def create_25dmesh(inPointCloud,
outMesh,
dsm_radius=0.07,
dsm_resolution=0.05,
depth=8,
samples=1,
maxVertexCount=100000,
verbose=False,
available_cores=None,
method='gridded'):
# Create DSM from point cloud
# Create temporary directory
mesh_directory = os.path.dirname(outMesh)
tmp_directory = os.path.join(mesh_directory, 'tmp')
if os.path.exists(tmp_directory):
shutil.rmtree(tmp_directory)
os.mkdir(tmp_directory)
log.ODM_INFO('Created temporary directory: %s' % tmp_directory)
radius_steps = [dsm_radius]
log.ODM_INFO('Creating DSM for 2.5D mesh')
commands.create_dem(inPointCloud,
'mesh_dsm',
output_type='max',
radiuses=map(str, radius_steps),
gapfill=True,
outdir=tmp_directory,
resolution=dsm_resolution,
verbose=verbose,
max_workers=available_cores)
if method == 'gridded':
mesh = dem_to_mesh_gridded(os.path.join(tmp_directory, 'mesh_dsm.tif'),
outMesh, maxVertexCount, verbose)
elif method == 'poisson':
dsm_points = dem_to_points(
os.path.join(tmp_directory, 'mesh_dsm.tif'),
os.path.join(tmp_directory, 'dsm_points.ply'), verbose)
mesh = screened_poisson_reconstruction(dsm_points,
outMesh,
depth=depth,
samples=samples,
maxVertexCount=maxVertexCount,
threads=available_cores,
verbose=verbose)
else:
raise 'Not a valid method: ' + method
# Cleanup tmp
if os.path.exists(tmp_directory):
shutil.rmtree(tmp_directory)
return mesh
def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05, depth=8, samples=1, maxVertexCount=100000, verbose=False, available_cores=None, method='gridded'):
# Create DSM from point cloud
# Create temporary directory
mesh_directory = os.path.dirname(outMesh)
tmp_directory = os.path.join(mesh_directory, 'tmp')
if os.path.exists(tmp_directory):
shutil.rmtree(tmp_directory)
os.mkdir(tmp_directory)
log.ODM_INFO('Created temporary directory: %s' % tmp_directory)
radius_steps = [dsm_radius]
log.ODM_INFO('Creating DSM for 2.5D mesh')
commands.create_dem(
inPointCloud,
'mesh_dsm',
output_type='max',
radiuses=map(str, radius_steps),
gapfill=True,
outdir=tmp_directory,
resolution=dsm_resolution,
verbose=verbose,
max_workers=available_cores
)
if method == 'gridded':
mesh = dem_to_mesh_gridded(os.path.join(tmp_directory, 'mesh_dsm.tif'), outMesh, maxVertexCount, verbose)
elif method == 'poisson':
dsm_points = dem_to_points(os.path.join(tmp_directory, 'mesh_dsm.tif'), os.path.join(tmp_directory, 'dsm_points.ply'), verbose)
mesh = screened_poisson_reconstruction(dsm_points, outMesh, depth=depth,
samples=samples,
maxVertexCount=maxVertexCount,
threads=available_cores,
verbose=verbose)
else:
raise 'Not a valid method: ' + method
# Cleanup tmp
if os.path.exists(tmp_directory):
shutil.rmtree(tmp_directory)
return mesh
def process(self, args, outputs):
tree = outputs['tree']
las_model_found = io.file_exists(tree.odm_georeferencing_model_laz)
log.ODM_INFO('Classify: ' + str(args.pc_classify))
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_laz, str(las_model_found)))
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify and las_model_found:
pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or self.rerun():
log.ODM_INFO("Classifying {} using Simple Morphological Filter".format(tree.odm_georeferencing_model_laz))
commands.classify(tree.odm_georeferencing_model_laz,
args.smrf_scalar,
args.smrf_slope,
args.smrf_threshold,
args.smrf_window,
verbose=args.verbose
)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: smrf\n')
f.write('Scalar: {}\n'.format(args.smrf_scalar))
f.write('Slope: {}\n'.format(args.smrf_slope))
f.write('Threshold: {}\n'.format(args.smrf_threshold))
f.write('Window: {}\n'.format(args.smrf_window))
progress = 20
self.update_progress(progress)
