2017 5月更新在前面：
Hi,大家好，我现在也没有opencv2.4.5的配置文件,尝试下载亦未有成功。
另一方面，原理及配置方法都是相通。大家不妨试一下配置新的版本及环境。
谢谢！
实质是通过JNI调到OPENCV的DLL，javacv相当于接口而已
首先看下软件的效果
由上可知 ,环境对识别率影响很大，超人前传里的一段视频一直都低甚至会出错，而CCTV的新闻的识别率则挺高。
人脸识别过程（主要方法）
public boolean recognizeFromCam() throws Exception //从摄像头中识别人脸
{
OpenCVFrameGrabber grabber = null;
FileDialog fd;
IplImage pFrame=null;
int keypress = 0;
int count=0;
if(LoginShell.video_flag)//如果被选，则用视频文件
{
fd=new FileDialog(LoginShell.sShell,SWT.OPEN);
fd.setFilterExtensions(new String[]{"*.*"});
fd.setFilterNames(new String[]{".avi",".wmv",".mp4"});
String filename=fd.open();
grabber = OpenCVFrameGrabber.createDefault(filename);
}
else
grabber = OpenCVFrameGrabber.createDefault(0);
grabber.start();
pFrame = grabber.grab();
while( pFrame!=null ){
//if(count==6){
detectAndCropAndPre( pFrame,cascade,CV_HAAR_DO_CANNY_PRUNING | CV_HAAR_DO_ROUGH_SEARCH);
//count=0;
//}
cvShowImage("Press 'Esc' to Stop!",pFrame);
pFrame = grabber.grab();
keypress=cvWaitKey(24);
System.out.println(g_confidence);
grabber.getTimeout();
if( keypress== 27){
grabber.release();
cvDestroyWindow("Press 'Esc' to Stop!");
break;
}
//count++;
}
cvReleaseImage(pFrame);
//cvDestroyWindow("BP_FaceRecognizer_FaceLogin");
cvClearMemStorage(storage);
// grabber.stop();
grabber.release();
return false;
}
public void recongizeFormImage(String filePath,String fileNames[]){//从图片中识别人脸
IplImage signleImage=null;
//System.out.println(filePath);
//signleImage=cvLoadImage(filePath);
//if(!signleImage.isNull());
detectAndCropFromImg(cascade,CV_HAAR_DO_CANNY_PRUNING | CV_HAAR_DO_ROUGH_SEARCH,fileNames,filePath);
//cvNamedWindow("Press 'Esc' to exit");
}
private void detectForRegister(IplImage src,CvHaarClassifierCascade cascade,int flag,String name){
//从摄像头获取五张人脸注册
IplImage greyImg=null;
IplImage faceImg=null;
IplImage sizedImg=null;
IplImage equalizedImg=null;
//int countForResgister=0;//for pause to rejust facial expression
CvRect r ;
CvFont font = new CvFont(CV_FONT_HERSHEY_COMPLEX_SMALL, 1, 1);
cvInitFont(font,CV_FONT_HERSHEY_COMPLEX_SMALL, 1.0, 0.8,1,1,CV_AA);
greyImg = cvCreateImage( cvGetSize(src), IPL_DEPTH_8U, 1 );
greyImg=convertImageToGreyscale(src);
//CvMemStorage storage = CvMemStorage.create();
CvSeq sign = cvHaarDetectObjects(
greyImg,
cascade,
storage,
1.1,
3,
flag);
cvClearMemStorage(storage);
if(sign.total()==1)//只会有一个脸部
{
r = new CvRect(cvGetSeqElem(sign, 0));
faceImg = cropImage(greyImg, r);
sizedImg = resizeImage(faceImg);
equalizedImg = cvCreateImage(cvGetSize(sizedImg), 8, 1);
cvEqualizeHist(sizedImg, equalizedImg);
cvRectangle (
src,
cvPoint(r.x(), r.y()),
cvPoint(r.width() + r.x(), r.height() + r.y()),
CvScalar.WHITE,
1,
CV_AA,
0);
cvPutText(src, "Need 5 photos" ,cvPoint(r.x()+30, r.y()- 50), font, CvScalar.GREEN);
cvPutText(src, "This is No."+String.valueOf(countSavedFace)+" photos. " ,cvPoint(r.x()+30, r.y()- 30), font, CvScalar.GREEN);
if(countForResgister==10){
cvSaveImage("img\\"+name+countSavedFace+".jpg",equalizedImg);
