def compute_sequence_pairs(mapping_path: str, output_path: str,
window_size: int, loop: bool, expand_window: bool,
max_interval: int):
"""
compute image pairs from sequence, and write the result in a text file
"""
skip_heavy = [
kapture.RecordsLidar, kapture.RecordsWifi, kapture.Keypoints,
kapture.Descriptors, kapture.GlobalFeatures, kapture.Matches,
kapture.Points3d, kapture.Observations
]
logger.info(f'compute_sequence_pairs. loading mapping: {mapping_path}')
kdata = kapture_from_dir(mapping_path, skip_list=skip_heavy)
assert kdata.sensors is not None
assert kdata.records_camera is not None
os.umask(0o002)
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
image_pairs = get_pairs_sequence(kdata, window_size, loop,
expand_window, max_interval)
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
logger.info('all done')
def compute_observations_pairs(mapping_path: str,
query_path: Optional[str],
output_path: str,
topk: int,
keypoints_type: Optional[str],
iou: bool,
max_number_of_threads: Optional[int] = None):
"""
compute image pairs from observations, and write the result in a text file
"""
skip_heavy_features = [
kapture.Descriptors, kapture.GlobalFeatures, kapture.Matches
]
skip_heavy = [kapture.RecordsLidar, kapture.RecordsWifi
] + skip_heavy_features
logger.info(f'compute_observations_pairs. loading mapping: {mapping_path}')
# the content of the keypoints is not important, we do not need to keep a reference to the tar
with get_all_tar_handlers(mapping_path,
skip_list=skip_heavy_features) as tar_handlers:
kdata = kapture_from_dir(mapping_path,
skip_list=skip_heavy,
tar_handlers=tar_handlers)
assert kdata.sensors is not None
assert kdata.records_camera is not None
if keypoints_type is None:
keypoints_type = try_get_only_key_from_collection(kdata.keypoints)
assert keypoints_type is not None
assert kdata.observations is not None
assert kdata.keypoints is not None
assert keypoints_type in kdata.keypoints
assert kdata.points3d is not None
if query_path is None or mapping_path == query_path:
logger.info('computing mapping pairs from observations...')
kdata_query = None
else:
logger.info('computing query pairs from observations...')
with get_all_tar_handlers(
query_path,
skip_list=skip_heavy_features) as query_tar_handlers:
kdata_query = kapture_from_dir(query_path,
skip_list=skip_heavy,
tar_handlers=query_tar_handlers)
assert kdata_query.sensors is not None
assert kdata_query.records_camera is not None
os.umask(0o002)
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
image_pairs = get_pairs_observations(kdata, kdata_query,
keypoints_type,
max_number_of_threads, iou, topk)
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
logger.info('all done')
def pairsfile_fusion(input_path: List[str], output_path: str,
topk: Optional[int], method: LateFusionMethod,
additional_parameters: dict):
"""
fuse pairsfile scores and write a pairsfile with the fused scores
"""
assert len(input_path) > 1
logger.info(f'pairsfile_fusion. loading {input_path}')
similarity_dicts: List[dict] = []
for file_path in input_path:
loaded_pairs = get_ordered_pairs_from_file(file_path)
similarity_dicts.append(loaded_pairs)
if method == LateFusionMethod.round_robin:
image_pairs = round_robin_from_similarity_dicts(similarity_dicts, topk)
else:
pairs = {}
for loaded_pairs in similarity_dicts:
for query_name, pairlist in loaded_pairs.items():
for map_name, score in pairlist:
pairtuple = (query_name, map_name)
if pairtuple not in pairs:
pairs[pairtuple] = []
pairs[pairtuple].append(score)
# keep entries with correct count
similarity_dict = {}
for pairtuple, scores in pairs.items():
(query_name, map_name) = pairtuple
if len(scores) != len(similarity_dicts):
logger.warning(
f'pair {pairtuple} did not have a line in all pairsfile, skipped'
)
continue
scores_as_matrices = [
np.array([[score]], dtype=np.float64) for score in scores
]
final_score = fuse_similarities(scores_as_matrices, method,
additional_parameters)[0, 0]
if query_name not in similarity_dict:
similarity_dict[query_name] = []
similarity_dict[query_name].append((map_name, final_score))
image_pairs = get_image_pairs(similarity_dict, topk)
logger.info('saving to file ...')
