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Merge branch 'master' of github.com:opendronemap/opendronemap into add_smvs

pull/889/head
Dakota Benjamin 2018-06-30 20:59:59 -04:00
rodzic 85cb052acc 4a00c4d841
commit a41ce33b93
9 zmienionych plików z 94 dodań i 25 usunięć
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49
README.md
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@ -30,7 +30,11 @@ instructions through "Create a Docker group". The Docker image workflow
has equivalent procedures for Mac OS X and Windows found at [docs.docker.com](docs.docker.com). Then run the following command which will build a pre-built image and run on images found in `$(pwd)/images` (you can change this if you need to, see the [wiki](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Docker) for more detailed instructions.
```
docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing opendronemap/opendronemap
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
-v "$(pwd)/odm_texturing:/code/odm_texturing" \
opendronemap/opendronemap
```
### Native Install (Ubuntu 16.04)
@ -126,7 +130,7 @@ When the process finishes, the results will be organized as follows:
|-- texture_N.jpg # Associated textured images used by the model
|-- odm_georeferencing/
|-- odm_georeferenced_model.ply # A georeferenced dense point cloud
|-- odm_georeferenced_model.ply.laz # LAZ format point cloud
|-- odm_georeferenced_model.laz # LAZ format point cloud
|-- odm_georeferenced_model.csv # XYZ format point cloud
|-- odm_georeferencing_log.txt # Georeferencing log
|-- odm_georeferencing_transform.txt# Transform used for georeferencing
@ -155,12 +159,20 @@ installed on your machine. Docker software is free to install and use in this co
see the [Docker Ubuntu installation tutorial](https://docs.docker.com/engine/installation/linux/ubuntulinux/) and follow the
instructions through "Create a Docker group". Once Docker is installed, the fastest way to use OpenDroneMap is to run a pre-built image by typing:
docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing opendronemap/opendronemap
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
-v "$(pwd)/odm_texturing:/code/odm_texturing" \
opendronemap/opendronemap
If you want to build your own Docker image from sources, type:
docker build -t my_odm_image .
docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing my_odm_image
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
-v "$(pwd)/odm_texturing:/code/odm_texturing" \
my_odm_image
Using this method, the containerized ODM will process the images in the OpenDroneMap/images directory and output results
to the OpenDroneMap/odm_orthophoto and OpenDroneMap/odm_texturing directories as described in the [Viewing Results](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Output-and-Results) section.
@ -168,19 +180,40 @@ If you want to view other results outside the Docker image simply add which dire
established above. For example, if you're interested in the dense cloud results generated by PMVS and in the orthophoto,
simply use the following `docker run` command after building the image:
docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_georeferencing:/code/odm_georeferencing -v $(pwd)/odm_orthophoto:/code/odm_orthophoto my_odm_image
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_georeferencing:/code/odm_georeferencing" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
my_odm_image
If you want to get all intermediate outputs, run the following command:
docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_georeferencing:/code/odm_georeferencing -v $(pwd)/odm_meshing:/code/odm_meshing -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing -v $(pwd)/opensfm:/code/opensfm -v $(pwd)/pmvs:/code/pmvs opendronemap/opendronemap
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_georeferencing:/code/odm_georeferencing" \
-v "$(pwd)/odm_meshing:/code/odm_meshing" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
-v "$(pwd)/odm_texturing:/code/odm_texturing" \
-v "$(pwd)/opensfm:/code/opensfm" \
-v "$(pwd)/pmvs:/code/pmvs" \
opendronemap/opendronemap
To pass in custom parameters to the run.py script, simply pass it as arguments to the `docker run` command. For example:
docker run -it --rm -v $(pwd)/images:/code/images v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing opendronemap/opendronemap --resize-to 1800 --force-ccd 6.16
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
-v "$(pwd)/odm_texturing:/code/odm_texturing" \
opendronemap/opendronemap --resize-to 1800 --force-ccd 6.16
If you want to pass in custom parameters using the settings.yaml file, you can pass it as a -v volume binding:
docker run -it --rm -v $(pwd)/images:/code/images v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing -v $(pwd)/settings.yaml:/code/settings.yaml opendronemap/opendronemap
docker run -it --rm \
-v "$(pwd)/images:/code/images" \
-v "$(pwd)/odm_orthophoto:/code/odm_orthophoto" \
-v "$(pwd)/odm_texturing:/code/odm_texturing" \
-v "$(pwd)/settings.yaml:/code/settings.yaml" \
opendronemap/opendronemap
When building your own Docker image, if image size is of importance to you, you should use the ```--squash``` flag, like so:

