def hloc_pipeline_from_kapture_dataset(
kapture_path_map: str, kapture_path_query: str,
pairsfile_path_map: str, pairsfile_path_query: str, output_dir: str,
feature_conf_str: str, matcher_conf_str: str,
covisibility_clustering: bool, bins_as_str: List[str],
benchmark_format_style: BenchmarkFormatStyle, colmap_binary: str,
python_binary: Optional[str], skip_list: List[str]) -> None:
"""
run hloc on kapture data
"""
feature_conf = extract_features.confs[feature_conf_str]
matcher_conf = match_features.confs[matcher_conf_str]
images_map = get_record_fullpath(kapture_path_map)
images_query = get_record_fullpath(kapture_path_query)
os.makedirs(output_dir, exist_ok=True)
if "convert_pairsfile_map" not in skip_list:
map_pairs_hloc = path.join(
output_dir, 'pairfiles/db_pairs',
path.basename(pairsfile_path_map) + "_hloc.txt")
convert_pairs_to_hloc_format(pairsfile_path_map, map_pairs_hloc)
pairsfile_path_map = map_pairs_hloc
if "convert_pairsfile_query" not in skip_list:
query_pairs_hloc = path.join(
output_dir, 'pairfiles/query',
path.basename(pairsfile_path_query) + "_hloc.txt")
convert_pairs_to_hloc_format(pairsfile_path_query, query_pairs_hloc)
pairsfile_path_query = query_pairs_hloc
feature_path = Path(output_dir, feature_conf['output'] + '.h5')
if "extract_features_map" not in skip_list:
image_list_map_path = path.join(output_dir, 'image_list_map.txt')
convert_kapture_to_hloc_image_list(kapture_path_map,
image_list_map_path)
feature_path_map = extract_features.main(
feature_conf,
Path(images_map),
Path(output_dir),
image_list=Path(image_list_map_path))
assert feature_path_map.resolve() == feature_path.resolve()
if "extract_features_query" not in skip_list:
image_list_query_path = path.join(output_dir, 'image_list_query.txt')
convert_kapture_to_hloc_image_list(kapture_path_query,
image_list_query_path)
feature_path_query = extract_features.main(
feature_conf,
Path(images_query),
Path(output_dir),
image_list=Path(image_list_query_path))
assert feature_path_query.resolve() == feature_path.resolve()
pairsfile_path_map_pathlib = Path(pairsfile_path_map)
match_name_map = feature_conf['output'] + '_' + matcher_conf[
"output"] + f'_{pairsfile_path_map_pathlib.stem}'
map_match_path = Path(output_dir, match_name_map + '.h5')
if 'match_map_pairs' not in skip_list:
map_match_path_actual = match_features.main(
matcher_conf, pairsfile_path_map_pathlib, feature_conf['output'],
Path(output_dir))
assert map_match_path_actual.resolve() == map_match_path.resolve()
exported_mapping_path = path.join(output_dir,
'3D-models/exported_from_kapture')
if 'kapture_export_map_to_colmap' not in skip_list:
export_colmap(kapture_path_map,
path.join(exported_mapping_path, 'colmap.db'),
exported_mapping_path,
force_overwrite_existing=True)
# convert .txt to .bin
run_model_converter(colmap_binary, exported_mapping_path,
exported_mapping_path, 'BIN')
triangulate_path = path.join(
output_dir, 'sfm_' + feature_conf_str + '_' + matcher_conf_str)
if 'triangulate' not in skip_list:
triangulation.main(Path(triangulate_path), Path(exported_mapping_path),
Path(images_map), pairsfile_path_map_pathlib,
feature_path, map_match_path, colmap_binary)
pairsfile_path_query_pathlib = Path(pairsfile_path_query)
match_name_query = feature_conf['output'] + '_' + matcher_conf[
"output"] + f'_{pairsfile_path_query_pathlib.stem}'
