def recognize_from_image():
# prepare input data
img = cv2.imread(args.input, cv2.IMREAD_UNCHANGED)
img = preprocess_image(img)
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
classifier = ailia.Classifier(MODEL_PATH,
WEIGHT_PATH,
env_id=env_id,
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
range=IMAGE_RANGE)
# compute execution time
for i in range(5):
start = int(round(time.time() * 1000))
classifier.compute(img, MAX_CLASS_COUNT)
end = int(round(time.time() * 1000))
print(f'ailia processing time {end - start} ms')
# get result
count = classifier.get_class_count()
print(f'class_count: {count}')
for idx in range(count):
print(f"+ idx={idx}")
info = classifier.get_class(idx)
print(f" category={info.category} " +
f"[ {resnet50_labels.imagenet_category[info.category]} ]")
print(f" prob={info.prob}")
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
org_img = load_image(
args.input,
(IMAGE_HEIGHT, IMAGE_WIDTH),
)
input_data = load_image(args.input, (IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='127.5',
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
detections = postprocess(preds_ailia)
# generate detections
for detection in detections:
plot_detections(org_img, detection, save_image_path=args.savepath)
print('Script finished successfully.')
def extract_feature_vec_from_image():
# prepare input data
input_img = load_image(
args.input,
(IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None',
)
input_data = prepare_input_data(input_img)
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
fe_net = ailia.Net(FE_MODEL_PATH, FE_WEIGHT_PATH, env_id=env_id)
fe_net.set_input_shape(input_data.shape)
input_dict = {'data': input_data}
# inference
print('Start inference...')
if args.benchmark:
print('BENCHMARK mode')
for i in range(5):
start = int(round(time.time() * 1000))
_ = fe_net.predict(input_dict)[0]
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
_ = fe_net.predict(input_dict)[0]
# Extracting the output of a specifc layer
idx = fe_net.find_blob_index_by_name('encode1')
preds_ailia = fe_net.get_blob_data(idx)
print(preds_ailia.reshape(preds_ailia.shape[0], -1))
print('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)
src_img = load_image(args.input, (IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None')
# 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
res_img = postprocess(src_img, preds_ailia)
cv2.imwrite(args.savepath, res_img)
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
src_img = cv2.imread(args.input)
input_data = load_image(
args.input,
(IMAGE_HEIGHT, IMAGE_WIDTH),
)
input_data = input_data[np.newaxis, :, :, :]
# 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)
net.set_input_shape(input_data.shape)
# compute execution time
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'ailia processing time {end - start} ms')
# postprocessing
pred = preds_ailia.reshape((IMAGE_HEIGHT, IMAGE_WIDTH))
dst = transfer(src_img, pred)
cv2.imwrite(args.savepath, dst)
def compare_images():
# 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)
features = []
# prepare input data
for img_path in args.inputs:
input_data = load_and_preprocess(img_path)
# compute execution time
for i in range(1):
start = int(round(time.time() * 1000))
_ = net.predict(input_data)
end = int(round(time.time() * 1000))
print(f'ailia processing time {end - start} ms')
blob = net.get_blob_data(net.find_blob_index_by_name('conv5_3'))
features.append(blob)
# get result
fname1 = os.path.basename(args.inputs[0])
fname2 = os.path.basename(args.inputs[1])
dist = distance(features[0], features[1])
print(f'{fname1} vs {fname2} = {dist}')
if dist < THRESHOLD:
print('Same person')
else:
print('Not same person')
print('Script finished successfully.')
