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():
# 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_video():
etl_word = codecs.open(ETL_PATH, 'r', 'utf-8').readlines()
# net initialize
classifier = ailia.Classifier(
MODEL_PATH,
WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32,
)
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))
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
in_frame, frame = webcamera_utils.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()
logger.info('=' * 80)
logger.info(f'class_count: {count}')
for idx in range(count):
logger.info(f"+ idx={idx}")
info = classifier.get_class(idx)
logger.info(
f" category={info.category} [ {etl_word[info.category].rstrip()} ]"
)
logger.info(f" prob={info.prob}")
cv2.imshow('frame', in_frame)
# save results
if writer is not None:
writer.write(in_frame)
time.sleep(SLEEP_TIME)
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def recognize_from_video():
# 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,
)
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))
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
# prepare input data
input_image, input_data = webcamera_utils.adjust_frame_size(
frame, IMAGE_HEIGHT, IMAGE_WIDTH)
input_data = cv2.cvtColor(input_data, cv2.COLOR_BGR2BGRA)
# inference
classifier.compute(input_data, MAX_CLASS_COUNT)
# get result
count = classifier.get_class_count()
logger.info('=' * 80)
for idx in range(count):
# logger.info result
logger.info(f'+ idx={idx}')
info = classifier.get_class(idx)
logger.info(
f' category={info.category}'
f'[ {inceptionv3_labels.imagenet_category[info.category]} ]')
logger.info(f' prob={info.prob}')
cv2.imshow('frame', input_image)
time.sleep(SLEEP_TIME)
# save results
if writer is not None:
writer.write(input_image)
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=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():
# prepare input data
etl_word = codecs.open(ETL_PATH, 'r', 'utf-8').readlines()
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_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32
)
# 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} [ {etl_word[info.category]} ]" )
print(f" prob={info.prob}")
def recognize_from_image():
# prepare input data
input_img = load_image(
args.input,
(IMAGE_HEIGHT, IMAGE_WIDTH),
normalize_type='None',
)
input_data = cv2.cvtColor(input_img, cv2.COLOR_BGR2BGRA)
# 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)
# compute execution time
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'ailia processing time {end - start} ms')
# postprocessing
for idx in range(count):
# print result
print(f'+ idx={idx}')
info = classifier.get_class(idx)
print(f' category={info.category}' +\
f'[ {inceptionv3_labels.imagenet_category[info.category]} ]')
print(f' prob={info.prob}')
print('Script finished successfully.')
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_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 recognize_from_video():
# net initialize
classifier = ailia.Classifier(
MODEL_PATH,
WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_RGB,
range=IMAGE_RANGE,
)
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))
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
in_frame, frame = webcamera_utils.adjust_frame_size(
frame, IMAGE_HEIGHT, IMAGE_WIDTH)
frame = preprocess_image(frame)
# inference
classifier.compute(frame, MAX_CLASS_COUNT)
# get result
# count = classifier.get_class_count()
plot_results(in_frame, classifier, resnet50_labels.imagenet_category)
cv2.imshow('frame', in_frame)
time.sleep(SLEEP_TIME)
# save results
if writer is not None:
writer.write(in_frame)
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def recognize_from_video():
# 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,
)
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
_, resized_frame = webcamera_utils.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
plot_results(frame, classifier, efficientnet_labels.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(5):
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)
count = classifier.get_class_count()
# postprocessing
for idx in range(count):
# logger.info result
logger.info(f'+ idx={idx}')
info = classifier.get_class(idx)
logger.info(
f' category={info.category}'
f'[ {inceptionv3_labels.imagenet_category[info.category]} ]')
logger.info(f' prob={info.prob}')
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_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_U_FP32,
)
# input image loop
for image_path in args.input:
# prepare input data
logger.info(image_path)
etl_word = codecs.open(ETL_PATH, 'r', 'utf-8').readlines()
img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
if img is None:
logger.error("can not open " + image_path)
continue
img = preprocess_image(img)
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
for i in range(5):
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)
# get result
count = classifier.get_class_count()
logger.info(f'class_count: {count}')
for idx in range(count):
logger.info(f"+ idx={idx}")
info = classifier.get_class(idx)
logger.info(
f" category={info.category} [ {etl_word[info.category].rstrip()} ]"
)
logger.info(f" prob={info.prob}")
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
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)
# 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)
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(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}')
print('Script finished successfully.')
def recognize_from_image():
# prepare input
# load input image and convert to BGRA
img = cv2.imread(args.input, cv2.IMREAD_UNCHANGED)
if img.shape[2] == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
elif img.shape[2] == 1:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGRA)
# net initialize
emotion_classifier = ailia.Classifier(
EMOTION_MODEL_PATH,
EMOTION_WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
)
gender_classifier = ailia.Classifier(
GENDER_MODEL_PATH,
GENDER_WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
)
# inference emotion
print('Start inference...')
