def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
outputs['large'] = len(photos) > args.split
if outputs['large']:
# If we have a cluster address, we'll use a distributed workflow
local_workflow = not bool(args.sm_cluster)
octx = OSFMContext(tree.opensfm)
split_done_file = octx.path("split_done.txt")
if not io.file_exists(split_done_file) or self.rerun():
orig_max_concurrency = args.max_concurrency
if not local_workflow:
args.max_concurrency = max(1, args.max_concurrency - 1)
log.ODM_INFO("Setting max-concurrency to %s to better handle remote splits" % args.max_concurrency)
log.ODM_INFO("Large dataset detected (%s photos) and split set at %s. Preparing split merge." % (
len(photos), args.split))
config = [
"submodels_relpath: ../submodels/opensfm",
"submodel_relpath_template: ../submodels/submodel_%04d/opensfm",
"submodel_images_relpath_template: ../submodels/submodel_%04d/images",
"submodel_size: %s" % args.split,
"submodel_overlap: %s" % args.split_overlap,
]
octx.setup(args, tree.dataset_raw, reconstruction=reconstruction, append_config=config,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(5)
if local_workflow:
octx.feature_matching(self.rerun())
self.update_progress(20)
# Create submodels
if not io.dir_exists(tree.submodels_path) or self.rerun():
if io.dir_exists(tree.submodels_path):
log.ODM_WARNING("Removing existing submodels directory: %s" % tree.submodels_path)
shutil.rmtree(tree.submodels_path)
octx.run("create_submodels")
else:
log.ODM_WARNING("Submodels directory already exist at: %s" % tree.submodels_path)
# Find paths of all submodels
mds = metadataset.MetaDataSet(tree.opensfm)
submodel_paths = [os.path.abspath(p) for p in mds.get_submodel_paths()]
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
# Copy filtered GCP file if needed
# One in OpenSfM's directory, one in the submodel project directory
if reconstruction.gcp and reconstruction.gcp.exists():
submodel_gcp_file = os.path.abspath(sp_octx.path("..", "gcp_list.txt"))
submodel_images_dir = os.path.abspath(sp_octx.path("..", "images"))
if reconstruction.gcp.make_filtered_copy(submodel_gcp_file, submodel_images_dir):
log.ODM_INFO("Copied filtered GCP file to %s" % submodel_gcp_file)
io.copy(submodel_gcp_file, os.path.abspath(sp_octx.path("gcp_list.txt")))
else:
log.ODM_INFO(
"No GCP will be copied for %s, not enough images in the submodel are referenced by the GCP" % sp_octx.name())
# Reconstruct each submodel
log.ODM_INFO(
"Dataset has been split into %s submodels. Reconstructing each submodel..." % len(submodel_paths))
self.update_progress(25)
if local_workflow:
for sp in submodel_paths:
log.ODM_INFO("Reconstructing %s" % sp)
OSFMContext(sp).reconstruct(self.rerun())
else:
lre = LocalRemoteExecutor(args.sm_cluster, self.rerun())
lre.set_projects([os.path.abspath(os.path.join(p, "..")) for p in submodel_paths])
lre.run_reconstruction()
self.update_progress(50)
# TODO: this is currently not working and needs a champion to fix it
# https://community.opendronemap.org/t/filenotfound-error-cameras-json/6047/2
# resplit_done_file = octx.path('resplit_done.txt')
# if not io.file_exists(resplit_done_file) and bool(args.split_multitracks):
# submodels = mds.get_submodel_paths()
# i = 0
# for s in submodels:
# template = octx.path("../aligned_submodels/submodel_%04d")
# with open(s+"/reconstruction.json", "r") as f:
# j = json.load(f)
# for k in range(0, len(j)):
# v = j[k]
# path = template % i
# #Create the submodel path up to opensfm
# os.makedirs(path+"/opensfm")
# os.makedirs(path+"/images")
# #symlinks for common data
# images = os.listdir(octx.path("../images"))
# for image in images:
# os.symlink("../../../images/"+image, path+"/images/"+image)
# os.symlink("../../../opensfm/exif", path+"/opensfm/exif")
# os.symlink("../../../opensfm/features", path+"/opensfm/features")
# os.symlink("../../../opensfm/matches", path+"/opensfm/matches")
# os.symlink("../../../opensfm/reference_lla.json", path+"/opensfm/reference_lla.json")
# os.symlink("../../../opensfm/camera_models.json", path+"/opensfm/camera_models.json")
# shutil.copy(s+"/../cameras.json", path+"/cameras.json")
# shutil.copy(s+"/../images.json", path+"/images.json")