countSavedFace++;
countForResgister=0;
cvWaitKey(800);
}
countForResgister++;
cvReleaseImage(greyImg);
cvReleaseImage(faceImg);
cvReleaseImage(sizedImg);
cvReleaseImage(equalizedImg);
}
private void detectAndCropAndPre(IplImage src,CvHaarClassifierCascade cascade,int flag)//从一帧图像中获取脸部并识别
{
int nearest=0;
IplImage greyImg=null;
IplImage faceImg=null;
IplImage sizedImg=null;
IplImage equalizedImg=null;
boolean faceIsTrue=false;
CvRect r ;
CvFont font = new CvFont(CV_FONT_HERSHEY_COMPLEX_SMALL, 1, 1);
cvInitFont(font,CV_FONT_HERSHEY_COMPLEX_SMALL, 1.0, 0.8,1,1,CV_AA);
greyImg = cvCreateImage( cvGetSize(src), IPL_DEPTH_8U, 1 );
greyImg=convertImageToGreyscale(src);
//CvMemStorage storage = CvMemStorage.create();
CvSeq sign = cvHaarDetectObjects(
greyImg,
cascade,
storage,
1.5,
3,
flag);
//cvClearMemStorage(storage);
if(sign.total()>0)
{
for(int i=0;i<sign.total();i++)
{
r = new CvRect(cvGetSeqElem(sign, i));
faceImg = cropImage(greyImg, r);
sizedImg = resizeImage(faceImg);
if(i==0)
equalizedImg = cvCreateImage(cvGetSize(sizedImg), 8, 1);
cvEqualizeHist(sizedImg, equalizedImg);
cvRectangle (
src,
cvPoint(r.x(), r.y()),
cvPoint(r.width() + r.x(), r.height() + r.y()),
CvScalar.WHITE,
1,
CV_AA,
0);
eigenDecomImg(equalizedImg);
if(g_confidence*100>50){
cvPutText(src, textName,cvPoint(r.x()-10, r.y() + r.height() + 20), font, CvScalar.WHITE);
cvPutText(src, " conf="+Integer.valueOf((int) (g_confidence*100))+"%",cvPoint(r.x()-10, r.y() + r.height() + 40), font, CvScalar.GREEN);
textName="unknow";
}
else {
cvPutText(src, "unknow",cvPoint(r.x()-10, r.y() + r.height() + 20), font, CvScalar.WHITE);
cvPutText(src, " conf="+Integer.valueOf((int) (g_confidence*100))+"%",cvPoint(r.x()-10, r.y() + r.height() + 40), font, CvScalar.GREEN);
}
}
cvReleaseImage(greyImg);
cvReleaseImage(faceImg);
cvReleaseImage(sizedImg);
cvReleaseImage(equalizedImg);
}
}
private void detectAndCropFromImg(CvHaarClassifierCascade cascade,int flag,String fileNames[],String filePath)
{
CvRect r ;
CvFont font = new CvFont(CV_FONT_HERSHEY_COMPLEX_SMALL, 1, 1);
cvInitFont(font,CV_FONT_HERSHEY_COMPLEX_SMALL, 1.0, 0.8,1,1,CV_AA);
int fileNum=0;
fileNum=fileNames.length;
IplImage src[]=new IplImage[fileNum];
IplImage tempImg[]=new IplImage[fileNum];
IplImage greyImg[]=new IplImage[fileNum];
IplImage faceImg[]=new IplImage[fileNum];
IplImage sizedImg[]=new IplImage[fileNum];
IplImage equalizedImg[]=new IplImage[fileNum];
for(int i=0;i<fileNum;i++){
System.out.println(filePath+fileNames[i]);
src[i]=cvLoadImage(filePath+fileNames[i]);
if(src[i].width()>3200||src[i].height()>2400)
{
tempImg[i] = cvCreateImage(new CvSize(src[i].width()/4,src[i].height()/4), IPL_DEPTH_8U, 1 );
tempImg[i]=resizeImage(src[i],src[i].width()/4,src[i].height()/4);
}
else if(src[i].width()>1600||src[i].height()>1200)
{
tempImg[i] = cvCreateImage(new CvSize(src[i].width()/2,src[i].height()/2), IPL_DEPTH_8U, 1 );
tempImg[i]=resizeImage(src[i],src[i].width()/2,src[i].height()/2);
