def process_local(self): octx = OSFMContext(self.path("opensfm")) log.ODM_INFO("==================================") log.ODM_INFO("Local Reconstruction %s" % octx.name()) log.ODM_INFO("==================================") octx.feature_matching(self.params['rerun']) octx.reconstruct(self.params['rerun'])
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): 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 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
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 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): 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)