def check_val_rep(num_points=25):
total_rep_avg = []
num_examples = dataset_class.get_num_patches(True)
fetches = [src_score_maps_activation, dst_score_maps_activation]
for _ in tqdm(range(num_examples)):
images_batch, images_dst_batch, h_src_2_dst_batch, h_dst_2_src_batch = sess.run(next_val_batch)
feed_dict = {
input_network_src: images_batch,
input_network_dst: images_dst_batch,
h_src_2_dst: h_src_2_dst_batch,
h_dst_2_src: h_dst_2_src_batch,
phase_train: False,
dimension_image: np.array(
[images_batch.shape[0], images_batch.shape[1], images_batch.shape[2]], dtype=np.int32),
dimension_image_dst: np.array(
[images_dst_batch.shape[0], images_dst_batch.shape[1], images_dst_batch.shape[2]], dtype=np.int32),
}
src_scores, dst_scores = sess.run(fetches, feed_dict=feed_dict)
# Apply NMS
src_scores = rep_tools.apply_nms(src_scores[0, :, :, 0], args.nms_size)
dst_scores = rep_tools.apply_nms(dst_scores[0, :, :, 0], args.nms_size)
hom = geo_tools.prepare_homography(h_dst_2_src_batch[0])
mask_src, mask_dst = geo_tools.create_common_region_masks(hom, images_batch[0].shape, images_dst_batch[0].shape)
src_scores = np.multiply(src_scores, mask_src)
dst_scores = np.multiply(dst_scores, mask_dst)
src_pts = geo_tools.get_point_coordinates(src_scores, num_points=num_points, order_coord='xysr')
dst_pts = geo_tools.get_point_coordinates(dst_scores, num_points=num_points, order_coord='xysr')
dst_to_src_pts = geo_tools.apply_homography_to_points(dst_pts, hom)
repeatability_results = rep_tools.compute_repeatability(src_pts, dst_to_src_pts)
total_rep_avg.append(repeatability_results['rep_single_scale'])
return np.asarray(total_rep_avg).mean()
def extract_features(image):
pyramid = pyramid_gaussian(image,
max_layer=args.pyramid_levels,
downscale=args.scale_factor_levels)
score_maps = {}
for (j, resized) in enumerate(pyramid):
im = resized.reshape(1, resized.shape[0], resized.shape[1], 1)
feed_dict = {
input_network:
im,
phase_train:
False,
dimension_image:
np.array([1, im.shape[1], im.shape[2]], dtype=np.int32),
}
im_scores = sess.run(maps, feed_dict=feed_dict)
im_scores = geo_tools.remove_borders(im_scores,
borders=args.border_size)
score_maps['map_' +
str(j + 1 + args.upsampled_levels)] = im_scores[0, :, :,
0]
if args.upsampled_levels:
for j in range(args.upsampled_levels):
factor = args.scale_factor_levels**(args.upsampled_levels - j)
up_image = cv2.resize(image, (0, 0), fx=factor, fy=factor)
im = np.reshape(up_image,
(1, up_image.shape[0], up_image.shape[1], 1))
feed_dict = {
input_network:
im,
phase_train:
False,
dimension_image:
np.array([1, im.shape[1], im.shape[2]], dtype=np.int32),
}
im_scores = sess.run(maps, feed_dict=feed_dict)
im_scores = geo_tools.remove_borders(im_scores,
borders=args.border_size)
score_maps['map_' + str(j + 1)] = im_scores[0, :, :, 0]
im_pts = []
for idx_level in range(levels):
scale_value = (args.scale_factor_levels**(idx_level -
args.upsampled_levels))
scale_factor = 1. / scale_value
h_scale = np.asarray([[scale_factor, 0., 0.],
[0., scale_factor, 0.], [0., 0., 1.]])
