def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if not os.path.exists(tree.odm_filterpoints):
system.mkdir_p(tree.odm_filterpoints)
# check if reconstruction was done before
if not io.file_exists(tree.filtered_point_cloud) or self.rerun():
if args.fast_orthophoto:
inputPointCloud = os.path.join(tree.opensfm,
'reconstruction.ply')
elif args.use_opensfm_dense:
inputPointCloud = tree.opensfm_model
else:
inputPointCloud = tree.mve_model
point_cloud.filter(inputPointCloud,
tree.filtered_point_cloud,
standard_deviation=args.pc_filter,
confidence=None,
sample_radius=args.pc_sample,
verbose=args.verbose)
else:
log.ODM_WARNING('Found a valid point cloud file in: %s' %
tree.filtered_point_cloud)
if args.optimize_disk_space:
os.remove(inputPointCloud)
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM FilterPoints Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_filterpoints') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_filterpoints' in args.rerun_from)
if not os.path.exists(tree.odm_filterpoints):
system.mkdir_p(tree.odm_filterpoints)
# check if reconstruction was done before
if not io.file_exists(tree.filtered_point_cloud) or rerun_cell:
if args.fast_orthophoto:
inputPointCloud = os.path.join(tree.opensfm,
'reconstruction.ply')
elif args.use_opensfm_dense:
inputPointCloud = tree.opensfm_model
else:
inputPointCloud = tree.mve_model
confidence = None
if not args.use_opensfm_dense and not args.fast_orthophoto:
confidence = args.mve_confidence
point_cloud.filter(inputPointCloud,
tree.filtered_point_cloud,
standard_deviation=args.pc_filter,
confidence=confidence,
verbose=args.verbose)
else:
log.ODM_WARNING('Found a valid point cloud file in: %s' %
tree.filtered_point_cloud)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'MVE')
log.ODM_INFO('Running ODM FilterPoints Cell - Finished')
return ecto.OK if args.end_with != 'odm_filterpoints' else ecto.QUIT
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM FilterPoints Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_filterpoints') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_filterpoints' in args.rerun_from)
if not os.path.exists(tree.odm_filterpoints): system.mkdir_p(tree.odm_filterpoints)
# check if reconstruction was done before
if not io.file_exists(tree.filtered_point_cloud) or rerun_cell:
if args.fast_orthophoto:
inputPointCloud = os.path.join(tree.opensfm, 'reconstruction.ply')
elif args.use_opensfm_dense:
inputPointCloud = tree.opensfm_model
else:
inputPointCloud = tree.mve_model
confidence = None
if not args.use_opensfm_dense and not args.fast_orthophoto:
confidence = args.mve_confidence
point_cloud.filter(inputPointCloud, tree.filtered_point_cloud, standard_deviation=args.pc_filter, confidence=confidence, verbose=args.verbose)
else:
log.ODM_WARNING('Found a valid point cloud file in: %s' %
tree.filtered_point_cloud)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'MVE')
log.ODM_INFO('Running ODM FilterPoints Cell - Finished')
return ecto.OK if args.end_with != 'odm_filterpoints' else ecto.QUIT
def filter_points(odm_filterpoints_folder, mve_model, filter_points_ply_file,
max_concurrency):
if not os.path.exists(odm_filterpoints_folder):
system.mkdir_p(odm_filterpoints_folder)
pc_filter = 2.5
pc_sample = 0
verbose = False
# check if reconstruction was done before
if not io.file_exists(filter_points_ply_file):
inputPointCloud = mve_model
point_cloud.filter(inputPointCloud,
filter_points_ply_file,
standard_deviation=pc_filter,
sample_radius=pc_sample,
verbose=verbose,
max_concurrency=max_concurrency)
else:
log.ODM_WARNING('Found a valid point cloud file in: %s' %
filter_points_ply_file)
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running SMVS Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start SMVS')
return ecto.QUIT
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'smvs') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'smvs' in args.rerun_from)
# check if reconstruction was done before
if not io.file_exists(tree.smvs_model) or rerun_cell:
# cleanup if a rerun
if io.dir_exists(tree.mve_path) and rerun_cell:
shutil.rmtree(tree.mve_path)
# make bundle directory
if not io.file_exists(tree.mve_bundle):
system.mkdir_p(tree.mve_path)
system.mkdir_p(io.join_paths(tree.mve_path, 'bundle'))
io.copy(tree.opensfm_image_list, tree.mve_image_list)
io.copy(tree.opensfm_bundle, tree.mve_bundle)
# mve makescene wants the output directory
# to not exists before executing it (otherwise it
# will prompt the user for confirmation)
if io.dir_exists(tree.smvs):
shutil.rmtree(tree.smvs)
# run mve makescene
if not io.dir_exists(tree.mve_views):
system.run('%s %s %s' % (context.makescene_path, tree.mve_path, tree.smvs))
# config
config = [
"-t%s" % self.params.threads,
"-a%s" % self.params.alpha,
"--max-pixels=%s" % int(self.params.max_pixels),
"-o%s" % self.params.output_scale,
