def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Parse dataset to obtain query/index images, and ground-truth.
print('Parsing dataset...')
query_list, index_list, ground_truth = dataset.ReadDatasetFile(
FLAGS.dataset_file_path)
num_query_images = len(query_list)
num_index_images = len(index_list)
(_, medium_ground_truth,
hard_ground_truth) = dataset.ParseEasyMediumHardGroundTruth(ground_truth)
print('done! Found %d queries and %d index images' %
(num_query_images, num_index_images))
# Read global features.
query_global_features = _ReadDelgGlobalDescriptors(
FLAGS.query_features_dir, query_list)
index_global_features = _ReadDelgGlobalDescriptors(
FLAGS.index_features_dir, index_list)
# Compute similarity between query and index images, potentially re-ranking
# with geometric verification.
ranks_before_gv = np.zeros([num_query_images, num_index_images],
dtype='int32')
if FLAGS.use_geometric_verification:
medium_ranks_after_gv = np.zeros([num_query_images, num_index_images],
dtype='int32')
hard_ranks_after_gv = np.zeros([num_query_images, num_index_images],
dtype='int32')
for i in range(num_query_images):
print('Performing retrieval with query %d (%s)...' %
(i, query_list[i]))
start = time.time()
# Compute similarity between global descriptors.
similarities = np.dot(index_global_features, query_global_features[i])
ranks_before_gv[i] = np.argsort(-similarities)
# Re-rank using geometric verification.
if FLAGS.use_geometric_verification:
medium_ranks_after_gv[
i] = image_reranking.RerankByGeometricVerification(
input_ranks=ranks_before_gv[i],
initial_scores=similarities,
query_name=query_list[i],
index_names=index_list,
query_features_dir=FLAGS.query_features_dir,
index_features_dir=FLAGS.index_features_dir,
junk_ids=set(medium_ground_truth[i]['junk']),
local_feature_extension=_DELG_LOCAL_EXTENSION,
ransac_seed=0,
feature_distance_threshold=FLAGS.
local_feature_distance_threshold,
ransac_residual_threshold=FLAGS.ransac_residual_threshold)
hard_ranks_after_gv[
i] = image_reranking.RerankByGeometricVerification(
input_ranks=ranks_before_gv[i],
initial_scores=similarities,
query_name=query_list[i],
index_names=index_list,
query_features_dir=FLAGS.query_features_dir,
index_features_dir=FLAGS.index_features_dir,
junk_ids=set(hard_ground_truth[i]['junk']),
local_feature_extension=_DELG_LOCAL_EXTENSION,
ransac_seed=0,
feature_distance_threshold=FLAGS.
local_feature_distance_threshold,
ransac_residual_threshold=FLAGS.ransac_residual_threshold)
elapsed = (time.time() - start)
print('done! Retrieval for query %d took %f seconds' % (i, elapsed))
# Create output directory if necessary.
if not tf.io.gfile.exists(FLAGS.output_dir):
tf.io.gfile.makedirs(FLAGS.output_dir)
# Compute metrics.
medium_metrics = dataset.ComputeMetrics(ranks_before_gv,
medium_ground_truth, _PR_RANKS)
hard_metrics = dataset.ComputeMetrics(ranks_before_gv, hard_ground_truth,
_PR_RANKS)
if FLAGS.use_geometric_verification:
medium_metrics_after_gv = dataset.ComputeMetrics(
medium_ranks_after_gv, medium_ground_truth, _PR_RANKS)
hard_metrics_after_gv = dataset.ComputeMetrics(hard_ranks_after_gv,
hard_ground_truth,
_PR_RANKS)
# Write metrics to file.
mean_average_precision_dict = {
'medium': medium_metrics[0],
'hard': hard_metrics[0]
}
mean_precisions_dict = {
'medium': medium_metrics[1],
'hard': hard_metrics[1]
}
mean_recalls_dict = {'medium': medium_metrics[2], 'hard': hard_metrics[2]}
if FLAGS.use_geometric_verification:
mean_average_precision_dict.update({
'medium_after_gv':
medium_metrics_after_gv[0],
'hard_after_gv':
hard_metrics_after_gv[0]
})
mean_precisions_dict.update({
'medium_after_gv':
medium_metrics_after_gv[1],
'hard_after_gv':
hard_metrics_after_gv[1]
})
mean_recalls_dict.update({
'medium_after_gv': medium_metrics_after_gv[2],
'hard_after_gv': hard_metrics_after_gv[2]
})
dataset.SaveMetricsFile(mean_average_precision_dict, mean_precisions_dict,
mean_recalls_dict, _PR_RANKS,
os.path.join(FLAGS.output_dir, _METRICS_FILENAME))
def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Parse dataset to obtain query/index images, and ground-truth.