}
else
{
tempImg[i] = cvCreateImage(new CvSize(src[i].width(),src[i].height()), IPL_DEPTH_8U, 1 );
tempImg[i]=cvCloneImage(src[i]);
}
greyImg[i] = cvCreateImage( cvGetSize(tempImg[i]), IPL_DEPTH_8U, 1 );
greyImg[i]=convertImageToGreyscale(tempImg[i]);
//CvMemStorage storage = CvMemStorage.create();
CvSeq sign = cvHaarDetectObjects(
greyImg[i],
cascade,
storage,
1.2,
3,
flag);
if(sign.total()>0)
{
for(int j=0;j<sign.total();j++)
{
r = new CvRect(cvGetSeqElem(sign, j));
if(j==0){
faceImg[i]=cvCreateImage(new CvSize(r.width(),r.height()), IPL_DEPTH_8U, 1);
sizedImg[i]=cvCreateImage(new CvSize(newWidth,newHeight), IPL_DEPTH_8U, 1);
equalizedImg[i] = cvCreateImage(cvGetSize(sizedImg[i]), IPL_DEPTH_8U, 1);
}
faceImg[i] = cropImage(greyImg[i], r);
sizedImg[i] = resizeImage(faceImg[i]);
cvEqualizeHist(sizedImg[i], equalizedImg[i]);
cvRectangle (
tempImg[i],
cvPoint(r.x(), r.y()),
cvPoint(r.width() + r.x(), r.height() + r.y()),
CvScalar.WHITE,
1,
CV_AA,
0);
eigenDecomImg(equalizedImg[i]);
if(g_confidence*100>50){
cvPutText(tempImg[i], textName,cvPoint(r.x()-10, r.y() + r.height() + 20), font, CvScalar.WHITE);
cvPutText(tempImg[i], " conf="+Integer.valueOf((int) (g_confidence*100))+"%",cvPoint(r.x()-10, r.y() + r.height() + 40), font, CvScalar.GREEN);
textName="unknow";
}
else{
cvPutText(tempImg[i], "Unknow",cvPoint(r.x()-10, r.y() + r.height() + 20), font, CvScalar.WHITE);
cvPutText(tempImg[i], " conf="+Integer.valueOf((int) (g_confidence*100))+"%",cvPoint(r.x()-10, r.y() + r.height() + 40), font, CvScalar.GREEN);
}
}
cvReleaseImage(faceImg[i]);
cvReleaseImage(sizedImg[i]);
cvReleaseImage(equalizedImg[i]);
}
else
cvPutText(tempImg[i], "can't find any face!",cvPoint(src[i].width()/2, src[i].height()/2), font, CvScalar.GREEN);
cvReleaseImage(greyImg[i]);
cvReleaseImage(src[i]);
cvShowImage("Press 'Esc' to exit "+filePath+fileNames[i],tempImg[i]);
cvReleaseImage(tempImg[i]);
}
cvWaitKey(0);
cvClearMemStorage(storage);
cvDestroyAllWindows();
}
private void eigenDecomImg(IplImage src){//人脸对比的核心方法
//CvMat trainPersonNumMat=null;
float confidence = 0.0f;
int nearest=0;
int iNearest=0;
LOGGER.info("=====================================Waiting For the camera .....");
//
if( trainPersonNumMat==null) {
LOGGER.info("ERROR in recognizeFromCam(): Couldn't load the training data!\n");
System.exit(1);
}
float[] projectedTestFace = new float[nEigens];
cvEigenDecomposite(
src,
nEigens,
eigenVectArr,
0,
null,
pAvgTrainImg,
projectedTestFace);
final FloatPointer pConfidence = new FloatPointer(confidence);
iNearest = findNearestNeighbor(projectedTestFace, new FloatPointer(pConfidence));
confidence = pConfidence.get();
nearest = trainPersonNumMat.data_i().get(iNearest);
personName="";
textName=personNames.get(nearest-1);
personName=personNames.get(nearest-1);
g_confidence=confidence;
}
private void doPCA() {//人脸对比的核心方法
int i;
CvTermCriteria calcLimit;
CvSize faceImgSize = new CvSize();
