def test_exists_table(self) -> None:
with tempfile.TemporaryDirectory() as tmpdirname:
colmap_db_path = path.join(tmpdirname, 'colmap.db')
colmap_db = COLMAPDatabase.connect(colmap_db_path)
self.assertFalse(exists_table('two_view_geometries', colmap_db))
colmap_db.create_tables()
self.assertTrue(exists_table('two_view_geometries', colmap_db))
colmap_db.close()
def colmap_build_map_from_loaded_data(kapture_data: kapture.Kapture,
kapture_path: str,
colmap_path: str,
colmap_binary: str,
pairsfile_path: Optional[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 pairsfile_path: Optional[str],
: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 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 run_colmap_gv_from_loaded_data(kapture_none_matches: kapture.Kapture,
kapture_colmap_matches: kapture.Kapture,
kapture_none_matches_dirpath: str,
kapture_colmap_matches_dirpath: str,
tar_handlers_none_matches: Optional[TarCollection],
tar_handlers_colmap_matches: Optional[TarCollection],
colmap_binary: str,
keypoints_type: Optional[str],
skip_list: List[str],
force: bool):
logger.info('run_colmap_gv...')
if not (kapture_none_matches.records_camera and kapture_none_matches.sensors and
kapture_none_matches.keypoints and kapture_none_matches.matches):
raise ValueError('records_camera, sensors, keypoints, matches are mandatory')
# COLMAP does not fully support rigs.
if kapture_none_matches.rigs is not None and kapture_none_matches.trajectories is not None:
# make sure, rigs are not used in trajectories.
logger.info('remove rigs notation.')
rigs_remove_inplace(kapture_none_matches.trajectories, kapture_none_matches.rigs)
# Set fixed name for COLMAP database
colmap_db_path = os.path.join(kapture_colmap_matches_dirpath, 'colmap.db')
if 'delete_existing' not in skip_list:
safe_remove_file(colmap_db_path, force)
if keypoints_type is None:
keypoints_type = try_get_only_key_from_collection(kapture_none_matches.matches)
assert keypoints_type is not None
assert keypoints_type in kapture_none_matches.keypoints
assert keypoints_type in kapture_none_matches.matches
if 'matches_importer' not in skip_list:
logger.debug('compute matches difference.')
if kapture_colmap_matches.matches is not None and keypoints_type in kapture_colmap_matches.matches:
colmap_matches = kapture_colmap_matches.matches[keypoints_type]
else:
colmap_matches = kapture.Matches()
matches_to_verify = kapture.Matches(kapture_none_matches.matches[keypoints_type].difference(colmap_matches))
kapture_data_to_export = kapture.Kapture(sensors=kapture_none_matches.sensors,
trajectories=kapture_none_matches.trajectories,
records_camera=kapture_none_matches.records_camera,
keypoints={
keypoints_type: kapture_none_matches.keypoints[keypoints_type]
},
matches={
keypoints_type: matches_to_verify
})
# creates a new database with matches
logger.debug('export matches difference to db.')
colmap_db = COLMAPDatabase.connect(colmap_db_path)
database_extra.kapture_to_colmap(kapture_data_to_export, kapture_none_matches_dirpath,
tar_handlers_none_matches,
colmap_db,
keypoints_type,
None,
export_two_view_geometry=False)
# close db before running colmap processes in order to avoid locks
colmap_db.close()
logger.debug('run matches_importer command.')
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=matches_to_verify,
force=force
)
if 'import' not in skip_list:
logger.debug('import verified matches.')
os.umask(0o002)
colmap_db = COLMAPDatabase.connect(colmap_db_path)
kapture_data = kapture.Kapture()
kapture_data.records_camera, _ = get_images_and_trajectories_from_database(colmap_db)
kapture_data.matches = {
keypoints_type: get_matches_from_database(colmap_db, kapture_data.records_camera,
kapture_colmap_matches_dirpath,
tar_handlers_colmap_matches,
keypoints_type,
no_geometric_filtering=False)
}
colmap_db.close()
if kapture_colmap_matches.matches is None:
kapture_colmap_matches.matches = {}
if keypoints_type not in kapture_colmap_matches.matches:
kapture_colmap_matches.matches[keypoints_type] = kapture.Matches()
kapture_colmap_matches.matches[keypoints_type].update(kapture_data.matches[keypoints_type])
if 'delete_db' not in skip_list:
logger.debug('delete intermediate colmap db.')
os.remove(colmap_db_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_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 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