you have virtualization enabled you can use the Microsoft® Hardware-Assisted Virtualization Detection Tool instead.

                      If virtualization is disabled, you’ll need to enable it. The procedure unfortunately is a bit different for each computer model, so the best way to do this is to look up on a search engine “how to enable vtx for <type your computer model here>”. Often times it’s a matter of restarting the computer, immediately pressing F2 or F12 during startup, navigating the boot menu and changing the settings to enable virtualization (often called “VT-X”).

                      -
                      +
                      Table of different bios keys -
                      +

                      Common keys to press at computer startup to access the boot menu for various PC vendors

                      -
                      -
                      +
                      +

                      Step 2. Install Requirements

                      First, you’ll need to install:

                      For Python 3, make sure you check Add Python 3.x to PATH during the installation.

                      -
                      +
                      Screenshot of Python3 installation process -
                      +

                      Don’t forget to add the Python executable to your PATH (so that you can run commands with it)

                      Then, only if you are on Windows 10 Home, Windows 8 (any version) or Windows 7 (any version), install:

                        @@ -394,8 +395,8 @@ versions of Windows are not supported.

                      Please do NOT install both docker programs. They are different and will create a mess if they are both installed.

                      After installing docker, launch it from the Desktop icon that is created from the installation (Docker Quickstart in the case of Docker Toolbox, Docker for Windows for Docker for Windows). This is important, do not skip this step. If there are errors, follow the prompts on screen to fix them.

                      -
                      -
                      +
                      +

                      Step 3. Check Memory and CPU Allocation

                      Docker on Windows works by running a VM in the background (think of a VM as a “computer emulator”). This VM has a certain amount of memory allocated and WebODM can only use as much memory as it’s allocated.

                      If you installed Docker Toolbox (see below if you installed Docker for Windows instead):

                      @@ -405,9 +406,9 @@ versions of Windows are not supported.

                    • Right click the default VM and press Settings…

                    • Move the Base Memory slider from the System panel and allocate 60-70% of all available memory, optionally adding 50% of the available processors from the Processor tab also

                      • -
                      +
                      Screenshot of VirtualBox Settings -
                      +

                      VirtualBox default VM settings

                      Then press OK, right click the default VM and press Start.

                      If you installed Docker for Windows instead:

                      @@ -417,16 +418,16 @@ versions of Windows are not supported.

                    • From the panel, click Advanced and use the sliders to allocate 60-70% of available memory and use half of all available CPUs.

                    • Press Apply.

                      • -
                      +
                      Screenshot of Docker Icon -
                      +

                      Step 1 Docker icon

                      -
                      +
                      Screenshot of Docker Settings -
                      +

                      Step 3 & 4 Docker settings

                      -
                      -
                      + +

                      Step 4. Download WebODM

                      Open the Git Gui program that comes installed with Git. From there:

                        @@ -435,27 +436,27 @@ versions of Windows are not supported.

                      • In Target Directory click browse and navigate to a folder of your choosing (create one if necessary)

                      • Press Clone

                      -
                      +
                      Screenshot of Git Gui -
                      +

                      Git Gui

                      If the download succeeded, you should now see this window:

                      -
                      +
                      Screenshot of Git Gui after successful download -
                      +

                      Git Gui after successful download (clone)

                      Go to the Repository menu, then click Create Desktop Icon. This will allow you to come back to this application easily in the future.

                      -
                      -
                      + +

                      Step 4. Launch WebODM

                      From Git Gui, go to the Repository menu, then click Git Bash. From the command line terminal type:

                      $ ./webodm.sh start&

                      Several components will download to your machine at this point, including WebODM, NodeODM and ODM. After the download you should be greeted by the following screen:

                      -
                      +
                      Screenshot of after successfully downloading WebODM -
                      +

                      Console output after starting WebODM for the first time

                      • If you are using Docker for Windows, open a web browser to http://localhost:8000

                        • @@ -469,12 +470,12 @@ versions of Windows are not supported.

                      Then connect to http://192.168.1.100:8000 (replacing the IP address with the proper one).

                      - - -
                      + + +

                      macOS

                      Most modern (post 2010) Mac computers running macOS Sierra 10.12 or higher can run OpenDroneMap using docker, as long as hardware virtualization is supported (see below).

                      -
                      +

                      Step 1. Check Virtualization Support

                      Open a Terminal window and type:

                      $ sysctl kern.hv_support
@@ -486,8 +487,8 @@ versions of Windows are not supported.

                      If the result is kern.hv_support: 1, then your Mac is supported! Continue with Step 2.

                      If the result is kern.hv_support: 0, unfortunately it means your Mac is too old to run OpenDroneMap. :(

                      -
                      -
                      +
                      +

                      Step 2. Install Requirements

                      There are only two programs to install:

                        @@ -495,9 +496,9 @@ versions of Windows are not supported.

                      1. Git: https://sourceforge.net/projects/git-osx-installer/files/

                      After installing docker you should find an icon that looks like a whale in the task bar.

                      -
                      +
                      Screenshot of Docker whale -
                      +

                      Docker app running

                      You can verify that docker is running properly by opening the Terminal app and typing:

                      $ docker run hello-world
@@ -516,8 +517,8 @@ versions of Windows are not supported.

                      If you get a “bash: git: command not found”, try to restart your Terminal app and double-check for any errors during the install process.

                      -
                      -
                      +
                      +

                      Step 3. Check Memory and CPU Allocation

                      Docker on macOS works by running a VM in the background (think of it as a “computer emulator”). This VM has a certain amount of memory allocated and WebODM can only use as much memory as it’s allocated.

                        @@ -526,12 +527,12 @@ versions of Windows are not supported.

                      1. Adjust the CPUs slider to use half of all available CPUs and the memory to use 60-70% of all available memory

                      2. Press Apply & Restart

                      -
                      +
                      Screenshot of Docker advanced settings -
                      +

                      Docker advanced settings

                      -
                      -
                      + +

                      Step 4. Download and Launch WebODM

                      From a Terminal type:

                      $ git clone https://github.com/OpenDroneMap/WebODM
@@ -540,12 +541,12 @@ $ ./webodm.sh start

                      Then open a web browser to http://localhost:8000.

                      -
                      - -
                      + + +

                      Linux

                      OpenDroneMap can run on any Linux distribution that supports docker. According to docker’s documentation website the officially supported distributions are CentOS, Debian, Ubuntu and Fedora, with static binaries available for others. If you have to pick a distribution solely for running OpenDroneMap, Ubuntu is the recommended way to go.

                      -
                      +

                      Step 1. Install Requirements

                      There are four programs that need to be installed:

                        @@ -555,7 +556,7 @@ $ ./webodm.sh start

                      1. Pip

                      We cannot possibly cover the installation process for every Linux distribution out there, so we’ll limit the instructions to those that are distributions officially supported by docker. In all cases it’s just a matter of opening a terminal prompt and typing a few commands.

                      -
                      +
                      Install on Ubuntu / Debian

                      Commands to type:

                      $ sudo apt update
@@ -564,8 +565,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
                      -
                      -
                      +
                      +
                      Install on CentOS / RHEL

                      Commands to type:

                      $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -573,8 +574,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
                      -
                      -
                      +
                      +
                      Install on Fedora

                      Commands to type:

                      $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -582,16 +583,16 @@ $ sh get-docker.sh
 $ sudo dnf install git python python-pip
                      -
                      -
                      + +
                      Install on Arch

                      Commands to type:

                      $ sudo pacman -Sy docker git python python-pip
                      -
                      - -
                      + + +

                      Step 2. Check Additional Requirements

                      In addition to the three programs above, the dockercompose script is also needed. Sometimes it’s already installed with docker, but sometimes it isn’t. To verify if it’s installed try to type:

                      $ docker-compose --version
@@ -609,8 +610,8 @@ $ sudo dnf install git python python-pip
 
                      $ sudo pip install docker-compose
 
                      
 
                      
-
                      -
                      +
                      +

                      Step 3. Download and Launch WebODM

                      From a terminal type:

                      $ git clone https://github.com/OpenDroneMap/WebODM
@@ -619,10 +620,10 @@ $ ./webodm.sh start

                      Then open a web browser to http://localhost:8000.

                      -
                      - - -
                      + + + +

                      Basic Commands and Troubleshooting

                      The cool thing about using docker is that 99% of the tasks you’ll ever need to perform while using WebODM can be done via the ./webodm.sh script. You have already encountered one of them:

                      $ ./webodm.sh start
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                      The community forum is a great place to ask for help if you get stuck during any of the installation steps and for general questions on using the ./webodm.sh script.

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                      Hello, WebODM!

                      After running ./webodm.sh start and opening WebODM in the browser, you will be greeted with a welcome message and will be asked to create the first user. Take some time to familiarize yourself with the web interface and explore its various menus.

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                      Screenshot of WebODM Dashboard -
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                      WebODM Dashboard

                      Notice that under the Processing Nodes menu there’s a “node-odm-1” node already configured for you to use. This is a NodeODM node and has been created automatically by WebODM. This node is running on the same machine as WebODM.

                      If you’ve made it this far, congratulations! Now it’s time to start processing some data.

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                      Running on more than one machine

                      Optionally: If you have another computer, you can repeat the installation process (install docker, git, python, etc.) and launch a new NodeODM node by typing from a Terminal/Git Bash window:

                      docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -677,9 +678,9 @@ $ ./webodm.sh --help
 
                      From WebODM you can then press the Add New button under Processing Nodes. For the hostname/IP field type the IP of the second computer. For the port field type “3000”. For the token field type “secret”. You can also add an optional label for your node, such as “second computer”. Then press Save.
                      
 
                      If everything went well, you should now have two processing nodes! You will be able to process multiple tasks in parallel using two different machines.
                      
 
                      Learn to edit and help improve this page!
                      
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                      diff --git a/large.html b/large.html index 5eb4c90f5..883a2a78f 100644 --- a/large.html +++ b/large.html @@ -4,10 +4,11 @@ - + + - Splitting Large Datasets — OpenDroneMap 2.4.7 documentation + Splitting Large Datasets — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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                      Splitting Large Datasets

                      Starting with ODM version 0.6.0 you can split up very large datasets into manageable chunks (called submodels), running the pipeline on each chunk, and then producing merged DEMs, orthophotos and point clouds. The process is referred to as “split-merge”.

                      Why might you use the split-merge pipeline? If you have a very large number of images in your dataset, split-merge will help make the processing more manageable on a large machine (it will require less memory). If you have many machines all connected to the same network you can also process the submodels in parallel, thus allowing for horizontal scaling and processing thousands of images more quickly.

                      Split-merge works in WebODM out of the box as long as the processing nodes support split-merge, by enabling the --split option when creating a new task.

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                      Calibrate images

                      Image calibration is recommended (but not required) for large datasets because error propagation due to image distortion could cause a bowl effect on the models. Calibration instructions can be found at Calibrate Images.

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                      image of lens distortion effect on bowling of data -
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                      Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

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                      Local Split-Merge

                      Splitting a dataset into more manageable submodels and sequentially processing all submodels on the same machine is easy! Just use --split and --split-overlap to decide the the average number of images per submodels and the overlap (in meters) between submodels respectively

                      docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
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                      will create 3 submodels. Make sure to pass --split-overlap 0 if you manually provide a image_groups.txt file.

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                      Distributed Split-Merge

                      ODM can also automatically distribute the processing of each submodel to multiple machines via NodeODM nodes, orchestrated via ClusterODM.

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                      image of lens distortion effect on bowling of data -
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                      Getting Started with Distributed Split-Merge

                      The first step is start ClusterODM

                      docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -355,8 +356,8 @@ Escape character is '^]'.
 
                      docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
                      
 
                      
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                      Understanding the Cluster

                      When connected via telnet, it is possible to interrogate what is happening on the cluster. For example, we can use the command HELP to find out available commands

                      # HELP
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 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
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                      Accessing the Logs

                      While a process is running, it is also possible to list the tasks, and view the task output

                      # TASK LIST
 # TASK OUTPUT <taskId> [lines]
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                      Autoscaling ClusterODM

                      ClusterODM also includes the option to autoscale on multiple platforms, including, to date, Amazon and Digital Ocean. This allows users to reduce costs associated with always-on instances as well as being able to scale processing based on demand.

                      To setup autoscaling you must:

                      @@ -436,19 +437,19 @@ info: Found docker-machine executable

                      This way all tasks will be automatically forwarded to the autoscaler.

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                      Limitations

                      The 3D textured meshes are currently not being merged as part of the workflow (only point clouds, DEMs and orthophotos are).

                      GCPs are fully supported, however, there needs to be at least 3 GCP points on each submodel for the georeferencing to take place. If a submodel has fewer than 3 GCPs, a combination of the remaining GCPs + EXIF data will be used instead (which is going to be less accurate). We recommend using the image_groups.txt file to accurately control the submodel split when using GCPs.

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                      Acknowledgments

                      Huge props to Pau and the folks at Mapillary for their amazing contributions to OpenDroneMap through their OpenSfM code, which is a key component of the split-merge pipeline. We look forward to further pushing the limits of OpenDroneMap and seeing how big a dataset we can process.

                      Learn to edit and help improve this page!

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                      - + + diff --git a/map-accuracy.html b/map-accuracy.html index 0312a8419..6e67c45d2 100644 --- a/map-accuracy.html +++ b/map-accuracy.html @@ -4,10 +4,11 @@ - + + - Map accuracy — OpenDroneMap 2.4.7 documentation + Map accuracy — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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                      Map accuracy

                      Accuracy can be defined as the degree or closeness to which the information on a map matches the values in the real world. Therefore, when we refer to accuracy, we are talking about quality of data and about number of errors contained in a certain dataset (Pascual 2011).

                      Relative or Local accuracy

                      Local or relative accuracy can be defined as the degree to which de distances between two points on a map correspond to the actually distances between those points in the real world.

                      Relative accuracy is independent of the location of the map in the world, so a map can have a high relative accuracy (in size and shape) but its position in the world can be shifted (Figure 1).

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                      Model showing high relative accuracy -
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                      Figure 1. Model showing high relative accuracy but misplaced according to its real world position

                      Absolute or global Accuracy

                      Absolute accuracy is the accuracy of the reconstruction in relation to its true position on the planet (Pix4D 2019). Figure 2 shows a relative and absolute accurate model, as the points are correctly placed according to its real world position.

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                      Model showing high absolute accuracy -
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                      Figure 2. Model showing high relative and absolute accuracy. Placed correctly according to its real world position

                      An Accuracy level for each project

                      Each project has specific accuracy needs to be met. For instance assessing the progress in a construction site or measuring an area affected by a fire does not require the use of GCP, since absolute accuracy will not impact the decision making process. In the other hand, there are tasks on which accuracy is critical, for example project compliance evaluations and land title surveying, which require a higher relative and absolute accuracy.

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                      What to expect

                      In general terms, one can expect the relative accuracy to be in the order of 1 to 3 times the average GSD for the dataset. And as for the absolute accuracy, one must consider that it is dependent of the GPS unit mounted in the UAV but the horizontal accuracy of a standard GPS is usually in the range of 2 to 6 meters and the vertical accuracy between 3 to 4 times the horizontal accuracy.

                      When using GCP, absolute accuracy can be improved to 2.5 times GSD for the horizontal accuracy and 4 times the GSD for the vertical accuracy (Madawalagama 2016).

                      At a GSD of 1cm, the accuracy is to that of the RTK GNSS, and is within 1:200 scales according to NSDI & FGDC mapping accuracy standards during sub-optimal conditions (Barry 2013).

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                      Aspects impacting map accuracy

                      Weather

                      Weather conditions have direct impact in the photogrammetry results, so it is important to consider cloud coverage, wind speed, humidity, sun’s altitude and other factors influencing the UAV stability and terrain illumination.

                      @@ -324,9 +325,9 @@

                      The higher the flight altitude, the larger the image footprint and GSD. The resulting larger GSD the accuracy will be decreased as there will be less detail in the recognizable features. When a smaller GSD is required an altitude of 3 to 4 times the height of the highest point is recommended.

                      Flight speed

                      Flight speed have special effect in cameras equipped with rolling shutter, while those equipped with global or mechanical shutter tends to reduce this effect. UAV equipped with RTK positioning systems are also affected with the speed, but with hover at each photo taken, you can get very good accuracy. If instead you are moving during each photo take, the accuracy is going to be limited by two factors: the speed at which you are moving multiplied by the 1 second increments of RTK (Mather 2020).

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                      References

                      Barry, P., & Coakley, R. «Accuracy of UAV photogrammetry compared with Network RTK GPS.» Baseline Surveys. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (accessed 10 13, 2020).

                      Drone Deploy. How Do I Use Ground Control Points?: A guide to using ground control points with drone mapping software. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (accessed 7 9, 2020).

                      @@ -335,8 +336,8 @@

                      Pascual, Manuel S. GIS Lounge: GIS Data: A Look at Accuracy, Precision, and Types of Errors. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (accessed 07 09, 2020).

                      Pix4D. «What is accuracy in an aerial mapping project?» Pix4D. 25 de 05 de 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (accessed 10 13, 2020).

                      Learn to edit and help improve this page!

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                      diff --git a/masks.html b/masks.html index 0dd2f64a9..75930d409 100644 --- a/masks.html +++ b/masks.html @@ -4,10 +4,11 @@ - + + - Using Image Masks — OpenDroneMap 2.4.7 documentation + Using Image Masks — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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                      Using Image Masks

                      Starting from ODM 2.0 people can supply image masks to inform the software to skip reconstruction over certain areas. This is useful for cases where the sky was accidentally included in the input photos from oblique shots, or simply to limit the reconstruction of a single subject.

                      To add a mask, simply create a new black and white image of the same dimension as the target image you want to mask (you can use a program such as GIMP to do this). Color in black the areas to exclude from the reconstruction.

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                      Target image -
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                      Image mask -
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                      3D result (building is masked) -
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                      Name your file:

                      <filename>_mask.JPG

                      For example, DJI_0018.JPG can have a mask by creating a DJI_0018_mask.JPG file and include that in the list of images. You can use .JPG, .PNG, .BMP and .TIF formats for image masks.

                      Learn to edit and help improve this page!

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                      diff --git a/multispectral.html b/multispectral.html index 841351e1a..510ee2ecf 100644 --- a/multispectral.html +++ b/multispectral.html @@ -4,10 +4,11 @@ - + + - Multispectral Support — OpenDroneMap 2.4.7 documentation + Multispectral Support — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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                      Multispectral Support

                      Since version 0.9.9 ODM has basic support for radiometric normalization, which is able to generate reflectance orthophotos from multispectral cameras. Multispectral cameras capture multiple shots of the scene using different band sensors.

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                      Hardware

                      While we aim to support as many cameras as possible, multispectral support has been developed using the following cameras, so they will work better:

                      @@ -304,13 +305,13 @@

                    Other cameras might also work. You can help us expand this list by sharing datasets captured with other cameras.

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                    Usage

                    Process all the images from all bands at once (do not separate the bands into multiple folders) and pass the –radiometric-calibration parameter to enable radiometric normalization. If the images are part of a multi-camera setup, the resulting orthophoto will have N bands, one for each camera (+ alpha).

                    Learn to edit and help improve this page!

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                    - + + diff --git a/objects.inv b/objects.inv index e83467260..f88739afd 100644 --- a/objects.inv +++ b/objects.inv @@ -1,6 +1,6 @@ # Sphinx inventory version 2 # Project: OpenDroneMap -# Version: 2.4.7 +# Version: 2.5.5 # The remainder of this file is compressed using zlib. xڅSKn0ڭ t]n1b]OLwFדH Nzy yDI'?dtQ diff --git a/outputs.html b/outputs.html index 41fd6142d..55438b835 100644 --- a/outputs.html +++ b/outputs.html @@ -4,10 +4,11 @@ - + + - OpenDroneMap Outputs — OpenDroneMap 2.4.7 documentation + OpenDroneMap Outputs — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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                    OpenDroneMap Outputs

                    Listed below are some of the useful outputs ODM produces.

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                    Point Cloud

                    odm_georeferencing/odm_georeferenced_model.ply/laz/csv – The georeferenced point cloud in different file formats

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                    Point cloud over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                    3D Textured Model

                    odm_texturing/odm_textured_model.obj – The textured surface mesh odm_texturing/odm_textured_model_geo.obj – The georeferenced and textured surface mesh

                    You can access the point cloud and textured meshes using MeshLab. Open MeshLab, and choose File:Import Mesh and choose your textured mesh from a location similar to the following: odm_texturing\odm_textured_model.obj

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                    Textured mesh courtesy of OpenDroneMap

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                    Orthophoto

                    odm_orthophoto/odm_orthphoto.png – The orthophoto, but this is a simple png, which doesn’t have any georeferencing information

                    odm_orthophoto/odm_orthphoto.tif – GeoTIFF Orthophoto. You can use it in QGIS as a raster layer.

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                    Orthophoto over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                    DTM/DSM

                    DTM/DSM will only be created if the --dtm or --dsm options are used. See tutorial on elevation models for more options in creating.

                    Data will be stored in:

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                  • odm_dem/dsm.tif

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                  Digital surface model over State University Zanzibar, courtesy of Khadija Abdullah Ali

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                  List of all outputs

                  project/
 ├── images/
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                  Learn to edit and help improve this page!

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                  How To Request Features

                  All software needs user feedback and feature requests, to grow and maintain alignment with the needs of its users.

                  @@ -319,7 +320,7 @@ then there are two answers that work really well in response:

                  and some are easier to implement. We’ll do our best to help you understand where your request falls, and we appreciate any support you can provide.

                  Learn to edit and help improve this page!