print('Parsing dataset...')
query_list, index_list, ground_truth = dataset.ReadDatasetFile(
cmd_args.dataset_file_path)
num_query_images = len(query_list)
num_index_images = len(index_list)
(_, medium_ground_truth,
hard_ground_truth) = dataset.ParseEasyMediumHardGroundTruth(ground_truth)
print('done! Found %d queries and %d index images' %
(num_query_images, num_index_images))
# Parse AggregationConfig protos.
query_config = aggregation_config_pb2.AggregationConfig()
with tf.io.gfile.GFile(cmd_args.query_aggregation_config_path, 'r') as f:
text_format.Merge(f.read(), query_config)
index_config = aggregation_config_pb2.AggregationConfig()
with tf.io.gfile.GFile(cmd_args.index_aggregation_config_path, 'r') as f:
text_format.Merge(f.read(), index_config)
# Read aggregated descriptors.
query_aggregated_descriptors, query_visual_words = _ReadAggregatedDescriptors(
cmd_args.query_aggregation_dir, query_list, query_config)
index_aggregated_descriptors, index_visual_words = _ReadAggregatedDescriptors(
cmd_args.index_aggregation_dir, index_list, index_config)
# Create similarity computer.
similarity_computer = (feature_aggregation_similarity.
SimilarityAggregatedRepresentation(index_config))
# Compute similarity between query and index images, potentially re-ranking
# with geometric verification.
ranks_before_gv = np.zeros([num_query_images, num_index_images],
dtype='int32')
if cmd_args.use_geometric_verification:
medium_ranks_after_gv = np.zeros([num_query_images, num_index_images],
dtype='int32')
hard_ranks_after_gv = np.zeros([num_query_images, num_index_images],
dtype='int32')
for i in range(num_query_images):
print('Performing retrieval with query %d (%s)...' %
(i, query_list[i]))
start = time.clock()
# Compute similarity between aggregated descriptors.
similarities = np.zeros([num_index_images])
for j in range(num_index_images):
similarities[j] = similarity_computer.ComputeSimilarity(
query_aggregated_descriptors[i],
index_aggregated_descriptors[j], query_visual_words[i],
index_visual_words[j])
ranks_before_gv[i] = np.argsort(-similarities)
# Re-rank using geometric verification.
if cmd_args.use_geometric_verification:
medium_ranks_after_gv[
i] = image_reranking.RerankByGeometricVerification(
ranks_before_gv[i], similarities, query_list[i],
index_list, cmd_args.query_features_dir,
cmd_args.index_features_dir,
set(medium_ground_truth[i]['junk']))
hard_ranks_after_gv[
i] = image_reranking.RerankByGeometricVerification(
ranks_before_gv[i], similarities, query_list[i],
index_list, cmd_args.query_features_dir,
cmd_args.index_features_dir,
set(hard_ground_truth[i]['junk']))
elapsed = (time.clock() - start)
print('done! Retrieval for query %d took %f seconds' % (i, elapsed))
# Create output directory if necessary.
if not tf.io.gfile.exists(cmd_args.output_dir):
tf.io.gfile.makedirs(cmd_args.output_dir)
# Compute metrics.
medium_metrics = dataset.ComputeMetrics(ranks_before_gv,
medium_ground_truth, _PR_RANKS)
hard_metrics = dataset.ComputeMetrics(ranks_before_gv, hard_ground_truth,
_PR_RANKS)
if cmd_args.use_geometric_verification:
medium_metrics_after_gv = dataset.ComputeMetrics(
medium_ranks_after_gv, medium_ground_truth, _PR_RANKS)
hard_metrics_after_gv = dataset.ComputeMetrics(hard_ranks_after_gv,
hard_ground_truth,
_PR_RANKS)
# Write metrics to file.
mean_average_precision_dict = {
'medium': medium_metrics[0],
'hard': hard_metrics[0]
}
mean_precisions_dict = {
'medium': medium_metrics[1],
'hard': hard_metrics[1]
}
mean_recalls_dict = {'medium': medium_metrics[2], 'hard': hard_metrics[2]}
if cmd_args.use_geometric_verification:
mean_average_precision_dict.update({
'medium_after_gv':
medium_metrics_after_gv[0],
'hard_after_gv':
hard_metrics_after_gv[0]
})
mean_precisions_dict.update({
'medium_after_gv':
medium_metrics_after_gv[1],
'hard_after_gv':
hard_metrics_after_gv[1]
})
mean_recalls_dict.update({
'medium_after_gv': medium_metrics_after_gv[2],
'hard_after_gv': hard_metrics_after_gv[2]
})
dataset.SaveMetricsFile(
mean_average_precision_dict, mean_precisions_dict, mean_recalls_dict,
_PR_RANKS, os.path.join(cmd_args.output_dir, _METRICS_FILENAME))
def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Parse dataset to obtain query/index images, and ground-truth.