query_match_path = Path(output_dir, match_name_query + '.h5')
if 'match_query_pairs' not in skip_list:
query_match_path_actual = match_features.main(
matcher_conf, pairsfile_path_query_pathlib, feature_conf['output'],
Path(output_dir))
assert query_match_path_actual.resolve() == query_match_path.resolve()
query_as_txt = path.join(output_dir, 'image_list_with_intrinsics.txt')
export_image_list(kapture_path_query, query_as_txt)
results_file = path.join(
output_dir, f'results_{feature_conf_str}_{matcher_conf_str}.txt')
if 'localize' not in skip_list:
localize_sfm.main(Path(triangulate_path),
Path(query_as_txt),
pairsfile_path_query_pathlib,
feature_path,
query_match_path,
Path(results_file),
covisibility_clustering=covisibility_clustering)
results_full = path.join(
output_dir,
f'results_{feature_conf_str}_{matcher_conf_str}_fullnames.txt')
results_kapture = path.join(
output_dir, f'results_{feature_conf_str}_{matcher_conf_str}_kapture')
if 'convert_results' not in skip_list:
convert_results_format(query_as_txt, results_file, results_full)
convert_results_to_kapture(kapture_path_query, results_full,
results_kapture)
if 'evaluate' not in skip_list and path.isfile(
get_csv_fullpath(kapture.Trajectories, kapture_path_query)):
local_evaluate_path = path.join(pipeline_import_paths.HERE_PATH,
'../tools/kapture_evaluate.py')
evaluate_args = [
'-v',
str(logger.level), '-i', results_kapture, '--labels',
f'hloc_{feature_conf_str}_{matcher_conf_str}', '-gt',
kapture_path_query, '-o',
path.join(results_kapture, 'eval')
]
evaluate_args += ['--bins'] + bins_as_str
evaluate_args.append('-f')
run_python_command(local_evaluate_path, evaluate_args, python_binary)
LTVL2020_output_path = path.join(
output_dir,
f'results_{feature_conf_str}_{matcher_conf_str}_LTVL2020_style.txt')
if 'export_LTVL2020' not in skip_list:
export_LTVL2020_script_name, export_LTVL2020_args = get_benchmark_format_command(
benchmark_format_style, results_kapture, LTVL2020_output_path,
True, logger)
local_export_LTVL2020_path = path.join(
pipeline_import_paths.HERE_PATH,
f'../../kapture/tools/{export_LTVL2020_script_name}')
run_python_command(local_export_LTVL2020_path, export_LTVL2020_args,
python_binary)
def colmap_localize_from_loaded_data(kapture_data: kapture.Kapture,
kapture_path: str,
tar_handlers: Optional[TarCollection],
colmap_path: str,
input_database_path: str,
input_reconstruction_path: str,
colmap_binary: str,
keypoints_type: Optional[str],
use_colmap_matches_importer: bool,
image_registrator_options: List[str],
skip_list: List[str],
force: bool) -> None:
"""
Localize images on a colmap model with the kapture data.
:param kapture_data: kapture data to use
:param kapture_path: path to the kapture to use
:param tar_handler: collection of preloaded tar archives
:param colmap_path: path to the colmap build
:param input_database_path: path to the map colmap.db
:param input_database_path: path to the map colmap.db
:param input_reconstruction_path: path to the map reconstruction folder
:param colmap_binary: path to the colmap binary executable
:param keypoints_type: type of keypoints, name of the keypoints subfolder
:param use_colmap_matches_importer: bool,
:param image_registrator_options: options for the image registrator
:param skip_list: list of steps to skip
:param force: Silently overwrite kapture files if already exists.