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                  diff --git a/resources.html b/resources.html index 476790ae4..6cc8a2599 100644 --- a/resources.html +++ b/resources.html @@ -4,10 +4,11 @@ - + + - Additional References — OpenDroneMap 2.4.7 documentation + Additional References — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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                  Additional References

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                  For Users

                  The following resources are a good place to start:

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                  For Developers

                  In addition to user resources, we recommend to also read the following:

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                  • We keep a section in our forum dedicated to research papers. This is a valuable place where to read more about state of the art research related to structure from motion, multi-view stereo, meshing, texturing, etc. which can be used to improve the software.

                  Learn to edit and help improve this page!

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                  diff --git a/search.html b/search.html index c83330cc7..940b9e23e 100644 --- a/search.html +++ b/search.html @@ -7,7 +7,7 @@ - Search — OpenDroneMap 2.4.7 documentation + Search — OpenDroneMap 2.5.5 documentation @@ -63,7 +63,7 @@
                  - 2.4.7 + 2.5.5
                  diff --git a/searchindex.js b/searchindex.js index 063b1f76b..4ff95f4c0 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ 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and Flags","How To Contribute","Flying Tips","Ground Control Points","Image Geolocation Files","Welcome to OpenDroneMap\u2019s documentation","Installation and Getting Started","Splitting Large Datasets","Map accuracy","Using Image Masks","Multispectral Support","OpenDroneMap Outputs","How To Request Features","Additional References","Tutorials"],titleterms:{"long":14,For:[1,13],Using:[9,14],access:[7,14],accuraci:[8,14],acknowledg:7,addit:[6,13],after:14,all:11,alloc:6,arch:6,argument:0,aspect:8,assum:14,autosc:7,awai:14,bandwidth:14,basic:6,bug:1,calibr:[7,14],camera:14,cento:6,check:6,clean:14,cloud:11,cluster:7,clusterodm:7,command:[6,14],commun:1,contribut:1,control:3,cpu:6,creat:14,data:14,dataset:7,debian:6,develop:13,digit:14,directori:14,distribut:7,docker:14,document:5,download:6,droplet:14,dsm:11,dtm:11,editor:3,elev:14,expect:[8,14],featur:12,fedora:6,fieldwork:14,file:[3,4],finish:14,flag:0,flight:14,fly:2,format:3,forum:1,from:14,gcp:[3,14],gcpi:3,geoloc:4,get:[6,7],ground:3,hardwar:[6,10],height:14,hello:6,high:14,how:[1,12],imag:[4,7,9],impact:8,instal:[6,14],instanc:14,interfac:3,larg:7,launch:6,limit:7,line:14,linux:6,list:[11,14],local:7,locat:14,log:[7,14],low:14,machin:[6,14],maco:6,map:8,mask:9,measur:14,memori:6,merg:7,model:[11,14],more:6,multispectr:10,odm:14,one:6,opendronemap:[5,11],option:0,orthophoto:[11,14],output:11,paramet:14,pattern:14,pin:14,plan:14,point:[3,11],posm:3,practic:3,prep:14,pro:3,process:14,project:14,pull:[1,14],qualiti:14,recommend:[3,6],refer:[3,8,13],report:1,request:[1,12],requir:6,resiz:14,rhel:6,run:[6,14],set:3,split:7,start:[6,7],step:[6,14],stockpil:14,structur:14,submit:1,support:[6,10],surviv:14,templat:1,textur:11,than:6,thi:14,tip:2,troubleshoot:6,tutori:14,ubuntu:6,understand:7,usag:10,user:[3,13],virtual:6,volum:14,webodm:6,welcom:5,what:[8,14],who:14,window:6,you:14}}) \ No newline at end of file diff --git a/sw/.buildinfo b/sw/.buildinfo index 4a3f81307..29194b900 100644 --- a/sw/.buildinfo +++ b/sw/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d65f1dafa7b72460ef2a1d4b173d4c7d +config: 826481e42566ded7f032c01892b9785c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/sw/.doctrees/arguments.doctree b/sw/.doctrees/arguments.doctree index 1a46bc634..39c8f581a 100644 Binary files a/sw/.doctrees/arguments.doctree and b/sw/.doctrees/arguments.doctree differ diff --git a/sw/.doctrees/contributing.doctree b/sw/.doctrees/contributing.doctree index 85040f928..b76182a49 100644 Binary files a/sw/.doctrees/contributing.doctree and b/sw/.doctrees/contributing.doctree differ diff --git a/sw/.doctrees/environment.pickle b/sw/.doctrees/environment.pickle index bdcfd0f48..a77f3737c 100644 Binary files a/sw/.doctrees/environment.pickle and b/sw/.doctrees/environment.pickle differ diff --git a/sw/.doctrees/flying.doctree b/sw/.doctrees/flying.doctree index ad5c9851f..fd61d6e7a 100644 Binary files a/sw/.doctrees/flying.doctree and b/sw/.doctrees/flying.doctree differ diff --git a/sw/.doctrees/gcp.doctree b/sw/.doctrees/gcp.doctree index 314a4c7c0..922ac4ae7 100644 Binary files a/sw/.doctrees/gcp.doctree and b/sw/.doctrees/gcp.doctree differ diff --git a/sw/.doctrees/geo.doctree b/sw/.doctrees/geo.doctree index ab91e95ab..18ac0efa2 100644 Binary files a/sw/.doctrees/geo.doctree and b/sw/.doctrees/geo.doctree differ diff --git a/sw/.doctrees/index.doctree b/sw/.doctrees/index.doctree index 63ebb48e7..50cc3621c 100644 Binary files a/sw/.doctrees/index.doctree and b/sw/.doctrees/index.doctree differ diff --git a/sw/.doctrees/installation.doctree b/sw/.doctrees/installation.doctree index b586cbad5..cf0e2fb79 100644 Binary files a/sw/.doctrees/installation.doctree and b/sw/.doctrees/installation.doctree differ diff --git a/sw/.doctrees/large.doctree b/sw/.doctrees/large.doctree index 01d0400ed..b75828866 100644 Binary files a/sw/.doctrees/large.doctree and b/sw/.doctrees/large.doctree differ diff --git a/sw/.doctrees/map-accuracy.doctree b/sw/.doctrees/map-accuracy.doctree index b7a041b99..a1c98f8f9 100644 Binary files a/sw/.doctrees/map-accuracy.doctree and b/sw/.doctrees/map-accuracy.doctree differ diff --git a/sw/.doctrees/masks.doctree b/sw/.doctrees/masks.doctree index 3289118cd..e29dc360f 100644 Binary files a/sw/.doctrees/masks.doctree and b/sw/.doctrees/masks.doctree differ diff --git a/sw/.doctrees/multispectral.doctree b/sw/.doctrees/multispectral.doctree index 096718185..e7df9f7ca 100644 Binary files a/sw/.doctrees/multispectral.doctree and b/sw/.doctrees/multispectral.doctree differ diff --git a/sw/.doctrees/outputs.doctree b/sw/.doctrees/outputs.doctree index 41612ab85..0aaed77dc 100644 Binary files a/sw/.doctrees/outputs.doctree and b/sw/.doctrees/outputs.doctree differ diff --git a/sw/.doctrees/requesting-features.doctree b/sw/.doctrees/requesting-features.doctree index 341a66f4d..3adfbe773 100644 Binary files a/sw/.doctrees/requesting-features.doctree and b/sw/.doctrees/requesting-features.doctree differ diff --git a/sw/.doctrees/resources.doctree b/sw/.doctrees/resources.doctree index 6e4da1ca6..76da547a6 100644 Binary files a/sw/.doctrees/resources.doctree and b/sw/.doctrees/resources.doctree differ diff --git a/sw/.doctrees/tutorials.doctree b/sw/.doctrees/tutorials.doctree index d1d947d05..6afd1c6c3 100644 Binary files a/sw/.doctrees/tutorials.doctree and b/sw/.doctrees/tutorials.doctree differ diff --git a/sw/_sources/arguments.rst.txt b/sw/_sources/arguments.rst.txt index 84ae87529..ce49943dd 100644 --- a/sw/_sources/arguments.rst.txt +++ b/sw/_sources/arguments.rst.txt @@ -15,6 +15,12 @@ Arguments ``--cameras`` Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/sw/_static/documentation_options.js b/sw/_static/documentation_options.js index 4b874974c..05fa9d8c6 100644 --- a/sw/_static/documentation_options.js +++ b/sw/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'sw', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/sw/arguments.html b/sw/arguments.html index 4bbfee4b0..bcbf11ee5 100644 --- a/sw/arguments.html +++ b/sw/arguments.html @@ -4,10 +4,11 @@ - + + - Chagizi na Bendera — OpenDroneMap 2.4.7 documentation + Chagizi na Bendera — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                  - 2.4.7 + 2.5.5
                  @@ -291,9 +292,9 @@
                  -
                  +

                  Chagizi na Bendera

                  -
                  +

                  Arguments

                  --build-overviews

                  Build orthophoto overviews for faster display in programs such as QGIS. Default: False

                  @@ -302,6 +303,10 @@
                  --cameras <json>

                  Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: ``

                  +
                  --cog

                  Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

                  +
                  +
                  --copy-to <path>

                  Copy output results to this folder after processing.

                  +
                  --crop <positive float>

                  Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: 3

                  --debug

                  Print debug messages. Default: False

                  @@ -356,7 +361,7 @@
                  --min-num-features <integer>

                  Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

                  -
                  name <project name>

                  Name of dataset (i.e subfolder name within project folder). Default: code

                  +
                  name <dataset name>

                  Name of dataset (i.e subfolder name within project folder). Default: code

                  --optimize-disk-space

                  Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don't have sufficient disk space available. Default: False

                  @@ -364,6 +369,8 @@
                  --orthophoto-cutline

                  Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

                  +
                  --orthophoto-kmz

                  Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

                  +
                  --orthophoto-no-tiled

                  Set this parameter if you want a striped GeoTIFF. Default: False

                  --orthophoto-png

                  Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

                  @@ -392,7 +399,7 @@
                  --project-path <path>

                  Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder.

                  -
                  --radiometric-calibration none | camera | camera+sun

                  Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

                  +
                  --radiometric-calibration none | camera | camera+sun

                  Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

                  --rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

                  Rerun this stage only and stop. . Default: ``

                  @@ -426,6 +433,8 @@
                  --texturing-data-term gmi | area

                  When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

                  +
                  --texturing-keep-unseen-faces

                  Keep faces in the mesh that are not seen in any camera. Default: False

                  +
                  --texturing-outlier-removal-type none | gauss_clamping | gauss_damping

                  Type of photometric outlier removal method. . Default: gauss_clamping

                  --texturing-skip-global-seam-leveling

                  Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

                  @@ -452,8 +461,8 @@

                  Learn to edit and help improve this page!

                  -
                  -
                  + +
                  diff --git a/sw/contributing.html b/sw/contributing.html index 32b976d2d..4e694ede8 100644 --- a/sw/contributing.html +++ b/sw/contributing.html @@ -4,10 +4,11 @@ - + + - Jinsi ya kushiriki — OpenDroneMap 2.4.7 documentation + Jinsi ya kushiriki — OpenDroneMap 2.5.5 documentation @@ -62,7 +63,7 @@
                  - 2.4.7 + 2.5.5
                  @@ -290,14 +291,14 @@
                  -
                  +

                  Jinsi ya kushiriki

                  OpenDroneMap inategemea na ushiriki wa kijamii. unaweza kushiriki kwa njia nyingi, hata ukiwa sio programa.

                  -
                  +

                  Jukwaa la kijamii

                  Ikiwa unataka kushiriki, katika matatizo yalioshinda, au yanayotaka kutatuliwa, jukwaa ni sehemu nzuri ya kuanzia. Unaweza kupata maswali yaliojibiwa au unaweza kupata njia zitakazokusaidia au rasilimali. Pia unaweza kushiriki data zako za wazi kwa wengine kutumia. Ni sehemu nzuri kabla kutuma makosa au kutumia pamoja na wasanifu kuandika vitu vipya.

                  -
                  -
                  +
                  +

                  Kuwasilisha Bugs

                  Bugs imefuatwa kama suala la Github. Tafadhali tengeneza kitu ndani sehemu ya kuhifadhi na lebo pamoja na makosa.

                  Elezea tatizo na uambatanishe maelezo ya ziada kuwasaidia wanaotengeneza kugundua tatizo:

                  @@ -317,7 +318,7 @@

                • Ni jina gani na toleo gani la OS unayotumia?

                • Unatumia ODM katika virtual mashine au Docker? Ikiwa ni hivyo, ni VM software gani unayotumia na opereting system gani na tolea unalotumia kwa host na guest?

                -
                +

                Kigezo kwa kuwasilisha maelezo ya bug

                [Short description of problem here]
 
@@ -349,9 +350,9 @@
 * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
                -
                - -
                + + +

                Tatua maombi

                • Ikiwemo screenshots na animated GIFs katika kutatua maombi yako vyovyote iwezekanavyo.

                  • @@ -375,8 +376,8 @@

                Learn to edit and help improve this page!

                -
                - + + diff --git a/sw/flying.html b/sw/flying.html index 84dd9af03..bd422aa70 100644 --- a/sw/flying.html +++ b/sw/flying.html @@ -4,10 +4,11 @@ - + + - Kidokeza cha kurusha ndege nyuki — OpenDroneMap 2.4.7 documentation + Kidokeza cha kurusha ndege nyuki — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                - 2.4.7 + 2.5.5
                @@ -291,7 +292,7 @@
                -
                +

                Kidokeza cha kurusha ndege nyuki

                The Humanitarian OpenStreetMap team ina muongozo juu ya kurusha UAV kwa ramani:

                +
                diff --git a/sw/gcp.html b/sw/gcp.html index 16b00655a..509a36fe5 100644 --- a/sw/gcp.html +++ b/sw/gcp.html @@ -4,10 +4,11 @@ - + + - Pointi za Udhibiti wa chini — OpenDroneMap 2.4.7 documentation + Pointi za Udhibiti wa chini — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
                - 2.4.7 + 2.5.5
                @@ -291,19 +292,19 @@
                -
                +

                Pointi za Udhibiti wa chini

                Ground control points ni muhimu kurekebisha mzunguko wa taarifa na kumbukumbu data ili kujua mfumo wa muunganiko.

                A Ground Control Point (GCP) is a position measurement made on the ground, typically using a high precision GPS. (Toffanin 2019)

                Ground control points can be set existing structures like pavement corners, lines on a parking lot or contrasting color floor tiles, otherwise can be set using targets placed on the ground.

                Targets can be purchased or build with an ample variety of materials ranging from bucket lids to floor tiles.

                - -
                +
                +

                GCP file format

                Muundo wa GCP file ni mwepesi.

                @@ -332,8 +333,8 @@

                Pembe zenye ncha kali ni nzuri kuchagua kwa GCP. Pia unaweza kuweka/kutafuta GCP hata katika eneo lako la utafiti.

                Faili la gcp_list.txt lazima litengenezwe wakati ukitengeza folder la project yako.

                Kwa matokeo mazuri, faili lako liwe na mistari iziozidi 15 baada ya ufunguzi (point 5 pamoja na picha 3 kwa kila point).

                -
                -
                + +

                interface ya mtumiaji

                Unaweza kutumia moja kati ya interface mbili kutengeneza GCP faili:

                @@ -342,7 +343,7 @@

              • GCP Editor Pro

              -
              +

              POSM GCPi

              POSM GCPi linabebwa na chaguo msingi la WebODM. Mfano unapatikana katika onesho la WebODM. Kutumia thamani hii ijulikanayo na udhibiti wa ardhi XYZ, Moja itafanya ifuatavyo:

              Tengeneza idadi ya GCP ambazo zitakuwa na majina gcp (hii itaandikwa amabyo itaonekana katika kiwasilishi cha GCP),x,y,na z, pamoja na utangulizicha proj4 string ya GCP yako (hakikisha zipo katika mfumo wa ramani, kama UTM. Lazima ionekane ka hivi:

              @@ -355,8 +356,8 @@

              Kisha inaweza pakia idadi ya GCP hii katika kiwasilishwa, pakia picha, na weka kila GCP katika picha.

              - -
              + +

              GCP Editor Pro

              Aplication nii inahitaji kuingizwa tofauti au inaweza kuingizwa kama WebODM plugin kutoka https://github.com/uav4geo/GCPEditorPro

              Tengeneza CSV faili litalochukua jina la gcp, kaskazini, kusini na muinuko.

              @@ -368,14 +369,14 @@

              Kisha ingiza CSV kutoka screen kuu na andika +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs ndani ya sanduku la EPSG/PROJ.

              Screen zifuatazo zitaonesha ramani kutoka sehemu na kuiita GCP kwa jina na kuita picha nyengine zilipo.

              - - -
              + + +

              References

              Toffanin, Piero. Open Drone Map: The Missing Guide. MasseranoLabs LLC, 2019.

              Learn to edit and help improve this page!

              -
              - + + diff --git a/sw/genindex.html b/sw/genindex.html index cc64bc8da..13a2cde12 100644 --- a/sw/genindex.html +++ b/sw/genindex.html @@ -7,7 +7,7 @@ - Index — OpenDroneMap 2.4.7 documentation + Index — OpenDroneMap 2.5.5 documentation @@ -61,7 +61,7 @@
              - 2.4.7 + 2.5.5
              diff --git a/sw/geo.html b/sw/geo.html index 5ace63bb2..0e68bddfc 100644 --- a/sw/geo.html +++ b/sw/geo.html @@ -4,10 +4,11 @@ - + + - Image Geolocation Files — OpenDroneMap 2.4.7 documentation + Image Geolocation Files — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
              - 2.4.7 + 2.5.5
              @@ -291,7 +292,7 @@
              -
              +

              Image Geolocation Files

              By default ODM will use the GPS information embedded in the images, if it is available. Sometimes images do not contain GPS information, or a user wishes to override the information with more accurate data (such as RTK).

              Starting from ODM 2.0 people can supply an image geolocation file (geo) for this purpose.

              @@ -319,7 +320,7 @@

              If you supply a file called geo.txt then ODM will automatically detect it. If it has another name you can specify using --geo <path>.

              The geo.txt file must be created in the base of your project folder.

              Learn to edit and help improve this page!

              -
              +
              diff --git a/sw/index.html b/sw/index.html index 0b3445918..98509cc3f 100644 --- a/sw/index.html +++ b/sw/index.html @@ -4,10 +4,11 @@ - + + - Karibu OpenDroneMap Kitabu — OpenDroneMap 2.4.7 documentation + Karibu OpenDroneMap Kitabu — OpenDroneMap 2.5.5 documentation @@ -62,7 +63,7 @@
              - 2.4.7 + 2.5.5
              @@ -290,10 +291,10 @@
              -
              +
              OpenDroneMap Logo -
              -
              + +

              Karibu OpenDroneMap Kitabu


              @@ -313,9 +314,9 @@


              -
              +
              image of seedling -
              +
              -
              +
              image of pencil -
              +
              • Somo
                  @@ -471,7 +472,7 @@
              -
              +
              diff --git a/sw/installation.html b/sw/installation.html index 210bf64b0..ddc99e5c3 100644 --- a/sw/installation.html +++ b/sw/installation.html @@ -4,10 +4,11 @@ - + + - Kuingiza na kuanza kutumia — OpenDroneMap 2.4.7 documentation + Kuingiza na kuanza kutumia — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
              - 2.4.7 + 2.5.5
              @@ -291,50 +292,50 @@
              -
              +

              Kuingiza na kuanza kutumia

              Kipengele hiki kimekubalika na kurahisishwa pamoja na ruhusa kutoka OpenDroneMap: Maelekezo yaliokosekana, by Piero Toffanin.

              Hadi sasa OpenDroneMap imekua ni term inayotumika kuwakilisha mstari mmoja wa camand application (ambayo sasa inajuulika kama ODM project). Pamoja na maendeleo ya sasa ya web interface ya API na tools nyengine, OpenDroneMap imekua tegemezi kwa application tofauti kwa kuchakata, kuchambua na kuonesha data za ardhini. Utegemeo huu umetengeneza baadhi ya vipengele:

              -
              +
              ODM Logo -
              +

              • ODM ni mashine ya kuchakata, ambayo inaweza kutumika kutoka camand line. itachukua picha kama kiingizio na kutoa matokeo tofauti, ikiwemo point clouds, 3D model na orthophotos.

              -
              +
              NodeODM Logo -
              +

              • NodeODM ni light-weight API iliojengwa juu ya ODM. Inaruhusu watumiaji na application kutumia function za ODM kwenye computer mtandao

              -
              +
              WebODM Logo -
              +

              • WebODM ni muonekano rahisi wa mtumiaji inayokusanya muonekano wa ramani, muonesho wa 3D, user login, mfumo wa plagin na vipengele vyengine ambavyo vinategemewa na drone mapping platform za kisasa

              -
              +
              CloudODM Logo -
              +

              • CloudODM ni mtari mdogo wa camand ya mteja kwa kuwasiliana na ODM kupitia NodeODM API

              -
              +
              PyODM Logo -
              +

              • PyODM ni Python SDK kwa kutengeza kazi kupitia NodeODM API. Tunavalisha maelezo zaidi ndani ya sura ya “Automated Processing With Python”

              -
              +
              ClusterODM Logo -
              +

              • ClusterODM ni usawa wa kubeba kwa kuunganisha pamoja NodeODM instance nyingi

              ODM, NodeODM na WebODM zinapatika katika platform kuu (Windows,macOS na Linux) kupitia programu inayoitwa docker, ambayo unatakiwa ifanye kazi software. Docker inatoa njia ya kutumia “containers”. Container zimekusanywa kukopiwa kwa system yote, ni software na inajitegemea. Container hii inarun ndani ya mazingira ya virtual. Katika Linux mazingira ya virtual hii yanapatika ndani ya operating system na ni rahisi kwa macOS na Windows, container hio inatumika ndani ya VM, kwa hiyo kuna nafasi mbele. lakini bado ni sawa kutumia software. Mara ikiingizwa mtumiaji hana haja ya kujali kuhusu docker, kama inafanya kazi sawa (kiujumla).

