//加载保存过的训练结果
int loadTrainingData(CvMat ** pTrainPersonNumMat)
{
CvFileStorage * fileStorage;
int i;
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);
}
cvReleaseFileStorage( &fileStorage );
return 1;
}
//////////////////////////////////
// loadTrainingData()
//
int EigenFace::loadTrainingData()
{
LOGD("load from %s", mTrainingDataFile);
CvFileStorage * fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage( mTrainingDataFile, 0, CV_STORAGE_READ );
if( !fileStorage )
{
LOGE("Can't open facedata.xml\n");
return 0;
}
nEigens = cvReadIntByName(fileStorage, 0, "nEigens", 0);
nTrainFaces = cvReadIntByName(fileStorage, 0, "nTrainFaces", 0);
trainPersonNumMat = (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 ReadFeature(
char* fileName,
FEATURE* feature){
CvFileStorage* fs = cvOpenFileStorage( fileName, 0, CV_STORAGE_READ, NULL);
if (fs==NULL){
fprintf(stderr, "can't open feature file!\n");
return;
}
feature->radius = cvReadIntByName(fs, NULL, "radius", RADIUS);
feature->neighbors = cvReadIntByName(fs, NULL, "neighbors", NEIGHBORS);
feature->grid_x = cvReadIntByName(fs, NULL, "grid_x", GRID_X);
feature->grid_y = cvReadIntByName(fs, NULL, "grid_y", GRID_Y);
feature->num_faces = cvReadIntByName(fs, NULL, "num_faces", 0);
char faceid[1024];
char id[20];
feature->histogram = (CvMat**) malloc(feature->num_faces*sizeof(CvMat*));
for (int i=0; i<feature->num_faces; i++){
strcpy(faceid, "face-");
sprintf(id, "%d", i);
strcat(faceid, id);
feature->histogram[i] = cvReadByName(fs, NULL, faceid, NULL);
}
if (HUELBP_ON){
feature->hue_histogram = (CvMat**) malloc(feature->num_faces*sizeof(CvMat*));
for (int i=0; i<feature->num_faces; i++){
strcpy(faceid, "hue-face-");
sprintf(id, "%d", i);
strcat(faceid, id);
feature->hue_histogram[i] = cvReadByName(fs, NULL, faceid, NULL);
}
}
cvReleaseFileStorage( &fs );
}
// Open the training data from the file 'facedata.xml'.
int loadTrainingData(CvMat ** pTrainPersonNumMat)
{
CvFileStorage * fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage( facedata, 0, CV_STORAGE_READ );
if( !fileStorage ) {
printf("Can't open training database file 'facedata.xml'.\n");
return 0;
}
// Load the person names. Added by Shervin.
personNames.clear(); // Make sure it starts as empty.
nPersons = cvReadIntByName( fileStorage, 0, "nPersons", 0 );
if (nPersons == 0) {
printf("No people found in the training database 'facedata.xml'.\n");
return 0;
}
// Load each person's name.
for (i=0; i<nPersons; i++) {
string sPersonName;
char varname[200];
sprintf( varname, "personName_%d", (i+1) );
sPersonName = cvReadStringByName(fileStorage, 0, varname );
personNames.push_back( sPersonName );
}
// Load the data
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 );
printf("Training data loaded (%d training images of %d people):\n", nTrainFaces, nPersons);
printf("People: ");
if (nPersons > 0)
printf("<%s>", personNames[0].c_str());
for (i=1; i<nPersons; i++) {
printf(", <%s>", personNames[i].c_str());
}
printf(".\n");
return 1;
}
void loadremap(CvMat* l_remap0, CvMat* l_remap1, CvMat* r_remap0, CvMat* r_remap1)
{
/*
* Load remap information from saved remap.yml file,
* which is generated by stereo calibration function from c++.
* Because write a pure C-based one is too f*****g stupid.
