def main(image_path):
# load configs and set random seed
configs = json.load(open('./configs/fer2013_config.json'))
image_size = (configs['image_size'], configs['image_size'])
# model = densenet121(in_channels=3, num_classes=7)
model = resmasking_dropout1(in_channels=3, num_classes=7)
model.cuda()
state = torch.load('./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33')
model.load_state_dict(state['net'])
model.eval()
image = cv2.imread(image_path)
faces = face_cascade.detectMultiScale(image, 1.15, 5)
gray = ensure_gray(image)
for x, y, w, h in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (179, 255, 179), 2)
face = gray[y:y + h, x:x + w]
face = ensure_color(face)
face = cv2.resize(face, image_size)
face = transform(face).cuda()
face = torch.unsqueeze(face, dim=0)
output = torch.squeeze(model(face), 0)
proba = torch.softmax(output, 0)
emo_proba, emo_idx = torch.max(proba, dim=0)
emo_idx = emo_idx.item()
emo_proba = emo_proba.item()
emo_label = FER_2013_EMO_DICT[emo_idx]
label_size, base_line = cv2.getTextSize('{}: 000'.format(emo_label), cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
cv2.rectangle(
image,
(x + w, y + 1 - label_size[1]),
(x + w + label_size[0], y + 1 + base_line),
(223, 128, 255),
cv2.FILLED
)
cv2.putText(
image,
'{}: {}'.format(emo_label, int(emo_proba * 100)),
(x + w, y + 1),
cv2.FONT_HERSHEY_SIMPLEX,
0.8, (0, 0, 0), 2
)
from barez import show
show(image)
'''
def main(video_path):
# load configs and set random seed
configs = json.load(open('./configs/fer2013_config.json'))
image_size = (configs['image_size'], configs['image_size'])
# model = densenet121(in_channels=3, num_classes=7)
model = resmasking_dropout1(in_channels=3, num_classes=7)
model.cuda()
state = torch.load(
'./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33')
model.load_state_dict(state['net'])
model.eval()
video = cv2.VideoCapture(video_path)
cv2.namedWindow('disp', cv2.WINDOW_NORMAL)
cv2.resizeWindow('disp', width=1500, height=800)
cnt = 0
passed_frame = 0
with torch.no_grad():
while True:
cnt += 1
ret, image = video.read()
if image is None or ret is not True or passed_frame != 0:
passed_frame -= 1
passed_frame = max(passed_frame, 0)
continue
image = image.astype(np.uint8)
faces = face_cascade.detectMultiScale(image, 1.15, 5)
gray = ensure_gray(image)
for x, y, w, h in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (179, 255, 179),
2)
face = gray[y:y + h, x:x + w]
face = ensure_color(face)
face = cv2.resize(face, image_size)
face = transform(face).cuda()
face = torch.unsqueeze(face, dim=0)
output = torch.squeeze(model(face), 0)
proba = torch.softmax(output, 0)
emo_proba, emo_idx = torch.max(proba, dim=0)
emo_idx = emo_idx.item()
emo_proba = emo_proba.item()
emo_label = FER_2013_EMO_DICT[emo_idx]
label_size, base_line = cv2.getTextSize(
'{}: 000'.format(emo_label), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
2)
cv2.rectangle(image, (x + w, y + 1 - label_size[1]),
(x + w + label_size[0], y + 1 + base_line),
(223, 128, 255), cv2.FILLED)
cv2.putText(image, '{}: {}'.format(emo_label,
int(emo_proba * 100)),
(x + w, y + 1), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 0), 2)
cv2.imshow('disp', image.astype(np.uint8))
key = cv2.waitKey(0)
if key == ord('w'):
cv2.imwrite('./real_life_demo/matbiec_{}.png'.format(cnt),
image)
elif key == ord('q'):
break
elif key == ord('f'):
passed_frame = 10
elif key == ord('g'):
passed_frame = 100
elif key == ord('h'):
passed_frame = 1000
cv2.destroyAllWindows()
def main():
# load configs and set random seed
configs = json.load(open('./configs/fer2013_config.json'))
image_size = (configs['image_size'], configs['image_size'])
# model = densenet121(in_channels=3, num_classes=7)
model = resmasking_dropout1(in_channels=3, num_classes=7)
model.cuda()
# state = torch.load('./saved/checkpoints/densenet121_rot30_2019Nov11_14.23')
state = torch.load(
'./saved/checkpoints/resmasking_dropout1_rot30_2019Nov17_14.33')
model.load_state_dict(state['net'])
model.eval()
vid = cv2.VideoCapture(0)
with torch.no_grad():
while True:
ret, frame = vid.read()
if frame is None or ret is not True:
continue
frame = np.fliplr(frame).astype(np.uint8)
gray = ensure_gray(frame)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for x, y, w, h in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (179, 255, 179),
2)
face = gray[y:y + h, x:x + w]
face = ensure_color(face)
face = cv2.resize(face, image_size)
face = transform(face).cuda()
face = torch.unsqueeze(face, dim=0)
output = torch.squeeze(model(face), 0)
proba = torch.softmax(output, 0)
'''
for idx, value in enumerate(output.data.tolist()):
cv2.putText(
frame,
"{}:\t {:.3f}".format(FER_2013_EMO_DICT[idx], value),
(x + w, y + 1 + 20 * (idx + 1)),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(0, 255, 0), 2
)
'''
# emo_idx = torch.argmax(proba, dim=0).item()
emo_proba, emo_idx = torch.max(proba, dim=0)
emo_idx = emo_idx.item()
emo_proba = emo_proba.item()
emo_label = FER_2013_EMO_DICT[emo_idx]
label_size, base_line = cv2.getTextSize(
'{} {}'.format(emo_label, int()), cv2.FONT_HERSHEY_SIMPLEX,
0.8, 2)
cv2.rectangle(frame, (x + w, y + 1 - label_size[1]),
(x + w + label_size[0], y + 1 + base_line),
(223, 128, 255), cv2.FILLED)
cv2.putText(frame, '{} {}'.format(emo_label,
int(emo_proba * 100)),
(x + w, y + 1), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 0), 2)
'''
cv2.putText(
frame,
emo_label,
(x + w, y + 1),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(0, 255, 0), 2
)
'''
cv2.imshow('disp', frame)
if cv2.waitKey(1) == ord('q'):
break
cv2.destroyAllWindows()