# Do we need to process anything here?
if (args.dsm or args.dtm) and las_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
self.rerun():
products = []
if args.dsm: products.append('dsm')
if args.dtm: products.append('dtm')
resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_error_estimate=-3, ignore_gsd=args.ignore_gsd)
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dem(
tree.odm_georeferencing_model_laz,
product,
output_type='idw' if product == 'dtm' else 'max',
radiuses=map(str, radius_steps),
gapfill=args.dem_gapfill_steps > 0,
outdir=odm_dem_root,
resolution=resolution / 100.0,
decimation=args.dem_decimation,
verbose=args.verbose,
max_workers=args.max_concurrency,
keep_unfilled_copy=args.dem_euclidean_map
)
dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product))
bounds_file_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
if args.crop > 0:
# Crop DEM
Cropper.crop(bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args))
if args.dem_euclidean_map:
unfilled_dem_path = io.related_file_path(dem_geotiff_path, postfix=".unfilled")
if args.crop > 0:
# Crop unfilled DEM
Cropper.crop(bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args))
commands.compute_euclidean_map(unfilled_dem_path,
io.related_file_path(dem_geotiff_path, postfix=".euclideand"),
overwrite=True)
progress += 30
self.update_progress(progress)
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
default=3,
type=int,
help='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: %(default)s')
args = parser.parse_args()
if not os.path.exists(args.point_cloud):
print("%s does not exist" % args.point_cloud)
exit(1)
outdir = os.path.dirname(args.point_cloud)
radius_steps = [args.resolution / 2.0]
for _ in range(args.gapfill_steps - 1):
radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value?
commands.create_dem(args.point_cloud,
args.type,
output_type='idw' if args.type == 'dtm' else 'max',
radiuses=list(map(str, radius_steps)),
gapfill=args.gapfill_steps > 0,
outdir=outdir,
resolution=args.resolution,
decimation=1,
verbose=True,
max_workers=multiprocessing.cpu_count(),
keep_unfilled_copy=False
)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
dem_input = tree.odm_georeferencing_model_laz
pc_model_found = io.file_exists(dem_input)
ignore_resolution = False
pseudo_georeference = False
if not reconstruction.is_georeferenced():
# Special case to clear previous run point cloud
# (NodeODM will generate a fake georeferenced laz during postprocessing
# with non-georeferenced datasets). odm_georeferencing_model_laz should
# not be here! Perhaps we should improve this.
if io.file_exists(
tree.odm_georeferencing_model_laz) and self.rerun():
os.remove(tree.odm_georeferencing_model_laz)
log.ODM_WARNING(
"Not georeferenced, using ungeoreferenced point cloud...")
dem_input = tree.path("odm_filterpoints", "point_cloud.ply")
pc_model_found = io.file_exists(dem_input)
ignore_resolution = True
pseudo_georeference = True
resolution = gsd.cap_resolution(args.dem_resolution,
tree.opensfm_reconstruction,
gsd_error_estimate=-3,
ignore_gsd=args.ignore_gsd,
ignore_resolution=ignore_resolution,
has_gcp=reconstruction.has_gcp())
log.ODM_INFO('Classify: ' + str(args.pc_classify))
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(
dem_input, str(pc_model_found)))
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify and pc_model_found:
pc_classify_marker = os.path.join(odm_dem_root,
'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or self.rerun():
log.ODM_INFO(
"Classifying {} using Simple Morphological Filter".format(
dem_input))
commands.classify(dem_input,
args.smrf_scalar,
args.smrf_slope,
args.smrf_threshold,
args.smrf_window,
verbose=args.verbose)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: smrf\n')
f.write('Scalar: {}\n'.format(args.smrf_scalar))
f.write('Slope: {}\n'.format(args.smrf_slope))
f.write('Threshold: {}\n'.format(args.smrf_threshold))
f.write('Window: {}\n'.format(args.smrf_window))
progress = 20
self.update_progress(progress)
if args.pc_rectify:
commands.rectify(dem_input, args.debug)
