def get_descriptors_from_database( database: COLMAPDatabase, images: kapture.RecordsCamera, kapture_dirpath: str, descriptor_name: str = 'SIFT') -> Optional[kapture.Descriptors]: """ Writes descriptors files and return the list in kapture format from the colmap database. :param database: colmap database. :param images: list of images (as RecordsCamera). :param kapture_dirpath: input root path to kapture. :param descriptor_name: name of the keypoints descriptor (by default, in colmap, its SIFT, but can be imported) :return: kapture descriptors """ image_filenames = set() dtype = np.uint8 # values in the range 0…255 # see https://colmap.github.io/tutorial.html#feature-detection-and-extraction dsize = None # usually uint8, 128, will be retrieved on first descriptor of DB colmap_descriptors = ((image_id, blob_to_array( data, dtype, (rows, cols)) if (rows > 0 and cols > 0) else np.zeros( (0, dsize))) for image_id, rows, cols, data in database.execute( "SELECT image_id, rows, cols, data FROM descriptors")) hide_progressbar = logger.getEffectiveLevel() > logging.INFO for image_id, image_descriptors in tqdm(colmap_descriptors, disable=hide_progressbar): # retrieve image path from image_id (actually the timestamp) if dsize is None: dsize = int(image_descriptors.shape[1]) elif dsize != image_descriptors.shape[ 1] or dtype != image_descriptors.dtype: raise ValueError('inconsistent descriptors size or type.') image_filename = next((v for v in images[image_id].values()), None) assert image_filename descriptors_filepath = kapture.io.features.get_descriptors_fullpath( kapture_dirpath, image_filename) if image_descriptors.shape[0] == 0: logger.warning( f'image={image_id}:{image_filename} has 0 descriptors.') # save the actual file kapture.io.features.image_keypoints_to_file(descriptors_filepath, image_descriptors) # register it into to kapture image_filenames.add(image_filename) if image_filenames: return kapture.Descriptors(descriptor_name, dtype, dsize, image_filenames) else: return None
def sub_kapture_from_img_list(kdata, kdata_path, img_list, pairs): trajectories = kapture.Trajectories() sensors = kapture.Sensors() records = kapture.RecordsCamera() keypoints = kapture.Keypoints(kdata.keypoints._tname, kdata.keypoints._dtype, kdata.keypoints._dsize) if kdata.descriptors != None: descriptors = kapture.Descriptors(kdata.descriptors._tname, kdata.descriptors._dtype, kdata.descriptors._dsize) else: descriptors = None matches = kapture.Matches() timestamp_sensor_id_from_image_name = { img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in kapture.flatten( kdata.records_camera) } for img in img_list: timestamp, sensor_id = timestamp_sensor_id_from_image_name[img] pose = kdata.trajectories[timestamp][sensor_id] sensors[sensor_id] = kdata.sensors[sensor_id] records[timestamp, sensor_id] = img trajectories[timestamp, sensor_id] = pose keypoints.add(img) if kdata.descriptors != None: descriptors.add(img) for i in pairs: image_matches_filepath = get_matches_fullpath((i[0], i[1]), kdata_path) if os.path.exists(image_matches_filepath): matches.add(i[0], i[1]) matches.normalize() return kapture.Kapture(sensors=sensors, trajectories=trajectories, records_camera=records, descriptors=descriptors, keypoints=keypoints, matches=matches)
def sub_kapture_from_img_list(kdata, img_list, pairs, keypoints_type, descriptors_type): trajectories = kapture.Trajectories() sensors = kapture.Sensors() records = kapture.RecordsCamera() keypoints = kapture.Keypoints(kdata.keypoints[keypoints_type].type_name, kdata.keypoints[keypoints_type].dtype, kdata.keypoints[keypoints_type].dsize) if kdata.descriptors is not None and descriptors_type in kdata.descriptors: descriptors = kapture.Descriptors(kdata.descriptors[descriptors_type].type_name, kdata.descriptors[descriptors_type].dtype, kdata.descriptors[descriptors_type].dsize, kdata.descriptors[descriptors_type].keypoints_type, kdata.descriptors[descriptors_type].metric_type) else: descriptors = None matches = kapture.Matches() timestamp_sensor_id_from_image_name = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in kapture.flatten(kdata.records_camera)} for img in img_list: timestamp, sensor_id = timestamp_sensor_id_from_image_name[img] sensors[sensor_id] = kdata.sensors[sensor_id] records[timestamp, sensor_id] = img if (timestamp, sensor_id) in kdata.trajectories: pose = kdata.trajectories[timestamp][sensor_id] trajectories[timestamp, sensor_id] = pose keypoints.add(img) if kdata.descriptors is not None: descriptors.add(img) for i in pairs: if i in kdata.matches[keypoints_type]: matches.add(i[0], i[1]) matches.normalize() return kapture.Kapture(sensors=sensors, trajectories=trajectories, records_camera=records, descriptors={descriptors_type: descriptors}, keypoints={keypoints_type: keypoints}, matches={keypoints_type: matches})
def import_openmvg_regions(openmvg_regions_directory_path, kapture_data, kapture_path): # look for the "image_describer.json" image_describer_path = path.join(openmvg_regions_directory_path, 'image_describer.json') if not path.isfile(image_describer_path): logger.debug(f'file not found : {image_describer_path}') return with open(image_describer_path) as f: image_describer = json.load(f) # retrieve what type of keypoints it is. keypoints_type = image_describer.get('regions_type', {}).get('polymorphic_name', 'UNDEFINED') keypoints_name = { 'SIFT_Regions': 'SIFT', 'AKAZE_Float_Regions': 'AKAZE' }.get(keypoints_type, keypoints_type) kapture_keypoints = kapture.Keypoints(type_name=keypoints_name, dtype=float, dsize=4) # retrieve what type of descriptors it is. descriptors_type = image_describer.get('image_describer', {}).get('polymorphic_name', 'UNDEFINED') descriptors_props = { 'SIFT_Image_describer': dict(type_name='SIFT', dtype=np.int32, dsize=128), 'AKAZE_Image_describer_SURF': dict(type_name='AKAZE', dtype=np.int32, dsize=128), }.get(descriptors_type) if not descriptors_props: raise ValueError( f'conversion of {descriptors_type} descriptors not implemented.') kapture_descriptors = kapture.Descriptors(**descriptors_props) # populate regions files in openMVG directory # https://github.com/openMVG/openMVG/blob/master/src/openMVG/features/scalar_regions.hpp#L23 for _, _, image_name in kapture.flatten(kapture_data.records_camera): openmvg_image_name = path.splitext(path.basename(image_name))[0] # keypoints openmvg_keypoints_filepath = path.join(openmvg_regions_directory_path, openmvg_image_name + '.feat') if path.isfile(openmvg_keypoints_filepath): # there is a keypoints file in openMVG, lets add it to kapture keypoints_data = np.loadtxt(openmvg_keypoints_filepath) assert keypoints_data.shape[1] == 4 kapture_keypoints.add(image_name) # and convert file kapture_keypoints_filepath = kapture.io.features.get_keypoints_fullpath( kapture_path, image_name) array_to_file(kapture_keypoints_filepath, keypoints_data) # descriptors openmvg_descriptors_filepath = path.join( openmvg_regions_directory_path, openmvg_image_name + '.desc') if path.isfile(openmvg_descriptors_filepath): assert path.isfile(openmvg_keypoints_filepath) # there is a keypoints file in openMVG, lets add it to kapture # assumes descriptors shape from keypoints_data shape descriptors_data_bytes = np.fromfile(openmvg_descriptors_filepath, dtype=np.uint8) nb_features = keypoints_data.shape[0] descriptors_shape = descriptors_data_bytes[0:8].view( descriptors_props['dtype']) assert descriptors_shape[0] == nb_features descriptors_data = descriptors_data_bytes[8:].view( np.uint8).reshape((nb_features, 128)) # descriptors_data.reshape((keypoints_data.shape[0], -1)) kapture_descriptors.add(image_name) # and convert file kapture_descriptors_filepath = kapture.io.features.get_descriptors_fullpath( kapture_path, image_name) array_to_file(kapture_descriptors_filepath, descriptors_data) kapture_data.keypoints = kapture_keypoints kapture_data.descriptors = kapture_descriptors