os.umask(0o002)
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
logger.info('all done')
def slice_pairsfile(pairsfile_path: str, output_path: str, topk: int,
threshold: float, startk: int, skip_if_na: bool):
logger.info('slice_pairsfile...')
similarity_dict = get_ordered_pairs_from_file(pairsfile_path)
# apply topk override + skip_if_na
image_pairs = []
for name_query, paired_images in sorted(similarity_dict.items()):
paired_images_threshold = [
x for x in paired_images if x[1] >= threshold
]
if math.isfinite(
topk) and startk + topk > len(paired_images_threshold):
logger.debug(
f'image {name_query} has {len(paired_images_threshold)} pairs, '
f'less than topk={topk} (with startk={startk})')
if skip_if_na:
logger.debug(f'skipping {name_query}')
continue
if math.isinf(topk):
paired_images_threshold = paired_images_threshold[startk:]
else:
paired_images_threshold = paired_images_threshold[startk:startk +
topk]
for name_map, score in paired_images_threshold:
image_pairs.append((name_query, name_map, score))
if len(image_pairs) > 0:
os.umask(0o002)
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
else:
logger.info('no pairs written')
logger.info('all done')
def _image_retrieval_late_fusion_from_loaded_data(
input_path: str, map_tar_handlers: TarCollection,
kdata_map: kapture.Kapture, query_path: str,
query_tar_handlers: TarCollection, kdata_query: kapture.Kapture,
global_features_types: List[str], output_path: str,
topk: Optional[int], method: LateFusionMethod,
additional_parameters: dict):
image_list_map = [
name for _, _, name in kapture.flatten(kdata_map.records_camera,
is_sorted=True)
]
image_list_query = [
name for _, _, name in kapture.flatten(kdata_query.records_camera,
is_sorted=True)
]
if len(global_features_types) == 0:
global_features_types = list(
set(kdata_map.global_features.keys()).intersection(
kdata_query.global_features.keys()))
similarity_matrices = []
stacked_query_index = None
stacked_map_index = None
for global_features_type in global_features_types:
if global_features_type not in kdata_map.global_features:
logger.warning(
f'could not use {global_features_type}, it was missing in kdata_map'
)
continue
if global_features_type not in kdata_query.global_features:
logger.warning(
f'could not use {global_features_type}, it was missing in kdata_query'
)
continue
mapping_gfeats = kdata_map.global_features[global_features_type]
query_gfeats = kdata_query.global_features[global_features_type]
assert mapping_gfeats.dtype == query_gfeats.dtype
assert mapping_gfeats.dsize == query_gfeats.dsize
assert mapping_gfeats.metric_type == query_gfeats.metric_type
global_features_config = GlobalFeaturesConfig(
mapping_gfeats.type_name, mapping_gfeats.dtype,
mapping_gfeats.dsize, mapping_gfeats.metric_type)
# force the same order for all global features
mapping_global_features_to_filepaths = [
(image_filename,
get_features_fullpath(kapture.GlobalFeatures,
global_features_type, input_path,
image_filename, map_tar_handlers))
for image_filename in image_list_map
]
mapping_stacked_features = stack_global_features(
global_features_config, mapping_global_features_to_filepaths)
if input_path == query_path:
query_stacked_features = mapping_stacked_features
else:
query_global_features_to_filepaths = [
(image_filename,
get_features_fullpath(kapture.GlobalFeatures,
global_features_type, query_path,
image_filename, query_tar_handlers))
for image_filename in image_list_query
]
query_stacked_features = stack_global_features(
global_features_config, query_global_features_to_filepaths)
# additional step to really make sure the order or the matrix is the same, and to remember it
if stacked_map_index is None:
stacked_map_index = mapping_stacked_features.index
else:
assert stacked_map_index.tolist(
) == mapping_stacked_features.index.tolist()
if stacked_query_index is None:
stacked_query_index = query_stacked_features.index
else:
assert stacked_query_index.tolist(
) == query_stacked_features.index.tolist()
similarity_matrices.append(
get_similarity_matrix(query_stacked_features,
mapping_stacked_features))
if method == LateFusionMethod.round_robin:
logger.info('Compute fused similarity from round_robin')
similarity_dicts = [
get_similarity_dict_from_similarity_matrix(similarity,
stacked_query_index,
stacked_map_index)
for similarity in similarity_matrices
]
image_pairs = round_robin_from_similarity_dicts(similarity_dicts, topk)
else:
logger.info(f'Compute fused similarity from {method.value} ...')