h_scale_inv = np.linalg.inv(h_scale)
h_scale_inv = h_scale_inv / h_scale_inv[2, 2]
num_points_level = point_level[idx_level]
if idx_level > 0:
res_points = int(
np.asarray(
[point_level[a]
for a in range(0, idx_level + 1)]).sum() -
len(im_pts))
num_points_level = res_points
im_scores = rep_tools.apply_nms(
score_maps['map_' + str(idx_level + 1)], args.nms_size)
im_pts_tmp = geo_tools.get_point_coordinates(
im_scores, num_points=num_points_level, order_coord='xysr')
im_pts_tmp = geo_tools.apply_homography_to_points(
im_pts_tmp, h_scale_inv)
if not idx_level:
im_pts = im_pts_tmp
else:
im_pts = np.concatenate((im_pts, im_pts_tmp), axis=0)
if args.order_coord == 'yxsr':
im_pts = np.asarray(
list(map(lambda x: [x[1], x[0], x[2], x[3]], im_pts)))
im_pts = im_pts[(-1 * im_pts[:, 3]).argsort()]
im_pts = im_pts[:args.num_points]
# Extract descriptor from features
descriptors = []
im = image.reshape(1, image.shape[0], image.shape[1], 1)
for idx_desc_batch in range(int(len(im_pts) / 250 + 1)):
points_batch = im_pts[idx_desc_batch * 250:(idx_desc_batch + 1) *
250]
if not len(points_batch):
break
feed_dict = {
input_network:
im,
phase_train:
False,
kpts_coord:
points_batch[:, :2],
kpts_scale:
args.scale_factor * points_batch[:, 2],
kpts_batch:
np.zeros(len(points_batch)),
dimension_image:
np.array([1, im.shape[1], im.shape[2]], dtype=np.int32),
}
patch_batch = sess.run(input_patches, feed_dict=feed_dict)
patch_batch = np.reshape(patch_batch,
(patch_batch.shape[0], 1, 32, 32))
data_a = torch.from_numpy(patch_batch)
data_a = data_a.cuda()
data_a = Variable(data_a)
with torch.no_grad():
out_a = model(data_a)
desc_batch = out_a.data.cpu().numpy().reshape(-1, 128)
if idx_desc_batch == 0:
descriptors = desc_batch
else:
descriptors = np.concatenate([descriptors, desc_batch], axis=0)
return im_pts, descriptors
def hsequences_metrics():
parser = argparse.ArgumentParser(description='HSequences Compute Repeatability')
parser.add_argument('--data-dir', type=str, default='hpatches-sequences-release/',
help='The root path to HSequences dataset.')
parser.add_argument('--results-bench-dir', type=str, default='HSequences_bench/results/',
help='The output path to save the results.')
parser.add_argument('--detector-name', type=str, default='KeyNet_default',
help='The name of the detector to compute metrics.')
parser.add_argument('--results-dir', type=str, default='extracted_features/',
help='The path to the extracted points.')
parser.add_argument('--split', type=str, default='view',
help='The name of the HPatches (HSequences) split. Use full, debug_view, debug_illum, view or illum.')
parser.add_argument('--split-path', type=str, default='HSequences_bench/splits.json',
help='The path to the split json file.')
parser.add_argument('--top-k-points', type=int, default=1000,
help='The number of top points to use for evaluation. Set to None to use all points')
parser.add_argument('--overlap', type=float, default=0.6,
help='The overlap threshold for a correspondence to be considered correct.')
parser.add_argument('--pixel-threshold', type=int, default=5,
help='The distance of pixels for a matching correspondence to be considered correct.')
parser.add_argument('--dst-to-src-evaluation', type=bool, default=True,
help='Order to apply homography to points. Use True for dst to src, False otherwise.')
parser.add_argument('--order-coord', type=str, default='xysr',
help='The coordinate order that follows the extracted points. Use either xysr or yxsr.')
