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 DPGrimsonGMMBGS::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/DPGrimsonGMMBGS.xml", 0, CV_STORAGE_READ);
threshold = cvReadRealByName(fs, 0, "threshold", 9.0);
alpha = cvReadRealByName(fs, 0, "alpha", 0.01);
gaussians = cvReadIntByName(fs, 0, "gaussians", 3);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void DPWrenGABGS::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/DPWrenGABGS.xml", 0, CV_STORAGE_READ);
threshold = cvReadRealByName(fs, 0, "threshold", 12.25f);
alpha = cvReadRealByName(fs, 0, "alpha", 0.005f);
learningFrames = cvReadIntByName(fs, 0, "learningFrames", 30);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void T2FGMM_UV::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/T2FGMM_UV.xml", 0, CV_STORAGE_READ);
threshold = cvReadRealByName(fs, 0, "threshold", 9.0);
alpha = cvReadRealByName(fs, 0, "alpha", 0.01);
km = cvReadRealByName(fs, 0, "km", 1.5);
kv = cvReadRealByName(fs, 0, "kv", 0.6);
gaussians = cvReadIntByName(fs, 0, "gaussians", 3);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void VuMeter::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/VuMeter.xml", 0, CV_STORAGE_READ);
enableFilter = cvReadIntByName(fs, 0, "enableFilter", true);
binSize = cvReadIntByName(fs, 0, "binSize", 8);
alpha = cvReadRealByName(fs, 0, "alpha", 0.995);
threshold = cvReadRealByName(fs, 0, "threshold", 0.03);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void FuzzyChoquetIntegral::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("../config/FuzzyChoquetIntegral.xml", 0, CV_STORAGE_READ);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
framesToLearn = cvReadIntByName(fs, 0, "framesToLearn", 10);
alphaLearn = cvReadRealByName(fs, 0, "alphaLearn", 0.1);
alphaUpdate = cvReadRealByName(fs, 0, "alphaUpdate", 0.01);
colorSpace = cvReadIntByName(fs, 0, "colorSpace", 1);
option = cvReadIntByName(fs, 0, "option", 2);
smooth = cvReadIntByName(fs, 0, "smooth", true);
threshold = cvReadRealByName(fs, 0, "threshold", 0.67);
cvReleaseFileStorage(&fs);
}
void EntropyOfEntropyArea::loadConfig(){
CvFileStorage* fs1 = cvOpenFileStorage("./Config/ImageResize.xml", 0, CV_STORAGE_READ);
CvFileStorage* fs2 = cvOpenFileStorage("./Config/EntropyOfEntropyArea.xml", 0, CV_STORAGE_READ);
entropyAreaSize = cvReadIntByName(fs1, 0, "entropyAreaSize", 3);
monitorAreaSize = cvReadIntByName(fs1, 0, "monitorAreaSize", 3);
windowWidth = cvReadIntByName(fs2, 0, "windowWidth", 5);
grayLevel = cvReadIntByName(fs2, 0, "grayLevel", 6);
C0 = cvReadRealByName(fs2, 0, "C0", 0.05);
learningRate = cvReadRealByName(fs2, 0, "learningRate", 0.7);
deltaENToOldENRatioEntropy = cvReadRealByName(fs2, 0, "deltaENToOldENRatioEntropy", 0.1);
staticRatioEntropy = cvReadRealByName(fs2, 0, "staticRatioEntropy", 0.7);
cvReleaseFileStorage(&fs1);
cvReleaseFileStorage(&fs2);
}
int
main (int argc, char **argv)
{
char filename[] = "save_cv.xml"; // file name
int i,j,k;
CvFileStorage *cvfs;
CvFileNode *node, *fn;
CvSeq *s;
int total;
// (1)memory space for loading data
int a;
float b;
CvMat** mat = (CvMat**)cvAlloc(3*sizeof(CvMat*));
// (2)open file storage
cvfs = cvOpenFileStorage(filename, NULL, CV_STORAGE_READ);
// (3)read data from file storage
node = cvGetFileNodeByName(cvfs, NULL, ""); // Get Top Node
a = cvReadIntByName(cvfs, node, "a", 0);
b = cvReadRealByName(cvfs, node, "b", 0);
fn = cvGetFileNodeByName(cvfs,node,"mat_array");
s = fn->data.seq;
total = s->total;
for(i=0;i<total;i++){
mat[i] = (CvMat*)cvRead(cvfs,(CvFileNode*)cvGetSeqElem(s,i), NULL);
}
// (4)close file storage
cvReleaseFileStorage(&cvfs);
// (5)print loaded data
printf("a:%d\n", a);
printf("b:%f\n", b);
for(i=0; i<3; i++){
printf("mat%d:\n",i);
for(j=0;j<mat[i]->rows;j++){
for(k=0;k<mat[i]->cols;k++){
printf("%f,",cvmGet(mat[i],j,k));
}
printf("\n");
}
}
// release mat
for(i=0; i<3; i++){
cvReleaseMat(mat+i);
}
cvFree(mat);
return 0;
}
void GMG::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/GMG.xml", 0, CV_STORAGE_READ);
initializationFrames = cvReadIntByName(fs, 0, "initializationFrames", 20);
decisionThreshold = cvReadRealByName(fs, 0, "decisionThreshold", 0.7);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void MixtureOfGaussianV2BGS::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/MixtureOfGaussianV2BGS.xml", 0, CV_STORAGE_READ);