"--debug-lvl=%s" % ('1' if self.params.verbose else '0'),
"%s" % '-S' if self.params.shading else '',
"%s" % '-g' if self.params.gamma_srgb and self.params.shading else '',
"--force" if rerun_cell else ''
]
# run smvs
system.run('%s %s %s' % (context.smvs_path, ' '.join(config), tree.smvs))
# find and rename the output file for simplicity
smvs_files = glob.glob(os.path.join(tree.smvs, 'smvs-*'))
smvs_files.sort(key=os.path.getmtime) # sort by last modified date
if len(smvs_files) > 0:
old_file = smvs_files[-1]
if not (io.rename_file(old_file, tree.smvs_model)):
log.ODM_WARNING("File %s does not exist, cannot be renamed. " % old_file)
# Filter
point_cloud.filter(tree.smvs_model, standard_deviation=args.pc_filter, verbose=args.verbose)
else:
log.ODM_WARNING("Cannot find a valid point cloud (smvs-XX.ply) in %s. Check the console output for errors." % tree.smvs)
else:
log.ODM_WARNING('Found a valid SMVS reconstruction file in: %s' %
tree.smvs_model)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'SMVS')
log.ODM_INFO('Running ODM SMVS Cell - Finished')
return ecto.OK if args.end_with != 'smvs' else ecto.QUIT
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM OpenSfM Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
return ecto.QUIT
# create working directories
system.mkdir_p(tree.opensfm)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'opensfm') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'opensfm' in args.rerun_from)
if args.fast_orthophoto:
output_file = io.join_paths(tree.opensfm, 'reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
# check if reconstruction was done before
if not io.file_exists(output_file) or rerun_cell:
# create file list
list_path = io.join_paths(tree.opensfm, 'image_list.txt')
has_alt = True
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
fout.write('%s\n' %
io.join_paths(tree.dataset_raw, photo.filename))
# create config file for OpenSfM
config = [
"use_exif_size: %s" %
('no' if not self.params.use_exif_size else 'yes'),
"feature_process_size: %s" % self.params.feature_process_size,
"feature_min_frames: %s" % self.params.feature_min_frames,
"processes: %s" % self.params.processes,
"matching_gps_neighbors: %s" %
self.params.matching_gps_neighbors,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.depthmap_resolution,
"depthmap_min_patch_sd: %s" %
args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" %
args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" %
('no' if self.params.fixed_camera_params else 'yes')
]
if has_alt:
log.ODM_DEBUG(
"Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
config.append("align_method: naive")
else:
config.append("align_method: orientation_prior")
config.append("align_orientation_prior: vertical")
if args.use_hybrid_bundle_adjustment:
log.ODM_DEBUG("Enabling hybrid bundle adjustment")
config.append(
"bundle_interval: 100"
) # Bundle after adding 'bundle_interval' cameras
config.append(
"bundle_new_points_ratio: 1.2"
) # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append(
"local_bundle_radius: 1"
) # Max image graph distance for images to be included in local bundle adjustment
if args.matcher_distance > 0:
config.append("matching_gps_distance: %s" %
self.params.matching_gps_distance)
if tree.odm_georeferencing_gcp:
config.append("bundle_use_gcp: yes")
io.copy(tree.odm_georeferencing_gcp, tree.opensfm)
# write config file
log.ODM_DEBUG(config)
config_filename = io.join_paths(tree.opensfm, 'config.yaml')
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
# run OpenSfM reconstruction
matched_done_file = io.join_paths(tree.opensfm,
'matching_done.txt')
if not io.file_exists(matched_done_file) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm extract_metadata %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm detect_features %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm match_features %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
with open(matched_done_file, 'w') as fout:
fout.write("Matching done!\n")
else:
log.ODM_WARNING(
'Found a feature matching done progress file in: %s' %
matched_done_file)
if not io.file_exists(tree.opensfm_tracks) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm create_tracks %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM tracks file in: %s' %
tree.opensfm_tracks)
if not io.file_exists(tree.opensfm_reconstruction) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm reconstruct %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# Check that a reconstruction file has been created
if not io.file_exists(tree.opensfm_reconstruction):
log.ODM_ERROR(
"The program could not process this dataset using the current settings. "
"Check that the images have enough overlap, "
"that there are enough recognizable features "
"and that the images are in focus. "
"You could also try to increase the --min-num-features parameter."
