void exportHistograms(){
TreeReader reader;
reader.openSeries(257,"D");
reader.openSeries(258,"D");
reader.openSeries(260,"D");
reader.openSeries(261,"D");
reader.openSeries(262,"D");
TH1F hist1("hist1","hist1",100,0,30000);
TH1F hist2("hist2","hist2",100,0,30000);
TH1F hist3("hist3","hist3",100,0,30000);
for(Long64_t i=0;i<reader.tree->GetEntries();i++){
reader.tree->GetEntry(i);
if(fabs(reader.recon.t_gamma[2]-600)<10){
hist1.Fill(reader.recon.E_gamma[2]);
}
if(fabs(reader.recon.t_gamma[6]-600)<10){
hist2.Fill(reader.recon.E_gamma[6]);
}
if(fabs(reader.recon.t_gamma[13]-600)<10){
hist3.Fill(reader.recon.E_gamma[13]);
}
}
ofstream output(Form("%s/output.csv",RDKANAL));
output<<"bin center,det02,det06,det16"<<endl;
for(Long64_t i=1;i<=100;i++){
output<<hist1.GetBinCenter(i)<<","<<hist1.GetBinContent(i)<<",";
output<<hist2.GetBinContent(i)<<","<<hist3.GetBinContent(i)<<endl;
}
output.close();
}
void MainWindow::on_actionShape_histogram_distance_triggered()
{
QPixmap pixmap = pixmapItem->pixmap().copy();
QImage image = pixmap.toImage();
int width = image.width();
int height = image.height();
if (width == 0 || height == 0)
{
ui->statusBar->showMessage( tr("Error. Image bad size"), 3000 );
return;
}
double threshold = 0.8;
picturesR.clear();
QString dirName = QFileDialog::getExistingDirectory(this, tr("Choose directory") );
QDir dir(dirName);
QDirIterator it(dir.absolutePath(), QDir::Files);
std::vector<double> origHist = hist2(pixmap, 20, 20);
for ( ; it.hasNext(); it.next() )
{
QString curFileName = it.filePath();
QPixmap curPixmap(curFileName);
if ( curPixmap.isNull() )
continue;
std::vector<double> curHist = hist2(curPixmap, 20, 20);
double dist = histDistance2(origHist, curHist);
qDebug() << dist;
if (dist < threshold)
picturesR.append(curPixmap);
}
QString mes = tr("Images: ") + QString::number(picturesR.size());
ui->statusBar->showMessage(mes, 3000);
if (picturesR.size() > 0)
{
picturesRind = 0;
pixmapItem_2->setPixmap(picturesR[0]);
scene_2->setSceneRect(QRectF(picturesR[0].rect()));
}
}
void CompareChiDialog::startMining()
{
QString logsDir = medianFilter->isChecked() ? "/CleanLogs" : "/Logs";
QDir logs_dir = QApplication::applicationDirPath();
logs_dir = logs_dir.path() + logsDir;
logs_dir = logs_dir.path() + "/" + this->userNameEdit1->text();
if (this->userNameEdit1->text().isEmpty() || this->userNameEdit2->text().isEmpty())
{
QMessageBox::critical(this, tr("Error"), tr("Usernames cannot be empty!"));
return;
}
if (!logs_dir.exists()) {
QMessageBox::critical(this, tr("Error"),
tr("Can't find logs for user \"") +
this->userNameEdit1->text() +
tr("\" in the directory ") +
logs_dir.path());
return;
}
logs_dir = QApplication::applicationDirPath();
logs_dir = logs_dir.path() + logsDir;
logs_dir = logs_dir.path() + "/" + this->userNameEdit2->text();
if (!logs_dir.exists()) {
QMessageBox::critical(this, tr("Error"),
tr("Can't find logs for user \"") +
this->userNameEdit2->text() +
tr("\" in the directory ") +
logs_dir.path());
return;
}
if (theresoldCut->isChecked()){
Histogram hist1 (this->steps->value(), this->maxValue->value(), this->userNameEdit1->text(), medianFilter->isChecked());
int end1 = hist1.findMinGoodEnd();
Histogram hist2 (this->steps->value(), this->maxValue->value(), this->userNameEdit2->text(), medianFilter->isChecked());
int end2 = hist2.findMinGoodEnd();
