def match_features(self, kapture_data):
image_list = [
filename
for _, _, filename in kapture.flatten(kapture_data.records_camera)
]
descriptors = []
descriptor_type = kapture_data.descriptors.dtype
descriptor_size = kapture_data.descriptors.dsize
for image_path in image_list:
descriptors_full_path = get_descriptors_fullpath(
kapture_data.kapture_path, image_path)
descriptors.append(
image_descriptors_from_file(descriptors_full_path,
descriptor_type, descriptor_size))
kapture_data.matches = kapture.Matches()
if self._sequential_length is None:
self._sequential_length = len(image_list)
for i in tqdm(range(len(image_list))):
for j in range(i + 1,
min(len(image_list), i + self._sequential_length)):
matches = self._matcher.match_descriptors(
descriptors[i], descriptors[j])
if self._minimal_score is not None:
mask = matches[:, 2] > self._minimal_score
matches = matches[mask]
kapture_data.matches.add(image_list[i], image_list[j])
matches_full_path = get_matches_fullpath(
(image_list[i], image_list[j]), kapture_data.kapture_path)
image_matches_to_file(matches_full_path, matches)
def add_image_to_kapture(kdata_src,
kdata_src_path,
kdata_trg,
img_name,
pairs,
add_pose=False):
timestamp_sensor_id_from_image_name = {
img_name: (timestamp, sensor_id)
for timestamp, sensor_id, img_name in kapture.flatten(
kdata_src.records_camera)
}
timestamp, sensor_id = timestamp_sensor_id_from_image_name[img_name]
kdata_trg.sensors[sensor_id] = kdata_src.sensors[sensor_id]
kdata_trg.records_camera[timestamp, sensor_id] = img_name
kdata_trg.keypoints.add(img_name)
if kdata_trg.descriptors != None:
kdata_trg.descriptors.add(img_name)
if add_pose:
kdata_trg.trajectories[timestamp,
sensor_id] = kdata_src.trajectories[timestamp,
sensor_id]
if os.path.exists(kdata_src_path) and len(pairs) != 0:
kdata_trg.matches = kapture.Matches()
for i in pairs:
image_matches_filepath = get_matches_fullpath((i[0], i[1]),
kdata_src_path)
if os.path.exists(image_matches_filepath):
kdata_trg.matches.add(i[0], i[1])
kdata_trg.matches.normalize()
return kdata_trg
def compute_matches(input_path: str, pairsfile_path: str):
"""
compute matches from descriptors. images to match are selected from a pairsfile (csv with name1, name2, score)
:param input_path: input path to kapture input root directory
:type input_path: str
:param pairsfile_path: path to pairs file (csv with 3 fields, name1, name2, score)
:type pairsfile_path: str
"""
logger.info(f'compute_matches. loading input: {input_path}')
kdata = kapture_from_dir(input_path)
assert kdata.sensors is not None
assert kdata.records_camera is not None
assert kdata.descriptors is not None
image_pairs = get_pairs_from_file(pairsfile_path)
matcher = MatchPairNnTorch(use_cuda=torch.cuda.is_available())
new_matches = kapture.Matches()
logger.info('compute_matches. entering main loop...')
hide_progress_bar = logger.getEffectiveLevel() > logging.INFO
for image_path1, image_path2 in tqdm(image_pairs,
disable=hide_progress_bar):
if image_path1 == image_path2:
continue
if image_path1 > image_path2:
image_path1, image_path2 = image_path2, image_path1
if image_path1 not in kdata.descriptors or image_path2 not in kdata.descriptors:
logger.warning('unable to find descriptors for image pair : '
'\n\t{} \n\t{}'.format(image_path1, image_path2))
continue
descriptor1 = load_descriptors(input_path, image_path1,
kdata.descriptors.dtype,
kdata.descriptors.dsize)
descriptor2 = load_descriptors(input_path, image_path2,
kdata.descriptors.dtype,
kdata.descriptors.dsize)
matches = matcher.match_descriptors(descriptor1, descriptor2)
matches_path = get_matches_fullpath((image_path1, image_path2),
input_path)
image_matches_to_file(matches_path, matches)
new_matches.add(image_path1, image_path2)
if not matches_check_dir(new_matches, input_path):
logger.critical(
'matching ended successfully but not all files were saved')
logger.info('all done')
def compute_matches_from_loaded_data(input_path: str,
kdata: kapture.Kapture,
image_pairs: list,
overwrite_existing: bool = False):
assert kdata.sensors is not None
assert kdata.records_camera is not None
assert kdata.descriptors is not None
matcher = MatchPairNnTorch(use_cuda=torch.cuda.is_available())
new_matches = kapture.Matches()
logger.info('compute_matches. entering main loop...')
