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+// Beispielhafter Aufruf: BUILD_x86_64/progs/testSemanticSegmentation -config <CONFIGFILE>
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+
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+/**
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+* @file testSemanticSegmentation.cpp
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+* @brief test semantic segmentation routines
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+* @author Erik Rodner
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+* @date 03/20/2008
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+*/
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+
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+#ifdef NICE_USELIB_OPENMP
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+#include <omp.h>
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+#endif
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+
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+#include <objrec/baselib/Config.h>
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+#include <objrec/baselib/StringTools.h>
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+#include <objrec/baselib/ICETools.h>
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+
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+#include <objrec/semanticsegmentation/SemanticSegmentation.h>
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+#include <objrec/semanticsegmentation/SemSegLocal.h>
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+#include <objrec/semanticsegmentation/SemSegSTF.h>
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+#include <objrec/semanticsegmentation/SemSegCsurka.h>
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+#include <objrec/semanticsegmentation/SemSegRegionBased.h>
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+
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+#include <fstream>
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+
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+using namespace OBJREC;
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+
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+using namespace NICE;
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+
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+using namespace std;
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+
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+void updateMatrix(const NICE::Image & img, const NICE::Image & gt,
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+ NICE::Matrix & M, const set<int> & forbidden_classes)
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+{
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+ double subsamplex = gt.width() / (double)img.width();
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+ double subsampley = gt.height() / (double)img.height();
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+
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+ for (int y = 0 ; y < gt.height() ; y++)
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+ for (int x = 0 ; x < gt.width() ; x++)
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+ {
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+ int xx = (int)(x / subsamplex);
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+ int yy = (int)(y / subsampley);
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+
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+ if (xx < 0) xx = 0;
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+
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+ if (yy < 0) yy = 0;
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+
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+ if (xx > img.width() - 1) xx = img.width() - 1;
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+
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+ if (yy > img.height() - 1) yy = img.height() - 1;
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+
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+ int cimg = img.getPixel(xx, yy);
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+
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+ int gimg = gt.getPixel(x, y);
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+
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+ if (forbidden_classes.find(gimg) == forbidden_classes.end())
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+ {
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+ M(gimg, cimg)++;
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+ }
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+ }
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+}
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+
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+/**
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+ test semantic segmentation routines
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+*/
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+int main(int argc, char **argv)
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+{
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+ std::set_terminate(__gnu_cxx::__verbose_terminate_handler);
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+
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+ Config conf(argc, argv);
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+
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+ bool show_result = conf.gB("debug", "show_results", false);
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+
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+ bool write_results = conf.gB("debug", "write_results", false);
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+
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+ bool write_results_pascal = conf.gB("debug", "write_results_pascal", false);
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+
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+ std::string resultdir = conf.gS("debug", "resultdir", ".");
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+
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+ if (write_results)
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+ {
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+ cerr << "Writing Results to " << resultdir << endl;
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+ }
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+
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+ MultiDataset md(&conf);
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+
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+ const ClassNames & classNames = md.getClassNames("train");
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+
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+ //SemanticSegmentation *semseg = new SemSegLocal ( &conf, &md );
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+ //SemanticSegmentation *semseg = new SemSegSTF ( &conf, &md );
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+ //SemanticSegmentation *semseg = new SemSegCsurka ( &conf, &md);
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+
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+ SemanticSegmentation *semseg = new SemSegRegionBased(&conf, &md);
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+
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+ const LabeledSet *testFiles = md["test"];
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+ NICE::Matrix M(classNames.getMaxClassno() + 1, classNames.getMaxClassno() + 1);
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+ M.set(0);
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+
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+ set<int> forbidden_classes;
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+
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+ std::string forbidden_classes_s = conf.gS("analysis", "forbidden_classes", "");
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+
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+ classNames.getSelection(forbidden_classes_s, forbidden_classes);
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+
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+ ProgressBar pb("Semantic Segmentation Analysis");
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+ pb.show();
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+
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+ int fileno = 0;
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+
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+ LOOP_ALL_S(*testFiles)
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+ {
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+ EACH_INFO(classno, info);
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+ pb.update(testFiles->count());
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+
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+ std::string file = info.img();
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+
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+ NICE::Image lm;
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+ GenericImage<double> probabilities;
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+
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+ if (info.hasLocalizationInfo())
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+ {
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+ const LocalizationResult *l_gt = info.localization();
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+
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+ lm.resize(l_gt->xsize, l_gt->ysize);
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+ lm.set(0);
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+ l_gt->calcLabeledImage(lm, classNames.getBackgroundClass());
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+ }
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+
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+ semseg->semanticseg(file, lm, probabilities);
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+ fprintf(stderr, "testSemanticSegmentation: Segmentation finished !\n");
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+
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+ NICE::Image lm_gt;
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+
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+ if (info.hasLocalizationInfo())
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+ {
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+ const LocalizationResult *l_gt = info.localization();
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+
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+ lm_gt.resize(l_gt->xsize, l_gt->ysize);
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+ lm_gt.set(0);
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+
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+ fprintf(stderr, "testSemanticSegmentation: Generating Labeled NICE::Image (Ground-Truth)\n");