similarity = fuse_similarities(similarity_matrices, method,
additional_parameters)
similarity_dict = get_similarity_dict_from_similarity_matrix(
similarity, stacked_query_index, stacked_map_index)
image_pairs = get_image_pairs(similarity_dict, topk)
logger.info('saving to file ...')
os.umask(0o002)
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
logger.info('all done')
def compute_image_pairs(mapping_path: str,
query_path: str,
output_path: str,
topk: int):
"""
compute image pairs between query -> mapping from global features, and write the result in a text file
:param mapping_path: input path to kapture input root directory
:type mapping_path: str
:param query_path: input path to a kapture root directory
:type query_path: str
:param output_path: output path to pairsfile
:type output_path: str
:param topk: the max number of top retained images
:type topk: int
"""
logger.info(f'compute_image_pairs. loading mapping: {mapping_path}')
kdata_mapping = kapture_from_dir(mapping_path, None, skip_list=[kapture.Keypoints,
kapture.Descriptors,
kapture.Matches,
kapture.Observations,
kapture.Points3d])
assert kdata_mapping.sensors is not None
assert kdata_mapping.records_camera is not None
assert kdata_mapping.global_features is not None
if mapping_path == query_path:
kdata_query = kdata_mapping
else:
logger.info(f'compute_image_pairs. loading query: {query_path}')
kdata_query = kapture_from_dir(query_path, None, skip_list=[kapture.Keypoints,
kapture.Descriptors,
kapture.Matches,
kapture.Observations,
kapture.Points3d])
assert kdata_query.sensors is not None
assert kdata_query.records_camera is not None
assert kdata_query.global_features is not None
assert kdata_mapping.global_features is not None
assert kdata_query.global_features is not None
assert kdata_mapping.global_features.type_name == kdata_query.global_features.type_name
assert kdata_mapping.global_features.dtype == kdata_query.global_features.dtype
assert kdata_mapping.global_features.dsize == kdata_query.global_features.dsize
global_features_config = ImageFeatureConfig(kdata_mapping.global_features.type_name,
kdata_mapping.global_features.dtype,
kdata_mapping.global_features.dsize)
logger.info(f'computing pairs from with {kdata_mapping.global_features.type_name}...')
mapping_global_features_to_filepaths = global_features_to_filepaths(kdata_mapping.global_features,
mapping_path)
mapping_list = list(kapture.flatten(mapping_global_features_to_filepaths, is_sorted=True))
mapping_stacked_features = stack_global_features(global_features_config, mapping_list)
if mapping_path == query_path:
query_stacked_features = mapping_stacked_features
else:
query_global_features_to_filepaths = global_features_to_filepaths(kdata_query.global_features,
query_path)
query_list = list(kapture.flatten(query_global_features_to_filepaths, is_sorted=True))
query_stacked_features = stack_global_features(global_features_config, query_list)
similarity = get_similarity(query_stacked_features, mapping_stacked_features)
# get list of image pairs
image_pairs = get_image_pairs(similarity, topk)
logger.info('saving to file ...')
