我正在尝试使用网络摄像头实现在线人脸识别。我使用这两个网站作为参考
shervinemami.co.cc
cognotics.com
我几乎没有问题：
在面部识别中，有6个步骤：
我能够完成前五个步骤。最后一步我无法做到。我不知道如何将第5步与第6步联系起来。
我已经创建了train.txt文件和test.txt文件，其中包含培训和测试图像的信息。我已经将诸如learn（），doPCA（）等函数添加到代码中......
但关键是如何在main中使用这些函数来识别已经预处理的图像。
需要一些帮助...
附上以下代码：
// Real-time.cpp : Defines the entry point for the console application.
#include "stdafx.h"
#include <cv.h>
#include <cxcore.h>
#include <highgui.h>
#include <cvaux.h>
IplImage ** faceImgArr = 0; // array of face images
CvMat * personNumTruthMat = 0; // array of person numbers
int nTrainFaces = 0; // the number of training images
int nEigens = 0; // the number of eigenvalues
IplImage * pAvgTrainImg = 0; // the average image
IplImage ** eigenVectArr = 0; // eigenvectors
CvMat * eigenValMat = 0; // eigenvalues
CvMat * projectedTrainFaceMat = 0; // projected training faces
IplImage* getCameraFrame(CvCapture* &camera);
IplImage* detectFaces( IplImage *img ,CvHaarClassifierCascade* facecascade,CvMemStorage* storage );
CvRect detectFaceInImage(IplImage *inputImg, CvHaarClassifierCascade* cascade);
IplImage* preprocess( IplImage* inputImg);
IplImage* resizeImage(const IplImage *origImg, int newWidth,
int newHeight, bool keepAspectRatio);
void learn();
void recognize();
void doPCA();
void storeTrainingData();
int loadTrainingData(CvMat ** pTrainPersonNumMat);
int findNearestNeighbor(float * projectedTestFace);
int loadFaceImgArray(char * filename);
int _tmain(int argc, _TCHAR* argv[])
{
CvCapture* camera = 0; // The camera device.
CvMemStorage *storage;
cvNamedWindow( "Realtime:", CV_WINDOW_AUTOSIZE);
char *faceCascadeFilename = "C:/OpenCV2.1/data/haarcascades/haarcascade_frontalface_alt.xml";
CvHaarClassifierCascade* faceCascade;
faceCascade = (CvHaarClassifierCascade*)cvLoad(faceCascadeFilename, 0, 0, 0);
storage = cvCreateMemStorage( 0 );
learn();
while ( cvWaitKey(10) != 27 ) // Quit on "Escape" key
{
IplImage *frame = getCameraFrame(camera);
//IplImage* resized=cvCreateImage(cvSize(420,240),frame->depth,3);
//cvResizeWindow( "Image:", 640, 480);
//cvResize(frame,resized);
//cvShowImage( "Realtime:", resized );
IplImage *imgA = resizeImage(frame, 420,240, true);
IplImage *frame1 = detectFaces(imgA,faceCascade,storage);
frame1 = preprocess(frame1);
}
// Free the camera.
cvReleaseCapture( &camera );
cvReleaseMemStorage( &storage );
return 0;
}
IplImage* getCameraFrame(CvCapture* &camera)
{
IplImage *frame;
int w, h;
// If the camera hasn't been initialized, then open it.
if (!camera) {
printf("Acessing the camera ...\n");
camera = cvCreateCameraCapture( 0 );
if (!camera) {
printf("Couldn't access the camera.\n");
exit(1);
}
// Try to set the camera resolution to 320 x 240.
cvSetCaptureProperty(camera, CV_CAP_PROP_FRAME_WIDTH, 320);
cvSetCaptureProperty(camera, CV_CAP_PROP_FRAME_HEIGHT, 240);
// Get the first frame, to make sure the camera is initialized.
frame = cvQueryFrame( camera );
if (frame) {
w = frame->width;
h = frame->height;
printf("Got the camera at %dx%d resolution.\n", w, h);
}
// Wait a little, so that the camera can auto-adjust its brightness.
