def __init__(self):

self.model_path = './model'

self.net = Net(is_training=False)

self.size = 96

def ad_threshold(self, img):

th2 = cv2.adaptiveThreshold(img, 255,

cv2.ADAPTIVE_THRESH_GAUSSIAN_C,

cv2.THRESH_BINARY, 19, 4) # 自适应二值化

return th2

def CatchUsbVideo(self, window_name, camera_index):

# 定义主函数

cv2.namedWindow(window_name) # 创建摄像头窗口

cap = cv2.VideoCapture(camera_index) # 调用摄像头（一般电脑自带摄像头index为0）

# 调用分类（人脸识别分类器是cv一个预训练的模型，文件名为haarcascade_frontalface_alt2.xml）

# 在我的电脑里查找就可以找到，找到后复制到当前文件夹内

# 我的电脑的储存路径是C:\Users\dell\AppData\Roaming\Python\Python37\site-packages\cv2\data

classfier = cv2.CascadeClassifier('./haarcascade_frontalface_alt2.xml')

# 设置边框颜色（用于框出人脸）

color = (0, 255, 0)

font = cv2.FONT_HERSHEY_SIMPLEX # 创建摄像头前置的文字框

with tf.Session() as sess:

sess.run(tf.compat.v1.global_variables_initializer())

ckpt = tf.train.get_checkpoint_state(self.model_path)

if ckpt and ckpt.model_checkpoint_path:

# 如果保存过模型，则在保存的模型的基础上继续训练

self.net.saver.restore(sess, ckpt.model_checkpoint_path)

print('Model Reload Successfully!')

while cap.isOpened():

catch, frame = cap.read() # 读取每一帧图片

if not catch:

raise Exception('Check if the camera if on.')

break

# 转换为灰度图片

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

gray = self.ad_threshold(gray) # 自适应二值化处理

# scaleFactor 为图片缩放比例

# minNeighbors 是至少检查为3次是人脸才作为标记，适当增大可以有效抗干扰

# minSize 是检测的最小人脸的大小

faceRects = classfier.detectMultiScale(

gray, scaleFactor=1.2, minNeighbors=2, minSize=(32, 32))

if len(faceRects) > 0:

# 历遍每次检测的所有脸

for face in faceRects:

x, y, w, h = face # face是一个元祖，返回了分类器的检测结果，包括起始点的坐标和高度宽度

image = frame[y-10:y+h+10, x-10:x+w+10] # 对原图片进行裁剪

cv2.rectangle(frame, (x-5, y-5), (x+w+5, y+h+5),

color, 2) # 绘制人脸检测的线框

cv2.putText(frame, 'face', (x + 30, y + 30),

font, 1, (255, 0, 255), 4)

image_x = cv2.resize(cv2.cvtColor(

image, cv2.COLOR_BGR2GRAY), (self.size, self.size))

points = self.net.test_net(image_x, sess)

points_x = points[::2] / self.size * w + x

points_y = points[1::2] / self.size * h + y

points_x = points_x.astype(np.int)

points_y = points_y.astype(np.int)

for x_, y_ in zip(points_x, points_y):

cv2.circle(frame, (x_, y_), 2, (0,0,255), -1)

cv2.imshow(window_name, frame) # 显示人脸检测结果

c = cv2.waitKey(10)

if c & 0xFF == ord('q'):

# 按q退出

break

if cv2.getWindowProperty(window_name, cv2.WND_PROP_AUTOSIZE) < 1:

# 点x退出

break

# 释放摄像头

cap.release()