args = parser.parse_args()
print(args.detector_name + ': ' + args.split)
aux.check_directory(args.results_bench_dir)
# create the dataloader
data_loader = HSequences_dataset(args.data_dir, args.split, args.split_path)
results = aux.create_overlapping_results(args.detector_name, args.overlap)
# matching method
matcher = OpencvBruteForceMatcher('l2')
count_seq = 0
# load data and compute the keypoints
for sample_id, sample_data in enumerate(data_loader.extract_hsequences()):
sequence = sample_data['sequence_name']
count_seq += 1
image_src = sample_data['im_src']
images_dst = sample_data['images_dst']
h_src_2_dst = sample_data['h_src_2_dst']
h_dst_2_src = sample_data['h_dst_2_src']
print('\nComputing ' + sequence + ' sequence {0} / {1} \n'.format(count_seq, len(data_loader.sequences)))
for idx_im in tqdm(range(len(images_dst))):
# create the mask to filter out the points outside of the common areas
mask_src, mask_dst = geo_tools.create_common_region_masks(h_dst_2_src[idx_im], image_src.shape, images_dst[idx_im].shape)
# compute the files paths
src_pts_filename = os.path.join(args.results_dir, args.detector_name,
'hpatches-sequences-release', '{}/1.ppm.kpt.npy'.format(sample_data['sequence_name']))
src_dsc_filename = os.path.join(args.results_dir, args.detector_name,
'hpatches-sequences-release', '{}/1.ppm.dsc.npy'.format(sample_data['sequence_name']))
dst_pts_filename = os.path.join(args.results_dir, args.detector_name,
'hpatches-sequences-release', '{}/{}.ppm.kpt.npy'.format(sample_data['sequence_name'], idx_im+2))
dst_dsc_filename = os.path.join(args.results_dir, args.detector_name,
'hpatches-sequences-release', '{}/{}.ppm.dsc.npy'.format(sample_data['sequence_name'], idx_im+2))
if not os.path.isfile(src_pts_filename):
print("Could not find the file: " + src_pts_filename)
return False
if not os.path.isfile(src_dsc_filename):
print("Could not find the file: " + src_dsc_filename)
return False
if not os.path.isfile(dst_pts_filename):
print("Could not find the file: " + dst_pts_filename)
return False
if not os.path.isfile(dst_dsc_filename):
print("Could not find the file: " + dst_dsc_filename)
return False
# load the points
src_pts = np.load(src_pts_filename)
src_dsc = np.load(src_dsc_filename)
dst_pts = np.load(dst_pts_filename)
dst_dsc = np.load(dst_dsc_filename)
if args.order_coord == 'xysr':
src_pts = np.asarray(list(map(lambda x: [x[1], x[0], x[2], x[3]], src_pts)))
dst_pts = np.asarray(list(map(lambda x: [x[1], x[0], x[2], x[3]], dst_pts)))
# Check Common Points
src_idx = rep_tools.check_common_points(src_pts, mask_src)
src_pts = src_pts[src_idx]
src_dsc = src_dsc[src_idx]
dst_idx = rep_tools.check_common_points(dst_pts, mask_dst)
dst_pts = dst_pts[dst_idx]
dst_dsc = dst_dsc[dst_idx]
# Select top K points
if args.top_k_points:
src_idx = rep_tools.select_top_k(src_pts, args.top_k_points)
src_pts = src_pts[src_idx]
src_dsc = src_dsc[src_idx]
dst_idx = rep_tools.select_top_k(dst_pts, args.top_k_points)
dst_pts = dst_pts[dst_idx]
dst_dsc = dst_dsc[dst_idx]
src_pts = np.asarray(list(map(lambda x: [x[1], x[0], x[2], x[3]], src_pts)))
dst_pts = np.asarray(list(map(lambda x: [x[1], x[0], x[2], x[3]], dst_pts)))
src_to_dst_pts = geo_tools.apply_homography_to_points(
src_pts, h_src_2_dst[idx_im])
dst_to_src_pts = geo_tools.apply_homography_to_points(
dst_pts, h_dst_2_src[idx_im])
if args.dst_to_src_evaluation:
points_src = src_pts
points_dst = dst_to_src_pts
else:
points_src = src_to_dst_pts
points_dst = dst_pts
# compute repeatability
repeatability_results = rep_tools.compute_repeatability(points_src, points_dst, overlap_err=1-args.overlap,
