def process(self, args, outputs):
tree = outputs['tree']
if outputs['large']:
if not os.path.exists(tree.submodels_path):
log.ODM_ERROR(
"We reached the merge stage, but %s folder does not exist. Something must have gone wrong at an earlier stage. Check the log and fix possible problem before restarting?" % tree.submodels_path)
exit(1)
# Merge point clouds
if args.merge in ['all', 'pointcloud']:
if not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
all_point_clouds = get_submodel_paths(tree.submodels_path, "odm_georeferencing",
"odm_georeferenced_model.laz")
try:
point_cloud.merge(all_point_clouds, tree.odm_georeferencing_model_laz)
point_cloud.post_point_cloud_steps(args, tree)
except Exception as e:
log.ODM_WARNING("Could not merge point cloud: %s (skipping)" % str(e))
else:
log.ODM_WARNING("Found merged point cloud in %s" % tree.odm_georeferencing_model_laz)
self.update_progress(25)
# Merge crop bounds
merged_bounds_file = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
if not io.file_exists(merged_bounds_file) or self.rerun():
all_bounds = get_submodel_paths(tree.submodels_path, 'odm_georeferencing',
'odm_georeferenced_model.bounds.gpkg')
log.ODM_INFO("Merging all crop bounds: %s" % all_bounds)
if len(all_bounds) > 0:
# Calculate a new crop area
# based on the convex hull of all crop areas of all submodels
# (without a buffer, otherwise we are double-cropping)
Cropper.merge_bounds(all_bounds, merged_bounds_file, 0)
else:
log.ODM_WARNING("No bounds found for any submodel.")
# Merge orthophotos
if args.merge in ['all', 'orthophoto']:
if not io.dir_exists(tree.odm_orthophoto):
system.mkdir_p(tree.odm_orthophoto)
if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
all_orthos_and_ortho_cuts = get_all_submodel_paths(tree.submodels_path,
os.path.join("odm_orthophoto",
"odm_orthophoto_feathered.tif"),
os.path.join("odm_orthophoto",
"odm_orthophoto_cut.tif"),
)
if len(all_orthos_and_ortho_cuts) > 1:
log.ODM_INFO(
"Found %s submodels with valid orthophotos and cutlines" % len(all_orthos_and_ortho_cuts))
# TODO: histogram matching via rasterio
# currently parts have different color tones
if io.file_exists(tree.odm_orthophoto_tif):
os.remove(tree.odm_orthophoto_tif)
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
orthophoto.merge(all_orthos_and_ortho_cuts, tree.odm_orthophoto_tif, orthophoto_vars)
orthophoto.post_orthophoto_steps(args, merged_bounds_file, tree.odm_orthophoto_tif,
tree.orthophoto_tiles)
elif len(all_orthos_and_ortho_cuts) == 1:
# Simply copy
log.ODM_WARNING("A single orthophoto/cutline pair was found between all submodels.")
shutil.copyfile(all_orthos_and_ortho_cuts[0][0], tree.odm_orthophoto_tif)
else:
log.ODM_WARNING(
"No orthophoto/cutline pairs were found in any of the submodels. No orthophoto will be generated.")