*/
CvFileStorage *fs = cvOpenFileStorage("remap.yml", 0, CV_STORAGE_READ, NULL);
l_remap0 = cvReadByName(fs, NULL, "left_remap0", NULL);
l_remap1 = cvReadByName(fs, NULL, "left_remap1", NULL);
r_remap0 = cvReadByName(fs, NULL, "right_remap0", NULL);
r_remap1 = cvReadByName(fs, NULL, "right_remap1", NULL);
if (l_remap0 == NULL || l_remap1 == NULL || r_remap0 == NULL || r_remap1 == NULL) {
printf("what the f**k dude why is this happenening I hate my life.\n");
}
}
int main (int argc, char *argv[])
{
IplImage *img;
CvFileStorage *fs;
CvSeq* seq;
CvMemStorage *str;
int N,i;
parse_args(argc,argv);
str = cvCreateMemStorage(0);
img = cvCreateImage(cvSize(WIDTH,HEIGHT), 8, 3);
cvZero(img);
fs = cvOpenFileStorage(infile, str, CV_STORAGE_READ, NULL);
assert(fs);
N = cvReadIntByName(fs, NULL, "N", 0);
seq = cvReadByName(fs, NULL, "seq", 0);
printf("N=%d\n", N);
for (i=0; i<seq->total; i++) {
CvPoint p;
p = *(CvPoint*)cvGetSeqElem(seq, i);
cvCircle(img, p, R, CV_RGB(255,0,0), -1, 8, 0);
}
cvShowImage("proto", img);
cvWaitKey(0);
cvReleaseFileStorage(&fs);
cvReleaseImage(&img);
return 0;
}
int main(int argc, const char * argv[]) {
CvMat *cmatrix = cvCreateMat(5,5,CV_32FC1);
float element_3_2 = 7.7;
*((float*)CV_MAT_ELEM_PTR( *cmatrix, 3,2) ) = element_3_2;
cvmSet(cmatrix,4,4,0.5000);
cvSetReal2D(cmatrix,3,3,0.5000);
CvFileStorage* fs1 = cvOpenFileStorage("cfg.xml", 0, CV_STORAGE_WRITE);
cvWriteInt( fs1, "frame_count", 10 );
cvStartWriteStruct( fs1, "frame_size", CV_NODE_SEQ );
cvWriteInt( fs1 , 0, 320 );
cvWriteInt( fs1 , 0, 200 );
cvEndWriteStruct( fs1 );
cvWrite( fs1, "color_cvt_matrix", cmatrix );
cvReleaseFileStorage( &fs1 );
CvFileStorage* fs2 = cvOpenFileStorage("cfg.xml", 0, CV_STORAGE_READ);
int frame_count = cvReadIntByName( fs2 , 0, "frame_count");
CvSeq* s = cvGetFileNodeByName( fs2,0,"frame_size" )->data.seq;
int frame_width = cvReadInt( (CvFileNode*)cvGetSeqElem(s,0) );
int frame_height = cvReadInt( (CvFileNode*)cvGetSeqElem(s,1) );
CvMat* color_cvt_matrix = (CvMat*) cvReadByName( fs2, 0 , "color_cvt_matrix");
printf("color_cvt_matrix: width=%d, height=%d\n",color_cvt_matrix->width, color_cvt_matrix->height );
printf("frame_count=%d, frame_width=%d, frame_height=%d\n",frame_count,frame_width,frame_height);
cvReleaseFileStorage( &fs2 );
return 0;
}
bool CameraParameters::loadParameters(const char* filename){
CvFileStorage* fstorage = 0;
fstorage = cvOpenFileStorage(filename, NULL, CV_STORAGE_READ);
if(!fstorage)
return false;
this->m_mat_camera_matrix = cvCloneMat( (CvMat*) cvReadByName(fstorage, 0, CAMERA_MATRIX_TAG));
this->m_mat_distortion_coeff = cvCloneMat((CvMat*) cvReadByName(fstorage, 0, DISTORTION_COEF_TAG));
cvReleaseFileStorage(&fstorage);
return true;
}
CvMat * icvReadSVHNLabels(char * filename, const int max_samples)
{
CvFileStorage * fs = cvOpenFileStorage(filename,0,CV_STORAGE_READ);
if (!fs){fprintf(stderr,"file loading error: %s\n",filename);return 0;}
CvFileNode * fnode = cvGetRootFileNode(fs);
char tagname[20]; int ii;
const int nparams = 4;
CvMat * data = cvCreateMat(max_samples,1+nparams*10,CV_32S); cvZero(data);
for (ii=0;;ii++){
sprintf(tagname,"img%d",ii+1);
CvMat * sample = (CvMat*)cvReadByName(fs,fnode,tagname);
if (!sample || ii==max_samples){break;}