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_image():
# prepare input data
img = load_image(args.input)
print(f'input image shape: {img.shape}')
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
detector = ailia.Detector(
MODEL_PATH,
WEIGHT_PATH,
len(VOC_CATEGORY),
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
algorithm=ailia.DETECTOR_ALGORITHM_YOLOV1,
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))
detector.compute(img, THRESHOLD, IOU)
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
detector.compute(img, THRESHOLD, IOU)
# plot result
res_img = plot_results(detector, img, VOC_CATEGORY)
cv2.imwrite(args.savepath, res_img)
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
img = load_image(args.input, (IMAGE_HEIGHT, IMAGE_WIDTH),
rgb=False,
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(img)[0]
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
preds_ailia = net.predict(img)[0]
# postprocess
fig = gen_img_from_predsailia(img, preds_ailia)
fig.savefig(args.savepath)
print('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)
net.set_input_shape((1, IMAGE_HEIGHT, IMAGE_WIDTH, 3))
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)
fig = create_figure()
tight_layout = True
while(True):
ret, frame = capture.read()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not ret:
continue
input_image, resized_img = adjust_frame_size(
frame, IMAGE_HEIGHT, IMAGE_WIDTH
)
resized_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB)
if args.apply_rotate:
rotation_angle = np.random.randint(360)
rotated_img = generate_rotated_image(
resized_img,
rotation_angle,
size=(IMAGE_HEIGHT, IMAGE_WIDTH),
crop_center=True,
crop_largest_rect=True
)
input_data = rotated_img.reshape((1, IMAGE_HEIGHT, IMAGE_WIDTH, 3))
else:
rotation_angle = 0
rotated_img = resized_img
input_data = rotated_img.reshape((1, IMAGE_HEIGHT, IMAGE_WIDTH, 3))
# inference
preds_ailia = net.predict(input_data)
# visualize
predicted_angle = np.argmax(preds_ailia, axis=1)[0]
plt = visualize(fig, rotated_img, rotation_angle, predicted_angle, tight_layout)
plt.pause(.01)
tight_layout = False
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
img = io.imread(args.input)
img = preProcess(img)
input_data = padCropImg(img)
input_data = input_data.astype(np.float32) / 255.0
ynum = input_data.shape[0]
xnum = input_data.shape[1]
preds_ailia = np.zeros((ynum, xnum, 128, 128, 3), dtype=np.float32)
# 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 c in range(5):
start = int(round(time.time() * 1000))
for j in range(ynum):
for i in range(xnum):
patchImg = input_data[j, i]
patchImg = (patchImg - 0.5) / 0.5
patchImg = patchImg.transpose((2, 0, 1))
patchImg = patchImg[np.newaxis, :, :, :]
out = net.predict(patchImg)
out = out.transpose((0, 2, 3, 1))[0]
out = (np.clip(out, 0, 1) * 255).astype(np.uint8)
preds_ailia[j, i] = out
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
start = int(round(time.time() * 1000))
for j in range(ynum):
for i in range(xnum):
patchImg = input_data[j, i]
patchImg = (patchImg - 0.5) / 0.5
patchImg = patchImg.transpose((2, 0, 1))
patchImg = patchImg[np.newaxis, :, :, :]
out = net.predict(patchImg)
out = out.transpose((0, 2, 3, 1))[0]
out = (np.clip(out, 0, 1) * 255).astype(np.uint8)
preds_ailia[j, i] = out
end = int(round(time.time() * 1000))
# postprocessing
resImg = composePatch(preds_ailia)
resImg = postProcess(resImg)
resImg.save(args.savepath)
print('Script finished successfully.')
def recognize_from_image_tiling():
# 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)
# padding input image
img = cv2.imread(args.input)
h, w = img.shape[0], img.shape[1]
padding_w = int((w + IMAGE_WIDTH - 1) / IMAGE_WIDTH) * IMAGE_WIDTH
padding_h = int((h+IMAGE_HEIGHT-1) / IMAGE_HEIGHT) * IMAGE_HEIGHT
scale = int(OUTPUT_HEIGHT / IMAGE_HEIGHT)
output_padding_w = padding_w * scale
output_padding_h = padding_h * scale
output_w = w * scale
output_h = h * scale
print(f'input image : {h}x{w}')
print(f'output image : {output_w}x{output_h}')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img / 255.0
img = img.transpose(2, 0, 1)
img = img[np.newaxis, :, :, :]
pad_img = np.zeros((1, 3, padding_h, padding_w))
pad_img[:, :, 0:h, 0:w] = img
output_pad_img = np.zeros((1, 3, output_padding_h, output_padding_w))
tile_x = int(padding_w / IMAGE_WIDTH)
tile_y = int(padding_h / IMAGE_HEIGHT)
# Inference
start = int(round(time.time() * 1000))
for y in range(tile_y):
for x in range(tile_x):
output_pad_img[
:,
:,
y*OUTPUT_HEIGHT:(y+1)*OUTPUT_HEIGHT,
x*OUTPUT_WIDTH:(x+1)*OUTPUT_WIDTH
] = net.predict(pad_img[
:,
:,
y*IMAGE_HEIGHT:(y+1)*IMAGE_HEIGHT,
x*IMAGE_WIDTH:(x+1)*IMAGE_WIDTH
])
end = int(round(time.time() * 1000))
print(f'ailia processing time {end - start} ms')
# Postprocessing
output_img = output_pad_img[0, :, :output_h, :output_w]
output_img = output_img.transpose(1, 2, 0).astype(np.float32)
output_img = cv2.cvtColor(output_img, cv2.COLOR_RGB2BGR)
cv2.imwrite(args.savepath, output_img * 255)
print('Script finished successfully.')