"""
os.makedirs(colmap_path, exist_ok=True)
if not (kapture_data.records_camera and kapture_data.sensors and kapture_data.keypoints and kapture_data.matches):
raise ValueError('records_camera, sensors, keypoints, matches are mandatory')
if kapture_data.trajectories:
logger.warning("Input data contains trajectories: they will be ignored")
kapture_data.trajectories.clear()
else:
kapture_data.trajectories = kapture.Trajectories()
# COLMAP does not fully support rigs.
if kapture_data.rigs is not None and kapture_data.trajectories is not None:
# make sure, rigs are not used in trajectories.
logger.info('remove rigs notation.')
rigs_remove_inplace(kapture_data.trajectories, kapture_data.rigs)
kapture_data.rigs.clear()
# Prepare output
# Set fixed name for COLMAP database
colmap_db_path = path.join(colmap_path, 'colmap.db')
image_list_path = path.join(colmap_path, 'images.list')
reconstruction_path = path.join(colmap_path, "reconstruction")
if 'delete_existing' not in skip_list:
safe_remove_file(colmap_db_path, force)
safe_remove_file(image_list_path, force)
safe_remove_any_path(reconstruction_path, force)
os.makedirs(reconstruction_path, exist_ok=True)
# Copy colmap db to output
if not os.path.exists(colmap_db_path):
shutil.copy(input_database_path, colmap_db_path)
# find correspondences between the colmap db and the kapture data
images_all = {image_path: (ts, cam_id)
for ts, shot in kapture_data.records_camera.items()
for cam_id, image_path in shot.items()}
colmap_db = COLMAPDatabase.connect(colmap_db_path)
colmap_image_ids = database_extra.get_colmap_image_ids_from_db(colmap_db)
colmap_images = database_extra.get_images_from_database(colmap_db)
colmap_db.close()
# dict ( kapture_camera -> colmap_camera_id )
colmap_camera_ids = {images_all[image_path][1]: colmap_cam_id
for image_path, colmap_cam_id in colmap_images if image_path in images_all}
images_to_add = {image_path: value
for image_path, value in images_all.items()
if image_path not in colmap_image_ids}
flatten_images_to_add = [(ts, kapture_cam_id, image_path)
for image_path, (ts, kapture_cam_id) in images_to_add.items()]
if 'import_to_db' not in skip_list:
logger.info("Step 1: Add precomputed keypoints and matches to colmap db")
if keypoints_type is None:
keypoints_type = try_get_only_key_from_collection(kapture_data.keypoints)
assert keypoints_type is not None
assert keypoints_type in kapture_data.keypoints
assert keypoints_type in kapture_data.matches
cameras_to_add = kapture.Sensors()
for _, (_, kapture_cam_id) in images_to_add.items():
if kapture_cam_id not in colmap_camera_ids:
kapture_cam = kapture_data.sensors[kapture_cam_id]
cameras_to_add[kapture_cam_id] = kapture_cam
colmap_db = COLMAPDatabase.connect(colmap_db_path)
colmap_added_camera_ids = database_extra.add_cameras_to_database(cameras_to_add, colmap_db)
colmap_camera_ids.update(colmap_added_camera_ids)
colmap_added_image_ids = database_extra.add_images_to_database_from_flatten(
colmap_db, flatten_images_to_add, kapture_data.trajectories, colmap_camera_ids)
colmap_image_ids.update(colmap_added_image_ids)
colmap_image_ids_reversed = {v: k for k, v in colmap_image_ids.items()} # colmap_id : name
# add new features
colmap_keypoints = database_extra.get_keypoints_set_from_database(colmap_db, colmap_image_ids_reversed)
keypoints_all = kapture_data.keypoints[keypoints_type]
keypoints_to_add = {name for name in keypoints_all if name not in colmap_keypoints}
keypoints_to_add = kapture.Keypoints(keypoints_all.type_name, keypoints_all.dtype, keypoints_all.dsize,
keypoints_to_add)
database_extra.add_keypoints_to_database(colmap_db, keypoints_to_add,
keypoints_type, kapture_path,
tar_handlers,
colmap_image_ids)
# add new matches
colmap_matches = kapture.Matches(database_extra.get_matches_set_from_database(colmap_db,
colmap_image_ids_reversed))
colmap_matches.normalize()
matches_all = kapture_data.matches[keypoints_type]
matches_to_add = kapture.Matches({pair for pair in matches_all if pair not in colmap_matches})
# print(list(matches_to_add))
database_extra.add_matches_to_database(colmap_db, matches_to_add,
keypoints_type, kapture_path,
tar_handlers,
colmap_image_ids,
export_two_view_geometry=not use_colmap_matches_importer)
colmap_db.close()
if use_colmap_matches_importer:
logger.info('Step 2: Run geometric verification')
logger.debug('running colmap matches_importer...')
