def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] max_dim = find_largest_photo_dim(reconstruction.photos) max_texture_size = 8 * 1024 # default if max_dim > 8000: log.ODM_INFO("Large input images (%s pixels), increasing maximum texture size." % max_dim) max_texture_size *= 3 class nonloc: runs = [] def add_run(nvm_file, primary=True, band=None): subdir = "" if not primary and band is not None: subdir = band if not args.skip_3dmodel and (primary or args.use_3dmesh): nonloc.runs += [{ 'out_dir': os.path.join(tree.odm_texturing, subdir), 'model': tree.odm_mesh, 'nadir': False, 'primary': primary, 'nvm_file': nvm_file, 'labeling_file': os.path.join(tree.odm_texturing, "odm_textured_model_geo_labeling.vec") if subdir else None }] if not args.use_3dmesh: nonloc.runs += [{ 'out_dir': os.path.join(tree.odm_25dtexturing, subdir), 'model': tree.odm_25dmesh, 'nadir': True, 'primary': primary, 'nvm_file': nvm_file, 'labeling_file': os.path.join(tree.odm_25dtexturing, "odm_textured_model_geo_labeling.vec") if subdir else None }] if reconstruction.multi_camera: for band in reconstruction.multi_camera: primary = band['name'] == get_primary_band_name(reconstruction.multi_camera, args.primary_band) nvm_file = os.path.join(tree.opensfm, "undistorted", "reconstruction_%s.nvm" % band['name'].lower()) add_run(nvm_file, primary, band['name'].lower()) # Sort to make sure primary band is processed first nonloc.runs.sort(key=lambda r: r['primary'], reverse=True) else: add_run(tree.opensfm_reconstruction_nvm) progress_per_run = 100.0 / len(nonloc.runs) progress = 0.0 for r in nonloc.runs: if not io.dir_exists(r['out_dir']): system.mkdir_p(r['out_dir']) odm_textured_model_obj = os.path.join(r['out_dir'], tree.odm_textured_model_obj) if not io.file_exists(odm_textured_model_obj) or self.rerun(): log.ODM_INFO('Writing MVS Textured file in: %s' % odm_textured_model_obj) # Format arguments to fit Mvs-Texturing app skipGlobalSeamLeveling = "" skipLocalSeamLeveling = "" keepUnseenFaces = "" nadir = "" if args.texturing_skip_global_seam_leveling: skipGlobalSeamLeveling = "--skip_global_seam_leveling" if args.texturing_skip_local_seam_leveling: skipLocalSeamLeveling = "--skip_local_seam_leveling" if args.texturing_keep_unseen_faces: keepUnseenFaces = "--keep_unseen_faces" if (r['nadir']): nadir = '--nadir_mode' # mvstex definitions kwargs = { 'bin': context.mvstex_path, 'out_dir': os.path.join(r['out_dir'], "odm_textured_model_geo"), 'model': r['model'], 'dataTerm': args.texturing_data_term, 'outlierRemovalType': args.texturing_outlier_removal_type, 'skipGlobalSeamLeveling': skipGlobalSeamLeveling, 'skipLocalSeamLeveling': skipLocalSeamLeveling, 'keepUnseenFaces': keepUnseenFaces, 'toneMapping': args.texturing_tone_mapping, 'nadirMode': nadir, 'maxTextureSize': '--max_texture_size=%s' % max_texture_size, 'nvm_file': r['nvm_file'], 'intermediate': '--no_intermediate_results' if (r['labeling_file'] or not reconstruction.multi_camera) else '', 'labelingFile': '-L "%s"' % r['labeling_file'] if r['labeling_file'] else '' } mvs_tmp_dir = os.path.join(r['out_dir'], 'tmp') # Make sure tmp directory is empty if io.dir_exists(mvs_tmp_dir): log.ODM_INFO("Removing old tmp directory {}".format(mvs_tmp_dir)) shutil.rmtree(mvs_tmp_dir) # run texturing binary system.run('"{bin}" "{nvm_file}" "{model}" "{out_dir}" ' '-d {dataTerm} -o {outlierRemovalType} ' '-t {toneMapping} ' '{intermediate} ' '{skipGlobalSeamLeveling} ' '{skipLocalSeamLeveling} ' '{keepUnseenFaces} ' '{nadirMode} ' '{labelingFile} ' '{maxTextureSize} '.format(**kwargs)) # Backward