alpha = cvReadRealByName(fs, 0, "alpha", 0.05);
enableThreshold = cvReadIntByName(fs, 0, "enableThreshold", true);
threshold = cvReadIntByName(fs, 0, "threshold", 15);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void CvGBTrees::read( CvFileStorage* fs, CvFileNode* node )
{
CV_FUNCNAME( "CvGBTrees::read" );
__BEGIN__;
CvSeqReader reader;
CvFileNode* trees_fnode;
CvMemStorage* storage;
int i, ntrees;
cv::String s;
clear();
read_params( fs, node );
if( !data )
EXIT;
base_value = (float)cvReadRealByName( fs, node, "base_value", 0.0 );
class_count = cvReadIntByName( fs, node, "class_count", 1 );
weak = new pCvSeq[class_count];
for (int j=0; j<class_count; ++j)
{
s = cv::format("trees_%d", j);
trees_fnode = cvGetFileNodeByName( fs, node, s.c_str() );
if( !trees_fnode || !CV_NODE_IS_SEQ(trees_fnode->tag) )
CV_ERROR( CV_StsParseError, "<trees_x> tag is missing" );
cvStartReadSeq( trees_fnode->data.seq, &reader );
ntrees = trees_fnode->data.seq->total;
if( ntrees != params.weak_count )
CV_ERROR( CV_StsUnmatchedSizes,
"The number of trees stored does not match <ntrees> tag value" );
CV_CALL( storage = cvCreateMemStorage() );
weak[j] = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvDTree*), storage );
for( i = 0; i < ntrees; i++ )
{
CvDTree* tree = new CvDTree();
CV_CALL(tree->read( fs, (CvFileNode*)reader.ptr, data ));
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
cvSeqPush( weak[j], &tree );
}
}
__END__;
}
void AdaptiveBackgroundLearning::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("AdaptiveBackgroundLearning.xml", 0, CV_STORAGE_READ);
alpha = cvReadRealByName(fs, 0, "alpha", 0.05);
limit = cvReadIntByName(fs, 0, "limit", -1);
enableThreshold = cvReadIntByName(fs, 0, "enableThreshold", true);
threshold = cvReadIntByName(fs, 0, "threshold", 15);
showForeground = cvReadIntByName(fs, 0, "showForeground", true);
showBackground = cvReadIntByName(fs, 0, "showBackground", true);
cvReleaseFileStorage(&fs);
}
void PixelBasedAdaptiveSegmenter::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("./config/PixelBasedAdaptiveSegmenter.xml", 0, CV_STORAGE_READ);
enableInputBlur = cvReadIntByName(fs, 0, "enableInputBlur", true);
enableOutputBlur = cvReadIntByName(fs, 0, "enableOutputBlur", true);
alpha = cvReadRealByName(fs, 0, "alpha", 7.0);
beta = cvReadRealByName(fs, 0, "beta", 1.0);
N = cvReadIntByName(fs, 0, "N", 20);
Raute_min = cvReadIntByName(fs, 0, "Raute_min", 2);
R_incdec = cvReadRealByName(fs, 0, "R_incdec", 0.05);
R_lower = cvReadIntByName(fs, 0, "R_lower", 18);
R_scale = cvReadIntByName(fs, 0, "R_scale", 5);
T_dec = cvReadRealByName(fs, 0, "T_dec", 0.05);
T_inc = cvReadIntByName(fs, 0, "T_inc", 1);
T_init = cvReadIntByName(fs, 0, "T_init", 18);
T_lower = cvReadIntByName(fs, 0, "T_lower", 2);
T_upper = cvReadIntByName(fs, 0, "T_upper", 200);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
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;
}
void MixtureOfGaussianV1BGS::loadConfig()
{
QDir dir(QDir::home());
if(!dir.exists("NoobaVSS")) {
dir.mkdir("NoobaVSS");
}
dir.cd("NoobaVSS");
if(!dir.exists("config")) {
dir.mkdir("config");
}
dir.cd("config");
CvFileStorage* fs = cvOpenFileStorage(dir.absoluteFilePath("MixtureOfGaussianV1BGS.xml").toLocal8Bit(), 0, CV_STORAGE_READ);
alpha = cvReadRealByName(fs, 0, "alpha", 0.05);
enableThreshold = cvReadIntByName(fs, 0, "enableThreshold", true);
threshold = cvReadIntByName(fs, 0, "threshold", 15);
showOutput = cvReadIntByName(fs, 0, "showOutput", false);
cvReleaseFileStorage(&fs);
}
void GMG::loadConfig()
{
QDir dir(QDir::home());
if(!dir.exists("NoobaVSS")){
dir.mkdir("NoobaVSS");
}
dir.cd("NoobaVSS");
if(!dir.exists("config")){
dir.mkdir("config");
}
dir.cd("config");
CvFileStorage* fs = cvOpenFileStorage(dir.absoluteFilePath("GMG.xml").toLocal8Bit(), 0, CV_STORAGE_WRITE);
initializationFrames = cvReadIntByName(fs, 0, "initializationFrames", 20);
decisionThreshold = cvReadRealByName(fs, 0, "decisionThreshold", 0.7);
showOutput = cvReadIntByName(fs, 0, "showOutput", false);
cvReleaseFileStorage(&fs);
}
void CConfig::LoadXmlFile()
{
if (!QDir(CONF_PATH).exists())
QDir().mkdir(CONF_PATH);
CvFileStorage* fs = NULL;
try
{
fs = cvOpenFileStorage(XML_FILE, 0, CV_STORAGE_READ);
}
catch (...)