"The program will now exit.")
sys.exit(1)
# Always export VisualSFM's reconstruction and undistort images
# as we'll use these for texturing (after GSD estimation and resizing)
if not args.ignore_gsd:
image_scale = gsd.image_scale_factor(
args.orthophoto_resolution, tree.opensfm_reconstruction)
else:
image_scale = 1.0
if not io.file_exists(
tree.opensfm_reconstruction_nvm) or rerun_cell:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_visualsfm --image_extension png --scale_focal %s %s'
% (context.pyopencv_path, context.opensfm_path,
image_scale, tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
# These will be used for texturing
system.run(
'PYTHONPATH=%s %s/bin/opensfm undistort --image_format png --image_scale %s %s'
% (context.pyopencv_path, context.opensfm_path, image_scale,
tree.opensfm))
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_ply --no-cameras %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
# Filter
point_cloud.filter(os.path.join(tree.opensfm,
'reconstruction.ply'),
standard_deviation=args.pc_filter,
verbose=args.verbose)
elif args.use_opensfm_dense:
# Undistort images at full scale in JPG
# (TODO: we could compare the size of the PNGs if they are < than depthmap_resolution
# and use those instead of re-exporting full resolution JPGs)
system.run('PYTHONPATH=%s %s/bin/opensfm undistort %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run(
'PYTHONPATH=%s %s/bin/opensfm compute_depthmaps %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
# Filter
point_cloud.filter(tree.opensfm_model,
standard_deviation=args.pc_filter,
verbose=args.verbose)
else:
log.ODM_WARNING(
'Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# check if reconstruction was exported to bundler before
if not io.file_exists(tree.opensfm_bundle_list) or rerun_cell:
# convert back to bundler's format
system.run(
'PYTHONPATH=%s %s/bin/export_bundler %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid Bundler file in: %s' %
tree.opensfm_reconstruction)
if reconstruction.georef:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_geocoords %s --transformation --proj \'%s\''
% (context.pyopencv_path, context.opensfm_path, tree.opensfm,
reconstruction.georef.projection.srs))
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'OpenSfM')
log.ODM_INFO('Running ODM OpenSfM Cell - Finished')
return ecto.OK if args.end_with != 'opensfm' else ecto.QUIT
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if not os.path.exists(tree.odm_filterpoints):
system.mkdir_p(tree.odm_filterpoints)
inputPointCloud = ""
# check if reconstruction was done before
if not io.file_exists(tree.filtered_point_cloud) or self.rerun():
if args.fast_orthophoto:
inputPointCloud = os.path.join(tree.opensfm,
'reconstruction.ply')
else:
inputPointCloud = tree.openmvs_model
# Check if we need to compute boundary
if args.auto_boundary:
if reconstruction.is_georeferenced():
if not 'boundary' in outputs:
avg_gsd = gsd.opensfm_reconstruction_average_gsd(
tree.opensfm_reconstruction)
if avg_gsd is not None:
outputs['boundary'] = compute_boundary_from_shots(
tree.opensfm_reconstruction, avg_gsd * 20,
reconstruction.get_proj_offset()
) # 20 is arbitrary
if outputs['boundary'] is None:
log.ODM_WANING(
"Cannot compute boundary from camera shots"
)
else:
log.ODM_WARNING(
"Cannot compute boundary (GSD cannot be estimated)"
)
else:
log.ODM_WARNING(
"--auto-boundary set but so is --boundary, will use --boundary"
)
else:
log.ODM_WARNING(
"Not a georeferenced reconstruction, will ignore --auto-boundary"
)
point_cloud.filter(inputPointCloud,
tree.filtered_point_cloud,
standard_deviation=args.pc_filter,
sample_radius=args.pc_sample,
boundary=boundary_offset(
outputs.get('boundary'),
reconstruction.get_proj_offset()),
verbose=args.verbose,
max_concurrency=args.max_concurrency)
# Quick check
info = point_cloud.ply_info(tree.filtered_point_cloud)
if info["vertex_count"] == 0:
extra_msg = ''
if 'boundary' in outputs:
extra_msg = '. Also, since you used a boundary setting, make sure that the boundary polygon you specified covers the reconstruction area correctly.'
raise system.ExitException(
"Uh oh! We ended up with an empty point cloud. This means that the reconstruction did not succeed. Have you followed best practices for data acquisition? See https://docs.opendronemap.org/flying/%s"
% extra_msg)
else:
log.ODM_WARNING('Found a valid point cloud file in: %s' %
tree.filtered_point_cloud)
if args.optimize_disk_space and inputPointCloud:
if os.path.isfile(inputPointCloud):
os.remove(inputPointCloud)