print('Parsing dataset...')
query_list, ground_truth = _read_query_csv_file(FLAGS.query_file_path)
index_list = _read_index_csv_file(FLAGS.index_file_path)
num_query_images = len(query_list)
num_index_images = len(index_list)
id_ground_truth = []
for i in range(num_query_images):
id_gt = []
for gt in ground_truth[i]:
if gt not in index_list:
continue
else:
id_gt.append(index_list.index(gt))
id_ground_truth.append(id_gt)
id_ground_truth = np.array(id_ground_truth)
print(type(id_ground_truth))
print('Done! Found %d queries and %d index images' %
(num_query_images, num_index_images))
# Read global features.
query_global_features = _ReadDelgGlobalDescriptors(FLAGS.query_features_dir, query_list)
index_global_features = _ReadDelgGlobalDescriptors(FLAGS.index_features_dir, index_list)
# Compute similarity between query and index images, potentially re-ranking
# with geometric verification.
ranks_before_gv = np.zeros([num_query_images, num_index_images], dtype='int32')
if FLAGS.use_geometric_verification:
ranks_after_gv = np.zeros([num_query_images, num_index_images], dtype='int32')
total_query_time = 0
for i in range(num_query_images):
print('Performing retrieval with query %d (%s)...' % (i, query_list[i]))
start = time.time()
# Compute similarity between global descriptors.
similarities = np.dot(index_global_features, query_global_features[i])
ranks_before_gv[i] = np.argsort(-similarities)
print(f'Global rank {ranks_before_gv[i][:20]}')
# Re-rank using geometric verification.
if FLAGS.use_geometric_verification:
ranks_after_gv[i] = image_reranking.RerankByGeometricVerification(
input_ranks=ranks_before_gv[i],
initial_scores=similarities,
query_name=query_list[i],
index_names=index_list,
query_features_dir=FLAGS.query_features_dir,
index_features_dir=FLAGS.index_features_dir,
junk_ids=[],
local_feature_extension=_DELG_LOCAL_EXTENSION,
ransac_seed=0,
descriptor_matching_threshold=FLAGS.local_descriptor_matching_threshold,
ransac_residual_threshold=FLAGS.ransac_residual_threshold,
use_ratio_test=FLAGS.use_ratio_test)
print(f'local_rank {ranks_after_gv[i][:20]}')
elapsed = (time.time() - start)
total_query_time += elapsed
print('Done! Retrieval for query %d took %f seconds' % (i, elapsed))
average_query_time = total_query_time / num_query_images
# Create output directory if necessary.
if not tf.io.gfile.exists(FLAGS.output_dir):
tf.io.gfile.makedirs(FLAGS.output_dir)
# global_path = os.path.join(FLAGS.output_dir, 'global_rank.txt')
# np.savetxt(global_path, ranks_before_gv.astype(np.int32))
# if FLAGS.use_geometric_verification:
# local_path = os.path.join(FLAGS.output_dir, 'local_rank.txt')
# np.savetxt(global_path, ranks_after_gv.astype(np.int32))
# Write total time to query all query image set
write_time_path = os.path.join(FLAGS.output_dir, 'query_time.txt')
with tf.io.gfile.GFile(write_time_path, 'w') as f:
f.write(f'Total query time: {total_query_time} seconds\n')
f.write(f'Avarage query time: {average_query_time} seconds')
# Write metric to file
metric_bf_path = os.path.join(FLAGS.output_dir, _METRICS_BF_FILENAME)
metrics = ComputeMetrics(ranks_before_gv, id_ground_truth, _PR_RANKS)
with tf.io.gfile.GFile(metric_bf_path, 'w') as f:
f.write('mAP={}\n mP@k{} {}\n mR@k{} {}\n'.format(
np.around(metrics[0] * 100, decimals=2),
np.array(_PR_RANKS), np.around(metrics[1] * 100, decimals=2),
np.array(_PR_RANKS), np.around(metrics[2] * 100, decimals=2)))
if FLAGS.use_geometric_verification:
metrics_a = ComputeMetrics(ranks_after_gv, id_ground_truth, _PR_RANKS)
metric_af_path = os.path.join(FLAGS.output_dir, _METRICS_AF_FILENAME)
with tf.io.gfile.GFile(metric_af_path, 'w') as f:
f.write('mAP={}\n mP@k{} {}\n mR@k{} {}\n'.format(
np.around(metrics_a[0] * 100, decimals=2),
np.array(_PR_RANKS), np.around(metrics_a[1] * 100, decimals=2),
np.array(_PR_RANKS), np.around(metrics_a[2] * 100, decimals=2)))