hide_progress_bar = logger.getEffectiveLevel() > logging.INFO
skip_count = 0
for image_path1, image_path2 in tqdm(image_pairs, disable=hide_progress_bar):
if image_path1 == image_path2:
continue
if image_path1 > image_path2:
image_path1, image_path2 = image_path2, image_path1
# skip existing matches
if (not overwrite_existing) and (kdata.matches is not None) and ((image_path1, image_path2) in kdata.matches):
new_matches.add(image_path1, image_path2)
skip_count += 1
continue
if image_path1 not in kdata.descriptors or image_path2 not in kdata.descriptors:
logger.warning('unable to find descriptors for image pair : '
'\n\t{} \n\t{}'.format(image_path1, image_path2))
continue
descriptor1 = load_descriptors(input_path, image_path1, kdata.descriptors.dtype, kdata.descriptors.dsize)
descriptor2 = load_descriptors(input_path, image_path2, kdata.descriptors.dtype, kdata.descriptors.dsize)
matches = matcher.match_descriptors(descriptor1, descriptor2)
matches_path = get_matches_fullpath((image_path1, image_path2), input_path)
image_matches_to_file(matches_path, matches)
new_matches.add(image_path1, image_path2)
if not overwrite_existing:
logger.debug(f'{skip_count} pairs were skipped because the match file already existed')
if not matches_check_dir(new_matches, input_path):
logger.critical('matching ended successfully but not all files were saved')
logger.info('all done')
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 get_correspondences(kapture_data: kapture.Kapture, keypoints_type: str,
kapture_path: str, tar_handlers: TarCollection,
img_query: str, pairs: List[str],
point_id_from_obs: Dict[Tuple[str, int], int],
kpts_query: Optional[np.ndarray],
kpts_query_undistorted: Optional[np.ndarray],
duplicate_strategy: DuplicateCorrespondencesStrategy,
rerank_strategy: RerankCorrespondencesStrategy):
"""
get 2D-3D correspondences for a given query image, a list of paired map images, and a kapture map
"""
# first list all correspondences
correspondences = {}
for img_map in pairs:
# get matches
if img_query < img_map:
if (img_query,
img_map) not in kapture_data.matches[keypoints_type]:
getLogger().warning(
f'pair {img_query}, {img_map} do not have a match file, skipped'
)
continue
matches_path = get_matches_fullpath((img_query, img_map),
keypoints_type, kapture_path,
tar_handlers)
else:
if (img_map,
img_query) not in kapture_data.matches[keypoints_type]:
getLogger().warning(
f'pair {img_query}, {img_map} do not have a match file, skipped'
)
matches_path = get_matches_fullpath((img_map, img_query),
keypoints_type, kapture_path,
tar_handlers)
matches = image_matches_from_file(matches_path)
num_matches = matches.shape[0]
corrs = []
for m in matches:
if img_query < img_map:
kpid_query = m[0]
kpid_map = m[1]
else:
kpid_query = m[1]
kpid_map = m[0]
# match_score = m[2]
if not (img_map, kpid_map) in point_id_from_obs:
continue
# get 3D point
p3did = point_id_from_obs[(img_map, kpid_map)]
corrs.append((kpid_query, p3did))
correspondences[img_map] = (num_matches, corrs)
if rerank_strategy == RerankCorrespondencesStrategy.none:
reranked_pairs = pairs
elif rerank_strategy == RerankCorrespondencesStrategy.matches_count:
reranked_pairs = [
img_map for img_map, _ in sorted(
correspondences.items(), key=lambda x: x[1][0], reverse=True)
]
elif rerank_strategy == RerankCorrespondencesStrategy.correspondences_count:
reranked_pairs = [
img_map for img_map, _ in sorted(correspondences.items(),
key=lambda x: len(x[1][1]),
reverse=True)
]
else:
raise NotImplementedError(f'{rerank_strategy} not implemented')
# N number of correspondences
# points2D - Nx2 array with pixel coordinates
# points3D - Nx3 array with world coordinates
points2D = []