def test_init_descriptors_unknown(self): descriptors = kapture.Descriptors( 'R2D2', float, 64, ['a/a.jpg', 'b/b.jpg', 'c/c.jpg', 'c/c.jpg']) self.assertEqual('R2D2', descriptors.type_name) self.assertEqual(3, len(descriptors)) self.assertIn('a/a.jpg', descriptors)
def extract_kapture_keypoints(kapture_root, config, output_dir='', overwrite=False): """ Extract r2d2 keypoints and descritors to the kapture format directly """ print('extract_kapture_keypoints...') kdata = kapture_from_dir(kapture_root, matches_pairsfile_path=None, skip_list= [kapture.GlobalFeatures, kapture.Matches, kapture.Points3d, kapture.Observations]) export_dir = output_dir if output_dir else kapture_root # root of output directory for features os.makedirs(export_dir, exist_ok=True) assert kdata.records_camera is not None image_list = [filename for _, _, filename in kapture.flatten(kdata.records_camera)] # resume extraction if some features exist try: # load existing features, if any kdata.keypoints = keypoints_from_dir(export_dir, None) kdata.descriptors = descriptors_from_dir(export_dir, None) if kdata.keypoints is not None and kdata.descriptors is not None and not overwrite: image_list = [name for name in image_list if name not in kdata.keypoints or name not in kdata.descriptors] except FileNotFoundError: pass except: logging.exception("Error with importing existing local features.") # clear features first if overwriting if overwrite: delete_existing_kapture_files(export_dir, True, only=[kapture.Descriptors, kapture.Keypoints]) if len(image_list) == 0: print('All features were already extracted') return else: print(f'Extracting r2d2 features for {len(image_list)} images') iscuda = common.torch_set_gpu([torch.cuda.is_available()]) # load the network... net = load_network(config['checkpoint']) if iscuda: net = net.cuda() # create the non-maxima detector detector = NonMaxSuppression( rel_thr = config['reliability_thr'], rep_thr = config['repeatability_thr']) keypoints_dtype = None if kdata.keypoints is None else kdata.keypoints.dtype descriptors_dtype = None if kdata.descriptors is None else kdata.descriptors.dtype keypoints_dsize = None if kdata.keypoints is None else kdata.keypoints.dsize descriptors_dsize = None if kdata.descriptors is None else kdata.descriptors.dsize for image_name in image_list: img_path = get_image_fullpath(kapture_root, image_name) if img_path.endswith('.txt'): images = open(img_path).read().splitlines() + images continue print(f"\nExtracting features for {img_path}") img = Image.open(img_path).convert('RGB') W, H = img.size img = norm_RGB(img)[None] if iscuda: img = img.cuda() # extract keypoints/descriptors for a single image xys, desc, scores = extract_multiscale(net, img, detector, scale_f = config['scale_f'], min_scale = config['min_scale'], max_scale = config['max_scale'], min_size = config['min_size'], max_size = config['max_size'], verbose = True) xys = xys.cpu().numpy() desc = desc.cpu().numpy() scores = scores.cpu().numpy() idxs = scores.argsort()[-config['top_k'] or None:] xys = xys[idxs] desc = desc[idxs] if keypoints_dtype is None or descriptors_dtype is None: keypoints_dtype = xys.dtype descriptors_dtype = desc.dtype keypoints_dsize = xys.shape[1] descriptors_dsize = desc.shape[1] kdata.keypoints = kapture.Keypoints('r2d2', keypoints_dtype, keypoints_dsize) kdata.descriptors = kapture.Descriptors('r2d2', descriptors_dtype, descriptors_dsize) keypoints_config_absolute_path = get_csv_fullpath(kapture.Keypoints, export_dir) descriptors_config_absolute_path = get_csv_fullpath(kapture.Descriptors, export_dir) keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints) descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors) else: assert kdata.keypoints.type_name == 'r2d2' assert kdata.descriptors.type_name == 'r2d2' assert kdata.keypoints.dtype == xys.dtype assert kdata.descriptors.dtype == desc.dtype assert kdata.keypoints.dsize == xys.shape[1] assert kdata.descriptors.dsize == desc.shape[1] keypoints_fullpath = get_keypoints_fullpath(export_dir, image_name) print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}") image_keypoints_to_file(keypoints_fullpath, xys) kdata.keypoints.add(image_name) descriptors_fullpath = get_descriptors_fullpath(export_dir, image_name) print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}") image_descriptors_to_file(descriptors_fullpath, desc) kdata.descriptors.add(image_name) if not keypoints_check_dir(kdata.keypoints, export_dir) or \ not descriptors_check_dir(kdata.descriptors, export_dir): print('local feature extraction ended successfully but not all files were saved')
if args.max_keypoints != float("+inf"): # keep the last (the highest) indexes idx_keep = scores.argsort()[-min(len(keypoints), args.max_keypoints):] keypoints = keypoints[idx_keep] descriptors = descriptors[idx_keep] if keypoints_dtype is None or descriptors_dtype is None: keypoints_dtype = keypoints.dtype descriptors_dtype = descriptors.dtype keypoints_dsize = keypoints.shape[1] descriptors_dsize = descriptors.shape[1] kdata.keypoints = kapture.Keypoints('d2net', keypoints_dtype, keypoints_dsize) kdata.descriptors = kapture.Descriptors('d2net', descriptors_dtype, descriptors_dsize) keypoints_config_absolute_path = get_csv_fullpath(kapture.Keypoints, args.kapture_root) descriptors_config_absolute_path = get_csv_fullpath(kapture.Descriptors, args.kapture_root) keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints) descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors) else: assert kdata.keypoints.type_name == 'd2net' assert kdata.descriptors.type_name == 'd2net' assert kdata.keypoints.dtype == keypoints.dtype assert kdata.descriptors.dtype == descriptors.dtype assert kdata.keypoints.dsize == keypoints.shape[1] assert kdata.descriptors.dsize == descriptors.shape[1] keypoints_fullpath = get_keypoints_fullpath(args.kapture_root, image_name)
def extract_kapture_keypoints(args): """ Extract r2d2 keypoints and descritors to the kapture format directly """ print('extract_kapture_keypoints...') kdata = kapture_from_dir(args.kapture_root, matches_pairsfile_path=None, skip_list=[ kapture.GlobalFeatures, kapture.Matches, kapture.Points3d, kapture.Observations ]) assert kdata.records_camera is not None image_list = [ filename for _, _, filename in kapture.flatten(kdata.records_camera) ] if kdata.keypoints is not None and kdata.descriptors is not None: image_list = [ name for name in image_list if name not in kdata.keypoints or name not in kdata.descriptors ] if len(image_list) == 0: print('All features were already extracted') return else: print(f'Extracting r2d2 features for {len(image_list)} images') iscuda = common.torch_set_gpu(args.gpu) # load the network... net = load_network(args.model) if iscuda: net = net.cuda() # create the non-maxima detector detector = NonMaxSuppression(rel_thr=args.reliability_thr, rep_thr=args.repeatability_thr) keypoints_dtype = None if kdata.keypoints is None else kdata.keypoints.dtype descriptors_dtype = None if kdata.descriptors is None else kdata.descriptors.dtype keypoints_dsize = None if kdata.keypoints is None else kdata.keypoints.dsize descriptors_dsize = None if kdata.descriptors is None else kdata.descriptors.dsize for image_name in image_list: img_path = get_image_fullpath(args.kapture_root, image_name) print(f"\nExtracting features for {img_path}") img = Image.open(img_path).convert('RGB') W, H = img.size img = norm_RGB(img)[None] if iscuda: img = img.cuda() # extract keypoints/descriptors for a single image xys, desc, scores = extract_multiscale(net, img, detector, scale_f=args.scale_f, min_scale=args.min_scale, max_scale=args.max_scale, min_size=args.min_size, max_size=args.max_size, verbose=True) xys = xys.cpu().numpy() desc = desc.cpu().numpy() scores = scores.cpu().numpy() idxs = scores.argsort()[-args.top_k or None:] xys = xys[idxs] desc = desc[idxs] if keypoints_dtype is None or descriptors_dtype is None: keypoints_dtype = xys.dtype descriptors_dtype = desc.dtype keypoints_dsize = xys.shape[1] descriptors_dsize = desc.shape[1] kdata.keypoints = kapture.Keypoints('r2d2', keypoints_dtype, keypoints_dsize) kdata.descriptors = kapture.Descriptors('r2d2', descriptors_dtype, descriptors_dsize) keypoints_config_absolute_path = get_csv_fullpath( kapture.Keypoints, args.kapture_root) descriptors_config_absolute_path = get_csv_fullpath( kapture.Descriptors, args.kapture_root) keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints) descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors) else: assert kdata.keypoints.type_name == 'r2d2' assert kdata.descriptors.type_name == 'r2d2' assert kdata.keypoints.dtype == xys.dtype assert kdata.descriptors.dtype == desc.dtype assert kdata.keypoints.dsize == xys.shape[1] assert kdata.descriptors.dsize == desc.shape[1] keypoints_fullpath = get_keypoints_fullpath(args.kapture_root, image_name) print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}") image_keypoints_to_file(keypoints_fullpath, xys) kdata.keypoints.add(image_name) descriptors_fullpath = get_descriptors_fullpath( args.kapture_root, image_name) print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}") image_descriptors_to_file(descriptors_fullpath, desc) kdata.descriptors.add(image_name) if not keypoints_check_dir(kdata.keypoints, args.kapture_root) or \ not descriptors_check_dir(kdata.descriptors, args.kapture_root): print( 'local feature extraction ended successfully but not all files were saved' )
def _import_features_and_matches(opensfm_root_dir, kapture_root_dir, disable_tqdm)\ -> Tuple[kapture.Descriptors, kapture.Keypoints, kapture.Matches]: # import features (keypoints + descriptors) kapture_keypoints = None # kapture.Keypoints(type_name='opensfm', dsize=4, dtype=np.float64) kapture_descriptors = None # kapture.Descriptors(type_name='opensfm', dsize=128, dtype=np.uint8) opensfm_features_dir_path = path.join(opensfm_root_dir, 'features') opensfm_features_suffix = '.features.npz' if path.isdir(opensfm_features_dir_path): logger.info('importing keypoints and descriptors ...') opensfm_features_file_list = (path.join( dp, fn) for dp, _, fs in os.walk(opensfm_features_dir_path) for fn in fs) opensfm_features_file_list = ( filepath for filepath in opensfm_features_file_list if filepath.endswith(opensfm_features_suffix)) for opensfm_feature_filename in tqdm(opensfm_features_file_list, disable=disable_tqdm): image_filename = path.relpath( opensfm_feature_filename, opensfm_features_dir_path)[:-len(opensfm_features_suffix)] opensfm_image_features = np.load(opensfm_feature_filename) opensfm_image_keypoints = opensfm_image_features['points'] opensfm_image_descriptors = opensfm_image_features['descriptors'] logger.debug( f'parsing keypoints and descriptors in {opensfm_feature_filename}' ) if kapture_keypoints is None: # print(type(opensfm_image_keypoints.dtype)) # HAHOG = Hessian Affine feature point detector + HOG descriptor kapture_keypoints = kapture.Keypoints( type_name='HessianAffine', dsize=opensfm_image_keypoints.shape[1], dtype=opensfm_image_keypoints.dtype) if kapture_descriptors is None: kapture_descriptors = kapture.Descriptors( type_name='HOG', dsize=opensfm_image_descriptors.shape[1], dtype=opensfm_image_descriptors.dtype) # convert keypoints file keypoint_file_path = kapture.io.features.get_features_fullpath( data_type=kapture.Keypoints, kapture_dirpath=kapture_root_dir, image_filename=image_filename) kapture.io.features.image_keypoints_to_file( filepath=keypoint_file_path, image_keypoints=opensfm_image_keypoints) # register