{
setDefault();
return;
}
if (fs == NULL)
{
setDefault();
return;
}
_config.ipCam = cvReadIntByName(fs, 0, "IpCam", 0);
_config.strCamUrl_GV = cvReadStringByName(fs, 0, "VsCamGV", CAM_URL_GV);
_config.strCamUrl_GV_ir = cvReadStringByName(fs, 0, "IrCamGV", CAM_URL_GV_IR);
_config.strCamUrl_IP = cvReadStringByName(fs, 0, "VsCamIP", CAM_URL_IP);
_config.strCamUrl_IP_ir = cvReadStringByName(fs, 0, "IrCamIP", CAM_URL_IP_IR);
_config.trkWidth = cvReadIntByName(fs, 0, "TrkWidth", QApplication::desktop()->screenGeometry(0).width()/6);
_config.trkHeight = cvReadIntByName(fs, 0, "TrkHeight", QApplication::desktop()->screenGeometry(0).height()/6);
_config.frmPosX = cvReadIntByName(fs, 0, "FrmPosX", 0);
_config.frmPosY = cvReadIntByName(fs, 0, "FrmPosY", 0);
_config.frmWidth = cvReadIntByName(fs, 0, "FrmWidth", QApplication::desktop()->screenGeometry(0).width());
_config.frmHeight = cvReadIntByName(fs, 0, "FrmHeight", QApplication::desktop()->screenGeometry(0).height());
_config.fps = cvReadRealByName(fs, 0, "Fps", 18.5);
cvReleaseFileStorage(&fs);
}
void AdaptiveBackgroundLearning::loadConfig()
{
QDir dir(QDir::home());
if(!dir.exists("NoobaVSS")){
dir.mkdir("NoobaVSS");
}
dir.cd("NoobaVSS");
if(!dir.exists("config")){
dir.mkdir("config");
}
dir.cd("config");
CvFileStorage* fs = cvOpenFileStorage(dir.absoluteFilePath("AdaptiveBackgroundLearning.xml").toLocal8Bit(), 0, CV_STORAGE_READ);
alpha = cvReadRealByName(fs, 0, "alpha", 0.05);
limit = cvReadIntByName(fs, 0, "limit", -1);
enableThreshold = cvReadIntByName(fs, 0, "enableThreshold", true);
threshold = cvReadIntByName(fs, 0, "threshold", 15);
showForeground = cvReadIntByName(fs, 0, "showForeground", true);
showBackground = cvReadIntByName(fs, 0, "showBackground", true);
cvReleaseFileStorage(&fs);
}
static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs, CvFileNode* node)
{
int noise_type = CV_NOISE_NONE;;
CvTestSeqElem* pElem = NULL;
const char* pVideoName = cvReadStringByName( fs, node,"Video", NULL);
const char* pVideoObjName = cvReadStringByName( fs, node,"VideoObj", NULL);
if(pVideoName)
{ /* Check to noise flag: */
if( cv_stricmp(pVideoName,"noise_gaussian") == 0 ||
cv_stricmp(pVideoName,"noise_normal") == 0) noise_type = CV_NOISE_GAUSSIAN;
if( cv_stricmp(pVideoName,"noise_uniform") == 0) noise_type = CV_NOISE_UNIFORM;
if( cv_stricmp(pVideoName,"noise_speckle") == 0) noise_type = CV_NOISE_SPECKLE;
if( cv_stricmp(pVideoName,"noise_salt_and_pepper") == 0) noise_type = CV_NOISE_SALT_AND_PEPPER;
}
if((pVideoName || pVideoObjName ) && noise_type == CV_NOISE_NONE)
{ /* Read other elements: */
if(pVideoName) pElem = icvTestSeqReadElemAll(pTS, fs, pVideoName);
if(pVideoObjName)
{
CvTestSeqElem* pE;
pElem = icvTestSeqReadElemAll(pTS, fs, pVideoObjName);
for(pE=pElem;pE;pE=pE->next)
{
pE->ObjID = pTS->ObjNum;
pE->pObjName = pVideoObjName;
}
pTS->ObjNum++;
}
} /* Read other elements. */
else
{ /* Create new element: */
CvFileNode* pPosNode = cvGetFileNodeByName( fs, node,"Pos");
CvFileNode* pSizeNode = cvGetFileNodeByName( fs, node,"Size");
int AutoSize = (pSizeNode && CV_NODE_IS_STRING(pSizeNode->tag) && cv_stricmp("auto",cvReadString(pSizeNode,""))==0);
int AutoPos = (pPosNode && CV_NODE_IS_STRING(pPosNode->tag) && cv_stricmp("auto",cvReadString(pPosNode,""))==0);
const char* pFileName = cvReadStringByName( fs, node,"File", NULL);
pElem = (CvTestSeqElem*)cvAlloc(sizeof(CvTestSeqElem));
memset(pElem,0,sizeof(CvTestSeqElem));
pElem->ObjID = -1;
