def main(cfg):
'''Main function to compute matches.
Parameters
----------
cfg: Namespace
Configurations for running this part of the code.
'''
# Get data directory
data_dir = get_data_path(cfg)
# Load pre-computed pairs with the new visibility criteria
pairs_per_th = get_pairs_per_threshold(data_dir)
# Check if all files exist
if is_stereo_complete(cfg):
print(' -- already exists, skipping stereo eval')
return
# Load keypoints and matches
keypoints_dict = load_h5(get_kp_file(cfg))
matches_dict = load_h5(get_match_file(cfg))
geom_dict = load_h5(get_geom_file(cfg))
geom_inl_dict = load_h5(get_geom_inl_file(cfg))
filter_matches_dict = load_h5(get_filter_match_file(cfg))
# Load visiblity and images
images_list = get_fullpath_list(data_dir, 'images')
vis_list = get_fullpath_list(data_dir, 'visibility')
if cfg.dataset != 'googleurban':
depth_maps_list = get_fullpath_list(data_dir, 'depth_maps')
image_names = get_item_name_list(images_list)
# Load camera information
calib_list = get_fullpath_list(data_dir, 'calibration')
calib_dict = load_calib(calib_list)
# Generate all possible pairs
print('Generating list of all possible pairs')
pairs = compute_image_pairs(vis_list, len(image_names), cfg.vis_th)
print('Old pairs with the point-based visibility threshold: {} '
'(for compatibility)'.format(len(pairs)))
for k, v in pairs_per_th.items():
print('New pairs at visibility threshold {}: {}'.format(k, len(v)))
# Evaluate each stereo pair in parallel
# Compute it for all pairs (i.e. visibility threshold 0)
print('Compute stereo metrics for all pairs')
#num_cores = int(multiprocessing.cpu_count() * 0.9)
num_cores = int(len(os.sched_getaffinity(0)) * 0.9)
result = Parallel(n_jobs=num_cores)(delayed(compute_stereo_metrics_from_E)(
images_list[image_names.index(pair.split('-')[0])], images_list[
image_names.index(pair.split('-')[1])],
depth_maps_list[image_names.index(pair.split('-')[0])] if cfg.
dataset != 'googleurban' else None, depth_maps_list[image_names.index(
pair.split('-')[1])] if cfg.dataset != 'googleurban' else None,
np.asarray(keypoints_dict[pair.split('-')[0]]),
np.asarray(keypoints_dict[pair.split('-')[1]]), calib_dict[pair.split(
'-')[0]], calib_dict[pair.split('-')
[1]], geom_dict[pair], matches_dict[pair],
filter_matches_dict[pair], geom_inl_dict[pair], cfg)
for pair in tqdm(pairs_per_th['0.0']))
# Convert previous visibility list to strings
old_keys = []
for pair in pairs:
old_keys.append('{}-{}'.format(image_names[pair[0]],
image_names[pair[1]]))
# Extract scores, err_q, err_t from results
all_keys = pairs_per_th['0.0']
err_dict, rep_s_dict = {}, {}
geo_s_dict_pre_match, geo_s_dict_refined_match, \
geo_s_dict_final_match = {}, {}, {}
true_s_dict_pre_match, true_s_dict_refined_match, \
true_s_dict_final_match = {}, {}, {}
for i in range(len(result)):
if all_keys[i] in old_keys:
if result[i][5]:
geo_s_dict_pre_match[
all_keys[i]] = result[i][0][0] if result[i][0] else None
geo_s_dict_refined_match[
all_keys[i]] = result[i][0][1] if result[i][0] else None
geo_s_dict_final_match[
all_keys[i]] = result[i][0][2] if result[i][0] else None
true_s_dict_pre_match[
all_keys[i]] = result[i][1][0] if result[i][1] else None
true_s_dict_refined_match[
all_keys[i]] = result[i][1][1] if result[i][1] else None
true_s_dict_final_match[
all_keys[i]] = result[i][1][2] if result[i][1] else None
err_q = result[i][2]
err_t = result[i][3]
rep_s_dict[all_keys[i]] = result[i][4]
err_dict[all_keys[i]] = [err_q, err_t]
