def main(argv: Tuple[str]) -> None:
"""Runs the TILDE model and saves the results.
Arguments:
argv {Tuple[str]} -- List of one parameters. There should be exactly
one parameter - the path to the config file inside the tmp dir.
This config file will be used to get all other information and
process the correct images.
"""
if len(argv) <= 0:
raise RuntimeError("Missing argument <path_to_config_file>. Abort")
with open(argv[0], 'rb') as src:
config_file = pickle.load(src, encoding='utf-8')
config, file_list = config_file
model = load_tfeat()
if config['task'] == 'descriptors':
for file in tqdm(file_list):
descriptors = compute(file, config, model)
if descriptors is not None:
io_utils.save_descriptor_output(file, config, descriptors)
elif config['task'] == 'patches':
for file in tqdm(file_list):
descriptors = computeForPatchImages(file, config, model)
if descriptors is not None:
io_utils.save_descriptor_output(file, config, descriptors)
def main(argv: Tuple[str]) -> None:
"""Runs the LIFT model and saves the results.
Arguments:
argv {Tuple[str]} -- List of one parameters. There should be exactly
one parameter - the path to the config file inside the tmp dir.
This config file will be used to get all other information and
"""
if len(argv) <= 0:
raise RuntimeError("Missing argument <path_to_config_file>. Abort")
with open(argv[0], 'rb') as src:
config_file = pickle.load(src, encoding='utf-8')
config, file_list = config_file
model = load_model()
if config['task'] == 'keypoints':
for file in tqdm(file_list):
keypoints, keypoints_image, heatmap_image = detect(
file, config, model)
if keypoints is not None:
io_utils.save_detector_output(file, config['detector_name'],
config, keypoints,
keypoints_image, heatmap_image)
elif config['task'] == 'descriptors':
for file in tqdm(file_list):
descriptors = compute(file, config, model)
if descriptors is not None:
io_utils.save_descriptor_output(file, config, descriptors)
def main(argv: Tuple[str]) -> None:
"""Runs the TILDE model and saves the results.
Arguments:
argv {Tuple[str]} -- List of one parameters. There should be exactly
one parameter - the path to the config file inside the tmp dir.
This config file will be used to get all other information and
"""
if len(argv) <= 0:
raise RuntimeError("Missing argument <path_to_config_file>. Abort")
with open(argv[0], 'rb') as src:
config_file = pickle.load(src, encoding='utf-8')
_config, file_list = config_file
# Since we cannot split detector and descriptor in the superpoint model,
# we handle SuperPoint as a detector and add the property `descriptor_name`
# with value 'superpoint' to be able to save the descriptors.
# Note, that superpoint can only handle superpoint keypoints to generate
# descriptors, but the found keypoints can still be used by other descriptors.
config = copy.deepcopy(_config)
config['descriptor_name'] = 'superpoint'
model = load_superpoint()
if config['task'] == 'keypoints':
for file in tqdm(file_list):
keypoints, descriptors, keypoints_image, heatmap_image = detect(
file, config, model)
# Save detector output
io_utils.save_detector_output(file, config['detector_name'],
config, keypoints, keypoints_image,
heatmap_image)
# Save descriptor output
io_utils.save_descriptor_output(file, config, descriptors)
elif config['task'] == 'patches':
for file in tqdm(file_list):
descriptors = computeForPatchImages(file, config, model)
if descriptors is not None:
io_utils.save_descriptor_output(file, config, descriptors)