# Do we need to process anything here?
if (args.dsm or args.dtm) and pc_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
self.rerun():
products = []
if args.dsm or (args.dtm and args.dem_euclidean_map):
products.append('dsm')
if args.dtm: products.append('dtm')
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(
radius_steps[-1] *
2) # 2 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dem(
dem_input,
product,
output_type='idw' if product == 'dtm' else 'max',
radiuses=map(str, radius_steps),
gapfill=args.dem_gapfill_steps > 0,
outdir=odm_dem_root,
resolution=resolution / 100.0,
decimation=args.dem_decimation,
verbose=args.verbose,
max_workers=args.max_concurrency,
keep_unfilled_copy=args.dem_euclidean_map)
dem_geotiff_path = os.path.join(odm_dem_root,
"{}.tif".format(product))
bounds_file_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.gpkg')
if args.crop > 0:
# Crop DEM
Cropper.crop(bounds_file_path, dem_geotiff_path,
utils.get_dem_vars(args))
if args.dem_euclidean_map:
unfilled_dem_path = io.related_file_path(
dem_geotiff_path, postfix=".unfilled")
if args.crop > 0:
# Crop unfilled DEM
Cropper.crop(bounds_file_path, unfilled_dem_path,
utils.get_dem_vars(args))
commands.compute_euclidean_map(
unfilled_dem_path,
io.related_file_path(dem_geotiff_path,
postfix=".euclideand"),
overwrite=True)
if pseudo_georeference:
# 0.1 is arbitrary
pseudogeo.add_pseudo_georeferencing(
dem_geotiff_path, 0.1)
progress += 30
self.update_progress(progress)
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
def create_25dmesh(inPointCloud,
outMesh,
dsm_radius=0.07,
dsm_resolution=0.05,
depth=8,
samples=1,
maxVertexCount=100000,
verbose=False,
available_cores=None,
method='gridded',
smooth_dsm=True):
# Create DSM from point cloud
# Create temporary directory
mesh_directory = os.path.dirname(outMesh)
tmp_directory = os.path.join(mesh_directory, 'tmp')
if os.path.exists(tmp_directory):
shutil.rmtree(tmp_directory)
os.mkdir(tmp_directory)
log.ODM_INFO('Created temporary directory: %s' % tmp_directory)
radius_steps = [dsm_radius]
for _ in range(2):
radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary
log.ODM_INFO('Creating DSM for 2.5D mesh')
commands.create_dem(inPointCloud,
'mesh_dsm',
output_type='max',
radiuses=list(map(str, radius_steps)),
gapfill=True,
outdir=tmp_directory,
resolution=dsm_resolution,
verbose=verbose,
max_workers=available_cores,
apply_smoothing=smooth_dsm)
if method == 'gridded':
mesh = dem_to_mesh_gridded(os.path.join(tmp_directory, 'mesh_dsm.tif'),
outMesh,
maxVertexCount,
verbose,
maxConcurrency=max(1, available_cores))
elif method == 'poisson':
dsm_points = dem_to_points(
os.path.join(tmp_directory, 'mesh_dsm.tif'),
os.path.join(tmp_directory, 'dsm_points.ply'), verbose)
mesh = screened_poisson_reconstruction(
dsm_points,
outMesh,
depth=depth,
samples=samples,
maxVertexCount=maxVertexCount,
threads=max(
1, available_cores - 1
), # poissonrecon can get stuck on some machines if --threads == all cores
verbose=verbose)
else:
raise 'Not a valid method: ' + method
# Cleanup tmp
if os.path.exists(tmp_directory):
shutil.rmtree(tmp_directory)
return mesh
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
dem_input = tree.odm_georeferencing_model_laz
pc_model_found = io.file_exists(dem_input)
ignore_resolution = False
pseudo_georeference = False
if not reconstruction.is_georeferenced():
log.ODM_WARNING(
"Not georeferenced, using ungeoreferenced point cloud...")
ignore_resolution = True
pseudo_georeference = True
# It is probably not reasonable to have accurate DEMs a the same resolution as the source photos, so reduce it
# by a factor!
gsd_scaling = 2.0
resolution = gsd.cap_resolution(args.dem_resolution,
tree.opensfm_reconstruction,
gsd_scaling=gsd_scaling,
ignore_gsd=args.ignore_gsd,
ignore_resolution=ignore_resolution
and args.ignore_gsd,
has_gcp=reconstruction.has_gcp())
log.ODM_INFO('Classify: ' + str(args.pc_classify))
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(
dem_input, str(pc_model_found)))
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify and pc_model_found:
pc_classify_marker = os.path.join(odm_dem_root,
'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or self.rerun():
log.ODM_INFO(
"Classifying {} using Simple Morphological Filter".format(
dem_input))
commands.classify(dem_input,
args.smrf_scalar,
args.smrf_slope,
args.smrf_threshold,
args.smrf_window,
verbose=args.verbose)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: smrf\n')
f.write('Scalar: {}\n'.format(args.smrf_scalar))
f.write('Slope: {}\n'.format(args.smrf_slope))
f.write('Threshold: {}\n'.format(args.smrf_threshold))
f.write('Window: {}\n'.format(args.smrf_window))
progress = 20
self.update_progress(progress)
if args.pc_rectify:
commands.rectify(dem_input, args.debug)