else:
log.ODM_WARNING("Found merged orthophoto in %s" % tree.odm_orthophoto_tif)
self.update_progress(75)
# Merge DEMs
def merge_dems(dem_filename, human_name):
if not io.dir_exists(tree.path('odm_dem')):
system.mkdir_p(tree.path('odm_dem'))
dem_file = tree.path("odm_dem", dem_filename)
if not io.file_exists(dem_file) or self.rerun():
all_dems = get_submodel_paths(tree.submodels_path, "odm_dem", dem_filename)
log.ODM_INFO("Merging %ss" % human_name)
# Merge
dem_vars = utils.get_dem_vars(args)
eu_map_source = None # Default
# Use DSM's euclidean map for DTMs
# (requires the DSM to be computed)
if human_name == "DTM":
eu_map_source = "dsm"
euclidean_merge_dems(all_dems, dem_file, dem_vars, euclidean_map_source=eu_map_source)
if io.file_exists(dem_file):
# Crop
if args.crop > 0:
Cropper.crop(merged_bounds_file, dem_file, dem_vars,
keep_original=not args.optimize_disk_space)
log.ODM_INFO("Created %s" % dem_file)
if args.tiles:
generate_dem_tiles(dem_file, tree.path("%s_tiles" % human_name.lower()),
args.max_concurrency)
else:
log.ODM_WARNING("Cannot merge %s, %s was not created" % (human_name, dem_file))
else:
log.ODM_WARNING("Found merged %s in %s" % (human_name, dem_filename))
if args.merge in ['all', 'dem'] and args.dsm:
merge_dems("dsm.tif", "DSM")
if args.merge in ['all', 'dem'] and args.dtm:
merge_dems("dtm.tif", "DTM")
self.update_progress(95)
# Merge reports
if not io.dir_exists(tree.odm_report):
system.mkdir_p(tree.odm_report)
geojson_shots = tree.path(tree.odm_report, "shots.geojson")
if not io.file_exists(geojson_shots) or self.rerun():
geojson_shots_files = get_submodel_paths(tree.submodels_path, "odm_report", "shots.geojson")
log.ODM_INFO("Merging %s shots.geojson files" % len(geojson_shots_files))
merge_geojson_shots(geojson_shots_files, geojson_shots)
else:
log.ODM_WARNING("Found merged shots.geojson in %s" % tree.odm_report)
# Stop the pipeline short! We're done.
self.next_stage = None
else:
log.ODM_INFO("Normal dataset, nothing to merge.")
self.progress = 0.0
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
# Export GCP information if available
gcp_export_file = tree.path("odm_georeferencing", "ground_control_points.gpkg")
gcp_gml_export_file = tree.path("odm_georeferencing", "ground_control_points.gml")
gcp_geojson_export_file = tree.path("odm_georeferencing", "ground_control_points.geojson")
if reconstruction.has_gcp() and (not io.file_exists(gcp_export_file) or self.rerun()):
octx = OSFMContext(tree.opensfm)
gcps = octx.ground_control_points(reconstruction.georef.proj4())
if len(gcps):
gcp_schema = {
'geometry': 'Point',
'properties': OrderedDict([
('id', 'str'),
('observations_count', 'int'),
('observations_list', 'str'),
('error_x', 'float'),
('error_y', 'float'),
('error_z', 'float'),
])
}
# Write GeoPackage
with fiona.open(gcp_export_file, 'w', driver="GPKG",
crs=fiona.crs.from_string(reconstruction.georef.proj4()),
schema=gcp_schema) as f:
for gcp in gcps:
f.write({
'geometry': {
'type': 'Point',
'coordinates': gcp['coordinates'],
},
'properties': OrderedDict([
('id', gcp['id']),
('observations_count', len(gcp['observations'])),
('observations_list', ",".join([obs['shot_id'] for obs in gcp['observations']])),
('error_x', gcp['error'][0]),
('error_y', gcp['error'][1]),
('error_z', gcp['error'][2]),
])
})
# Write GML
try:
system.run('ogr2ogr -of GML "{}" "{}"'.format(gcp_gml_export_file, gcp_export_file))
except Exception as e:
log.ODM_WARNING("Cannot generate ground control points GML file: %s" % str(e))
# Write GeoJSON
geojson = {
'type': 'FeatureCollection',
'features': []
}
from_srs = CRS.from_proj4(reconstruction.georef.proj4())
to_srs = CRS.from_epsg(4326)
transformer = location.transformer(from_srs, to_srs)
for gcp in gcps:
properties = gcp.copy()
del properties['coordinates']
geojson['features'].append({
'type': 'Feature',
'geometry': {
'type': 'Point',
'coordinates': transformer.TransformPoint(*gcp['coordinates']),
},
'properties': properties
})
with open(gcp_geojson_export_file, 'w') as f:
f.write(json.dumps(geojson, indent=4))
else:
log.ODM_WARNING("GCPs could not be loaded for writing to %s" % gcp_export_file)
if not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
cmd = ('pdal translate -i "%s" -o \"%s\"' % (tree.filtered_point_cloud, tree.odm_georeferencing_model_laz))
stages = ["ferry"]
params = [
'--filters.ferry.dimensions="views => UserData"',