# with open(octx.path("config.yaml")) as f:
# doc = yaml.safe_load(f)
# dmcv = "depthmap_min_consistent_views"
# if dmcv in doc:
# if len(v["shots"]) < doc[dmcv]:
# doc[dmcv] = len(v["shots"])
# print("WARNING: Reduced "+dmcv+" to accommodate short track")
# with open(path+"/opensfm/config.yaml", "w") as f:
# yaml.dump(doc, f)
# #We need the original tracks file for the visualsfm export, since
# #there may still be point matches between the tracks
# shutil.copy(s+"/tracks.csv", path+"/opensfm/tracks.csv")
# #Create our new reconstruction file with only the relevant track
# with open(path+"/opensfm/reconstruction.json", "w") as o:
# json.dump([v], o)
# #Create image lists
# with open(path+"/opensfm/image_list.txt", "w") as o:
# o.writelines(list(map(lambda x: "../images/"+x+'\n', v["shots"].keys())))
# with open(path+"/img_list.txt", "w") as o:
# o.writelines(list(map(lambda x: x+'\n', v["shots"].keys())))
# i+=1
# os.rename(octx.path("../submodels"), octx.path("../unaligned_submodels"))
# os.rename(octx.path("../aligned_submodels"), octx.path("../submodels"))
# octx.touch(resplit_done_file)
mds = metadataset.MetaDataSet(tree.opensfm)
submodel_paths = [os.path.abspath(p) for p in mds.get_submodel_paths()]
# Align
octx.align_reconstructions(self.rerun())
self.update_progress(55)
# Aligned reconstruction is in reconstruction.aligned.json
# We need to rename it to reconstruction.json
remove_paths = []
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
aligned_recon = sp_octx.path('reconstruction.aligned.json')
unaligned_recon = sp_octx.path('reconstruction.unaligned.json')
main_recon = sp_octx.path('reconstruction.json')
if io.file_exists(main_recon) and io.file_exists(unaligned_recon) and not self.rerun():
log.ODM_INFO("Submodel %s has already been aligned." % sp_octx.name())
continue
if not io.file_exists(aligned_recon):
log.ODM_WARNING("Submodel %s does not have an aligned reconstruction (%s). "
"This could mean that the submodel could not be reconstructed "
" (are there enough features to reconstruct it?). Skipping." % (
sp_octx.name(), aligned_recon))
remove_paths.append(sp)
continue
if io.file_exists(main_recon):
shutil.move(main_recon, unaligned_recon)
shutil.move(aligned_recon, main_recon)
log.ODM_INFO("%s is now %s" % (aligned_recon, main_recon))
# Remove invalid submodels
submodel_paths = [p for p in submodel_paths if not p in remove_paths]
# Run ODM toolchain for each submodel
if local_workflow:
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
log.ODM_INFO("========================")
log.ODM_INFO("Processing %s" % sp_octx.name())
log.ODM_INFO("========================")
argv = get_submodel_argv(args, tree.submodels_path, sp_octx.name())
# Re-run the ODM toolchain on the submodel
system.run(" ".join(map(quote, map(str, argv))), env_vars=os.environ.copy())
else:
lre.set_projects([os.path.abspath(os.path.join(p, "..")) for p in submodel_paths])
lre.run_toolchain()
# Restore max_concurrency value
args.max_concurrency = orig_max_concurrency
octx.touch(split_done_file)
else:
log.ODM_WARNING('Found a split done file in: %s' % split_done_file)
else:
log.ODM_INFO("Normal dataset, will process all at once.")
self.progress = 0.0
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
if reconstruction.multi_camera:
# Export only the primary band
primary = reconstruction.multi_camera[0]
image_list = os.path.join(tree.opensfm, "image_list_%s.txt" % primary['name'].lower())
cmd += ' --image_list "%s"' % image_list
octx.run(cmd)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = math.floor(math.log(outputs['undist_image_max_size'] / float(depthmap_resolution)) / math.log(2))
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
log.ODM_INFO("Running dense reconstruction. This might take a while.")
system.run('%s "%s" %s' % (context.omvs_densify_path,
os.path.join(tree.openmvs, 'scene.mvs'),
' '.join(config)))
self.update_progress(85)
# Filter points
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense,
"-v 0"
]
system.run('%s %s' % (context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING("Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting...")