this->maxValue->setValue(end1 < end2 ? end2 : end1);
}
long long int info = Mining::useChiSquareApprox(this->userNameEdit1->text(),
this->userNameEdit2->text(),
this->steps->value(),
this->maxValue->value(),
medianFilter->isChecked());
QMessageBox::information(this, "Mining complete", "The probability that it is one person is " + QString::number(info) + "%");
}
template<class Storage1, class Storage2> static int foaOperations(Storage1, Storage2)
{
const int SIZE = 720;
typedef Storage1::value_type Val;
typedef Storage1::idx_type Idx;
CVHistogram<Storage1> hist1( SIZE );
CVHistogram<Storage2> hist2( SIZE );
int Errors = 0;
for( Idx i = 0; i < SIZE; i++ )
{
hist1[i] = (Val)(i % 10);
hist2[i] = (Val)(9 - hist1[i]);
}
if( calc_histogram_intersection( hist1, hist2 ) != SIZE * 2 )
{
Errors++;
trsWrite( ATS_CON | ATS_LST, "Error Intersection function\n" );
}
double res;
if( (res = fabs(calc_histogram_chi_square( hist1, hist2 ) - SIZE * 330 / 90)) > 0.0001 )
{
Errors++;
trsWrite( ATS_CON | ATS_LST, "Error ChiSqr function\n" );
}
for( i = 0; i < SIZE; i++ )
{
hist1[i] = (Val)(i % 9);
hist2[i] = (Val)(8 - hist1[i]);
}
if( fabs(calc_histogram_correlation( hist1, hist2 ) + 1 ) >= 0.0001 )
{
Errors++;
trsWrite( ATS_CON | ATS_LST, "Error Correl function\n" );
}
return Errors == 0 ? TRS_OK : trsResult( TRS_FAIL, "Fixed %d errors", Errors );
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
const int *dimsf, *dimsg;
float s[3];
if (nrhs>4 || nrhs<3 || nlhs>1) mexErrMsgTxt("Incorrect usage.");
if (!mxIsNumeric(prhs[0]) || !mxIsUint8(prhs[0]) || mxIsComplex(prhs[0]))
mexErrMsgTxt("Wrong sort of data (1).");
if (mxGetNumberOfDimensions(prhs[0]) != 3) mexErrMsgTxt("Wrong number of dims (1).");;
dimsg = mxGetDimensions(prhs[0]);
if (!mxIsNumeric(prhs[1]) || !mxIsUint8(prhs[1]) || mxIsComplex(prhs[1]))
mexErrMsgTxt("Wrong sort of data (2).");
if (mxGetNumberOfDimensions(prhs[0]) != 3) mexErrMsgTxt("Wrong number of dims (1).");;
dimsf = mxGetDimensions(prhs[1]);
if (!mxIsNumeric(prhs[2]) || !mxIsDouble(prhs[2]) || mxIsComplex(prhs[2]))
mexErrMsgTxt("Wrong sort of matrix.");
if (mxGetM(prhs[2]) != 4 || mxGetN(prhs[2]) != 4)
mexErrMsgTxt("Matrix must be 4x4.");
if (nrhs == 4)
{
if (!mxIsNumeric(prhs[3]) || !mxIsDouble(prhs[3]) || mxIsComplex(prhs[3]) ||
mxGetM(prhs[3])*mxGetN(prhs[3]) != 3)
mexErrMsgTxt("Invalid skips.");
s[0] = mxGetPr(prhs[3])[0];
s[1] = mxGetPr(prhs[3])[1];
s[2] = mxGetPr(prhs[3])[2];
}
else
{
s[0] = s[1] = s[2] = 1.0;
}
plhs[0] = mxCreateDoubleMatrix(256,256,mxREAL);
hist2(mxGetPr(prhs[2]), (unsigned char *)mxGetPr(prhs[0]), (unsigned char *)mxGetPr(prhs[1]),
dimsg, dimsf, mxGetPr(plhs[0]), s);
}
int test_calculate_histogram()
{
// Blog: http://blog.csdn.net/fengbingchun/article/details/76417598
const int length{ 10 * 1024 * 1024 }; // 100MB
std::unique_ptr<unsigned char[]> data(new unsigned char[length]);
generator_random_number<unsigned char>(data.get(), length, 0, 255);
const int hist_size{ 256 };
std::unique_ptr<unsigned int[]> hist1(new unsigned int[hist_size]), hist2(new unsigned int[hist_size]);
std::for_each(hist1.get(), hist1.get() + hist_size, [](unsigned int& n) {n = 0; });
std::for_each(hist2.get(), hist2.get() + hist_size, [](unsigned int& n) {n = 0; });
float elapsed_time1{ 0.f }, elapsed_time2{ 0.f }; // milliseconds
unsigned int value1{ 0 }, value2{ 0 };