points2D_undistorted = []
points3D = []
assigned_keypoints_ids = {}
assigned_3d_points_ids = {}
true_duplicates_count = 0
same_2d_multiple_3d_count = 0
same_2d_multiple_3d_max = 0
same_3d_multiple_2d_count = 0
same_3d_multiple_2d_max = 0
rejected_correspondences = 0
for img_map in reranked_pairs:
if img_map not in correspondences:
continue
for kpid_query, p3did in correspondences[img_map][1]:
if kpid_query in assigned_keypoints_ids and p3did in assigned_keypoints_ids[
kpid_query]:
true_duplicates_count += 1
if duplicate_strategy == DuplicateCorrespondencesStrategy.ignore or \
duplicate_strategy == DuplicateCorrespondencesStrategy.ignore_strict or \
duplicate_strategy == DuplicateCorrespondencesStrategy.ignore_same_kpid or \
duplicate_strategy == DuplicateCorrespondencesStrategy.ignore_same_p3did:
rejected_correspondences += 1
continue
elif duplicate_strategy == DuplicateCorrespondencesStrategy.ignore and \
(kpid_query in assigned_keypoints_ids or p3did in assigned_3d_points_ids):
rejected_correspondences += 1
continue
else:
if duplicate_strategy == DuplicateCorrespondencesStrategy.ignore_same_kpid and \
kpid_query in assigned_keypoints_ids:
rejected_correspondences += 1
continue
elif kpid_query not in assigned_keypoints_ids:
assigned_keypoints_ids[kpid_query] = {p3did}
else:
# p3did not in assigned_keypoints_ids[kpid_query]
same_2d_multiple_3d_count += 1
assigned_keypoints_ids[kpid_query].add(p3did)
same_2d_multiple_3d_max = max(
same_2d_multiple_3d_max,
len(assigned_keypoints_ids[kpid_query]))
if duplicate_strategy == DuplicateCorrespondencesStrategy.ignore_same_p3did and \
p3did in assigned_3d_points_ids:
rejected_correspondences += 1
continue
elif p3did not in assigned_3d_points_ids:
assigned_3d_points_ids[p3did] = {kpid_query}
else:
# kpid_query not in assigned_3d_points_ids[p3did]
same_3d_multiple_2d_count += 1
assigned_3d_points_ids[p3did].add(p3did)
same_3d_multiple_2d_max = max(
same_3d_multiple_2d_max,
len(assigned_3d_points_ids[p3did]))
if kpts_query is not None:
kp_query = kpts_query[int(kpid_query)]
points2D.append(kp_query[0:2])
if kpts_query_undistorted is not None:
kp_query_undistorted = kpts_query_undistorted[int(kpid_query)]
points2D_undistorted.append(kp_query_undistorted[0:2])
p3d_map = kapture_data.points3d[p3did]
points3D.append(p3d_map[0:3])
stats = {
"true_duplicates_count": true_duplicates_count,
"same_2d_multiple_3d_count": same_2d_multiple_3d_count,
"same_2d_multiple_3d_max": same_2d_multiple_3d_max,
"same_3d_multiple_2d_count": same_3d_multiple_2d_count,
"same_3d_multiple_2d_max": same_3d_multiple_2d_max,
"rejected_correspondences": rejected_correspondences
}
return points2D, points2D_undistorted, points3D, stats
def export_opensfm(
kapture_rootdir: str,
opensfm_rootdir: str,
force_overwrite_existing: bool = False,
images_import_method: TransferAction = TransferAction.copy) -> None:
"""
:param kapture_rootdir:
:param opensfm_rootdir:
:param force_overwrite_existing:
:param images_import_method:
:return:
"""
disable_tqdm = logger.getEffectiveLevel(
) > logging.INFO # dont display tqdm for non-verbose levels
# load reconstruction
kapture_data = kapture.io.csv.kapture_from_dir(
kapture_dirpath=kapture_rootdir)
# export cameras
opensfm_cameras = {}
kapture_cameras = {
cam_id: cam
for cam_id, cam in kapture_data.sensors.items()
if cam.sensor_type == 'camera'
}
for cam_id, kapture_camera in kapture_cameras.items():
opensfm_cameras[cam_id] = export_opensfm_camera(kapture_camera)
# export shots
opensfm_shots = {}
for timestamp, camera_id, image_filename in tqdm(kapture.flatten(
kapture_data.records_camera),
disable=disable_tqdm):