Sleep(1000); // (in milliseconds)
}
// Wait until the next camera frame is ready, then grab it.
frame = cvQueryFrame( camera );
if (!frame) {
printf("Couldn't grab a camera frame.\n");
exit(1);
}
return frame;
}
CvRect detectFaceInImage(IplImage *inputImg, CvHaarClassifierCascade* cascade)
{
// Smallest face size.
CvSize minFeatureSize = cvSize(20, 20);
// Only search for 1 face.
int flags = CV_HAAR_FIND_BIGGEST_OBJECT | CV_HAAR_DO_ROUGH_SEARCH;
// How detailed should the search be.
float search_scale_factor = 1.1f;
IplImage *detectImg;
IplImage *greyImg = 0;
CvMemStorage* storage;
CvRect rc;
double t;
CvSeq* rects;
CvSize size;
int i, ms, nFaces;
storage = cvCreateMemStorage(0);
cvClearMemStorage( storage );
// If the image is color, use a greyscale copy of the image.
detectImg = (IplImage*)inputImg;
if (inputImg->nChannels > 1) {
size = cvSize(inputImg->width, inputImg->height);
greyImg = cvCreateImage(size, IPL_DEPTH_8U, 1 );
cvCvtColor( inputImg, greyImg, CV_BGR2GRAY );
detectImg = greyImg; // Use the greyscale image.
}
// Detect all the faces in the greyscale image.
t = (double)cvGetTickCount();
rects = cvHaarDetectObjects( detectImg, cascade, storage,
search_scale_factor, 3, flags, minFeatureSize);
t = (double)cvGetTickCount() - t;
ms = cvRound( t / ((double)cvGetTickFrequency() * 1000.0) );
nFaces = rects->total;
printf("Face Detection took %d ms and found %d objects\n", ms, nFaces);
// Get the first detected face (the biggest).
if (nFaces > 0)
rc = *(CvRect*)cvGetSeqElem( rects, 0 );
else
rc = cvRect(-1,-1,-1,-1); // Couldn't find the face.
if (greyImg)
cvReleaseImage( &greyImg );
cvReleaseMemStorage( &storage );
//cvReleaseHaarClassifierCascade( &cascade );
return rc; // Return the biggest face found, or (-1,-1,-1,-1).
}
IplImage* detectFaces( IplImage *img ,CvHaarClassifierCascade* facecascade,CvMemStorage* storage )
{
int i;
CvRect *r;
CvSeq *faces = cvHaarDetectObjects(
img,
facecascade,
storage,
1.1,
3,
0 /*CV_HAAR_DO_CANNY_PRUNNING*/,
cvSize( 40, 40 ) );
int padding_width = 30; // pixels
int padding_height = 30; // pixels
for( i = 0 ; i < ( faces ? faces->total : 0 ) ; i++ ) {
r = ( CvRect* )cvGetSeqElem( faces, i );
cvRectangle( img,
cvPoint( r->x, r->y ),
cvPoint( r->x + r->width, r->y + r->height ),
CV_RGB( 255, 0, 0 ), 1, 8, 0 );
}
cvShowImage( "Realtime:", img );
//cropping the face
cvSetImageROI(img, cvRect(r->x,r->y,r->width,r->height));
IplImage *img2 = cvCreateImage(cvGetSize(img),
img->depth,
img->nChannels);
cvCopy(img, img2, NULL);
cvResetImageROI(img);
return img;
}
IplImage* preprocess( IplImage* inputImg){
IplImage *detectImg, *greyImg = 0;
IplImage *imageProcessed;
CvSize size;
detectImg = (IplImage*)inputImg;
if (inputImg->nChannels > 1) {
size = cvSize(inputImg->width, inputImg->height);
greyImg = cvCreateImage(size, IPL_DEPTH_8U, 1 );
cvCvtColor( inputImg, greyImg, CV_BGR2GRAY );
detectImg = greyImg; // Use the greyscale image.