              Bila ya docker itakuwa sio rahisi ODM na Windows au macOS kufanya kazi. Katika platform hizi ODM haziwezi kukufanya kazi inavyotakiwa. Maendeleo ya nguvu za baadae yatazingatia juu ya nguvu mpya za Windows subsystem kwa Linux (WSL) na rahisi kuanzisha port kwa tegemezi zote za macOS, ambazo zitafanyak uingizwa software kwa rahisi zaidi.

              Katika Ubuntu Linux 18.04 inawezekana kurun OpenDroneMap software zote wenyewe. Hata hivyo, kwa sababu kuna kuongezeka ufanisi kwa kufanya kazi docker kwenye Linux na docker ina njia nyepesi ya kupangilia katika platform hii, hatushauri hio. Katika Linux ya faida ya chombo kisichozidi uwezowe uliopewa. Kwa watumiaji wa docker pia wanapata urahisi katika kupangilia kuapdate software, kwa hio ni vizuri.

              -
              +

              Sifa za Hardware

              Nafasi ndogo inayotakiwa kwa kutumia software ni:

                @@ -349,28 +350,28 @@

              • 16 GB RAM

              Sifa hizo zitaruhusu kwa picha kidogo mia moja kuchakatwa bila uzito mwingi. CPU iliyo na core nyingi itaruhu kuchakata haraka, wakati kadi ya picha (CPU) kwa wakati huo haina faida juu ya utendaji kazi. Kwa kuchakata picha zaidi, engeza kiendshi diski na RAM kulingana na picha unazotaka kuchakata.

              -
              -
              +
              +

              Uingizaji

              Tunapendekeza watu kutumia docker kwa kurun ODM, kama unatumia Windows, macOS au Linux.

              -
              +

              Windows

              Kutumia OpenDroneMap unahitaji angalau Windows 7. Toleo la nyuma la windows halihimili.

              -
              +

              Jia ya 1. Angalia msaada wa uvumbuzi

              Docker inahitaji vipengele kutoka kwenye CPU yako inayoitwa virtualization, ambayo inaruhusu kufanya kazi virtual mashine (VMs). Hakikisha unaweka enabled! baadhi ya muda huwa disabled. Kuangalia, katika windows 8 au ya juu zaidi unaweza kufungua Task Manager (Bonyeza CTRL+SHIFT+ESC) na washa Performance tab.

              -
              +
              Image of checking virtualization in Windows 8 or higher -
              +

              Virtualization lazima iruhusiwe

              Katika Window 7 kuangalia kama una virtualization ilioruhusiwa, unaweza kutumia Microsoft® Hardware-Assisted Virtualization Detection Tool badala yake.

              Ikiwa virtualization haijaruhusiwa, unahitajika uiruhusu. Kwa sasa watoaji ni tofauti kidogo kwa kila aina ya computer, Njia nzuri kufanya hivi ni kuangalia katika search engine “how to enable vtx for <type your computer model here>”. Kawaida muda hutegemea na kurestart computer, haraka bonyeza F2 au F12 wakati inawaka,angalia boot menu na badilisha mpangilo kwa kuruhusu virtualization (kawaida inaitwa "VT-X").

              -
              +
              Table of different bios keys -
              +

              Key iliyozoeleka kubonyeza kwa kuwasha computer kutumia boot menu kwa watengezaji wengi wa PC

              -
              -
              +
              +

              Njia ya 2. Ingiza Vinavyohitajika

              Kwanza, Unahitaji kuingiza:

              Kwa Python 3, hakikisha unaangalia Add Python 3.x to PATH wakati wa uingizaji.

              -
              +
              Screenshot of Python3 installation process -
              +

              Usisahau kuingiza Python executable katika PATH (kwa maana hio unaweza kurun camand pamoja).

              Kisha, ikiwa upo katika Windows 10 home pekee, Windows 8 (toleo lolote) au Windows 7 (toleo lolote), ingiza:

                @@ -392,8 +393,8 @@

              Tafadhali usingize programu zote za docker. Ni tofauti na utatengeneza tatizo ikiwa zote zimeingizwa.

              Baada ya kuingiza docker, fungua kutoka Desktop icon ambayo imetengenezwa ilipoingizwa (Docker Quickstart kwa Docker Toolbox, Docker for Windows kwa docker kwa ajili ya Windows). Hii ni muhimu, usikatishe hatua hii. ikiwa kuna makosa fuata haraka katika koo na uyatatue.

              -
              -
              +
              +

              Njia ya 3. Angalia nafasi na mgao wa CPU

              Docker katika Windows inafanya kazi kwa kufungua VM katika msingi (Fikiria VM kama “computer emulator”). VM hii ina kiwango fulani cha nafasi kilichogaiwa na WebODM, kinaweza kutumika tu kwa kiasi ambacho kimetengwa.

              Ikiwa utaingiza Docker Toolbox (angalia chini ikiwa utaingiza Docker badala ya windows):

              @@ -403,9 +404,9 @@

            • Right click default VM na bonyeza Settings...

            • Move the Base Memory slider from the System panel and allocate 60-70% of all available memory, optionally adding 50% of the available processors from the Processor tab also

              • -
              +
              Screenshot of VirtualBox Settings -
              +

              Chaguo msingi la VirtualBox kwa Mpangilio wa VM

              Kisha bonyeza OK", right click default VM na bonyeza Start.

              Ikiwa umeingiza Docker kwa Windows badala yake:

              @@ -415,16 +416,16 @@

            • kutoka kwenye ubao, bonyeza Advanced na tumia sliders kuonesha 60-70% ya nafasi inayotumika na tumia nusu ya CPU ilobakia.

            • Bonyeza Apply.

              • -
              +
              Screenshot of Docker Icon -
              +

              Njia ya 1. Docker icon

              -
              +
              Screenshot of Docker Settings -
              +

              Njia ya 3 & 4 Mpangilo wa Docker

              -
              -
              + +

              Njia ya 4. Pakua WebODM

              Fungua Git Gui programu iliyoingizwa pamoja na Git. Kutoka hapo:

                @@ -433,27 +434,27 @@

              • Ndani ya Target Directory bonyeza browse na tembea hadi folder ulilochagua (tengeneza moja kama lazima)

              • Bonyeza Clone

              -
              +
              Screenshot of Git Gui -
              +

              Git Gui

              Ikiwa kupakua kumefanikiwa, utaona window ifuatayo:

              -
              +
              Screenshot of Git Gui after successful download -
              +

              Git Gui baada ya kufanikiwa kupakua (clone)

              Nenda hadi Repository menu, kisha bonyeza Create Desktop Icon. Hii itakuruhusu kurudi nyuma kwenda katika application hii kiurahisi zaidi baadae.

              -
              -
              + +

              Njia ya 4. Zindua WebODM

              Kutoka Git Gui, nenda kwenye Repository menu, kisha bonyeza Git Bash. Kutoka kwenye mstari wa camand aina ya terminal:

              $ ./webodm.sh start&

              Vipengele tofauti vitapakuliwa katika mashine yako katika hatua hii, ikiwemo WebODM, NodeODM na ODM. Baada ya kupakua utapeleka kwa screen zifuatazo:

              -
              +
              Screenshot of after successfully downloading WebODM -
              +

              Console output baada ya kuanzisha WebODM kwa mara ya mwanzo

              Kisha utaunganisha kwa http://192.168.1.100:8000 (badilisha anuani IP kuweka iliyo sahihi zaidi).

              -
              -
              -
              + + +

              macOS

              Modem nyingi (post 2010) za Mac computer zinafanya kazi MacOS Sierra 10.12 au kubwa inafanyakazi OpenDroneMap kutumia docker, ikiwa hardware virtualization inakubali (angalia chini).

              -
              +

              Jia ya 1. Angalia msaada wa uvumbuzi

              Fungua terminal window na andika:

              $ sysctl kern.hv_support
@@ -484,8 +485,8 @@

              Ikiwa jawabu ni kern.hv_support: 1, Mac yako inakuali! Endelea na step ya 2.

              Ikiwa majibu ni kern.hv_support: 0, inamaanisha Mac yako ni ya zamani sana kwa kutumia OpenDroneMap. :(

              -
              -
              +
              +

              Njia ya 2. Ingiza Vinavyohitajika

              Kuna programu mbili tu za kuingiza:

                @@ -493,9 +494,9 @@

              1. Git: https://sourceforge.net/projects/git-osx-installer/files/

              Baada ya kuingiza docker itaona icon ambyo muonekano wake kama nyumbani katika task bar.

              -
              +
              Screenshot of Docker whale -
              +

              Docker app running

              Unaweza kuhakikisha kwamba docker inafanya kazi sawa kwa kufungua Terminal app na kuanza kuandika:

              $ docker run hello-world
@@ -514,8 +515,8 @@

              Ikiwa umepata “bash: git: command not found”, jaribu kuwasha tena Terminal app yako na angalia kwa mara nyengine ikiwa kuna makosa wakati wa mchakato wa kuingiza.

              -
              -
              +
              +

              Njia ya 3. Angalia nafasi na mgao wa CPU

              Docker katika macOS inafanya kazi kwa kuwasha VM katika background (fikiria hilo kama computer emulator”). VM hii ina kiwango cha nafasi kilichowekwa na WebODM inaweza kutumia nafasi ya kutosha iliotengwa.

                @@ -524,12 +525,12 @@

              1. Rekebisha CPU slider kwa kutumia nusu ya CPU inayopatikana na nafasi ya kutumia 60-70% ya nafasi yote ilyobakia

              2. Bonyeza Apply & Restart

              -
              +
              Screenshot of Docker advanced settings -
              +

              Docker advanced settings

              -
              -
              + +

              Njia ya 4. Pakua na fungua WebODM

              Kutoka Terminal andika:

              $ git clone https://github.com/OpenDroneMap/WebODM
@@ -538,12 +539,12 @@ $ ./webodm.sh start

              Kisha fungua web browser kwenda http://localhost:8000.

              -
              -
              -
              + + +

              Linux

              OpenDroneMap inaweza kufanya kazi kwenye Linux yoyote ambayo inaruhusu docker. Kwa mujibu wa nyaraka za website ya docker kwa mujibu wa msaada rasmi uliothibitishwa ni CentOS, Debian, Ubuntu na fedora, pamoja na bainari tuli zinazotumika kwa wengine. Ikiwa unataka kuchakua distribution peke yake kwa kumia OpenDroneMap, Ubuntu ni chaguo la kutumia.

              -
              +

              Hatua 1. Mahitaji ya kuingiza

              Kuna program nne ambazo zinahitaji kuingizwa:

                @@ -553,7 +554,7 @@ $ ./webodm.sh start

              1. Pip

              Hatuweza kuweza kumaliza mchakato wa uwingizaji kwa kila usambazaji wa Linux nje ya hapo, kwa hio tutazuiya maelekezo kwa wote kusambaza wanasaidiwa na docker. Katika kesi zote ni jambo la kufungua terminal prompt na kuandika comand.

              -
              +
              Ingiza ndani ya Ubuntu / Debian

              Camand za kuandika

              $ sudo apt update
@@ -562,8 +563,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
              -
              -
              +
              +
              Ingiza ndani ya CentOS / RHEL

              Camand za kuandika

              $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -571,8 +572,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
              -
              -
              +
              +
              Ingiza ndani ya Fedora

              Camand za kuandika

              $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -580,16 +581,16 @@ $ sh get-docker.sh
 $ sudo dnf install git python python-pip
              -
              -
              + +
              Ingiza ndani ya Arch

              Camand za kuandika

              $ sudo pacman -Sy docker git python python-pip
              -
              - -
              + + +

              Njia ya 2. Angalia Mahitaji ya Ziada

              Kwa kuongezea kwa programu tatu juu, dockercompose script pia inahitajika. Baadhi ya muda inakuwa ishahifadhiwa ndani ya docker, lakini kuna muda haijaingizwa. Kuthibitisha kama imeingizwa jaribu kuandika:

              $ docker-compose --version
@@ -607,8 +608,8 @@ $ sudo dnf install git python python-pip
 
              $ sudo pip install docker-compose
 
              
 
              
-
              -
              +
              +

              Njia ya 3. Pakua na zindua WebODM

              Kutoka aina ya terminal:

              $ git clone https://github.com/OpenDroneMap/WebODM
@@ -617,10 +618,10 @@ $ ./webodm.sh start

              Kisha fungua web browser kwenda http://localhost:8000.

              -
              - - -
              + + + +

              Camand za msingi na utatuzi shida

              Kitu kizuri kuhusu kutumia docker ni 99% ya kazi unayohitaji kufanya ikiwa unatumia WebODM, inaweza kufanywa kwa kutumia ./webodm.sh script. Unaweza kutimiza moja kati ya hizo:

              $ ./webodm.sh start
@@ -652,20 +653,20 @@ $ ./webodm.sh --help

              Jukwaa la kijamii ni sehemu nzuri kuomba msaada ikiwa umekwama wakati unapingiza kwa mpangilio na kwa maswali ya ujumla kutumia ./webodm.sh script.

              -
              -
              + +

              Habari, WebODM!

              Baada kuwa inatumika ./webodm.sh ikianza na kufungua WebODM ndani ya browser, utasalimia pamoja na ujumbe wa karibu na utaulizwa kutengeneza mtumiaji wa kwanza. Chukua muda kumalizia mwenyewe pamoja na web interface na kuchunguza menu tofauti.

              -
              +
              Screenshot of WebODM Dashboard -
              +

              WebODM Dashboard

              Tazama kwa Processing Nodes menu kuna "node-odm-1" node tayari zishapangwa kwa ajili yako kutumia. NodeODM node hii na imetengenezwa wenyewe kwa WebODM. Node hii inatumika katika mashine moja kama WebODM.

              Ikiwa umefikia hapo, Hongera! Sasa ni mda wa kuanza kuchakata baadhi ya data.

              -
              +
              Image of celebratory dance -
              -
              + +

              Kuendesha mashine zaidi ya moja

              Optionally: Ikiwa una computer nyengine, unaweza kurejea mchakato wa uwingizaji (install docker, git, python, etc.) na anzisha NodeODM mpya kwa kuandika kutoka dirisha la Terminal/Git Bash:

              docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -675,9 +676,9 @@ $ ./webodm.sh --help
 
              Kutoka WebODM kisha unawesha kuwasha Add New button juu ya Processin Nodes Kwa hostname/IP aina ya anuani ya field katika computer nyengine. Kwa port field type "3000". Kwa token field type "secret". Pia unaweza kuengeza label kwa node yako, kama vile computer ya pili. Kisha bonyeza Save.
              
 
              Ikiwa kila kitu kimeenda sawa, lazima uwe na processing node mbili! Utaweza kuchakata kazi nyingi kwa wakati mmoja kwa kutumia mashine tofauti.
              
 
              Learn to edit and help improve this page!
              
-
              -
              -
              +
              + +
              diff --git a/sw/large.html b/sw/large.html index 1897055f8..8fb564fd0 100644 --- a/sw/large.html +++ b/sw/large.html @@ -4,10 +4,11 @@ - + + - Kugawa Database Kubwa — OpenDroneMap 2.4.7 documentation + Kugawa Database Kubwa — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
              - 2.4.7 + 2.5.5
              @@ -291,20 +292,20 @@
              -
              +

              Kugawa Database Kubwa

              Anza na toleo la ODM 0.6.0 unaweza kugawa group kubwa la data linaloweza kudhibitiwa (liitwalo submodels), kutumia pipeline katika kila kundi, na kisha zalisha DEM itakayounganisha, orthophoto na pointcloud. Mchakato utawekwa kama "split-merge."

              Kwa nini unaweza kutumia split-merge pipeline? Ikiwa una idadi ya picha nyingi kwenye dataseti yako, split-merge itasaidia mchakato kuongozwa vizuri katika mashine kubwa (itahitaji nafasi ndogo). Ikiwa una mashine nyingi zilizounganishwa katika mtandao mmoja pia unaweza kuchakata submodel kwa pamoja, ndivyo itakavyoruhu kuongeza mstari mlalo na kuchakata maelfu ya picha kwa urahisi zaidi.

              Split-merge inafanya kazi ndani ya ODM nje ya boxi ikiwa node zinasaidia split-merge, kwa kuruhusu chaguo la --split unapotengeneza njia mpya.

              -
              +

              Kurekebisha Picha

              Kurekebisha picha ni kupendekeza (lakini sio lazima) kwa dataset nyingi, kwa sababu upatikanaji wa makosa wakati wa kuvuruga picha kunaweza kusababisha kubonyea katika muundo. Maelezo ya muinuko yanapatika kwenye Calibrate Images.

              -
              +
              image of lens distortion effect on bowling of data -
              +

              Madhara ya kubonyea picha juu ya point cloudi kwa kundi kubwa la picha zaidi ya 13,000+ zilizokusanywa na World Bank Tanzania katika maeneo ya mafuriko Msimbasi Basin, Dar es Salaam, Tanzania.

              -
              -
              +
              +

              Split-Merge ya Ndani

              Mgawio wa kundi data katika submodel, urahisi na katika kuchakata wepesi zaidi ndani ya mashine moja kwa wepesi! Tumia --split na --split-overlap kuchagua nambari ya picha inayotakiwa kwa kila submodel na mpishano (katika meters) baina ya submodel mfululizo

              docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
@@ -320,14 +321,14 @@

              utatengeza submodel 3. Hakikisha unapitisha --split-overlap 0 ikiwa unatengeneza mwenyewe image_groups.txt faili.

              -
              -
              + +

              Kugawanya Split-Merge

              ODM pia inaweza kugawanya wenyewe mchakato wa kila submodel kwa mashine tofauti kupitia NodeODM nodes, orchestrated via ClusterODM.

              -
              +
              image of lens distortion effect on bowling of data -
              -
              + +

              Kupata kuanza pamoja na kugawanya Split-Merge

              Njia ya mwanzo ni kustart ClusterODM

              docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -355,8 +356,8 @@ Escape character is '^]'.
 
              docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
              
 
              
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              Kufahamu Cluster

              Ukiunganisha kupitia telnet, ni rahisi kufahamu nini kinaendelea ndani ya cluster. Kwa Mfano, tunaweza kutumia camand HELP kutafuta camand zinazopatika

              # HELP
@@ -391,16 +392,16 @@ OK
 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
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              Kutumia Logs

              Wakati mchakato unaendelea, pia ni rahisi kuorodhesha kazi, na muonekano wa matokeo ya kazi

              # TASK LIST
 # TASK OUTPUT <taskId> [lines]
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              Kuongeza kiotomatiki ClusterODM

              ClusterODM pia inakusanya njia za kujipima wenyewe platform tofauti, ikiwemo, to date, Amazon na digital Ocean. Hii inawezesha watumiaji kupunguza gharama zitokanazo na always-on instance vile vile kuweza kupima mchakano kutokana na mahitaji.

              Kupanga autoscaling lazima:

              @@ -436,19 +437,19 @@ info: Found docker-machine executable

              Njia hii hii kazi zote zinapelekwa wenyewe kwa autoscaler.

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              Mipaka

              Mfumo wa 3D meshes kwa sasa haijaunganishwa kama sehemu ya mpangilio kazi (Ni point cloud pekee, DEM na orthophoto).

              GCPs inasaidiwa kikamilifu, lakini kuna mahitaji angalau point 3 za GCP kwa kila submodel kwa georeferencing kuchukua nafasi. Ikiwa submodel ina unafuu kuliko GCP 3, muungano wa GCP zilobakia + EXIF data zitatumika badala yake (ambayo itakua na usahihi mdogo). Tunapendekeza kutumia image_groups.txt faili kupelekea udhibiti sahihi wa mgawanyo wa submodel ukitumia GCP.

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              Tunakiri

              Sifa kubwa kwa Pau na folks kwa Mapillary kushiriki kwema kwa OpenDroneMap kupitia OpenSfM code, ambacho ni kijenzi cha msingi kwa split-merge pipeline. Tunaangalia mbele kwa kusukuma zaidi upeo wa OpenDroneMap na kuangalia vipi dataset kubwa zinaweza kuchakatwa.

              Learn to edit and help improve this page!

              -
              - + + diff --git a/sw/map-accuracy.html b/sw/map-accuracy.html index 60261d0c2..51a5dc065 100644 --- a/sw/map-accuracy.html +++ b/sw/map-accuracy.html @@ -4,10 +4,11 @@ - + + - Map accuracy — OpenDroneMap 2.4.7 documentation + Map accuracy — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
              - 2.4.7 + 2.5.5
              @@ -291,30 +292,30 @@
              -
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              Map accuracy

              Accuracy can be defined as the degree or closeness to which the information on a map matches the values in the real world. Therefore, when we refer to accuracy, we are talking about quality of data and about number of errors contained in a certain dataset (Pascual 2011).

              Relative or Local accuracy

              Local or relative accuracy can be defined as the degree to which de distances between two points on a map correspond to the actually distances between those points in the real world.

              Relative accuracy is independent of the location of the map in the world, so a map can have a high relative accuracy (in size and shape) but its position in the world can be shifted (Figure 1).

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              Model showing high relative accuracy -
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              Figure 1. Model showing high relative accuracy but misplaced according to its real world position

              Absolute or global Accuracy

              Absolute accuracy is the accuracy of the reconstruction in relation to its true position on the planet (Pix4D 2019). Figure 2 shows a relative and absolute accurate model, as the points are correctly placed according to its real world position.