int ret = calculate_histogram_cpu(data.get(), length, hist1.get(), value1, &elapsed_time1);
if (ret != 0) PRINT_ERROR_INFO(calculate_histogram_cpu);
ret = calculate_histogram_gpu(data.get(), length, hist2.get(), value2, &elapsed_time2);
if (ret != 0) PRINT_ERROR_INFO(calculate_histogram_gpu);
if (value1 != value2) {
fprintf(stderr, "their values are different: val1: %d, val2: %d\n", value1, value2);
return -1;
}
for (int i = 0; i < hist_size; ++i) {
if (hist1[i] != hist2[i]) {
fprintf(stderr, "their values are different at: %d, val1: %d, val2: %d\n",
i, hist1[i], hist2[i]);
return -1;
}
}
fprintf(stderr, "test calculate histogram: cpu run time: %f ms, gpu run time: %f ms\n", elapsed_time1, elapsed_time2);
return 0;
}
bool brightRGB::getMedian(const image& img,dvector& dest) const{
// image empty?
if (img.empty()) {
setStatusString("image empty");
dest.resize(0);
return false;
}
const rgbPixel transColor = getParameters().transColor;
dest.resize(3);
ivector hist0(256,0);
ivector hist1(256,0);
ivector hist2(256,0);
image::const_iterator it = img.begin();
if(getParameters().transparent) {
while(it != img.end()) {
if(*it != transColor) {
++hist0.at((*it).getRed());
++hist1.at((*it).getGreen());
++hist2.at((*it).getBlue());
}
it++;
}
const int counterHalf = hist0.sumOfElements()/2;
// check for complete image transparent
if (counterHalf==0) {
setStatusString("only transparent pixels");
dest.resize(0);
return false;
}
int i,s;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist0.at(i);
}
dest.at(0) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist1.at(i);
}
dest.at(1) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist2.at(i);
}
dest.at(2) = i-1;
} else { // no transparent color
while(it != img.end()) {
++hist0.at((*it).getRed());
++hist1.at((*it).getGreen());
++hist2.at((*it).getBlue());
it++;
}
const int counterHalf = img.columns()*img.rows()/2;
int i,s;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist0.at(i);
}
dest.at(0) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist1.at(i);
}
dest.at(1) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist2.at(i);
}
dest.at(2) = i-1;
}
// normalize to 0..1
dest.divide(255);
return true;
};
double pewarpcost(float *x, float *bX, float *bY, float *dbY, float *bZ,
mwSignedIndex *bdims, uint8_T *target, float *source, uint8_T *mask,
mwSignedIndex *ddims, float *skrn, int skrnl, float xscale,
float regstrength, float *H, uint8_T *warped, float *def)
{
int nvox = ddims[0] * ddims[1] * ddims[2];
/* float *def = mxMalloc(sizeof(float) * nvox); */
float *jac = mxMalloc(sizeof(float) * nvox);
/* uint8_T *warped = mxMalloc(sizeof(uint8_T) * nvox); */
/* float *H = mxMalloc(sizeof(float) * 256 * 256); */
float *Htmp = mxMalloc(sizeof(float) * 256 * 256);
double *s1 = mxMalloc(sizeof(double) * 256);
double *s2 = mxMalloc(sizeof(double) * 256);
double s, sL, s1L, s2L;
double cost;
/* mexPrintf(">>> Start -> %lu (CLOCKS_PER_SEC: %lu)\n", clock(), CLOCKS_PER_SEC); */
getDef(def, ddims, bdims[0], bX, bdims[1], bY, bdims[2], bZ, x, xscale);
/* The actual Jacobian is 1 + jac, but it is easier to add 1 later. */
getDef(jac, ddims, bdims[0], bX, bdims[1], dbY, bdims[2], bZ, x, xscale);
/* mexPrintf("getDef done -> %lu\n", clock()); */
/* For some reason, execution is single-threaded without the num_threads clause.