pElem->noise_type = noise_type;
cvRandInit( &pElem->rnd_state, 1, 0, 0,CV_RAND_NORMAL);
if(pFileName && pElem->noise_type == CV_NOISE_NONE)
{ /* If AVI or BMP: */
size_t l = strlen(pFileName);
pElem->pFileName = pFileName;
pElem->type = SRC_TYPE_IMAGE;
if(cv_stricmp(".avi",pFileName+l-4) == 0)pElem->type = SRC_TYPE_AVI;
if(pElem->type == SRC_TYPE_IMAGE)
{
//pElem->pImg = cvLoadImage(pFileName);
if(pElem->pImg)
{
pElem->FrameNum = 1;
if(pElem->pImgMask)cvReleaseImage(&(pElem->pImgMask));
pElem->pImgMask = cvCreateImage(
cvSize(pElem->pImg->width,pElem->pImg->height),
IPL_DEPTH_8U,1);
icvTestSeqCreateMask(pElem->pImg,pElem->pImgMask,FG_BG_THRESHOLD);
}
}
if(pElem->type == SRC_TYPE_AVI && pFileName)
{
//pElem->pAVI = cvCaptureFromFile(pFileName);
if(pElem->pAVI)
{
IplImage* pImg = 0;//cvQueryFrame(pElem->pAVI);
pElem->pImg = cvCloneImage(pImg);
pElem->pImg->origin = 0;
//cvSetCaptureProperty(pElem->pAVI,CV_CAP_PROP_POS_FRAMES,0);
pElem->FrameBegin = 0;
pElem->AVILen = pElem->FrameNum = 0;//(int)cvGetCaptureProperty(pElem->pAVI, CV_CAP_PROP_FRAME_COUNT);
//cvReleaseCapture(&pElem->pAVI);
pElem->pAVI = NULL;
}
else
{
printf("WARNING!!! Cannot open avi file %s\n",pFileName);
}
}
} /* If AVI or BMP. */
if(pPosNode)
{ /* Read positions: */
if(CV_NODE_IS_SEQ(pPosNode->tag))
{
int num = pPosNode->data.seq->total;
pElem->pPos = (CvPoint2D32f*)cvAlloc(sizeof(float)*num);
cvReadRawData( fs, pPosNode, pElem->pPos, "f" );
pElem->PosNum = num/2;
if(pElem->FrameNum == 0) pElem->FrameNum = pElem->PosNum;
}
}
if(pSizeNode)
{ /* Read sizes: */
if(CV_NODE_IS_SEQ(pSizeNode->tag))
{
int num = pSizeNode->data.seq->total;
pElem->pSize = (CvPoint2D32f*)cvAlloc(sizeof(float)*num);
cvReadRawData( fs, pSizeNode, pElem->pSize, "f" );
pElem->SizeNum = num/2;
}
}
if(AutoPos || AutoSize)
{ /* Auto size and pos: */
int i;
int num = (pElem->type == SRC_TYPE_AVI)?pElem->AVILen:1;
if(AutoSize)
{
pElem->pSize = (CvPoint2D32f*)cvAlloc(sizeof(CvPoint2D32f)*num);
pElem->SizeNum = num;
}
if(AutoPos)
{
pElem->pPos = (CvPoint2D32f*)cvAlloc(sizeof(CvPoint2D32f)*num);
pElem->PosNum = num;
}
for(i=0; i<num; ++i)
{
IplImage* pFG = NULL;
CvPoint2D32f* pPos = AutoPos?(pElem->pPos + i):NULL;
CvPoint2D32f* pSize = AutoSize?(pElem->pSize + i):NULL;
icvTestSeqQureyFrameElem(pElem,i);
pFG = pElem->pImgMask;
if(pPos)
{
pPos->x = 0.5f;
pPos->y = 0.5f;
}
if(pSize)
{
pSize->x = 0;
pSize->y = 0;
}
if(pFG)
{
double M00;
CvMoments m;
cvMoments( pElem->pImgMask, &m, 0 );
M00 = cvGetSpatialMoment( &m, 0, 0 );
if(M00 > 0 && pSize )
{
double X = cvGetSpatialMoment( &m, 1, 0 )/M00;
double Y = cvGetSpatialMoment( &m, 0, 1 )/M00;
double XX = (cvGetSpatialMoment( &m, 2, 0 )/M00) - X*X;
double YY = (cvGetSpatialMoment( &m, 0, 2 )/M00) - Y*Y;
pSize->x = (float)(4*sqrt(XX))/(pElem->pImgMask->width-1);
pSize->y = (float)(4*sqrt(YY))/(pElem->pImgMask->height-1);
}
if(M00 > 0 && pPos)
{
pPos->x = (float)(cvGetSpatialMoment( &m, 1, 0 )/(M00*(pElem->pImgMask->width-1)));
pPos->y = (float)(cvGetSpatialMoment( &m, 0, 1 )/(M00*(pElem->pImgMask->height-1)));
}
if(pPos)
{ /* Another way to calculate y pos
* using object median:
*/
int y0=0, y1=pFG->height-1;
for(y0=0; y0<pFG->height; ++y0)
{
CvMat m;
CvScalar s = cvSum(cvGetRow(pFG, &m, y0));
if(s.val[0] > 255*7) break;
}
for(y1=pFG->height-1; y1>0; --y1)
{
CvMat m;
CvScalar s = cvSum(cvGetRow(pFG, &m, y1));
if(s.val[0] > 255*7) break;
}
pPos->y = (y0+y1)*0.5f/(pFG->height-1);
}
} /* pFG */
} /* Next frame. */