exit(1)
self.update_progress(95)
if args.optimize_disk_space:
files = [scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs, 'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
]
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING('Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
raise system.ExitException(
'Not enough photos in photos array to start OpenSfM')
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
reconstruction=reconstruction,
rerun=self.rerun())
octx.photos_to_metadata(photos, self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
octx.extract_cameras(tree.path("cameras.json"), self.rerun())
self.update_progress(70)
def cleanup_disk_space():
if args.optimize_disk_space:
for folder in ["features", "matches", "reports"]:
folder_path = octx.path(folder)
if os.path.exists(folder_path):
if os.path.islink(folder_path):
os.unlink(folder_path)
else:
shutil.rmtree(folder_path)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
cleanup_disk_space()
return
# Stats are computed in the local CRS (before geoprojection)
if not args.skip_report:
# TODO: this will fail to compute proper statistics if
# the pipeline is run with --skip-report and is subsequently
# rerun without --skip-report a --rerun-* parameter (due to the reconstruction.json file)
# being replaced below. It's an isolated use case.
octx.export_stats(self.rerun())
self.update_progress(75)
# We now switch to a geographic CRS
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_topocentric_reconstruction) or self.rerun()):
octx.run(
'export_geocoords --reconstruction --proj "%s" --offset-x %s --offset-y %s'
% (reconstruction.georef.proj4(),
reconstruction.georef.utm_east_offset,
reconstruction.georef.utm_north_offset))
shutil.move(tree.opensfm_reconstruction,
tree.opensfm_topocentric_reconstruction)
shutil.move(tree.opensfm_geocoords_reconstruction,
tree.opensfm_reconstruction)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_geocoords_reconstruction)
self.update_progress(80)
updated_config_flag_file = octx.path('updated_config.txt')
# Make sure it's capped by the depthmap-resolution arg,
# since the undistorted images are used for MVS
outputs['undist_image_max_size'] = max(
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
has_gcp=reconstruction.has_gcp()),
get_depthmap_resolution(args, photos))
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
outputs['undist_image_max_size']
})
octx.touch(updated_config_flag_file)
# Undistorted images will be used for texturing / MVS
alignment_info = None
primary_band_name = None
largest_photo = None
undistort_pipeline = []
def undistort_callback(shot_id, image):
for func in undistort_pipeline:
image = func(shot_id, image)
return image
def resize_thermal_images(shot_id, image):
photo = reconstruction.get_photo(shot_id)
if photo.is_thermal():
return thermal.resize_to_match(image, largest_photo)
else:
return image
def radiometric_calibrate(shot_id, image):
photo = reconstruction.get_photo(shot_id)
if photo.is_thermal():
return thermal.dn_to_temperature(photo, image,
tree.dataset_raw)
else:
return multispectral.dn_to_reflectance(
photo,
image,
use_sun_sensor=args.radiometric_calibration ==
"camera+sun")
def align_to_primary_band(shot_id, image):
photo = reconstruction.get_photo(shot_id)
# No need to align if requested by user
if args.skip_band_alignment:
return image
# No need to align primary
if photo.band_name == primary_band_name:
return image
ainfo = alignment_info.get(photo.band_name)
if ainfo is not None:
return multispectral.align_image(image, ainfo['warp_matrix'],
ainfo['dimension'])
else:
log.ODM_WARNING(
"Cannot align %s, no alignment matrix could be computed. Band alignment quality might be affected."
% (shot_id))
return image
if reconstruction.multi_camera:
largest_photo = find_largest_photo(photos)
undistort_pipeline.append(resize_thermal_images)
if args.radiometric_calibration != "none":
undistort_pipeline.append(radiometric_calibrate)
image_list_override = None
if reconstruction.multi_camera:
# Undistort only secondary bands
image_list_override = [
os.path.join(tree.dataset_raw, p.filename) for p in photos
] # if p.band_name.lower() != primary_band_name.lower()
# We backup the original reconstruction.json, tracks.csv
# then we augment them by duplicating the primary band
# camera shots with each band, so that exports, undistortion,
# etc. include all bands
# We finally restore the original files later
added_shots_file = octx.path('added_shots_done.txt')
s2p, p2s = None, None
if not io.file_exists(added_shots_file) or self.rerun():
primary_band_name = multispectral.get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
s2p, p2s = multispectral.compute_band_maps(
reconstruction.multi_camera, primary_band_name)
if not args.skip_band_alignment:
alignment_info = multispectral.compute_alignment_matrices(
reconstruction.multi_camera,
primary_band_name,
tree.dataset_raw,
s2p,
p2s,
max_concurrency=args.max_concurrency)
else:
log.ODM_WARNING("Skipping band alignment")
alignment_info = {}
log.ODM_INFO("Adding shots to reconstruction")
octx.backup_reconstruction()
octx.add_shots_to_reconstruction(p2s)
octx.touch(added_shots_file)
undistort_pipeline.append(align_to_primary_band)
octx.convert_and_undistort(self.rerun(), undistort_callback,
image_list_override)
self.update_progress(95)
if reconstruction.multi_camera:
octx.restore_reconstruction_backup()
# Undistort primary band and write undistorted
# reconstruction.json, tracks.csv
octx.convert_and_undistort(self.rerun(),