int nnumbers = sample->rows;
CV_MAT_ELEM(*data,int,ii,0)=nnumbers;
for (int jj=0;jj<nnumbers;jj++){
float xx = CV_MAT_ELEM(*sample,float,jj,0);
float yy = CV_MAT_ELEM(*sample,float,jj,1);
float ww = CV_MAT_ELEM(*sample,float,jj,2);
float hh = CV_MAT_ELEM(*sample,float,jj,3);
float ll = CV_MAT_ELEM(*sample,float,jj,4);
CV_MAT_ELEM(*data,int,ii,1+nparams*jj+0)=cvRound(xx+ww*.5f); // x
CV_MAT_ELEM(*data,int,ii,1+nparams*jj+1)=cvRound(yy+hh*.5f); // y
CV_MAT_ELEM(*data,int,ii,1+nparams*jj+2)=cvRound(MAX(ww,hh)); // scale
CV_MAT_ELEM(*data,int,ii,1+nparams*jj+3)=cvRound(ll); // label
}
cvReleaseMat(&sample);
}
data->rows = ii;
cvReleaseFileStorage(&fs);
return data;
}
bool CvMatrix::read( CvFileStorage* fs, const char* mapname, const char* matname )
{
void* obj = 0;
CvMat* m = 0;
if( mapname )
{
CvFileNode* mapnode = cvGetFileNodeByName( fs, 0, mapname );
if( !mapnode )
obj = cvReadByName( fs, mapnode, matname );
}
else
obj = cvReadByName( fs, 0, matname );
m = icvRetrieveMatrix(obj);
set( m, false );
return m != 0;
}
bool CvImage::read( CvFileStorage* fs, const char* mapname, const char* imgname )
{
void* obj = 0;
IplImage* img = 0;
if( mapname )
{
CvFileNode* mapnode = cvGetFileNodeByName( fs, 0, mapname );
if( !mapnode )
obj = cvReadByName( fs, mapnode, imgname );
}
else
obj = cvReadByName( fs, 0, imgname );
img = icvRetrieveImage(obj);
attach( img );
return img != 0;
}
void CvGBTrees::read_params( CvFileStorage* fs, CvFileNode* fnode )
{
CV_FUNCNAME( "CvGBTrees::read_params" );
__BEGIN__;
CvFileNode* temp;
if( !fnode || !CV_NODE_IS_MAP(fnode->tag) )
return;
data = new CvDTreeTrainData();
CV_CALL( data->read_params(fs, fnode));
data->shared = true;
params.max_depth = data->params.max_depth;
params.min_sample_count = data->params.min_sample_count;
params.max_categories = data->params.max_categories;
params.priors = data->params.priors;
params.regression_accuracy = data->params.regression_accuracy;
params.use_surrogates = data->params.use_surrogates;
temp = cvGetFileNodeByName( fs, fnode, "loss_function" );
if( !temp )
EXIT;
if( temp && CV_NODE_IS_STRING(temp->tag) )
{
const char* loss_function_type_str = cvReadString( temp, "" );
params.loss_function_type = strcmp( loss_function_type_str, "SquaredLoss" ) == 0 ? SQUARED_LOSS :
strcmp( loss_function_type_str, "AbsoluteLoss" ) == 0 ? ABSOLUTE_LOSS :
strcmp( loss_function_type_str, "HuberLoss" ) == 0 ? HUBER_LOSS :
strcmp( loss_function_type_str, "DevianceLoss" ) == 0 ? DEVIANCE_LOSS : -1;
}
else
params.loss_function_type = cvReadInt( temp, -1 );
if( params.loss_function_type < SQUARED_LOSS || params.loss_function_type > DEVIANCE_LOSS || params.loss_function_type == 2)
CV_ERROR( CV_StsBadArg, "Unknown loss function" );
params.weak_count = cvReadIntByName( fs, fnode, "ensemble_length" );
params.shrinkage = (float)cvReadRealByName( fs, fnode, "shrinkage", 0.1 );
params.subsample_portion = (float)cvReadRealByName( fs, fnode, "subsample_portion", 1.0 );
if (data->is_classifier)
{
class_labels = (CvMat*)cvReadByName( fs, fnode, "class_labels" );
if( class_labels && !CV_IS_MAT(class_labels))
CV_ERROR( CV_StsParseError, "class_labels must stored as a matrix");
}
data->is_classifier = 0;
__END__;
}