if args.benchmark:
print('BENCHMARK mode')
for i in range(5):
start = int(round(time.time() * 1000))
emotion_classifier.compute(img, EMOTION_MAX_CLASS_COUNT)
end = int(round(time.time() * 1000))
print(f'\t[EMOTION MODEL] ailia processing time {end - start} ms')
else:
emotion_classifier.compute(img, EMOTION_MAX_CLASS_COUNT)
count = emotion_classifier.get_class_count()
print(f'emotion_class_count={count}')
# print result
for idx in range(count):
print(f'+ idx={idx}')
info = emotion_classifier.get_class(idx)
print(
f' category={info.category} [ {EMOTION_CATEGORY[info.category]} ]'
)
print(f' prob={info.prob}')
print('')
# inference gender
if args.benchmark:
print('BENCHMARK mode')
for i in range(5):
start = int(round(time.time() * 1000))
gender_classifier.compute(img, GENDER_MAX_CLASS_COUNT)
end = int(round(time.time() * 1000))
print(f'\t[EMOTION MODEL] ailia processing time {end - start} ms')
else:
gender_classifier.compute(img, GENDER_MAX_CLASS_COUNT)
count = gender_classifier.get_class_count()
print(f'gender_class_count={count}')
# print reuslt
for idx in range(count):
print(f'+ idx={idx}')
info = gender_classifier.get_class(idx)
print(
f' category={info.category} [ {GENDER_CATEGORY[info.category]} ]'
)
print(f' prob={info.prob}')
print('Script finished successfully.')
def recognize_from_video():
# net initialize
emotion_classifier = ailia.Classifier(
EMOTION_MODEL_PATH,
EMOTION_WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
)
gender_classifier = ailia.Classifier(
GENDER_MODEL_PATH,
GENDER_WEIGHT_PATH,
env_id=args.env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
)
detector = ailia.Net(FACE_MODEL_PATH, FACE_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:
print('[WARNING] currently video results output feature '
'is not supported in this model!')
# TODO: shape should be debugged!
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
# detect face
# WIP: FIXME: AiliaInvalidArgumentException error
detections = compute_blazeface(
detector,
frame,
anchor_path='../../face_detection/blazeface/anchors.npy',
)
for obj in detections:
# get detected face
crop_img, top_left, bottom_right = crop_blazeface(
obj, FACE_MARGIN, frame
)
if crop_img.shape[0] <= 0 or crop_img.shape[1] <= 0:
continue
crop_img = cv2.cvtColor(crop_img, cv2.COLOR_BGR2BGRA)
# emotion inference
emotion_classifier.compute(crop_img, EMOTION_MAX_CLASS_COUNT)
count = emotion_classifier.get_class_count()
print('=' * 80)
print(f'emotion_class_count={count}')
# print result
emotion_text = ""
for idx in range(count):
print(f'+ idx={idx}')
info = emotion_classifier.get_class(idx)
print(
f' category={info.category} ' +
f'[ {EMOTION_CATEGORY[info.category]} ]'
)
print(f' prob={info.prob}')
if idx == 0:
emotion_text = (f'[ {EMOTION_CATEGORY[info.category]} ] '
f'prob={info.prob:.3f}')
print('')
# gender inference
gender_text = ""
gender_classifier.compute(crop_img, GENDER_MAX_CLASS_COUNT)
count = gender_classifier.get_class_count()
# print reuslt
for idx in range(count):
print(f'+ idx={idx}')
info = gender_classifier.get_class(idx)
print(
f' category={info.category} ' +
f'[ {GENDER_CATEGORY[info.category]} ]'
)
print(f' prob={info.prob}')
if idx == 0:
gender_text = (f'[ {GENDER_CATEGORY[info.category]} ] '
f'prob={info.prob:.3f}')
print('')
# display label
LABEL_WIDTH = 400
LABEL_HEIGHT = 20
color = (255, 255, 255)
cv2.rectangle(frame, top_left, bottom_right, color, thickness=2)
cv2.rectangle(
frame,
top_left,
(top_left[0]+LABEL_WIDTH, top_left[1]+LABEL_HEIGHT),
color,
thickness=-1,
)
text_position = (top_left[0], top_left[1]+LABEL_HEIGHT//2)
color = (0, 0, 0)
fontScale = 0.5
cv2.putText(
frame,
emotion_text + " " + gender_text,
text_position,
cv2.FONT_HERSHEY_SIMPLEX,
fontScale,
color,
1,
)
# show result
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()
print('Script finished successfully.')
def recognize_from_video():
# net initialize
env_id = ailia.get_gpu_environment_id()
print(f'env_id: {env_id}')
emotion_classifier = ailia.Classifier(
EMOTION_MODEL_PATH,
EMOTION_WEIGHT_PATH,
env_id=env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST)
gender_classifier = ailia.Classifier(
GENDER_MODEL_PATH,
GENDER_WEIGHT_PATH,
env_id=env_id,
format=ailia.NETWORK_IMAGE_FORMAT_GRAY,
range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST)
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()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not ret:
continue
# emotion inference
emotion_classifier.compute(frame, EMOTION_MAX_CLASS_COUNT)
count = emotion_classifier.get_class_count()
print('===========================================================')
print(f'emotion_class_count={count}')
# print result
for idx in range(count):
print(f'+ idx={idx}')
info = emotion_classifier.get_class(idx)
print(
f' category={info.category} ' +\
f'[ {EMOTION_CATEGORY[info.category]} ]'
)
print(f' prob={info.prob}')
print('')
# gender inference
gender_classifier.compute(frame, GENDER_MAX_CLASS_COUNT)
count = gender_classifier.get_class_count()
# print reuslt
for idx in range(count):
print(f'+ idx={idx}')
info = gender_classifier.get_class(idx)
print(
f' category={info.category} ' +\
f'[ {GENDER_CATEGORY[info.category]} ]'
)
print(f' prob={info.prob}')
print('')
cv2.imshow('frame', frame)
time.sleep(SLEEP_TIME)
capture.release()
cv2.destroyAllWindows()
print('Script finished successfully.')