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
table_to_file(fid, image_pairs, header='# query_image, map_image, score')
logger.info('all done')
def compute_distance_pairs(mapping_path: str, query_path: Optional[str],
output_path: str, topk: int, block_size: int,
min_distance: float, max_distance: float,
max_angle: float, keep_rejected: bool):
"""
compute image pairs from distance, and write the result in a text file
"""
skip_heavy = [
kapture.RecordsLidar, kapture.RecordsWifi, kapture.Keypoints,
kapture.Descriptors, kapture.GlobalFeatures, kapture.Matches,
kapture.Points3d, kapture.Observations
]
logger.info(f'compute_distance_pairs. loading mapping: {mapping_path}')
kdata = kapture_from_dir(mapping_path, skip_list=skip_heavy)
assert kdata.sensors is not None
assert kdata.records_camera is not None
assert kdata.trajectories is not None
if query_path is None or mapping_path == query_path:
logger.info('computing mapping pairs from distance...')
kdata_query = None
else:
logger.info('computing query pairs from distance...')
kdata_query = kapture_from_dir(query_path, skip_list=skip_heavy)
assert kdata_query.sensors is not None
assert kdata_query.records_camera is not None
assert kdata_query.trajectories is not None
os.umask(0o002)
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
if kdata_query is None:
kdata_query = kdata
if kdata_query.rigs is not None:
assert kdata_query.trajectories is not None # for ide
kapture.rigs_remove_inplace(kdata_query.trajectories,
kdata_query.rigs)
records_camera_list = [
k for k in sorted(kapture.flatten(kdata_query.records_camera),
key=lambda x: x[2])
]
number_of_iteration = math.ceil(len(records_camera_list) / block_size)
table_to_file(fid, [], header='# query_image, map_image, score')
for i in tqdm(range(number_of_iteration),
disable=logging.getLogger().level >= logging.CRITICAL):
sliced_records = kapture.RecordsCamera()
for ts, sensor_id, img_name in records_camera_list[i *
block_size:(i +
1) *
block_size]:
if (ts, sensor_id) not in kdata_query.trajectories:
continue
sliced_records[(ts, sensor_id)] = img_name
kdata_slice_query = kapture.Kapture(
sensors=kdata_query.sensors,
records_camera=sliced_records,
trajectories=kdata_query.trajectories)
image_pairs = get_pairs_distance(kdata, kdata_slice_query, topk,
min_distance, max_distance,
max_angle, keep_rejected)
table_to_file(fid, image_pairs)
logger.info('all done')
def compute_image_pairs(mapping_path: str, query_path: str, output_path: str,
topk: int):
"""
compute image pairs between query -> mapping from global features, and write the result in a text file
:param mapping_path: input path to kapture input root directory
:type mapping_path: str
:param query_path: input path to a kapture root directory
:type query_path: str
:param output_path: output path to pairsfile
:type output_path: str
:param topk: the max number of top retained images
:type topk: int
"""
logger.info(f'compute_image_pairs. loading mapping: {mapping_path}')
kdata_mapping = kapture_from_dir(mapping_path)
assert kdata_mapping.sensors is not None
assert kdata_mapping.records_camera is not None
assert kdata_mapping.global_features is not None
if mapping_path == query_path:
kdata_query = kdata_mapping
else:
logger.info(f'compute_image_pairs. loading query: {query_path}')
kdata_query = kapture_from_dir(query_path)
assert kdata_query.sensors is not None
assert kdata_query.records_camera is not None
assert kdata_query.global_features is not None
assert kdata_mapping.global_features is not None
assert kdata_query.global_features is not None
assert kdata_mapping.global_features.type_name == kdata_query.global_features.type_name
assert kdata_mapping.global_features.dtype == kdata_query.global_features.dtype
assert kdata_mapping.global_features.dsize == kdata_query.global_features.dsize
global_features_config = ImageFeatureConfig(
kdata_mapping.global_features.type_name,
kdata_mapping.global_features.dtype,
kdata_mapping.global_features.dsize)
logger.info(
f'computing pairs from with {kdata_mapping.global_features.type_name}...'