* TODO: Find out why and change. */
#pragma omp parallel num_threads(4) shared(s, sL, s1L, s2L)
{
peResampleAndApplyJacobian(warped, source, def, /* jac, */ mask, ddims);
/* if (omp_get_thread_num() == 1)
mexPrintf("peResampleAndApplyJacobian done -> %lu\n", clock()); */
hist2(H, target, warped, mask, nvox);
/* if (omp_get_thread_num() == 1)
mexPrintf("hist2 done -> %lu\n", clock()); */
smoothRows(Htmp, H, 256, 256, skrn, skrnl);
smoothCols(H, Htmp, 256, 256, skrn, skrnl);
/* if (omp_get_thread_num() == 1)
mexPrintf("smooth... done -> %lu\n", clock()); */
#pragma omp single
{
s = histSumAll(H, 65536);
}
#pragma omp sections
{
#pragma omp section
{
sL = histLogAll(H, s, 65536);
}
#pragma omp section
{
s1L = histLogVert(H, s1, s, 256, 256);
}
#pragma omp section
{
s2L = histLogHorz(H, s2, s, 256, 256);
}
}
/* if (omp_get_thread_num() == 1)
mexPrintf("hist... done -> %lu\n", clock()); */
}
cost = - (s1L + s2L) / sL + regstrength * regularisation(jac, nvox);
/* mexPrintf("regularisation done -> %lu\n", clock());
mexPrintf("<<<\n"); */
/* mexPrintf("cost: %f sL: %f s1L: %f s2L: %f #voxels: %d\n", cost, sL, s1L, s2L, nvox); */
/* mxFree(def); */
mxFree(jac);
/* mxFree(warped); */
/* mxFree(H); */
mxFree(Htmp);
mxFree(s1);
mxFree(s2);
return cost;
}
int main( int argc, char* argv[])
{
if( argc != 3)
{
std::cerr << "Usage: "<<argv[0]<<" [input.nc] [output.nc]\n";
return -1;
}
std::cout << argv[1]<< " -> "<<argv[2]<<std::endl;
//----------------
const unsigned Nhist = 50;
const unsigned nhist = 1;
const unsigned Ninput =100;
const double Nsigma =4.;
std::vector<double> input1(Ninput,0.);
std::vector<double> input2(Ninput,0.);
thrust::random::minstd_rand generator;
thrust::random::normal_distribution<double> d1;
thrust::random::normal_distribution<double> d2;
std::vector<double> rand1(Ninput,0.);
std::vector<double> rand2(Ninput,0.);
for (unsigned i=0;i<rand1.size();i++) { rand1[i] = d1(generator); }
for (unsigned i=0;i<rand2.size();i++) { rand2[i] = d2(generator); }
for (unsigned i=0;i<input1.size();i++) {
double t = (double)(i/(input1.size()-1));
double omega1 =2.*M_PI* 20.;
input1[i] = (rand1[i]*0.1*cos( omega1*t)+1.);
}
for (unsigned i=0;i<input2.size();i++) {
double t = (double)(i/(input2.size()-1));
//double omega1 = 2.*M_PI*20.;
double omega2= 2.*M_PI*30.;
