def get_descriptors_from_database(
database: COLMAPDatabase,
images: kapture.RecordsCamera,
kapture_dirpath: str,
descriptor_name: str = 'SIFT') -> Optional[kapture.Descriptors]:
"""
Writes descriptors files and return the list in kapture format from the colmap database.
:param database: colmap database.
:param images: list of images (as RecordsCamera).
:param kapture_dirpath: input root path to kapture.
:param descriptor_name: name of the keypoints descriptor (by default, in colmap, its SIFT, but can be imported)
:return: kapture descriptors
"""
image_filenames = set()
dtype = np.uint8 # values in the range 0…255
# see https://colmap.github.io/tutorial.html#feature-detection-and-extraction
dsize = None # usually uint8, 128, will be retrieved on first descriptor of DB
colmap_descriptors = ((image_id, blob_to_array(
data, dtype, (rows, cols)) if (rows > 0 and cols > 0) else np.zeros(
(0, dsize))) for image_id, rows, cols, data in database.execute(
"SELECT image_id, rows, cols, data FROM descriptors"))
hide_progressbar = logger.getEffectiveLevel() > logging.INFO
for image_id, image_descriptors in tqdm(colmap_descriptors,
disable=hide_progressbar):
# retrieve image path from image_id (actually the timestamp)
if dsize is None:
dsize = int(image_descriptors.shape[1])
elif dsize != image_descriptors.shape[
1] or dtype != image_descriptors.dtype:
raise ValueError('inconsistent descriptors size or type.')
image_filename = next((v for v in images[image_id].values()), None)
assert image_filename
descriptors_filepath = kapture.io.features.get_descriptors_fullpath(
kapture_dirpath, image_filename)
if image_descriptors.shape[0] == 0:
logger.warning(
f'image={image_id}:{image_filename} has 0 descriptors.')
# save the actual file
kapture.io.features.image_keypoints_to_file(descriptors_filepath,
image_descriptors)
# register it into to kapture
image_filenames.add(image_filename)
if image_filenames:
return kapture.Descriptors(descriptor_name, dtype, dsize,
image_filenames)
else:
return None
def sub_kapture_from_img_list(kdata, kdata_path, img_list, pairs):
trajectories = kapture.Trajectories()
sensors = kapture.Sensors()
records = kapture.RecordsCamera()
keypoints = kapture.Keypoints(kdata.keypoints._tname,
kdata.keypoints._dtype,
kdata.keypoints._dsize)
if kdata.descriptors != None:
descriptors = kapture.Descriptors(kdata.descriptors._tname,
kdata.descriptors._dtype,
kdata.descriptors._dsize)
else:
descriptors = None
matches = kapture.Matches()
timestamp_sensor_id_from_image_name = {
img_name: (timestamp, sensor_id)
for timestamp, sensor_id, img_name in kapture.flatten(
kdata.records_camera)
}
for img in img_list:
timestamp, sensor_id = timestamp_sensor_id_from_image_name[img]
pose = kdata.trajectories[timestamp][sensor_id]
sensors[sensor_id] = kdata.sensors[sensor_id]
records[timestamp, sensor_id] = img
trajectories[timestamp, sensor_id] = pose
keypoints.add(img)
if kdata.descriptors != None:
descriptors.add(img)
for i in pairs:
image_matches_filepath = get_matches_fullpath((i[0], i[1]), kdata_path)
if os.path.exists(image_matches_filepath):
matches.add(i[0], i[1])
matches.normalize()
return kapture.Kapture(sensors=sensors,
trajectories=trajectories,
records_camera=records,
descriptors=descriptors,
keypoints=keypoints,
matches=matches)
def sub_kapture_from_img_list(kdata, img_list, pairs, keypoints_type, descriptors_type):
trajectories = kapture.Trajectories()
sensors = kapture.Sensors()
records = kapture.RecordsCamera()
keypoints = kapture.Keypoints(kdata.keypoints[keypoints_type].type_name,
kdata.keypoints[keypoints_type].dtype,
kdata.keypoints[keypoints_type].dsize)
if kdata.descriptors is not None and descriptors_type in kdata.descriptors:
descriptors = kapture.Descriptors(kdata.descriptors[descriptors_type].type_name,
kdata.descriptors[descriptors_type].dtype,
kdata.descriptors[descriptors_type].dsize,
kdata.descriptors[descriptors_type].keypoints_type,
kdata.descriptors[descriptors_type].metric_type)
else:
descriptors = None
matches = kapture.Matches()
timestamp_sensor_id_from_image_name = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in
kapture.flatten(kdata.records_camera)}
for img in img_list:
