src.rar_SRC 人脸识别_java 人脸识别_java人脸识别_人脸识别_人脸识别Java

import com.googlecode.javacpp.FloatPointer; import com.googlecode.javacpp.Pointer; import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import java.util.ArrayList; import java.util.List; import java.util.logging.Logger; import static com.googlecode.javacv.cpp.opencv_highgui.*; import static com.googlecode.javacv.cpp.opencv_highgui.cvDestroyWindow; import static com.googlecode.javacv.cpp.opencv_legacy.*; import static com.googlecode.javacv.cpp.opencv_objdetect.CV_HAAR_DO_CANNY_PRUNING; import static com.googlecode.javacv.cpp.opencv_objdetect.CV_HAAR_FIND_BIGGEST_OBJECT; import static com.googlecode.javacv.cpp.opencv_objdetect.CV_HAAR_DO_ROUGH_SEARCH; import static com.googlecode.javacv.cpp.opencv_objdetect.cvHaarDetectObjects; import static com.googlecode.javacv.cpp.opencv_imgproc.CV_BGR2GRAY; import static com.googlecode.javacv.cpp.opencv_imgproc.cvEqualizeHist; import com.googlecode.javacpp.Loader; import com.googlecode.javacv.*; import com.googlecode.javacv.FrameGrabber.Exception; import com.googlecode.javacv.cpp.opencv_core; import com.googlecode.javacv.cpp.opencv_core.CvFont; import com.googlecode.javacv.cpp.opencv_core.CvMemStorage; import com.googlecode.javacv.cpp.opencv_core.CvRect; import com.googlecode.javacv.cpp.opencv_core.CvScalar; import com.googlecode.javacv.cpp.opencv_core.CvSeq; import com.googlecode.javacv.cpp.opencv_core.IplImage; import com.googlecode.javacv.cpp.opencv_objdetect.CvHaarClassifierCascade; import static com.googlecode.javacv.cpp.opencv_core.*; import static com.googlecode.javacv.cpp.opencv_imgproc.*; import static com.googlecode.javacv.cpp.opencv_calib3d.*; import static com.googlecode.javacv.cpp.opencv_objdetect.*; import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import java.io.*; public class FaceRecognizer{ private static final Logger LOGGER = Logger.getLogger(FaceRecognizer.class.getName()); private int nTrainFaces = 0; private int nPersons=0; private int nEigens = 0; private int count=1; private int countForFace=0; private int countSavedFace=1; private CvMat personNumTruthMat; private CvMat eigenValMat; private CvMat projectedTrainFaceMat; private CvMat trainPersonNumMat=null; final static List<String> personNames = new ArrayList<String>(); private CvHaarClassifierCascade cascade = new CvHaarClassifierCascade(cvLoad("data\\haarcascade_frontalface_alt2.xml")); IplImage[] eigenVectArr; IplImage[] trainingFaceImgArr; IplImage[] testFaceImgArr; IplImage pAvgTrainImg; public static String personName; private static String textName="unknow"; public static double g_confidence=0; public FaceRecognizer() { trainPersonNumMat = loadTrainingData(); } private void learn(final String trainingFileName) { int i; // load training data LOGGER.info("==========================================="); LOGGER.info("Loading the training images in " + trainingFileName); trainingFaceImgArr = loadFaceImgArray(trainingFileName); nTrainFaces = trainingFaceImgArr.length; LOGGER.info("Got " + nTrainFaces + " training images"); if (nTrainFaces < 3) { LOGGER.severe("Need 3 or more training faces\n" + "Input file contains only " + nTrainFaces); return; } // do Principal Component Analysis on the training faces doPCA(); LOGGER.info("projecting the training images onto the PCA subspace"); // project the training images onto the PCA subspace projectedTrainFaceMat = cvCreateMat( nTrainFaces, // rows nEigens, // cols CV_32FC1); // type, 