//if(pElem->pAVI) cvReleaseCapture(&pElem->pAVI);
pElem->pAVI = NULL;
} /* End auto position creation. */
} /* Create new element. */
if(pElem)
{ /* Read transforms and: */
int FirstFrame, LastFrame;
CvTestSeqElem* p=pElem;
CvFileNode* pTransNode = NULL;
CvFileNode* pS = NULL;
int ShiftByPos = 0;
int KeyFrames[1024];
CvSeq* pTransSeq = NULL;
int KeyFrameNum = 0;
pTransNode = cvGetFileNodeByName( fs, node,"Trans");
while( pTransNode &&
CV_NODE_IS_STRING(pTransNode->tag) &&
cv_stricmp("auto",cvReadString(pTransNode,""))!=0)
{ /* Trans is reference: */
pTransNode = cvGetFileNodeByName( fs, NULL,cvReadString(pTransNode,""));
}
pS = cvGetFileNodeByName( fs, node,"Shift");
ShiftByPos = 0;
pTransSeq = pTransNode?(CV_NODE_IS_SEQ(pTransNode->tag)?pTransNode->data.seq:NULL):NULL;
KeyFrameNum = pTransSeq?pTransSeq->total:1;
if( (pS && CV_NODE_IS_STRING(pS->tag) && cv_stricmp("auto",cvReadString(pS,""))==0)
||(pTransNode && CV_NODE_IS_STRING(pTransNode->tag) && cv_stricmp("auto",cvReadString(pTransNode,""))==0))
{
ShiftByPos = 1;
}
FirstFrame = pElem->FrameBegin;
LastFrame = pElem->FrameBegin+pElem->FrameNum-1;
/* Calculate length of video and reallocate
* transformation array:
*/
for(p=pElem; p; p=p->next)
{
int v;
v = cvReadIntByName( fs, node, "BG", -1 );
if(v!=-1)p->BG = v;
v = cvReadIntByName( fs, node, "Mask", -1 );
if(v!=-1)p->Mask = v;
p->FrameBegin += cvReadIntByName( fs, node, "FrameBegin", 0 );
p->FrameNum = cvReadIntByName( fs, node, "FrameNum", p->FrameNum );
p->FrameNum = cvReadIntByName( fs, node, "Dur", p->FrameNum );
{
int LastFrame = cvReadIntByName( fs, node, "LastFrame", p->FrameBegin+p->FrameNum-1 );
p->FrameNum = MIN(p->FrameNum,LastFrame - p->FrameBegin+1);
}
icvTestSeqAllocTrans(p);
{ /* New range estimation: */
int LF = p->FrameBegin+p->FrameNum-1;
if(p==pElem || FirstFrame > p->FrameBegin)FirstFrame = p->FrameBegin;
if(p==pElem || LastFrame < LF)LastFrame = LF;
} /* New range estimation. */
} /* End allocate new transfrom array. */
if(ShiftByPos)
{
for(p=pElem;p;p=p->next)
{ /* Modify transformation to make autoshift: */
int i;
int num = p->FrameNum;
assert(num <= p->TransNum);
p->TransNum = MAX(1,num);
for(i=0; i<num; ++i)
{
CvTSTrans* pT = p->pTrans+i;
//float t = (num>1)?((float)i/(num-1)):0.0f;
float newx = p->pPos[i%p->PosNum].x;
float newy = p->pPos[i%p->PosNum].y;
pT->Shift.x = -newx*pT->Scale.x;
pT->Shift.y = -newy*pT->Scale.y;
if(p->pImg)
{
newx *= p->pImg->width-1;
newy *= p->pImg->height-1;
}
pT->T[2] = -(pT->T[0]*newx+pT->T[1]*newy);
pT->T[5] = -(pT->T[3]*newx+pT->T[4]*newy);
}
} /* Modify transformation old. */
} /* Next record. */
/* Initialize frame number array: */
KeyFrames[0] = FirstFrame;
if(pTransSeq&&KeyFrameNum>1)
{
int i0,i1,i;
for(i=0; i<KeyFrameNum; ++i)
{
CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,i);
KeyFrames[i] = cvReadIntByName(fs,pTN,"frame",-1);
}
if(KeyFrames[0]<0)KeyFrames[0]=FirstFrame;
if(KeyFrames[KeyFrameNum-1]<0)KeyFrames[KeyFrameNum-1]=LastFrame;
for(i0=0, i1=1; i1<KeyFrameNum;)
{
int i;
for(i1=i0+1; i1<KeyFrameNum && KeyFrames[i1]<0; i1++);
assert(i1<KeyFrameNum);
assert(i1>i0);
for(i=i0+1; i<i1; ++i)
{
KeyFrames[i] = cvRound(KeyFrames[i0] + (float)(i-i0)*(float)(KeyFrames[i1] - KeyFrames[i0])/(float)(i1-i0));
}
i0 = i1;
i1++;
} /* Next key run. */
} /* Initialize frame number array. */
if(pTransNode || pTransSeq)
{ /* More complex transform. */
int param;
CvFileNode* pTN = pTransSeq?(CvFileNode*)cvGetSeqElem(pTransSeq,0):pTransNode;