def recognize_from_video():
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)
# # 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)
net.set_input_shape((1, 3, args.duration, IMAGE_HEIGHT, IMAGE_WIDTH))
# prepare input data
original_queue = deque([])
input_blob = np.empty((1, 3, args.duration, IMAGE_HEIGHT, IMAGE_WIDTH))
for i in range(args.duration - 1):
ret, frame = capture.read()
if not ret:
continue
original_queue.append(frame)
input_blob[0, :, i, :, :] = convert_input_frame(frame)
next_input_index = args.duration - 1
input_frame_size = capture.get(cv2.CAP_PROP_FRAME_COUNT)
while (next_input_index <= input_frame_size or input_frame_size == 0):
ret, frame = capture.read()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not ret:
continue
original_queue.append(frame)
input_blob[0, :, args.duration - 1, :, :] = convert_input_frame(frame)
if args.ailia:
result = net.predict(input_blob)
else:
result = session.run(
[output_name], {input_name: input_blob.astype(np.float32)})[0]
print_mars_result(result)
preview_img = original_queue.popleft()
cv2.imshow('preview', preview_img)
for i in range(args.duration - 1):
input_blob[0, :, i, :, :] = input_blob[0, :, i + 1, :, :]
next_input_index += 1
capture.release()
print('Script finished successfully.')
def estimate_from_video():
# net initialize
env_id = ailia.get_gpu_environment_id()
print(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,
HEIGHT,
WIDTH,
data_rgb=False,
normalize_type='None')
# inference
preds_ailia = net.predict(input_data)
# estimated crowd count
et_count = int(np.sum(preds_ailia))
# density map
density_map = (255 * preds_ailia / np.max(preds_ailia))[0][0]
density_map = cv2.resize(density_map,
(input_image.shape[1], input_image.shape[0]))
heatmap = cv2.applyColorMap(density_map.astype(np.uint8),
cv2.COLORMAP_JET)
cv2.putText(
heatmap,
f'Est Count: {et_count}',
(40, 440), # position
cv2.FONT_HERSHEY_SIMPLEX, # font
0.8, # fontscale
(255, 255, 255), # color
2 # thickness
)
res_img = np.hstack((input_image, heatmap))
cv2.imshow('frame', res_img)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def recognize_from_video():
etl_word = codecs.open(ETL_PATH, 'r', 'utf-8').readlines()
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
classifier = ailia.Classifier(
MODEL_PATH,
WEIGHT_PATH,
env_id=env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32
)
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
in_frame, frame = adjust_frame_size(frame, IMAGE_HEIGHT, IMAGE_WIDTH)
frame = preprocess_image(frame)
# inference
# compute execution time
classifier.compute(frame, MAX_CLASS_COUNT)
# get result
count = classifier.get_class_count()
print('==============================================================')
print(f'class_count: {count}')
for idx in range(count) :
print(f"+ idx={idx}")
info = classifier.get_class(idx)
print(f" category={info.category} [ {etl_word[info.category]} ]" )
print(f" prob={info.prob}")
cv2.imshow('frame', in_frame)
time.sleep(SLEEP_TIME)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def _initialize_net(args, face_parser_path):
env_id = ailia.get_gpu_environment_id()
logger.info(f"env_id (face parser): {env_id}")
if not args.onnx:
net = ailia.Net(face_parser_path[0], face_parser_path[1], env_id=env_id)
else:
import onnxruntime
net = onnxruntime.InferenceSession(face_parser_path[1])
return net
def recognize_from_video():
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
classifier = ailia.Classifier(
MODEL_PATH,
WEIGHT_PATH,
env_id=env_id,
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32
)
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
_, resized_frame = adjust_frame_size(frame, IMAGE_HEIGHT, IMAGE_WIDTH)
input_data = cv2.cvtColor(
resized_frame.astype(np.float32),
cv2.COLOR_RGB2BGRA
).astype(np.uint8)
classifier.compute(input_data, MAX_CLASS_COUNT)
count = classifier.get_class_count()
# show results
print('==============================================================')
print(f'class_count: {count}')
for idx in range(count):
print(f'+ idx={idx}')
info = classifier.get_class(idx)
print(f' category={info.category} [ ' +\
f'{googlenet_labels.imagenet_category[info.category]} ]')
print(f' prob={info.prob}')
cv2.imshow('frame', frame)
time.sleep(SLEEP_TIME)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
num = lambda val: int(re.sub("\\D", "", val))
sorted_inputs_path = sorted(os.listdir(args.input), key=num)
input_blob = np.empty((1, 3, args.duration, IMAGE_HEIGHT, IMAGE_WIDTH))
for i, input_path in enumerate(sorted_inputs_path[0:args.duration]):
img = load_image(args.input + '/' + input_path,
(IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None',
gen_input_ailia=True)
input_blob[0, :, i, :, :] = img
next_input_index = args.duration
input_frame_size = len(sorted_inputs_path)
# # 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)
net.set_input_shape((1, 3, args.duration, IMAGE_HEIGHT, IMAGE_WIDTH))
# inferece
print('Start inference...')