)
mapping_global_features_to_filepaths = global_features_to_filepaths(
kdata_mapping.global_features, mapping_path)
mapping_list = list(
kapture.flatten(mapping_global_features_to_filepaths, is_sorted=True))
mapping_indexes, mapping_features = stack_global_features(
global_features_config, mapping_list)
if mapping_path == query_path:
query_indexes, query_features = mapping_indexes, mapping_features
else:
query_global_features_to_filepaths = global_features_to_filepaths(
kdata_query.global_features, query_path)
query_list = list(
kapture.flatten(query_global_features_to_filepaths,
is_sorted=True))
query_indexes, query_features = stack_global_features(
global_features_config, query_list)
# compute similarity matrix
similarity_matrix = query_features.dot(mapping_features.T)
# convert similarity matrix to dictionary query_name -> sorted (high score first) list [(mapping_name, score), ...]
similarity_dict = {}
for i, line in enumerate(similarity_matrix):
scores = line
indexes = np.argsort(-scores)
query_name = query_indexes[i]
similarity_dict[query_name] = list(
zip(mapping_indexes[indexes], scores[indexes]))
# get list of image pairs
image_pairs = []
for query_image_name, images_to_match in sorted(similarity_dict.items()):
for mapping_image_name, score in images_to_match[:topk]:
image_pairs.append([query_image_name, mapping_image_name, score])
logger.info('saving to file ...')
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
logger.info('all done')
def compute_image_pairs(mapping_path: str, query_path: str, output_path: str,
global_features_type: Optional[str], topk: int):
"""
compute image pairs between query -> mapping from global features, and write the result in a text file
:param mapping_path: input path to kapture input root directory
:type mapping_path: str
:param query_path: input path to a kapture root directory
:type query_path: str
:param output_path: output path to pairsfile
:type output_path: str
:param global_features_type: type of global_features, name of the global_features subfolder
:param topk: the max number of top retained images
:type topk: int
"""
logger.info(f'compute_image_pairs. loading mapping: {mapping_path}')
with get_all_tar_handlers(mapping_path) as mapping_tar_handlers:
kdata_mapping = kapture_from_dir(mapping_path,
None,
skip_list=[
kapture.Keypoints,
kapture.Descriptors,
kapture.Matches,
kapture.Observations,
kapture.Points3d
],
tar_handlers=mapping_tar_handlers)
assert kdata_mapping.sensors is not None
assert kdata_mapping.records_camera is not None
assert kdata_mapping.global_features is not None
if global_features_type is None:
global_features_type = try_get_only_key_from_collection(
kdata_mapping.global_features)
assert global_features_type is not None
assert global_features_type in kdata_mapping.global_features
global_features_config = GlobalFeaturesConfig(
kdata_mapping.global_features[global_features_type].type_name,
kdata_mapping.global_features[global_features_type].dtype,
kdata_mapping.global_features[global_features_type].dsize,
kdata_mapping.global_features[global_features_type].metric_type)
logger.info(f'computing pairs with {global_features_type}...')
mapping_global_features_to_filepaths = global_features_to_filepaths(
kdata_mapping.global_features[global_features_type],
global_features_type, mapping_path, mapping_tar_handlers)
mapping_list = list(
sorted(mapping_global_features_to_filepaths.items()))
mapping_stacked_features = stack_global_features(
global_features_config, mapping_list)
if mapping_path == query_path:
kdata_query = kdata_mapping
query_stacked_features = mapping_stacked_features
else:
logger.info(f'compute_image_pairs. loading query: {query_path}')
with get_all_tar_handlers(query_path) as query_tar_handlers:
kdata_query = kapture_from_dir(query_path,
None,
skip_list=[
kapture.Keypoints,
kapture.Descriptors,
kapture.Matches,
kapture.Observations,
kapture.Points3d
],
tar_handlers=query_tar_handlers)
assert kdata_query.sensors is not None
assert kdata_query.records_camera is not None
assert kdata_query.global_features is not None
assert global_features_type in kdata_query.global_features
kdata_mapping_gfeat = kdata_mapping.global_features[
global_features_type]
kdata_query_gfeat = kdata_query.global_features[
global_features_type]
assert kdata_mapping_gfeat.type_name == kdata_query_gfeat.type_name
assert kdata_mapping_gfeat.dtype == kdata_query_gfeat.dtype
assert kdata_mapping_gfeat.dsize == kdata_query_gfeat.dsize
query_global_features_to_filepaths = global_features_to_filepaths(
kdata_query_gfeat, global_features_type, query_path,
query_tar_handlers)
query_list = list(
sorted(query_global_features_to_filepaths.items()))
query_stacked_features = stack_global_features(
global_features_config, query_list)
similarity = get_similarity(query_stacked_features,
mapping_stacked_features)
# get list of image pairs
image_pairs = get_image_pairs(similarity, topk)
logger.info('saving to file ...')
