for bdbox in bdboxes:
font = cv2.FONT_HERSHEY_SIMPLEX
output_image = cv2.rectangle(
output_image,
(int(bdbox[0] - bdbox[2] / 2), int(bdbox[1] - bdbox[3] / 2)),
(int(bdbox[0] + bdbox[2] / 2), int(bdbox[1] + bdbox[3] / 2)),
(200, 0, 0), 1)
output_image = cv2.putText(
output_image, bdbox[4],
(int(bdbox[0] - bdbox[2] / 2), int(bdbox[1] - bdbox[3] / 2)),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 0), 1)
# output_image = np.multiply( output_image, 255 )
generate_image = FLAGS.save_dir + '/res.jpg'
if not os.path.exists(FLAGS.save_dir):
os.makedirs(FLAGS.save_dir)
cv2.imwrite(generate_image, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR))
end_time = time.time()
print('Use time: ', end_time - start_time)
plt.imshow(output_image)
plt.show()
if __name__ == '__main__':
tf.reset_default_graph()
args = parse_args()
FLAGS = read_config.read_config_file(args.conf)
main(FLAGS)
from run_ova import compute_similarity_ova
from run_wem import compute_similarity_wem
from run_basic import compute_similarity_basic
if __name__ == "__main__":
start_time, start_resources = time.time(), resource.getrusage(resource.RUSAGE_SELF)
signal.signal(signal.SIGINT, signal_handler)
####################################################################### PARAMETERS
# Read configuration file for default options + set cmd line options
main_dir = os.path.dirname(os.path.realpath(__file__))
custom, cfg, verbose, debug = parse_cmd_line()
config_file = cfg if (cfg is not None) else os.path.join(main_dir, 'configuration.ini')
n_iter, n_cores, n_locks, data_type, input_file, ground_truth_file, temp_folder, output_folder, annotation_params, classification_params, classifier_binary, task_params, cvg_step, cvg_criterion, cfg = read_config_file(config_file, *custom)
# Set temporary and output folders
base_name = time.strftime("%Y-%m-%d_%H-%M")
output_folder = init_folder(os.path.join(output_folder, 'Outputs_%s'%base_name))
temp_folder = init_folder(temp_folder)
# Save configuration for the experiment in the output folder
with open(os.path.join(output_folder, 'exp_configuration.ini'), 'w') as f:
cfg.write(f)
# Read and parse data file
n_samples, data, data_occurrences, index_to_label, label_to_index = parse_data(data_type, classification_params, input_file)
os.environ['CLASSIFIER'] = classifier_binary
# Additional parameters