cap = cv2.VideoCapture(0)
count = 0
while cap.isOpened():
ret, frame = cap.read()
if ret:
count += 1
# if count % 1 == 0:
# 转换通道, 转成Image 格式
b, g, r = cv2.split(frame)
img = cv2.merge([r, g, b])
img = Image.fromarray(img.astype(np.uint8))
# P网络
pboxs = utils.PnetDetect(pnet, img, imgshow=False)
# R网络
rboxs = utils.RnetDetect(rnet, img, pboxs, imgshow=False)
# O网络
oboxs = utils.OnetDetect(onet,
img,
rboxs,
imgshow=True,
show_conf=False,
isuse=True)
img = np.array(img, dtype=np.uint8)
# #转换通道BGR
r, g, b = cv2.split(img)
img = cv2.merge([b, g, r])
cv2.imshow("img", img)
# else:
# cv2.imshow("img", frame)
rnet = nets.R_Net(istraining=False)
if torch.cuda.is_available():
pnet = pnet.cuda()
rnet = rnet.cuda()
pnet.eval()
rnet.eval()
pnet.load_state_dict(
torch.load(
r'C:\Users\liev\Desktop\myproject\face_recognition\params\p_params.pkl'
)) # 导入训练参数
rnet.load_state_dict(
torch.load(
r'C:\Users\liev\Desktop\myproject\face_recognition\params\r_params.pkl'
)) # 导入训练参数
#输入图片
img = Image.open(
r"C:\Users\liev\Desktop\data\face_match_pic\14ce36d3d539b6009c2f88f9ea50352ac75cb79f.jpg"
)
# P网络
start_tim = time.time()
pboxs = utils.PnetDetect(pnet, img, imgshow=False)
pnet_tim = time.time()
# R网络
rboxs = utils.RnetDetect(rnet, img, pboxs, imgshow=True, show_conf=False)
rnet_tim = time.time()
print("pnet_time:", pnet_tim - start_tim, "rnet_time:",
rnet_tim - pnet_tim)