p = pathlib.Path(output_path)
os.makedirs(str(p.parent.resolve()), exist_ok=True)
with open(output_path, 'w') as fid:
table_to_file(fid,
image_pairs,
header='# query_image, map_image, score')
logger.info('all done')
def local_sfm_from_loaded_data(kdata_map: kapture.Kapture,
kdata_map_gv: kapture.Kapture,
kdata_query: kapture.Kapture,
map_plus_query_path: str,
map_plus_query_gv_path: str,
tar_handlers_map: Optional[TarCollection],
tar_handlers_map_gv: Optional[TarCollection],
descriptors_type: Optional[str],
pairsfile_path: str,
output_path_root: str,
colmap_binary: str,
force: bool):
"""
Localize query images in a COLMAP model built from topk retrieved images.
:param map_plus_query_path: path to the kapture data consisting of mapping and query data (sensors and reconstruction)
:param map_plus_query_gv_path: path to the kapture data consisting of mapping and query data after geometric verification (sensors and reconstruction)
:param query_path: path to the query kapture data (sensors)
:param descriptors_type: type of descriptors, name of the descriptors subfolder
:param pairsfile_path: path to the pairsfile that contains the topk retrieved mapping images for each query image
:param output_path_root: root path where outputs should be stored
:param colmap_binary: path to the COLMAP binary
:param force: silently overwrite already existing results
"""
# load query kapture (we use query kapture to reuse sensor_ids etc.)
if kdata_query.trajectories:
logger.warning("Query data contains trajectories: they will be ignored")
kdata_query.trajectories.clear()
else:
kdata_query.trajectories = kapture.Trajectories()
# clear query trajectories in map_plus_query
kdata_map_cleared_trajectories = kapture.Trajectories()
query_image_list = set(kdata_query.records_camera.data_list())
for timestamp, subdict in kdata_map.records_camera.items():
for sensor_id, image_name in subdict.items():
if image_name in query_image_list:
continue
if (timestamp, sensor_id) in kdata_map.trajectories:
pose = kdata_map.trajectories.get(timestamp)[sensor_id]
kdata_map_cleared_trajectories.setdefault(timestamp, {})[sensor_id] = pose
kdata_map.trajectories = kdata_map_cleared_trajectories
# load output kapture
output_path = os.path.join(output_path_root, 'localized')
if os.path.exists(os.path.join(output_path, 'sensors/trajectories.txt')):
kdata_output = kapture_from_dir(output_path)
if kdata_query.records_camera == kdata_output.records_camera and len(
kdata_output.trajectories) != 0 and not force:
kdata_query.trajectories = kdata_output.trajectories
if kdata_map.rigs is not None:
rigs_remove_inplace(kdata_map.trajectories, kdata_map.rigs)
if kdata_map_gv.rigs is not None:
rigs_remove_inplace(kdata_map_gv.trajectories, kdata_map_gv.rigs)
# load pairsfile
pairs = {}
with open(pairsfile_path, 'r') as fid:
table = table_from_file(fid)
for img_query, img_map, _ in table:
if img_query not in pairs:
pairs[img_query] = []
pairs[img_query].append(img_map)
kdata_sub_colmap_path = os.path.join(output_path_root, 'colmap')
kdata_reg_query_path = os.path.join(output_path_root, 'query_registered')
sub_kapture_pairsfile_path = os.path.join(output_path_root, 'tmp_pairs.txt')
if descriptors_type is None:
descriptors_type = try_get_only_key_from_collection(kdata_map.descriptors)
assert descriptors_type is not None
assert descriptors_type in kdata_map.descriptors
keypoints_type = kdata_map.descriptors[descriptors_type].keypoints_type
# init matches for kdata_map and kdata_map_gv
if kdata_map.matches is None:
kdata_map.matches = {}
if keypoints_type not in kdata_map.matches:
kdata_map.matches[keypoints_type] = kapture.Matches()
if kdata_map_gv.matches is None:
kdata_map_gv.matches = {}
if keypoints_type not in kdata_map_gv.matches:
kdata_map_gv.matches[keypoints_type] = kapture.Matches()
# run all matching
# loop over query images
img_skip_list = set()
for img_query, img_list_map in pairs.items():
if pose_found(kdata_query, img_query):
logger.info(f'{img_query} already processed, skipping...')