print('Aggregating results for the old visibility constraint: '
'{}/{}'.format(len(geo_s_dict_pre_match), len(result)))
# Repeat with the new visibility threshold
err_dict_th, rep_s_dict_th = {}, {}
geo_s_dict_pre_match_th, geo_s_dict_refined_match_th, \
geo_s_dict_final_match_th = {}, {}, {}
true_s_dict_pre_match_th, true_s_dict_refined_match_th, \
true_s_dict_final_match_th = {}, {}, {}
for th, cur_pairs in pairs_per_th.items():
_err_dict, _rep_s_dict = {}, {}
_geo_s_dict_pre_match, _geo_s_dict_refined_match, \
_geo_s_dict_final_match = {}, {}, {}
_true_s_dict_pre_match, _true_s_dict_refined_match, \
_true_s_dict_final_match = {}, {}, {}
for i in range(len(all_keys)):
if len(cur_pairs) > 0 and all_keys[i] in cur_pairs:
if result[i][5]:
_geo_s_dict_pre_match[all_keys[
i]] = result[i][0][0] if result[i][0] else None
_geo_s_dict_refined_match[all_keys[
i]] = result[i][0][1] if result[i][0] else None
_geo_s_dict_final_match[all_keys[
i]] = result[i][0][2] if result[i][0] else None
_true_s_dict_pre_match[all_keys[
i]] = result[i][1][0] if result[i][1] else None
_true_s_dict_refined_match[all_keys[
i]] = result[i][1][1] if result[i][1] else None
_true_s_dict_final_match[all_keys[
i]] = result[i][1][2] if result[i][1] else None
err_q = result[i][2]
err_t = result[i][3]
_rep_s_dict[
all_keys[i]] = result[i][4] if result[i][4] else None
_err_dict[all_keys[i]] = [err_q, err_t]
geo_s_dict_pre_match_th[th] = _geo_s_dict_pre_match
geo_s_dict_refined_match_th[th] = _geo_s_dict_refined_match
geo_s_dict_final_match_th[th] = _geo_s_dict_final_match
true_s_dict_pre_match_th[th] = _true_s_dict_pre_match
true_s_dict_refined_match_th[th] = _true_s_dict_refined_match
true_s_dict_final_match_th[th] = _true_s_dict_final_match
err_dict_th[th] = _err_dict
rep_s_dict_th[th] = _rep_s_dict
print('Aggregating results for threshold "{}": {}/{}'.format(
th, len(geo_s_dict_pre_match_th[th]), len(result)))
# Create results folder if it does not exist
if not os.path.exists(get_stereo_path(cfg)):
os.makedirs(get_stereo_path(cfg))
# Finally, save packed scores and errors
if cfg.dataset != 'googleurban':
save_h5(geo_s_dict_pre_match, get_stereo_epipolar_pre_match_file(cfg))
save_h5(geo_s_dict_refined_match,
get_stereo_epipolar_refined_match_file(cfg))
save_h5(geo_s_dict_final_match,
get_stereo_epipolar_final_match_file(cfg))
save_h5(true_s_dict_pre_match,
get_stereo_depth_projection_pre_match_file(cfg))
save_h5(true_s_dict_refined_match,
get_stereo_depth_projection_refined_match_file(cfg))
save_h5(true_s_dict_final_match,
get_stereo_depth_projection_final_match_file(cfg))
save_h5(rep_s_dict, get_repeatability_score_file(cfg))
save_h5(err_dict, get_stereo_pose_file(cfg))
for th in pairs_per_th:
if cfg.dataset != 'googleurban':
save_h5(geo_s_dict_pre_match_th[th],
get_stereo_epipolar_pre_match_file(cfg, th))
save_h5(geo_s_dict_refined_match_th[th],
get_stereo_epipolar_refined_match_file(cfg, th))
save_h5(geo_s_dict_final_match_th[th],
get_stereo_epipolar_final_match_file(cfg, th))
save_h5(true_s_dict_pre_match_th[th],
get_stereo_depth_projection_pre_match_file(cfg, th))
save_h5(true_s_dict_refined_match_th[th],
get_stereo_depth_projection_refined_match_file(cfg, th))
save_h5(true_s_dict_final_match_th[th],
get_stereo_depth_projection_final_match_file(cfg, th))
save_h5(rep_s_dict_th[th], get_repeatability_score_file(cfg, th))
save_h5(err_dict_th[th], get_stereo_pose_file(cfg, th))
def main(cfg):
'''Main function to compute model.
