# -*- coding: utf-8 -*-
# import 进openCV的库
import cv2
import os


# 调用电脑摄像头或者保存好的视频检测人脸并截图
def CatchPICFromVideo(window_name, camera_idx, catch_pic_num, path_name):
 cv2.namedWindow(window_name)

 # 视频来源,可以来自一段已存好的视频,也可以直接来自USB摄像头
 cap = cv2.VideoCapture(camera_idx)

 # 告诉OpenCV使用人脸识别分类器
 classfier = cv2.CascadeClassifier(os.getcwd()+"\\haarcascade\\haarcascade_frontalface_alt.xml")

 # 识别出人脸后要画的边框的颜色,RGB格式, color是一个不可增删的数组
 color = (0, 255, 0)

 num = 0
 while cap.isOpened():
 ok, frame = cap.read() # 读取一帧数据
 if not ok:
 break

 grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 将当前桢图像转换成灰度图像

 # 人脸检测,1.2和2分别为图片缩放比例和需要检测的有效点数
 faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
 if len(faceRects) > 0: # 大于0则检测到人脸
 for faceRect in faceRects: # 单独框出每一张人脸
 x, y, w, h = faceRect

 # 将当前帧保存为图片
 img_name = "%s/%d.jpg" % (path_name, num)
 # print(img_name)
 image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
 cv2.imwrite(img_name, image, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])

 num += 1
 if num > (catch_pic_num): # 如果超过指定最大保存数量退出循环
 break

 # 画出矩形框
 cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)

 # 显示当前捕捉到了多少人脸图片了,这样站在那里被拍摄时心里有个数,不用两眼一抹黑傻等着
 font = cv2.FONT_HERSHEY_SIMPLEX
 cv2.putText(frame, 'num:%d/100' % (num), (x + 30, y + 30), font, 1, (255, 0, 255), 4)

 # 超过指定最大保存数量结束程序
 if num > (catch_pic_num): break

 # 显示图像
 cv2.imshow(window_name, frame)
 c = cv2.waitKey(10)
 if c & 0xFF == ord('q'):
 break

 # 释放摄像头并销毁所有窗口
 cap.release()
 cv2.destroyAllWindows()


if __name__ == '__main__':
 # 连续截100张图像,存进image文件夹中
 CatchPICFromVideo("get face", os.getcwd()+"\\video\\kelake.mp4", 1000, "E:\\VideoCapture")