if args.benchmark:
print('BENCHMARK mode')
for i in range(5):
start = int(round(time.time() * 1000))
result = net.predict(input_blob)
end = int(round(time.time() * 1000))
print(f'\tailia processing time {end - start} ms')
else:
while (next_input_index < input_frame_size):
if cv2.waitKey(1) & 0xFF == ord('q'):
break
result = net.predict(input_blob)
print_mars_result(result)
preview_img = cv2.imread(args.input + '/' +
sorted_inputs_path[next_input_index -
args.duration])
cv2.imshow('preview', preview_img)
for i in range(args.duration - 1):
input_blob[0, :, i, :, :] = input_blob[0, :, i + 1, :, :]
img = load_image(args.input + '/' +
sorted_inputs_path[next_input_index],
(IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None',
gen_input_ailia=True)
input_blob[0, :, args.duration - 1, :, :] = img
next_input_index += 1
print('Script finished successfully.')
def recognize_from_video():
# [WARNING] This is test impl
print('[WARNING] This is test implementation')
# 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
img = preProcess(frame)
input_data = padCropImg(img)
input_data = input_data.astype(np.float32) / 255.0
ynum = input_data.shape[0]
xnum = input_data.shape[1]
preds_ailia = np.zeros((ynum, xnum, 128, 128, 3), dtype=np.float32)
for j in range(ynum):
for i in range(xnum):
patchImg = input_data[j, i]
patchImg = (patchImg - 0.5) / 0.5
patchImg = patchImg.transpose((2, 0, 1))
patchImg = patchImg[np.newaxis, :, :, :]
out = net.predict(patchImg)
out = out.transpose((0, 2, 3, 1))[0]
out = (np.clip(out, 0, 1) * 255).astype(np.uint8)
preds_ailia[j, i] = out
resImg = composePatch(preds_ailia)
resImg = postProcess(resImg)
cv2.imshow('frame', img_as_ubyte(resImg))
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def recognize_from_video():
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
enc_net = ailia.Net(ENC_MODEL_PATH, ENC_WEIGHT_PATH, env_id=env_id)
dec_net = ailia.Net(DEC_MODEL_PATH, DEC_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)
ret, frame = capture.read()
org_height, org_width, _ = frame.shape
while (True):
ret, frame = capture.read()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not ret:
continue
_, input_data = preprocess_frame(frame, IMAGE_HEIGHT, IMAGE_WIDTH)
# encoder
enc_input_blobs = enc_net.get_input_blob_list()
enc_net.set_input_blob_data(input_data, enc_input_blobs[0])
enc_net.update()
features = enc_net.get_results()
# decoder
dec_inputs_blobs = dec_net.get_input_blob_list()
for f_idx in range(len(features)):
dec_net.set_input_blob_data(features[f_idx],
dec_inputs_blobs[f_idx])
dec_net.update()
preds_ailia = dec_net.get_results()
# postprocessing
disp = preds_ailia[-1]
disp_resized, vmax = result_plot(disp, org_width, org_height)
plt.imshow(disp_resized, cmap='magma', vmax=vmax)
plt.pause(.01)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def compare_videoframe_image():
# 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)
# img part
fname = args.video[1]
input_data = load_and_preprocess(fname)
_ = net.predict(input_data)
i_feature = net.get_blob_data(net.find_blob_index_by_name('conv5_3'))
# video part
if args.video[0] == '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
_, resized_frame = adjust_frame_size(frame, IMAGE_HEIGHT, IMAGE_WIDTH)
input_data = preprocess(resized_frame, input_is_bgr=True)
# inference
_ = net.predict(input_data)
v_feature = net.get_blob_data(net.find_blob_index_by_name('conv5_3'))
# show result
dist = distance(i_feature, v_feature)
print('=============================================================')
print(f'{os.path.basename(fname)} vs video frame = {dist}')
if dist < THRESHOLD:
print('Same person')
else:
print('Not same person')
cv2.imshow('frame', resized_frame)
time.sleep(SLEEP_TIME)
capture.release()
cv2.destroyAllWindows()