void CvANN_MLP::read( CvFileStorage* fs, CvFileNode* node )
{
CvMat* _layer_sizes = 0;
CV_FUNCNAME( "CvANN_MLP::read" );
__BEGIN__;
CvFileNode* w;
CvSeqReader reader;
int i, l_count;
_layer_sizes = (CvMat*)cvReadByName( fs, node, "layer_sizes" );
CV_CALL( create( _layer_sizes, SIGMOID_SYM, 0, 0 ));
l_count = layer_sizes->cols;
CV_CALL( read_params( fs, node ));
w = cvGetFileNodeByName( fs, node, "input_scale" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != layer_sizes->data.i[0]*2 )
CV_ERROR( CV_StsParseError, "input_scale tag is not found or is invalid" );
CV_CALL( cvReadRawData( fs, w, weights[0], "d" ));
w = cvGetFileNodeByName( fs, node, "output_scale" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != layer_sizes->data.i[l_count-1]*2 )
CV_ERROR( CV_StsParseError, "output_scale tag is not found or is invalid" );
CV_CALL( cvReadRawData( fs, w, weights[l_count], "d" ));
w = cvGetFileNodeByName( fs, node, "inv_output_scale" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != layer_sizes->data.i[l_count-1]*2 )
CV_ERROR( CV_StsParseError, "inv_output_scale tag is not found or is invalid" );
CV_CALL( cvReadRawData( fs, w, weights[l_count+1], "d" ));
w = cvGetFileNodeByName( fs, node, "weights" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != l_count - 1 )
CV_ERROR( CV_StsParseError, "weights tag is not found or is invalid" );
cvStartReadSeq( w->data.seq, &reader );
for( i = 1; i < l_count; i++ )
{
w = (CvFileNode*)reader.ptr;
CV_CALL( cvReadRawData( fs, w, weights[i], "d" ));
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
}
__END__;
}
int Eigenfaces::loadConfig(const string& dir)
{
d->configFile = dir + string("/libface-config.xml");
if (DEBUG)
cout << "Load training data" << endl;
CvFileStorage* fileStorage = cvOpenFileStorage(d->configFile.data(), 0, CV_STORAGE_READ);
cout << "opened" << endl;
if (!fileStorage)
{
if (DEBUG)
cout << "Can't open config file for reading :" << d->configFile << endl;
return 1;
}
//d->clearTrainingStructures();
int nIds = cvReadIntByName(fileStorage, 0, "nIds", 0), i;
d->FACE_WIDTH = cvReadIntByName(fileStorage, 0, "FACE_WIDTH",d->FACE_WIDTH);
d->FACE_HEIGHT = cvReadIntByName(fileStorage, 0, "FACE_HEIGHT",d->FACE_HEIGHT);
d->THRESHOLD = cvReadRealByName(fileStorage, 0, "THRESHOLD", d->THRESHOLD);
//LibFaceUtils::printMatrix(d->projectedTrainFaceMat);
for ( i = 0; i < nIds; i++ )
{
char facename[200];
sprintf(facename, "person_%d", i);
d->faceImgArr.push_back( (IplImage*)cvReadByName(fileStorage, 0, facename, 0) );
}
for ( i = 0; i < nIds; i++ )
{
char idname[200];
sprintf(idname, "id_%d", i);
d->indexMap.push_back( cvReadIntByName(fileStorage, 0, idname, 0));
}
// Release file storage
cvReleaseFileStorage(&fileStorage);
return 0;
}
int CvOneWayDescriptor::ReadByName(CvFileStorage* fs, CvFileNode* parent, const char* name)
{
CvMat* mat = (CvMat*)cvReadByName(fs, parent, name);
if(!mat)
{
return 0;
}
for(int i = 0; i < m_pose_count; i++)
{
for(int y = 0; y < m_samples[i]->height; y++)
{
for(int x = 0; x < m_samples[i]->width; x++)
{
float val = cvmGet(mat, i, y*m_samples[i]->width + x);
*((float*)(m_samples[i]->imageData + y*m_samples[i]->widthStep) + x) = val;
}
}
}
cvReleaseMat(&mat);
return 1;
}