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              Model showing high absolute accuracy -
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              Figure 2. Model showing high relative and absolute accuracy. Placed correctly according to its real world position

              An Accuracy level for each project

              Each project has specific accuracy needs to be met. For instance assessing the progress in a construction site or measuring an area affected by a fire does not require the use of GCP, since absolute accuracy will not impact the decision making process. In the other hand, there are tasks on which accuracy is critical, for example project compliance evaluations and land title surveying, which require a higher relative and absolute accuracy.

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              What to expect

              In general terms, one can expect the relative accuracy to be in the order of 1 to 3 times the average GSD for the dataset. And as for the absolute accuracy, one must consider that it is dependent of the GPS unit mounted in the UAV but the horizontal accuracy of a standard GPS is usually in the range of 2 to 6 meters and the vertical accuracy between 3 to 4 times the horizontal accuracy.

              When using GCP, absolute accuracy can be improved to 2.5 times GSD for the horizontal accuracy and 4 times the GSD for the vertical accuracy (Madawalagama 2016).

              At a GSD of 1cm, the accuracy is to that of the RTK GNSS, and is within 1:200 scales according to NSDI & FGDC mapping accuracy standards during sub-optimal conditions (Barry 2013).

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              Aspects impacting map accuracy

              Weather

              Weather conditions have direct impact in the photogrammetry results, so it is important to consider cloud coverage, wind speed, humidity, sun’s altitude and other factors influencing the UAV stability and terrain illumination.

              @@ -324,9 +325,9 @@

              The higher the flight altitude, the larger the image footprint and GSD. The resulting larger GSD the accuracy will be decreased as there will be less detail in the recognizable features. When a smaller GSD is required an altitude of 3 to 4 times the height of the highest point is recommended.

              Flight speed

              Flight speed have special effect in cameras equipped with rolling shutter, while those equipped with global or mechanical shutter tends to reduce this effect. UAV equipped with RTK positioning systems are also affected with the speed, but with hover at each photo taken, you can get very good accuracy. If instead you are moving during each photo take, the accuracy is going to be limited by two factors: the speed at which you are moving multiplied by the 1 second increments of RTK (Mather 2020).

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              References

              Barry, P., & Coakley, R. «Accuracy of UAV photogrammetry compared with Network RTK GPS.» Baseline Surveys. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (accessed 10 13, 2020).

              Drone Deploy. How Do I Use Ground Control Points?: A guide to using ground control points with drone mapping software. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (accessed 7 9, 2020).

              @@ -335,8 +336,8 @@

              Pascual, Manuel S. GIS Lounge: GIS Data: A Look at Accuracy, Precision, and Types of Errors. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (accessed 07 09, 2020).

              Pix4D. «What is accuracy in an aerial mapping project?» Pix4D. 25 de 05 de 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (accessed 10 13, 2020).

              Learn to edit and help improve this page!

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              diff --git a/sw/masks.html b/sw/masks.html index 0bcd28de9..1df940f3b 100644 --- a/sw/masks.html +++ b/sw/masks.html @@ -4,10 +4,11 @@ - + + - Using Image Masks — OpenDroneMap 2.4.7 documentation + Using Image Masks — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
              - 2.4.7 + 2.5.5
              @@ -291,24 +292,24 @@
              -
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              Using Image Masks

              Starting from ODM 2.0 people can supply image masks to inform the software to skip reconstruction over certain areas. This is useful for cases where the sky was accidentally included in the input photos from oblique shots, or simply to limit the reconstruction of a single subject.

              To add a mask, simply create a new black and white image of the same dimension as the target image you want to mask (you can use a program such as GIMP to do this). Color in black the areas to exclude from the reconstruction.

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              Target image -
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              Image mask -
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              3D result (building is masked) -
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              Name your file:

              <filename>_mask.JPG

              For example, DJI_0018.JPG can have a mask by creating a DJI_0018_mask.JPG file and include that in the list of images. You can use .JPG, .PNG, .BMP and .TIF formats for image masks.

              Learn to edit and help improve this page!

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              diff --git a/sw/multispectral.html b/sw/multispectral.html index 3f685af74..baa9c7c84 100644 --- a/sw/multispectral.html +++ b/sw/multispectral.html @@ -4,10 +4,11 @@ - + + - Msaada wa Multispectral — OpenDroneMap 2.4.7 documentation + Msaada wa Multispectral — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
              - 2.4.7 + 2.5.5
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              Msaada wa Multispectral

              Tangu toleo 0.9.9 ODM lina msaada wa msingi kwa uchambuaji radiometric, ambayo inaweza kutengeneza tashira picha kutoka multispectral camera. Multispectral camera inapiga picha nyingi za sehemu kutumia band sensor tofauti.

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              Hardware

              Wakati tumedhamiria kusaidia camera nyingi iwezekanavyo, msaada wa multispectral umekuzwa kwa kutumia camera zifuatazo, kwa hiyo zitafanya kazi vizuri:

              @@ -304,13 +305,13 @@

            Kamera nyengine zinaweza kufanya kazi. Unaweza kutusaidia kuengeza idadi hii ` kugawa dataseti <https://community.opendronemap.org/c/datasets/10>`_ zilopigwa na kamera nyengine.

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            Matumizi

            Chakata picha zote kutoka band zote kwa pamoja (usitofautishe band katika mafolder tofauti) na ingiza --radiometric-calibration parameter kuruhusu radiometric normalization.Ikiwa picha ni sehemu ya mpangilio wa multi-camera, matokeo ya orthophoto yatakuwa na N band, kwa kila camera (+ alpha).

            Learn to edit and help improve this page!

            -
            - + + diff --git a/sw/objects.inv b/sw/objects.inv index ca28f7fac..94c074493 100644 Binary files a/sw/objects.inv and b/sw/objects.inv differ diff --git a/sw/outputs.html b/sw/outputs.html index 922d938a0..b36cf6a8d 100644 --- a/sw/outputs.html +++ b/sw/outputs.html @@ -4,10 +4,11 @@ - + + - OpenDroneMap Matokeo — OpenDroneMap 2.4.7 documentation + OpenDroneMap Matokeo — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
            - 2.4.7 + 2.5.5
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            OpenDroneMap Matokeo

            Vilivyo orodheshwa chini ni matumizi ya matokeo ya ODM.

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            Point Cloud

            odm_georeferencing/odm_georeferenced_model.ply/laz/csv -- Ni georeferenced point cloud katika format tofauti

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            image of OpenDroneMap derived point cloud -
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            Picha za anga katika State University Zanzibar, zilizoandaliwa na Khadija Abdullah Ali

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            3D Textured Model

            odm_texturing/odm_textured_model.obj -- The textured surface mesh odm_texturing/odm_textured_model_geo.obj -- The georeferenced na textured surface mesh

            Unaweza kupata pointcloud na textured mesh kutumia MeshLab. Fungua MeshLab, na chagua file:Ingiza Mesh na chagua textured mesh yako kutoka eneo sawa na lifuatalo: odm_texturing\odm_textured_model.obj

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            image of OpenDroneMap derived textured mesh -
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            Textured mesh courtesy of OpenDroneMap

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            Orthophoto

            odm_orthophoto/odm_orthphoto.png -- The orthophoto, lakini hii ni simple png, ambayo haina maelezo ya georeferencing

            odm_orthophoto/odm_orthphoto.tif -- GeoTIFF Orthophoto. Unaweza kuitumia kwenye QGIS kama tabaka la rasta.

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            image of OpenDroneMap orthophoto -
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            Picha juu ya State University Zanzibar, courtesy of* Khadija Abdullah Ali

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            DTM/DSM

            DTM/DSM itatengenezwa ikiwa --dtm au --dsm zitatumika. Angalia tutorial on elevation models kwa njia zaidi za kutengeneza.

            Data zitahifadhiwa ndani ya:

            @@ -330,12 +331,12 @@

          • odm_dem/dsm.tif

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          image of OpenDroneMap derived digital surface model -
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          Digital surface model over State University Zanzibar, courtesy of Khadija Abdullah Ali

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          Mpangilo wa matokeo yote

          project/
 ├── images/
@@ -381,8 +382,8 @@

          Learn to edit and help improve this page!

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          - + + diff --git a/sw/requesting-features.html b/sw/requesting-features.html index 198e26ccd..44e862a60 100644 --- a/sw/requesting-features.html +++ b/sw/requesting-features.html @@ -4,10 +4,11 @@ - + + - Vipi Utaomba Vipengele — OpenDroneMap 2.4.7 documentation + Vipi Utaomba Vipengele — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
          - 2.4.7 + 2.5.5
          @@ -291,7 +292,7 @@
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          Vipi Utaomba Vipengele

          Programu zote zinahitaji mrejosho kutoka kwa watumiaji na vipengele vya kuomba, kwa kukuza na kuimarisha mahitaji ya mtumiaji.

          OpenDroneMap ni FOSS programu. Free and open source (FOSS) mradi wa kupendeza kutoka ndani na nje: kutoka nje, mafanikio zaidi hasa kama wanaweza kufanya kila kitu, na ni ngumu kujua ni yapi maombi ya msingi. Kutoka ndani ya mradi wanaweza kuhisi jambo zito: muda mkubwa, pesa na fursa nyingi.

          @@ -306,7 +307,7 @@

          Tunafurahi unahamu kuona vitu vipya vinaongezwa katika mradi. Baadhi ya vitu vipya vinahitaji msaada, na baadhi ni rahisi kuvitengeneza. Tutafanya tuwezalo kukusaidia wewe kufahamu wapi maombi yako hayakufanikiwa, na tunathamini msaada wowote utakaoweza kutupa.

          Learn to edit and help improve this page!

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          diff --git a/sw/resources.html b/sw/resources.html index e7ff6e424..c48655863 100644 --- a/sw/resources.html +++ b/sw/resources.html @@ -4,10 +4,11 @@ - + + - Kumbukumbu za ziada — OpenDroneMap 2.4.7 documentation + Kumbukumbu za ziada — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
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          Kumbukumbu za ziada

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          Kwa watumiaji

          Rasilimali zifuatazo ni sehemu nzuri kuanzia:

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          Kwa msanidi programu

          Kwa kuongezea kwa mtumiaji rasilimali, tunapendekeza kusoma zifuatazo:

            @@ -314,8 +315,8 @@

          • Tunaweka sehemu katika jukwaa maalum kwa karatasi za utafiti. Hii ni sehemu ya thamani ambayo utasoma kuhusu tafiti za sanaa za vitu vinavyoshabihiana na umbile kutoka kwenye mwendo, multi-view sauti, mtandao, mifumo, etc. ambayo inaweza kutumia kuboresha software.

          Learn to edit and help improve this page!

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          diff --git a/sw/search.html b/sw/search.html index 53beb68cb..f51133391 100644 --- a/sw/search.html +++ b/sw/search.html @@ -7,7 +7,7 @@ - Search — OpenDroneMap 2.4.7 documentation + Search — OpenDroneMap 2.5.5 documentation @@ -63,7 +63,7 @@
          - 2.4.7 + 2.5.5
          diff --git a/sw/searchindex.js b/sw/searchindex.js index a81214e36..b1241aa0c 100644 --- a/sw/searchindex.js +++ b/sw/searchindex.js @@ -1 +1 @@ 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na Bendera","Jinsi ya kushiriki","Kidokeza cha kurusha ndege nyuki","Pointi za Udhibiti wa chini","Image Geolocation Files","Karibu OpenDroneMap Kitabu","Kuingiza na kuanza kutumia","Kugawa Database Kubwa","Map accuracy","Using Image Masks","Msaada wa Multispectral","OpenDroneMap Matokeo","Vipi Utaomba Vipengele","Kumbukumbu za ziada","Somo"],titleterms:{Using:9,accuraci:[8,14],angalia:6,anuani:14,arch:6,argument:0,aspect:8,baada:14,bandwidth:14,bendera:0,bug:1,camand:[6,14],camera:14,cento:6,cha:2,chagizi:0,chini:3,cloud:11,cluster:7,clusterodm:7,cpu:6,data:14,databas:7,debian:6,digit:14,docker:14,droplet:14,dsm:11,dtm:11,editor:3,elev:14,eneo:14,expect:[8,14],fedora:6,fieldwork:14,file:[3,4],flight:14,format:3,fungua:6,gcp:[3,14],gcpi:3,geoloc:4,habari:6,hardwar:[6,10],hatua:6,height:14,hiki:14,imag:[4,9],impact:8,ingiza:6,instanc:14,interfac:3,jia:6,jinsi:1,jukwaa:1,juu:14,karibu:5,kidokeza:2,kigezo:1,kijamii:1,kiotomatiki:7,kisia:14,kitaalamu:14,kitabu:5,kuandaa:14,kuanza:[6,7],kubadilisha:14,kubwa:7,kuendesha:6,kufahamu:7,kugawa:7,kugawanya:7,kuingiza:[6,14],kumaliza:14,kumbukumbu:13,kuongeza:7,kupata:7,kurekebisha:[7,14],kurusha:2,kushiriki:1,kutengeneza:14,kutoka:14,kutumia:[6,7,14],kuvuta:14,kuwasilisha:1,kwa:[1,13,14],kweny:14,leny:14,line:14,linux:6,log:[7,14],low:14,maco:6,maelezo:1,mahitaji:6,maombi:1,map:8,mashin:[6,14],mask:9,matokeo:11,matumizi:[10,14],mda:14,measur:14,merg:7,mgao:6,mifano:14,mipaka:7,mkubwa:14,model:[11,14],moja:6,mpangilo:11,msaada:[6,10],msanidi:13,msingi:6,mtumiaji:3,multispectr:10,muundo:14,nafasi:6,nani:14,ndani:[6,7],ndege:2,nini:14,njia:[6,14],nyuki:2,odm:14,opendronemap:[5,11],orthophoto:[11,14],pakua:6,pamoja:7,paramet:14,pattern:14,pekua:14,picha:7,pini:14,plan:14,point:11,pointi:3,posm:3,practic:3,pro:3,process:14,programu:13,project:14,recommend:3,refer:[3,8],rhel:6,safisha:14,set:3,shida:6,sifa:6,somo:14,split:7,stockpil:14,tatua:1,tengeneza:14,textur:11,tumia:14,tunakiri:7,ubuntu:6,udhib:3,uingizaji:6,utachukua:14,utaomba:12,utatuzi:6,uvumbuzi:6,vinavyohitajika:6,vipengel:12,vipi:12,volum:14,watumiaji:13,webodm:6,what:8,window:6,yote:11,zaidi:6,ziada:[6,13],zindua:6}}) \ No newline at end of file diff --git a/sw/tutorials.html b/sw/tutorials.html index fd0f10d3a..9b61e2687 100644 --- a/sw/tutorials.html +++ b/sw/tutorials.html @@ -4,10 +4,11 @@ - + + - Somo — OpenDroneMap 2.4.7 documentation + Somo — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
          - 2.4.7 + 2.5.5
          @@ -291,14 +292,14 @@
          -
          +

          Somo

          Hapo chini utapata maelezo kwa baadhi ya mifano muhimu.

          -
          +

          Tengeneza Orthophotos ya kitaalamu

          -
          +
          image of OpenDroneMap orthophoto -
          +

          Bila ya kuengeza paramiter, ODM inachagua nzuri baina ya ubora, kasi na nafasi ya kutumia. Ikiwa unataka kupata matokeo bora zaidi, utadadafua baadhi ya paramiter:

            @@ -309,39 +310,39 @@

          • --mesh-size lazima iongezeke hadi 300000-600000 na --mesh-octree-depth lazima iongezeke hadi 10-11 mjini kwa kutengeneza upya majengo mazuri/ mapaa.

          -
          -
          +
          +

          Kurekebisha Camera

          Kurekebisha Camera ni changamoto maalum pamoja na thamani ya camera. mabadiliko ya hali joto, mtetemeko, muelekeo, na na vitu vyengine vinavyoweza kuathiri data itakayopatikana. Automatic au matengenezo binafsi yanaweza kufanywa na kutakiwa na drone ukiruka, lakini inategemea na njia itakayopita ndege, marekebisho ya automatic haiwezi kuondosha njia zote kutoka mwanzo. James and Robson (2014) katika kitabu chao Mitigating systematic error in topographic models derived from UAV and ground‐based image networks imeonyesha jinsi kupunguza makosa kweye marekebisho binafsi.

          -
          +
          image of lens distortion effect on bowling of data -
          +

          Athatri za shimo katika point cloud kwa makundi ya picha zaidi ya 13,000 zilizokusanywa na benki ya dunia katika eneo la mafuriko Msimbasi Basin, Dar es Salaam, Tanzania.

          Kupunguza athari,kuna njia kidogo lakini njia rahisi ni kama: rusha njia mbili tofauti kwa 20°, kuliko kutumia nadir kamera (kuelekeza pont chini), tumia ambayo itainamisha camera kwa 5°.

          -
          +
          animation showing optimum -
          +

          Kama itafikia kuruka drone itachukua muda mrefu kuliko kuruka kwa kawaida, rubani au kikundi kinaweza kurusha drone kwa kumia njia hio hapo juu. OpenDroneMap itatengeneza file lilorekebishwa linaloitwa cameras.json ambayo baadae itachukuliwa kutumika kwa mruko mwengine, itakua ni mruko wa ufanisi zaidi.

          Vyenginevyo, njia za majaribio yafuatayo zinaweza kutumika: rusha drone kwa kupishanisha picha kidogo, lakini miruko crossgrid miwili (wakati mwengine huitwa crosshatch) iliotenganishwa kwa 20° na 5° moja kwa moja ikiangalia camera.

          • Crossgrid asilimia za kupishana zinaweza kuwa ndogo kuliko flight sambamba. Kwa kupata matukio mazuri ya 3D, utatakiwa 68% za kupishana na ubavuni kushabihiana na 83% kupishana na ubavuni.

          • Kupata matokeo ya 2D na 2.5D (digital elevation model), unatakiwa 42% mpishano na msimamo kwa mshabihiano 70% overlap na sidelap.

          -
          +
          animation showing experimental optimum -
          +

          Mistari ya kusimama iliyopishana ya njia ya kupitisha ndege pia inaongeza usahihi, lakini kidogo kwa hiyo camera iangalie moja kwa moja na 5°.

          -
          +
          figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
          +

          Kutoka James na Robson (2014),`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0>`_

          -
          -
          + +

          Kutengeneza Digital Elevation Model

          Chaguo msingi ODM haitengenezi DEMs. Kutengeneza digital terrain model, hakikisha unatumia --dtm flag. Kutengeneza digital surface model, hakikisha unatumia --dsm flag.

          -
          +
          image of OpenDroneMap derived digital surface model -
          +

          Kizazi cha DTM, Simple Morphological Filter (smrf) inatumika kugawa point katika ground na non-ground pia ground point pekee inatumika. Ni smrf kichujo kinaweza kidhibitiwa kutumia mihimili tofauti:

            @@ -354,9 +355,9 @@

            Kubadilisha njia hizi kunaweza kubadilisha maana ya matokeo ya DTM. Mwanzo mzuri kusoma kwa kufahamu vipi paramita zinabadilisha matokeo ni kusoma pepa ya awali An improved simple morphological filter for the terrain classification of airborne LIDAR data (Inapatikana bure PDF).

            Majumuisho ya njia smrf-threshold yanaleta matokeo makubwa.

            SMRF ni nzuri kwa kupunguza makosa ya uwandishi (nambari ndogo ya makosa ya groundpoint imegawanywa kama non-ground) lakini "acceptable" pekee kuepuka makosa Type II (nambari kubwa ya non grond point kimakosa imegaiwa kama ground). Mahitaji haya yanachukuwa kwa uwangalifu wakati inatengenezwa DTM ambayo ilikusudiwa kutumika kuonekana, tangu makosa ya kitu kwa muonekano wa ground mfano mdogo katika DTM ya mwisho.

            -
            +
            image of lens distortion effect on bowling of data -
            +

            Paramita nyengine mbili zinabadilisha kizazi cha DEM:

              @@ -368,36 +369,36 @@ 
              docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
              -
            -
            +
          +

          Measuring stockpile volume

          -
          +

          Fieldwork planning

          Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

          -
          -
          +
          +

          Flight pattern

          Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

          In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

          -
          +
          a simple lawnmower flight pattern can produce accurate results -
          +

          Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

          -
          -
          + +

          Flight height

          Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

          -
          -
          + +

          GCPs

          To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

          For further information on the use of GCPs, please refer to the Ground Control Points section.

          -
          -
          + +

          Processing parameters

          A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

          If using ODM, these this reference values can help you configure the process settings.

          @@ -406,69 +407,69 @@ When placing or measuring GCP, equipment accuracy should be greater than the GSD

          --orthophoto-resolution 1.0

          --feature-quality high

          --pc-quality high

          -
          -
          + +

          Measuring

          As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

          -
          +
          almost 50% of the material will be found in the first 20% of the stockpile height -
          +

          In WebODM Dashboard, clic on "view map" to start a 2D view of your project.

          Once in the 2D map view, clic on the "Measure volume, area and length" button.

          -
          +
          clic on the "Measure volume, area and length" button -
          +

          then clic on "Create a new measurement"

          -
          +
          clic on "Create a new measurement" -
          +

          Start placing the points to define the stockpile base plane

          -
          +
          Define the stockpile base plane -
          +

          Clic on "Finish measurement" to finish the process.

          -
          +
          Clic on "Finish measurement" to finish the process -
          +

          Dialog box will show the message "Computing ..." for a few seconds, and after the computing is finished the volume measurement value will be displayed.