if keypoints_type is None:
keypoints_type = try_get_only_key_from_collection(kapture_data.matches)
assert keypoints_type is not None
assert keypoints_type in kapture_data.matches
# compute two view geometry
colmap_lib.run_matches_importer_from_kapture_matches(
colmap_binary,
colmap_use_cpu=True,
colmap_gpu_index=None,
colmap_db_path=colmap_db_path,
kapture_matches=kapture_data.matches[keypoints_type],
force=force)
else:
logger.info('Step 2: Run geometric verification - skipped')
if 'image_registrator' not in skip_list:
logger.info("Step 3: Run image_registrator")
# run image_registrator
colmap_lib.run_image_registrator(
colmap_binary,
colmap_db_path,
input_reconstruction_path,
reconstruction_path,
image_registrator_options
)
# run model_converter
if 'model_converter' not in skip_list:
logger.info("Step 4: Export reconstruction results to txt")
colmap_lib.run_model_converter(
colmap_binary,
reconstruction_path,
reconstruction_path
)
def colmap_build_sift_map(kapture_path: str, colmap_path: str,
colmap_binary: str, colmap_use_cpu: bool,
colmap_gpu_index: str, vocab_tree_path: str,
point_triangulator_options: List[str],
skip_list: List[str], force: bool) -> None:
"""
Build a colmap model using default SIFT features with the kapture data.
:param kapture_path: path to the kapture to use
:param colmap_path: path to the colmap build
:param colmap_binary: path to the colmap executable
:param colmap_use_cpu: to use cpu only (and ignore gpu) or to use also gpu
:param colmap_gpu_index: gpu index for sift extractor and mapper
:param vocab_tree_path: path to the colmap vocabulary tree file
:param point_triangulator_options: options for the point triangulator
:param skip_list: list of steps to skip
:param force: Silently overwrite kapture files if already exists.
"""
os.makedirs(colmap_path, exist_ok=True)
# Load input files first to make sure it is OK
logger.info('loading kapture files...')
kapture_data = kapture.io.csv.kapture_from_dir(kapture_path)
if not (kapture_data.records_camera and kapture_data.sensors):
raise ValueError('records_camera, sensors are mandatory')
if not kapture_data.trajectories:
logger.info(
'there are no trajectories, running mapper instead of point_triangulator'
)
if not os.path.isfile(vocab_tree_path):
raise ValueError(
f'Vocabulary Tree file does not exist: {vocab_tree_path}')
# COLMAP does not fully support rigs.
if kapture_data.rigs is not None and kapture_data.trajectories is not None:
# make sure, rigs are not used in trajectories.
logger.info('remove rigs notation.')