timestamp, sensor_id = timestamp_sensor_id_from_image_name[img]
sensors[sensor_id] = kdata.sensors[sensor_id]
records[timestamp, sensor_id] = img
if (timestamp, sensor_id) in kdata.trajectories:
pose = kdata.trajectories[timestamp][sensor_id]
trajectories[timestamp, sensor_id] = pose
keypoints.add(img)
if kdata.descriptors is not None:
descriptors.add(img)
for i in pairs:
if i in kdata.matches[keypoints_type]:
matches.add(i[0], i[1])
matches.normalize()
return kapture.Kapture(sensors=sensors, trajectories=trajectories, records_camera=records,
descriptors={descriptors_type: descriptors},
keypoints={keypoints_type: keypoints},
matches={keypoints_type: matches})
def import_openmvg_regions(openmvg_regions_directory_path, kapture_data,
kapture_path):
# look for the "image_describer.json"
image_describer_path = path.join(openmvg_regions_directory_path,
'image_describer.json')
if not path.isfile(image_describer_path):
logger.debug(f'file not found : {image_describer_path}')
return
with open(image_describer_path) as f:
image_describer = json.load(f)
# retrieve what type of keypoints it is.
keypoints_type = image_describer.get('regions_type',
{}).get('polymorphic_name',
'UNDEFINED')
keypoints_name = {
'SIFT_Regions': 'SIFT',
'AKAZE_Float_Regions': 'AKAZE'
}.get(keypoints_type, keypoints_type)
kapture_keypoints = kapture.Keypoints(type_name=keypoints_name,
dtype=float,
dsize=4)
# retrieve what type of descriptors it is.
descriptors_type = image_describer.get('image_describer',
{}).get('polymorphic_name',
'UNDEFINED')
descriptors_props = {
'SIFT_Image_describer':
dict(type_name='SIFT', dtype=np.int32, dsize=128),
'AKAZE_Image_describer_SURF':
dict(type_name='AKAZE', dtype=np.int32, dsize=128),
}.get(descriptors_type)
if not descriptors_props:
raise ValueError(
f'conversion of {descriptors_type} descriptors not implemented.')
kapture_descriptors = kapture.Descriptors(**descriptors_props)
# populate regions files in openMVG directory
# https://github.com/openMVG/openMVG/blob/master/src/openMVG/features/scalar_regions.hpp#L23
for _, _, image_name in kapture.flatten(kapture_data.records_camera):
openmvg_image_name = path.splitext(path.basename(image_name))[0]
# keypoints
openmvg_keypoints_filepath = path.join(openmvg_regions_directory_path,
openmvg_image_name + '.feat')
if path.isfile(openmvg_keypoints_filepath):
# there is a keypoints file in openMVG, lets add it to kapture
keypoints_data = np.loadtxt(openmvg_keypoints_filepath)
assert keypoints_data.shape[1] == 4
kapture_keypoints.add(image_name)
# and convert file
kapture_keypoints_filepath = kapture.io.features.get_keypoints_fullpath(
kapture_path, image_name)
array_to_file(kapture_keypoints_filepath, keypoints_data)
# descriptors
openmvg_descriptors_filepath = path.join(
openmvg_regions_directory_path, openmvg_image_name + '.desc')
if path.isfile(openmvg_descriptors_filepath):
assert path.isfile(openmvg_keypoints_filepath)
# there is a keypoints file in openMVG, lets add it to kapture
# assumes descriptors shape from keypoints_data shape
descriptors_data_bytes = np.fromfile(openmvg_descriptors_filepath,
dtype=np.uint8)
nb_features = keypoints_data.shape[0]
descriptors_shape = descriptors_data_bytes[0:8].view(
descriptors_props['dtype'])
assert descriptors_shape[0] == nb_features
descriptors_data = descriptors_data_bytes[8:].view(
np.uint8).reshape((nb_features, 128))
# descriptors_data.reshape((keypoints_data.shape[0], -1))
kapture_descriptors.add(image_name)
# and convert file
kapture_descriptors_filepath = kapture.io.features.get_descriptors_fullpath(
kapture_path, image_name)
array_to_file(kapture_descriptors_filepath, descriptors_data)
kapture_data.keypoints = kapture_keypoints
kapture_data.descriptors = kapture_descriptors
def test_init_descriptors_unknown(self):
descriptors = kapture.Descriptors(
'R2D2', float, 64, ['a/a.jpg', 'b/b.jpg', 'c/c.jpg', 'c/c.jpg'])
self.assertEqual('R2D2', descriptors.type_name)
self.assertEqual(3, len(descriptors))
self.assertIn('a/a.jpg', descriptors)
def extract_kapture_keypoints(kapture_root,
config,
output_dir='',
overwrite=False):
"""
Extract r2d2 keypoints and descritors to the kapture format directly
"""
print('extract_kapture_keypoints...')