Parameters
----------
cfg: Namespace
Configurations for running this part of the code.
'''
if os.path.exists(get_geom_file(cfg)):
print(' -- already exists, skipping model computation')
return
# Get data directory
keypoints_dict = load_h5(get_kp_file(cfg))
# Load keypoints and matches
matches_dict = load_h5(get_filter_match_file_for_computing_model(cfg))
# Feature Matching
print('Computing model')
num_cores = cfg.num_opencv_threads if cfg.num_opencv_threads > 0 else int(
len(os.sched_getaffinity(0)) * 0.9)
# Load camera information
data_dir = get_data_path(cfg)
images_list = get_fullpath_list(data_dir, 'images')
image_names = get_item_name_list(images_list)
calib_list = get_fullpath_list(data_dir, 'calibration')
calib_dict = load_calib(calib_list)
pairs_per_th = get_pairs_per_threshold(data_dir)
# Get data directory
try:
desc_dict = defaultdict(list)
desc_dict = load_h5(get_desc_file(cfg))
for k, v in desc_dict.items():
desc_dict[k] = v
except Exception:
desc_dict = defaultdict(list)
try:
aff_dict = defaultdict(list)
aff_dict1 = load_h5(get_affine_file(cfg))
for k, v in aff_dict1.items():
aff_dict[k] = v
except Exception:
aff_dict = defaultdict(list)
try:
ori_dict = defaultdict(list)
ori_dict1 = load_h5(get_angle_file(cfg))
for k, v in ori_dict1.items():
ori_dict[k] = v
except Exception:
ori_dict = defaultdict(list)
try:
scale_dict = defaultdict(list)
scale_dict1 = load_h5(get_scale_file(cfg))
for k, v in scale_dict1.items():
scale_dict[k] = v
except Exception:
scale_dict = defaultdict(list)
random.shuffle(pairs_per_th['0.0'])
result = Parallel(n_jobs=num_cores)(delayed(compute_model)(
cfg, np.asarray(matches_dict[pair]),
np.asarray(keypoints_dict[pair.split('-')[0]]),
np.asarray(keypoints_dict[pair.split('-')[1]]), calib_dict[pair.split(
'-')[0]], calib_dict[pair.split('-')[1]], images_list[
image_names.index(pair.split('-')[0])], images_list[
image_names.index(pair.split('-')[1])],
np.asarray(scale_dict[pair.split('-')[0]]),
np.asarray(scale_dict[pair.split('-')[1]]),
np.asarray(ori_dict[pair.split('-')[0]]),
np.asarray(ori_dict[pair.split('-')[1]]),
np.asarray(aff_dict[pair.split('-')[0]]),
np.asarray(aff_dict[pair.split('-')[1]]),
np.asarray(desc_dict[pair.split('-')[0]]),
np.asarray(desc_dict[pair.split('-')[1]]))
for pair in tqdm(pairs_per_th['0.0']))
# Make model dictionary
model_dict = {}
inl_dict = {}
timings_list = []
for i, pair in enumerate(pairs_per_th['0.0']):
model_dict[pair] = result[i][0]
inl_dict[pair] = result[i][1]
timings_list.append(result[i][2])
# Check model directory
if not os.path.exists(get_geom_path(cfg)):
os.makedirs(get_geom_path(cfg))
# Finally save packed models
save_h5(model_dict, get_geom_file(cfg))
save_h5(inl_dict, get_geom_inl_file(cfg))
# Save computational cost
save_h5({'cost': np.mean(timings_list)}, get_geom_cost_file(cfg))
print('Geometry cost (averaged over image pairs): {:0.2f} sec'.format(
np.mean(timings_list)))
def is_geom_complete(cfg):
'''Checks if match computation is complete.'''
is_complete = os.path.exists(get_geom_file(cfg))
return is_complete