def recognize_from_image():
# prepare input data
src_img = cv2.imread(args.input)
img256, _, scale, pad = but.resize_pad(src_img[:, :, ::-1])
input_data = img256.astype('float32') / 255.
input_data = np.expand_dims(np.moveaxis(input_data, -1, 0), 0)
# 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 _ in range(5):
start = int(round(time.time() * 1000))
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds)[0]
detections = but.denormalize_detections(normalized_detections,
scale, pad)
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds)[0]
detections = but.denormalize_detections(normalized_detections, scale,
pad)
# postprocessing
display_result(src_img, detections)
cv2.imwrite(args.savepath, src_img)
print('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:
f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
writer = 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
img256, _, scale, pad = but.resize_pad(frame[:, :, ::-1], resolution=IMAGE_WIDTH)
input_data = img256.astype('float32') / 255.
input_data = np.expand_dims(np.moveaxis(input_data, -1, 0), 0)
if not CHANNEL_FIRST:
input_data = input_data.transpose((0,2,3,1))
# inference
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds, anchor_path=ANCHOR_PATH, resolution=IMAGE_WIDTH)[0]
detections = but.denormalize_detections(
normalized_detections, scale, pad, resolution=IMAGE_WIDTH
)
# postprocessing
display_result(frame, detections)
visual_img = frame
if args.video == '0': # Flip horizontally if camera
visual_img = np.ascontiguousarray(frame[:,::-1,:])
cv2.imshow('frame', visual_img)
# save results
if writer is not None:
writer.write(frame)
capture.release()
if writer is not None:
writer.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def recognize_from_image():
# net initialize
if args.onnx:
import onnxruntime
net = onnxruntime.InferenceSession(WEIGHT_PATH)
else:
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)
src_img = cv2.imread(image_path)
img256, _, scale, pad = but.resize_pad(src_img[:, :, ::-1],IMAGE_WIDTH)
input_data = img256.astype('float32') / 255.
input_data = np.expand_dims(np.moveaxis(input_data, -1, 0), 0)
if not CHANNEL_FIRST:
input_data = input_data.transpose((0,2,3,1))
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for _ in range(5):
start = int(round(time.time() * 1000))
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds,anchor_path=ANCHOR_PATH,resolution=IMAGE_WIDTH)[0]
detections = but.denormalize_detections(
normalized_detections, scale, pad, resolution=IMAGE_WIDTH
)
end = int(round(time.time() * 1000))
logger.info(f'\tailia processing time {end - start} ms')
else:
if args.onnx:
input_name = net.get_inputs()[0].name
preds = net.run(None, {input_name: input_data.astype(np.float32)})
else:
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds, anchor_path=ANCHOR_PATH,resolution=IMAGE_WIDTH)[0]
detections = but.denormalize_detections(
normalized_detections, scale, pad, resolution=IMAGE_WIDTH
)
# postprocessing
display_result(src_img, detections)
savepath = get_savepath(args.savepath, image_path)
logger.info(f'saved at : {savepath}')
cv2.imwrite(savepath, src_img)
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)
capture = get_capture(args.video)
# create video writer if savepath is specified as video format
if args.savepath != SAVE_IMAGE_PATH:
f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
save_h, save_w = webcamera_utils.calc_adjust_fsize(
f_h, f_w, IMAGE_HEIGHT, IMAGE_WIDTH)
writer = webcamera_utils.get_writer(args.savepath, save_h, save_w)
else:
writer = None
while (True):
ret, frame = capture.read()
if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
break
img256, _, scale, pad = but.resize_pad(frame[:, :, ::-1])
input_data = img256.astype('float32') / 255.
input_data = np.expand_dims(np.moveaxis(input_data, -1, 0), 0)
# inference
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds)[0]
detections = but.denormalize_detections(normalized_detections, scale,
pad)
# postprocessing
display_result(frame, detections)
cv2.imshow('frame', frame)
# save results
if writer is not None:
writer.write(frame)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
pass
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)
src_img = cv2.imread(image_path)
img256, _, scale, pad = but.resize_pad(src_img[:, :, ::-1])
input_data = img256.astype('float32') / 255.
input_data = np.expand_dims(np.moveaxis(input_data, -1, 0), 0)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for _ in range(5):
start = int(round(time.time() * 1000))
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds)[0]
detections = but.denormalize_detections(
normalized_detections, scale, pad)
end = int(round(time.time() * 1000))
logger.info(f'\tailia processing time {end - start} ms')
else:
preds = net.predict([input_data])
normalized_detections = but.postprocess(preds)[0]
detections = but.denormalize_detections(normalized_detections,
scale, pad)
# postprocessing
display_result(src_img, detections)
savepath = get_savepath(args.savepath, image_path)
logger.info(f'saved at : {savepath}')
cv2.imwrite(savepath, src_img)
logger.info('Script finished successfully.')