# Do we need to process anything here?
if (args.dsm or args.dtm) and pc_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
self.rerun():
products = []
if args.dsm or (args.dtm and args.dem_euclidean_map):
products.append('dsm')
if args.dtm: products.append('dtm')
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(
radius_steps[-1] *
2) # 2 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dem(
dem_input,
product,
output_type='idw' if product == 'dtm' else 'max',
radiuses=list(map(str, radius_steps)),
gapfill=args.dem_gapfill_steps > 0,
outdir=odm_dem_root,
resolution=resolution / 100.0,
decimation=args.dem_decimation,
verbose=args.verbose,
max_workers=args.max_concurrency,
keep_unfilled_copy=args.dem_euclidean_map)
dem_geotiff_path = os.path.join(odm_dem_root,
"{}.tif".format(product))
bounds_file_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.gpkg')
if args.crop > 0 or args.boundary:
# Crop DEM
Cropper.crop(
bounds_file_path,
dem_geotiff_path,
utils.get_dem_vars(args),
keep_original=not args.optimize_disk_space)
if args.dem_euclidean_map:
unfilled_dem_path = io.related_file_path(
dem_geotiff_path, postfix=".unfilled")
if args.crop > 0 or args.boundary:
# Crop unfilled DEM
Cropper.crop(
bounds_file_path,
unfilled_dem_path,
utils.get_dem_vars(args),
keep_original=not args.optimize_disk_space)
commands.compute_euclidean_map(
unfilled_dem_path,
io.related_file_path(dem_geotiff_path,
postfix=".euclideand"),
overwrite=True)
if pseudo_georeference:
pseudogeo.add_pseudo_georeferencing(dem_geotiff_path)
if args.tiles:
generate_dem_tiles(dem_geotiff_path,
tree.path("%s_tiles" % product),
args.max_concurrency)
if args.cog:
convert_to_cogeo(dem_geotiff_path,
max_workers=args.max_concurrency)
progress += 30
self.update_progress(progress)
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM DEM Cell')
# get inputs
args = self.inputs.args
tree = self.inputs.tree
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
args.rerun == 'odm_dem') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_dem' in args.rerun_from)
log.ODM_INFO('Classify: ' + str(args.pc_classify))
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_laz, str(las_model_found)))
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify and las_model_found:
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 Simple Morphological Filter".format(
tree.odm_georeferencing_model_laz))
commands.classify(tree.odm_georeferencing_model_laz,
args.smrf_scalar,
args.smrf_slope,
args.smrf_threshold,
args.smrf_window,
verbose=args.verbose)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: smrf\n')
f.write('Scalar: {}\n'.format(args.smrf_scalar))
f.write('Slope: {}\n'.format(args.smrf_slope))
f.write('Threshold: {}\n'.format(args.smrf_threshold))
f.write('Window: {}\n'.format(args.smrf_window))
# Do we need to process anything here?
if (args.dsm or args.dtm) and las_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
rerun_cell:
products = []
if args.dsm: products.append('dsm')
if args.dtm: products.append('dtm')
resolution = gsd.cap_resolution(args.dem_resolution,
tree.opensfm_reconstruction,
gsd_error_estimate=-3,
ignore_gsd=args.ignore_gsd)
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(
radius_steps[-1] *
2) # 2 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dem(
tree.odm_georeferencing_model_laz,
product,
output_type='idw' if product == 'dtm' else 'max',
radiuses=map(str, radius_steps),
gapfill=args.dem_gapfill_steps > 0,
outdir=odm_dem_root,
resolution=resolution / 100.0,
decimation=args.dem_decimation,
verbose=args.verbose,
max_workers=args.max_concurrency)
if args.crop > 0:
bounds_shapefile_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.shp')
if os.path.exists(bounds_shapefile_path):
Cropper.crop(
bounds_shapefile_path,
os.path.join(odm_dem_root,
"{}.tif".format(product)),
{
'TILED': 'YES',
'COMPRESS': 'LZW',
'BLOCKXSIZE': 512,
'BLOCKYSIZE': 512,
'NUM_THREADS': self.params.max_concurrency
})
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Dem')
log.ODM_INFO('Running ODM DEM Cell - Finished')
return ecto.OK if args.end_with != 'odm_dem' else ecto.QUIT