# retrieve pose (if there is one).
# opensfm_shots = {image_filename: shot}
# shot = {camera , rotation, translation, capture_time, gps_position, ...}
opensfm_shot = {
'capture_time': 0, # in ms != timestamp
'camera': camera_id,
}
if (timestamp, camera_id) in kapture_data.trajectories:
pose = kapture_data.trajectories[timestamp, camera_id]
rotation_vector = quaternion.as_rotation_vector(pose.r)
translation_vector = pose.t.flatten()
opensfm_shot.update({
'rotation': rotation_vector.tolist(),
'translation': translation_vector.tolist()
})
opensfm_shots[image_filename] = opensfm_shot
# pack it
opensfm_reconstruction = {
'cameras': opensfm_cameras,
'shots': opensfm_shots,
}
# images
logger.info(
f'writing image files "{path.join(opensfm_rootdir, "images")}".')
image_filenames = [
f for _, _, f in kapture.flatten(kapture_data.records_camera)
]
kapture_image_filepaths = [
get_record_fullpath(kapture_rootdir, image_filename)
for image_filename in image_filenames
]
opensfm_image_filepaths = [
path.join(opensfm_rootdir, 'images', image_filename)
for image_filename in image_filenames
]
transfer_files_from_dir(
source_filepath_list=kapture_image_filepaths,
destination_filepath_list=opensfm_image_filepaths,
force_overwrite=force_overwrite_existing,
copy_strategy=images_import_method,
)
# export features files (keypoints + descriptors)
opensfm_features_suffix = '.features.npz'
opensfm_features_dirpath = path.join(opensfm_rootdir, 'features')
logger.info(
f'exporting keypoint and descriptors to {opensfm_features_dirpath}')
os.makedirs(opensfm_features_dirpath, exist_ok=True)
for image_filename in tqdm(image_filenames, disable=disable_tqdm):
opensfm_features = {}
# look and load for keypoints in kapture
if kapture_data.keypoints is not None and image_filename in kapture_data.keypoints:
kapture_keypoints_filepath = get_keypoints_fullpath(
kapture_dirpath=kapture_rootdir, image_filename=image_filename)
logger.debug(f'loading {kapture_keypoints_filepath}')
kapture_keypoint = image_keypoints_from_file(
kapture_keypoints_filepath,
dtype=kapture_data.keypoints.dtype,
dsize=kapture_data.keypoints.dsize)
opensfm_features['points'] = kapture_keypoint
# look and load for descriptors in kapture
if kapture_data.descriptors is not None and image_filename in kapture_data.descriptors:
kapture_descriptor_filepath = get_descriptors_fullpath(
kapture_dirpath=kapture_rootdir, image_filename=image_filename)
logger.debug(f'loading {kapture_descriptor_filepath}')
kapture_descriptor = image_descriptors_from_file(
kapture_descriptor_filepath,
dtype=kapture_data.descriptors.dtype,
dsize=kapture_data.descriptors.dsize)
opensfm_features['descriptors'] = kapture_descriptor
# writing opensfm feature file
if len(opensfm_features) > 0:
opensfm_features_filepath = path.join(
opensfm_features_dirpath,
image_filename + opensfm_features_suffix)
logger.debug(f'writing {opensfm_features_filepath}')
os.makedirs(path.dirname(opensfm_features_filepath), exist_ok=True)
np.save(opensfm_features_filepath, opensfm_features)
# export matches files
if kapture_data.matches is not None:
opensfm_matches_suffix = '_matches.pkl.gz'
opensfm_matches_dirpath = path.join(opensfm_rootdir, 'matches')
os.makedirs(opensfm_matches_dirpath, exist_ok=True)
logger.info(f'exporting matches to {opensfm_matches_dirpath}')
opensfm_pairs = {}
for image_filename1, image_filename2 in kapture_data.matches:
opensfm_pairs.setdefault(image_filename1,
[]).append(image_filename2)
for image_filename1 in tqdm(image_filenames, disable=disable_tqdm):
opensfm_matches = {}
opensfm_matches_filepath = path.join(
opensfm_matches_dirpath,
image_filename1 + opensfm_matches_suffix)
logger.debug(f'loading matches for {image_filename1}')