kdata = kapture_from_dir(kapture_root, matches_pairsfile_path=None,
skip_list= [kapture.GlobalFeatures,
kapture.Matches,
kapture.Points3d,
kapture.Observations])
export_dir = output_dir if output_dir else kapture_root # root of output directory for features
os.makedirs(export_dir, exist_ok=True)
assert kdata.records_camera is not None
image_list = [filename for _, _, filename in kapture.flatten(kdata.records_camera)]
# resume extraction if some features exist
try:
# load existing features, if any
kdata.keypoints = keypoints_from_dir(export_dir, None)
kdata.descriptors = descriptors_from_dir(export_dir, None)
if kdata.keypoints is not None and kdata.descriptors is not None and not overwrite:
image_list = [name for name in image_list if name not in kdata.keypoints or name not in kdata.descriptors]
except FileNotFoundError:
pass
except:
logging.exception("Error with importing existing local features.")
# clear features first if overwriting
if overwrite: delete_existing_kapture_files(export_dir, True, only=[kapture.Descriptors, kapture.Keypoints])
if len(image_list) == 0:
print('All features were already extracted')
return
else:
print(f'Extracting r2d2 features for {len(image_list)} images')
iscuda = common.torch_set_gpu([torch.cuda.is_available()])
# load the network...
net = load_network(config['checkpoint'])
if iscuda: net = net.cuda()
# create the non-maxima detector
detector = NonMaxSuppression(
rel_thr = config['reliability_thr'],
rep_thr = config['repeatability_thr'])
keypoints_dtype = None if kdata.keypoints is None else kdata.keypoints.dtype
descriptors_dtype = None if kdata.descriptors is None else kdata.descriptors.dtype
keypoints_dsize = None if kdata.keypoints is None else kdata.keypoints.dsize
descriptors_dsize = None if kdata.descriptors is None else kdata.descriptors.dsize
for image_name in image_list:
img_path = get_image_fullpath(kapture_root, image_name)
if img_path.endswith('.txt'):
images = open(img_path).read().splitlines() + images
continue
print(f"\nExtracting features for {img_path}")
img = Image.open(img_path).convert('RGB')
W, H = img.size
img = norm_RGB(img)[None]
if iscuda: img = img.cuda()
# extract keypoints/descriptors for a single image
xys, desc, scores = extract_multiscale(net, img, detector,
scale_f = config['scale_f'],
min_scale = config['min_scale'],
max_scale = config['max_scale'],
min_size = config['min_size'],
max_size = config['max_size'],
verbose = True)
xys = xys.cpu().numpy()
desc = desc.cpu().numpy()
scores = scores.cpu().numpy()
idxs = scores.argsort()[-config['top_k'] or None:]
xys = xys[idxs]
desc = desc[idxs]
if keypoints_dtype is None or descriptors_dtype is None:
keypoints_dtype = xys.dtype
descriptors_dtype = desc.dtype
keypoints_dsize = xys.shape[1]
descriptors_dsize = desc.shape[1]
kdata.keypoints = kapture.Keypoints('r2d2', keypoints_dtype, keypoints_dsize)
kdata.descriptors = kapture.Descriptors('r2d2', descriptors_dtype, descriptors_dsize)
keypoints_config_absolute_path = get_csv_fullpath(kapture.Keypoints, export_dir)
descriptors_config_absolute_path = get_csv_fullpath(kapture.Descriptors, export_dir)
keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints)
descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors)
else:
assert kdata.keypoints.type_name == 'r2d2'
assert kdata.descriptors.type_name == 'r2d2'
assert kdata.keypoints.dtype == xys.dtype
assert kdata.descriptors.dtype == desc.dtype
assert kdata.keypoints.dsize == xys.shape[1]
assert kdata.descriptors.dsize == desc.shape[1]
keypoints_fullpath = get_keypoints_fullpath(export_dir, image_name)
print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}")
image_keypoints_to_file(keypoints_fullpath, xys)
kdata.keypoints.add(image_name)
descriptors_fullpath = get_descriptors_fullpath(export_dir, image_name)
print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}")
image_descriptors_to_file(descriptors_fullpath, desc)
