def main(unused_argv):
# Read list of images.
print('Reading list of images...')
image_paths = _ReadImageList(cmd_args.list_images_path)
num_images = len(image_paths)
print(f'done! Found {num_images} images')
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(cmd_args.config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directory if necessary.
if not tf.io.gfile.exists(cmd_args.output_dir):
tf.io.gfile.makedirs(cmd_args.output_dir)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
start = time.clock()
for i in range(num_images):
# Write to log-info once in a while.
if i == 0:
print('Starting to extract DELF features from images...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.clock() - start)
print(
f'Processing image {i} out of {num_images}, last '
f'{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds'
)
start = time.clock()
# If descriptor already exists, skip its computation.
out_desc_filename = os.path.splitext(os.path.basename(
image_paths[i]))[0] + _DELF_EXT
out_desc_fullpath = os.path.join(cmd_args.output_dir, out_desc_filename)
if tf.io.gfile.exists(out_desc_fullpath):
print(f'Skipping {image_paths[i]}')
continue
im = np.array(utils.RgbLoader(image_paths[i]))
# Extract and save features.
extracted_features = extractor_fn(im)
locations_out = extracted_features['local_features']['locations']
descriptors_out = extracted_features['local_features']['descriptors']
feature_scales_out = extracted_features['local_features']['scales']
attention_out = extracted_features['local_features']['attention']
feature_io.WriteToFile(out_desc_fullpath, locations_out,
feature_scales_out, descriptors_out,
attention_out)
def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
# Read list of query images from dataset file.
tf.compat.v1.logging.info(
'Reading list of query images and boxes from dataset file...')
query_list, _, ground_truth = dataset.ReadDatasetFile(
cmd_args.dataset_file_path)
num_images = len(query_list)
tf.compat.v1.logging.info('done! Found %d images', num_images)
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(cmd_args.delf_config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directory if necessary.
if not tf.io.gfile.exists(cmd_args.output_features_dir):
tf.io.gfile.makedirs(cmd_args.output_features_dir)
with tf.Graph().as_default():
with tf.compat.v1.Session() as sess:
# Initialize variables, construct DELF extractor.
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
start = time.clock()
for i in range(num_images):
query_image_name = query_list[i]
input_image_filename = os.path.join(
cmd_args.images_dir, query_image_name + _IMAGE_EXTENSION)
output_feature_filename = os.path.join(
cmd_args.output_features_dir,
query_image_name + _DELF_EXTENSION)
if tf.io.gfile.exists(output_feature_filename):
tf.compat.v1.logging.info('Skipping %s', query_image_name)
continue
# Crop query image according to bounding box.
bbox = [int(round(b)) for b in ground_truth[i]['bbx']]
im = np.array(_PilLoader(input_image_filename).crop(bbox))
# Extract and save features.
(locations_out, descriptors_out, feature_scales_out,
attention_out) = extractor_fn(im)
feature_io.WriteToFile(output_feature_filename, locations_out,
feature_scales_out, descriptors_out,
attention_out)
elapsed = (time.clock() - start)
print('Processed %d query images in %f seconds' %
(num_images, elapsed))
def extract_features(list_images_path,
output_dir,
config_path=r"src\delf_config.pbtxt"):
output_dir = str(output_dir)
# Read list of images.
print('Reading list of images...')
image_paths = [str(path) for path in list_images_path]
num_images = len(image_paths)
print(f'done! Found {num_images} images')
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directory if necessary.