void CalTarget::load(CvFileStorage *in, CvFileNode *node) {
point.load(in, cvGetFileNodeByName(in, node, "point"));
image.reset((cv::Mat*) cvReadByName(in, node, "image"));
origImage.reset((cv::Mat*) cvReadByName(in, node, "origImage"));
}
void * cvReadByName_wrap(CvFileStorage * fs , const CvFileNode * map , const char * name , CvAttrList * attributes ){
return cvReadByName(/*CvFileStorage*//***/fs , /*const*//*CvFileNode*//***/map , /*const*//*char*//***/name , /*CvAttrList*//***/attributes);
}
LKInverseComp::LKInverseComp(char *path)
{
CvFileStorage *fs;
fs = cvOpenFileStorage(path, 0, CV_STORAGE_READ );
nA = (int)cvReadRealByName(fs, 0, "numberOfAppearanceEigenVectors", 0);
nS = (int)cvReadRealByName(fs, 0, "numberOfShapeEigenVectors", 0);
printf("%d %d \n",nS,nA);
//nS=16;
//
//nA=2;
//nS=4;/
nA=10;
nA+=1;
negNewParam = (float *)malloc(sizeof(float)*(nS+4));
negNewParamnS = (float *)malloc(sizeof(float)*(nS+4));
negNewParam4 = (float *)malloc(sizeof(float)*(nS+4));
ParamnS = (float *)malloc(sizeof(float)*(nS+4));
Param4 = (float *)malloc(sizeof(float)*(nS+4));
ptemp1 = (float *)malloc(sizeof(float)*(nS+4));
ptemp2 = (float *)malloc(sizeof(float)*(nS+4));
param = (float *) malloc((4+nS)*sizeof(float));
nonRigidShapeVectors=(IplImage**)cvAlloc(sizeof(IplImage*) * nS);
appearanceVectors=(IplImage**)cvAlloc(sizeof(IplImage*) * nA);
globalShapeVectors=(IplImage**)cvAlloc(sizeof(IplImage*) * 4);
combineshapevectors=(IplImage**)cvAlloc(sizeof(IplImage*) * (4+nS));
srcShape =cvCreateImage(cvSize(totalnumberofpoints,1),IPL_DEPTH_64F,1);
destShape =cvCreateImage(cvSize(totalnumberofpoints,1),IPL_DEPTH_64F,1);
destShapeTemp =cvCreateImage(cvSize(totalnumberofpoints,1),IPL_DEPTH_64F,1);
destShapewithoutQ =cvCreateImage(cvSize(totalnumberofpoints,1),IPL_DEPTH_64F,1);
for (int m=0;m<(nS+4);m++)
{
combineshapevectors[m]=cvCreateImage(cvSize(totalnumberofpoints,1),IPL_DEPTH_64F,1);
param[m]=0;
negNewParam[m]=0;
negNewParamnS[m]=0;
negNewParam4[m]=0;
ParamnS[m]=0;
Param4[m]=0;
ptemp1[m]=0;
ptemp2[m]=0;
}
for (int m=0;m<4;m++)
globalShapeVectors[m]=cvCreateImage(cvSize(totalnumberofpoints,1),IPL_DEPTH_64F,1);
CvMat * meanShapeDel=cvCreateMat(numberofpoints,2,CV_64FC1);
eigenVal= (CvMat *)cvReadByName( fs,0,"eigenVal",0 );
// minProjVal=(IplImage *)cvReadByName( fs,0,"minProjVal",0 );
// maxProjVal=(IplImage *)cvReadByName( fs,0,"maxProjVal",0 );
meanAppearance=(IplImage *)cvReadByName( fs,0,"avgApp",0 );
ErrorImage = cvCreateMat((meanAppearance->width*meanAppearance->height),1, CV_64FC1);
IplImage* meanShapeTemp=(IplImage *)cvReadByName( fs,0,"avgShape",0 );
meanShape=cvCreateImage(cvSize(meanShapeTemp->width,meanShapeTemp->height),IPL_DEPTH_64F,1);
cvConvertScale( meanShapeTemp,meanShape,1,0);
for (int m=0;m<nS;m++)
{
char temp[200];
sprintf(temp,"shapeEigenVectors%d",m);
nonRigidShapeVectors[m]=(IplImage *)cvReadByName( fs,0,temp,0 );
}
for (int m=0;m<nA-1;m++)
{
char temp[200];
sprintf(temp,"appEigenVectors%d",m);
appearanceVectors[m]=(IplImage *)cvReadByName( fs,0,temp,0 );
}
appearanceVectors[nA-1]=cvCreateImage(cvSize(appearanceVectors[nA-2]->width,appearanceVectors[nA-2]->height),IPL_DEPTH_32F,1);
for(int i=0;i<(appearanceVectors[nA-2]->width);i++)
{