rigs_remove_inplace(kapture_data.trajectories, kapture_data.rigs)
kapture_data.rigs.clear()
# Set fixed name for COLMAP database
colmap_db_path = path.join(colmap_path, 'colmap.db')
image_list_path = path.join(colmap_path, 'images.list')
reconstruction_path = path.join(colmap_path, "reconstruction")
if 'delete_existing' not in skip_list:
safe_remove_file(colmap_db_path, force)
safe_remove_file(image_list_path, force)
safe_remove_any_path(reconstruction_path, force)
os.makedirs(reconstruction_path, exist_ok=True)
if 'feature_extract' not in skip_list:
logger.info("Step 1: Feature extraction using colmap")
with open(image_list_path, 'w') as fid:
for timestamp, sensor_id in sorted(
kapture_data.records_camera.key_pairs()):
fid.write(kapture_data.records_camera[timestamp][sensor_id] +
"\n")
colmap_lib.run_feature_extractor(colmap_binary, colmap_use_cpu,
colmap_gpu_index, colmap_db_path,
get_image_fullpath(kapture_path),
image_list_path)
# Update cameras in COLMAP:
# - use only one camera for all images taken with the same camera (update all camera IDs)
# - import camera intrinsics
# - import camera pose
if 'update_db_cameras' not in skip_list:
logger.info("Step 2: Populate COLMAP DB with cameras and poses")
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.update_DB_cameras_and_poses(colmap_db, kapture_data)
# close db before running colmap processes in order to avoid locks
colmap_db.close()
# Extract matches with COLMAP
if 'matches' not in skip_list:
logger.info("Step 3: Compute matches with colmap")
colmap_lib.run_vocab_tree_matcher(colmap_binary, colmap_use_cpu,
colmap_gpu_index, colmap_db_path,
vocab_tree_path)
if kapture_data.trajectories is not None:
# Generate priors for reconstruction
txt_path = path.join(colmap_path, "priors_for_reconstruction")
os.makedirs(txt_path, exist_ok=True)
if 'priors_for_reconstruction' not in skip_list:
logger.info('Step 4: Exporting priors for reconstruction.')
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.generate_priors_for_reconstruction(
kapture_data, colmap_db, txt_path)
colmap_db.close()
# Point triangulator
reconstruction_path = path.join(colmap_path, "reconstruction")
os.makedirs(reconstruction_path, exist_ok=True)
if 'triangulation' not in skip_list:
logger.info("Step 5: Triangulation")
colmap_lib.run_point_triangulator(colmap_binary, colmap_db_path,
get_image_fullpath(kapture_path),
txt_path, reconstruction_path,
point_triangulator_options)
else:
# mapper
reconstruction_path = path.join(colmap_path, "reconstruction")
os.makedirs(reconstruction_path, exist_ok=True)
if 'triangulation' not in skip_list:
logger.info("Step 5: Triangulation")
colmap_lib.run_mapper(colmap_binary, colmap_db_path,
get_image_fullpath(kapture_path), None,
reconstruction_path,
point_triangulator_options)
# use reconstruction 0 as main
first_reconstruction = os.path.join(reconstruction_path, '0')
files = os.listdir(first_reconstruction)
for f in files:
shutil.move(os.path.join(first_reconstruction, f),
os.path.join(reconstruction_path, f))
shutil.rmtree(first_reconstruction)
# run model_converter
if 'model_converter' not in skip_list:
logger.info("Step 6: Export reconstruction results to txt")
colmap_lib.run_model_converter(colmap_binary, reconstruction_path,
reconstruction_path)
def colmap_localize_sift(kapture_path: str, colmap_path: str,
input_database_path: str,
input_reconstruction_path: str, colmap_binary: str,
colmap_use_cpu: bool, colmap_gpu_index: str,
vocab_tree_path: str,
image_registrator_options: List[str],
skip_list: List[str], force: bool) -> None:
"""
Localize images on a colmap model using default SIFT features with the kapture data.
:param kapture_path: path to the kapture to use
:param colmap_path: path to the colmap build
:param input_database_path: path to the map colmap.db
:param input_database_path: path to the map colmap.db
:param input_reconstruction_path: path to the map reconstruction folder
:param colmap_binary: path to the colmap binary executable
:param colmap_use_cpu: to use cpu only (and ignore gpu) or to use also gpu
:param colmap_gpu_index: gpu index for sift extractor and mapper
:param vocab_tree_path: path to the colmap vocabulary tree file
:param image_registrator_options: options for the image registrator
:param skip_list: list of steps to skip
:param force: Silently overwrite kapture files if already exists.