undistort_callback,
runId='primary')
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --points')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
if reconstruction.multi_camera:
log.ODM_INFO("Multiple bands found")
# Write NVM files for the various bands
for band in reconstruction.multi_camera:
nvm_file = octx.path(
"undistorted",
"reconstruction_%s.nvm" % band['name'].lower())
if not io.file_exists(nvm_file) or self.rerun():
img_map = {}
if primary_band_name is None:
primary_band_name = multispectral.get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
if p2s is None:
s2p, p2s = multispectral.compute_band_maps(
reconstruction.multi_camera, primary_band_name)
for fname in p2s:
# Primary band maps to itself
if band['name'] == primary_band_name:
img_map[add_image_format_extension(
fname, 'tif')] = add_image_format_extension(
fname, 'tif')
else:
band_filename = next(
(p.filename for p in p2s[fname]
if p.band_name == band['name']), None)
if band_filename is not None:
img_map[add_image_format_extension(
fname,
'tif')] = add_image_format_extension(
band_filename, 'tif')
else:
log.ODM_WARNING(
"Cannot find %s band equivalent for %s" %
(band, fname))
nvm.replace_nvm_images(tree.opensfm_reconstruction_nvm,
img_map, nvm_file)
else:
log.ODM_WARNING("Found existing NVM file %s" % nvm_file)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras --point-num-views')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
cleanup_disk_space()
if args.optimize_disk_space:
os.remove(octx.path("tracks.csv"))
if io.file_exists(octx.recon_backup_file()):
os.remove(octx.recon_backup_file())
if io.dir_exists(octx.path("undistorted", "depthmaps")):
files = glob.glob(
octx.path("undistorted", "depthmaps", "*.npz"))
for f in files:
os.remove(f)
# Keep these if using OpenMVS
if args.fast_orthophoto:
files = [
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
]
for f in files:
if os.path.exists(f):
os.remove(f)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
reconstruction=reconstruction,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
octx.extract_cameras(tree.path("cameras.json"), self.rerun())
self.update_progress(70)
if args.optimize_disk_space:
for folder in ["features", "matches", "exif", "reports"]:
folder_path = octx.path(folder)
if os.path.islink(folder_path):
os.unlink(folder_path)
else:
shutil.rmtree(folder_path)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
updated_config_flag_file = octx.path('updated_config.txt')
# Make sure it's capped by the depthmap-resolution arg,
# since the undistorted images are used for MVS
outputs['undist_image_max_size'] = max(
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
has_gcp=reconstruction.has_gcp()),
args.depthmap_resolution)
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
outputs['undist_image_max_size']
})
octx.touch(updated_config_flag_file)
# These will be used for texturing / MVS
if args.radiometric_calibration == "none":
octx.convert_and_undistort(self.rerun())
else:
def radiometric_calibrate(shot_id, image):
photo = reconstruction.get_photo(shot_id)
return multispectral.dn_to_reflectance(
photo,
image,
use_sun_sensor=args.radiometric_calibration ==
"camera+sun")
octx.convert_and_undistort(self.rerun(), radiometric_calibrate)
self.update_progress(80)
if reconstruction.multi_camera:
# Dump band image lists
log.ODM_INFO("Multiple bands found")
for band in reconstruction.multi_camera:
log.ODM_INFO("Exporting %s band" % band['name'])
image_list_file = octx.path("image_list_%s.txt" %
band['name'].lower())
if not io.file_exists(image_list_file) or self.rerun():
with open(image_list_file, "w") as f:
f.write("\n".join([p.filename
for p in band['photos']]))
log.ODM_INFO("Wrote %s" % image_list_file)
else:
log.ODM_WARNING(
"Found a valid image list in %s for %s band" %
(image_list_file, band['name']))
nvm_file = octx.path(
"undistorted",
"reconstruction_%s.nvm" % band['name'].lower())
if not io.file_exists(nvm_file) or self.rerun():
octx.run('export_visualsfm --points --image_list "%s"' %
image_list_file)
os.rename(tree.opensfm_reconstruction_nvm, nvm_file)
else:
log.ODM_WARNING(
"Found a valid NVM file in %s for %s band" %
(nvm_file, band['name']))
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --points')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
self.update_progress(90)
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.proj4())
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
if args.optimize_disk_space:
os.remove(octx.path("tracks.csv"))
os.remove(octx.path("undistorted", "tracks.csv"))
os.remove(octx.path("undistorted", "reconstruction.json"))
if io.dir_exists(octx.path("undistorted", "depthmaps")):
files = glob.glob(
octx.path("undistorted", "depthmaps", "*.npz"))
for f in files:
os.remove(f)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
outputs['large'] = len(photos) > args.split
if outputs['large']:
# If we have a cluster address, we'll use a distributed workflow
local_workflow = not bool(args.sm_cluster)
octx = OSFMContext(tree.opensfm)
split_done_file = octx.path("split_done.txt")
if not io.file_exists(split_done_file) or self.rerun():
orig_max_concurrency = args.max_concurrency
if not local_workflow:
args.max_concurrency = max(1, args.max_concurrency - 1)
log.ODM_INFO(
"Setting max-concurrency to %s to better handle remote splits"
% args.max_concurrency)
log.ODM_INFO(
"Large dataset detected (%s photos) and split set at %s. Preparing split merge."