          -
          +
          Clic on "Finish measurement" to finish the process -
          +

          If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

          Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

          -
          -
          + +

          Expected accuracy

          For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

          -
          -
          -
          + + +

          Kutumia Docker

          Tangu watumiaji wengi kutumia docker kuingiza OpenDroneMap itakua ni vizuri kufanhamu camandi za msingi kuitathmini mifano ya docker wakati ikitokea makosa, au tukiwa na wasiwasi wa matokeo. Docker ni mkusanyiko wa muonekano uliopangwa, miongoni mwa vitu vyengine kufanya urahisi wa kuingiza software kwa kujitegemea kwenye mazingira ya nyumbani. Kwa njia hii ni sawa na virtual mashine.

          Comand chache rahisi zinaweza kufanya docker yetu kwa nzuri zaidi.

          -
          +

          Kutumia Docker mashine

          Tunaweza kuanza kwa kusikiliza docker zilizopo katika mashine ulionayo tunaanza kama ifuatavyo:

          > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

          Ikiwa tunataka kuona mashine ambayo haifanyi kazi lakini docker inapatikana, tunaingiza -a flag:

          > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
          -
          -
          +
          +

          Tumia logs kwenye instance

          Au tumia CONTAINER ID au jina, tunamia logs pekee inapatika kwenye mashine kama ifuatavyo:

          > docker logs 2518817537ce
@@ -499,8 +500,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

          Nambari -5 inaiambia tail camand kutupa mistari 5 ya mwisho ya log.

          -
          -
          + +

          Matumizy ya camand line kwa mifano

          Baadhi ya wakati tunataka kuenda ndani kidogo katika mchakato wa utafiti wa OpenDroneMap. Kwa hilitunaweza kupata kutumia mistari ya camand moja kwa moja kwenye mashine. Kwa hili, tunawza kutumia docker exec kufikia na camand line bash muonekano ndani ya mashine kwa umuhimu ufuatao:

          > docker exec -ti 2518817537ce bash
@@ -508,24 +509,24 @@ The resulting accuracy is comparable to the commercially available photogrammetr

          Sasa tumeingia ndani ya kundi docker na mashine itaonesha.

          -
          -
          + +

          Safisha juu baada ya Docker

          Docker haina msaada kutumia nafasi na kawaida haiondoi kufanya kazi data na mashine mchakato ukimaliza. Hii inaweza kuwa faida ikiwa tunahitaji kutumia mchakato ambao ulikatika, lakini unachukua matatizo ya kuengeza matumizi ya nafasi kwa mda. Maciej Łebkowski ana mtazamo mzuri ya vipi kuongoza kutumia nafasi iliotumika kutumia ndani ya docker.

          -
          -
          -
          + + +

          Tumia ODM kutoka eneo lenye low-bandwidth

          -
          +

          Nini hiki na kwa nani

          Kutoka kwa Ivan Gayton's repo.

          OpenDroneMap haiwezi kuwa kawada kuingizwa local inataka mashine yenye nguvu kwa mchakato wa data nyingi—kwa hivyo cloud mashine inaweza baadhi ya wakati kujibu kwa watu wanotaka kutumia. Hata hivyo nafasi ni tatizo kwa marekebisho ya kiwango cha chini. Kizuwizi hiki hakiwezi kuondoa tatizo moja kwa moja, bali njia zifuatazo zinaweza kuwa sababu za kupunguza mahitaji ya nafasi kwa kuchakata picha za ndege nyuki katika claud kutoka maeneo ya Afrika.

          Hapa tutawakilisha wepesi lakini mchakato unaofanya kazi wa kutengeneza OpenDroneMap cloud mashine (sio CloudODM, kumbuka, ni cloud-based instance ya ODM ambayo unaweza kutumia kutoka kwa camand line) na tumia kuprocess kundi picha nyingi ukiwa mbali. Inahitaji ujuzi wa matumizi ya Unix camand line, ssh, Digital Ocean account (Amazon AWS inafanya kazi pia, inawezekana ni tofauti ndogo katika mpangilio), njia ya kati na kati ya usomaji Computer kwa ujumla. Ikiwa huna computer nzuri na unataka kutumia njia ya slightly setup, CloudODM ni lazima kuiangalia.

          Mchakato wote huo umekusudiwa kwa mtu anerusha ndege nyuki eneo kubwa ndani ya Afrika au eneo linalofanana kutaka kuprocess data haraka wakati yupo eneo la kazi. Kwa hio itasaidia kazi iliopangwa kwa kupunguza nafasi/ kusafirisha data, kuliko njia rahisi ya kutumia ODM.

          -
          -
          +
          +

          Njia

          -
          +

          Kuingiza

          • Tengeneza Digital Ocean droplet yenye ukubwa angalau 4GB ya RAM. Ambayo makisio gharama ni $20/mwezi, chini ya 4GB na haitakubali kuingia. Wakati ukiwasha ODM process tutapunguza kuwa ukubwa na cloud mashine ya gharama, Lakini wakati ikitumika unaweza kupunguza kwa droplet ya pili rahisi zaidi ambayo ina gharama ya $10/mwezi (ni droplet rahisi zaidi, at $5/mwezi , utatumia kwa nafasi ndogo ambayo huwezi kuirudisha mwanzo).

            @@ -562,8 +563,8 @@ export PATH=$PATH:/home/odm/ODM/

            • Sasa utahitaji hard drive ya pili ("nafasi" katika Digital Ocean jargon) inatosha kutengeneza project yako. Sheria ya thumb inaonesha kuwa mara 10 ya nafasi ya data picha, tumepata 100GB hadi kufikia ukubwa wa 1000GB (ukimaliza kurun unaweza kusafisha nafasi kubwa , lakini inahitajika kumaliza mchakato wote). Panga hadi nafasi ikifika, weka katika droplet yako, na configure hio nafasi uitakayo (katika mfano huu tunapanga ndani ya /mnt/odmdata/).

            -
          -
          +
          +

          Kuandaa data na project

          • Sasa peleka picha zako ndani ya server. Unaweza kutumia Secure Copy (scp) Mfano: scp -r /path/to/my/imagefolder odm@xxx.xxx.xxx.xxx:/mnt/odmdata/.

            @@ -573,7 +574,7 @@ export PATH=$PATH:/home/odm/ODM/
        -
        +
        Muundo wa Anuani

        ODM inahitaji kuandaliwa nafasi ya orodha ndani ya mshine. Nafasi ngumu ni kuingiza folder (Ikiwa utahifadhi kama hapo juu ni "/home/odm/ODM/) na folder la project (i.e. /mnt/odmdata/myproject/)

          @@ -583,9 +584,9 @@ export PATH=$PATH:/home/odm/ODM/

        • Badilisha settings.yaml kurahisisha anuani kuu ya file project (katika kesi kama hii nafasi tuliotengeneza, /mnt/odmdata/). Hakikisha picha zipo katika sehemu husika, i.e /mnt/odmdata/myproject/images na mafile mengine ya ziada (gcp_list.txt and image_groups.txt) imo katika semu ya folder /mnt/odmdata/myproject/

        • Ikiwa una picha katika mafolder tofauti kwa kila mabloki AOI au flight (ikiwa mpangilio wa flight zako utapangika) unaweza kutengeneza picha kwenye file image_groups.txt pamoja na for i in *; do cd $i; for j in *; do echo \"$j $i\" >> ../$i.txt; done; cd ../; done; and cd ../, for i in myproject/*.txt; do cat $i >> image_groups.txt; done;. Hio itatengenea file na "group name" baada kila moja (kwa kesi hii itakuwa rahisi kuandika jina la folder llkotoka). Kisha ondosha file zote za picha katika anuani moja inayoitwa images ndani ya anuani ya project dir (kwa /mnt/odmdata/myproject/images/). file la image_groups.txt litaruhusu ODM kujua kila picha itokapo kutoka kundi moa,hata ikiwa zimetoka katika anuani moja.

        -
        - -
        + + +

        Kubadilisha droplet, kuvuta pini, pekua

        • Zima na badilisha mashine yako kwa mpangilio maalum wa CPU na lkiasi cha kumbukumbu. Natumia memory-optimized mashine kiasi 24 vCPUs na 192GB ya RAM (ghara yake ni kiasi $1.60/hr ambayo itaengeza haraka, ni juu $1000/mwezi). Washa upya na fanya kazi kwa haraka ili usipoteze mda big-droplet.

          • @@ -610,8 +611,8 @@ export PATH=$PATH:/home/odm/ODM/

          • Unaweza kuangalia htop (ili kupata kujua matumizi uliotumia kwa maana siku za usoni unaweza kuongeza ukubwa wa mashine)

          -
        -
        + +

        Baada kumaliza (kisia utachukua mda mkubwa)

        • Mara tu processing ikimaliza, zima mashine na rudisha chini kwa uwezo mdogo.

          • @@ -630,10 +631,10 @@ export PATH=$PATH:/home/odm/ODM/

        Learn to edit and help improve this page!

        - - - - + + + + diff --git a/te/.buildinfo b/te/.buildinfo index 4a3f81307..29194b900 100644 --- a/te/.buildinfo +++ b/te/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d65f1dafa7b72460ef2a1d4b173d4c7d +config: 826481e42566ded7f032c01892b9785c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/te/.doctrees/arguments.doctree b/te/.doctrees/arguments.doctree index 2680b1294..7981d5e3f 100644 Binary files a/te/.doctrees/arguments.doctree and b/te/.doctrees/arguments.doctree differ diff --git a/te/.doctrees/contributing.doctree b/te/.doctrees/contributing.doctree index c3dde54e5..b6dee4c39 100644 Binary files a/te/.doctrees/contributing.doctree and b/te/.doctrees/contributing.doctree differ diff --git a/te/.doctrees/environment.pickle b/te/.doctrees/environment.pickle index 80a10aa3a..4b74c4344 100644 Binary files a/te/.doctrees/environment.pickle and b/te/.doctrees/environment.pickle differ diff --git a/te/.doctrees/flying.doctree b/te/.doctrees/flying.doctree index 13e5a25e4..1fdb89cc2 100644 Binary files a/te/.doctrees/flying.doctree and b/te/.doctrees/flying.doctree differ diff --git a/te/.doctrees/gcp.doctree b/te/.doctrees/gcp.doctree index 6279e4cc9..459786e23 100644 Binary files a/te/.doctrees/gcp.doctree and b/te/.doctrees/gcp.doctree differ diff --git a/te/.doctrees/geo.doctree b/te/.doctrees/geo.doctree index 47a33b4be..77e0e3cd8 100644 Binary files a/te/.doctrees/geo.doctree and b/te/.doctrees/geo.doctree differ diff --git a/te/.doctrees/index.doctree b/te/.doctrees/index.doctree index 7f641f693..a4c2e2465 100644 Binary files a/te/.doctrees/index.doctree and b/te/.doctrees/index.doctree differ diff --git a/te/.doctrees/installation.doctree b/te/.doctrees/installation.doctree index 0e631225f..fc04db74b 100644 Binary files a/te/.doctrees/installation.doctree and b/te/.doctrees/installation.doctree differ diff --git a/te/.doctrees/large.doctree b/te/.doctrees/large.doctree index 0d0fcaf6c..551666aa9 100644 Binary files a/te/.doctrees/large.doctree and b/te/.doctrees/large.doctree differ diff --git a/te/.doctrees/map-accuracy.doctree b/te/.doctrees/map-accuracy.doctree index 724381e29..75df3bc38 100644 Binary files a/te/.doctrees/map-accuracy.doctree and b/te/.doctrees/map-accuracy.doctree differ diff --git a/te/.doctrees/masks.doctree b/te/.doctrees/masks.doctree index fb9360400..5a35beaa0 100644 Binary files a/te/.doctrees/masks.doctree and b/te/.doctrees/masks.doctree differ diff --git a/te/.doctrees/multispectral.doctree b/te/.doctrees/multispectral.doctree index c1ed21074..fb2d061e2 100644 Binary files a/te/.doctrees/multispectral.doctree and b/te/.doctrees/multispectral.doctree differ diff --git a/te/.doctrees/outputs.doctree b/te/.doctrees/outputs.doctree index d918ebe9b..c39ac36b4 100644 Binary files a/te/.doctrees/outputs.doctree and b/te/.doctrees/outputs.doctree differ diff --git a/te/.doctrees/requesting-features.doctree b/te/.doctrees/requesting-features.doctree index c9e962e66..3f9a90c5b 100644 Binary files a/te/.doctrees/requesting-features.doctree and b/te/.doctrees/requesting-features.doctree differ diff --git a/te/.doctrees/resources.doctree b/te/.doctrees/resources.doctree index 60bc5c617..e3cebaff6 100644 Binary files a/te/.doctrees/resources.doctree and b/te/.doctrees/resources.doctree differ diff --git a/te/.doctrees/tutorials.doctree b/te/.doctrees/tutorials.doctree index 018aeb31d..4e7fd901d 100644 Binary files a/te/.doctrees/tutorials.doctree and b/te/.doctrees/tutorials.doctree differ diff --git a/te/_sources/arguments.rst.txt b/te/_sources/arguments.rst.txt index 84ae87529..ce49943dd 100644 --- a/te/_sources/arguments.rst.txt +++ b/te/_sources/arguments.rst.txt @@ -15,6 +15,12 @@ Arguments ``--cameras`` Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: `` +``--cog`` + Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: ``False`` + +``--copy-to`` + Copy output results to this folder after processing. + ``--crop`` Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: ``3`` @@ -96,7 +102,7 @@ Arguments ``--min-num-features`` Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: ``8000`` -``name`` +``name`` Name of dataset (i.e subfolder name within project folder). Default: ``code`` ``--optimize-disk-space`` @@ -108,6 +114,9 @@ Arguments ``--orthophoto-cutline`` Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: ``False`` +``--orthophoto-kmz`` + Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: ``False`` + ``--orthophoto-no-tiled`` Set this parameter if you want a striped GeoTIFF. Default: ``False`` @@ -151,7 +160,7 @@ Arguments Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder. ``--radiometric-calibration`` none | camera | camera+sun - Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` + Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: ``none`` ``--rerun,-r`` dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report Rerun this stage only and stop. . Default: `` @@ -201,6 +210,9 @@ Arguments ``--texturing-data-term`` gmi | area When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: ``gmi`` +``--texturing-keep-unseen-faces`` + Keep faces in the mesh that are not seen in any camera. Default: ``False`` + ``--texturing-outlier-removal-type`` none | gauss_clamping | gauss_damping Type of photometric outlier removal method. . Default: ``gauss_clamping`` diff --git a/te/_static/documentation_options.js b/te/_static/documentation_options.js index ddf8f086c..f1ad4db21 100644 --- a/te/_static/documentation_options.js +++ b/te/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.4.7', + VERSION: '2.5.5', LANGUAGE: 'te', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/te/arguments.html b/te/arguments.html index 96cb97645..f5c567261 100644 --- a/te/arguments.html +++ b/te/arguments.html @@ -4,10 +4,11 @@ - + + - ఎంపికలు మరియు ఫ్లాగ్స్ — OpenDroneMap 2.4.7 documentation + ఎంపికలు మరియు ఫ్లాగ్స్ — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
        - 2.4.7 + 2.5.5
        @@ -292,9 +293,9 @@
        -
        +

        ఎంపికలు మరియు ఫ్లాగ్స్

        -
        +

        ఆర్గుమెంట్స్

        --build-overviews

        Build orthophoto overviews for faster display in programs such as QGIS. Default: False

        @@ -303,6 +304,10 @@
        --cameras <json>

        Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: ``

        +
        --cog

        Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: False

        +
        +
        --copy-to <path>

        Copy output results to this folder after processing.

        +
        --crop <positive float>

        Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrinked by N meters. Use 0 to disable cropping. Default: 3

        --debug

        Print debug messages. Default: False

        @@ -357,7 +362,7 @@
        --min-num-features <integer>

        Minimum number of features to extract per image. More features can be useful for finding more matches between images, potentially allowing the reconstruction of areas with little overlap or insufficient features. More features also slow down processing. Default: 8000

        -
        name <project name>

        Name of dataset (i.e subfolder name within project folder). Default: code

        +
        name <dataset name>

        Name of dataset (i.e subfolder name within project folder). Default: code

        --optimize-disk-space

        Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don't have sufficient disk space available. Default: False

        @@ -365,6 +370,8 @@
        --orthophoto-cutline

        Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: False

        +
        --orthophoto-kmz

        Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: False

        +
        --orthophoto-no-tiled

        Set this parameter if you want a striped GeoTIFF. Default: False

        --orthophoto-png

        Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: False

        @@ -393,7 +400,7 @@
        --project-path <path>

        Path to the project folder. Your project folder should contain subfolders for each dataset. Each dataset should have an "images" folder.

        -
        --radiometric-calibration none | camera | camera+sun

        Set the radiometric calibration to perform on images. When processing multispectral images you should set this option to obtain reflectance values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found). [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

        +
        --radiometric-calibration none | camera | camera+sun

        Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. . Default: none

        --rerun,-r dataset | split | merge | opensfm | openmvs | odm_filterpoints | odm_meshing | mvs_texturing | odm_georeferencing | odm_dem | odm_orthophoto | odm_report

        Rerun this stage only and stop. . Default: ``

        @@ -427,6 +434,8 @@
        --texturing-data-term gmi | area

        When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: gmi

        +
        --texturing-keep-unseen-faces

        Keep faces in the mesh that are not seen in any camera. Default: False

        +
        --texturing-outlier-removal-type none | gauss_clamping | gauss_damping

        Type of photometric outlier removal method. . Default: gauss_clamping

        --texturing-skip-global-seam-leveling

        Skip normalization of colors across all images. Useful when processing radiometric data. Default: False

        @@ -453,8 +462,8 @@

        Learn to edit and help improve this page!

        -
        -
        + +
        diff --git a/te/contributing.html b/te/contributing.html index e8469536d..5823b4e24 100644 --- a/te/contributing.html +++ b/te/contributing.html @@ -4,10 +4,11 @@ - + + - ఎలా సహకరించాలి — OpenDroneMap 2.4.7 documentation + ఎలా సహకరించాలి — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
        - 2.4.7 + 2.5.5
        @@ -291,14 +292,14 @@
        -
        +

        ఎలా సహకరించాలి

        OpenDroneMap సంఘం రచనలపై ఆధారపడుతుంది. మీరు ప్రోగ్రామర్ కాకపోయినా మీరు అనేక విధాలుగా సహకరించవచ్చు.

        -
        +

        కమ్యూనిటీ ఫోరం

        మీరు పాల్గొనడానికి చూస్తున్నట్లయితే, సమస్యలో చిక్కుకున్నా, లేదా చేరుకోవాలనుకుంటే, ఫోరమ్ ప్రారంభించడానికి గొప్ప ప్రదేశం. మీ ప్రశ్నలకు ఇప్పటికే సమాధానమివ్వవచ్చు లేదా మీరు ఇతర ఉపయోగకరమైన చిట్కాలు మరియు వనరులను కనుగొనవచ్చు. ఇతరులు అన్వేషించడానికి మీరు మీ ఓపెన్ యాక్సెస్ డేటాసెట్లను కూడా అందించవచ్చు. క్రొత్త ఫీచర్‌ను వ్రాయడానికి ముందు బగ్ నివేదికలను సమర్పించడానికి లేదా డెవలపర్‌లతో సన్నిహితంగా ఉండటానికి ముందు ఇది మంచి ప్రదేశం.

        -
        -
        +
        +

        దోషాలు నివేదించడం

        దోషాలు గితుబ్ సమస్యలుగా ట్రాక్ చేయబడతాయి. దయచేసి రిపోజిటరీలో సమస్యను సృష్టించి, బగ్ ట్యాగ్‌తో ట్యాగ్ చేయండి.

        సమస్యను వివరించండి మరియు సమస్యను పునరుత్పత్తి చేసేవారికి సహాయపడటానికి అదనపు వివరాలను చేర్చండి:

        @@ -318,7 +319,7 @@

      • మీరు ఉపయోగిస్తున్న OS యొక్క పేరు మరియు వెర్షన్ ఏమిటి?

      • మీరు ODM ను వర్చువల్ మెషీన్ లేదా డాకర్‌లో నడుపుతున్నారా? అలా అయితే, మీరు ఏ VM సాఫ్ట్‌వేర్‌ను ఉపయోగిస్తున్నారు మరియు హోస్ట్ మరియు అతిథి కోసం ఏ ఆపరేటింగ్ సిస్టమ్స్ మరియు వెర్షన్లు ఉపయోగించబడుతున్నాయి?

      -
      +

      బగ్ నివేదికలను సమర్పించడానికి మూస

      [Short description of problem here]
 
@@ -350,9 +351,9 @@
 * Problem happens with all datasets and projects, not only some datasets or projects: [Yes/No]
      -
      - -
      + + +

      అభ్యర్థనలను లాగండి

      • స్క్రీన్షాట్లు మరియు యానిమేటెడ్ GIF లను మీ పుల్ అభ్యర్థనలో సాధ్యమైనప్పుడల్లా చేర్చండి.

        • @@ -376,8 +377,8 @@

      Learn to edit and help improve this page!

      -
      - + + diff --git a/te/flying.html b/te/flying.html index c613b300d..cf7d7c773 100644 --- a/te/flying.html +++ b/te/flying.html @@ -4,10 +4,11 @@ - + + - ఎగిరే చిట్కాలు — OpenDroneMap 2.4.7 documentation + ఎగిరే చిట్కాలు — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
      - 2.4.7 + 2.5.5
      @@ -292,7 +293,7 @@
      -
      +

      ఎగిరే చిట్కాలు

      హ్యూమానిటేరియన్ ఓపెన్‌స్ట్రీట్ మ్యాప్ బృందం UAV మ్యాపింగ్ కోసం ఎగురుతున్నందుకు మార్గదర్శకాలను కలిగి ఉంది <https://uav-guidelines.openaerialmap.org/>`_:

        @@ -305,7 +306,7 @@

        చివరగా, ఖచ్చితమైన 3D డేటా అవసరమయ్యే ప్రాజెక్టులలో లెన్స్ వక్రీకరణ ఒక సవాలు. కెమెరా కాలిబ్రేషన్‌లోని ఈ పత్రాల్లో మా విభాగాన్ని చూడండి tutorials.html#calibrating-the-camera.