if not tf.io.gfile.exists(output_dir):
tf.io.gfile.makedirs(output_dir)
extractor_fn = extractor.MakeExtractor(config)
start = time.time()
for i in range(num_images):
# Report progress once in a while.
if i == 0:
print('Starting to extract DELF features from images...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.time() - start)
print(f'Processing image {i} out of {num_images}, last '
f'{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds')
start = time.time()
# If descriptor already exists, skip its computation.
out_desc_filename = os.path.splitext(os.path.basename(
image_paths[i]))[0] + _DELF_EXT
out_desc_fullpath = os.path.join(output_dir, out_desc_filename)
if tf.io.gfile.exists(out_desc_fullpath):
print(f'Skipping {image_paths[i]}')
continue
im = np.array(utils.RgbLoader(image_paths[i]))
# Extract and save features.
extracted_features = extractor_fn(im)
locations_out = extracted_features['local_features']['locations']
descriptors_out = extracted_features['local_features']['descriptors']
feature_scales_out = extracted_features['local_features']['scales']
attention_out = extracted_features['local_features']['attention']
feature_io.WriteToFile(out_desc_fullpath, locations_out,
feature_scales_out, descriptors_out,
attention_out)
def main(delf_config_path, dataset_file_path, images_dir, output_features_dir):
image_list = []
image_list.append(dataset_file_path)
num_images = len(image_list)
print('done! Found %d images' % num_images)
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(delf_config_path, 'r') as f:
text_format.Parse(f.read(), config)
extractor_fn = extractor.MakeExtractor(config)
start = time.time()
for i in range(num_images):
if i == 0:
print('Starting to extract features...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.time() - start)
print('Processing image %d out of %d, last %d '
'images took %f seconds' %
(i, num_images, _STATUS_CHECK_ITERATIONS, elapsed))
start = time.time()
image_name = image_list[i]
input_image_filename = os.path.join(images_dir,
image_name + _IMAGE_EXTENSION)
if config.use_local_features:
output_local_feature_filename = os.path.join(
output_features_dir, image_name + _DELG_LOCAL_EXTENSION)
if not tf.io.gfile.exists(output_local_feature_filename):
should_skip_local = False
pil_im = utils.RgbLoader(input_image_filename)
resize_factor = 1.0
im = np.array(pil_im)
# Extract and save features.
extracted_features = extractor_fn(im, resize_factor)
#if config.use_global_features:
# global_descriptor = extracted_features['global_descriptor']
# datum_io.WriteToFile(global_descriptor, output_global_feature_filename)
if config.use_local_features:
locations = extracted_features['local_features']['locations']
descriptors = extracted_features['local_features']['descriptors']
feature_scales = extracted_features['local_features']['scales']
attention = extracted_features['local_features']['attention']
feature_io.WriteToFile(output_local_feature_filename, locations,
feature_scales, descriptors, attention)
def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Read list of query images from dataset file.
print('Reading list of query images and boxes from dataset file...')
query_list, _, ground_truth = dataset.ReadDatasetFile(
cmd_args.dataset_file_path)
num_images = len(query_list)
print(f'done! Found {num_images} images')
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(cmd_args.delf_config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directory if necessary.
if not tf.io.gfile.exists(cmd_args.output_features_dir):
tf.io.gfile.makedirs(cmd_args.output_features_dir)
extractor_fn = extractor.MakeExtractor(config)
start = time.time()
for i in range(num_images):
query_image_name = query_list[i]
input_image_filename = os.path.join(
cmd_args.images_dir, query_image_name + _IMAGE_EXTENSION)
output_feature_filename = os.path.join(
cmd_args.output_features_dir, query_image_name + _DELF_EXTENSION)
if tf.io.gfile.exists(output_feature_filename):
print(f'Skipping {query_image_name}')
continue
# Crop query image according to bounding box.
bbox = [int(round(b)) for b in ground_truth[i]['bbx']]
im = np.array(utils.RgbLoader(input_image_filename).crop(bbox))
# Extract and save features.
extracted_features = extractor_fn(im)
locations_out = extracted_features['local_features']['locations']
descriptors_out = extracted_features['local_features']['descriptors']
feature_scales_out = extracted_features['local_features']['scales']
attention_out = extracted_features['local_features']['attention']
feature_io.WriteToFile(output_feature_filename, locations_out,
feature_scales_out, descriptors_out,
attention_out)
elapsed = (time.time() - start)
print('Processed %d query images in %f seconds' % (num_images, elapsed))
def main(args, kwargs, centroids, n_values):
"""Generates graph."""