1
SuperBuild/CMakeLists.txt
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@ -124,6 +124,7 @@ set(custom_libs OpenGV
CMVS
Catkin
Ecto
LASzip
PDAL
MvsTexturing
)

29
SuperBuild/cmake/External-LASzip.cmake 100644
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@ -0,0 +1,29 @@
set(_proj_name laszip)
set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
ExternalProject_Add(${_proj_name}
DEPENDS
PREFIX ${_SB_BINARY_DIR}
TMP_DIR ${_SB_BINARY_DIR}/tmp
STAMP_DIR ${_SB_BINARY_DIR}/stamp
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}/${_proj_name}
URL https://github.com/LASzip/LASzip/archive/master.zip
#--Update/Patch step----------
UPDATE_COMMAND ""
#--Configure step-------------
SOURCE_DIR ${SB_SOURCE_DIR}/${_proj_name}
CMAKE_ARGS
-DBUILD_SHARED_LIBS=ON
-DBUILD_STATIC_LIBS=OFF
-DCMAKE_INSTALL_PREFIX=${SB_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=lib
#--Build step-----------------
BINARY_DIR ${_SB_BINARY_DIR}
#--Install step---------------
INSTALL_DIR ${SB_INSTALL_DIR}
#--Output logging-------------
LOG_DOWNLOAD OFF
LOG_CONFIGURE OFF
LOG_BUILD OFF
)

2
SuperBuild/cmake/External-MvsTexturing.cmake
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@ -7,7 +7,7 @@ ExternalProject_Add(${_proj_name}
TMP_DIR ${_SB_BINARY_DIR}/tmp
STAMP_DIR ${_SB_BINARY_DIR}/stamp
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}/${_proj_name}
URL https://github.com/OpenDroneMap/mvs-texturing/archive/master.zip
#--Update/Patch step----------
UPDATE_COMMAND ""

5
SuperBuild/cmake/External-PDAL.cmake
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@ -2,14 +2,13 @@ set(_proj_name pdal)
set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
ExternalProject_Add(${_proj_name}
DEPENDS hexer
DEPENDS hexer laszip
PREFIX ${_SB_BINARY_DIR}
TMP_DIR ${_SB_BINARY_DIR}/tmp
STAMP_DIR ${_SB_BINARY_DIR}/stamp
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
URL https://github.com/PDAL/PDAL/archive/e881b581e3b91a928105d67db44c567f3b6d1afe.tar.gz
URL_MD5 cadbadf1c83d69d6525cfffd41473323
URL https://github.com/PDAL/PDAL/releases/download/1.7.2/PDAL-1.7.2-src.tar.gz
#--Update/Patch step----------
UPDATE_COMMAND ""
#--Configure step-------------