// set the number of eigenvalues to use
nEigens = nTrainFaces-1 ;
LOGGER.info("allocating images for principal component analysis, using " + nEigens + (nEigens == 1 ? " eigenvalue" : " eigenvalues"));
// allocate the eigenvector images
faceImgSize.width(trainingFaceImgArr[0].width());
faceImgSize.height(trainingFaceImgArr[0].height());
eigenVectArr = new IplImage[nEigens];
for (i = 0; i < nEigens; i++) {
eigenVectArr[i] = cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
}
// allocate the eigenvalue array
eigenValMat = cvCreateMat(
1, // rows
nEigens, // cols
CV_32FC1); // type, 32-bit float, 1 channel
// allocate the averaged image
pAvgTrainImg = cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
// set the PCA termination criterion
calcLimit = cvTermCriteria(
CV_TERMCRIT_ITER, // type
nEigens, // max_iter
1); // epsilon
LOGGER.info("computing average image, eigenvalues and eigenvectors");
// compute average image, eigenvalues, and eigenvectors
cvCalcEigenObjects(
nTrainFaces, // nObjects
trainingFaceImgArr, // input
eigenVectArr, // output
CV_EIGOBJ_NO_CALLBACK, // ioFlags
0, // ioBufSize
null, // userData
calcLimit,
pAvgTrainImg, // avg
eigenValMat.data_fl()); // eigVals
LOGGER.info("normalizing the eigenvectors");
cvNormalize(
eigenValMat, // src (CvArr)
eigenValMat, // dst (CvArr)
1, // a
0, // b
CV_L1, // norm_type
null); // mask
}
配置javacv+android请看这篇文章http://blog.csdn.net/viviwen123/article/details/6386302，android是Linux内核，所以不用dll用so
配置opencv动态库
（http://wiki.opencv.org.cn/index.php/VC_2010%E4%B8%8B%E5%AE%89%E8%A3%85OpenCV2.4.4，在“三，配置VS2010”之前的步骤），另一种方法是将要配的opencv环境变量目录下的DLL文件全部复制到C盘下Windows下的System32中
配置eclipse+javacv,
下载eclipse。
下载javacv（http://code.google.com/p/javacv/downloads/detail?name=javacv-0.6-bin.zip）链接是0.6版的，opencv也要是最新版的.即现在的2.4.6
在eclipse新建项目“T”，
解压出Javacv
导入javacv中的包到eclipse工程项目中（3个即可javacpp.jar, javacv.jar和javacv-windows-x86.jar 或javacv-windows-x86_64.jar(这里X86和X64区别是32位和64位的系统，3个就行)）
新建class“T”，
import static com.googlecode.javacv.cpp.opencv_core.*;
import static com.googlecode.javacv.cpp.opencv_highgui.*;
public class B {
public static void main(String[] args) {
IplImage srcImg=cvLoadImage("D:\\lena.jpg");
cvNamedWindow("Test");
cvShowImage("Test",srcImg);
cvWaitKey(0);
cvReleaseImage(srcImg);
cvDestroyWindow("Test");
}
}
粘贴代码，运行。
如出错或无法运行主要有两原因：
1）Exception in thread "main" java.lang.UnsatisfiedLinkError: C:\Users\User\AppData\Local\Temp\javacpp2057959839616\jniopencv_core.dll: %1 不是有效的 Win32 应用程序。at java.lang.ClassLoader$NativeLibrary.load(Native Method)
如上，系统变量配置不正确。但有切换工作空间workspace时也会出错，可能是配置文件没有更新。解决，直接将..opencv\build\x86\vc10\bin（32位）的DLL复制到windows下的system32里。
2）如果是导入包有错的话
如
Windows-->Preference-->java-->compiler-->errors/warnings-->Deprecated and restricted API-->Forbidden reference(access rules) 将Error改为warning
下载
人脸识别项目下载
http://download.csdn.net/detail/anqing715/6560059。
项目导入eclipse后直接可用，附带测试图片及视频。你也可以用自己的脸来识别。这软件不仅可以识别摄像头中的人脸，还可以识别图片，视频文件中的人脸。