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.gfile.FastGFile(args.config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
bench(args, kwargs, sess, extractor_fn, centroids, n_values)
def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Read list of images.
print('Reading list of images...')
image_paths = _ReadImageList(FLAGS.list_images_path)
num_images = len(image_paths)
print(f'done! Found {num_images} images')
# Load images in memory.
print('Loading images, %d times per image...' % FLAGS.repeat_per_image)
im_array = []
for filename in image_paths:
im = np.array(utils.RgbLoader(filename))
for _ in range(FLAGS.repeat_per_image):
im_array.append(im)
np.random.shuffle(im_array)
print('done!')
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(FLAGS.delf_config_path, 'r') as f:
text_format.Parse(f.read(), config)
extractor_fn = extractor.MakeExtractor(config)
start = time.time()
for i, im in enumerate(im_array):
if i == 0:
print('Starting to extract DELF features from images...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.time() - start)
print(f'Processing image {i} out of {len(im_array)}, last '
f'{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds,'
f'ie {elapsed/_STATUS_CHECK_ITERATIONS} secs/image.')
start = time.time()
# Extract and save features.
extracted_features = extractor_fn(im)
def ExtractBoxesAndFeaturesToFiles(image_names, image_paths, delf_config_path,
detector_model_dir, detector_thresh,
output_features_dir, output_boxes_dir,
output_mapping):
"""Extracts boxes and features, saving them to files.
Boxes are saved to <image_name>.boxes files. DELF features are extracted for
the entire image and saved into <image_name>.delf files. In addition, DELF
features are extracted for each high-confidence bounding box in the image, and
saved into files named <image_name>_0.delf, <image_name>_1.delf, etc.
It checks if descriptors/boxes already exist, and skips computation for those.
Args:
image_names: List of image names. These are used to compose output file
names for boxes and features.
image_paths: List of image paths. image_paths[i] is the path for the image
named by image_names[i]. `image_names` and `image_paths` must have the
same number of elements.
delf_config_path: Path to DelfConfig proto text file.
detector_model_dir: Directory where detector SavedModel is located.
detector_thresh: Threshold used to decide if an image's detected box
undergoes feature extraction.
output_features_dir: Directory where DELF features will be written to.
output_boxes_dir: Directory where detected boxes will be written to.
output_mapping: CSV file which maps each .delf file name to the image ID and
detected box ID.
Raises:
ValueError: If len(image_names) and len(image_paths) are different.
"""
num_images = len(image_names)
if len(image_paths) != num_images:
raise ValueError(
'image_names and image_paths have different number of items')
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(delf_config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directories if necessary.
if not tf.io.gfile.exists(output_features_dir):
tf.io.gfile.makedirs(output_features_dir)
if not tf.io.gfile.exists(output_boxes_dir):
tf.io.gfile.makedirs(output_boxes_dir)
if not tf.io.gfile.exists(os.path.dirname(output_mapping)):
tf.io.gfile.makedirs(os.path.dirname(output_mapping))
names_ids_and_boxes = []
with tf.Graph().as_default():
with tf.compat.v1.Session() as sess:
# Initialize variables, construct detector and DELF extractor.
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
detector_fn = detector.MakeDetector(sess,
detector_model_dir,
import_scope='detector')
delf_extractor_fn = extractor.MakeExtractor(
sess, config, import_scope='extractor_delf')
start = time.clock()
for i in range(num_images):
if i == 0:
print('Starting to extract features/boxes...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.clock() - start)
print('Processing image %d out of %d, last %d '
'images took %f seconds' %
(i, num_images, _STATUS_CHECK_ITERATIONS, elapsed))
start = time.clock()
image_name = image_names[i]
output_feature_filename_whole_image = os.path.join(
output_features_dir, image_name + _DELF_EXTENSION)
output_box_filename = os.path.join(output_boxes_dir,
image_name + _BOX_EXTENSION)
pil_im = utils.RgbLoader(image_paths[i])