"""
os.makedirs(colmap_path, exist_ok=True)
# Set fixed name for COLMAP database
# Load input files first to make sure it is OK
logger.info('loading kapture files...')
kapture_data = kapture.io.csv.kapture_from_dir(kapture_path)
if not (kapture_data.records_camera and kapture_data.sensors):
raise ValueError('records_camera, sensors are mandatory')
if kapture_data.trajectories:
logger.warning(
"Input data contains trajectories: they will be ignored")
kapture_data.trajectories.clear()
else:
kapture_data.trajectories = kapture.Trajectories()
if not os.path.isfile(vocab_tree_path):
raise ValueError(
f'Vocabulary Tree file does not exist: {vocab_tree_path}')
# COLMAP does not fully support rigs.
if kapture_data.rigs is not None and kapture_data.trajectories is not None:
# make sure, rigs are not used in trajectories.
logger.info('remove rigs notation.')
rigs_remove_inplace(kapture_data.trajectories, kapture_data.rigs)
kapture_data.rigs.clear()
# Prepare output
# Set fixed name for COLMAP database
colmap_db_path = path.join(colmap_path, 'colmap.db')
image_list_path = path.join(colmap_path, 'images.list')
reconstruction_path = path.join(colmap_path, "reconstruction")
if 'delete_existing' not in skip_list:
safe_remove_file(colmap_db_path, force)
safe_remove_file(image_list_path, force)
safe_remove_any_path(reconstruction_path, force)
os.makedirs(reconstruction_path, exist_ok=True)
# Copy colmap db to output
if not os.path.exists(colmap_db_path):
shutil.copy(input_database_path, colmap_db_path)
# find correspondences between the colmap db and the kapture data
images_all = {
image_path: (ts, cam_id)
for ts, shot in kapture_data.records_camera.items()
for cam_id, image_path in shot.items()
}
colmap_db = COLMAPDatabase.connect(colmap_db_path)
colmap_image_ids = database_extra.get_colmap_image_ids_from_db(colmap_db)
colmap_cameras = database_extra.get_camera_ids_from_database(colmap_db)
colmap_images = database_extra.get_images_from_database(colmap_db)
colmap_db.close()
# dict ( kapture_camera -> colmap_camera_id )
colmap_camera_ids = {
images_all[image_path][1]: colmap_cam_id
for image_path, colmap_cam_id in colmap_images
if image_path in images_all
}
images_to_add = {
image_path: value
for image_path, value in images_all.items()
if image_path not in colmap_image_ids
}
flatten_images_to_add = [
(ts, kapture_cam_id, image_path)
for image_path, (ts, kapture_cam_id) in images_to_add.items()
]
if 'feature_extract' not in skip_list:
logger.info("Step 1: Feature extraction using colmap")
with open(image_list_path, 'w') as fid:
for image in images_to_add.keys():
fid.write(image + "\n")
colmap_lib.run_feature_extractor(colmap_binary, colmap_use_cpu,
colmap_gpu_index, colmap_db_path,
get_image_fullpath(kapture_path),
image_list_path)
if 'matches' not in skip_list:
logger.info("Step 2: Compute matches with colmap")
colmap_lib.run_vocab_tree_matcher(colmap_binary, colmap_use_cpu,
colmap_gpu_index, colmap_db_path,
vocab_tree_path, image_list_path)
if 'fix_db_cameras' not in skip_list:
logger.info(
"Step 3: Replace colmap generated cameras with kapture cameras")
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.foreign_keys_off(colmap_db)
# remove colmap generated cameras
after_feature_extraction_colmap_cameras = database_extra.get_camera_ids_from_database(
colmap_db)
colmap_cameras_to_remove = [
cam_id for cam_id in after_feature_extraction_colmap_cameras
if cam_id not in colmap_cameras
]
for cam_id in colmap_cameras_to_remove:
database_extra.remove_camera(colmap_db, cam_id)
# put the correct cameras and image extrinsic back into the database
cameras_to_add = kapture.Sensors()
for image_path, (ts, kapture_cam_id) in images_to_add.items():
if kapture_cam_id not in colmap_camera_ids:
kapture_cam = kapture_data.sensors[kapture_cam_id]