kdata.descriptors.add(image_name)
if not keypoints_check_dir(kdata.keypoints, export_dir) or \
not descriptors_check_dir(kdata.descriptors, export_dir):
print('local feature extraction ended successfully but not all files were saved')
if args.max_keypoints != float("+inf"):
# keep the last (the highest) indexes
idx_keep = scores.argsort()[-min(len(keypoints), args.max_keypoints):]
keypoints = keypoints[idx_keep]
descriptors = descriptors[idx_keep]
if keypoints_dtype is None or descriptors_dtype is None:
keypoints_dtype = keypoints.dtype
descriptors_dtype = descriptors.dtype
keypoints_dsize = keypoints.shape[1]
descriptors_dsize = descriptors.shape[1]
kdata.keypoints = kapture.Keypoints('d2net', keypoints_dtype, keypoints_dsize)
kdata.descriptors = kapture.Descriptors('d2net', descriptors_dtype, descriptors_dsize)
keypoints_config_absolute_path = get_csv_fullpath(kapture.Keypoints, args.kapture_root)
descriptors_config_absolute_path = get_csv_fullpath(kapture.Descriptors, args.kapture_root)
keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints)
descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors)
else:
assert kdata.keypoints.type_name == 'd2net'
assert kdata.descriptors.type_name == 'd2net'
assert kdata.keypoints.dtype == keypoints.dtype
assert kdata.descriptors.dtype == descriptors.dtype
assert kdata.keypoints.dsize == keypoints.shape[1]
assert kdata.descriptors.dsize == descriptors.shape[1]
keypoints_fullpath = get_keypoints_fullpath(args.kapture_root, image_name)
def extract_kapture_keypoints(args):
"""
Extract r2d2 keypoints and descritors to the kapture format directly
"""
print('extract_kapture_keypoints...')
kdata = kapture_from_dir(args.kapture_root,
matches_pairsfile_path=None,
skip_list=[
kapture.GlobalFeatures, kapture.Matches,
kapture.Points3d, kapture.Observations
])
assert kdata.records_camera is not None
image_list = [
filename for _, _, filename in kapture.flatten(kdata.records_camera)
]
if kdata.keypoints is not None and kdata.descriptors is not None:
image_list = [
name for name in image_list
if name not in kdata.keypoints or name not in kdata.descriptors
]
if len(image_list) == 0:
print('All features were already extracted')
return
else:
print(f'Extracting r2d2 features for {len(image_list)} images')
iscuda = common.torch_set_gpu(args.gpu)
# load the network...
net = load_network(args.model)
if iscuda: net = net.cuda()
# create the non-maxima detector
detector = NonMaxSuppression(rel_thr=args.reliability_thr,
rep_thr=args.repeatability_thr)
keypoints_dtype = None if kdata.keypoints is None else kdata.keypoints.dtype
descriptors_dtype = None if kdata.descriptors is None else kdata.descriptors.dtype
keypoints_dsize = None if kdata.keypoints is None else kdata.keypoints.dsize
descriptors_dsize = None if kdata.descriptors is None else kdata.descriptors.dsize
for image_name in image_list:
img_path = get_image_fullpath(args.kapture_root, image_name)
print(f"\nExtracting features for {img_path}")
img = Image.open(img_path).convert('RGB')
W, H = img.size
img = norm_RGB(img)[None]
if iscuda: img = img.cuda()
# extract keypoints/descriptors for a single image
xys, desc, scores = extract_multiscale(net,
img,
detector,
scale_f=args.scale_f,
min_scale=args.min_scale,
max_scale=args.max_scale,
min_size=args.min_size,
max_size=args.max_size,
verbose=True)
xys = xys.cpu().numpy()
desc = desc.cpu().numpy()
scores = scores.cpu().numpy()
idxs = scores.argsort()[-args.top_k or None:]
xys = xys[idxs]
desc = desc[idxs]
if keypoints_dtype is None or descriptors_dtype is None:
keypoints_dtype = xys.dtype
descriptors_dtype = desc.dtype
keypoints_dsize = xys.shape[1]
descriptors_dsize = desc.shape[1]
kdata.keypoints = kapture.Keypoints('r2d2', keypoints_dtype,
keypoints_dsize)
kdata.descriptors = kapture.Descriptors('r2d2', descriptors_dtype,
descriptors_dsize)
keypoints_config_absolute_path = get_csv_fullpath(
kapture.Keypoints, args.kapture_root)