print('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)
# 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
input_image, input_data = webcamera_utils.preprocess_frame(
frame, IMAGE_HEIGHT, IMAGE_WIDTH, normalize_type='127.5')
# inference
input_blobs = net.get_input_blob_list()
net.set_input_blob_data(input_data, input_blobs[0])
net.update()
preds_ailia = net.get_results()
# postprocessing
detections = postprocess(preds_ailia)
show_result(input_image, detections)
cv2.imshow('frame', input_image)
# save results
if writer is not None:
writer.write(input_image)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def main():
# model files check and download
check_and_download_models(WEIGHT_PATH, MODEL_PATH, REMOTE_PATH)
# bert tokenizer
if LANG == 'en':
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
elif LANG == 'jp':
tokenizer = BertTokenizer(
'vocab.txt',
do_lower_case=False,
do_basic_tokenize=False
)
# prepare data
dummy_input = np.ones((1, MAX_SEQ_LEN), dtype=np.int64)
tokens_ts, segments_ts, masked_index = text2token(
SENTENCE, tokenizer, lang=LANG
)
input_data = np.array([tokens_ts, segments_ts])
# 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)
# compute execution time
for i in range(5):
start = int(round(time.time() * 1000))
input_blobs = net.get_input_blob_list()
for i, idx in enumerate(input_blobs):
if i < len(input_data):
net.set_input_blob_data(input_data[i], idx)
else:
net.set_input_blob_data(dummy_input, idx)
net.update()
preds_ailia = net.get_results()
# preds_ailia = net.predict(dummy_input)[0]
end = int(round(time.time() * 1000))
print("ailia processing time {} ms".format(end-start))
# masked word prediction
predicted_indices = np.argsort(
preds_ailia[0][0][masked_index]
)[-NUM_PREDICT:][::-1]
predicted_tokens = tokenizer.convert_ids_to_tokens(predicted_indices)
print('Input sentence: ' + SENTENCE)
print(f'predicted top {NUM_PREDICT} words: {predicted_tokens}')
print('Script finished successfully.')
def segment_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)
ailia_input_w = net.get_input_shape()[3]
ailia_input_h = net.get_input_shape()[2]
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, ailia_input_h, ailia_input_w, normalize_type='127.5'
)
# inference
input_blobs = net.get_input_blob_list()
net.set_input_blob_data(input_data, input_blobs[0])
net.update()
preds_ailia = np.array(net.get_results())[0, 0] # TODO why?
# postprocessing
seg_map = np.argmax(preds_ailia.transpose(1, 2, 0), axis=2)
seg_image = label_to_color_image(seg_map).astype(np.uint8)
# showing the segmented image (simple)
seg_image = cv2.cvtColor(seg_image, cv2.COLOR_RGB2BGR)
seg_image = cv2.resize(
seg_image, (input_image.shape[1], input_image.shape[0])
)
cv2.imshow('frame', seg_image)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def recognize_from_image():
# prepare input data
src_img = cv2.imread(args.input)
crop_size = (max(src_img.shape[0],
src_img.shape[1]), max(src_img.shape[0],
src_img.shape[1]))
src_img = safe_crop(src_img, crop_size)
# net initialize
env_id = 0 # use cpu because overflow fp16 range
#env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=env_id)
net.set_input_shape((1, IMAGE_HEIGHT, IMAGE_WIDTH, 4))
if args.trimap == "":
input_data = src_img
env_id = ailia.get_gpu_environment_id()
seg_net = ailia.Net(SEGMENTATION_MODEL_PATH,
SEGMENTATION_WEIGHT_PATH,
env_id=env_id)
trimap_data, seg_data = generate_trimap(seg_net, input_data)
else:
trimap_data = cv2.imread(args.trimap)
trimap_data = safe_crop(trimap_data, crop_size)
seg_data = trimap_data.copy()
input_data, src_img, trimap_data = matting_preprocess(
src_img, trimap_data, seg_data)
# 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
res_img = postprocess(src_img, trimap_data, preds_ailia)
cv2.imwrite(args.savepath, res_img)
print('Script finished successfully.')