width, height = pil_im.size
# Extract and save boxes.
if tf.io.gfile.exists(output_box_filename):
print('Skipping box computation for %s' % image_name)
(boxes_out, scores_out, class_indices_out
) = box_io.ReadFromFile(output_box_filename)
else:
(boxes_out, scores_out, class_indices_out) = detector_fn(
np.expand_dims(pil_im, 0))
# Using only one image per batch.
boxes_out = boxes_out[0]
scores_out = scores_out[0]
class_indices_out = class_indices_out[0]
box_io.WriteToFile(output_box_filename, boxes_out,
scores_out, class_indices_out)
# Select boxes with scores greater than threshold. Those will be the
# ones with extracted DELF features (besides the whole image, whose DELF
# features are extracted in all cases).
num_delf_files = 1
selected_boxes = []
for box_ind, box in enumerate(boxes_out):
if scores_out[box_ind] >= detector_thresh:
selected_boxes.append(box)
num_delf_files += len(selected_boxes)
# Extract and save DELF features.
for delf_file_ind in range(num_delf_files):
if delf_file_ind == 0:
box_name = image_name
output_feature_filename = output_feature_filename_whole_image
else:
box_name = image_name + '_' + str(delf_file_ind - 1)
output_feature_filename = os.path.join(
output_features_dir, box_name + _DELF_EXTENSION)
names_ids_and_boxes.append(
[box_name, i, delf_file_ind - 1])
if tf.io.gfile.exists(output_feature_filename):
print('Skipping DELF computation for %s' % box_name)
continue
if delf_file_ind >= 1:
bbox_for_cropping = selected_boxes[delf_file_ind - 1]
bbox_for_cropping_pil_convention = [
int(math.floor(bbox_for_cropping[1] * width)),
int(math.floor(bbox_for_cropping[0] * height)),
int(math.ceil(bbox_for_cropping[3] * width)),
int(math.ceil(bbox_for_cropping[2] * height))
]
pil_cropped_im = pil_im.crop(
bbox_for_cropping_pil_convention)
im = np.array(pil_cropped_im)
else:
im = np.array(pil_im)
extracted_features = delf_extractor_fn(im)
locations_out = extracted_features['local_features'][
'locations']
descriptors_out = extracted_features['local_features'][
'descriptors']
feature_scales_out = extracted_features['local_features'][
'scales']
attention_out = extracted_features['local_features'][
'attention']
feature_io.WriteToFile(output_feature_filename,
locations_out, feature_scales_out,
descriptors_out, attention_out)
# Save mapping from output DELF name to image id and box id.
_WriteMappingBasenameToIds(names_ids_and_boxes, output_mapping)
def main(unused_argv):
print('Export descriptor on hpatches.')
print(cmd_args)
tf.logging.set_verbosity(tf.logging.INFO)
# Read list of images.
tf.logging.info('Reading list of images...')
image_paths = _ReadImageList(cmd_args.list_images_path)
num_images = len(image_paths)
tf.logging.info('done! Found %d images', num_images)
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.gfile.FastGFile(cmd_args.config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directory if necessary.
if os.path.isdir(cmd_args.output_dir) == False:
os.mkdir(cmd_args.output_dir)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Reading list of images.
filename_queue = tf.train.string_input_producer(image_paths, shuffle=False)
reader = tf.WholeFileReader()
_, value = reader.read(filename_queue)
image_tf = tf.image.decode_png(value, channels=3)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
start = time.clock()
for i in range(num_images):
# Write to log-info once in a while.
if i == 0:
tf.logging.info('Starting to extract DELF features from images...')