for(int j=0;j<appearanceVectors[nA-2]->height;j++)
{
CvScalar s;
s.val[0]=1;
cvSet2D(appearanceVectors[nA-1],j,i,s);
}
}
// IplImage * newImage = cvCreateImage(cvSize(meanAppearance->width,meanAppearance->height),IPL_DEPTH_8U,1);
for (int m=0;m<nA;m++)
{
// cvZero(newImage);
for (int i=0;i<meanAppearance->width;i++)
{
for (int j=0;j<meanAppearance->height;j++)
{
CvScalar s= cvGet2D(appearanceVectors[m],j,i);
s.val[0]*=255;
CvScalar s1= cvGet2D(meanAppearance,j,i);
s.val[0]+=s1.val[0];
////printf("%e \n",s.val[0]);
// cvSet2D(newImage,j,i,s);
}
}
// cvNamedWindow("yoyo",1);
//cvShowImage("yoyo",newImage);
// cvWaitKey(-1);
}
for (int m=0;m<numberofpoints;m++)
{
CvScalar s1,s2;
s1=cvGet2D(meanShape,0,2*m);
s2=cvGet2D(meanShape,0,2*m+1);
cvSet2D(meanShapeDel,m,0,s1);
cvSet2D(meanShapeDel,m,1,s2);
cvSet2D(globalShapeVectors[0],0,2*m,s1);
cvSet2D(globalShapeVectors[0],0,2*m+1,s2);
cvSet2D(combineshapevectors[0],0,2*m,s1);
cvSet2D(combineshapevectors[0],0,2*m+1,s2);
s2.val[0]*=-1;
cvSet2D(globalShapeVectors[1],0,2*m,s2);
cvSet2D(globalShapeVectors[1],0,2*m+1,s1);
cvSet2D(combineshapevectors[1],0,2*m,s2);
cvSet2D(combineshapevectors[1],0,2*m+1,s1);
CvScalar one,zero;
one.val[0]=1;
zero.val[0]=0;
cvSet2D(globalShapeVectors[2],0,2*m,one);
cvSet2D(globalShapeVectors[2],0,2*m+1,zero);
cvSet2D(globalShapeVectors[3],0,2*m,zero);
cvSet2D(globalShapeVectors[3],0,2*m+1,one);
cvSet2D(combineshapevectors[2],0,2*m,one);
cvSet2D(combineshapevectors[2],0,2*m+1,zero);
cvSet2D(combineshapevectors[3],0,2*m,zero);
cvSet2D(combineshapevectors[3],0,2*m+1,one);
}
for (int i=0;i<numberofpoints;i++)
{
CvScalar avx,avy;
avx=cvGet2D( meanShapeDel, i,0);
avy=cvGet2D( meanShapeDel, i,1);
// ////printf("%e %e \n",avx.val[0],avy.val[0]);
}
newDelaunayShapeCompute= new delaunay(meanShapeDel);
newDelaunay = new delaunay(meanShapeDel);
newDelaunayInverse = new delaunay(meanShapeDel);
for (int m=0;m<4;m++)
{
double val= cvNorm(globalShapeVectors[m],NULL , CV_L2, 0 );
cvConvertScale( globalShapeVectors[m],globalShapeVectors[m],1/val,0);
////printf(" %e \n",val);
// cvConvertScale( combineshapevectors[m],combineshapevectors[m],val,0);
}
for (int m=4;m<(nS+4);m++)
{
for (int j=0;j<totalnumberofpoints;j++)
{
CvScalar s1 = cvGet2D(nonRigidShapeVectors[m-4],0,j);
cvSet2D(combineshapevectors[m],0,j,s1);
}
}
for (int m=0;m<nS+4;m++)
{
double val= cvNorm(combineshapevectors[m],NULL , CV_L2, 0 );
cvConvertScale( combineshapevectors[m],combineshapevectors[m],1/val,0);
////printf(" %e \n",val);
// cvConvertScale( combineshapevectors[m],combineshapevectors[m],val,0);
}
for (int m=0;m<nA;m++)
{
double val= cvNorm(appearanceVectors[m],NULL , CV_L2, 0 );
cvConvertScale( appearanceVectors[m],appearanceVectors[m],1/val,0);
// //printf(" %e \n",val);
}
////printf("%d %d \n",meanAppearance->width,meanAppearance->height);
totalNumberOfPixels=newDelaunay->numberOfPixels();
interpolateMean=(float*)malloc(sizeof(float)*totalNumberOfPixels);
for (int i=0;i<totalNumberOfPixels;i++)
{
interpolateMean[i]=0;
pixel * pix = newDelaunay->getpixel(i);
interpolateMean[i]=interpolate<float>(meanAppearance,double(pix->x+pix->tx),double(pix->y+pix->ty));
}
preCompute();
////printf("%d %d \n",meanAppearance->width,meanAppearance->height);
// preCompute();
}