def main():
# model files check and download
check_and_download_models(WEIGHT_PATH, MODEL_PATH, REMOTE_PATH)
# check folder existing
if not os.path.exists(args.input):
print("error : directory not found "+args.input)
sys.exit(1)
if not os.path.exists(args.output):
os.mkdir(args.output)
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
if args.arch == 'blazeface':
detector = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=env_id)
else:
detector = ailia.Detector(
MODEL_PATH,
WEIGHT_PATH,
len(FACE_CATEGORY),
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32,
algorithm=ailia.DETECTOR_ALGORITHM_YOLOV3,
env_id=env_id
)
# process images
no = 0
for src_dir, dirs, files in os.walk(args.input):
files = sorted(files)
for file_ in files:
root, ext = os.path.splitext(file_)
if file_==".DS_Store":
continue
if file_=="Thumbs.db":
continue
if not(ext == ".jpg" or ext == ".png" or ext == ".bmp"):
continue
print(src_dir+"/"+file_)
folders=src_dir.split("/")
folder=folders[len(folders)-1]
dst_dir = args.output+"/"+folder
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
dst_path= dst_dir+ "/"+str(no)+".jpg"
recognize_from_image(detector,dst_path,src_dir,file_)
no=no+1
def recognize_from_video():
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
pose = ailia.PoseEstimator(MODEL_PATH,
WEIGHT_PATH,
env_id=env_id,
algorithm=ALGORITHM)
baseline = ailia.Net(BASELINE_MODEL_PATH,
BASELINE_WEIGHT_PATH,
env_id=env_id)
baseline.set_input_shape((1, 32))
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 = adjust_frame_size(
frame,
IMAGE_HEIGHT,
IMAGE_WIDTH,
)
input_data = cv2.cvtColor(input_data, cv2.COLOR_BGR2BGRA)
# inferece
_ = pose.compute(input_data)
# postprocessing
display_result(input_image, pose, baseline)
cv2.imshow('frame', input_image)
# display 3d pose
plt.pause(0.01)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def compare_image_and_video():
# prepare base image
base_imgs = prepare_input_data(args.video[1])
# net itinialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=env_id)
# web camera
if args.video[0] == '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[0]):
capture = cv2.VideoCapture(args.video[0])
# inference loop
while (True):
ret, frame = capture.read()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not ret:
continue
frame, resized_frame = adjust_frame_size(frame, IMAGE_HEIGHT,
IMAGE_WIDTH)
input_frame = preprocess_image(resized_frame, input_is_bgr=True)
input_data = np.concatenate([base_imgs, input_frame], axis=0)
# inference
preds_ailia = net.predict(input_data)
# postprocessing
fe_1 = np.concatenate([preds_ailia[0], preds_ailia[1]], axis=0)
fe_2 = np.concatenate([preds_ailia[2], preds_ailia[3]], axis=0)
sim = cosin_metric(fe_1, fe_2)
bool_sim = False if THRESHOLD > sim else True
frame = draw_result_on_img(
frame,
texts=[f"Similarity: {sim:06.3f}", f"SAME FACE: {bool_sim}"])
cv2.imshow('frame', frame)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def recognize_from_video():
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
detector = ailia.Detector(
MODEL_PATH,
WEIGHT_PATH,
len(FACE_CATEGORY),
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
range=RANGE,
algorithm=ALGORITHM,
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
_, resized_img = adjust_frame_size(frame, IMAGE_HEIGHT, IMAGE_WIDTH)
img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2BGRA)
detector.compute(img, THRESHOLD, IOU)
detections = []
for idx in range(detector.get_object_count()):
obj = detector.get_object(idx)
detections.append(obj)
detections=nms_between_categories(detections,frame.shape[1],frame.shape[0],categories=[0,1],iou_threshold=IOU)
res_img = plot_results(detections, resized_img, FACE_CATEGORY, False)
cv2.imshow('frame', res_img)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')
def __init__(self):
check_and_download_models(WEIGHT_PATH_YOLO, MODEL_PATH_YOLO,
REMOTE_PATH_YOLO)
# net initialize
env_id = ailia.get_gpu_environment_id()
self.detector = detector = ailia.Detector(
MODEL_PATH_YOLO,
WEIGHT_PATH_YOLO,
len(FACE_CATEGORY),
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32,
algorithm=ailia.DETECTOR_ALGORITHM_YOLOV3,
env_id=env_id)