15
opendm/types.py
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@ -255,16 +255,18 @@ class ODM_GeoRef(object):
'srs': self.projection.srs,
'json': json_file}
# create pipeline file transform.xml to enable transformation
# create pipeline file las.json to write odm_georeferenced_model.laz point cloud
pipeline = '{{' \
' "pipeline":[' \
' "untransformed.ply",' \
' {{' \
' "type":"writers.las",' \
' "a_srs":"{srs}",' \
' "offset_x":"{east}",' \
' "offset_y":"{north}",' \
' "offset_z":"0",' \
' "filename":"transformed.las"' \
' "compression":"laszip",' \
' "filename":"{f_out}"' \
' }}' \
' ]' \
'}}'.format(**kwargs)
@ -272,9 +274,14 @@ class ODM_GeoRef(object):
with open(json_file, 'w') as f:
f.write(pipeline)
<<<<<<< HEAD
# call pdal
system.run('{bin}/pdal pipeline -i {json} --readers.ply.filename={f_in} '
'--writers.las.filename={f_out}'.format(**kwargs))
=======
# call pdal
system.run('{bin}/pdal pipeline -i {json} --readers.ply.filename={f_in}'.format(**kwargs))
>>>>>>> 4a00c4d8411befc0c1b01a36f713e61633f002b3
def utm_to_latlon(self, _file, _photo, idx):
@ -484,8 +491,8 @@ class ODM_Tree(object):
self.odm_georeferencing, 'odm_georeferenced_model.csv')
self.odm_georeferencing_las_json = io.join_paths(
self.odm_georeferencing, 'las.json')
self.odm_georeferencing_model_las = io.join_paths(
self.odm_georeferencing, 'odm_georeferenced_model.las')
self.odm_georeferencing_model_laz = io.join_paths(
self.odm_georeferencing, 'odm_georeferenced_model.laz')
self.odm_georeferencing_dem = io.join_paths(
self.odm_georeferencing, 'odm_georeferencing_model_dem.tif')

10
scripts/odm_dem.py
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@ -28,7 +28,7 @@ class ODMDEMCell(ecto.Cell):
# get inputs
args = self.inputs.args
tree = self.inputs.tree
las_model_found = io.file_exists(tree.odm_georeferencing_model_las)
las_model_found = io.file_exists(tree.odm_georeferencing_model_laz)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
@ -40,7 +40,7 @@ class ODMDEMCell(ecto.Cell):
log.ODM_INFO('Classify: ' + str(args.pc_classify != "none"))
log.ODM_INFO('Create DSM: ' + str(args.dsm))
log.ODM_INFO('Create DTM: ' + str(args.dtm))
log.ODM_INFO('DEM input file {0} found: {1}'.format(tree.odm_georeferencing_model_las, str(las_model_found)))
log.ODM_INFO('DEM input file {0} found: {1}'.format(tree.odm_georeferencing_model_laz, str(las_model_found)))
# Setup terrain parameters
terrain_params_map = {
@ -61,8 +61,8 @@ class ODMDEMCell(ecto.Cell):
pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or rerun_cell:
log.ODM_INFO("Classifying {} using {}".format(tree.odm_georeferencing_model_las, args.pc_classify))
commands.classify(tree.odm_georeferencing_model_las,
log.ODM_INFO("Classifying {} using {}".format(tree.odm_georeferencing_model_laz, args.pc_classify))
commands.classify(tree.odm_georeferencing_model_laz,
args.pc_classify == "smrf",
slope,
cellsize,
@ -96,7 +96,7 @@ class ODMDEMCell(ecto.Cell):
for product in products:
commands.create_dems(
[tree.odm_georeferencing_model_las],
[tree.odm_georeferencing_model_laz],
product,
radius=map(str, radius_steps),
gapfill=True,

4
scripts/odm_georeferencing.py
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@ -138,7 +138,7 @@ class ODMGeoreferencingCell(ecto.Cell):
# convert ply model to LAS reference system
geo_ref.convert_to_las(odm_georeferencing_model_ply_geo,
tree.odm_georeferencing_model_las,
tree.odm_georeferencing_model_laz,
tree.odm_georeferencing_las_json)
reconstruction.georef = geo_ref
@ -171,7 +171,7 @@ class ODMGeoreferencingCell(ecto.Cell):
if args.crop > 0:
log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
cropper = Cropper(tree.odm_georeferencing, 'odm_georeferenced_model')
cropper.create_bounds_shapefile(tree.odm_georeferencing_model_las, args.crop)
cropper.create_bounds_shapefile(tree.odm_georeferencing_model_laz, args.crop)
# Do not execute a second time, since
# We might be doing georeferencing for

2
tests/test_odm.py
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@ -136,7 +136,7 @@ class TestGeoreferencing(unittest.TestCase):
def test_las_out(self):
self.assertTrue(os.path.isfile(os.path.join(self.app.georeferencing.inputs.tree.odm_georeferencing,
"odm_georeferenced_model.ply.las")))
"odm_georeferenced_model.laz")))
class TestOrthophoto(unittest.TestCase):