img_skip_list.add(img_query)
continue
else:
map_pairs = get_pairfile_from_img_list(img_list_map)
query_pairs = get_pairfile_img_vs_img_list(img_query, img_list_map)
with open(sub_kapture_pairsfile_path, 'w') as fid:
logger.info(f'matching for {img_query}')
table_to_file(fid, map_pairs)
table_to_file(fid, query_pairs)
pairs_all = map_pairs + query_pairs
pairs_all = [(i, j) for i, j, _ in pairs_all]
# match missing pairs
# kdata_map.matches is being updated by compute_matches_from_loaded_data
compute_matches_from_loaded_data(map_plus_query_path,
tar_handlers_map,
kdata_map,
descriptors_type,
pairs_all)
# if kdata_map have matches in tar, they need to be switched to read mode
matches_handler = retrieve_tar_handler_from_collection(kapture.Matches, keypoints_type, tar_handlers_map)
if matches_handler is not None:
matches_handler.close()
tarfile_path = get_feature_tar_fullpath(kapture.Matches, keypoints_type, map_plus_query_path)
tar_handlers_map.matches[keypoints_type] = TarHandler(tarfile_path, 'r')
# run all gv
# loop over query images
for img_query, img_list_map in pairs.items():
if img_query in img_skip_list:
continue
else:
# recompute the pairs
map_pairs = get_pairfile_from_img_list(img_list_map)
query_pairs = get_pairfile_img_vs_img_list(img_query, img_list_map)
with open(sub_kapture_pairsfile_path, 'w') as fid:
logger.info(f'geometric verification of {img_query}')
table_to_file(fid, map_pairs)
table_to_file(fid, query_pairs)
pairs_all = map_pairs + query_pairs
pairs_all = [(i, j) for i, j, _ in pairs_all]
if all(pair in kdata_map_gv.matches[keypoints_type] for pair in pairs_all):
continue
# create a sub kapture in order to minimize the amount of data exported to colmap
# kdata_sub needs to be re-created to add the new matches
kdata_sub = sub_kapture_from_img_list(kdata_map, img_list_map + [img_query], pairs_all,
keypoints_type, descriptors_type)
kdata_sub_gv = sub_kapture_from_img_list(kdata_map_gv, img_list_map + [img_query], pairs_all,
keypoints_type, descriptors_type)
# run colmap gv on missing pairs
run_colmap_gv_from_loaded_data(kdata_sub,
kdata_sub_gv,
map_plus_query_path,
map_plus_query_gv_path,
tar_handlers_map,
tar_handlers_map_gv,
colmap_binary,
keypoints_type,
[],
True)
# update kdata_map_gv.matches
kdata_map_gv.matches[keypoints_type].update(kdata_sub_gv.matches[keypoints_type])
# if kdata_map_gv have matches in tar, they need to be switched to read mode
matches_gv_handler = retrieve_tar_handler_from_collection(kapture.Matches, keypoints_type, tar_handlers_map_gv)
if matches_gv_handler is not None:
print(matches_gv_handler)
matches_gv_handler.close()
tarfile_path = get_feature_tar_fullpath(kapture.Matches, keypoints_type, map_plus_query_gv_path)
tar_handlers_map_gv.matches[keypoints_type] = TarHandler(tarfile_path, 'r')
# loop over query images
for img_query, img_list_map in pairs.items():
if img_query in img_skip_list:
continue
else:
map_pairs = get_pairfile_from_img_list(img_list_map)
with open(sub_kapture_pairsfile_path, 'w') as fid:
logger.info(f'mapping and localization for {img_query}')