cameras_to_add[kapture_cam_id] = kapture_cam
colmap_added_camera_ids = database_extra.add_cameras_to_database(
cameras_to_add, colmap_db)
colmap_camera_ids.update(colmap_added_camera_ids)
database_extra.update_images_in_database_from_flatten(
colmap_db, flatten_images_to_add, kapture_data.trajectories,
colmap_camera_ids)
database_extra.foreign_keys_on(colmap_db)
colmap_db.commit()
colmap_db.close()
if 'image_registrator' not in skip_list:
logger.info("Step 4: Run image_registrator")
# run image_registrator
colmap_lib.run_image_registrator(colmap_binary, colmap_db_path,
input_reconstruction_path,
reconstruction_path,
image_registrator_options)
# run model_converter
if 'model_converter' not in skip_list:
logger.info("Step 5: Export reconstruction results to txt")
colmap_lib.run_model_converter(colmap_binary, reconstruction_path,
reconstruction_path)
def colmap_build_map_from_loaded_data(kapture_data: kapture.Kapture,
kapture_path: str, colmap_path: str,
colmap_binary: str,
use_colmap_matches_importer: bool,
point_triangulator_options: List[str],
skip_list: List[str],
force: bool) -> None:
"""
Build a colmap model using custom features with the kapture data.
:param kapture_data: kapture data to use
:param kapture_path: path to the kapture to use
:param colmap_path: path to the colmap build
:param colmap_binary: path to the colmap executable
:param use_colmap_matches_importer: bool,
:param point_triangulator_options: options for the point triangulator
:param skip_list: list of steps to skip
:param force: Silently overwrite kapture files if already exists.
"""
os.makedirs(colmap_path, exist_ok=True)
if not (kapture_data.records_camera and kapture_data.sensors
and kapture_data.keypoints and kapture_data.matches):
raise ValueError(
'records_camera, sensors, keypoints, matches are mandatory')
if not kapture_data.trajectories:
logger.info(
'there are no trajectories, running mapper instead of point_triangulator'
)
# COLMAP does not fully support rigs.
if kapture_data.rigs is not None and kapture_data.trajectories is not None:
# make sure, rigs are not used in trajectories.
logger.info('remove rigs notation.')
rigs_remove_inplace(kapture_data.trajectories, kapture_data.rigs)
kapture_data.rigs.clear()
# Set fixed name for COLMAP database
colmap_db_path = path.join(colmap_path, 'colmap.db')
reconstruction_path = path.join(colmap_path, "reconstruction")
priors_txt_path = path.join(colmap_path, "priors_for_reconstruction")
if 'delete_existing' not in skip_list:
safe_remove_file(colmap_db_path, force)
safe_remove_any_path(reconstruction_path, force)
safe_remove_any_path(priors_txt_path, force)
os.makedirs(reconstruction_path, exist_ok=True)
if 'colmap_db' not in skip_list:
logger.info('Using precomputed keypoints and matches')
logger.info('Step 1: Export kapture format to colmap')
colmap_db = COLMAPDatabase.connect(colmap_db_path)
if kapture_data.descriptors is not None:
kapture_data.descriptors.clear()
database_extra.kapture_to_colmap(
kapture_data,
kapture_path,
colmap_db,
export_two_view_geometry=not use_colmap_matches_importer)
# close db before running colmap processes in order to avoid locks
colmap_db.close()
if use_colmap_matches_importer:
logger.info('Step 2: Run geometric verification')
logger.debug('running colmap matches_importer...')
colmap_lib.run_matches_importer_from_kapture(
colmap_binary,
colmap_use_cpu=True,
colmap_gpu_index=None,
colmap_db_path=colmap_db_path,
kapture_data=kapture_data,
force=force)
else:
logger.info('Step 2: Run geometric verification - skipped')
if kapture_data.trajectories is not None:
# Generate priors for reconstruction
os.makedirs(priors_txt_path, exist_ok=True)
if 'priors_for_reconstruction' not in skip_list:
logger.info('Step 3: Exporting priors for reconstruction.')
