人脸识别系统

#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;
int main(int argc, const char** argv)
{
//create the cascade classifier object used for the face detection
CascadeClassifier face_cascade;
//use the haarcascade_frontalface_alt.xml library
face_cascade.load("haarcascade_frontalface_alt2.xml");
const char* imagename = "test3.jpeg";
//从文件中读入图像
Mat img = imread(imagename);
//如果读入图像失败
if (img.empty())
{
fprintf(stderr, "Can not load image %s\n", imagename);
return -1;
}
//setup image files used in the process
Mat grayscaleFrame;
//convert captured image to gray scale and equalize
cvtColor(img, grayscaleFrame, CV_BGR2GRAY);
equalizeHist(grayscaleFrame, grayscaleFrame);
//create a vector array to store the face found
std::vector<Rect> faces;
//find faces and store them in the vector array
face_cascade.detectMultiScale(grayscaleFrame, faces, 1.1, 3, CV_HAAR_FIND_BIGGEST_OBJECT | CV_HAAR_SCALE_IMAGE, Size(30, 30));
//draw a rectangle for all found faces in the vector array on the original image
for (int i = 0; i < faces.size(); i++)
{
Point pt1(faces[i].x + faces[i].width, faces[i].y + faces[i].height);
Point pt2(faces[i].x, faces[i].y);
rectangle(img, pt1, pt2, cvScalar(0, 255, 0, 0), 3, 8, 0);
}
//print the output
imshow("Face Dection, Results", img);
//pause for 33ms
waitKey();
return 0;
}
请注意：成功运行此段代码需要做一些准备工作，比如要将“haarcascade_frontalface_alt2.xml”文件放到工程目录下，而这个文件可以在”C:\OPENCV3.0\opencv\sources\data\haarcascades”找到。

#include <opencv2/opencv.hpp>
#include <cstdio>
#include <cstdlib>
#include <Windows.h>
using namespace std;
const char *pcascadeName = "D:\\OpenCV\\opencv\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
const char *pImageName = "image.jpg";
void DetectAndMark();
int main(int argc, const char** argv)
{
DetectAndMark();
return 0;
}
void DetectAndMark()
{
// load the Haar classifier
CvHaarClassifierCascade *pHaarClassCascade;
pHaarClassCascade = (CvHaarClassifierCascade*)cvLoad(pcascadeName);
//load the test image
IplImage *pSrcImage = cvLoadImage(pImageName, CV_LOAD_IMAGE_UNCHANGED);
IplImage *pGrayImage = cvCreateImage(cvGetSize(pSrcImage), IPL_DEPTH_8U, 1);
if(pSrcImage == NULL || pGrayImage == NULL)
{
printf("can't load image!\n");
return;
}
cvCvtColor(pSrcImage, pGrayImage, CV_BGR2GRAY);
if (pHaarClassCascade != NULL && pSrcImage != NULL && pGrayImage != NULL)
{
const static CvScalar colors[] =
{
CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)
};
CvMemStorage *pcvMemStorage = cvCreateMemStorage(0);
cvClearMemStorage(pcvMemStorage);
//detect the face
int TimeStart, TimeEnd;
TimeStart = GetTickCount();
CvSeq *pcvSeqFaces = cvHaarDetectObjects(pGrayImage, pHaarClassCascade, pcvMemStorage);
TimeEnd = GetTickCount();
printf("the number of faces: %d\nSpending Time: %d ms\n", pcvSeqFaces->total, TimeEnd - TimeStart);
//mark the face
for(int i = 0; i <pcvSeqFaces->total; i++)
{
CvRect* r = (CvRect*)cvGetSeqElem(pcvSeqFaces, i);
CvPoint center;
int radius;
center.x = cvRound((r->x + r->width * 0.5));
center.y = cvRound((r->y + r->height * 0.5));
radius = cvRound((r->width + r->height) * 0.25);
cvCircle(pSrcImage, center, radius, colors[i % 8], 2);
}
cvReleaseMemStorage(&pcvMemStorage);
}
const char *pstrWindowsTitle = "FaceDetect Demo"; //从文件中读入图像
cvNamedWindow(pstrWindowsTitle, CV_WINDOW_AUTOSIZE);
cvShowImage(pstrWindowsTitle, pSrcImage);
cvWaitKey(0);
cvDestroyWindow(pstrWindowsTitle);
cvReleaseImage(&pSrcImage);
cvReleaseImage(&pGrayImage);
}
现在到了考验程序的准确度的时候了，先选了张简单的图片，结果如下：
还不错，准确检测出来了，来张多人脸的图测试下：
居然也都通过了，看来这个类Haar分类训练器果然训练有素啊，再来一张试试：。