        Learn to edit and help improve this page!

        -
      +
      diff --git a/te/gcp.html b/te/gcp.html index 48372bc7f..890a7ef5d 100644 --- a/te/gcp.html +++ b/te/gcp.html @@ -4,10 +4,11 @@ - + + - గ్రౌండ్ కంట్రోల్ పాయింట్లు — OpenDroneMap 2.4.7 documentation + గ్రౌండ్ కంట్రోల్ పాయింట్లు — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
      - 2.4.7 + 2.5.5
      @@ -292,19 +293,19 @@
      -
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      గ్రౌండ్ కంట్రోల్ పాయింట్లు

      డేటాలోని వక్రీకరణలను సరిదిద్దడానికి మరియు సమన్వయ వ్యవస్థలను తెలుసుకోవడానికి డేటాను సూచించడానికి గ్రౌండ్ కంట్రోల్ పాయింట్లు ఉపయోగపడతాయి.

      గ్రౌండ్ కంట్రోల్ పాయింట్ (జిసిపి) అనేది భూమిపై చేసిన స్థానం కొలత, సాధారణంగా అధిక ఖచ్చితత్వంతో కూడిన జిపిఎస్‌ను ఉపయోగిస్తుంది. (టోఫానిన్ 2019)

      గ్రౌండ్ కంట్రోల్ పాయింట్లను పేవ్మెంట్ కార్నర్స్, పార్కింగ్ స్థలంలో లైన్లు లేదా కలర్ ఫ్లోర్ టైల్స్ వంటి ప్రస్తుత నిర్మాణాలను సెట్ చేయవచ్చు, లేకపోతే భూమిపై ఉంచిన లక్ష్యాలను ఉపయోగించి సెట్ చేయవచ్చు.

      లక్ష్యాలను బకెట్ మూతలు నుండి నేల పలకలు వరకు అనేక రకాల పదార్థాలతో కొనుగోలు చేయవచ్చు లేదా నిర్మించవచ్చు.

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      GCP ఫైల్ ఫార్మాట్

      GCP ఫైల్ యొక్క ఆకృతి సులభం.

      @@ -333,8 +334,8 @@

      పదునైన మూలలు జిసిపిలకు మంచి ఎంపికలు. మీరు మీ సర్వే ప్రాంతం చుట్టూ సమానంగా GCP లను కూడా ఉంచాలి / కనుగొనాలి.

      మీ ప్రాజెక్ట్ ఫోల్డర్ యొక్క బేస్ లో `` gcp_list.txt`` ఫైల్ తప్పక సృష్టించబడాలి.

      మంచి ఫలితాల కోసం మీ ఫైల్‌లో హెడర్ తర్వాత కనీసం 15 పంక్తులు ఉండాలి (ప్రతి పాయింట్‌కు 3 చిత్రాలతో 5 పాయింట్లు).

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      వినియోగదారు ఇంటర్‌ఫేస్‌లు

      GCP ఫైళ్ళను సృష్టించడానికి మీరు రెండు యూజర్ ఇంటర్ఫేస్లలో ఒకదాన్ని ఉపయోగించవచ్చు:

      @@ -343,7 +344,7 @@

    • జిసిపి ఎడిటర్ ప్రో

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    POSM GCPi

    POSM GCPi అప్రమేయంగా WebODM లో లోడ్ అవుతుంది. WebODM డెమో <http://demo.webodm.org/plugins/posm-gcpi/> _ వద్ద ఒక ఉదాహరణ అందుబాటులో ఉంది. తెలిసిన గ్రౌండ్ కంట్రోల్ XYZ విలువలతో దీన్ని ఉపయోగించడానికి, ఈ క్రింది వాటిని చేస్తారు:

    మీ జిసిపిల యొక్క ప్రొజె 4 స్ట్రింగ్‌తో హెడర్‌తో జిసిపి పేరు (జిసిపి ఇంటర్‌ఫేస్‌లో కనిపించే లేబుల్ ఇది), x, y మరియు z మాత్రమే ఉండే జిసిపి జాబితాను సృష్టించండి (అవి ప్లానార్ కోఆర్డినేట్‌లో ఉన్నాయని నిర్ధారించుకోండి UTM వంటి వ్యవస్థ. ఇది ఇలా ఉండాలి:

    @@ -356,8 +357,8 @@

    అప్పుడు ఈ జిసిపి జాబితాను ఇంటర్‌ఫేస్‌లోకి లోడ్ చేయవచ్చు, చిత్రాలను లోడ్ చేయవచ్చు మరియు ప్రతి జిసిపిలను చిత్రంలో ఉంచవచ్చు.

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    జిసిపి ఎడిటర్ ప్రో

    ఈ అనువర్తనాన్ని విడిగా ఇన్‌స్టాల్ చేయాలి లేదా https://github.com/uav4geo/GCPEditorPro <https://github.com/uav4geo/GCPEditorPro>`_ నుండి వెబ్‌ఓడిఎం ప్లగిన్‌గా లోడ్ చేయవచ్చు.

    Gcp పేరు, నార్తింగ్, ఈస్టింగ్ మరియు ఎలివేషన్ ఉన్న CSV ఫైల్‌ను సృష్టించండి.

    @@ -369,14 +370,14 @@

    అప్పుడు ప్రధాన స్క్రీన్ నుండి CSV ని దిగుమతి చేసి, ʻEPSG / PROJ పెట్టెలో` +proj=utm +zone=37 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs అని టైప్ చేయండి.

    కింది స్క్రీన్ సంబంధిత చిత్రాలను ట్యాగ్ చేయడానికి మరియు దిగుమతి చేయడానికి GCP లను ఎన్నుకోవాల్సిన మ్యాప్‌ను ప్రదర్శిస్తుంది.

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    ప్రస్తావనలు

    టోఫానిన్, పియరో. ఓపెన్ డ్రోన్ మ్యాప్: ది మిస్సింగ్ గైడ్. మాస్సెరానో లాబ్స్ LLC, 2019.

    Learn to edit and help improve this page!

    -
    - + + diff --git a/te/genindex.html b/te/genindex.html index 8027c2371..46076ed8e 100644 --- a/te/genindex.html +++ b/te/genindex.html @@ -7,7 +7,7 @@ - Index — OpenDroneMap 2.4.7 documentation + Index — OpenDroneMap 2.5.5 documentation @@ -62,7 +62,7 @@
    - 2.4.7 + 2.5.5
    diff --git a/te/geo.html b/te/geo.html index 413b180ac..c20dac82e 100644 --- a/te/geo.html +++ b/te/geo.html @@ -4,10 +4,11 @@ - + + - చిత్రం జియోలొకేషన్ ఫైల్స్ — OpenDroneMap 2.4.7 documentation + చిత్రం జియోలొకేషన్ ఫైల్స్ — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
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    -
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    చిత్రం జియోలొకేషన్ ఫైల్స్

    డిఫాల్ట్‌గా ODM అందుబాటులో ఉంటే చిత్రాలలో పొందుపరిచిన GPS సమాచారాన్ని ఉపయోగిస్తుంది. కొన్నిసార్లు చిత్రాలలో GPS సమాచారం ఉండదు, లేదా వినియోగదారు మరింత ఖచ్చితమైన డేటాతో (RTK వంటివి) సమాచారాన్ని ఓవర్‌రైడ్ చేయాలని కోరుకుంటారు.

    ODM `` 2.0`` నుండి ప్రారంభించి ప్రజలు ఈ ప్రయోజనం కోసం ఇమేజ్ జియోలొకేషన్ ఫైల్ (జియో) ను సరఫరా చేయవచ్చు.

    @@ -320,7 +321,7 @@

    మీరు `` ge.txt`` అనే ఫైల్‌ను సరఫరా చేస్తే, ODM స్వయంచాలకంగా దాన్ని కనుగొంటుంది. దీనికి మరొక పేరు ఉంటే మీరు `` --geo 1`` ని ఉపయోగించి పేర్కొనవచ్చు.

    `` జియో.టెక్స్ట్`` ఫైల్ మీ ప్రాజెక్ట్ ఫోల్డర్ యొక్క బేస్ లో తప్పక సృష్టించబడాలి.

    Learn to edit and help improve this page!

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    diff --git a/te/index.html b/te/index.html index a59326877..743c3baa9 100644 --- a/te/index.html +++ b/te/index.html @@ -4,10 +4,11 @@ - + + - OpenDroneMap యొక్క డాక్యుమెంటేషన్‌కు స్వాగతం — OpenDroneMap 2.4.7 documentation + OpenDroneMap యొక్క డాక్యుమెంటేషన్‌కు స్వాగతం — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
    - 2.4.7 + 2.5.5
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    OpenDroneMap Logo -
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    OpenDroneMap యొక్క డాక్యుమెంటేషన్‌కు స్వాగతం


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    diff --git a/te/installation.html b/te/installation.html index 67f11247f..387d60036 100644 --- a/te/installation.html +++ b/te/installation.html @@ -4,10 +4,11 @@ - + + - Installation and Getting Started — OpenDroneMap 2.4.7 documentation + Installation and Getting Started — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    Installation and Getting Started

    This section is excerpted and modified with permission from OpenDroneMap: The Missing Guide, by Piero Toffanin.

    Until recently OpenDroneMap was the term used to refer to a single command line application (what is now known as the ODM project). With the recent development of a web interface, an API and other tools, OpenDroneMap has become an ecosystem of various applications to process, analyze and display aerial data. This ecosystem is made of several components:

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    ODM Logo -
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    • ODM is the processing engine, which can be used from the command line. It takes images as input and produces a variety of outputs, including point clouds, 3D models and orthophotos

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    NodeODM Logo -
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    • NodeODM is a light-weight API built on top of ODM. It allows users and applications to access the functions of ODM over a computer network

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    WebODM Logo -
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    • WebODM is a friendly user interface that includes a map viewer, a 3D viewer, user logins, a plugin system and many other features that are expected of modern drone mapping platforms

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    CloudODM Logo -
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    • CloudODM is a small command line client to communicate with ODM via the NodeODM API

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    PyODM Logo -
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    • PyODM is a Python SDK for creating tasks via the NodeODM API. We cover it in more detail in the “Automated Processing With Python” chapter

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    • ClusterODM is a load balancer for connecting together multiple NodeODM instances

    ODM, NodeODM and WebODM are available on all major platforms (Windows, macOS and Linux) via a program called docker, which is required to run the software. Docker offers a way to run “containers”. Containers are packaged copies of an entire system, its software and its dependencies. These containers run within a virtual environment. On Linux this virtual environment is available from the operating system and is very efficient. On macOS and Windows the containers run within a VM, so there’s a bit of a overhead. but it’s still very suitable for running the software. Once installed users do not have to worry much about docker, as it operates (almost) transparently.

    Without docker it would not be possible to run ODM on Windows or macOS. On these platforms ODM cannot run natively. Future development efforts are being focused on leveraging the new Windows Subsystem for Linux (WSL) and the possibility to make a native port of all dependencies to macOS, which is going to make the installation much easier.

    On Ubuntu Linux 18.04 it’s feasible to run all OpenDroneMap software natively. However, because there’s very little performance penalty for running docker on Linux and docker is straightforward to setup on this platform, we don’t recommend it. On Linux the advantages of containerization far outweigh a tiny performance penalty. With docker users also get easy one-step updates of the software, so that’s nice.

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    Hardware Recommendations

    The bare minimum requirements for running the software are:

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  • 16 GB RAM

    • The above will allow for a few hundred images to be processed without too many issues. A CPU with more cores will allow for faster processing, while a graphics card (GPU) currently has no impact on performance. For processing more images, add more disk space and RAM linearly to the number of images you need to process.

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    Installation

    We recommend people use docker for running ODM, whether you are on Windows, macOS or Linux.

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    Windows

    To run OpenDroneMap you need at least Windows 7. Previous versions of Windows are not supported.

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    Step 1. Check Virtualization Support

    Docker requires a feature from your CPU called virtualization, which allows it to run virtual machines (VMs). Make sure you have it enabled! Sometimes this is disabled. To check, on Windows 8 or higher you can open the Task Manager (press CTRL+SHIFT+ESC) and switch to the Performance tab.

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    Virtualization should be enabled

    On Windows 7 to see if you have virtualization enabled you can use the Microsoft® Hardware-Assisted Virtualization Detection Tool instead.

    If virtualization is disabled, you’ll need to enable it. The procedure unfortunately is a bit different for each computer model, so the best way to do this is to look up on a search engine “how to enable vtx for <type your computer model here>”. Often times it’s a matter of restarting the computer, immediately pressing F2 or F12 during startup, navigating the boot menu and changing the settings to enable virtualization (often called “VT-X”).

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    Common keys to press at computer startup to access the boot menu for various PC vendors

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    Step 2. Install Requirements

    First, you’ll need to install:

    For Python 3, make sure you check Add Python 3.x to PATH during the installation.

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    Don’t forget to add the Python executable to your PATH (so that you can run commands with it)

    Then, only if you are on Windows 10 Home, Windows 8 (any version) or Windows 7 (any version), install:

      @@ -395,8 +396,8 @@ versions of Windows are not supported.

    Please do NOT install both docker programs. They are different and will create a mess if they are both installed.

    After installing docker, launch it from the Desktop icon that is created from the installation (Docker Quickstart in the case of Docker Toolbox, Docker for Windows for Docker for Windows). This is important, do not skip this step. If there are errors, follow the prompts on screen to fix them.

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    Step 3. Check Memory and CPU Allocation

    Docker on Windows works by running a VM in the background (think of a VM as a “computer emulator”). This VM has a certain amount of memory allocated and WebODM can only use as much memory as it’s allocated.

    If you installed Docker Toolbox (see below if you installed Docker for Windows instead):

    @@ -406,9 +407,9 @@ versions of Windows are not supported.

  • Right click the default VM and press Settings...

  • Move the Base Memory slider from the System panel and allocate 60-70% of all available memory, optionally adding 50% of the available processors from the Processor tab also

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    VirtualBox default VM settings

    Then press OK, right click the default VM and press Start.

    If you installed Docker for Windows instead:

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  • From the panel, click Advanced and use the sliders to allocate 60-70% of available memory and use half of all available CPUs.

  • Press Apply.

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    Step 1 Docker icon

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    Step 3 & 4 Docker settings

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    Step 4. Download WebODM

    Open the Git Gui program that comes installed with Git. From there:

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    • In Target Directory click browse and navigate to a folder of your choosing (create one if necessary)

    • Press Clone

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    Git Gui

    If the download succeeded, you should now see this window:

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    Git Gui after successful download (clone)

    Go to the Repository menu, then click Create Desktop Icon. This will allow you to come back to this application easily in the future.

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    Step 4. Launch WebODM

    From Git Gui, go to the Repository menu, then click Git Bash. From the command line terminal type:

    $ ./webodm.sh start&

    Several components will download to your machine at this point, including WebODM, NodeODM and ODM. After the download you should be greeted by the following screen:

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    Console output after starting WebODM for the first time

    • If you are using Docker for Windows, open a web browser to http://localhost:8000

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    Then connect to http://192.168.1.100:8000 (replacing the IP address with the proper one).

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    macOS

    Most modern (post 2010) Mac computers running macOS Sierra 10.12 or higher can run OpenDroneMap using docker, as long as hardware virtualization is supported (see below).

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    Step 1. Check Virtualization Support

    Open a Terminal window and type:

    $ sysctl kern.hv_support
@@ -487,8 +488,8 @@ versions of Windows are not supported.

    If the result is kern.hv_support: 1, then your Mac is supported! Continue with Step 2.

    If the result is kern.hv_support: 0, unfortunately it means your Mac is too old to run OpenDroneMap. :(

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    Step 2. Install Requirements

    There are only two programs to install:

      @@ -496,9 +497,9 @@ versions of Windows are not supported.

    1. Git: https://sourceforge.net/projects/git-osx-installer/files/

    After installing docker you should find an icon that looks like a whale in the task bar.

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    Docker app running

    You can verify that docker is running properly by opening the Terminal app and typing:

    $ docker run hello-world
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    If you get a “bash: git: command not found”, try to restart your Terminal app and double-check for any errors during the install process.

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    Step 3. Check Memory and CPU Allocation

    Docker on macOS works by running a VM in the background (think of it as a “computer emulator”). This VM has a certain amount of memory allocated and WebODM can only use as much memory as it’s allocated.

      @@ -527,12 +528,12 @@ versions of Windows are not supported.

    1. Adjust the CPUs slider to use half of all available CPUs and the memory to use 60-70% of all available memory

    2. Press Apply & Restart

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    Docker advanced settings

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    Step 4. Download and Launch WebODM

    From a Terminal type:

    $ git clone https://github.com/OpenDroneMap/WebODM
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    Then open a web browser to http://localhost:8000.

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    Linux

    OpenDroneMap can run on any Linux distribution that supports docker. According to docker’s documentation website the officially supported distributions are CentOS, Debian, Ubuntu and Fedora, with static binaries available for others. If you have to pick a distribution solely for running OpenDroneMap, Ubuntu is the recommended way to go.

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    Step 1. Install Requirements

    There are four programs that need to be installed:

      @@ -556,7 +557,7 @@ $ ./webodm.sh start

    1. Pip

    We cannot possibly cover the installation process for every Linux distribution out there, so we’ll limit the instructions to those that are distributions officially supported by docker. In all cases it’s just a matter of opening a terminal prompt and typing a few commands.

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    Install on Ubuntu / Debian

    Commands to type:

    $ sudo apt update
@@ -565,8 +566,8 @@ $ sh get-docker.sh
 $ sudo apt install -y git python python-pip
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    Install on CentOS / RHEL

    Commands to type:

    $ curl -fsSL https://get.docker.com -o get-docker.sh
@@ -574,8 +575,8 @@ $ sh get-docker.sh
 $ sudo yum -y install git python python-pip
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    Install on Fedora

    Commands to type:

    $ curl -fsSL https://get.docker.com -o get-docker.sh
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 $ sudo dnf install git python python-pip
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    Install on Arch

    Commands to type:

    $ sudo pacman -Sy docker git python python-pip
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    Step 2. Check Additional Requirements

    In addition to the three programs above, the dockercompose script is also needed. Sometimes it’s already installed with docker, but sometimes it isn’t. To verify if it’s installed try to type:

    $ docker-compose --version
@@ -610,8 +611,8 @@ $ sudo dnf install git python python-pip
 
    $ sudo pip install docker-compose
 
    
 
    
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    Step 3. Download and Launch WebODM

    From a terminal type:

    $ git clone https://github.com/OpenDroneMap/WebODM
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    Then open a web browser to http://localhost:8000.

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    Basic Commands and Troubleshooting

    The cool thing about using docker is that 99% of the tasks you’ll ever need to perform while using WebODM can be done via the ./webodm.sh script. You have already encountered one of them:

    $ ./webodm.sh start
@@ -655,20 +656,20 @@ $ ./webodm.sh --help

    The community forum is a great place to ask for help if you get stuck during any of the installation steps and for general questions on using the ./webodm.sh script.

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    Hello, WebODM!

    After running ./webodm.sh start and opening WebODM in the browser, you will be greeted with a welcome message and will be asked to create the first user. Take some time to familiarize yourself with the web interface and explore its various menus.

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    Screenshot of WebODM Dashboard -
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    WebODM Dashboard

    Notice that under the Processing Nodes menu there’s a "node-odm-1" node already configured for you to use. This is a NodeODM node and has been created automatically by WebODM. This node is running on the same machine as WebODM.

    If you’ve made it this far, congratulations! Now it’s time to start processing some data.

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    Running on more than one machine

    Optionally: If you have another computer, you can repeat the installation process (install docker, git, python, etc.) and launch a new NodeODM node by typing from a Terminal/Git Bash window:

    docker run --rm -it -p 3000:3000 opendronemap/nodeodm -q 1 --token secret
@@ -678,9 +679,9 @@ $ ./webodm.sh --help
 
    From WebODM you can then press the Add New button under Processing Nodes. For the hostname/IP field type the IP of the second computer. For the port field type “3000”. For the token field type “secret”. You can also add an optional label for your node, such as “second computer”. Then press Save.
    
 
    If everything went well, you should now have two processing nodes! You will be able to process multiple tasks in parallel using two different machines.
    
 
    Learn to edit and help improve this page!
    
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    diff --git a/te/large.html b/te/large.html index 8363cb768..6a36ce7ae 100644 --- a/te/large.html +++ b/te/large.html @@ -4,10 +4,11 @@ - + + - పెద్ద డేటాసెట్లను విభజించడం — OpenDroneMap 2.4.7 documentation + పెద్ద డేటాసెట్లను విభజించడం — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    పెద్ద డేటాసెట్లను విభజించడం

    ODM సంస్కరణ `` 0.6.0`` తో ప్రారంభించి, మీరు చాలా పెద్ద డేటాసెట్లను నిర్వహించదగిన భాగాలుగా విభజించవచ్చు (సబ్‌మోడల్స్ అని పిలుస్తారు), ప్రతి భాగం పైప్‌లైన్‌ను నడుపుతుంది, ఆపై విలీనం చేసిన DEM లు, ఆర్థోఫోటోలు మరియు పాయింట్ మేఘాలను ఉత్పత్తి చేస్తుంది. ఈ ప్రక్రియను "స్ప్లిట్-విలీనం" గా సూచిస్తారు.