}
imageProcessed = cvCreateImage(cvSize(inputImg->width, inputImg->height), IPL_DEPTH_8U, 1);
cvResize(detectImg, imageProcessed, CV_INTER_LINEAR);
cvEqualizeHist(imageProcessed, imageProcessed);
return imageProcessed;
}
IplImage* resizeImage(const IplImage *origImg, int newWidth,
int newHeight, bool keepAspectRatio)
{
IplImage *outImg = 0;
int origWidth;
int origHeight;
if (origImg) {
origWidth = origImg->width;
origHeight = origImg->height;
}
if (newWidth <= 0 || newHeight <= 0 || origImg == 0
|| origWidth <= 0 || origHeight <= 0) {
//cerr << "ERROR: Bad desired image size of " << newWidth
// << "x" << newHeight << " in resizeImage().\n";
exit(1);
}
if (keepAspectRatio) {
// Resize the image without changing its aspect ratio,
// by cropping off the edges and enlarging the middle section.
CvRect r;
// input aspect ratio
float origAspect = (origWidth / (float)origHeight);
// output aspect ratio
float newAspect = (newWidth / (float)newHeight);
// crop width to be origHeight * newAspect
if (origAspect > newAspect) {
int tw = (origHeight * newWidth) / newHeight;
r = cvRect((origWidth - tw)/2, 0, tw, origHeight);
}
else { // crop height to be origWidth / newAspect
int th = (origWidth * newHeight) / newWidth;
r = cvRect(0, (origHeight - th)/2, origWidth, th);
}
IplImage *croppedImg = cropImage(origImg, r);
// Call this function again, with the new aspect ratio image.
// Will do a scaled image resize with the correct aspect ratio.
outImg = resizeImage(croppedImg, newWidth, newHeight, false);
cvReleaseImage( &croppedImg );
}
else {
// 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);
// CV_INTER_LINEAR: good at enlarging.
// CV_INTER_CUBIC: good at enlarging.
cvResize(origImg, outImg, CV_INTER_LINEAR);
}
else {
// Make the image smaller
cvResetImageROI((IplImage*)origImg);
// CV_INTER_AREA: good at shrinking (decimation) only.
cvResize(origImg, outImg, CV_INTER_AREA);
}
}
return outImg;
}
void learn()
{
int i, offset;
// load training data
nTrainFaces = loadFaceImgArray("C:/Users/HP/Desktop/OpenCV/50_images_of_15_people.txt");
if( nTrainFaces < 2 )
{
fprintf(stderr,
"Need 2 or more training faces\n"
"Input file contains only %d\n", nTrainFaces);
return;
}
// do PCA on the training faces
doPCA();
// project the training images onto the PCA subspace
projectedTrainFaceMat = cvCreateMat( nTrainFaces, nEigens, CV_32FC1 );
offset = projectedTrainFaceMat->step / sizeof(float);
for(i=0; i<nTrainFaces; i++)
{
//int offset = i * nEigens;
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
//projectedTrainFaceMat->data.fl + i*nEigens);
projectedTrainFaceMat->data.fl + i*offset);
}
// store the recognition data as an xml file
storeTrainingData();
}
void recognize()
{
int i, nTestFaces = 0; // the number of test images
CvMat * trainPersonNumMat = 0; // the person numbers during training
float * projectedTestFace = 0;
// load test images and ground truth for person number
nTestFaces = loadFaceImgArray("C:/Users/HP/Desktop/OpenCV/test.txt");
printf("%d test faces loaded\n", nTestFaces);
// load the saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) return;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
