【javacv】javacv配置+javacv人脸识别项目源码(不是检测)

 

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]);}elsecvPutText(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。