dist_match_thresh=args.pixel_threshold)
# match descriptors
matches = matcher.match(src_dsc, dst_dsc)
matches_np = aux.convert_opencv_matches_to_numpy(matches)
matches_inv = matcher.match(dst_dsc, src_dsc)
matches_inv_np = aux.convert_opencv_matches_to_numpy(matches_inv)
mask = matches_np[:, 0] == matches_inv_np[matches_np[:, 1], 1]
matches_np = matches_np[mask]
match_score, match_score_corr, num_matches = {}, {}, {}
# compute matching based on pixel distance
for th_i in range(1, 11):
match_score_i, match_score_corr_i, num_matches_i = match_tools.compute_matching_based_distance(points_src, points_dst, matches_np,
repeatability_results['total_num_points'],
pixel_threshold=th_i,
possible_matches=repeatability_results['possible_matches'])
match_score[str(th_i)] = match_score_i
match_score_corr[str(th_i)] = match_score_corr_i
num_matches[str(th_i)] = num_matches_i
mma = np.mean([match_score[str(idx)] for idx in match_score])
results['rep_single_scale'].append(
repeatability_results['rep_single_scale'])
results['rep_multi_scale'].append(
repeatability_results['rep_multi_scale'])
results['num_points_single_scale'].append(
repeatability_results['num_points_single_scale'])
results['num_points_multi_scale'].append(
repeatability_results['num_points_multi_scale'])
results['error_overlap_single_scale'].append(
repeatability_results['error_overlap_single_scale'])
results['error_overlap_multi_scale'].append(
repeatability_results['error_overlap_multi_scale'])
results['mma'].append(match_score[str(args.pixel_threshold)])
results['mma_corr'].append(match_score_corr[str(args.pixel_threshold)])
results['num_matches'].append(num_matches[str(args.pixel_threshold)])
results['num_mutual_corresp'].append(len(matches_np))
results['avg_mma'].append(mma)
results['num_features'].append(repeatability_results['total_num_points'])
# average the results
rep_single = np.array(results['rep_single_scale']).mean()
rep_multi = np.array(results['rep_multi_scale']).mean()
error_overlap_s = np.array(results['error_overlap_single_scale']).mean()
error_overlap_m = np.array(results['error_overlap_multi_scale']).mean()
mma = np.array(results['mma']).mean()
mma_corr = np.array(results['mma_corr']).mean()
num_matches = np.array(results['num_matches']).mean()
num_mutual_corresp = np.array(results['num_mutual_corresp']).mean()
avg_mma = np.array(results['avg_mma']).mean()
num_features = np.array(results['num_features']).mean()
# Matching Score: Matching Score taking into account all features that have been
# detected in any of the two images.
# Matching Score (possible matches): Matching Score only taking into account those features that have been
# detected in both images.
# MMA Score is computed based on the Matching Score (all detected features)
print('\n## Overlap @{0}:\n \
#### Rep. Multi: {1:.4f}\n \
#### Rep. Single: {2:.4f}\n \
#### Overlap Multi: {3:.4f}\n \
#### Overlap Single: {4:.4f}\n \
#### MMA: {5:.4f}\n \
#### MMA (possible matches): {6:.4f}\n \
#### Num matches: {7:.4f}\n \
#### Num Mutual Correspondences: {8:.4f}\n \
#### Avg. over Threshold MMA: {9:.4f}\n \
#### Num Feats: {10:.4f}'.format(
args.overlap, rep_multi, rep_single, error_overlap_s, error_overlap_m, mma,
mma_corr, num_matches, num_mutual_corresp, avg_mma, num_features))
# Store data (serialize)
output_file_path = os.path.join(args.results_bench_dir, '{0}_{1}.pickle'
.format(args.detector_name, args.split))
with open(output_file_path, 'wb') as handle:
pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)