    మీరు స్ప్లిట్-విలీన పైప్‌లైన్‌ను ఎందుకు ఉపయోగించవచ్చు? మీ డేటాసెట్‌లో మీకు చాలా ఎక్కువ సంఖ్యలో చిత్రాలు ఉంటే, స్ప్లిట్-విలీనం పెద్ద మెషీన్‌లో ప్రాసెసింగ్‌ను మరింత నిర్వహించగలిగేలా చేస్తుంది (దీనికి తక్కువ మెమరీ అవసరం). ఒకే నెట్‌వర్క్‌తో అనుసంధానించబడిన అనేక యంత్రాలు మీకు ఉంటే, మీరు సబ్‌మోడల్‌లను సమాంతరంగా ప్రాసెస్ చేయవచ్చు, తద్వారా క్షితిజ సమాంతర స్కేలింగ్ మరియు వేలాది చిత్రాలను మరింత త్వరగా ప్రాసెస్ చేయడానికి అనుమతిస్తుంది.

    Split-merge works in WebODM out of the box as long as the processing nodes support split-merge, by enabling the --split option when creating a new task.

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    Calibrate images

    Image calibration is recommended (but not required) for large datasets because error propagation due to image distortion could cause a bowl effect on the models. Calibration instructions can be found at Calibrate Images.

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    image of lens distortion effect on bowling of data -
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    Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

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    Local Split-Merge

    Splitting a dataset into more manageable submodels and sequentially processing all submodels on the same machine is easy! Just use --split and --split-overlap to decide the the average number of images per submodels and the overlap (in meters) between submodels respectively

    docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
@@ -321,14 +322,14 @@

    will create 3 submodels. Make sure to pass --split-overlap 0 if you manually provide a image_groups.txt file.

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    Distributed Split-Merge

    ODM can also automatically distribute the processing of each submodel to multiple machines via NodeODM nodes, orchestrated via ClusterODM.

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    Getting Started with Distributed Split-Merge

    The first step is start ClusterODM

    docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
@@ -356,8 +357,8 @@ Escape character is '^]'.
 
    docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
 
    
 
    
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    Understanding the Cluster

    When connected via telnet, it is possible to interrogate what is happening on the cluster. For example, we can use the command HELP to find out available commands

    # HELP
@@ -392,16 +393,16 @@ OK
 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
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    Accessing the Logs

    While a process is running, it is also possible to list the tasks, and view the task output

    # TASK LIST
 # TASK OUTPUT <taskId> [lines]
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    Autoscaling ClusterODM

    ClusterODM also includes the option to autoscale on multiple platforms, including, to date, Amazon and Digital Ocean. This allows users to reduce costs associated with always-on instances as well as being able to scale processing based on demand.

    To setup autoscaling you must:

    @@ -437,19 +438,19 @@ info: Found docker-machine executable

    This way all tasks will be automatically forwarded to the autoscaler.

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    Limitations

    The 3D textured meshes are currently not being merged as part of the workflow (only point clouds, DEMs and orthophotos are).

    GCPs are fully supported, however, there needs to be at least 3 GCP points on each submodel for the georeferencing to take place. If a submodel has fewer than 3 GCPs, a combination of the remaining GCPs + EXIF data will be used instead (which is going to be less accurate). We recommend using the image_groups.txt file to accurately control the submodel split when using GCPs.

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    Acknowledgments

    Huge props to Pau and the folks at Mapillary for their amazing contributions to OpenDroneMap through their OpenSfM code, which is a key component of the split-merge pipeline. We look forward to further pushing the limits of OpenDroneMap and seeing how big a dataset we can process.

    Learn to edit and help improve this page!

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    మ్యాప్ ఖచ్చితత్వం

    మ్యాప్‌లోని సమాచారం వాస్తవ ప్రపంచంలోని విలువలతో సరిపోయే డిగ్రీ లేదా సాన్నిహిత్యాన్ని ఖచ్చితత్వాన్ని నిర్వచించవచ్చు. అందువల్ల, మేము ఖచ్చితత్వాన్ని సూచించినప్పుడు, మేము డేటా నాణ్యత గురించి మరియు ఒక నిర్దిష్ట డేటాసెట్ (పాస్కల్ 2011) లో ఉన్న లోపాల సంఖ్య గురించి మాట్లాడుతున్నాము.

    ** సాపేక్ష లేదా స్థానిక ఖచ్చితత్వం **

    స్థానిక లేదా సాపేక్ష ఖచ్చితత్వాన్ని మ్యాప్‌లోని రెండు పాయింట్ల మధ్య దూరం వాస్తవ ప్రపంచంలో ఆ పాయింట్ల మధ్య దూరాలకు అనుగుణంగా ఉంటుంది.

    సాపేక్ష ఖచ్చితత్వం ప్రపంచంలోని మ్యాప్ యొక్క స్థానానికి స్వతంత్రంగా ఉంటుంది, కాబట్టి మ్యాప్ అధిక సాపేక్ష ఖచ్చితత్వాన్ని కలిగి ఉంటుంది (పరిమాణం మరియు ఆకారంలో) కానీ ప్రపంచంలో దాని స్థానాన్ని మార్చవచ్చు (మూర్తి 1).

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    Figure 1. Model showing high relative accuracy but misplaced according to its real world position

    ** సంపూర్ణ లేదా ప్రపంచ ఖచ్చితత్వం **

    సంపూర్ణ ఖచ్చితత్వం అంటే గ్రహం మీద దాని నిజమైన స్థానానికి సంబంధించి పునర్నిర్మాణం యొక్క ఖచ్చితత్వం (పిక్స్ 4 డి 2019). మూర్తి 2 సాపేక్ష మరియు సంపూర్ణ ఖచ్చితమైన నమూనాను చూపిస్తుంది, ఎందుకంటే పాయింట్లు దాని వాస్తవ ప్రపంచ స్థానం ప్రకారం సరిగ్గా ఉంచబడతాయి.

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    Model showing high absolute accuracy -
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    Figure 2. Model showing high relative and absolute accuracy. Placed correctly according to its real world position

    ** ప్రతి ప్రాజెక్ట్ కోసం ఖచ్చితత్వం స్థాయి **

    ప్రతి ప్రాజెక్ట్ నిర్దిష్ట ఖచ్చితత్వాన్ని కలిగి ఉండాలి. ఉదాహరణకు, నిర్మాణ స్థలంలో పురోగతిని అంచనా వేయడం లేదా అగ్నిప్రమాదం సంభవించిన ప్రాంతాన్ని కొలవడం GCP యొక్క ఉపయోగం అవసరం లేదు, ఎందుకంటే సంపూర్ణ ఖచ్చితత్వం నిర్ణయం తీసుకునే ప్రక్రియను ప్రభావితం చేయదు. మరోవైపు, ఖచ్చితత్వం కీలకమైన పనులు ఉన్నాయి, ఉదాహరణకు ప్రాజెక్ట్ వర్తింపు మూల్యాంకనాలు మరియు భూమి టైటిల్ సర్వేయింగ్, దీనికి అధిక సాపేక్ష మరియు సంపూర్ణ ఖచ్చితత్వం అవసరం.

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    ఏమి ఆశించను

    సాధారణ పరంగా, డేటాసెట్ కోసం సగటు ఖచ్చితత్వం 1 నుండి 3 రెట్లు సాపేక్ష ఖచ్చితత్వం ఉంటుందని ఎవరైనా ఆశించవచ్చు. మరియు సంపూర్ణ ఖచ్చితత్వానికి, ఇది UAV లో అమర్చిన GPS యూనిట్ మీద ఆధారపడి ఉంటుందని పరిగణించాలి కాని ప్రామాణిక GPS యొక్క క్షితిజ సమాంతర ఖచ్చితత్వం సాధారణంగా 2 నుండి 6 మీటర్ల పరిధిలో ఉంటుంది మరియు నిలువు ఖచ్చితత్వం 3 నుండి 4 రెట్లు మధ్య ఉంటుంది సమాంతర ఖచ్చితత్వం.

    GCP ని ఉపయోగిస్తున్నప్పుడు, క్షితిజ సమాంతర ఖచ్చితత్వానికి సంపూర్ణ ఖచ్చితత్వాన్ని 2.5 రెట్లు GSD మరియు నిలువు ఖచ్చితత్వానికి 4 రెట్లు GSD గా మెరుగుపరచవచ్చు (మాడవాలాగామా 2016).

    1 సెం.మీ. యొక్క GSD వద్ద, ఖచ్చితత్వం RTK GNSS కు ఉంటుంది మరియు ఇది ఉప-ఆప్టిమల్ పరిస్థితులలో (బారీ 2013) NSDI & FGDC మ్యాపింగ్ ఖచ్చితత్వ ప్రమాణాల ప్రకారం 1: 200 ప్రమాణాలలో ఉంటుంది.

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    మ్యాప్ ఖచ్చితత్వాన్ని ప్రభావితం చేసే అంశాలు

    ** వాతావరణం **

    ఫోటోగ్రామెట్రీ ఫలితాల్లో వాతావరణ పరిస్థితులు ప్రత్యక్ష ప్రభావాన్ని చూపుతాయి, కాబట్టి క్లౌడ్ కవరేజ్, గాలి వేగం, తేమ, సూర్యుడి ఎత్తు మరియు UAV స్థిరత్వం మరియు భూభాగ ప్రకాశాన్ని ప్రభావితం చేసే ఇతర అంశాలను పరిగణనలోకి తీసుకోవడం చాలా ముఖ్యం.

    @@ -325,9 +326,9 @@

    ఫ్లైట్ ఎత్తులో ఎక్కువ, ఇమేజ్ పాదముద్ర మరియు జిఎస్‌డి పెద్దవి. గుర్తించదగిన లక్షణాలలో తక్కువ వివరాలు ఉన్నందున ఫలితమయ్యే పెద్ద GSD ఖచ్చితత్వం తగ్గుతుంది. ఒక చిన్న GSD అవసరమైనప్పుడు ఎత్తైన ప్రదేశం యొక్క ఎత్తు 3 నుండి 4 రెట్లు ఎత్తుగా సిఫార్సు చేయబడింది.

    ** విమాన వేగం **

    రోలింగ్ షట్టర్ అమర్చిన కెమెరాల్లో విమాన వేగం ప్రత్యేక ప్రభావాన్ని చూపుతుంది, అయితే గ్లోబల్ లేదా మెకానికల్ షట్టర్ అమర్చినవి ఈ ప్రభావాన్ని తగ్గిస్తాయి. RTK పొజిషనింగ్ సిస్టమ్‌లతో కూడిన UAV కూడా వేగంతో ప్రభావితమవుతుంది, కానీ తీసిన ప్రతి ఫోటో వద్ద హోవర్‌తో, మీరు చాలా మంచి ఖచ్చితత్వాన్ని పొందవచ్చు. బదులుగా మీరు ప్రతి ఫోటో తీసేటప్పుడు కదులుతున్నట్లయితే, ఖచ్చితత్వం రెండు కారకాల ద్వారా పరిమితం చేయబడుతుంది: మీరు కదులుతున్న వేగం RTK (మాథర్ 2020) యొక్క 1 సెకండ్ ఇంక్రిమెంట్లతో గుణించబడుతుంది.

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    ప్రస్తావనలు

    బారీ, పి., & కోక్లీ, ఆర్. Network నెట్‌వర్క్ RTK GPS తో పోలిస్తే UAV ఫోటోగ్రామెట్రీ యొక్క ఖచ్చితత్వం. » బేస్లైన్ సర్వేలు. 2013. http://uav.ie/PDF/Accuracy_UAV_compare_RTK_GPS.pdf (10 13, 2020 న వినియోగించబడింది).

    డ్రోన్ డిప్లాయ్. నేను గ్రౌండ్ కంట్రోల్ పాయింట్లను ఎలా ఉపయోగించగలను ?: డ్రోన్ మ్యాపింగ్ సాఫ్ట్‌వేర్‌తో గ్రౌండ్ కంట్రోల్ పాయింట్లను ఉపయోగించటానికి ఒక గైడ్. 5 8, 2017. https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/ (యాక్సెస్ చేయబడింది 7 9, 2020).

    @@ -336,8 +337,8 @@

    పాస్కల్, మాన్యువల్ ఎస్. జిఐఎస్ లాంజ్: జిఐఎస్ డేటా: ఎ లుక్ ఎట్ ఖచ్చితత్వం, ప్రెసిషన్, మరియు రకాలు లోపాలు. 11 6, 2011. https://www.gislounge.com/gis-data-a-look-at-accuracy-precision-and-types-of-errors/ (యాక్సెస్ 07 09, 2020).

    పిక్స్ 4 డి. A ఏరియల్ మ్యాపింగ్ ప్రాజెక్టులో ఖచ్చితత్వం ఏమిటి? » పిక్స్ 4 డి. 25 డి 05 డి 2019. https://www.pix4d.com/blog/accuracy-aerial-mapping (యాక్సెస్డ్ 10 13, 2020).

    Learn to edit and help improve this page!

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    diff --git a/te/masks.html b/te/masks.html index 424c2b024..5603d6a3b 100644 --- a/te/masks.html +++ b/te/masks.html @@ -4,10 +4,11 @@ - + + - ఇమేజ్ మాస్క్‌లను ఉపయోగించడం — OpenDroneMap 2.4.7 documentation + ఇమేజ్ మాస్క్‌లను ఉపయోగించడం — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    ఇమేజ్ మాస్క్‌లను ఉపయోగించడం

    ODM `` 2.0`` నుండి ప్రారంభించి ప్రజలు కొన్ని ప్రాంతాలపై పునర్నిర్మాణాన్ని దాటవేయడానికి సాఫ్ట్‌వేర్‌కు తెలియజేయడానికి ఇమేజ్ మాస్క్‌లను సరఫరా చేయవచ్చు. వాలుగా ఉన్న షాట్ల నుండి ఇన్పుట్ ఫోటోలలో ఆకాశం అనుకోకుండా చేర్చబడిన సందర్భాలకు లేదా ఒకే విషయం యొక్క పునర్నిర్మాణాన్ని పరిమితం చేయడానికి ఇది ఉపయోగపడుతుంది.

    ముసుగుని జోడించడానికి, మీరు ముసుగు చేయాలనుకుంటున్న లక్ష్య చిత్రానికి సమానమైన కొత్త నలుపు మరియు తెలుపు చిత్రాన్ని సృష్టించండి (దీన్ని చేయడానికి మీరు GIMP వంటి ప్రోగ్రామ్‌ను ఉపయోగించవచ్చు). పునర్నిర్మాణం నుండి మినహాయించటానికి ప్రాంతాలను నలుపు రంగులో ఉంచండి.

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    Image mask -
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    3D result (building is masked) -
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    మీ ఫైల్‌కు పేరు పెట్టండి:

    1_mask.JPG

    ఉదాహరణకు, `` DJI_0018. JPG`` ఒక `` DJI_0018_mask.JPG`` ఫైల్‌ను సృష్టించడం ద్వారా ముసుగు కలిగి ఉండవచ్చు మరియు దానిని చిత్రాల జాబితాలో చేర్చండి. ఇమేజ్ మాస్క్‌ల కోసం మీరు `` .JPG``, `` .PNG``, `` .BMP`` మరియు `` .TIF`` ఫార్మాట్‌లను ఉపయోగించవచ్చు.

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    diff --git a/te/multispectral.html b/te/multispectral.html index 6e70993d8..d6af78f03 100644 --- a/te/multispectral.html +++ b/te/multispectral.html @@ -4,10 +4,11 @@ - + + - మల్టీస్పెక్ట్రల్ సపోర్ట్ — OpenDroneMap 2.4.7 documentation + మల్టీస్పెక్ట్రల్ సపోర్ట్ — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    మల్టీస్పెక్ట్రల్ సపోర్ట్

    వెర్షన్ 0.9.9 నుండి ODM కి రేడియోమెట్రిక్ సాధారణీకరణకు ప్రాథమిక మద్దతు ఉంది, ఇది మల్టీస్పెక్ట్రల్ కెమెరాల నుండి ప్రతిబింబ ఆర్థోఫోటోలను ఉత్పత్తి చేయగలదు. మల్టీస్పెక్ట్రల్ కెమెరాలు విభిన్న బ్యాండ్ సెన్సార్లను ఉపయోగించి సన్నివేశం యొక్క బహుళ షాట్లను సంగ్రహిస్తాయి.

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    హార్డ్వేర్

    మేము వీలైనన్ని కెమెరాలకు మద్దతు ఇవ్వాలని లక్ష్యంగా పెట్టుకున్నాము, కింది కెమెరాలను ఉపయోగించి మల్టీస్పెక్ట్రల్ సపోర్ట్ అభివృద్ధి చేయబడింది, కాబట్టి అవి బాగా పనిచేస్తాయి:

    @@ -305,13 +306,13 @@

    ఇతర కెమెరాలు కూడా పనిచేయవచ్చు. డేటాసెట్లను పంచుకోవడం <https://community.opendronemap.org/c/datasets/10> _ ఇతర కెమెరాలతో సంగ్రహించడం ద్వారా ఈ జాబితాను విస్తరించడానికి మీరు మాకు సహాయపడగలరు.

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    వాడుక

    రేడియోమెట్రిక్ సాధారణీకరణను ప్రారంభించడానికి అన్ని చిత్రాల నుండి అన్ని చిత్రాలను ఒకేసారి ప్రాసెస్ చేయండి (బ్యాండ్‌లను బహుళ ఫోల్డర్‌లుగా వేరు చేయవద్దు) మరియు - రేడియోమెట్రిక్-కాలిబ్రేషన్ పరామితిని పాస్ చేయండి. చిత్రాలు బహుళ-కెమెరా సెటప్‌లో భాగమైతే, ఫలితంగా వచ్చే ఆర్థోఫోటోలో N బ్యాండ్‌లు ఉంటాయి, ప్రతి కెమెరాకు ఒకటి (+ ఆల్ఫా).

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    diff --git a/te/objects.inv b/te/objects.inv index 11a45177e..f313ba581 100644 Binary files a/te/objects.inv and b/te/objects.inv differ diff --git a/te/outputs.html b/te/outputs.html index eeb8057fc..311a42680 100644 --- a/te/outputs.html +++ b/te/outputs.html @@ -4,10 +4,11 @@ - + + - OpenDroneMap అవుట్‌పుట్‌లు — OpenDroneMap 2.4.7 documentation + OpenDroneMap అవుట్‌పుట్‌లు — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    OpenDroneMap అవుట్‌పుట్‌లు

    ODM ఉత్పత్తి చేసే కొన్ని ఉపయోగకరమైన ఉత్పాదనలు క్రింద ఇవ్వబడ్డాయి.

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    పాయింట్ క్లౌడ్

    `` odm_georeferencing / odm_georeferenced_model.ply / laz / csv`` - వేర్వేరు ఫైల్ ఫార్మాట్లలో భౌగోళిక సూచించిన పాయింట్ క్లౌడ్

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    Point cloud over State University Zanzibar, courtesy of Khadija Abdullah Ali

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    3D ఆకృతి మోడల్

    `` odm_texturing / odm_textured_model.obj`` - ఆకృతి చేసిన ఉపరితల మెష్ `` odm_texturing / odm_textured_model_geo.obj`` - భౌగోళిక మరియు ఉపరితల ఉపరితల మెష్

    మీరు మెష్ లాబ్ ఉపయోగించి పాయింట్ క్లౌడ్ మరియు ఆకృతి మెష్లను యాక్సెస్ చేయవచ్చు. మెష్‌ల్యాబ్‌ను తెరిచి, ఫైల్‌ను ఎంచుకోండి: మెష్‌ను దిగుమతి చేయండి మరియు కింది వాటికి సమానమైన స్థానం నుండి మీ ఆకృతి మెష్‌ను ఎంచుకోండి: `` odm_texturing odm_textured_model.obj``

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    Textured mesh courtesy of OpenDroneMap

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    ఆర్థోఫోటో

    `` odm_orthophoto / odm_orthphoto.png`` - ఆర్థోఫోటో, కానీ ఇది సరళమైన png, దీనికి భౌగోళిక సమాచారం లేదు

    `` odm_orthophoto / odm_orthphoto.tif`` - GeoTIFF ఆర్థోఫోటో. మీరు దీన్ని QGIS లో రాస్టర్ లేయర్‌గా ఉపయోగించవచ్చు.

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    Orthophoto over State University Zanzibar, courtesy of Khadija Abdullah Ali

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    DTM / DSM

    `` --Dtm`` లేదా `` --dsm`` ఎంపికలు ఉపయోగించినట్లయితే మాత్రమే DTM / DSM సృష్టించబడుతుంది. సృష్టించే మరిన్ని ఎంపికల కోసం ఎలివేషన్ మోడళ్లపై <ట్యుటోరియల్ <https://docs.opendronemap.org/tutorials.html#creating-digital-elevation-models>`_ చూడండి.

    డేటా వీటిలో నిల్వ చేయబడుతుంది:

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  • odm_dem/dsm.tif

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    Digital surface model over State University Zanzibar, courtesy of Khadija Abdullah Ali

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    అన్ని ఉత్పాదనల జాబితా

    project/
 ├── images/
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    diff --git a/te/requesting-features.html b/te/requesting-features.html index fcc94f3bf..1d92ffcab 100644 --- a/te/requesting-features.html +++ b/te/requesting-features.html @@ -4,10 +4,11 @@ - + + - ఫీచర్లను ఎలా అభ్యర్థించాలి — OpenDroneMap 2.4.7 documentation + ఫీచర్లను ఎలా అభ్యర్థించాలి — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    ఫీచర్లను ఎలా అభ్యర్థించాలి

    అన్ని సాఫ్ట్‌వేర్‌లకు దాని వినియోగదారుల అవసరాలకు అనుగుణంగా అమరిక పెరగడానికి మరియు నిర్వహించడానికి వినియోగదారు అభిప్రాయం మరియు ఫీచర్ అభ్యర్థనలు అవసరం.