% (len(photos), args.split))
config = [
"submodels_relpath: ../submodels/opensfm",
"submodel_relpath_template: ../submodels/submodel_%04d/opensfm",
"submodel_images_relpath_template: ../submodels/submodel_%04d/images",
"submodel_size: %s" % args.split,
"submodel_overlap: %s" % args.split_overlap,
]
octx.setup(args,
tree.dataset_raw,
photos,
reconstruction=reconstruction,
append_config=config,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(5)
if local_workflow:
octx.feature_matching(self.rerun())
self.update_progress(20)
# Create submodels
if not io.dir_exists(tree.submodels_path) or self.rerun():
if io.dir_exists(tree.submodels_path):
log.ODM_WARNING(
"Removing existing submodels directory: %s" %
tree.submodels_path)
shutil.rmtree(tree.submodels_path)
octx.run("create_submodels")
else:
log.ODM_WARNING(
"Submodels directory already exist at: %s" %
tree.submodels_path)
# Find paths of all submodels
mds = metadataset.MetaDataSet(tree.opensfm)
submodel_paths = [
os.path.abspath(p) for p in mds.get_submodel_paths()
]
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
# Copy filtered GCP file if needed
# One in OpenSfM's directory, one in the submodel project directory
if reconstruction.gcp and reconstruction.gcp.exists():
submodel_gcp_file = os.path.abspath(
sp_octx.path("..", "gcp_list.txt"))
submodel_images_dir = os.path.abspath(
sp_octx.path("..", "images"))
if reconstruction.gcp.make_filtered_copy(
submodel_gcp_file, submodel_images_dir):
log.ODM_INFO("Copied filtered GCP file to %s" %
submodel_gcp_file)
io.copy(
submodel_gcp_file,
os.path.abspath(sp_octx.path("gcp_list.txt")))
else:
log.ODM_INFO(
"No GCP will be copied for %s, not enough images in the submodel are referenced by the GCP"
% sp_octx.name())
# Reconstruct each submodel
log.ODM_INFO(
"Dataset has been split into %s submodels. Reconstructing each submodel..."
% len(submodel_paths))
self.update_progress(25)
if local_workflow:
for sp in submodel_paths:
log.ODM_INFO("Reconstructing %s" % sp)
OSFMContext(sp).reconstruct(self.rerun())
else:
lre = LocalRemoteExecutor(args.sm_cluster, self.rerun())
lre.set_projects([
os.path.abspath(os.path.join(p, ".."))
for p in submodel_paths
])
lre.run_reconstruction()
self.update_progress(50)
# Align
octx.align_reconstructions(self.rerun())
self.update_progress(55)
# Aligned reconstruction is in reconstruction.aligned.json
# We need to rename it to reconstruction.json
remove_paths = []
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
aligned_recon = sp_octx.path('reconstruction.aligned.json')
unaligned_recon = sp_octx.path(
'reconstruction.unaligned.json')
main_recon = sp_octx.path('reconstruction.json')
if io.file_exists(main_recon) and io.file_exists(
unaligned_recon) and not self.rerun():
log.ODM_INFO("Submodel %s has already been aligned." %
sp_octx.name())
continue
if not io.file_exists(aligned_recon):
log.ODM_WARNING(
"Submodel %s does not have an aligned reconstruction (%s). "
"This could mean that the submodel could not be reconstructed "
" (are there enough features to reconstruct it?). Skipping."