table_to_file(fid, map_pairs)
map_pairs = [(i, j) for i, j, _ in map_pairs]
kdata_sub_gv = sub_kapture_from_img_list(kdata_map_gv, img_list_map, map_pairs,
keypoints_type, descriptors_type)
# sanity check
if len(map_pairs) != len(kdata_sub_gv.matches[keypoints_type]):
logger.info(f'not all mapping matches available')
# build COLMAP map
try:
colmap_build_map_from_loaded_data(
kdata_sub_gv,
map_plus_query_gv_path,
tar_handlers_map_gv,
kdata_sub_colmap_path,
colmap_binary,
keypoints_type,
False,
[],
['model_converter'],
True)
except ValueError:
logger.info(f'{img_query} was not localized')
continue
if not os.path.exists(os.path.join(kdata_sub_colmap_path, 'reconstruction/images.bin')):
logger.info(f'colmap mapping for {img_query} did not work, image was not localized')
continue
query_pairs = get_pairfile_img_vs_img_list(img_query, img_list_map)
with open(sub_kapture_pairsfile_path, 'w') as fid:
table_to_file(fid, query_pairs)
query_pairs = [(i, j) for i, j, _ in query_pairs]
query_img_kapture_gv = add_image_to_kapture(kdata_map_gv,
kdata_sub_gv, img_query, query_pairs,
keypoints_type, descriptors_type)
# sanity check
if len(query_pairs) != len(query_img_kapture_gv.matches[keypoints_type]):
logger.info(f'not all query matches available')
# localize in COLMAP map
try:
colmap_localize_from_loaded_data(
query_img_kapture_gv,
map_plus_query_gv_path,
tar_handlers_map_gv,
os.path.join(kdata_sub_colmap_path, 'registered'),
os.path.join(kdata_sub_colmap_path, 'colmap.db'),
os.path.join(kdata_sub_colmap_path, 'reconstruction'),
colmap_binary,
keypoints_type,
False,
['--Mapper.ba_refine_focal_length', '0',
'--Mapper.ba_refine_principal_point', '0',
'--Mapper.ba_refine_extra_params', '0',
'--Mapper.min_num_matches', '4',
'--Mapper.init_min_num_inliers', '4',
'--Mapper.abs_pose_min_num_inliers', '4',
'--Mapper.abs_pose_min_inlier_ratio', '0.05',
'--Mapper.ba_local_max_num_iterations', '50',
'--Mapper.abs_pose_max_error', '20',
'--Mapper.filter_max_reproj_error', '12'],
[],
True)
except ValueError:
logger.info(f'{img_query} was not localized')
continue
if not os.path.exists(os.path.join(os.path.join(kdata_sub_colmap_path, 'registered'),
'reconstruction/images.txt')):
logger.info(f'colmap localization of {img_query} did not work, image was not localized')
continue
# add to results kapture
kdata_reg_query = import_colmap(
kdata_reg_query_path,
os.path.join(os.path.join(kdata_sub_colmap_path, 'registered'), 'colmap.db'),
os.path.join(os.path.join(kdata_sub_colmap_path, 'registered'),
'reconstruction'),
None,
None,
True,
True,
True,
TransferAction.skip)
if add_pose_to_query_kapture(kdata_reg_query, kdata_query, img_query):
logger.info('successfully localized')
# write results (after each image to see the progress)
kapture_to_dir(output_path, kdata_query)
# clean up (e.g. remove temporal files and folders)
safe_remove_any_path(kdata_sub_colmap_path, True)
safe_remove_any_path(kdata_reg_query_path, True)
safe_remove_file(sub_kapture_pairsfile_path, True)
logger.info('all done')