compatibility: copy odm_textured_model_geo.mtl to odm_textured_model.mtl # for certain older WebODM clients which expect a odm_textured_model.mtl # to be present for visualization # We should remove this at some point in the future geo_mtl = os.path.join(r['out_dir'], 'odm_textured_model_geo.mtl') if io.file_exists(geo_mtl): nongeo_mtl = os.path.join(r['out_dir'], 'odm_textured_model.mtl') shutil.copy(geo_mtl, nongeo_mtl) progress += progress_per_run self.update_progress(progress) else: log.ODM_WARNING('Found a valid ODM Texture file in: %s' % odm_textured_model_obj) if args.optimize_disk_space: for r in nonloc.runs: if io.file_exists(r['model']): os.remove(r['model']) undistorted_images_path = os.path.join(tree.opensfm, "undistorted", "images") if io.dir_exists(undistorted_images_path): shutil.rmtree(undistorted_images_path)
def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] class nonloc: runs = [] def add_run(nvm_file, primary=True, band=None): subdir = "" if not primary and band is not None: subdir = band if not args.skip_3dmodel and (primary or args.use_3dmesh): nonloc.runs += [{ 'out_dir': os.path.join(tree.odm_texturing, subdir), 'model': tree.odm_mesh, 'nadir': False, 'primary': primary, 'nvm_file': nvm_file, 'labeling_file': os.path.join(tree.odm_texturing, "odm_textured_model_labeling.vec") if subdir else None }] if not args.use_3dmesh: nonloc.runs += [{ 'out_dir': os.path.join(tree.odm_25dtexturing, subdir), 'model': tree.odm_25dmesh, 'nadir': True, 'primary': primary, 'nvm_file': nvm_file, 'labeling_file': os.path.join(tree.odm_25dtexturing, "odm_textured_model_labeling.vec") if subdir else None }] if reconstruction.multi_camera: for band in reconstruction.multi_camera: primary = band['name'] == get_primary_band_name( reconstruction.multi_camera, args.primary_band) nvm_file = os.path.join( tree.opensfm, "undistorted", "reconstruction_%s.nvm" % band['name'].lower()) add_run(nvm_file, primary, band['name'].lower()) # Sort to make sure primary band is processed first nonloc.runs.sort(key=lambda r: r['primary'], reverse=True) else: add_run(tree.opensfm_reconstruction_nvm) progress_per_run = 100.0 / len(nonloc.runs) progress = 0.0 for r in nonloc.runs: if not io.dir_exists(r['out_dir']): system.mkdir_p(r['out_dir']) odm_textured_model_obj = os.path.join(r['out_dir'], tree.odm_textured_model_obj) if not io.file_exists(odm_textured_model_obj) or self.rerun(): log.ODM_INFO('Writing MVS Textured file in: %s' % odm_textured_model_obj) # Format arguments to fit Mvs-Texturing app skipGlobalSeamLeveling = "" skipLocalSeamLeveling = "" nadir = "" if (self.params.get('skip_glob_seam_leveling')): skipGlobalSeamLeveling = "--skip_global_seam_leveling" if (self.params.get('skip_loc_seam_leveling')): skipLocalSeamLeveling = "--skip_local_seam_leveling" if (r['nadir']): nadir = '--nadir_mode' # mvstex definitions kwargs = { 'bin': context.mvstex_path, 'out_dir': os.path.join(r['out_dir'], "odm_textured_model"), 'model': r['model'], 'dataTerm': self.params.get('data_term'), 'outlierRemovalType': self.params.get('outlier_rem_type'), 'skipGlobalSeamLeveling': skipGlobalSeamLeveling, 'skipLocalSeamLeveling': skipLocalSeamLeveling, 'toneMapping': self.params.get('tone_mapping'), 'nadirMode': nadir, 'nvm_file': r['nvm_file'], 'intermediate': '--no_intermediate_results' if (r['labeling_file'] or not reconstruction.multi_camera) else '', 'labelingFile': '-L "%s"' % r['labeling_file'] if