% (sp_octx.name(), aligned_recon))
remove_paths.append(sp)
continue
if io.file_exists(main_recon):
shutil.move(main_recon, unaligned_recon)
shutil.move(aligned_recon, main_recon)
log.ODM_INFO("%s is now %s" % (aligned_recon, main_recon))
# Remove invalid submodels
submodel_paths = [
p for p in submodel_paths if not p in remove_paths
]
# Run ODM toolchain for each submodel
if local_workflow:
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
log.ODM_INFO("========================")
log.ODM_INFO("Processing %s" % sp_octx.name())
log.ODM_INFO("========================")
argv = get_submodel_argv(args, tree.submodels_path,
sp_octx.name())
# Re-run the ODM toolchain on the submodel
system.run(" ".join(map(quote, argv)),
env_vars=os.environ.copy())
else:
lre.set_projects([
os.path.abspath(os.path.join(p, ".."))
for p in submodel_paths
])
lre.run_toolchain()
# Restore max_concurrency value
args.max_concurrency = orig_max_concurrency
octx.touch(split_done_file)
else:
log.ODM_WARNING('Found a split done file in: %s' %
split_done_file)
else:
log.ODM_INFO("Normal dataset, will process all at once.")
self.progress = 0.0
system.mkdir_p(os.path.join(submodel_path, 'opensfm'))
# Create reconstruction object
reconstruction = types.ODM_Reconstruction(photos)
opensfm_interface.invent_reference_lla(images_filepath, photo_list,os.path.join(submodel_path, 'opensfm'))
system.mkdir_p(os.path.join(submodel_path,'odm_georeferencing'))
odm_georeferencing = io.join_paths(submodel_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(odm_georeferencing, 'coords.txt')
reconstruction.georeference_with_gps(photos, odm_georeferencing_coords, True)
odm_geo_proj = io.join_paths(odm_georeferencing, 'proj.txt')
reconstruction.save_proj_srs(odm_geo_proj)
from opendm.osfm import OSFMContext
octx = OSFMContext(os.path.join(submodel_path, 'opensfm'))
print('----------Export geocroods--------')
octx.run('export_geocoords --transformation --proj \'%s\'' % reconstruction.georef.proj4())
print('----------Export Geocoords Ppppp--------')
lib.odm_mesh_function(opensfm_config,submodel_path, max_concurrency, reconstruction)
#lib.odm_mesh_function(submodel_path, max_concurrency)
end = timer()
odm_mesh_time = end - start
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
gcp_path=tree.odm_georeferencing_gcp,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
updated_config_flag_file = octx.path('updated_config.txt')
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd)
})
octx.touch(updated_config_flag_file)
# These will be used for texturing
undistorted_images_path = octx.path("undistorted")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort')
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --undistorted')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
# check if reconstruction was exported to bundler before
octx.export_bundler(tree.opensfm_bundle_list, self.rerun())
self.update_progress(90)
if reconstruction.georef and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.projection.srs)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
openmvs_scene_file = os.path.join(tree.openmvs, "scene.mvs")
if not io.file_exists(openmvs_scene_file) or self.rerun():
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
octx.run(cmd)
else:
log.ODM_WARNING("Found existing %s" % openmvs_scene_file)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if io.dir_exists(depthmaps_dir) and self.rerun():
shutil.rmtree(depthmaps_dir)
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = int(
round(
math.log(outputs['undist_image_max_size'] /
float(depthmap_resolution)) / math.log(2)))
log.ODM_INFO(
"Running dense reconstruction. This might take a while.")
log.ODM_INFO("Estimating depthmaps")
densify_ini_file = os.path.join(tree.openmvs, 'config.ini')
with open(densify_ini_file, 'w+') as f:
f.write("Optimize = 0\n") # Disable depth-maps re-filtering
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
"--number-views-fuse 2",
'-w "%s"' % depthmaps_dir, "-v 0"
]
if args.pc_tile:
config.append("--fusion-mode 1")
system.run('%s "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file, ' '.join(config)))
self.update_progress(85)
files_to_remove = []
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if args.pc_tile:
log.ODM_INFO("Computing sub-scenes")
config = [
"--sub-scene-area 660000",
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
system.run('%s "%s" %s' %
(context.omvs_densify_path, openmvs_scene_file,
' '.join(config)))
scene_files = glob.glob(
os.path.join(tree.openmvs,
"scene_[0-9][0-9][0-9][0-9].mvs"))
if len(scene_files) == 0:
log.ODM_ERROR(
"No OpenMVS scenes found. This could be a bug, or the reconstruction could not be processed."