for(i=0; i<nTestFaces; i++)
{
int iNearest, nearest, truth;
// project the test image onto the PCA subspace
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace);
truth = personNumTruthMat->data.i[i];
nearest = trainPersonNumMat->data.i[iNearest];
printf("nearest = %d, Truth = %d\n", nearest, truth);
}
}
int loadTrainingData(CvMat ** pTrainPersonNumMat)
{
CvFileStorage * fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_READ );
if( !fileStorage )
{
fprintf(stderr, "Can't open facedata.xml\n");
return 0;
}
nEigens = cvReadIntByName(fileStorage, 0, "nEigens", 0);
nTrainFaces = cvReadIntByName(fileStorage, 0, "nTrainFaces", 0);
*pTrainPersonNumMat = (CvMat *)cvReadByName(fileStorage, 0, "trainPersonNumMat", 0);
eigenValMat = (CvMat *)cvReadByName(fileStorage, 0, "eigenValMat", 0);
projectedTrainFaceMat = (CvMat *)cvReadByName(fileStorage, 0, "projectedTrainFaceMat", 0);
pAvgTrainImg = (IplImage *)cvReadByName(fileStorage, 0, "avgTrainImg", 0);
eigenVectArr = (IplImage **)cvAlloc(nTrainFaces*sizeof(IplImage *));
for(i=0; i<nEigens; i++)
{
char varname[200];
sprintf( varname, "eigenVect_%d", i );
eigenVectArr[i] = (IplImage *)cvReadByName(fileStorage, 0, varname, 0);
}
// release the file-storage interface
cvReleaseFileStorage( &fileStorage );
return 1;
}
void storeTrainingData()
{
CvFileStorage * fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_WRITE );
// store all the data
cvWriteInt( fileStorage, "nEigens", nEigens );
cvWriteInt( fileStorage, "nTrainFaces", nTrainFaces );
cvWrite(fileStorage, "trainPersonNumMat", personNumTruthMat, cvAttrList(0,0));
cvWrite(fileStorage, "eigenValMat", eigenValMat, cvAttrList(0,0));
cvWrite(fileStorage, "projectedTrainFaceMat", projectedTrainFaceMat, cvAttrList(0,0));
cvWrite(fileStorage, "avgTrainImg", pAvgTrainImg, cvAttrList(0,0));
for(i=0; i<nEigens; i++)
{
char varname[200];
sprintf( varname, "eigenVect_%d", i );
cvWrite(fileStorage, varname, eigenVectArr[i], cvAttrList(0,0));
}
// release the file-storage interface
cvReleaseFileStorage( &fileStorage );
}
int findNearestNeighbor(float * projectedTestFace)
{
//double leastDistSq = 1e12;
double leastDistSq = DBL_MAX;
int i, iTrain, iNearest = 0;
for(iTrain=0; iTrain<nTrainFaces; iTrain++)
{
double distSq=0;
for(i=0; i<nEigens; i++)
{
float d_i =
projectedTestFace[i] -
projectedTrainFaceMat->data.fl[iTrain*nEigens + i];
//distSq += d_i*d_i / eigenValMat->data.fl[i]; // Mahalanobis
distSq += d_i*d_i; // Euclidean
}
if(distSq < leastDistSq)
{
leastDistSq = distSq;
iNearest = iTrain;
}
}
return iNearest;
}
void doPCA()
{
int i;
CvTermCriteria calcLimit;
CvSize faceImgSize;
// set the number of eigenvalues to use
nEigens = nTrainFaces-1;
// allocate the eigenvector images
faceImgSize.width = faceImgArr[0]->width;
faceImgSize.height = faceImgArr[0]->height;
eigenVectArr = (IplImage**)cvAlloc(sizeof(IplImage*) * nEigens);
for(i=0; i<nEigens; i++)
eigenVectArr[i] = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1);
// allocate the eigenvalue array
eigenValMat = cvCreateMat( 1, nEigens, CV_32FC1 );
