def recognize_from_image():
# net initialize
classifier = ailia.Classifier(
MODEL_PATH,
WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
range=IMAGE_RANGE,
)
# input image loop
for image_path in args.input:
# prepare input data
logger.info(image_path)
img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
img = preprocess_image(img)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
classifier.compute(img, MAX_CLASS_COUNT)
end = int(round(time.time() * 1000))
logger.info(f'\tailia processing time {end - start} ms')
else:
classifier.compute(img, MAX_CLASS_COUNT)
# show results
print_results(classifier, resnet50_labels.imagenet_category)
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),
normalize_type='ImageNet',
gen_input_ailia=True,
)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for i in range(args.benchmark_count):
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)
print_results(preds_ailia, mobilenetv3_labels.imagenet_category)
logger.info('Script finished successfully.')
def recognize_from_image():
# prepare input data
input_data = load_image(
args.input,
(IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='ImageNet',
gen_input_ailia=True
)
# net initialize
net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.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
print_results(preds_ailia, vgg16_labels.imagenet_category)
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
input_data = load_image(args.input, (IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None',
gen_input_ailia=False)
input_data = cv2.cvtColor(input_data.astype(np.float32),
cv2.COLOR_RGB2BGRA).astype(np.uint8)
# net initialize
classifier = ailia.Classifier(MODEL_PATH,
WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32)
# inference
print('Start inference...')
if args.benchmark:
print('BENCHMARK mode')
for i in range(5):
start = int(round(time.time() * 1000))
classifier.compute(input_data, MAX_CLASS_COUNT)
# count = classifier.get_class_count()
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
classifier.compute(input_data, MAX_CLASS_COUNT)
# count = classifier.get_class_count()
# show results
print_results(classifier, efficientnet_labels.imagenet_category)
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_img = cv2.imread(image_path)
input_data = preprocess_input(input_img, IMAGE_HEIGHT, IMAGE_WIDTH)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
preds = net.predict(input_data)
end = int(round(time.time() * 1000))
logger.info(f'\tailia processing time {end - start} ms')
else:
preds = net.predict(input_data)
preds = softmax(preds)
# show results
print_results(preds, imagenet_category)
logger.info('Script finished successfully.')
def recognize_from_image(net):
# input image loop
for image_path in args.input:
logger.info(image_path)
img = load_image(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
total_time_estimation = 0
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
prob = predict(net, img)
end = int(round(time.time() * 1000))
estimation_time = (end - start)
# Logging
logger.info(
f'\tailia processing estimation time {estimation_time} ms')
if i != 0:
total_time_estimation = total_time_estimation + estimation_time
logger.info(
f'\taverage time estimation {total_time_estimation / (args.benchmark_count - 1)} ms'
)
else:
prob = predict(net, img)
# show result
print_results([prob], imagenet_classes)
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 is not None:
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
# prepare input data
_, input_data = webcamera_utils.adjust_frame_size(
frame, IMAGE_HEIGHT, IMAGE_WIDTH
)
input_data = preprocess_input(input_data, IMAGE_HEIGHT, IMAGE_WIDTH)
# inference
preds = net.predict(input_data)
preds = softmax(preds)
# get result
print_results(preds, imagenet_category)
cv2.imshow('frame', frame)
time.sleep(SLEEP_TIME)
# save results
if writer is not None:
writer.write(frame)
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def recognize_from_image():
# net initialize
classifier = ailia.Classifier(
MODEL_PATH,
WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32,
)
# input image loop
for image_path in args.input:
# prepare input data
logger.info(image_path)
input_img = load_image(
image_path,
(IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None',
)
input_data = cv2.cvtColor(input_img, cv2.COLOR_BGR2BGRA)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
classifier.compute(input_data, MAX_CLASS_COUNT)
end = int(round(time.time() * 1000))
logger.info(f'\tailia processing time {end - start} ms')
else:
classifier.compute(input_data, MAX_CLASS_COUNT)
# show results
print_results(classifier, inceptionv3_labels.imagenet_category)
logger.info('Script finished successfully.')
def recognize_from_image():
net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.env_id)
# input image loop
for i, image_path in enumerate(args.input):
input_batch = cv2.imread(image_path)
input_batch = _preprocess_image(input_batch)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
output = net.predict(input_batch)
end = int(round(time.time() * 1000))
logger.info(f'\tailia processing time {end - start} ms')
else:
output = net.predict(input_batch)
output = _softmax(output)
print_results(output, alexnet_labels.imagenet_category)
logger.info('Script finished successfully.')
