bool EigenfaceRecognizer::train()
{
isTrained = false;
if(!loadTrainingImages())
return false;
//do principal component analysis
doPCA();
// project the training images onto the PCA subspace
projectedTrainImage = cvCreateMat( numTrainedImages, numEigenvalues, CV_32FC1 );
int offset = projectedTrainImage->step / sizeof(float);
for(int i = 0; i < numTrainedImages; i++)
{
//int offset = i * nEigens;
cvEigenDecomposite(
images[i],
numEigenvalues,
eigenVectors,
0, 0,
averageImage,
//projectedTrainFaceMat->data.fl + i*nEigens);
projectedTrainImage->data.fl + i*offset);
}
isTrained = true;
return isTrained;
}
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();
}
/**
* 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();
}
//////////////////////////////////
// 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();
}
/**
* 頂点を、順番にcollapseしていく。
* ただし、当該頂点から出るエッジの長さが短いものから、優先的にcollapseしていく。
*/
void MTT::collapse(RoadGraph* roads, float w1, float w2, float w3, std::vector<RoadGraph*>* sequence) {
qDebug() << "collapse start.";
PCA pca;
doPCA(roads, pca);
RoadVertexDesc bdry1_desc, bdry2_desc;
findBoundaryVertices(roads, bdry1_desc, bdry2_desc);
while (true) {
int count = 0;
sequence->push_back(GraphUtil::copyRoads(roads));
float min_metric = std::numeric_limits<float>::max();
RoadVertexDesc min_v_desc1; // to be collapsed to the other one
RoadVertexDesc min_v_desc2;
RoadEdgeIter ei, eend;
for (boost::tie(ei, eend) = boost::edges(roads->graph); ei != eend; ++ei) {
if (!roads->graph[*ei]->valid) continue;
count++;
RoadVertexDesc src = boost::source(*ei, roads->graph);
RoadVertexDesc tgt = boost::target(*ei, roads->graph);
if (bdry2_desc == src || bdry2_desc == tgt) {
int k = 0;
}
float metric = getMetric(roads, src, tgt, w1, w2, w3, pca);
if (metric < min_metric) {
min_metric = metric;
min_v_desc1 = src;
min_v_desc2 = tgt;
}
metric = getMetric(roads, tgt, src, w1, w2, w3, pca);
if (metric < min_metric) {
min_metric = metric;
min_v_desc1 = tgt;
min_v_desc2 = src;
}
}
if (count <= 1) break;
// 頂点min_v_desc1を、min_v_desc2へcollapseする
GraphUtil::collapseVertex(roads, min_v_desc1, min_v_desc2);
}
qDebug() << "collapse done.";
}
// 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 learn()
{
int i, offset;
numberOfTrainingFaces = loadfaceImageArray("/etc/pam_face_authentication/facemanager/face.key");
doPCA();
projectedTrainFaceMat = cvCreateMat( numberOfTrainingFaces, numberOfEigenVectors, CV_32FC1 );
offset = projectedTrainFaceMat->step / sizeof(float);
for (i=0; i<numberOfTrainingFaces; i++)
{
//int offset = i * numberOfEigenVectors;
cvEigenDecomposite(
faceImageArray[i],
numberOfEigenVectors,
eigenVectorArray,
0, 0,
averageTrainingImage,
//projectedTrainFaceMat->data.fl + i*numberOfEigenVectors);
projectedTrainFaceMat->data.fl + i*offset);
}
storeTrainingData();
}
void MTT::findBoundaryVertices(RoadGraph* roads, RoadVertexDesc &v1_desc, RoadVertexDesc &v2_desc) {
PCA pca;
doPCA(roads, pca);
QVector2D evector;
evector.setX(pca.evectors.at<double>(0, 0));
evector.setY(pca.evectors.at<double>(1, 0));
float min_score = std::numeric_limits<float>::max();
float max_score = -std::numeric_limits<float>::max();
int min_pt_id, max_pt_id;
for (int i = 0; i < pca.score.rows; i++) {
float score = pca.score.at<double>(i, 0);
if (score < min_score) {
min_score = score;
min_pt_id = i;
}
if (score > max_score) {
max_score = score;
max_pt_id = i;
}
}
int count = 0;
RoadVertexIter vi, vend;
for (boost::tie(vi, vend) = boost::vertices(roads->graph); vi != vend; ++vi) {
if (!roads->graph[*vi]->valid) continue;
if (count == min_pt_id) {
v1_desc = *vi;
}
if (count == max_pt_id) {
v2_desc = * vi;
}
count++;
}
}
//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();
}