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+ l_gt->calcLabeledImage(lm_gt, classNames.getBackgroundClass());
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+ }
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+
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+ std::string fname = StringTools::baseName(file, false);
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+
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+ if (write_results_pascal)
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+ {
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+ NICE::Image pascal_lm(lm.width(), lm.height());
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+ int backgroundClass = classNames.getBackgroundClass();
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+
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+ for (int y = 0 ; y < lm.height(); y++)
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+ for (int x = 0 ; x < lm.width(); x++)
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+ {
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+ int v = lm.getPixel(x, y);
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+
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+ if (v == backgroundClass)
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+ pascal_lm.setPixel(x, y, 255);
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+ else
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+ pascal_lm.setPixel(x, y, 255 - v - 1);
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+ }
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+
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+ char filename[1024];
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+
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+ char *format = (char *)"pgm";
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+ sprintf(filename, "%s/%s.%s", resultdir.c_str(), fname.c_str(), format);
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+
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+ pascal_lm.write(filename);
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+ }
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+
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+ if (show_result || write_results)
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+ {
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+ NICE::ColorImage orig(file);
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+ NICE::ColorImage rgb;
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+ NICE::ColorImage rgb_gt;
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+ classNames.labelToRGB(lm, rgb);
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+ classNames.labelToRGB(lm_gt, rgb_gt);
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+
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+ if (write_results)
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+ {
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+ char filename[1024];
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+ char *format = "ppm";
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+ sprintf(filename, "%06d.%s", fileno, format);
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+ std::string origfilename = resultdir + "/orig_" + string(filename);
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+ cerr << "Writing to file " << origfilename << endl;
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+ orig.write(origfilename);
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+ rgb.write(resultdir + "/result_" + string(filename));
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+ rgb_gt.write(resultdir + "/groundtruth_" + string(filename));
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+ }
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+
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+ if (show_result)
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+ {
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+#ifndef NOVISUAL
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+ showImage(rgb, "Result");
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+ showImage(rgb_gt, "Groundtruth");
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+ showImage(orig, "Input");
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+#endif
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+ }
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+ }
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+
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+//#pragma omp critical
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+ updateMatrix(lm, lm_gt, M, forbidden_classes);
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+
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+ cerr << M << endl;
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+ fileno++;
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+ }
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+
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+ pb.hide();
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+
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+ double overall = 0.0;
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+ double sumall = 0.0;
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+ for(int r = 0; r < (int)M.rows(); r++)
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+ {
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+ for(int c = 0; c < (int)M.cols(); c++)
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+ {
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+ if(r == c)
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+ overall += M(r,c);
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+ sumall += M(r,c);
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+ }
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+ }
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+
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+ overall /= sumall;
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+
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+ // normalizing M using rows
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+ for (int r = 0 ; r < (int)M.rows() ; r++)
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+ {
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+ double sum = 0.0;
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+
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+ for (int c = 0 ; c < (int)M.cols() ; c++)
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+ sum += M(r, c);
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+
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+ if (fabs(sum) > 1e-4)
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+ for (int c = 0 ; c < (int)M.cols() ; c++)
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+ M(r, c) /= sum;
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+ }
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+
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+ cerr << M << endl;
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+
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+ double avg_perf = 0.0;
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+ int classes_trained = 0;
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+
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+ for (int r = 0 ; r < (int)M.rows() ; r++)
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+ {
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+ if ((classNames.existsClassno(r)) && (forbidden_classes.find(r) == forbidden_classes.end()))
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+ {
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+ avg_perf += M(r, r);
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+ classes_trained++;
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+ }
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+ }
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+
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+ if (write_results)
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+ {
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+ ofstream fout((resultdir + "/res.txt").c_str(), ios::out);
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+ fout << "overall: " << overall << endl;
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+ fout << "Average Performance " << avg_perf / (classes_trained) << endl;
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+ fout << "Lower Bound " << 1.0 / classes_trained << endl;
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+
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+ for (int r = 0 ; r < (int)M.rows() ; r++)
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+ {
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+ if ((classNames.existsClassno(r)) && (forbidden_classes.find(r) == forbidden_classes.end()))
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+ {
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+ std::string classname = classNames.text(r);
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+ fout << classname.c_str() << ": " << M(r, r) << endl;
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+ }
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+ }
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+
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+ fout.close();
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+ }
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+
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+ cerr << "overall: " << overall << endl;
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+ fprintf(stderr, "Average Performance %f\n", avg_perf / (classes_trained));
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+ //fprintf(stderr, "Lower Bound %f\n", 1.0 / classes_trained);
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+ for (int r = 0 ; r < (int)M.rows() ; r++)
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+ {
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+ if ((classNames.existsClassno(r)) && (forbidden_classes.find(r) == forbidden_classes.end()))
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+ {
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+ std::string classname = classNames.text(r);
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+ fprintf(stderr, "%s: %f\n", classname.c_str(), M(r, r));
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+ }
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+ }
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+
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+ delete semseg;
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+
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+ return 0;
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+}
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