descriptors_config_absolute_path = get_csv_fullpath(
kapture.Descriptors, args.kapture_root)
keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints)
descriptors_to_file(descriptors_config_absolute_path,
kdata.descriptors)
else:
assert kdata.keypoints.type_name == 'r2d2'
assert kdata.descriptors.type_name == 'r2d2'
assert kdata.keypoints.dtype == xys.dtype
assert kdata.descriptors.dtype == desc.dtype
assert kdata.keypoints.dsize == xys.shape[1]
assert kdata.descriptors.dsize == desc.shape[1]
keypoints_fullpath = get_keypoints_fullpath(args.kapture_root,
image_name)
print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}")
image_keypoints_to_file(keypoints_fullpath, xys)
kdata.keypoints.add(image_name)
descriptors_fullpath = get_descriptors_fullpath(
args.kapture_root, image_name)
print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}")
image_descriptors_to_file(descriptors_fullpath, desc)
kdata.descriptors.add(image_name)
if not keypoints_check_dir(kdata.keypoints, args.kapture_root) or \
not descriptors_check_dir(kdata.descriptors, args.kapture_root):
print(
'local feature extraction ended successfully but not all files were saved'
)
def _import_features_and_matches(opensfm_root_dir, kapture_root_dir, disable_tqdm)\
-> Tuple[kapture.Descriptors, kapture.Keypoints, kapture.Matches]:
# import features (keypoints + descriptors)
kapture_keypoints = None # kapture.Keypoints(type_name='opensfm', dsize=4, dtype=np.float64)
kapture_descriptors = None # kapture.Descriptors(type_name='opensfm', dsize=128, dtype=np.uint8)
opensfm_features_dir_path = path.join(opensfm_root_dir, 'features')
opensfm_features_suffix = '.features.npz'
if path.isdir(opensfm_features_dir_path):
logger.info('importing keypoints and descriptors ...')
opensfm_features_file_list = (path.join(
dp, fn) for dp, _, fs in os.walk(opensfm_features_dir_path)
for fn in fs)
opensfm_features_file_list = (
filepath for filepath in opensfm_features_file_list
if filepath.endswith(opensfm_features_suffix))
for opensfm_feature_filename in tqdm(opensfm_features_file_list,
disable=disable_tqdm):
image_filename = path.relpath(
opensfm_feature_filename,
opensfm_features_dir_path)[:-len(opensfm_features_suffix)]
opensfm_image_features = np.load(opensfm_feature_filename)
opensfm_image_keypoints = opensfm_image_features['points']
opensfm_image_descriptors = opensfm_image_features['descriptors']
logger.debug(
f'parsing keypoints and descriptors in {opensfm_feature_filename}'
)
if kapture_keypoints is None:
# print(type(opensfm_image_keypoints.dtype))
# HAHOG = Hessian Affine feature point detector + HOG descriptor
kapture_keypoints = kapture.Keypoints(
type_name='HessianAffine',
dsize=opensfm_image_keypoints.shape[1],
dtype=opensfm_image_keypoints.dtype)
if kapture_descriptors is None:
kapture_descriptors = kapture.Descriptors(
type_name='HOG',
dsize=opensfm_image_descriptors.shape[1],
dtype=opensfm_image_descriptors.dtype)
# convert keypoints file
keypoint_file_path = kapture.io.features.get_features_fullpath(
data_type=kapture.Keypoints,
kapture_dirpath=kapture_root_dir,
image_filename=image_filename)
kapture.io.features.image_keypoints_to_file(
filepath=keypoint_file_path,
image_keypoints=opensfm_image_keypoints)
# register the file
kapture_keypoints.add(image_filename)
# convert descriptors file
descriptor_file_path = kapture.io.features.get_features_fullpath(
data_type=kapture.Descriptors,
kapture_dirpath=kapture_root_dir,
image_filename=image_filename)
kapture.io.features.image_descriptors_to_file(
filepath=descriptor_file_path,
image_descriptors=opensfm_image_descriptors)
# register the file
kapture_descriptors.add(image_filename)
# import matches
kapture_matches = kapture.Matches()
opensfm_matches_suffix = '_matches.pkl.gz'
opensfm_matches_dir_path = path.join(opensfm_root_dir, 'matches')
if path.isdir(opensfm_matches_dir_path):
logger.info('importing matches ...')
