# -*- coding: utf-8 -*-
#该文件实现opencv 获取usb摄像头，展示实时画面
import cv2
import sys
from PIL import Imagedef CatchUsbVideo(window_name, camera_idx):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)while cap.isOpened():ok, frame = cap.read()  # 读取一帧数据if not ok:break# 显示图像并等待10毫秒按键输入，输入‘q’退出程序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__':if len(sys.argv) != 2:print("Usage:%s camera_id\r\n" % (sys.argv[0]))else:CatchUsbVideo("截取视频流", int(sys.argv[1]))

# -*- coding: utf-8 -*-#该项目实现人脸识别，使用opencv的haarcascade_frontalface_alt2.xml 人脸分类器

import cv2import sysfrom PIL import Image

def CatchUsbVideo(window_name, camera_idx):    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("E:/project/opencv/build/etc/haarcascades/haarcascade_frontalface_alt2.xml")

    # 识别出人脸后要画的边框的颜色，RGB格式    color = (0, 255, 0)

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

            # 将当前帧转换成灰度图像        grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

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

        # 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__':    if len(sys.argv) != 2:        print("Usage:%s camera_id\r\n" % (sys.argv[0]))    else:        CatchUsbVideo("face", int(sys.argv[1]))