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我正在嘗試使用網絡攝像頭進行在線人臉識別。我用這兩個網站作爲參考使用OpenCV進行在線人臉識別
shervinemami.co.cc
cognotics.com
我有幾個問題：
在面部識別中，有6個步驟：
- 抓鬥從相機架
- 檢測圖像中的臉部
- 裁剪框只顯示在臉上
- 轉換框架爲灰度
- 預處理圖像
- 認識的人的形象。
我能夠完成前五個步驟。最後一步我無法做到。我不知道如何鏈接步驟5到步驟6.
我已經創建了train.txt文件和test.txt文件，其中包含訓練和測試圖像的信息。我已經在代碼中添加了learn（），doPCA（）等函數...
但是關鍵是如何在主要中使用這些函數來識別已經過預處理的圖像。
需要一些關於它的幫助...
附代碼如下：
// 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;
}
您已經提出了一個相當模糊的問題「發現連接步驟5到步驟6時遇到困難」，並且發佈了大量代碼。爲了提高獲得良好答案的機會，請在說明您嘗試用於識別面部的方法之後，考慮只發布代碼的相關部分。 – Chris 2012-03-01 08:57:24