for image_filename2 in opensfm_pairs.get(image_filename1, []):
# print(image_filename1, image_filename2)
kapture_matches_filepath = get_matches_fullpath(
(image_filename1, image_filename2),
kapture_dirpath=kapture_rootdir)
kapture_matches = image_matches_from_file(
kapture_matches_filepath)
opensfm_matches[image_filename2] = kapture_matches[:,
0:2].astype(
np.int)
os.makedirs(path.dirname(opensfm_matches_filepath), exist_ok=True)
with gzip.open(opensfm_matches_filepath, 'wb') as f:
pickle.dump(opensfm_matches, f)
# export 3D-points files
if kapture_data.points3d is not None:
logger.info('exporting points 3-D')
opensfm_reconstruction['points'] = {}
for i, (x, y, z, r, g, b) in tqdm(enumerate(kapture_data.points3d),
disable=disable_tqdm):
opensfm_reconstruction['points'][i] = {
'coordinates': [x, y, z],
'color': [r, g, b]
}
# write json files #################################################################################################
os.makedirs(opensfm_rootdir, exist_ok=True)
# write reconstruction.json
opensfm_reconstruction_filepath = path.join(opensfm_rootdir,
'reconstruction.json')
logger.info(
f'writing reconstruction file "{opensfm_reconstruction_filepath}".')
with open(opensfm_reconstruction_filepath, 'wt') as f:
json.dump([opensfm_reconstruction], f, indent=4)
# write camera_models.json
opensfm_cameras_filepath = path.join(opensfm_rootdir, 'camera_models.json')
logger.info(f'writing camera models file "{opensfm_cameras_filepath}".')
with open(opensfm_cameras_filepath, 'wt') as f:
json.dump(opensfm_cameras, f, indent=4)
def _export_opensfm_features_and_matches(image_filenames, kapture_data,
kapture_root_dir, opensfm_root_dir,
disable_tqdm):
"""
export features files (keypoints + descriptors) and matches
"""
opensfm_features_suffix = '.features.npz'
opensfm_features_dir_path = path.join(opensfm_root_dir, 'features')
logger.info(
f'exporting keypoint and descriptors to {opensfm_features_dir_path}')
os.makedirs(opensfm_features_dir_path, exist_ok=True)
for image_filename in tqdm(image_filenames, disable=disable_tqdm):
opensfm_features = {}
# look and load for keypoints in kapture
if kapture_data.keypoints is not None and image_filename in kapture_data.keypoints:
kapture_keypoints_filepath = get_keypoints_fullpath(
kapture_dirpath=kapture_root_dir,
image_filename=image_filename)
logger.debug(f'loading {kapture_keypoints_filepath}')
kapture_keypoint = image_keypoints_from_file(
kapture_keypoints_filepath,
dtype=kapture_data.keypoints.dtype,
dsize=kapture_data.keypoints.dsize)
opensfm_features['points'] = kapture_keypoint
# look and load for descriptors in kapture
if kapture_data.descriptors is not None and image_filename in kapture_data.descriptors:
kapture_descriptor_filepath = get_descriptors_fullpath(
kapture_dirpath=kapture_root_dir,
image_filename=image_filename)
logger.debug(f'loading {kapture_descriptor_filepath}')
kapture_descriptor = image_descriptors_from_file(
kapture_descriptor_filepath,
dtype=kapture_data.descriptors.dtype,
dsize=kapture_data.descriptors.dsize)
opensfm_features['descriptors'] = kapture_descriptor
# writing opensfm feature file
if len(opensfm_features) > 0:
opensfm_features_filepath = path.join(
opensfm_features_dir_path,
image_filename + opensfm_features_suffix)
logger.debug(f'writing {opensfm_features_filepath}')
os.makedirs(path.dirname(opensfm_features_filepath), exist_ok=True)
np.save(opensfm_features_filepath, opensfm_features)
# export matches files
if kapture_data.matches is not None:
opensfm_matches_suffix = '_matches.pkl.gz'
opensfm_matches_dir_path = path.join(opensfm_root_dir, 'matches')
os.makedirs(opensfm_matches_dir_path, exist_ok=True)
