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 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 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')