def recognize_from_image(): # prepare input data input_data = load_image( args.input, (IMAGE_HEIGHT, IMAGE_WIDTH), gen_input_ailia=True ) # net initialize env_id = ailia.get_gpu_environment_id() print(f'env_id: {env_id}') net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=env_id) # inference print('Start inference...') if args.benchmark: print('BENCHMARK mode') for i in range(5): start = int(round(time.time() * 1000)) preds_ailia = net.predict(input_data) end = int(round(time.time() * 1000)) print(f'\tailia processing time {end - start} ms') else: preds_ailia = net.predict(input_data) # postprocessing if args.smooth: preds_ailia = smooth_output(preds_ailia) save_pred(preds_ailia, args.savepath, IMAGE_HEIGHT, IMAGE_WIDTH) print('Script finished successfully.')
def recognize_from_image(): # net initialize net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.env_id) # input image loop for image_path in args.input: # prepare input data logger.info(image_path) input_data = load_image( image_path, (IMAGE_HEIGHT, IMAGE_WIDTH), gen_input_ailia=True, ) # inference logger.info('Start inference...') if args.benchmark: logger.info('BENCHMARK mode') for i in range(5): start = int(round(time.time() * 1000)) preds_ailia = net.predict(input_data) end = int(round(time.time() * 1000)) logger.info(f'\tailia processing time {end - start} ms') else: preds_ailia = net.predict(input_data) # postprocessing if args.smooth: preds_ailia = smooth_output(preds_ailia) savepath = get_savepath(args.savepath, image_path) logger.info(f'saved at : {savepath}') save_pred(preds_ailia, savepath, IMAGE_HEIGHT, IMAGE_WIDTH) logger.info('Script finished successfully.')