    OpenDroneMap అనేది FOSS సాఫ్ట్‌వేర్. ఉచిత మరియు ఓపెన్ సోర్స్ (FOSS) ప్రాజెక్టులు లోపలి మరియు వెలుపల నుండి ఆసక్తికరంగా ఉంటాయి: బయటి నుండి, విజయవంతమైన వారు ఏదైనా చేయగలరని భావిస్తారు మరియు సహేతుకమైన అభ్యర్థన ఏమిటో తెలుసుకోవడం కష్టం. ప్రాజెక్ట్ లోపలి నుండి, వారు చాలా వనరులను నిర్బంధించినట్లు భావిస్తారు: ఎక్కువగా సమయం, డబ్బు మరియు అవకాశాల ఓవర్లోడ్ ద్వారా.

    @@ -307,7 +308,7 @@

    ప్రాజెక్ట్కు కొత్త ఫీచర్లు జోడించబడటం చూసి మీరు సంతోషిస్తున్నాము. కొన్ని క్రొత్త లక్షణాలకు మద్దతు అవసరం, మరికొన్ని అమలు చేయడం సులభం. మీ అభ్యర్థన ఎక్కడ పడిపోతుందో అర్థం చేసుకోవడానికి మేము మా వంతు కృషి చేస్తాము మరియు మీరు అందించగల మద్దతును మేము అభినందిస్తున్నాము.

    Learn to edit and help improve this page!

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    diff --git a/te/resources.html b/te/resources.html index ae2bb6803..af88221b3 100644 --- a/te/resources.html +++ b/te/resources.html @@ -4,10 +4,11 @@ - + + - అదనపు సూచనలు — OpenDroneMap 2.4.7 documentation + అదనపు సూచనలు — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
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    @@ -292,9 +293,9 @@
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    అదనపు సూచనలు

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    వినియోగదారుల కోసం

    కింది వనరులు ప్రారంభించడానికి మంచి ప్రదేశం:

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    డెవలపర్ల కోసం

    వినియోగదారు వనరులతో పాటు, ఈ క్రింది వాటిని కూడా చదవమని మేము సిఫార్సు చేస్తున్నాము:

      @@ -315,8 +316,8 @@

    • మేము మా ఫోరమ్‌లో కు అంకితమైన విభాగాన్ని ఉంచుతాము. సాఫ్ట్‌వేర్‌ను మెరుగుపరచడానికి ఉపయోగపడే మోషన్, మల్టీ-వ్యూ స్టీరియో, మెషింగ్, టెక్స్‌టరింగ్ మొదలైన వాటి నుండి నిర్మాణానికి సంబంధించిన ఆర్ట్ రీసెర్చ్ గురించి మరింత చదవడానికి ఇది ఒక విలువైన ప్రదేశం.

    Learn to edit and help improve this page!

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    -
    + +
    diff --git a/te/search.html b/te/search.html index a71c07d15..db3a53452 100644 --- a/te/search.html +++ b/te/search.html @@ -7,7 +7,7 @@ - Search — OpenDroneMap 2.4.7 documentation + Search — OpenDroneMap 2.5.5 documentation @@ -64,7 +64,7 @@
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\ No newline at end of file diff --git a/te/tutorials.html b/te/tutorials.html index 67b0ff62e..eccdd2feb 100644 --- a/te/tutorials.html +++ b/te/tutorials.html @@ -4,10 +4,11 @@ - + + - Tutorials — OpenDroneMap 2.4.7 documentation + Tutorials — OpenDroneMap 2.5.5 documentation @@ -64,7 +65,7 @@
    - 2.4.7 + 2.5.5
    @@ -292,14 +293,14 @@
    -
    +

    Tutorials

    Below you will find instructions for some common use cases.

    -
    +

    Creating High Quality Orthophotos

    -
    +
    image of OpenDroneMap orthophoto -
    +

    Without any parameter tweaks, ODM chooses a good compromise between quality, speed and memory usage. If you want to get higher quality results, you need to tweak some parameters:

      @@ -310,39 +311,39 @@

    • --mesh-size should be increased to 300000-600000 and --mesh-octree-depth should be increased to 10-11 in urban areas to recreate better buildings / roofs.

    -
    -
    +
    +

    Calibrating the Camera

    Camera calibration is a special challenge with commodity cameras. Temperature changes, vibrations, focus, and other factors can affect the derived parameters with substantial effects on resulting data. Automatic or self calibration is possible and desirable with drone flights, but depending on the flight pattern, automatic calibration may not remove all distortion from the resulting products. James and Robson (2014) in their paper Mitigating systematic error in topographic models derived from UAV and ground‐based image networks address how to minimize the distortion from self-calibration.

    -
    +
    image of lens distortion effect on bowling of data -
    +

    Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

    To mitigate this effect, there are a few options but the simplest are as follows: fly two patterns separated by 20°, and rather than having a nadir (straight down pointing) camera, use one that tilts forward by 5°.

    -
    +
    animation showing optimum -
    +

    As this approach to flying can be take longer than typical flights, a pilot or team can fly a small area using the above approach. OpenDroneMap will generate a calibration file called cameras.json that then can be imported to be used to calibrate another flight that is more efficiently flown.

    Alternatively, the following experimental method can be applied: fly with much lower overlap, but two crossgrid flights (sometimes called crosshatch) separated by 20° with a 5° forward facing camera.

    • Crossgrid overlap percentages can be lower than parallel flights. To get good 3D results, you will require 68% overlap and sidelap for an equivalent 83% overlap and sidelap.

    • To get good 2D and 2.5D (digital elevation model) results, you will require 42% overlap and sidelap for an equivalent 70% overlap and sidelap.

    -
    +
    animation showing experimental optimum -
    +

    Vertically separated flight lines also improve accuracy, but less so than a camera that is forward facing by 5°.

    -
    +
    figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
    +

    From James and Robson (2014), CC BY 4.0

    -
    -
    + +

    Creating Digital Elevation Models

    By default ODM does not create DEMs. To create a digital terrain model, make sure to pass the --dtm flag. To create a digital surface model, be sure to pass the --dsm flag.

    -
    +
    image of OpenDroneMap derived digital surface model -
    +

    For DTM generation, a Simple Morphological Filter (smrf) is used to classify points in ground vs. non-ground and only the ground points are used. The smrf filter can be controlled via several parameters:

      @@ -355,9 +356,9 @@

      Changing these options can affect the result of DTMs significantly. The best source to read to understand how the parameters affect the output is to read the original paper An improved simple morphological filter for the terrain classification of airborne LIDAR data (PDF freely available).

      Overall the --smrf-threshold option has the biggest impact on results.

      SMRF is good at avoiding Type I errors (small number of ground points mistakenly classified as non-ground) but only "acceptable" at avoiding Type II errors (large number non-ground points mistakenly classified as ground). This needs to be taken in consideration when generating DTMs that are meant to be used visually, since objects mistaken for ground look like artifacts in the final DTM.

      -
      +
      image of lens distortion effect on bowling of data -
      +

      Two other important parameters affect DEM generation:

        @@ -369,36 +370,36 @@ 
        docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
        -
      -
      +
    +

    Measuring stockpile volume

    -
    +

    Fieldwork planning

    Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

    -
    -
    +
    +

    Flight pattern

    Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

    In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

    -
    +
    a simple lawnmower flight pattern can produce accurate results -
    +

    Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

    -
    -
    + +

    Flight height

    Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

    -
    -
    + +

    GCPs

    To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

    For further information on the use of GCPs, please refer to the Ground Control Points section.

    -
    -
    + +

    Processing parameters

    A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

    If using ODM, these this reference values can help you configure the process settings.

    @@ -407,69 +408,69 @@ When placing or measuring GCP, equipment accuracy should be greater than the GSD

    --orthophoto-resolution 1.0

    --feature-quality high

    --pc-quality high

    -
    -
    + +

    Measuring

    As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

    -
    +
    almost 50% of the material will be found in the first 20% of the stockpile height -
    +

    In WebODM Dashboard, clic on "view map" to start a 2D view of your project.

    Once in the 2D map view, clic on the "Measure volume, area and length" button.

    -
    +
    clic on the "Measure volume, area and length" button -
    +

    then clic on "Create a new measurement"

    -
    +
    clic on "Create a new measurement" -
    +

    Start placing the points to define the stockpile base plane

    -
    +
    Define the stockpile base plane -
    +

    Clic on "Finish measurement" to finish the process.

    -
    +
    Clic on "Finish measurement" to finish the process -
    +

    Dialog box will show the message "Computing ..." for a few seconds, and after the computing is finished the volume measurement value will be displayed.

    -
    +
    Clic on "Finish measurement" to finish the process -
    +

    If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

    Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

    -
    -
    + +

    Expected accuracy

    For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

    -
    -
    -
    + + +

    Using Docker

    Since many users employ docker to deploy OpenDroneMap, it can be useful to understand some basic commands in order to interrogate the docker instances when things go wrong, or we are curious about what is happening. Docker is a containerized environment intended, among other things, to make it easier to deploy software independent of the local environment. In this way, it is similar to virtual machines.

    A few simple commands can make our docker experience much better.

    -
    +

    Listing Docker Machines

    We can start by listing available docker machines on the current machine we are running as follows:

    > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

    If we want to see machines that may not be running but still exist, we can add the -a flag:

    > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
    -
    -
    +
    +

    Accessing logs on the instance

    Using either the CONTAINER ID or the name, we can access any logs available on the machine as follows:

    > docker logs 2518817537ce
@@ -500,8 +501,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

    The value -5 tells the tail command to give us just the last 5 lines of the logs.

    -
    -
    + +

    Command line access to instances

    Sometimes we need to go a little deeper in our exploration of the process for OpenDroneMap. For this, we can get direct command line access to the machines. For this, we can use docker exec to execute a bash command line shell in the machine of interest as follows:

    > docker exec -ti 2518817537ce bash
@@ -509,15 +510,15 @@ The resulting accuracy is comparable to the commercially available photogrammetr

    Now we are logged into our docker instance and can explore the machine.

    -
    -
    + +

    Cleaning up after Docker

    Docker has a lamentable use of space and by default does not clean up excess data and machines when processes are complete. This can be advantageous if we need to access a process that has since terminated, but carries the burden of using increasing amounts of storage over time. Maciej Łebkowski has an excellent overview of how to manage excess disk usage in docker.

    -
    -
    -
    + + +

    Using ODM from low-bandwidth location

    -
    +

    What is this and who is it for?

    Via Ivan Gayton's repo.

    OpenDroneMap can’t always be @@ -542,10 +543,10 @@ missions in an African or similar location looking to process data ASAP while still in a field setting. Therefore it emphasizes a workflow intended to reduce bandwidth/data transfer, rather than just the simplest way of running ODM.

    -
    -
    +
    +

    Steps

    -
    +

    Install

    • Create a Digital Ocean droplet with at least 4GB of RAM. That’ll cost @@ -623,8 +624,8 @@ droplet, and /mnt/odmdata/).

    -
    -
    +
    +

    Prep data and project

    • Now push your images onto the server. You can use Secure Copy @@ -640,7 +641,7 @@ files.1, +

      Directory structure

      ODM requires the directories on the machine to be set up just so. The critical bits are the install folder (if you installed as above, it’s @@ -684,9 +685,9 @@ image_groups.txt file will allow ODM to keep track of which images belong to the same batch, even though they’re all in a single directory.

    -
    - -
    + + +

    Resize droplet, pull pin, run away

    • Shut down and resize your machine to an appropriately monstrous @@ -741,8 +742,8 @@ a large dataset (>1000 images), omit the +

    +

    After it finishes (assuming you survive that long)

    • As soon as processing is done, shut down the machine and resize it @@ -770,10 +771,10 @@ maybe smaller).

    Learn to edit and help improve this page!

    - - - - + + + + diff --git a/tutorials.html b/tutorials.html index b4a10b6fc..4ff554bae 100644 --- a/tutorials.html +++ b/tutorials.html @@ -4,10 +4,11 @@ - + + - Tutorials — OpenDroneMap 2.4.7 documentation + Tutorials — OpenDroneMap 2.5.5 documentation @@ -63,7 +64,7 @@
    - 2.4.7 + 2.5.5
    @@ -291,14 +292,14 @@
    -
    +

    Tutorials

    Below you will find instructions for some common use cases.

    -
    +

    Creating High Quality Orthophotos

    -
    +
    image of OpenDroneMap orthophoto -
    +

    Without any parameter tweaks, ODM chooses a good compromise between quality, speed and memory usage. If you want to get higher quality results, you need to tweak some parameters:

      @@ -309,39 +310,39 @@

    • --mesh-size should be increased to 300000-600000 and --mesh-octree-depth should be increased to 10-11 in urban areas to recreate better buildings / roofs.

    -
    -
    +
    +

    Calibrating the Camera

    Camera calibration is a special challenge with commodity cameras. Temperature changes, vibrations, focus, and other factors can affect the derived parameters with substantial effects on resulting data. Automatic or self calibration is possible and desirable with drone flights, but depending on the flight pattern, automatic calibration may not remove all distortion from the resulting products. James and Robson (2014) in their paper Mitigating systematic error in topographic models derived from UAV and ground‐based image networks address how to minimize the distortion from self-calibration.

    -
    +
    image of lens distortion effect on bowling of data -
    +

    Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.

    To mitigate this effect, there are a few options but the simplest are as follows: fly two patterns separated by 20°, and rather than having a nadir (straight down pointing) camera, use one that tilts forward by 5°.

    -
    +
    animation showing optimum -
    +

    As this approach to flying can be take longer than typical flights, a pilot or team can fly a small area using the above approach. OpenDroneMap will generate a calibration file called cameras.json that then can be imported to be used to calibrate another flight that is more efficiently flown.

    Alternatively, the following experimental method can be applied: fly with much lower overlap, but two crossgrid flights (sometimes called crosshatch) separated by 20° with a 5° forward facing camera.

    • Crossgrid overlap percentages can be lower than parallel flights. To get good 3D results, you will require 68% overlap and sidelap for an equivalent 83% overlap and sidelap.

    • To get good 2D and 2.5D (digital elevation model) results, you will require 42% overlap and sidelap for an equivalent 70% overlap and sidelap.

    -
    +
    animation showing experimental optimum -
    +

    Vertically separated flight lines also improve accuracy, but less so than a camera that is forward facing by 5°.

    -
    +
    figure showing effect of vertically separated flight lines and forward facing cameras on improving self calibration -
    +

    From James and Robson (2014), CC BY 4.0

    -
    -
    + +

    Creating Digital Elevation Models

    By default ODM does not create DEMs. To create a digital terrain model, make sure to pass the --dtm flag. To create a digital surface model, be sure to pass the --dsm flag.

    -
    +
    image of OpenDroneMap derived digital surface model -
    +

    For DTM generation, a Simple Morphological Filter (smrf) is used to classify points in ground vs. non-ground and only the ground points are used. The smrf filter can be controlled via several parameters:

      @@ -354,9 +355,9 @@

      Changing these options can affect the result of DTMs significantly. The best source to read to understand how the parameters affect the output is to read the original paper An improved simple morphological filter for the terrain classification of airborne LIDAR data (PDF freely available).

      Overall the --smrf-threshold option has the biggest impact on results.

      SMRF is good at avoiding Type I errors (small number of ground points mistakenly classified as non-ground) but only “acceptable” at avoiding Type II errors (large number non-ground points mistakenly classified as ground). This needs to be taken in consideration when generating DTMs that are meant to be used visually, since objects mistaken for ground look like artifacts in the final DTM.

      -
      +
      image of lens distortion effect on bowling of data -
      +

      Two other important parameters affect DEM generation:

        @@ -368,36 +369,36 @@ 
        docker run -ti --rm -v /my/project:/datasets/code <my_odm_image> --project-path /datasets --dtm --dem-resolution 2 --smrf-threshold 0.4 --smrf-window 24
        -
      -
      +
    +

    Measuring stockpile volume

    -
    +

    Fieldwork planning

    Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process. In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated. Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.

    -
    -
    +
    +

    Flight pattern

    Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.

    In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.

    -
    +
    a simple lawnmower flight pattern can produce accurate results -
    +

    Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.

    -
    -
    + +

    Flight height

    Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters. As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.

    -
    -
    + +

    GCPs

    To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results. When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.

    For further information on the use of GCPs, please refer to the Ground Control Points section.

    -
    -
    + +

    Processing parameters

    A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.

    If using ODM, these this reference values can help you configure the process settings.

    @@ -406,69 +407,69 @@ When placing or measuring GCP, equipment accuracy should be greater than the GSD

    –orthophoto-resolution 1.0

    –feature-quality high

    –pc-quality high

    -
    -
    + +

    Measuring

    As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.

    -
    +
    almost 50% of the material will be found in the first 20% of the stockpile height -
    +

    In WebODM Dashboard, clic on “view map” to start a 2D view of your project.

    Once in the 2D map view, clic on the “Measure volume, area and length” button.

    -
    +
    clic on the "Measure volume, area and length" button -
    +

    then clic on “Create a new measurement”

    -
    +
    clic on "Create a new measurement" -
    +

    Start placing the points to define the stockpile base plane

    -
    +
    Define the stockpile base plane -
    +

    Clic on “Finish measurement” to finish the process.

    -
    +
    Clic on "Finish measurement" to finish the process -
    +

    Dialog box will show the message “Computing …” for a few seconds, and after the computing is finished the volume measurement value will be displayed.

    -
    +
    Clic on "Finish measurement" to finish the process -
    +

    If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.

    Also consider that once the limits of the stockpile are set in software like QGis, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point. Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.

    -
    -
    + +

    Expected accuracy

    For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%. The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.

    -
    -
    -
    + + +

    Using Docker

    Since many users employ docker to deploy OpenDroneMap, it can be useful to understand some basic commands in order to interrogate the docker instances when things go wrong, or we are curious about what is happening. Docker is a containerized environment intended, among other things, to make it easier to deploy software independent of the local environment. In this way, it is similar to virtual machines.

    A few simple commands can make our docker experience much better.

    -
    +

    Listing Docker Machines

    We can start by listing available docker machines on the current machine we are running as follows:

    > docker ps
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                  zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours         0.0.0.0:3000->3000/tcp   flamboyant_dhawan

    If we want to see machines that may not be running but still exist, we can add the -a flag:

    > docker ps -a
 CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS                    PORTS                    NAMES
 2518817537ce        opendronemap/odm       "bash"                   36 hours ago        Up 36 hours                                        zen_wright
-1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
+1cdc7fadf688        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   37 hours ago        Up 37 hours               0.0.0.0:3000->3000/tcp   flamboyant_dhawan
 cd7b9585b8f6        opendronemap/odm       "bash"                   3 days ago          Exited (1) 37 hours ago                            nostalgic_lederberg
 e31010c00b9a        opendronemap/odm       "python /code/run.py…"   3 days ago          Exited (2) 3 days ago                              suspicious_kepler
 c44e0d0b8448        opendronemap/nodeodm   "/usr/bin/nodejs /va…"   3 days ago          Exited (0) 37 hours ago                            wonderful_burnell
    -
    -
    +
    +

    Accessing logs on the instance

    Using either the CONTAINER ID or the name, we can access any logs available on the machine as follows:

    > docker logs 2518817537ce
@@ -499,8 +500,8 @@ The resulting accuracy is comparable to the commercially available photogrammetr

    The value -5 tells the tail command to give us just the last 5 lines of the logs.

    -
    -
    + +

    Command line access to instances

    Sometimes we need to go a little deeper in our exploration of the process for OpenDroneMap. For this, we can get direct command line access to the machines. For this, we can use docker exec to execute a bash command line shell in the machine of interest as follows:

    > docker exec -ti 2518817537ce bash
@@ -508,15 +509,15 @@ The resulting accuracy is comparable to the commercially available photogrammetr

    Now we are logged into our docker instance and can explore the machine.

    -
    -
    + +

    Cleaning up after Docker

    Docker has a lamentable use of space and by default does not clean up excess data and machines when processes are complete. This can be advantageous if we need to access a process that has since terminated, but carries the burden of using increasing amounts of storage over time. Maciej Łebkowski has an excellent overview of how to manage excess disk usage in docker.

    -
    -
    -
    + + +

    Using ODM from low-bandwidth location

    -
    +

    What is this and who is it for?

    Via Ivan Gayton’s repo.

    OpenDroneMap can’t always be @@ -541,10 +542,10 @@ missions in an African or similar location looking to process data ASAP while still in a field setting. Therefore it emphasizes a workflow intended to reduce bandwidth/data transfer, rather than just the simplest way of running ODM.

    -
    -
    +
    +

    Steps

    -
    +

    Install

    • Create a Digital Ocean droplet with at least 4GB of RAM. That’ll cost @@ -622,8 +623,8 @@ droplet, and /mnt/odmdata/).

    -
    -
    +
    +

    Prep data and project

    • Now push your images onto the server. You can use Secure Copy @@ -639,7 +640,7 @@ files.1, +

      Directory structure

      ODM requires the directories on the machine to be set up just so. The critical bits are the install folder (if you installed as above, it’s @@ -683,9 +684,9 @@ image_groups.txt file will allow ODM to keep track of which images belong to the same batch, even though they’re all in a single directory.

    -
    - -
    + + +

    Resize droplet, pull pin, run away

    • Shut down and resize your machine to an appropriately monstrous @@ -740,8 +741,8 @@ a large dataset (>1000 images), omit the +

    +

    After it finishes (assuming you survive that long)

    • As soon as processing is done, shut down the machine and resize it @@ -769,10 +770,10 @@ maybe smaller).

    Learn to edit and help improve this page!

    - - - - + + + +