def main(**kwargs):
subset = kwargs['subset']
const = FlickrConstants()
io.mkdir_if_not_exists(const.flickr_paths['proc_dir'])
image_ids = io.read(const.subset_ids[subset])
image_ids = [idx.decode() for idx in image_ids.split()]
# Write boxes to json
boxes = {}
for image_id in tqdm(image_ids):
box_xml = os.path.join(const.flickr_paths['anno_dir'],
f'{image_id}.xml')
boxes[image_id] = get_annotations(box_xml)
io.dump_json_object(boxes, const.box_json[subset])
# Write sentence annos to json
sent = {}
for image_id in tqdm(image_ids):
sent_txt = os.path.join(const.flickr_paths['sent_dir'],
f'{image_id}.txt')
sent[image_id] = get_sentence_data(sent_txt)
io.dump_json_object(sent, const.sent_json[subset])
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--descriptorsFile', help='descriptors dict file', required=True)
parser.add_argument('-q', '--queryIDs', help='query IDs joined by + symbol', required=True)
parser.add_argument('-s', '--resultSize', help='result list size', required=True)
parser.add_argument('-f', '--distanceName', help='distance function name', required=True)
parser.add_argument('-i', '--img_path_expression', help='the img path expression in which %s is the ID', required=True)
parser.add_argument('-o', '--plot_file', help='the output file for plotting', required=True)
ARGS = parser.parse_args()
samplesDescriptors = io.read(ARGS.descriptorsFile)
queryIDs = ARGS.queryIDs.split('+')
resultSize = int(ARGS.resultSize)
distanceName = ARGS.distanceName
img_path_expression = ARGS.img_path_expression
plot_file = ARGS.plot_file
labelsByID = dict()
for id in samplesDescriptors.keys():
labelsByID[id] = id.rsplit('_')[0]
searchs_results = search.queries(distanceName, resultSize, samplesDescriptors, queryIDs)
for r in searchs_results:
print(r)
precisions, averagePrecisions = search.evaluate_results(searchs_results, labelsByID)
p_formatted = search.format_mean_std(precisions)
ap_formatted = search.format_mean_std(averagePrecisions)
print('results using distance %s:' % distanceName)
print('P@%d by query:' % resultSize, precisions)
print('AP@%d by query:' % resultSize, averagePrecisions)
print('P@%d: %s, AP@%d: %s' % (resultSize, p_formatted, resultSize, ap_formatted))
search.print_results(searchs_results, precisions, averagePrecisions, p_formatted, ap_formatted, img_path_expression, plot_file)
def main(**kwargs):
subset = kwargs['subset']
const = FlickrConstants()
image_ids = io.read(const.subset_ids[subset])
image_ids = [idx.decode() for idx in image_ids.split()]
image_dir = const.flickr_paths['image_dir']
det_input = []
for image_id in tqdm(image_ids):
image_path = os.path.join(image_dir, f'{image_id}.jpg')
det_input.append({'path': image_path, 'id': image_id})
io.dump_json_object(
det_input,
os.path.join(kwargs['out_dir'],
'det_input_' + kwargs['subset'] + '.json'))
def main(argv):
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('-il',
'--input_list',
help='Input list',
required=True)
parser.add_argument('-if',
'--input_folder',
help='Input folder',
required=True)
parser.add_argument('-de',
'--descriptor_name',
help='Descriptor name',
required=True)
parser.add_argument('-rf',
'--regions_file',
help='Regions input file',
required=True)
parser.add_argument('-nf',
'--n_features',
help='Number of features',
required=True)
parser.add_argument('-ro',
'--region_descriptors_output_file',
help='Output file',
required=True)
parser.add_argument('-do',
'--descriptors_output_file',
help='Output file',
required=True)
parser.add_argument('-vwo',
'--visual_words_output_file',
help='Output file',
required=True)
parser.add_argument('-d',
'--debug',
help='Debuggin mode',
action='store_true')
ARGS = parser.parse_args()
global DEBUG
DEBUG = ARGS.debug
regions = io.read(ARGS.regions_file)
region_descriptors_output_file = ARGS.region_descriptors_output_file
descriptors_output_file = ARGS.descriptors_output_file
visual_words_output_file = ARGS.visual_words_output_file
if os.path.isfile(region_descriptors_output_file) is False:
with open(ARGS.input_list, 'r') as input_list_files:
descriptors = {}
for line in input_list_files:
print("Reading:", line)
file_name = "{}/{}".format(ARGS.input_folder,
line).split("\n")[0]
img = cv2.imread(file_name)
labeled_image, n_regions = regions[line.split(".")[0]]
img_descriptor = desc.get_descriptors(
img,
labeled_image,
n_regions,
descriptor_name=ARGS.descriptor_name)
descriptors[line.split(".")[0]] = img_descriptor
io.write(descriptors, output_file=region_descriptors_output_file)
else:
descriptors = io.read(region_descriptors_output_file)
img_descriptors, visual_words = bovw.describe_images(
descriptors, int(ARGS.n_features))
io.write(img_descriptors, output_file=descriptors_output_file)
io.write(visual_words, output_file=visual_words_output_file)
def read_image_ids(self):
image_ids = io.read(self.const.image_ids_txt)
image_ids = [idx.decode() for idx in image_ids.split()]
return image_ids