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.')
def recognize_from_video():
# net initialize
net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.env_id)
capture = webcamera_utils.get_capture(args.video)
# create video writer if savepath is specified as video format
if args.savepath != SAVE_IMAGE_PATH:
logger.warning(
'currently, video results cannot be output correctly...')
f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
writer = webcamera_utils.get_writer(args.savepath, f_h, f_w)
else:
writer = None
while (True):
ret, frame = capture.read()
if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
break
input_image, input_data = webcamera_utils.preprocess_frame(
frame,
IMAGE_HEIGHT,
IMAGE_WIDTH,
)
# inference
preds_ailia = net.predict(input_data)
# postprocessing
if args.smooth:
preds_ailia = smooth_output(preds_ailia)
gen_img = gen_preds_img(preds_ailia, IMAGE_HEIGHT, IMAGE_WIDTH)
plt.imshow(gen_img)
plt.pause(.01)
if not plt.get_fignums():
break
# # save results
# if writer is not None:
# writer.write(res_img)
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def recognize_from_video():
# 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)
if args.video == '0':
print('[INFO] Webcam mode is activated')
capture = cv2.VideoCapture(0)
if not capture.isOpened():
print("[ERROR] webcamera not found")
sys.exit(1)
else:
if check_file_existance(args.video):
capture = cv2.VideoCapture(args.video)
while (True):
ret, frame = capture.read()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not ret:
continue
input_image, input_data = preprocess_frame(
frame,
IMAGE_HEIGHT,
IMAGE_WIDTH,
)
# inference
preds_ailia = net.predict(input_data)
# postprocessing
if args.smooth:
preds_ailia = smooth_output(preds_ailia)
gen_img = gen_preds_img(preds_ailia, IMAGE_HEIGHT, IMAGE_WIDTH)
plt.imshow(gen_img)
plt.pause(.01)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')