# Output node names to file.
#node_names = [node.name for node in tf.get_default_graph().as_graph_def().node]
#f = open('node_names.txt', 'w')
#for name in node_names:
# f.write(name + '\n')
#f.close()
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.clock() - start)
tf.logging.info(
'Processing image %d out of %d, last %d '
'images took %f seconds', i, num_images, _STATUS_CHECK_ITERATIONS,
elapsed)
start = time.clock()
# Get next image.
patches = sess.run(image_tf)
patches = patches.reshape((-1, 65, 65, 3))
num_patches = patches.shape[0]
input_dir, img_name = os.path.split(image_paths[i])
out_desc_dir = os.path.join(cmd_args.output_dir, input_dir.split('/')[-1])
out_desc_fullpath = os.path.join(out_desc_dir, img_name[:-3]+'csv')
if os.path.isdir(out_desc_dir) == False:
os.mkdir(out_desc_dir)
print(out_desc_fullpath)
output_file = open(out_desc_fullpath, 'w')
for i_patch in range(num_patches):
# Extract and save features.
im = patches[i_patch, :, :, :]
(locations_out, descriptors_out, feature_scales_out,
attention_out, feature_map_out) = extractor_fn(im)
# Output descriptors to file.
desc = feature_map_out[0, int(feature_map_out.shape[1]/2), int(feature_map_out.shape[2]/2), :]
for i_dim in range(desc.shape[0]-1):
output_file.write('{:>8.5f}'.format(desc[i_dim]) + ', ')
output_file.write(str(desc[i_dim+1]) + '\n')
output_file.close()
# Finalize enqueue threads.
coord.request_stop()
coord.join(threads)
def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Read list of images from dataset file.
print('Reading list of images from dataset file...')
query_list, index_list, ground_truth = dataset.ReadDatasetFile(
FLAGS.dataset_file_path)
if FLAGS.image_set == 'query':
image_list = query_list
else:
image_list = index_list
num_images = len(image_list)
print('done! Found %d images' % num_images)
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.io.gfile.GFile(FLAGS.delf_config_path, 'r') as f:
text_format.Parse(f.read(), config)
# Create output directory if necessary.
if not tf.io.gfile.exists(FLAGS.output_features_dir):
tf.io.gfile.makedirs(FLAGS.output_features_dir)
with tf.Graph().as_default():
with tf.compat.v1.Session() as sess:
# Initialize variables, construct DELG extractor.
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
start = time.time()
for i in range(num_images):
if i == 0:
print('Starting to extract features...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.time() - start)
print('Processing image %d out of %d, last %d '
'images took %f seconds' %
(i, num_images, _STATUS_CHECK_ITERATIONS, elapsed))
start = time.time()
image_name = image_list[i]
input_image_filename = os.path.join(
FLAGS.images_dir, image_name + _IMAGE_EXTENSION)
# Compose output file name and decide if image should be skipped.
should_skip_global = True
should_skip_local = True
if config.use_global_features:
output_global_feature_filename = os.path.join(
FLAGS.output_features_dir,
image_name + _DELG_GLOBAL_EXTENSION)
if not tf.io.gfile.exists(output_global_feature_filename):
should_skip_global = False
if config.use_local_features:
output_local_feature_filename = os.path.join(
FLAGS.output_features_dir,
image_name + _DELG_LOCAL_EXTENSION)
if not tf.io.gfile.exists(output_local_feature_filename):
should_skip_local = False
if should_skip_global and should_skip_local:
print('Skipping %s' % image_name)
continue
pil_im = utils.RgbLoader(input_image_filename)
resize_factor = 1.0
if FLAGS.image_set == 'query':
# Crop query image according to bounding box.
original_image_size = max(pil_im.size)
bbox = [int(round(b)) for b in ground_truth[i]['bbx']]
pil_im = pil_im.crop(bbox)
cropped_image_size = max(pil_im.size)
resize_factor = cropped_image_size / original_image_size
im = np.array(pil_im)
# Extract and save features.
extracted_features = extractor_fn(im, resize_factor)
if config.use_global_features:
global_descriptor = extracted_features['global_descriptor']
datum_io.WriteToFile(global_descriptor,
output_global_feature_filename)
if config.use_local_features:
locations = extracted_features['local_features'][
'locations']
descriptors = extracted_features['local_features'][
'descriptors']
feature_scales = extracted_features['local_features'][
'scales']
attention = extracted_features['local_features'][
'attention']
feature_io.WriteToFile(output_local_feature_filename,
locations, feature_scales,
descriptors, attention)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Read list of images.
