OpenCV读取摄像头人脸检测

#coding:utf-8import os
import numpy
from PIL import Image,ImageDraw
import cv2


cap = cv2.VideoCapture(0)
fps = cap.get(cv2.cv.CV_CAP_PROP_FPS)
size = (int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.cv.CV_FOURCC('I','4','2','0')
#video = cv2.VideoWriter("aaa.avi", fourcc, 5, size)
print cap.isOpened()

classifier=cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")

index = 0;
count=0
while count > -1:
    ret,img = cap.read()
    faceRects = classifier.detectMultiScale(img, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,(20,20))

    if len(faceRects)>0:
        for faceRect in faceRects:
                x, y, w, h = faceRect
                cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0),2,0)
                print "save faceimg"
                face_win = img[int(y):int(y) + int(h), int(x):int(x) + int(w)]
                cv2.imwrite('faceimg/index' + str(index) + '.bmp', face_win)
                index +=1
    #facenet
    #video.write(img)
    cv2.imshow('video',img)
    key=cv2.waitKey(1)
    if key==ord('q'):
        break

#video.release()
cap.release()
cv2.destroyAllWindows()

#coding:utf-8import os
import numpy
from PIL import Image,ImageDraw
import cv2


cap = cv2.VideoCapture(0)
fps = cap.get(cv2.cv.CV_CAP_PROP_FPS)
size = (int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.cv.CV_FOURCC('I','4','2','0')
#video = cv2.VideoWriter("aaa.avi", fourcc, 5, size)
print cap.isOpened()

classifier=cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")

count=0
while count > -1:
    ret,img = cap.read()
    faceRects = classifier.detectMultiScale(img, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,(20,20))
    if len(faceRects)>0:
        for faceRect in faceRects:
                x, y, w, h = faceRect
                cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0),2,0)
    #video.write(img)
    cv2.imshow('video',img)
    key=cv2.waitKey(1)
    if key==ord('q'):
        break

#video.release()
cap.release()
cv2.destroyAllWindows()


# import cv2
#
# capture=cv2.VideoCapture(0)
# #将capture保存为motion-jpeg,cv_fourcc为保存格式
# size = (int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)),
#         int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)))
# #cv_fourcc值要设置对，不然无法写入，而且不报错，坑
# #video=cv2.VideoWriter("VideoTest.avi", cv2.cv.CV_FOURCC('I','4','2','0'), 30, size)
# #isopened可以查看摄像头是否开启
# print capture.isOpened()
# num=0
# #要不断读取image需要设置一个循环
# while True:
#     ret,img=capture.read()
#     #视频中的图片一张张写入
#     #video.write(img)
#     cv2.imshow('Video',img)
#     key=cv2.waitKey(1)#里面数字为delay时间，如果大于0为刷新时间，
#     #超过指定时间则返回-1，等于0没有返回值,但也可以读取键盘数值，
#     #cv2.imwrite('%s.jpg'%(str(num)),img)
#     num=num+1
#     if key==ord('q'):#ord为键盘输入对应的整数,
#         break
# video.release()
# #如果不用release方法的话无法储存，要等结束程序再等摄像头关了才能显示保持成功
# capture.release()#把摄像头也顺便关了
#
# cv2.destroyAllWindows()



# OpenCV视频抓取好简单，主要用videowriter就可以了，主要要注意的是OpenCV中的抓取是放在内存中的，所以需要一个释放命令，不然就只能等到程序关闭后进行垃圾回收时才能释放了。视频抓取就不上图了。
#
# 然后是脸部识别，OpenCV自带了很多特征库有脸部，眼睛的还有很多，原理都一样，只是眼睛的库识别率视乎并不高，直接上代码：



# import cv2
# import cv2.cv as cv
#
# img = cv2.imread("face1.jpg")
#
# def detect(img, cascade):
#     '''detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg，
# faces表示检测到的人脸目标序列，1.3表示每次图像尺寸减小的比例为1.3，
#  4表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大小都可以检测到人脸),
#  CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测，而是缩放图像，Size(20, 20)为目标的最小最大尺寸'''
#     rects = cascade.detectMultiScale(img, scaleFactor=1.3,
#                                     minNeighbors=5, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
#     if len(rects) == 0:
#         return []
#     rects[:,2:] += rects[:,:2]
#     print rects
#     return rects
#
# #在img上绘制矩形
# def draw_rects(img, rects, color):
#     for x1, y1, x2, y2 in rects:
#         cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
#
#
# #转换为灰度图
# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# #直方图均衡处理
# gray = cv2.equalizeHist(gray)
#
# #脸部特征分类地址，里面还有其他
# cascade_fn = 'haarcascade_frontalface_alt.xml'
#
# #读取分类器,CascadeClassifier下面有一个detectMultiScale方法来得到矩形
# cascade = cv2.CascadeClassifier(cascade_fn)
#
# #通过分类器得到rects
# rects = detect(gray, cascade)
#
# #vis为img副本
# vis = img.copy()
#
# #画矩形
# draw_rects(vis, rects, (0, 255, 0))
#
# cv2.imshow('facedetect', vis)
#
# cv2.waitKey(0)
# cv2.destroyAllWindows() 

//---------------------------------【头文件、命名空间包含部分】----------------------------
// 描述：包含程序所使用的头文件和命名空间
//-------------------------------------------------------------------------------------------------
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;
void detectAndDisplay( Mat frame );
//--------------------------------【全局变量声明】----------------------------------------------
// 描述：声明全局变量
//-------------------------------------------------------------------------------------------------
//注意，需要把"haarcascade_frontalface_alt.xml"和"haarcascade_eye_tree_eyeglasses.xml"这两个文件复制到工程路径下
String face_cascade_name = "haarcascade_frontalface_alt.xml";
String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
string window_name = "Capture - Face detection";
RNG rng(12345);
//--------------------------------【help( )函数】----------------------------------------------
// 描述：输出帮助信息
//-------------------------------------------------------------------------------------------------
static void ShowHelpText()
{
//输出欢迎信息和OpenCV版本
cout <<"\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"
<<"\n\n\t\t\t此为本书OpenCV2版的第11个配套示例程序\n"
<< "\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION
<<"\n\n  ----------------------------------------------------------------------------" ;
}
//-----------------------------------【main( )函数】--------------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main( void )
{
  VideoCapture capture;
  Mat frame;
  //-- 1. 加载级联（cascades）
  if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
  if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
  //-- 2. 读取视频
  capture.open(0);
  ShowHelpText();
  if( capture.isOpened() )
  {
    for(;;)
    {
      capture >> frame;
      //-- 3. 对当前帧使用分类器（Apply the classifier to the frame）
      if( !frame.empty() )
       { detectAndDisplay( frame ); }
      else
       { printf(" --(!) No captured frame -- Break!"); break; }
      int c = waitKey(10);
      if( (char)c == 'c' ) { break; }
    }
  }
  return 0;
}
void detectAndDisplay( Mat frame )
{
   std::vector<Rect> faces;
   Mat frame_gray;
   cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
   equalizeHist( frame_gray, frame_gray );
   //-- 人脸检测
   face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
   for( size_t i = 0; i < faces.size(); i++ )
    {
      Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
      ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 2, 8, 0 );
      // Mat faceROI = frame_gray( faces[i] );
      // std::vector<Rect> eyes;
      // //-- 在脸中检测眼睛
      // eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
      // for( size_t j = 0; j < eyes.size(); j++ )
      //  {
      //    Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
      //    int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
      //    circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 3, 8, 0 );
      //  }
    }
   //-- 显示最终效果图
   imshow( window_name, frame );

}