// allocate the averaged image
pAvgTrainImg = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1);
// set the PCA termination criterion
calcLimit = cvTermCriteria( CV_TERMCRIT_ITER, nEigens, 1);
// compute average image, eigenvalues, and eigenvectors
cvCalcEigenObjects(
nTrainFaces,
(void*)faceImgArr,
(void*)eigenVectArr,
CV_EIGOBJ_NO_CALLBACK,
0,
0,
&calcLimit,
pAvgTrainImg,
eigenValMat->data.fl);
cvNormalize(eigenValMat, eigenValMat, 1, 0, CV_L1, 0);
}
int loadFaceImgArray(char * filename)
{
FILE * imgListFile = 0;
char imgFilename[512];
int iFace, nFaces=0;
// open the input file
if( !(imgListFile = fopen(filename, "r")) )
{
fprintf(stderr, "Can\'t open file %s\n", filename);
return 0;
}
// count the number of faces
while( fgets(imgFilename, 512, imgListFile) ) ++nFaces;
rewind(imgListFile);
// allocate the face-image array and person number matrix
faceImgArr = (IplImage **)cvAlloc( nFaces*sizeof(IplImage *) );
personNumTruthMat = cvCreateMat( 1, nFaces, CV_32SC1 );
// store the face images in an array
for(iFace=0; iFace<nFaces; iFace++)
{
// read person number and name of image file
fscanf(imgListFile,
"%d %s", personNumTruthMat->data.i+iFace, imgFilename);
// load the face image
faceImgArr[iFace] = cvLoadImage(imgFilename, CV_LOAD_IMAGE_GRAYSCALE);
if( !faceImgArr[iFace] )
{
fprintf(stderr, "Can\'t load image from %s\n", imgFilename);
return 0;
}
}
fclose(imgListFile);
return nFaces;
}
答案 0 :(得分：0)
我刚看完
int _tmain(int argc, _TCHAR* argv[])
{
.......
}
您的部分代码。此代码用于检测图像中的面部。让我们说它是Face_x。现在从Face_x中提取要素，将其称为F_x。在您的数据库中，您应存储从{F_1, F_2,..., F_N}个不同的面n中提取的功能{Face_1, Face_2,..Face_N}。
识别Face_x的简单算法是计算F_x和n要素之间的欧几里德距离。最小距离（低于阈值）给出相应的面。如果最小距离不低于阈值，则Face_x是新面孔。将功能F_x添加到数据库。这样您就可以增加数据库。您可以在数据库中没有功能的情况下开始算法。随着每个新面孔，数据库都在增长
我希望我建议的方法能引导您找到解决方案
答案 1 :(得分：0)
我的答案可能会迟到但如果我回答它可能对朋友有用。我正在研究一个类似的项目，我遇到了同样的问题。我通过写一个函数来解决它，保存或写入检测到的，裁剪的并将预处理后的图像放到我电脑的硬盘上（使用CvWrite）。并将保存图像的参数输入到代码的识别部分。它让我的生活变得更轻松。通过感兴趣区域的矩形参数我有点困难。如果您或其他人这样做，与我们分享代码可能会很棒。 您可以使用以下代码在使用代码上的resizeimage函数将图像调整为常量值后保存图像。
void saveCroppedFaces(CvSeq* tempon,IplImage* DetectedImage)
{
char* name;
int nFaces;
CvRect rect;
nFaces=tempon->total;
name =new char[nFaces];
IplImage* cropped = 0;
IplImage* croppedResized=0;
Mat croped;
for(int k=0;k<nFaces;k++)
{
itoa(k,(name+k),10);
rect = *(CvRect*)cvGetSeqElem( tempon, k );
cropped= cropImage(DetectedImage,rect);
//i can resize the cropped faces in to a fixed size here
//i can write a function to save images and call it so
//that it will save it in to hard drive
//cvNamedWindow((name+k),CV_WINDOW_AUTOSIZE);
//cvShowImage((name+k),cropped);
croppedResized=resizeImage(cropped,60,60);
croped=IplToMatConverter(croppedResized);
saveROI(croped,itoa(k,(name+k),10));
cvReleaseImage(&cropped);
}
name=NULL;
delete[] name;
}
void saveROI(Mat mat,String outputFileName)
{
string store_path("C://Users/sizusuzu/Desktop/Images/FaceDetection2
/"+outputFileName+".jpg");
bool write_success = imwrite(store_path,mat);
}
在此之后，您可以使用
将IplImage *更改为Mat Mat IplToMatConverter(IplImage* imageToMat)
{
Mat mat = cvarrToMat(imageToMat);
return mat;
}
在FaceRecognizer API中使用Mat。或者只是做其他/更难的方式。 感谢