opensfm_matches_file_list = (path.join(
dp, fn) for dp, _, fs in os.walk(opensfm_matches_dir_path)
for fn in fs)
opensfm_matches_file_list = (
filepath for filepath in opensfm_matches_file_list
if filepath.endswith(opensfm_matches_suffix))
for opensfm_matches_filename in tqdm(opensfm_matches_file_list,
disable=disable_tqdm):
image_filename_1 = path.relpath(
opensfm_matches_filename,
opensfm_matches_dir_path)[:-len(opensfm_matches_suffix)]
logger.debug(f'parsing matches in {image_filename_1}')
with gzip.open(opensfm_matches_filename, 'rb') as f:
opensfm_matches = pickle.load(f)
for image_filename_2, opensfm_image_matches in opensfm_matches.items(
):
image_pair = (image_filename_1, image_filename_2)
# register the pair to kapture
kapture_matches.add(*image_pair)
# convert the bin file to kapture
kapture_matches_filepath = kapture.io.features.get_matches_fullpath(
image_filename_pair=image_pair,
kapture_dirpath=kapture_root_dir)
kapture_image_matches = np.hstack([
opensfm_image_matches.astype(np.float64),
# no matches scoring = assume all to one
np.ones(shape=(opensfm_image_matches.shape[0], 1),
dtype=np.float64)
])
kapture.io.features.image_matches_to_file(
kapture_matches_filepath, kapture_image_matches)
return kapture_descriptors, kapture_keypoints, kapture_matches
idx_keep = scores.argsort(
)[-min(len(keypoints), args.max_keypoints):]
keypoints = keypoints[idx_keep]
descriptors = descriptors[idx_keep]
if keypoints_dtype is None or descriptors_dtype is None:
keypoints_dtype = keypoints.dtype
descriptors_dtype = descriptors.dtype
keypoints_dsize = keypoints.shape[1]
descriptors_dsize = descriptors.shape[1]
kdata.keypoints[args.keypoints_type] = kapture.Keypoints(
'd2net', keypoints_dtype, keypoints_dsize)
kdata.descriptors[args.descriptors_type] = kapture.Descriptors(
'd2net', descriptors_dtype, descriptors_dsize,
args.keypoints_type, 'L2')
keypoints_config_absolute_path = get_feature_csv_fullpath(
kapture.Keypoints, args.keypoints_type, args.kapture_root)
descriptors_config_absolute_path = get_feature_csv_fullpath(
kapture.Descriptors, args.descriptors_type, args.kapture_root)
keypoints_to_file(keypoints_config_absolute_path,
kdata.keypoints[args.keypoints_type])
descriptors_to_file(descriptors_config_absolute_path,
kdata.descriptors[args.descriptors_type])
else:
assert kdata.keypoints[
args.keypoints_type].dtype == keypoints.dtype
assert kdata.descriptors[
def import_opensfm(
opensfm_rootdir: str,
kapture_rootdir: str,
force_overwrite_existing: bool = False,
images_import_method: TransferAction = TransferAction.copy) -> None:
disable_tqdm = logger.getEffectiveLevel() != logging.INFO
# load reconstruction
opensfm_reconstruction_filepath = path.join(opensfm_rootdir,
'reconstruction.json')
with open(opensfm_reconstruction_filepath, 'rt') as f:
opensfm_reconstruction = json.load(f)
# remove the single list @ root
opensfm_reconstruction = opensfm_reconstruction[0]
# prepare space for output
os.makedirs(kapture_rootdir, exist_ok=True)
delete_existing_kapture_files(kapture_rootdir,
force_erase=force_overwrite_existing)
# import cameras
kapture_sensors = kapture.Sensors()
assert 'cameras' in opensfm_reconstruction
# import cameras
for osfm_camera_id, osfm_camera in opensfm_reconstruction['cameras'].items(
):
camera = import_camera(osfm_camera, name=osfm_camera_id)
kapture_sensors[osfm_camera_id] = camera
# import shots
logger.info('importing images and trajectories ...')