//double phase = 0.5*M_PI;
// input2[i] =input1[i]; //perfectly correlated
// input2[i] = (-rand1[i]*0.1*cos(omega1*t)+1.);//perfectly anticorrelated
// input2[i] = (rand2[i]*0.001*cos(omega2*t)+3.);//perfectly uncorrelated
input2[i] = (rand2[i]*0.001*cos(omega2*t)+3.);//uncorrelated
}
//normalize grid and compute sigma
NormalizeToFluc(input1);
NormalizeToFluc(input2);
dg::Grid1d g1d1(-Nsigma,Nsigma, nhist, Nhist,dg::DIR);
dg::Grid1d g1d2(-Nsigma,Nsigma, nhist, Nhist,dg::DIR);
dg::Grid2d g2d( -Nsigma,Nsigma,-Nsigma,Nsigma, nhist, Nhist,Nhist,dg::DIR,dg::DIR);
dg::Histogram<dg::HVec> hist1(g1d1,input1);
dg::Histogram<dg::HVec> hist2(g1d2,input2);
dg::Histogram2D<dg::HVec> hist12(g2d,input1,input2);
dg::HVec PA1 = dg::evaluate(hist1,g1d1);
dg::HVec A1 = dg::evaluate(dg::cooX1d,g1d1);
dg::HVec PA2= dg::evaluate(hist2,g1d2);
dg::HVec A2 = dg::evaluate(dg::cooX1d,g1d2);
dg::HVec PA1A2= dg::evaluate(hist12,g2d);
//-----------------NC output start
int dataIDs1[2],dataIDs2[2],dataIDs12[1];
int dim_ids1[1],dim_ids2[1],dim_ids12[2];
int ncid;
file::NC_Error_Handle err;
err = nc_create(argv[2],NC_NETCDF4|NC_CLOBBER, &ncid);
//plot 1
err = file::define_dimension( ncid,"A1_", &dim_ids1[0], g1d1);
err = nc_def_var( ncid, "P(A1)", NC_DOUBLE, 1, &dim_ids1[0], &dataIDs1[0]);
err = nc_def_var( ncid, "A1", NC_DOUBLE, 1, &dim_ids1[0], &dataIDs1[1]);
err = nc_enddef( ncid);
err = nc_put_var_double( ncid, dataIDs1[0], PA1.data() );
err = nc_put_var_double( ncid, dataIDs1[1], A1.data() );
err = nc_redef(ncid);
//plot 2
err = file::define_dimension( ncid,"A2_", &dim_ids2[0], g1d2);
err = nc_def_var( ncid, "P(A2)", NC_DOUBLE, 1, &dim_ids2[0], &dataIDs2[0]);
err = nc_def_var( ncid, "A2", NC_DOUBLE, 1, &dim_ids2[0], &dataIDs2[1]);
err = nc_enddef( ncid);
err = nc_put_var_double( ncid, dataIDs2[0], PA2.data() );
err = nc_put_var_double( ncid, dataIDs2[1], A2.data() );
err = nc_redef(ncid);
//plot12
// dim_ids12[0]=dim_ids1[0];
// dim_ids12[1]=dim_ids2[0];
dim_ids12[0]=dataIDs1[0];
dim_ids12[1]=dataIDs2[0];
err = file::define_dimensions( ncid, &dim_ids12[0], g2d);
err = nc_def_var( ncid, "P(A1,A2)", NC_DOUBLE, 2, &dim_ids12[0], &dataIDs12[0]);
err = nc_enddef( ncid);
err = nc_put_var_double( ncid, dataIDs12[0], PA1A2.data() );
err = nc_redef(ncid);
nc_close( ncid);
return 0;
}