for(p=pElem; p; p=p->next)
{
//int trans_num = p->TransNum;
for(param=0; param_name[param]; ++param)
{
const char* name = param_name[param];
float defv = param_defval[param];
if(KeyFrameNum==1)
{ /* Only one transform record: */
int i;
double val;
CvFileNode* node = cvGetFileNodeByName( fs, pTN,name);
if(node == NULL) continue;
val = cvReadReal(node,defv);
for(i=0; i<p->TransNum; ++i)
{
icvUpdateTrans(
p->pTrans+i, param, val,
p->pImg?(float)(p->pImg->width-1):1.0f,
p->pImg?(float)(p->pImg->height-1):1.0f);
}
} /* Next record. */
else
{ /* Several transforms: */
int i0,i1;
double v0;
double v1;
CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,0);
v0 = cvReadRealByName(fs, pTN,name,defv);
for(i1=1,i0=0; i1<KeyFrameNum; ++i1)
{
int f0,f1;
int i;
CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,i1);
CvFileNode* pVN = cvGetFileNodeByName(fs,pTN,name);
if(pVN)v1 = cvReadReal(pVN,defv);
else if(pVN == NULL && i1 == KeyFrameNum-1) v1 = defv;
else continue;
f0 = KeyFrames[i0];
f1 = KeyFrames[i1];
if(i1==(KeyFrameNum-1)) f1++;
for(i=f0; i<f1; ++i)
{
double val;
double t = (float)(i-f0);
int li = i - p->FrameBegin;
if(li<0) continue;
if(li>= p->TransNum) break;
if(KeyFrames[i1]>KeyFrames[i0]) t /=(float)(KeyFrames[i1]-KeyFrames[i0]);
val = t*(v1-v0)+v0;
icvUpdateTrans(
p->pTrans+li, param, val,
p->pImg?(float)(p->pImg->width-1):1.0f,
p->pImg?(float)(p->pImg->height-1):1.0f);
} /* Next transform. */
i0 = i1;
v0 = v1;
} /* Next value run. */
} /* Several transforms. */
} /* Next parameter. */
} /* Next record. */
} /* More complex transform. */
} /* Read transfroms. */
return pElem;
} /* icvTestSeqReadElemOne */
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();
}
void MultiLayerBGS::loadConfig()
{
CvFileStorage* fs = cvOpenFileStorage("MultiLayerBGS.xml", 0, CV_STORAGE_READ);
if(fs == NULL)
{
cout << "Do not open file MultiLayerBGS.xml - loadConfig()" << endl;
return;
}
bg_model_preload = cvReadStringByName(fs, 0, "preloadModel", "");
saveModel = cvReadIntByName(fs, 0, "saveModel", false);
detectAfter = cvReadIntByName(fs, 0, "detectAfter", 0);
disableDetectMode = cvReadIntByName(fs, 0, "disableDetectMode", true);
disableLearning = cvReadIntByName(fs, 0, "disableLearningInDetecMode", false);
loadDefaultParams = cvReadIntByName(fs, 0, "loadDefaultParams", true);
max_mode_num = cvReadIntByName(fs, 0, "max_mode_num", 5);
weight_updating_constant = cvReadRealByName(fs, 0, "weight_updating_constant", 5.0);
texture_weight = cvReadRealByName(fs, 0, "texture_weight", 0.5);
bg_mode_percent = cvReadRealByName(fs, 0, "bg_mode_percent", 0.6);
pattern_neig_half_size = cvReadIntByName(fs, 0, "pattern_neig_half_size", 4);
pattern_neig_gaus_sigma = cvReadRealByName(fs, 0, "pattern_neig_gaus_sigma", 3.0);
bg_prob_threshold = cvReadRealByName(fs, 0, "bg_prob_threshold", 0.2);
bg_prob_updating_threshold = cvReadRealByName(fs, 0, "bg_prob_updating_threshold", 0.2);
robust_LBP_constant = cvReadIntByName(fs, 0, "robust_LBP_constant", 3);
min_noised_angle = cvReadRealByName(fs, 0, "min_noised_angle", 0.01768);
shadow_rate = cvReadRealByName(fs, 0, "shadow_rate", 0.6);
highlight_rate = cvReadRealByName(fs, 0, "highlight_rate", 1.2);
bilater_filter_sigma_s = cvReadRealByName(fs, 0, "bilater_filter_sigma_s", 3.0);
bilater_filter_sigma_r = cvReadRealByName(fs, 0, "bilater_filter_sigma_r", 0.1);
frame_duration = cvReadRealByName(fs, 0, "frame_duration", 0.1);