32-bit float, 1 channel // initialize the training face matrix - for ease of debugging for (int i1 = 0; i1 < nTrainFaces; i1++) { for (int j1 = 0; j1 < nEigens; j1++) { projectedTrainFaceMat.put(i1, j1, 0.0); } } LOGGER.info("created projectedTrainFaceMat with " + nTrainFaces + " (nTrainFaces) rows and " + nEigens + " (nEigens) columns"); if (nTrainFaces < 5) { LOGGER.info("projectedTrainFaceMat contents:\n" + oneChannelCvMatToString(projectedTrainFaceMat)); } final FloatPointer floatPointer = new FloatPointer(nEigens); for (i = 0; i < nTrainFaces; i++) { cvEigenDecomposite( trainingFaceImgArr[i], // obj nEigens, // nEigObjs eigenVectArr, // eigInput (Pointer) 0, // ioFlags null, // userData (Pointer) pAvgTrainImg, // avg floatPointer); // coeffs (FloatPointer) if (nTrainFaces < 5) { LOGGER.info("floatPointer: " + floatPointerToString(floatPointer)); } for (int j1 = 0; j1 < nEigens; j1++) { projectedTrainFaceMat.put(i, j1, floatPointer.get(j1)); } } if (nTrainFaces < 5) { LOGGER.info("projectedTrainFaceMat after cvEigenDecomposite:\n" + projectedTrainFaceMat); } // store the recognition data as an xml file storeTrainingData(); // Save all the eigenvectors as images, so that they can be checked. storeEigenfaceImages(); } private IplImage convertImageToGreyscale(IplImage imageSrc) { IplImage imageGrey; // Either convert the image to greyscale, or make a copy of the existing greyscale image. // This is to make sure that the user can always call cvReleaseImage() on the output, whether it was greyscale or not. if (imageSrc.nChannels()==3) { imageGrey = cvCreateImage( cvGetSize(imageSrc), IPL_DEPTH_8U, 1 ); cvCvtColor( imageSrc, imageGrey, CV_BGR2GRAY ); } else { imageGrey = cvCloneImage(imageSrc); } return imageGrey; } private IplImage resizeImage(IplImage origImg, int newWidth, int newHeight) { IplImage outImg = null; int origWidth=0; int origHeight=0; if (origImg!=null) { origWidth = origImg.width(); origHeight = origImg.height(); } if (newWidth <= 0 || newHeight <= 0 || origImg == null || origWidth <= 0 || origHeight <= 0) { LOGGER.info("ERROR in resizeImage: Bad desired image size of"); LOGGER.info(String.valueOf(newWidth)+","+String.valueOf(newHeight)); System.exit(1); } // Scale the image to the new dimensions, even if the aspect ratio will be changed. outImg = cvCreateImage(cvSize(newWidth, newHeight), origImg.depth(), origImg.nChannels()); if (newWidth > origImg.width() && newHeight > origImg.height()) { // Make the image larger cvResetImageROI((IplImage)origImg); cvResize(origImg, outImg, CV_INTER_LINEAR); // CV_INTER_CUBIC or CV_INTER_LINEAR is good for enlarging } else { // Make the image smaller cvResetImageROI((IplImage)origImg); cvResize(origImg, outImg, CV_INTER_AREA); // CV_INTER_AREA is good for shrinking / decimation, but bad at enlarging. } return outImg; } private IplImage cropImage(IplImage img, CvRect region) { IplImage imageTmp; IplImage imageRGB; CvSize size; // size.height()=img.height(); // size.width() = img.width(); if (img.depth() != IPL_DEPTH_8U) { LOGGER.info("ERROR in cropImage: Unknown image depth of"); LOGGER.info(String.valueOf(img.depth())); LOGGER.info(" given in cropImage() instead of 8 bits per pixel."); System.exit(1); } // First create a new (color or greyscale) IPL Image and copy contents of img into it. imageTmp = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, img.nChannels()); cvCopy(img, imageTmp); // Create a new image of the detected region // Set