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.generate_priors_for_reconstruction(
kapture_data, colmap_db, priors_txt_path)
colmap_db.close()
# Point triangulator
reconstruction_path = path.join(colmap_path, "reconstruction")
os.makedirs(reconstruction_path, exist_ok=True)
if 'triangulation' not in skip_list:
logger.info("Step 4: Triangulation")
colmap_lib.run_point_triangulator(colmap_binary, colmap_db_path,
get_image_fullpath(kapture_path),
priors_txt_path,
reconstruction_path,
point_triangulator_options)
else:
# mapper
reconstruction_path = path.join(colmap_path, "reconstruction")
os.makedirs(reconstruction_path, exist_ok=True)
if 'triangulation' not in skip_list:
logger.info("Step 4: Triangulation")
colmap_lib.run_mapper(colmap_binary, colmap_db_path,
get_image_fullpath(kapture_path), None,
reconstruction_path,
point_triangulator_options)
# use reconstruction 0 as main
first_reconstruction = os.path.join(reconstruction_path, '0')
files = os.listdir(first_reconstruction)
for f in files:
shutil.move(os.path.join(first_reconstruction, f),
os.path.join(reconstruction_path, f))
shutil.rmtree(first_reconstruction)
# run model_converter
if 'model_converter' not in skip_list:
logger.info("Step 5: Export reconstruction results to txt")
colmap_lib.run_model_converter(colmap_binary, reconstruction_path,
reconstruction_path)
def reconstruct(self, kapture_data):
os.makedirs(self._colmap_path, exist_ok=True)
if not (kapture_data.records_camera and kapture_data.sensors
and kapture_data.keypoints and kapture_data.matches
and kapture_data.trajectories):
raise ValueError(
'records_camera, sensors, keypoints, matches, trajectories are mandatory'
)
# Set fixed name for COLMAP database
colmap_db_path = path.join(self._colmap_path, 'colmap.db')
reconstruction_path = path.join(self._colmap_path, "reconstruction")
priors_txt_path = path.join(self._colmap_path,
"priors_for_reconstruction")
safe_remove_file(colmap_db_path, True)
safe_remove_any_path(reconstruction_path, True)
safe_remove_any_path(priors_txt_path, True)
os.makedirs(reconstruction_path, exist_ok=True)
# COLMAP does not fully support rigs.
print("Step 1. Remove rigs")
if kapture_data.rigs is not None and kapture_data.trajectories is not None:
# make sure, rigs are not used in trajectories.
rigs_remove_inplace(kapture_data.trajectories, kapture_data.rigs)
kapture_data.rigs.clear()
print("Step 2. Kapture to colmap")
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.kapture_to_colmap(kapture_data,
kapture_data.kapture_path,
colmap_db,
export_two_view_geometry=True)
colmap_db.close()
os.makedirs(priors_txt_path, exist_ok=True)
print("Step 3. Generate priors for reconstruction")
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.generate_priors_for_reconstruction(
kapture_data, colmap_db, priors_txt_path)
colmap_db.close()
# Point triangulator
print("Step 4. Point triangulator")
reconstruction_path = path.join(self._colmap_path, "reconstruction")
os.makedirs(reconstruction_path, exist_ok=True)
run_point_triangulator(self._colmap_binary, colmap_db_path,
kapture_data.image_path, priors_txt_path,
reconstruction_path,
self._point_triangulator_options)
print("Step 5. Model converter")
run_model_converter(self._colmap_binary, reconstruction_path,
reconstruction_path)
print("Step 5. Reconstruction import")
points3d, observations = import_from_colmap_points3d_txt(
os.path.join(reconstruction_path, "points3D.txt"),
kapture_data.image_names)
kapture_data.observations = observations
kapture_data.points3d = points3d