the file kapture_keypoints.add(image_filename) # convert descriptors file descriptor_file_path = kapture.io.features.get_features_fullpath( data_type=kapture.Descriptors, kapture_dirpath=kapture_root_dir, image_filename=image_filename) kapture.io.features.image_descriptors_to_file( filepath=descriptor_file_path, image_descriptors=opensfm_image_descriptors) # register the file kapture_descriptors.add(image_filename) # import matches kapture_matches = kapture.Matches() opensfm_matches_suffix = '_matches.pkl.gz' opensfm_matches_dir_path = path.join(opensfm_root_dir, 'matches') if path.isdir(opensfm_matches_dir_path): logger.info('importing matches ...') opensfm_matches_file_list = (path.join( dp, fn) for dp, _, fs in os.walk(opensfm_matches_dir_path) for fn in fs) opensfm_matches_file_list = ( filepath for filepath in opensfm_matches_file_list if filepath.endswith(opensfm_matches_suffix)) for opensfm_matches_filename in tqdm(opensfm_matches_file_list, disable=disable_tqdm): image_filename_1 = path.relpath( opensfm_matches_filename, opensfm_matches_dir_path)[:-len(opensfm_matches_suffix)] logger.debug(f'parsing matches in {image_filename_1}') with gzip.open(opensfm_matches_filename, 'rb') as f: opensfm_matches = pickle.load(f) for image_filename_2, opensfm_image_matches in opensfm_matches.items( ): image_pair = (image_filename_1, image_filename_2) # register the pair to kapture kapture_matches.add(*image_pair) # convert the bin file to kapture kapture_matches_filepath = kapture.io.features.get_matches_fullpath( image_filename_pair=image_pair, kapture_dirpath=kapture_root_dir) kapture_image_matches = np.hstack([ opensfm_image_matches.astype(np.float64), # no matches scoring = assume all to one np.ones(shape=(opensfm_image_matches.shape[0], 1), dtype=np.float64) ]) kapture.io.features.image_matches_to_file( kapture_matches_filepath, kapture_image_matches) return kapture_descriptors, kapture_keypoints, kapture_matches
idx_keep = scores.argsort( )[-min(len(keypoints), args.max_keypoints):] keypoints = keypoints[idx_keep] descriptors = descriptors[idx_keep] if keypoints_dtype is None or descriptors_dtype is None: keypoints_dtype = keypoints.dtype descriptors_dtype = descriptors.dtype keypoints_dsize = keypoints.shape[1] descriptors_dsize = descriptors.shape[1] kdata.keypoints[args.keypoints_type] = kapture.Keypoints( 'd2net', keypoints_dtype, keypoints_dsize) kdata.descriptors[args.descriptors_type] = kapture.Descriptors( 'd2net', descriptors_dtype, descriptors_dsize, args.keypoints_type, 'L2') keypoints_config_absolute_path = get_feature_csv_fullpath( kapture.Keypoints, args.keypoints_type, args.kapture_root) descriptors_config_absolute_path = get_feature_csv_fullpath( kapture.Descriptors, args.descriptors_type, args.kapture_root) keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type]) descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors[args.descriptors_type]) else: assert kdata.keypoints[ args.keypoints_type].dtype == keypoints.dtype assert kdata.descriptors[
def import_opensfm( opensfm_rootdir: str, kapture_rootdir: str, force_overwrite_existing: bool = False, images_import_method: TransferAction = TransferAction.copy) -> None: disable_tqdm = logger.getEffectiveLevel() != logging.INFO # load reconstruction opensfm_reconstruction_filepath = path.join(opensfm_rootdir, 'reconstruction.json') with open(opensfm_reconstruction_filepath, 'rt') as f: opensfm_reconstruction = json.load(f) # remove the single list @ root opensfm_reconstruction = opensfm_reconstruction[0] # prepare space for output os.makedirs(kapture_rootdir, exist_ok=True) delete_existing_kapture_files(kapture_rootdir, force_erase=force_overwrite_existing) # import cameras kapture_sensors = kapture.Sensors() assert 'cameras' in opensfm_reconstruction # import cameras for osfm_camera_id, osfm_camera in opensfm_reconstruction['cameras'].items( ): camera = import_camera(osfm_camera, name=osfm_camera_id) kapture_sensors[osfm_camera_id] = camera # import shots logger.info('importing images and trajectories ...') kapture_images = kapture.RecordsCamera() kapture_trajectories = kapture.Trajectories() opensfm_image_dirpath = path.join(opensfm_rootdir, 'images') assert 'shots' in opensfm_reconstruction image_timestamps, image_sensors = {}, { } # used later to retrieve the timestamp of an image. for timestamp, (image_filename, shot) in enumerate( opensfm_reconstruction['shots'].items()): sensor_id = shot['camera'] image_timestamps[image_filename] = timestamp image_sensors[image_filename] = sensor_id # in OpenSfm, (sensor, timestamp) is not unique. rotation_vector = shot['rotation'] q = quaternion.from_rotation_vector(rotation_vector) translation = shot['translation'] # capture_time = shot['capture_time'] # may be invalid # gps_position = shot['gps_position'] kapture_images[timestamp, sensor_id] = image_filename kapture_trajectories[timestamp, sensor_id] = kapture.PoseTransform(r=q, t=translation) # copy image files filename_list = [f for _, _, f in kapture.flatten(kapture_images)] import_record_data_from_dir_auto( source_record_dirpath=opensfm_image_dirpath, destination_kapture_dirpath=kapture_rootdir, filename_list=filename_list, copy_strategy=images_import_method) # gps from pre-extracted exif, in exif/image_name.jpg.exif kapture_gnss = None opensfm_exif_dirpath = path.join(opensfm_rootdir, 'exif') opensfm_exif_suffix = '.exif' if path.isdir(opensfm_exif_dirpath): logger.info('importing GNSS from exif ...') camera_ids = set(image_sensors.values()) # add a gps sensor for each camera map_cam_to_gnss_sensor = { cam_id: 'GPS_' + cam_id for cam_id in camera_ids } for gnss_id in map_cam_to_gnss_sensor.values(): kapture_sensors[gnss_id] = kapture.Sensor( sensor_type='gnss', sensor_params=['EPSG:4326']) # build epsg_code for all cameras kapture_gnss = kapture.RecordsGnss() opensfm_exif_filepath_list = ( path.join(dirpath, filename) for dirpath, _, filename_list in os.walk(opensfm_exif_dirpath) for filename in filename_list if filename.endswith(opensfm_exif_suffix)) for opensfm_exif_filepath in tqdm(opensfm_exif_filepath_list, disable=disable_tqdm): image_filename = path.relpath( opensfm_exif_filepath, opensfm_exif_dirpath)[:-len(opensfm_exif_suffix)] image_timestamp = image_timestamps[image_filename] image_sensor_id = image_sensors[image_filename] gnss_timestamp = image_timestamp gnss_sensor_id = map_cam_to_gnss_sensor[image_sensor_id] with open(opensfm_exif_filepath, 'rt') as f: js_root = json.load(f) if 'gps' not in js_root: logger.warning(f'NO GPS data in "{opensfm_exif_filepath}"') continue gps_coords = { 'x': js_root['gps']['longitude'], 'y': js_root['gps']['latitude'], 'z': js_root['gps'].get('altitude', 0.0), 'dop': js_root['gps'].get('dop', 0), 'utc': 0, } logger.debug( f'found GPS data for ({gnss_timestamp}, {gnss_sensor_id}) in "{opensfm_exif_filepath}"' ) kapture_gnss[gnss_timestamp, gnss_sensor_id] = kapture.RecordGnss(**gps_coords) # import features (keypoints + descriptors) kapture_keypoints = None # kapture.Keypoints(type_name='opensfm', dsize=4, dtype=np.float64) kapture_descriptors = None # kapture.Descriptors(type_name='opensfm', dsize=128, dtype=np.uint8) opensfm_features_dirpath = path.join(opensfm_rootdir, 'features') opensfm_features_suffix = '.features.npz' if path.isdir(opensfm_features_dirpath): logger.info('importing keypoints and descriptors ...') opensfm_features_file_list = (path.join( dp, fn) for dp, _, fs in os.walk(opensfm_features_dirpath) for fn in fs) opensfm_features_file_list = ( filepath for filepath in opensfm_features_file_list if filepath.endswith(opensfm_features_suffix)) for opensfm_feature_filename in