learn_mode_learn_rate_per_second = cvReadRealByName(fs, 0, "learn_mode_learn_rate_per_second", 0.5);
learn_weight_learn_rate_per_second = cvReadRealByName(fs, 0, "learn_weight_learn_rate_per_second", 0.5);
learn_init_mode_weight = cvReadRealByName(fs, 0, "learn_init_mode_weight", 0.05);
detect_mode_learn_rate_per_second = cvReadRealByName(fs, 0, "detect_mode_learn_rate_per_second", 0.01);
detect_weight_learn_rate_per_second = cvReadRealByName(fs, 0, "detect_weight_learn_rate_per_second", 0.01);
detect_init_mode_weight = cvReadRealByName(fs, 0, "detect_init_mode_weight", 0.001);
showOutput = cvReadIntByName(fs, 0, "showOutput", true);
cvReleaseFileStorage(&fs);
}
void CvANN_MLP::read_params( CvFileStorage* fs, CvFileNode* node )
{
//CV_FUNCNAME( "CvANN_MLP::read_params" );
__BEGIN__;
const char* activ_func_name = cvReadStringByName( fs, node, "activation_function", 0 );
CvFileNode* tparams_node;
if( activ_func_name )
activ_func = strcmp( activ_func_name, "SIGMOID_SYM" ) == 0 ? SIGMOID_SYM :
strcmp( activ_func_name, "IDENTITY" ) == 0 ? IDENTITY :
strcmp( activ_func_name, "GAUSSIAN" ) == 0 ? GAUSSIAN : 0;
else
activ_func = cvReadIntByName( fs, node, "activation_function" );
f_param1 = cvReadRealByName( fs, node, "f_param1", 0 );
f_param2 = cvReadRealByName( fs, node, "f_param2", 0 );
set_activ_func( activ_func, f_param1, f_param2 );
min_val = cvReadRealByName( fs, node, "min_val", 0. );
max_val = cvReadRealByName( fs, node, "max_val", 1. );
min_val1 = cvReadRealByName( fs, node, "min_val1", 0. );
max_val1 = cvReadRealByName( fs, node, "max_val1", 1. );
tparams_node = cvGetFileNodeByName( fs, node, "training_params" );
params = CvANN_MLP_TrainParams();
if( tparams_node )
{
const char* tmethod_name = cvReadStringByName( fs, tparams_node, "train_method", "" );
CvFileNode* tcrit_node;
if( strcmp( tmethod_name, "BACKPROP" ) == 0 )
{
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = cvReadRealByName( fs, tparams_node, "dw_scale", 0 );
params.bp_moment_scale = cvReadRealByName( fs, tparams_node, "moment_scale", 0 );
}
else if( strcmp( tmethod_name, "RPROP" ) == 0 )
{
params.train_method = CvANN_MLP_TrainParams::RPROP;
params.rp_dw0 = cvReadRealByName( fs, tparams_node, "dw0", 0 );
params.rp_dw_plus = cvReadRealByName( fs, tparams_node, "dw_plus", 0 );
params.rp_dw_minus = cvReadRealByName( fs, tparams_node, "dw_minus", 0 );
params.rp_dw_min = cvReadRealByName( fs, tparams_node, "dw_min", 0 );
params.rp_dw_max = cvReadRealByName( fs, tparams_node, "dw_max", 0 );
}
tcrit_node = cvGetFileNodeByName( fs, tparams_node, "term_criteria" );
if( tcrit_node )
{
params.term_crit.epsilon = cvReadRealByName( fs, tcrit_node, "epsilon", -1 );
params.term_crit.max_iter = cvReadIntByName( fs, tcrit_node, "iterations", -1 );
params.term_crit.type = (params.term_crit.epsilon >= 0 ? CV_TERMCRIT_EPS : 0) +
(params.term_crit.max_iter >= 0 ? CV_TERMCRIT_ITER : 0);
}
}
__END__;
}
double cvReadRealByName_wrap(const CvFileStorage * fs , const CvFileNode * map , const char * name , double default_value ){
return cvReadRealByName(/*const*//*CvFileStorage*//***/fs , /*const*//*CvFileNode*//***/map , /*const*//*char*//***/name , /*double*/default_value);
}
void Point::load(CvFileStorage *in, CvFileNode *node) {
x = cvReadRealByName(in, node, "x");
y = cvReadRealByName(in, node, "y");
}
// Read XML-formatted configuration file.
void readConfiguration(const char* filename, struct slParams* sl_params){
// Open file storage for XML-formatted configuration file.