r['labeling_file'] else '' } mvs_tmp_dir = os.path.join(r['out_dir'], 'tmp') # Make sure tmp directory is empty if io.dir_exists(mvs_tmp_dir): log.ODM_INFO( "Removing old tmp directory {}".format(mvs_tmp_dir)) shutil.rmtree(mvs_tmp_dir) # run texturing binary system.run('{bin} {nvm_file} {model} {out_dir} ' '-d {dataTerm} -o {outlierRemovalType} ' '-t {toneMapping} ' '{intermediate} ' '{skipGlobalSeamLeveling} ' '{skipLocalSeamLeveling} ' '{nadirMode} ' '{labelingFile} '.format(**kwargs)) progress += progress_per_run self.update_progress(progress) else: log.ODM_WARNING('Found a valid ODM Texture file in: %s' % odm_textured_model_obj) if args.optimize_disk_space: for r in nonloc.runs: if io.file_exists(r['model']): os.remove(r['model']) undistorted_images_path = os.path.join(tree.opensfm, "undistorted", "images") if io.dir_exists(undistorted_images_path): shutil.rmtree(undistorted_images_path)
def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] doPointCloudGeo = True transformPointCloud = True verbose = '-verbose' if self.params.get('verbose') else '' class nonloc: runs = [] def add_run(primary=True, band=None): subdir = "" if not primary and band is not None: subdir = band # Make sure 2.5D mesh is georeferenced before the 3D mesh # Because it will be used to calculate a transform # for the point cloud. If we use the 3D model transform, # DEMs and orthophoto might not align! if not args.use_3dmesh: nonloc.runs += [{ 'georeferencing_dir': os.path.join(tree.odm_25dgeoreferencing, subdir), 'texturing_dir': os.path.join(tree.odm_25dtexturing, subdir), }] if not args.skip_3dmodel and (primary or args.use_3dmesh): nonloc.runs += [{ 'georeferencing_dir': tree.odm_georeferencing, 'texturing_dir': os.path.join(tree.odm_texturing, subdir), }] if reconstruction.multi_camera: for band in reconstruction.multi_camera: primary = band['name'] == get_primary_band_name( reconstruction.multi_camera, args.primary_band) add_run(primary, band['name'].lower()) else: add_run() progress_per_run = 100.0 / len(nonloc.runs) progress = 0.0 for r in nonloc.runs: if not io.dir_exists(r['georeferencing_dir']): system.mkdir_p(r['georeferencing_dir']) odm_georeferencing_model_obj_geo = os.path.join( r['texturing_dir'], tree.odm_georeferencing_model_obj_geo) odm_georeferencing_model_obj = os.path.join( r['texturing_dir'], tree.odm_textured_model_obj) odm_georeferencing_log = os.path.join(r['georeferencing_dir'], tree.odm_georeferencing_log) odm_georeferencing_transform_file = os.path.join( r['georeferencing_dir'], tree.odm_georeferencing_transform_file) odm_georeferencing_model_txt_geo_file = os.path.join( r['georeferencing_dir'], tree.odm_georeferencing_model_txt_geo) if not io.file_exists(odm_georeferencing_model_obj_geo) or \ not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun(): # odm_georeference definitions kwargs = { 'bin': context.odm_modules_path, 'input_pc_file': tree.filtered_point_cloud, 'bundle': tree.opensfm_bundle, 'imgs': tree.dataset_raw, 'imgs_list': tree.opensfm_bundle_list, 'model': odm_georeferencing_model_obj, 'log': odm_georeferencing_log, 'input_trans_file': tree.opensfm_transformation, 'transform_file': odm_georeferencing_transform_file, 'coords': tree.odm_georeferencing_coords, 'output_pc_file': tree.odm_georeferencing_model_laz, 'geo_sys': odm_georeferencing_model_txt_geo_file, 'model_geo': odm_georeferencing_model_obj_geo, 'verbose': verbose } if transformPointCloud: kwargs[ 'pc_params'] = '-inputPointCloudFile {input_pc_file} -outputPointCloudFile {output_pc_file}'.format( **kwargs) if reconstruction.is_georeferenced(): kwargs[ 'pc_params'] += ' -outputPointCloudSrs %s' % pipes.quote( reconstruction.georef.proj4()) else: log.ODM_WARNING( 'NO SRS: The output point cloud will not have a SRS.' ) else: kwargs['pc_params'] = '' if io.file_exists( tree.opensfm_transformation) and io.file_exists( tree.odm_georeferencing_coords): log.ODM_INFO( 'Running georeferencing with OpenSfM transformation matrix' ) system.run( '{bin}/odm_georef -bundleFile {bundle} -inputTransformFile {input_trans_file} -inputCoordFile {coords} ' '-inputFile {model} -outputFile {model_geo} ' '{pc_params} {verbose} ' '-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}' .format(**kwargs)) elif io.file_exists(tree.odm_georeferencing_coords): log.ODM_INFO( 'Running georeferencing with generated coords file.') system.run( '{bin}/odm_georef -bundleFile {bundle} -inputCoordFile {coords} ' '-inputFile {model} -outputFile {model_geo} ' '{pc_params} {verbose} ' '-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}' .format(**kwargs)) else: log.ODM_WARNING( 'Georeferencing failed. Make sure your ' 'photos have geotags in the EXIF or you have ' 'provided a GCP file. ') doPointCloudGeo = False # skip the rest of the georeferencing if doPointCloudGeo: reconstruction.georef.extract_offsets( odm_georeferencing_model_txt_geo_file) point_cloud.post_point_cloud_steps(args, tree) if args.crop > 0: log.ODM_INFO( "Calculating cropping area and generating bounds shapefile from point cloud" ) cropper = Cropper(tree.odm_georeferencing, 'odm_georeferenced_model') if args.fast_orthophoto: decimation_step = 10 else: decimation_step = 40 # More aggressive decimation for large datasets if not args.fast_orthophoto: decimation_step *= int( len(reconstruction.photos) / 1000) + 1 decimation_step = min(decimation_step, 95) try: cropper.create_bounds_gpkg( tree.odm_georeferencing_model_laz, args.crop, decimation_step=decimation_step) except: log.ODM_WARNING( "Cannot calculate crop bounds! We will skip cropping" ) args.crop = 0 # Do not execute a second time, since # We might be doing georeferencing for # multiple models (3D, 2.5D, ...) doPointCloudGeo = False transformPointCloud = False else: log.ODM_WARNING('Found a valid georeferenced model in: %s' % tree.odm_georeferencing_model_laz) if args.optimize_disk_space and io.file_exists( tree.odm_georeferencing_model_laz) and io.file_exists( tree.filtered_point_cloud): os.remove(tree.filtered_point_cloud) progress += progress_per_run self.update_progress(progress)
def process(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'] verbose = '-verbose' if args.verbose else '' # define paths and create working directories system.mkdir_p(tree.odm_orthophoto) if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun(): gsd_error_estimate = 0.1 ignore_resolution = False if not reconstruction.is_georeferenced(): # Match DEMs gsd_error_estimate = -3 ignore_resolution = True resolution = 1.0 / ( gsd.cap_resolution(args.orthophoto_resolution, tree.opensfm_reconstruction, gsd_error_estimate=gsd_error_estimate, ignore_gsd=args.ignore_gsd, ignore_resolution=ignore_resolution, has_gcp=reconstruction.has_gcp()) / 100.0) # odm_orthophoto definitions kwargs = { 'bin': context.odm_modules_path, 'log': tree.odm_orthophoto_log, 'ortho': tree.odm_orthophoto_render, 'corners': tree.odm_orthophoto_corners, 'res': resolution, 'bands': '', 'verbose': verbose } models = [] if args.use_3dmesh: base_dir = tree.odm_texturing