kapture_images = kapture.RecordsCamera()
kapture_trajectories = kapture.Trajectories()
opensfm_image_dirpath = path.join(opensfm_rootdir, 'images')
assert 'shots' in opensfm_reconstruction
image_timestamps, image_sensors = {}, {
} # used later to retrieve the timestamp of an image.
for timestamp, (image_filename, shot) in enumerate(
opensfm_reconstruction['shots'].items()):
sensor_id = shot['camera']
image_timestamps[image_filename] = timestamp
image_sensors[image_filename] = sensor_id
# in OpenSfm, (sensor, timestamp) is not unique.
rotation_vector = shot['rotation']
q = quaternion.from_rotation_vector(rotation_vector)
translation = shot['translation']
# capture_time = shot['capture_time'] # may be invalid
# gps_position = shot['gps_position']
kapture_images[timestamp, sensor_id] = image_filename
kapture_trajectories[timestamp,
sensor_id] = kapture.PoseTransform(r=q,
t=translation)
# copy image files
filename_list = [f for _, _, f in kapture.flatten(kapture_images)]
import_record_data_from_dir_auto(
source_record_dirpath=opensfm_image_dirpath,
destination_kapture_dirpath=kapture_rootdir,
filename_list=filename_list,
copy_strategy=images_import_method)
# gps from pre-extracted exif, in exif/image_name.jpg.exif
kapture_gnss = None
opensfm_exif_dirpath = path.join(opensfm_rootdir, 'exif')
opensfm_exif_suffix = '.exif'
if path.isdir(opensfm_exif_dirpath):
logger.info('importing GNSS from exif ...')
camera_ids = set(image_sensors.values())
# add a gps sensor for each camera
map_cam_to_gnss_sensor = {
cam_id: 'GPS_' + cam_id
for cam_id in camera_ids
}
for gnss_id in map_cam_to_gnss_sensor.values():
kapture_sensors[gnss_id] = kapture.Sensor(
sensor_type='gnss', sensor_params=['EPSG:4326'])
# build epsg_code for all cameras
kapture_gnss = kapture.RecordsGnss()
opensfm_exif_filepath_list = (
path.join(dirpath, filename)
for dirpath, _, filename_list in os.walk(opensfm_exif_dirpath)
for filename in filename_list
if filename.endswith(opensfm_exif_suffix))
for opensfm_exif_filepath in tqdm(opensfm_exif_filepath_list,
disable=disable_tqdm):
image_filename = path.relpath(
opensfm_exif_filepath,
opensfm_exif_dirpath)[:-len(opensfm_exif_suffix)]
image_timestamp = image_timestamps[image_filename]
image_sensor_id = image_sensors[image_filename]
gnss_timestamp = image_timestamp
gnss_sensor_id = map_cam_to_gnss_sensor[image_sensor_id]
with open(opensfm_exif_filepath, 'rt') as f:
js_root = json.load(f)
if 'gps' not in js_root:
logger.warning(f'NO GPS data in "{opensfm_exif_filepath}"')
continue
gps_coords = {
'x': js_root['gps']['longitude'],
'y': js_root['gps']['latitude'],
'z': js_root['gps'].get('altitude', 0.0),
'dop': js_root['gps'].get('dop', 0),
'utc': 0,
}
logger.debug(
f'found GPS data for ({gnss_timestamp}, {gnss_sensor_id}) in "{opensfm_exif_filepath}"'
)
kapture_gnss[gnss_timestamp,
gnss_sensor_id] = kapture.RecordGnss(**gps_coords)
# import features (keypoints + descriptors)
kapture_keypoints = None # kapture.Keypoints(type_name='opensfm', dsize=4, dtype=np.float64)
kapture_descriptors = None # kapture.Descriptors(type_name='opensfm', dsize=128, dtype=np.uint8)
opensfm_features_dirpath = path.join(opensfm_rootdir, 'features')
opensfm_features_suffix = '.features.npz'
if path.isdir(opensfm_features_dirpath):
logger.info('importing keypoints and descriptors ...')