tf.logging.info('Reading list of images...')
image_paths = _ReadImageList(cmd_args.list_images_path)
num_images = len(image_paths)
tf.logging.info('done! Found %d images', num_images)
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.gfile.FastGFile(cmd_args.config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Create output directory if necessary.
if not tf.gfile.Exists(cmd_args.output_dir):
tf.gfile.MakeDirs(cmd_args.output_dir)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Reading list of images.
filename_queue = tf.train.string_input_producer(image_paths, shuffle=False)
reader = tf.WholeFileReader()
_, value = reader.read(filename_queue)
image_tf = tf.image.decode_jpeg(value, channels=3)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
start = time.clock()
for i in range(num_images):
# Write to log-info once in a while.
if i == 0:
tf.logging.info('Starting to extract DELF features from images...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.clock() - start)
tf.logging.info(
'Processing image %d out of %d, last %d '
'images took %f seconds', i, num_images, _STATUS_CHECK_ITERATIONS,
elapsed)
start = time.clock()
# # Get next image.
im = sess.run(image_tf)
# If descriptor already exists, skip its computation.
out_desc_filename = os.path.splitext(os.path.basename(
image_paths[i]))[0] + _DELF_EXT
out_desc_fullpath = os.path.join(cmd_args.output_dir, out_desc_filename)
if tf.gfile.Exists(out_desc_fullpath):
tf.logging.info('Skipping %s', image_paths[i])
continue
# Extract and save features.
(locations_out, descriptors_out, feature_scales_out,
attention_out) = extractor_fn(im)
feature_io.WriteToFile(out_desc_fullpath, locations_out,
feature_scales_out, descriptors_out,
attention_out)
# Finalize enqueue threads.
coord.request_stop()
coord.join(threads)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Read list of images.
tf.logging.info('Reading list of images...')
image_paths = _ReadImageList(cmd_args.list_images_path, cmd_args.prefix)
num_images = len(image_paths)
tf.logging.info('done! Found %d images', num_images)
# Parse DelfConfig proto.
config = delf_config_pb2.DelfConfig()
with tf.gfile.FastGFile(cmd_args.config_path, 'r') as f:
text_format.Merge(f.read(), config)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Reading list of images.
filename_queue = tf.train.string_input_producer(image_paths, shuffle=False)
reader = tf.WholeFileReader()
_, value = reader.read(filename_queue)
image_tf = tf.image.decode_jpeg(value, channels=3)
image_tf = tf.image.resize(image_tf, (448, 448))
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
extractor_fn = extractor.MakeExtractor(sess, config)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
start = time.clock()
for i in range(num_images):
# Write to log-info once in a while.
if i == 0:
tf.logging.info('Starting to extract DELF features from images...')
elif i % _STATUS_CHECK_ITERATIONS == 0:
elapsed = (time.clock() - start)
tf.logging.info(
'Processing image %d out of %d, last %d '
'images took %f seconds', i, num_images, _STATUS_CHECK_ITERATIONS,
elapsed)
start = time.clock()
# # Get next image.
im = sess.run(image_tf)
# If descriptor already exists, skip its computation.
save_path = image_paths[i]
save_path = save_path.replace('undist_images', 'reg_feat')
save_path = save_path.replace('.jpg', '.bin')
if cmd_args.skip_extracted and tf.gfile.Exists(save_path):
tf.logging.info('Skipping %s', image_paths[i])
continue
# Extract and save features.
reg_feat_out = extractor_fn(im)
reg_feat_out = np.squeeze(reg_feat_out, axis=0)
dir_name = os.path.dirname(save_path)
if not os.path.exists(dir_name):
os.mkdir(dir_name)
reg_feat_out.astype(np.float32).tofile(save_path)
# Finalize enqueue threads.
coord.request_stop()
coord.join(threads)
