added a stockpile measurement tutorial

source/arguments.rst

@@ -99,15 +99,6 @@ Arguments
 ``name`` <project name>
   Name of dataset (i.e subfolder name within project folder). Default: ``code``
 
-``--opensfm-depthmap-method`` PATCH_MATCH |  BRUTE_FORCE |  PATCH_MATCH_SAMPLE
-  Raw depthmap computation algorithm. PATCH_MATCH and PATCH_MATCH_SAMPLE are faster, but might miss some valid points. BRUTE_FORCE takes longer but produces denser reconstructions. Default: ``PATCH_MATCH``
-
-``--opensfm-depthmap-min-consistent-views`` <integer: 2 <= x <= 9>
-  Minimum number of views that should reconstruct a point for it to be valid. Use lower values if your images have less overlap. Lower values result in denser point clouds but with more noise. Default: ``3``
-
-``--opensfm-depthmap-min-patch-sd`` <positive float>
-  When using PATCH_MATCH or PATCH_MATCH_SAMPLE, controls the standard deviation threshold to include patches. Patches with lower standard deviation are ignored. Default: ``1``
-
 ``--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``
 
@@ -148,7 +139,10 @@ Arguments
   Perform ground rectification on the point cloud. This means that wrongly classified ground points will be re-classified and gaps will be filled. Useful for generating DTMs. Default: ``False``
 
 ``--pc-sample`` <positive float>
-  Filters the point cloud by keeping only a single point around a radius N (in meters). This can be useful to limit the output resolution of the point cloud. Set to 0 to disable sampling. Default: ``0``
+  Filters the point cloud by keeping only a single point around a radius N (in meters). This can be useful to limit the output resolution of the point cloud and remove duplicate points. Set to 0 to disable sampling. Default: ``0``
+
+``--pc-tile`` 
+  Reduce the memory usage needed for depthmap fusion by splitting large scenes into tiles. Turn this on if your machine doesn't have much RAM and/or you've set --pc-quality to high or ultra. Experimental. Default: ``False``
 
 ``--primary-band`` <string>
   When processing multispectral datasets, you can specify the name of the primary band that will be used for reconstruction. It's recommended to choose a band which has sharp details and is in focus. Default: ``auto``
@@ -177,6 +171,9 @@ Arguments
 ``--skip-band-alignment`` 
   When processing multispectral datasets, ODM will automatically align the images for each band. If the images have been postprocessed and are already aligned, use this option. Default: ``False``
 
+``--skip-report`` 
+  Skip generation of PDF report. This can save time if you don't need a report. Default: ``False``
+
 ``--sm-cluster`` <string>
   URL to a ClusterODM instance for distributing a split-merge workflow on multiple nodes in parallel. Default: ``None``
 
@@ -195,6 +192,9 @@ Arguments
 ``--split`` <positive integer>
   Average number of images per submodel. When splitting a large dataset into smaller submodels, images are grouped into clusters. This value regulates the number of images that each cluster should have on average. Default: ``999999``
 
+``--split-image-groups`` <path string>
+  Path to the image groups file that controls how images should be split into groups. The file needs to use the following format: image_name group_nameDefault: ``None``
+
 ``--split-overlap`` <positive integer>
   Radius of the overlap between submodels. After grouping images into clusters, images that are closer than this radius to a cluster are added to the cluster. This is done to ensure that neighboring submodels overlap. Default: ``150``
 
@@ -231,9 +231,6 @@ Arguments
 ``--use-hybrid-bundle-adjustment`` 
   Run local bundle adjustment for every image added to the reconstruction and a global adjustment every 100 images. Speeds up reconstruction for very large datasets. Default: ``False``
 
-``--use-opensfm-dense`` 
-  Use OpenSfM to compute the dense point cloud instead of OpenMVS. Default: ``False``
-
 ``--verbose,-v`` 
   Print additional messages to the console. Default: ``False``
 

source/tutorials.rst

@@ -97,6 +97,116 @@ Example of how to generate a DTM::
 
     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.
+
+.. figure:: images/lawnmower_pattern.png
+   :alt: a simple lawnmower flight pattern can produce accurate results
+   :align: center
+   
+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 <https://docs.opendronemap.org/gcp.html>`_.
+
+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.
+
+--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.
+
+.. figure:: images/stockpile.png
+   :alt: almost 50% of the material will be found in the first 20% of the stockpile height 
+   :align: center
+
+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.
+
+.. figure:: images/measurement1.png
+   :alt: clic on the "Measure volume, area and length" button
+   :align: center
+   
+then clic on "Create a new measurement"
+
+.. figure:: images/measurement2.png
+   :alt: clic on "Create a new measurement"
+   :align: center
+
+Start placing the points to define the stockpile base plane
+
+.. figure:: images/measurement3.png
+   :alt: Define the stockpile base plane
+   :align: center
+ 
+Clic on "Finish measurement" to finish the process.
+
+.. figure:: images/measurement4.png
+   :alt: Clic on "Finish measurement" to finish the process
+   :align: center
+   
+Dialog box will show the  message "Computing ..." for a few seconds, and after the computing is finished the volume measurement value will be displayed.
+
+.. figure:: images/measurement7.png
+   :alt: Clic on "Finish measurement" to finish the process
+   :align: center
+
+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 <https://www.qgis.org>`_, 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.
+
+
+
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 Using Docker
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