'--writers.las.compression="lazip"',
]
if reconstruction.is_georeferenced():
log.ODM_INFO("Georeferencing point cloud")
stages.append("transformation")
params += [
'--filters.transformation.matrix="1 0 0 %s 0 1 0 %s 0 0 1 0 0 0 0 1"' % reconstruction.georef.utm_offset(),
'--writers.las.offset_x=%s' % reconstruction.georef.utm_east_offset,
'--writers.las.offset_y=%s' % reconstruction.georef.utm_north_offset,
'--writers.las.scale_x=0.001',
'--writers.las.scale_y=0.001',
'--writers.las.scale_z=0.001',
'--writers.las.offset_z=0',
'--writers.las.a_srs="%s"' % reconstruction.georef.proj4()
]
if reconstruction.has_gcp() and io.file_exists(gcp_gml_export_file):
log.ODM_INFO("Embedding GCP info in point cloud")
params += [
'--writers.las.vlrs="{\\\"filename\\\": \\\"%s\\\", \\\"user_id\\\": \\\"ODM_GCP\\\", \\\"description\\\": \\\"Ground Control Points (GML)\\\"}"' % gcp_gml_export_file.replace(os.sep, "/")
]
system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
self.update_progress(50)
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')
if args.fast_orthophoto:
decimation_step = 4
else:
decimation_step = 40
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(len(reconstruction.photos) / 1000) + 1
decimation_step = min(decimation_step, 95)
try:
cropper.create_bounds_gpkg(tree.odm_georeferencing_model_laz, args.crop,
decimation_step=decimation_step)
except:
log.ODM_WARNING("Cannot calculate crop bounds! We will skip cropping")
args.crop = 0
if 'boundary' in outputs and args.crop == 0:
log.ODM_INFO("Using boundary JSON as cropping area")
bounds_base, _ = os.path.splitext(tree.odm_georeferencing_model_laz)
bounds_json = bounds_base + ".bounds.geojson"
bounds_gpkg = bounds_base + ".bounds.gpkg"
export_to_bounds_files(outputs['boundary'], reconstruction.get_proj_srs(), bounds_json, bounds_gpkg)
else:
log.ODM_INFO("Converting point cloud (non-georeferenced)")
system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
point_cloud.post_point_cloud_steps(args, tree, self.rerun())
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s'
% tree.odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(tree.odm_georeferencing_model_laz) and io.file_exists(tree.filtered_point_cloud):
os.remove(tree.filtered_point_cloud)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if not io.file_exists(
tree.odm_georeferencing_model_laz) or self.rerun():
cmd = (
'pdal translate -i "%s" -o \"%s\"' %
(tree.filtered_point_cloud, tree.odm_georeferencing_model_laz))
stages = ["ferry"]
params = [
'--filters.ferry.dimensions="views => UserData"',
'--writers.las.compression="lazip"',
]
if reconstruction.is_georeferenced():
log.ODM_INFO("Georeferencing point cloud")
stages.append("transformation")
params += [
'--filters.transformation.matrix="1 0 0 %s 0 1 0 %s 0 0 1 0 0 0 0 1"'
% reconstruction.georef.utm_offset(),
'--writers.las.offset_x=%s' %
reconstruction.georef.utm_east_offset,
'--writers.las.offset_y=%s' %
reconstruction.georef.utm_north_offset,
'--writers.las.offset_z=0',
'--writers.las.a_srs="%s"' % reconstruction.georef.proj4()
]
system.run(cmd + ' ' + ' '.join(stages) + ' ' +
' '.join(params))
self.update_progress(50)
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')
if args.fast_orthophoto:
decimation_step = 10
else:
decimation_step = 40
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(
len(reconstruction.photos) / 1000) + 1
decimation_step = min(decimation_step, 95)
try:
cropper.create_bounds_gpkg(
tree.odm_georeferencing_model_laz,
args.crop,
decimation_step=decimation_step)
except:
log.ODM_WARNING(
"Cannot calculate crop bounds! We will skip cropping"
)
args.crop = 0
else:
log.ODM_INFO("Converting point cloud (non-georeferenced)")
system.run(cmd + ' ' + ' '.join(stages) + ' ' +
' '.join(params))
point_cloud.post_point_cloud_steps(args, tree, self.rerun())
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s' %
tree.odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(
tree.odm_georeferencing_model_laz) and io.file_exists(
tree.filtered_point_cloud):
os.remove(tree.filtered_point_cloud)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
doPointCloudGeo = True
transformPointCloud = True
verbose = '-verbose' if self.params.get('verbose') else ''
class nonloc:
runs = []
def add_run(primary=True, band=None):
subdir = ""
if not primary and band is not None:
subdir = band
# Make sure 2.5D mesh is georeferenced before the 3D mesh
# Because it will be used to calculate a transform
# for the point cloud. If we use the 3D model transform,
# DEMs and orthophoto might not align!
if not args.use_3dmesh:
nonloc.runs += [{
'georeferencing_dir':
os.path.join(tree.odm_25dgeoreferencing, subdir),
'texturing_dir':
os.path.join(tree.odm_25dtexturing, subdir),
}]
if not args.skip_3dmodel and (primary or args.use_3dmesh):
nonloc.runs += [{
'georeferencing_dir':
tree.odm_georeferencing,
'texturing_dir':
os.path.join(tree.odm_texturing, subdir),
}]
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band['name'] == get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
add_run(primary, band['name'].lower())
else:
add_run()
progress_per_run = 100.0 / len(nonloc.runs)
progress = 0.0
for r in nonloc.runs:
if not io.dir_exists(r['georeferencing_dir']):
system.mkdir_p(r['georeferencing_dir'])
odm_georeferencing_model_obj_geo = os.path.join(
r['texturing_dir'], tree.odm_georeferencing_model_obj_geo)
odm_georeferencing_model_obj = os.path.join(
r['texturing_dir'], tree.odm_textured_model_obj)
odm_georeferencing_log = os.path.join(r['georeferencing_dir'],
tree.odm_georeferencing_log)
odm_georeferencing_transform_file = os.path.join(
r['georeferencing_dir'],
tree.odm_georeferencing_transform_file)
odm_georeferencing_model_txt_geo_file = os.path.join(
r['georeferencing_dir'], tree.odm_georeferencing_model_txt_geo)
if not io.file_exists(odm_georeferencing_model_obj_geo) or \
not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
# odm_georeference definitions
kwargs = {
'bin': context.odm_modules_path,
'input_pc_file': tree.filtered_point_cloud,
'bundle': tree.opensfm_bundle,
'imgs': tree.dataset_raw,
'imgs_list': tree.opensfm_bundle_list,
'model': odm_georeferencing_model_obj,
'log': odm_georeferencing_log,
'input_trans_file': tree.opensfm_transformation,
'transform_file': odm_georeferencing_transform_file,
'coords': tree.odm_georeferencing_coords,
'output_pc_file': tree.odm_georeferencing_model_laz,
'geo_sys': odm_georeferencing_model_txt_geo_file,
'model_geo': odm_georeferencing_model_obj_geo,
'verbose': verbose
}
if transformPointCloud:
kwargs[
'pc_params'] = '-inputPointCloudFile {input_pc_file} -outputPointCloudFile {output_pc_file}'.format(
**kwargs)
if reconstruction.is_georeferenced():
kwargs[
'pc_params'] += ' -outputPointCloudSrs %s' % pipes.quote(
reconstruction.georef.proj4())
else:
log.ODM_WARNING(
'NO SRS: The output point cloud will not have a SRS.'