)
exit(1)
log.ODM_INFO("Fusing depthmaps for %s scenes" %
len(scene_files))
scene_ply_files = []
for sf in scene_files:
p, _ = os.path.splitext(sf)
scene_ply = p + "_dense_dense_filtered.ply"
scene_dense_mvs = p + "_dense.mvs"
files_to_remove += [scene_ply, sf, scene_dense_mvs]
scene_ply_files.append(scene_ply)
if not io.file_exists(scene_ply) or self.rerun():
# Fuse
config = [
'--resolution-level %s' % int(resolution_level),
'--min-resolution %s' % depthmap_resolution,
'--max-resolution %s' %
int(outputs['undist_image_max_size']),
'--dense-config-file "%s"' % densify_ini_file,
'--number-views-fuse 2',
'--max-threads %s' % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
'-v 0',
]
try:
system.run('%s "%s" %s' %
(context.omvs_densify_path, sf,
' '.join(config)))
# Filter
system.run(
'%s "%s" --filter-point-cloud -1 -v 0' %
(context.omvs_densify_path, scene_dense_mvs))
except:
log.ODM_WARNING(
"Sub-scene %s could not be reconstructed, skipping..."
% sf)
if not io.file_exists(scene_ply):
scene_ply_files.pop()
log.ODM_WARNING(
"Could not compute PLY for subscene %s" % sf)
else:
log.ODM_WARNING("Found existing dense scene file %s" %
scene_ply)
# Merge
log.ODM_INFO("Merging %s scene files" % len(scene_ply_files))
if len(scene_ply_files) == 0:
log.ODM_ERROR(
"Could not compute dense point cloud (no PLY files available)."
)
if len(scene_ply_files) == 1:
# Simply rename
os.rename(scene_ply_files[0], tree.openmvs_model)
log.ODM_INFO("%s --> %s" %
(scene_ply_files[0], tree.openmvs_model))
else:
# Merge
fast_merge_ply(scene_ply_files, tree.openmvs_model)
else:
# Filter all at once
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense, "-v 0"
]
system.run('%s %s' %
(context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING(
"Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting..."
)
exit(1)
# TODO: add support for image masks
self.update_progress(95)
if args.optimize_disk_space:
files = [
scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs,
'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
] + files_to_remove
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING(
'Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
octx = OSFMContext(tree.opensfm)
if not photos:
raise system.ExitException('Not enough photos in photos array to start OpenMVS')
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
openmvs_scene_file = os.path.join(tree.openmvs, "scene.mvs")
if not io.file_exists(openmvs_scene_file) or self.rerun():
cmd = 'export_openmvs'
octx.run(cmd)
else:
log.ODM_WARNING("Found existing %s" % openmvs_scene_file)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if io.dir_exists(depthmaps_dir) and self.rerun():
shutil.rmtree(depthmaps_dir)
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
log.ODM_INFO("Depthmap resolution set to: %spx" % depthmap_resolution)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = int(round(math.log(outputs['undist_image_max_size'] / float(depthmap_resolution)) / math.log(2)))
log.ODM_INFO("Running dense reconstruction. This might take a while.")
log.ODM_INFO("Estimating depthmaps")
number_views_fuse = 2
densify_ini_file = os.path.join(tree.openmvs, 'Densify.ini')
subres_levels = 2 # The number of lower resolutions to process before estimating output resolution depthmap.
config = [
" --resolution-level %s" % int(resolution_level),
'--dense-config-file "%s"' % densify_ini_file,
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
"--number-views-fuse %s" % number_views_fuse,
"--sub-resolution-levels %s" % subres_levels,
'-w "%s"' % depthmaps_dir,
"-v 0"
]
gpu_config = []
if not has_gpu(args):
gpu_config.append("--cuda-device -2")
if args.pc_tile:
config.append("--fusion-mode 1")
extra_config = []
if not args.pc_geometric:
extra_config.append("--geometric-iters 0")
masks_dir = os.path.join(tree.opensfm, "undistorted", "masks")
masks = os.path.exists(masks_dir) and len(os.listdir(masks_dir)) > 0
if masks:
extra_config.append("--ignore-mask-label 0")
sharp = args.pc_geometric
with open(densify_ini_file, 'w+') as f:
f.write("Optimize = %s\n" % (7 if sharp else 3))
def run_densify():
system.run('"%s" "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file,
' '.join(config + gpu_config + extra_config)))
try:
run_densify()
except system.SubprocessException as e:
# If the GPU was enabled and the program failed,
# try to run it again without GPU
if e.errorCode == 1 and len(gpu_config) == 0:
log.ODM_WARNING("OpenMVS failed with GPU, is your graphics card driver up to date? Falling back to CPU.")