else: base_dir = tree.odm_25dtexturing model_file = tree.odm_textured_model_obj if reconstruction.multi_camera: for band in reconstruction.multi_camera: primary = band['name'] == get_primary_band_name( reconstruction.multi_camera, args.primary_band) subdir = "" if not primary: subdir = band['name'].lower() models.append(os.path.join(base_dir, subdir, model_file)) kwargs['bands'] = '-bands %s' % (','.join( [quote(b['name']) for b in reconstruction.multi_camera])) else: models.append(os.path.join(base_dir, model_file)) kwargs['models'] = ','.join(map(quote, models)) # run odm_orthophoto system.run( '{bin}/odm_orthophoto -inputFiles {models} ' '-logFile {log} -outputFile {ortho} -resolution {res} {verbose} ' '-outputCornerFile {corners} {bands}'.format(**kwargs)) # Create georeferenced GeoTiff geotiffcreated = False if reconstruction.is_georeferenced(): ulx = uly = lrx = lry = 0.0 with open(tree.odm_orthophoto_corners) as f: for lineNumber, line in enumerate(f): if lineNumber == 0: tokens = line.split(' ') if len(tokens) == 4: ulx = float(tokens[0]) + \ float(reconstruction.georef.utm_east_offset) lry = float(tokens[1]) + \ float(reconstruction.georef.utm_north_offset) lrx = float(tokens[2]) + \ float(reconstruction.georef.utm_east_offset) uly = float(tokens[3]) + \ float(reconstruction.georef.utm_north_offset) log.ODM_INFO('Creating GeoTIFF') orthophoto_vars = orthophoto.get_orthophoto_vars(args) kwargs = { 'ulx': ulx, 'uly': uly, 'lrx': lrx, 'lry': lry, 'vars': ' '.join([ '-co %s=%s' % (k, orthophoto_vars[k]) for k in orthophoto_vars ]), 'proj': reconstruction.georef.proj4(), 'input': tree.odm_orthophoto_render, 'output': tree.odm_orthophoto_tif, 'log': tree.odm_orthophoto_tif_log, 'max_memory': get_max_memory(), } system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} ' '{vars} ' '-a_srs \"{proj}\" ' '--config GDAL_CACHEMAX {max_memory}% ' '--config GDAL_TIFF_INTERNAL_MASK YES ' '{input} {output} > {log}'.format(**kwargs)) bounds_file_path = os.path.join( tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg') # Cutline computation, before cropping # We want to use the full orthophoto, not the cropped one. if args.orthophoto_cutline: cutline_file = os.path.join(tree.odm_orthophoto, "cutline.gpkg") compute_cutline(tree.odm_orthophoto_tif, bounds_file_path, cutline_file, args.max_concurrency, tmpdir=os.path.join( tree.odm_orthophoto, "grass_cutline_tmpdir"), scale=0.25) orthophoto.compute_mask_raster( tree.odm_orthophoto_tif, cutline_file, os.path.join(tree.odm_orthophoto, "odm_orthophoto_cut.tif"), blend_distance=20, only_max_coords_feature=True) orthophoto.post_orthophoto_steps(args, bounds_file_path, tree.odm_orthophoto_tif, tree.orthophoto_tiles) # Generate feathered orthophoto also if args.orthophoto_cutline: orthophoto.feather_raster( tree.odm_orthophoto_tif, os.path.join(tree.odm_orthophoto, "odm_orthophoto_feathered.tif"), blend_distance=20) geotiffcreated = True if not geotiffcreated: if io.file_exists(tree.odm_orthophoto_render): pseudogeo.add_pseudo_georeferencing( tree.odm_orthophoto_render) log.ODM_INFO( "Renaming %s --> %s" % (tree.odm_orthophoto_render, tree.odm_orthophoto_tif)) os.rename(tree.odm_orthophoto_render, tree.odm_orthophoto_tif) else: log.ODM_WARNING( "Could not generate an orthophoto (it did not render)") else: log.ODM_WARNING('Found a valid orthophoto in: %s' % tree.odm_orthophoto_tif) if args.optimize_disk_space and io.file_exists( tree.odm_orthophoto_render): os.remove(tree.odm_orthophoto_render)