)
else:
kwargs['pc_params'] = ''
if io.file_exists(
tree.opensfm_transformation) and io.file_exists(
tree.odm_georeferencing_coords):
log.ODM_INFO(
'Running georeferencing with OpenSfM transformation matrix'
)
system.run(
'{bin}/odm_georef -bundleFile {bundle} -inputTransformFile {input_trans_file} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'
.format(**kwargs))
elif io.file_exists(tree.odm_georeferencing_coords):
log.ODM_INFO(
'Running georeferencing with generated coords file.')
system.run(
'{bin}/odm_georef -bundleFile {bundle} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'
.format(**kwargs))
else:
log.ODM_WARNING(
'Georeferencing failed. Make sure your '
'photos have geotags in the EXIF or you have '
'provided a GCP file. ')
doPointCloudGeo = False # skip the rest of the georeferencing
if doPointCloudGeo:
reconstruction.georef.extract_offsets(
odm_georeferencing_model_txt_geo_file)
point_cloud.post_point_cloud_steps(args, tree)
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')
if args.fast_orthophoto:
decimation_step = 10
else:
decimation_step = 40
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(
len(reconstruction.photos) / 1000) + 1
decimation_step = min(decimation_step, 95)
try:
cropper.create_bounds_gpkg(
tree.odm_georeferencing_model_laz,
args.crop,
decimation_step=decimation_step)
except:
log.ODM_WARNING(
"Cannot calculate crop bounds! We will skip cropping"
)
args.crop = 0
# Do not execute a second time, since
# We might be doing georeferencing for
# multiple models (3D, 2.5D, ...)
doPointCloudGeo = False
transformPointCloud = False
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s' %
tree.odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(
tree.odm_georeferencing_model_laz) and io.file_exists(
tree.filtered_point_cloud):
os.remove(tree.filtered_point_cloud)
progress += progress_per_run
self.update_progress(progress)
def process(args, tree, reconstruction, current_path):
odm_orthophoto = io.join_paths(current_path, 'orthophoto')
odm_orthophoto_path = odm_orthophoto
odm_orthophoto_render = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_render.tif')
odm_orthophoto_tif = io.join_paths(odm_orthophoto_path, 'odm_orthophoto.tif')
odm_orthophoto_corners = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_corners.tif')
odm_orthophoto_log = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_log.tif')
odm_orthophoto_tif_log = io.join_paths(odm_orthophoto_path, 'gdal_translate_log.txt')
odm_25dgeoreferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(
odm_georeferencing, 'coords.txt')
odm_georeferencing_gcp = io.find('gcp_list.txt', current_path)
odm_georeferencing_gcp_utm = io.join_paths(odm_georeferencing, 'gcp_list_utm.txt')
odm_georeferencing_utm_log = io.join_paths(
odm_georeferencing, 'odm_georeferencing_utm_log.txt')
odm_georeferencing_log = 'odm_georeferencing_log.txt'
odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
odm_georeferencing_proj = 'proj.txt'
odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
odm_georeferencing_xyz_file = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.csv')
odm_georeferencing_las_json = io.join_paths(
odm_georeferencing, 'las.json')
odm_georeferencing_model_laz = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.laz')
odm_georeferencing_model_las = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.las')
odm_georeferencing_dem = io.join_paths(
odm_georeferencing, 'odm_georeferencing_model_dem.tif')
opensfm_reconstruction = io.join_paths(current_path,'reconstruction.json')
odm_texturing = io.join_paths(current_path,'mvs')
odm_textured_model_obj = io.join_paths(odm_texturing, 'odm_textured_model.obj')
images_dir = io.join_paths(current_path, 'images')
tree = tree
opensfm_bundle = os.path.join(current_path, 'opensfm', 'bundle_r000.out')
opensfm_bundle_list = os.path.join(current_path, 'opensfm', 'list_r000.out')
opensfm_transformation = os.path.join(current_path, 'opensfm', 'geocoords_transformation.txt')
filtered_point_cloud = os.path.join(current_path, 'filterpoints', 'point_cloud.ply')
doPointCloudGeo = True
transformPointCloud = True
verbose =''
class nonloc:
runs = []
def add_run(primary=True, band=None):
subdir = ""
if not primary and band is not None:
subdir = band
# Make sure 2.5D mesh is georeferenced before the 3D mesh
# Because it will be used to calculate a transform
# for the point cloud. If we use the 3D model transform,
# DEMs and orthophoto might not align!
b = True
if b:
nonloc.runs += [{
'georeferencing_dir': os.path.join(odm_georeferencing, subdir),
'texturing_dir': os.path.join(odm_texturing, subdir),
}]
if not args.skip_3dmodel and (primary or args.use_3dmesh):
nonloc.runs += [{
'georeferencing_dir': odm_georeferencing,
'texturing_dir': os.path.join(odm_texturing, subdir),
}]
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band == reconstruction.multi_camera[0]
add_run(primary, band['name'].lower())
else:
add_run()
#progress_per_run = 100.0 / len(nonloc.runs)
#progress = 0.0
for r in nonloc.runs:
if not io.dir_exists(r['georeferencing_dir']):
system.mkdir_p(r['georeferencing_dir'])
odm_georeferencing_model_obj_geo = os.path.join(r['texturing_dir'], odm_georeferencing_model_obj_geo)
odm_georeferencing_model_obj = os.path.join(r['texturing_dir'], odm_textured_model_obj)
odm_georeferencing_log = os.path.join(r['georeferencing_dir'], odm_georeferencing_log)
odm_georeferencing_transform_file = os.path.join(r['georeferencing_dir'], odm_georeferencing_transform_file)
odm_georeferencing_model_txt_geo_file = os.path.join(r['georeferencing_dir'], odm_georeferencing_model_txt_geo)
pio = True
if pio:
#if not io.file_exists(odm_georeferencing_model_obj_geo) or \
#not io.file_exists(odm_georeferencing_model_laz)
# odm_georeference definitions
kwargs = {
'bin': context.odm_modules_path,
'input_pc_file': filtered_point_cloud,
'bundle': opensfm_bundle,
'imgs': images_dir,
'imgs_list': opensfm_bundle_list,
'model': odm_georeferencing_model_obj,
'log': odm_georeferencing_log,
'input_trans_file': opensfm_transformation,
'transform_file': odm_georeferencing_transform_file,
'coords': odm_georeferencing_coords,
'output_pc_file': odm_georeferencing_model_laz,
'geo_sys': odm_georeferencing_model_txt_geo_file,
'model_geo': odm_georeferencing_model_obj_geo,
'verbose': verbose
}
if transformPointCloud:
kwargs['pc_params'] = '-inputPointCloudFile {input_pc_file} -outputPointCloudFile {output_pc_file}'.format(**kwargs)
if reconstruction.is_georeferenced():
kwargs['pc_params'] += ' -outputPointCloudSrs %s' % pipes.quote(reconstruction.georef.proj4())
else:
log.ODM_WARNING('NO SRS: The output point cloud will not have a SRS.')
else:
kwargs['pc_params'] = ''
if io.file_exists(opensfm_transformation) and io.file_exists(odm_georeferencing_coords):
log.ODM_INFO('Running georeferencing with OpenSfM transformation matrix')
system.run('{bin}/odm_georef -bundleFile {bundle} -inputTransformFile {input_trans_file} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'.format(**kwargs))
elif io.file_exists(odm_georeferencing_coords):
print('running georeferencing')
log.ODM_INFO('Running georeferencing with generated coords file.')
system.run('{bin}/odm_georef -bundleFile {bundle} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'.format(**kwargs))
else:
log.ODM_WARNING('Georeferencing failed. Make sure your '
'photos have geotags in the EXIF or you have '
'provided a GCP file. ')
doPointCloudGeo = False # skip the rest of the georeferencing
if doPointCloudGeo:
reconstruction.georef.extract_offsets(odm_georeferencing_model_txt_geo_file)
point_cloud.post_point_cloud_steps(args, tree)
if args.crop > 0:
log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
cropper = Cropper(odm_georeferencing, 'odm_georeferenced_model')
decimation_step = 40 if args.fast_orthophoto or args.use_opensfm_dense else 90
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(len(reconstruction.photos) / 1000) + 1
cropper.create_bounds_gpkg(odm_georeferencing_model_laz, args.crop,
decimation_step=decimation_step)
# Do not execute a second time, since
# We might be doing georeferencing for
# multiple models (3D, 2.5D, ...)
doPointCloudGeo = False
transformPointCloud = False
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s'
% odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(odm_georeferencing_model_laz) and io.file_exists(filtered_point_cloud):
os.remove(filtered_point_cloud)