gpu_config.append("--cuda-device -2")
run_densify()
else:
raise e
self.update_progress(85)
files_to_remove = []
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if args.pc_tile:
log.ODM_INFO("Computing sub-scenes")
subscene_densify_ini_file = os.path.join(tree.openmvs, 'subscene-config.ini')
with open(subscene_densify_ini_file, 'w+') as f:
f.write("Optimize = 0\n")
config = [
"--sub-scene-area 660000",
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
system.run('"%s" "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file,
' '.join(config + gpu_config)))
scene_files = glob.glob(os.path.join(tree.openmvs, "scene_[0-9][0-9][0-9][0-9].mvs"))
if len(scene_files) == 0:
raise system.ExitException("No OpenMVS scenes found. This could be a bug, or the reconstruction could not be processed.")
log.ODM_INFO("Fusing depthmaps for %s scenes" % len(scene_files))
scene_ply_files = []
for sf in scene_files:
p, _ = os.path.splitext(sf)
scene_ply_unfiltered = p + "_dense.ply"
scene_ply = p + "_dense_dense_filtered.ply"
scene_dense_mvs = p + "_dense.mvs"
files_to_remove += [scene_ply, sf, scene_dense_mvs, scene_ply_unfiltered]
scene_ply_files.append(scene_ply)
if not io.file_exists(scene_ply) or self.rerun():
# Fuse
config = [
'--resolution-level %s' % int(resolution_level),
'--min-resolution %s' % depthmap_resolution,
'--max-resolution %s' % int(outputs['undist_image_max_size']),
'--dense-config-file "%s"' % subscene_densify_ini_file,
'--number-views-fuse %s' % number_views_fuse,
'--max-threads %s' % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
'-v 0',
]
try:
system.run('"%s" "%s" %s' % (context.omvs_densify_path, sf, ' '.join(config + gpu_config + extra_config)))
# Filter
if args.pc_filter > 0:
system.run('"%s" "%s" --filter-point-cloud -1 -v 0 %s' % (context.omvs_densify_path, scene_dense_mvs, ' '.join(gpu_config)))
else:
# Just rename
log.ODM_INFO("Skipped filtering, %s --> %s" % (scene_ply_unfiltered, scene_ply))
os.rename(scene_ply_unfiltered, scene_ply)
except:
log.ODM_WARNING("Sub-scene %s could not be reconstructed, skipping..." % sf)
if not io.file_exists(scene_ply):
scene_ply_files.pop()
log.ODM_WARNING("Could not compute PLY for subscene %s" % sf)
else:
log.ODM_WARNING("Found existing dense scene file %s" % scene_ply)
# Merge
log.ODM_INFO("Merging %s scene files" % len(scene_ply_files))
if len(scene_ply_files) == 0:
log.ODM_ERROR("Could not compute dense point cloud (no PLY files available).")
if len(scene_ply_files) == 1:
# Simply rename
os.replace(scene_ply_files[0], tree.openmvs_model)
log.ODM_INFO("%s --> %s"% (scene_ply_files[0], tree.openmvs_model))
else:
# Merge
fast_merge_ply(scene_ply_files, tree.openmvs_model)
else:
# Filter all at once
if args.pc_filter > 0:
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense,
"-v 0"
]
system.run('"%s" %s' % (context.omvs_densify_path, ' '.join(config + gpu_config + extra_config)))
else:
raise system.ExitException("Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting...")
else:
# Just rename
scene_dense_ply = os.path.join(tree.openmvs, 'scene_dense.ply')
log.ODM_INFO("Skipped filtering, %s --> %s" % (scene_dense_ply, tree.openmvs_model))
os.rename(scene_dense_ply, tree.openmvs_model)
self.update_progress(95)
if args.optimize_disk_space:
files = [scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs, 'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
] + files_to_remove
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING('Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
gcp_path=tree.odm_georeferencing_gcp,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
# 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 self.rerun():
octx.run(
'export_visualsfm --image_extension png --scale_focal %s' %
image_scale)
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
# These will be used for texturing
undistorted_images_path = octx.path("undistorted")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort --image_format png --image_scale %s' %
image_scale)
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
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)
if not io.file_exists(output_file) or self.rerun():
octx.run('undistort')
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
# check if reconstruction was exported to bundler before
octx.export_bundler(tree.opensfm_bundle_list, self.rerun())
self.update_progress(90)
if reconstruction.georef and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.projection.srs)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