opensfm_features_file_list = (path.join(
dp, fn) for dp, _, fs in os.walk(opensfm_features_dirpath)
for fn in fs)
opensfm_features_file_list = (
filepath for filepath in opensfm_features_file_list
if filepath.endswith(opensfm_features_suffix))
for opensfm_feature_filename in tqdm(opensfm_features_file_list,
disable=disable_tqdm):
image_filename = path.relpath(
opensfm_feature_filename,
opensfm_features_dirpath)[:-len(opensfm_features_suffix)]
opensfm_image_features = np.load(opensfm_feature_filename)
opensfm_image_keypoints = opensfm_image_features['points']
opensfm_image_descriptors = opensfm_image_features['descriptors']
logger.debug(
f'parsing keypoints and descriptors in {opensfm_feature_filename}'
)
if kapture_keypoints is None:
# print(type(opensfm_image_keypoints.dtype))
# HAHOG = Hessian Affine feature point detector + HOG descriptor
kapture_keypoints = kapture.Keypoints(
type_name='HessianAffine',
dsize=opensfm_image_keypoints.shape[1],
dtype=opensfm_image_keypoints.dtype)
if kapture_descriptors is None:
kapture_descriptors = kapture.Descriptors(
type_name='HOG',
dsize=opensfm_image_descriptors.shape[1],
dtype=opensfm_image_descriptors.dtype)
# convert keypoints file
keypoint_filpath = kapture.io.features.get_features_fullpath(
data_type=kapture.Keypoints,
kapture_dirpath=kapture_rootdir,
image_filename=image_filename)
kapture.io.features.image_keypoints_to_file(
filepath=keypoint_filpath,
image_keypoints=opensfm_image_keypoints)
# register the file
kapture_keypoints.add(image_filename)
# convert descriptors file
descriptor_filpath = kapture.io.features.get_features_fullpath(
data_type=kapture.Descriptors,
kapture_dirpath=kapture_rootdir,
image_filename=image_filename)
kapture.io.features.image_descriptors_to_file(
filepath=descriptor_filpath,
image_descriptors=opensfm_image_descriptors)
# register the file
kapture_descriptors.add(image_filename)
# import matches
kapture_matches = kapture.Matches()
opensfm_matches_suffix = '_matches.pkl.gz'
opensfm_matches_dirpath = path.join(opensfm_rootdir, 'matches')
if path.isdir(opensfm_matches_dirpath):
logger.info('importing matches ...')
opensfm_matches_file_list = (path.join(
dp, fn) for dp, _, fs in os.walk(opensfm_matches_dirpath)
for fn in fs)
opensfm_matches_file_list = (
filepath for filepath in opensfm_matches_file_list
if filepath.endswith(opensfm_matches_suffix))
for opensfm_matches_filename in tqdm(opensfm_matches_file_list,
disable=disable_tqdm):
image_filename_1 = path.relpath(
opensfm_matches_filename,
opensfm_matches_dirpath)[:-len(opensfm_matches_suffix)]
logger.debug(f'parsing mathes in {image_filename_1}')
with gzip.open(opensfm_matches_filename, 'rb') as f:
opensfm_matches = pickle.load(f)
for image_filename_2, opensfm_image_matches in opensfm_matches.items(
):
image_pair = (image_filename_1, image_filename_2)
# register the pair to kapture
kapture_matches.add(*image_pair)
# convert the bin file to kapture
kapture_matches_filepath = kapture.io.features.get_matches_fullpath(
image_filename_pair=image_pair,
kapture_dirpath=kapture_rootdir)
kapture_image_matches = np.hstack([
opensfm_image_matches.astype(np.float64),
# no macthes scoring = assume all to one
np.ones(shape=(opensfm_image_matches.shape[0], 1),
dtype=np.float64)
])
kapture.io.features.image_matches_to_file(
kapture_matches_filepath, kapture_image_matches)
# import 3-D points
if 'points' in opensfm_reconstruction:
logger.info('importing points 3-D')
opensfm_points = opensfm_reconstruction['points']
points_data = []
for point_id in sorted(opensfm_points):
point_data = opensfm_points[point_id]
point_data = point_data['coordinates'] + point_data['color']
points_data.append(point_data)
kapture_points = kapture.Points3d(points_data)
else:
kapture_points = None
# saving kapture csv files
logger.info('saving kapture files')
kapture_data = kapture.Kapture(sensors=kapture_sensors,
records_camera=kapture_images,
records_gnss=kapture_gnss,
trajectories=kapture_trajectories,
keypoints=kapture_keypoints,
descriptors=kapture_descriptors,
matches=kapture_matches,
points3d=kapture_points)
kapture.io.csv.kapture_to_dir(dirpath=kapture_rootdir,
kapture_data=kapture_data)