CvFileStorage* fs = cvOpenFileStorage(filename, 0, CV_STORAGE_READ);
// Read output directory and object (or sequence) name.
CvFileNode* m = cvGetFileNodeByName(fs, 0, "output");
strcpy(sl_params->outdir, cvReadStringByName(fs, m, "output_directory", "./output"));
strcpy(sl_params->object, cvReadStringByName(fs, m, "object_name", "./output"));
sl_params->save = (cvReadIntByName(fs, m, "save_intermediate_results", 0) != 0);
// Read camera parameters.
m = cvGetFileNodeByName(fs, 0, "camera");
sl_params->cam_w = cvReadIntByName(fs, m, "width", 960);
sl_params->cam_h = cvReadIntByName(fs, m, "height", 720);
sl_params->Logitech_9000 = (cvReadIntByName(fs, m, "Logitech_Quickcam_9000_raw_mode", 0) != 0);
// Read projector parameters.
m = cvGetFileNodeByName(fs, 0, "projector");
sl_params->proj_w = cvReadIntByName(fs, m, "width", 1024);
sl_params->proj_h = cvReadIntByName(fs, m, "height", 768);
sl_params->proj_invert = (cvReadIntByName(fs, m, "invert_projector", 0) != 0);
// Read camera and projector gain parameters.
m = cvGetFileNodeByName(fs, 0, "gain");
sl_params->cam_gain = cvReadIntByName(fs, m, "camera_gain", 50);
sl_params->proj_gain = cvReadIntByName(fs, m, "projector_gain", 50);
// Read distortion model parameters.
m = cvGetFileNodeByName(fs, 0, "distortion_model");
sl_params->cam_dist_model[0] = (cvReadIntByName(fs, m, "enable_tangential_camera", 0) != 0);
sl_params->cam_dist_model[1] = (cvReadIntByName(fs, m, "enable_6th_order_radial_camera", 0) != 0);
sl_params->proj_dist_model[0] = (cvReadIntByName(fs, m, "enable_tangential_projector", 0) != 0);
sl_params->proj_dist_model[1] = (cvReadIntByName(fs, m, "enable_6th_order_radial_projector", 0) != 0);
// Read camera calibration chessboard parameters.
m = cvGetFileNodeByName(fs, 0, "camera_chessboard");
sl_params->cam_board_w = cvReadIntByName(fs, m, "interior_horizontal_corners", 8);
sl_params->cam_board_h = cvReadIntByName(fs, m, "interior_vertical_corners", 6);
sl_params->cam_board_w_mm = (float)cvReadRealByName(fs, m, "square_width_mm", 30.0);
sl_params->cam_board_h_mm = (float)cvReadRealByName(fs, m, "square_height_mm", 30.0);
// Read projector calibration chessboard parameters.
m = cvGetFileNodeByName(fs, 0, "projector_chessboard");
sl_params->proj_board_w = cvReadIntByName(fs, m, "interior_horizontal_corners", 8);
sl_params->proj_board_h = cvReadIntByName(fs, m, "interior_vertical_corners", 6);
sl_params->proj_board_w_pixels = cvReadIntByName(fs, m, "square_width_pixels", 75);
sl_params->proj_board_h_pixels = cvReadIntByName(fs, m, "square_height_pixels", 75);
// Read scanning and reconstruction parameters.
m = cvGetFileNodeByName(fs, 0, "scanning_and_reconstruction");
sl_params->mode = cvReadIntByName(fs, m, "mode", 2);
sl_params->scan_cols = (cvReadIntByName(fs, m, "reconstruct_columns", 1) != 0);
sl_params->scan_rows = (cvReadIntByName(fs, m, "reconstruct_rows", 1) != 0);
sl_params->delay = cvReadIntByName(fs, m, "frame_delay_ms", 200);
sl_params->thresh = cvReadIntByName(fs, m, "minimum_contrast_threshold", 32);
sl_params->dist_range[0] = (float) cvReadRealByName(fs, m, "minimum_distance_mm", 0.0);
sl_params->dist_range[1] = (float) cvReadRealByName(fs, m, "maximum_distance_mm", 1.0e4);
sl_params->dist_reject = (float) cvReadRealByName(fs, m, "maximum_distance_variation_mm", 10.0);
sl_params->background_depth_thresh = (float) cvReadRealByName(fs, m, "minimum_background_distance_mm", 20.0);
// Read visualization options.
m = cvGetFileNodeByName(fs, 0, "visualization");
sl_params->display = (cvReadIntByName(fs, m, "display_intermediate_results", 1) != 0);
sl_params->window_w = cvReadIntByName(fs, m, "display_window_width_pixels", 640);
// Enable both row and column scanning, if "ray-ray" reconstruction mode is enabled.
if(sl_params->mode == 2){
sl_params->scan_cols = true;
sl_params->scan_rows = true;
}
// Set camera visualization window dimensions.
sl_params->window_h = (int)ceil((float)sl_params->window_w*((float)sl_params->cam_h/(float)sl_params->cam_w));
// Close file storage for XML-formatted configuration file.
cvReleaseFileStorage(&fs);
}