logger.info(f'exporting matches to {opensfm_matches_dir_path}')
opensfm_pairs = {}
for image_filename1, image_filename2 in kapture_data.matches:
opensfm_pairs.setdefault(image_filename1,
[]).append(image_filename2)
for image_filename1 in tqdm(image_filenames, disable=disable_tqdm):
opensfm_matches = {}
opensfm_matches_filepath = path.join(
opensfm_matches_dir_path,
image_filename1 + opensfm_matches_suffix)
logger.debug(f'loading matches for {image_filename1}')
for image_filename2 in opensfm_pairs.get(image_filename1, []):
# print(image_filename1, image_filename2)
kapture_matches_filepath = get_matches_fullpath(
(image_filename1, image_filename2),
kapture_dirpath=kapture_root_dir)
kapture_matches = image_matches_from_file(
kapture_matches_filepath)
opensfm_matches[image_filename2] = kapture_matches[:,
0:2].astype(
np.int)
os.makedirs(path.dirname(opensfm_matches_filepath), exist_ok=True)
with gzip.open(opensfm_matches_filepath, 'wb') as f:
pickle.dump(opensfm_matches, f)
def compute_matches_from_loaded_data(input_path: str,
tar_handlers: Optional[TarCollection],
kdata: kapture.Kapture,
descriptors_type: Optional[str],
image_pairs: list,
overwrite_existing: bool = False):
assert kdata.sensors is not None
assert kdata.records_camera is not None
assert kdata.descriptors is not None
os.umask(0o002)
if descriptors_type is None:
descriptors_type = try_get_only_key_from_collection(kdata.descriptors)
assert descriptors_type is not None
assert descriptors_type in kdata.descriptors
keypoints_type = kdata.descriptors[descriptors_type].keypoints_type
# assert kdata.descriptors[descriptors_type].metric_type == "L2"
matcher = MatchPairNnTorch(use_cuda=torch.cuda.is_available())
new_matches = kapture.Matches()
logger.info('compute_matches. entering main loop...')
hide_progress_bar = logger.getEffectiveLevel() > logging.INFO
skip_count = 0
for image_path1, image_path2 in tqdm(image_pairs,
disable=hide_progress_bar):
if image_path1 == image_path2:
continue
if image_path1 > image_path2:
image_path1, image_path2 = image_path2, image_path1
# skip existing matches
if (not overwrite_existing) \
and (kdata.matches is not None) \
and keypoints_type in kdata.matches \
and ((image_path1, image_path2) in kdata.matches[keypoints_type]):
new_matches.add(image_path1, image_path2)
skip_count += 1
continue
if image_path1 not in kdata.descriptors[descriptors_type] \
or image_path2 not in kdata.descriptors[descriptors_type]:
logger.warning('unable to find descriptors for image pair : '
'\n\t{} \n\t{}'.format(image_path1, image_path2))
continue
descriptor1 = load_descriptors(
descriptors_type, input_path, tar_handlers, image_path1,
kdata.descriptors[descriptors_type].dtype,
kdata.descriptors[descriptors_type].dsize)
descriptor2 = load_descriptors(
descriptors_type, input_path, tar_handlers, image_path2,
kdata.descriptors[descriptors_type].dtype,
kdata.descriptors[descriptors_type].dsize)
matches = matcher.match_descriptors(descriptor1, descriptor2)
matches_path = get_matches_fullpath((image_path1, image_path2),
keypoints_type, input_path,
tar_handlers)
image_matches_to_file(matches_path, matches)
new_matches.add(image_path1, image_path2)
if not overwrite_existing:
logger.debug(
f'{skip_count} pairs were skipped because the match file already existed'
)
if not matches_check_dir(new_matches, keypoints_type, input_path,
tar_handlers):
logger.critical(
'matching ended successfully but not all files were saved')
# update kapture matches
if kdata.matches is None:
kdata.matches = {}
if keypoints_type not in kdata.matches:
kdata.matches[keypoints_type] = kapture.Matches()
kdata.matches[keypoints_type].update(new_matches)
logger.info('all done')
