# cap = cv2.VideoCapture(r"C:\Users\liev\Desktop\myproject\face_recognition\test\test.mp4")
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)
import torch, time
import net.checknet as nets
import tool.utils as utils
from PIL import Image
import numpy as np
if __name__ == '__main__':
net = nets.P_Net(istraining=False)
if torch.cuda.is_available():
net = net.cuda()
net.eval()
net.load_state_dict(
torch.load(
r'C:\Users\liev\Desktop\myproject\face_recognition\params\p_params.pkl'
)) # 导入训练参数
#输入图片
img = Image.open(r"./5.jpg")
#P网络
start_tim = time.time()
pboxs = utils.PnetDetect(net, img, imgshow=True)
end_tim = time.time()
print("pnet_time:", end_tim - start_tim)