tqdm(opensfm_features_file_list, disable=disable_tqdm): image_filename = path.relpath( opensfm_feature_filename, opensfm_features_dirpath)[:-len(opensfm_features_suffix)] opensfm_image_features = np.load(opensfm_feature_filename) opensfm_image_keypoints = opensfm_image_features['points'] opensfm_image_descriptors = opensfm_image_features['descriptors'] logger.debug( f'parsing keypoints and descriptors in {opensfm_feature_filename}' ) if kapture_keypoints is None: # print(type(opensfm_image_keypoints.dtype)) # HAHOG = Hessian Affine feature point detector + HOG descriptor kapture_keypoints = kapture.Keypoints( type_name='HessianAffine', dsize=opensfm_image_keypoints.shape[1], dtype=opensfm_image_keypoints.dtype) if kapture_descriptors is None: kapture_descriptors = kapture.Descriptors( type_name='HOG', dsize=opensfm_image_descriptors.shape[1], dtype=opensfm_image_descriptors.dtype) # convert keypoints file keypoint_filpath = kapture.io.features.get_features_fullpath( data_type=kapture.Keypoints, kapture_dirpath=kapture_rootdir, image_filename=image_filename) kapture.io.features.image_keypoints_to_file( filepath=keypoint_filpath, image_keypoints=opensfm_image_keypoints) # register the file kapture_keypoints.add(image_filename) # convert descriptors file descriptor_filpath = kapture.io.features.get_features_fullpath( data_type=kapture.Descriptors, kapture_dirpath=kapture_rootdir, image_filename=image_filename) kapture.io.features.image_descriptors_to_file( filepath=descriptor_filpath, image_descriptors=opensfm_image_descriptors) # register the file kapture_descriptors.add(image_filename) # import matches kapture_matches = kapture.Matches() opensfm_matches_suffix = '_matches.pkl.gz' opensfm_matches_dirpath = path.join(opensfm_rootdir, 'matches') if path.isdir(opensfm_matches_dirpath): logger.info('importing matches ...') opensfm_matches_file_list = (path.join( dp, fn) for dp, _, fs in os.walk(opensfm_matches_dirpath) for fn in fs) opensfm_matches_file_list = ( filepath for filepath in opensfm_matches_file_list if filepath.endswith(opensfm_matches_suffix)) for opensfm_matches_filename in tqdm(opensfm_matches_file_list, disable=disable_tqdm): image_filename_1 = path.relpath( opensfm_matches_filename, opensfm_matches_dirpath)[:-len(opensfm_matches_suffix)] logger.debug(f'parsing mathes in {image_filename_1}') with gzip.open(opensfm_matches_filename, 'rb') as f: opensfm_matches = pickle.load(f) for image_filename_2, opensfm_image_matches in opensfm_matches.items( ): image_pair = (image_filename_1, image_filename_2) # register the pair to kapture kapture_matches.add(*image_pair) # convert the bin file to kapture kapture_matches_filepath = kapture.io.features.get_matches_fullpath( image_filename_pair=image_pair, kapture_dirpath=kapture_rootdir) kapture_image_matches = np.hstack([ opensfm_image_matches.astype(np.float64), # no macthes scoring = assume all to one np.ones(shape=(opensfm_image_matches.shape[0], 1), dtype=np.float64) ]) kapture.io.features.image_matches_to_file( kapture_matches_filepath, kapture_image_matches) # import 3-D points if 'points' in opensfm_reconstruction: logger.info('importing points 3-D') opensfm_points = opensfm_reconstruction['points'] points_data = [] for point_id in sorted(opensfm_points): point_data = opensfm_points[point_id] point_data = point_data['coordinates'] + point_data['color'] points_data.append(point_data) kapture_points = kapture.Points3d(points_data) else: kapture_points = None # saving kapture csv files logger.info('saving kapture files') kapture_data = kapture.Kapture(sensors=kapture_sensors, records_camera=kapture_images, records_gnss=kapture_gnss, trajectories=kapture_trajectories, keypoints=kapture_keypoints, descriptors=kapture_descriptors, matches=kapture_matches, points3d=kapture_points) kapture.io.csv.kapture_to_dir(dirpath=kapture_rootdir, kapture_data=kapture_data)