void learn()
{
int i;
// load training data
nTrainFaces = loadFaceImgArray("train.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();
printf("Starting Matrix creation.\n");
// project the training images onto the PCA subspace
projectedTrainFaceMat = cvCreateMat(nTrainFaces, nEigens, CV_32FC1);
printf("Entering for loop.\n");
for(i=0; i<nTrainFaces; i++)
{
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTrainFaceMat->data.fl + i*nEigens);
}
// store the recognition data as an xml file
storeTrainingData();
}
//识别阶段代码
void recognize()
{
int i, nTestFaces = 0; // 测试人脸数
CvMat * trainPersonNumMat = 0; // 训练阶段的人脸数
float * projectedTestFace = 0;
// 加载测试图像，并返回测试人脸数
nTestFaces = loadFaceImgArray("test.txt");
printf("%d test faces loaded\n", nTestFaces);
// 加载保存在.xml文件中的训练结果
if( !loadTrainingData( &trainPersonNumMat ) ) return;
//
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
for(i=0; i<nTestFaces; i++)
{
int iNearest, nearest, truth;
//将测试图像投影到子空间中
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);
}
}
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("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);
}
}
/**
* 顔を学習する
*/
void EigenFace::learn(char* trainFileName)
{
int i, offset;
// load training data
nTrainFaces = loadFaceImgArray(trainFileName);
if( nTrainFaces < 2 )
{
LOGE("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();
}
//////////////////////////////////
// recognize()
//返回本次训练通过的图片数
int faceRecognition::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(testFileListPath);
// load the saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) return 0;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
//处理输出结果,记录一次训练中正确匹配的图片数
int count=0;
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];
// AfxMessageBox("nearest = %d, Truth = %d", nearest, truth);
if(nearest==truth)
count++;
}//for
return count;
}
//////////////////////////////////
// learn()
//
void faceRecognition::learn()
{
AfxMessageBox("训练人脸识别模型");
int i, offset;
// load training data
nTrainFaces = loadFaceImgArray(learnFileListPath);
if( nTrainFaces < 2){
CString S;
S.Format("Need 2 or more training faces\nInput file contains only %d\n", nTrainFaces);AfxMessageBox(S); return;
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();
}
// Train from the data in the given text file, and store the trained data into the file 'facedata.xml'.
void learn(char szFileTrain[252])
{
int i, offset;
// load training data
printf("Loading the training images in '%s'\n", szFileTrain);
nTrainFaces = loadFaceImgArray(szFileTrain);
printf("Got %d training images.\n", nTrainFaces);
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();
// Save all the eigenvectors as images, so that they can be checked.
if (SAVE_EIGENFACE_IMAGES) {
storeEigenfaceImages();
}
}
void learn_eigenfaces() {
int i, offset;
nTrainFaces = loadFaceImgArray("train_eigen.txt");
if (nTrainFaces < 2) {
fprintf(stderr, "Need more than 2 faces to train\n");
return;
}
doPCA();
projectedTrainFaceMat = cvCreateMat(nTrainFaces, nEigens, CV_32FC1);
offset = projectedTrainFaceMat->step / sizeof(float);
for(i=0;i<nTrainFaces;i++) {
cvEigenDecomposite(faceImgArr[i], nEigens, eigenVectArr, 0, 0,
pAvgTrainImg, projectedTrainFaceMat->data.fl + i*nEigens);
}
storeTrainingData_eigenfaces();
}
void recognize_eigenfaces() {
int i, nTestFaces = 0;
CvMat *trainPersonNumMat = 0;
float *projectedTestFace = 0;
nTestFaces = loadFaceImgArray("test.txt");
printf("%d test faces loaded\n", nTestFaces);
if (!loadTrainingData_eigenfaces(&trainPersonNumMat)) return;
projectedTestFace = (float *)cvAlloc(nEigens*sizeof(float));
for(i=0;i<nTestFaces;i++) {
int iNearest, nearest, truth;
cvEigenDecomposite(faceImgArr[i], nEigens, eigenVectArr,
0, 0, pAvgTrainImg, projectedTestFace);
iNearest = findNearestNeighbor_eigenfaces(projectedTestFace);
truth = personNumTruthMat->data.i[i];
nearest = trainPersonNumMat->data.i[iNearest];
printf("nearest = %d, truth = %d\n", nearest, truth);
}
}
//学习阶段代码
void learn()
{
int i, offset;
//加载训练图像集
nTrainFaces = loadFaceImgArray("train.txt");
if( nTrainFaces < 2 )
{
fprintf(stderr,
"Need 2 or more training faces\n"
"Input file contains only %d\n", nTrainFaces);
return;
}
// 进行主成分分析
doPCA();
//将训练图集投影到子空间中
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);
}
//将训练阶段得到的特征值，投影矩阵等数据存为.xml文件，以备测试时使用
storeTrainingData();
}
// Recognize the face in each of the test images given, and compare the results with the truth.
void recognizeFileList(char *szFileTest)
{
int i, nTestFaces = 0; // the number of test images
CvMat * trainPersonNumMat = 0; // the person numbers during training
float * projectedTestFace = 0;
char *answer;
int nCorrect = 0;
int nWrong = 0;
double timeFaceRecognizeStart;
double tallyFaceRecognizeTime;
float confidence;
// load test images and ground truth for person number
nTestFaces = loadFaceImgArray(szFileTest);
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) );
timeFaceRecognizeStart = (double)cvGetTickCount(); // Record the timing.
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, &confidence);
truth = personNumTruthMat->data.i[i];
if(iNearest!=-1)
{
nearest = trainPersonNumMat->data.i[iNearest];
}
else
{
nearest = 0;
}
if (nearest == truth) {
answer = "Correct";
nCorrect++;
}
else {
answer = "WRONG!";
nWrong++;
}
printf("nearest = %d, Truth = %d (%s). Confidence = %f\n", nearest, truth, answer, confidence);
}
tallyFaceRecognizeTime = (double)cvGetTickCount() - timeFaceRecognizeStart;
if (nCorrect+nWrong > 0) {
printf("TOTAL ACCURACY: %d%% out of %d tests.\n", nCorrect * 100/(nCorrect+nWrong), (nCorrect+nWrong));
printf("TOTAL TIME: %.1fms average.\n", tallyFaceRecognizeTime/((double)cvGetTickFrequency() * 1000.0 * (nCorrect+nWrong) ) );
}
}
