Cleaning up debug prints/variables/flags/timers

modules/tracking/src/multiTracker.cpp

@@ -75,7 +75,6 @@ namespace cv
 
 	bool MultiTracker::update(const Mat& image)
 	{
-		printf("Naive-Loop MO-TLD Update....\n");
 		for (int i = 0; i < (int)trackers.size(); i++)
 			if (!trackers[i]->update(image, boundingBoxes[i]))
 				return false;
@@ -237,14 +236,7 @@ namespace cv
 
 
 		}
-		//Debug display candidates after Variance Filter
-		////////////////////////////////////////////////
-		Mat tmpImg = image;
-		for (int i = 0; i < (int)debugStack[0].size(); i++)
-			//rectangle(tmpImg, debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
-		debugStack[0].clear();
-		tmpImg.copyTo(image);
-		////////////////////////////////////////////////
+
 		return true;
 	}
 
@@ -342,7 +334,6 @@ namespace cv
 		} while (size.width >= initSize.width && size.height >= initSize.height);
 
 		//Encsemble classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -385,7 +376,6 @@ namespace cv
 		}
 
 		//NN classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -538,7 +528,6 @@ namespace cv
 		} while (size.width >= initSize.width && size.height >= initSize.height);
 
 		//Encsemble classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -581,7 +570,6 @@ namespace cv
 		}
 
 		//NN classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -589,7 +577,6 @@ namespace cv
 			tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
 			//TLD Model Extraction
 			tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
-			//Size InitSize = tldModel->getMinSize();
 			npos = 0;
 			nneg = 0;
 			maxSc = -5.0;

modules/tracking/src/tldDetector.hpp

@@ -81,7 +81,6 @@ namespace cv
 			double ocl_Sc(const Mat_<uchar>& patch);
 			void ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches);
 
-			std::vector <Rect2d> debugStack[10];
 			std::vector<TLDEnsembleClassifier> classifiers;
 			Mat *posExp, *negExp;
 			int *posNum, *negNum;

modules/tracking/src/tldModel.cpp

@@ -140,7 +140,6 @@ namespace cv
 						detector->classifiers[k].integrate(blurredPatch, false);
 				}
 			}
-			//dprintf(("positive patches: %d\nnegative patches: %d\n", (int)positiveExamples.size(), (int)negativeExamples.size()));
 		}
 
 
@@ -180,16 +179,6 @@ namespace cv
 						detector->classifiers[i].integrate(blurredPatch, patches[k].isObject);
 				}
 			}
-			/*
-			if( negativeIntoModel > 0 )
-			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
-			if( positiveIntoModel > 0)
-			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
-			if( negativeIntoEnsemble > 0 )
-			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
-			if( positiveIntoEnsemble > 0 )
-			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
-			dfprintf((stdout, "\n"));*/
 
 		}
 
@@ -198,9 +187,6 @@ namespace cv
 			int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
 			if ((int)eForModel.size() == 0) return;
 
-			//int64 e1, e2;
-			//double t;
-			//e1 = getTickCount();
 			for (int k = 0; k < (int)eForModel.size(); k++)
 			{
 				double sr = detector->Sr(eForModel[k]);
@@ -231,19 +217,6 @@ namespace cv
 						detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
 				}
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency() * 1000;
-			//printf("Integrate Additional: %fms\n", t);
-			/*
-			if( negativeIntoModel > 0 )
-			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
-			if( positiveIntoModel > 0 )
-			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
-			if( negativeIntoEnsemble > 0 )
-			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
-			if( positiveIntoEnsemble > 0 )
-			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
-			dfprintf((stdout, "\n"));*/
 		}
 
 		void TrackerTLDModel::ocl_integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
@@ -251,10 +224,6 @@ namespace cv
 			int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
 			if ((int)eForModel.size() == 0) return;
 
-			//int64 e1, e2;
-			//double t;
-			//e1 = getTickCount();
-
 			//Prepare batch of patches
 			int numOfPatches = (int)eForModel.size();
 			Mat_<uchar> stdPatches(numOfPatches, 225);
@@ -301,19 +270,6 @@ namespace cv
 						detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
 				}
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency() * 1000;
-			//printf("Integrate Additional OCL: %fms\n", t);
-			/*
-			if( negativeIntoModel > 0 )
-			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
-			if( positiveIntoModel > 0 )
-			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
-			if( negativeIntoEnsemble > 0 )
-			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
-			if( positiveIntoEnsemble > 0 )
-			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
-			dfprintf((stdout, "\n"));*/
 		}
 
 		//Push the patch to the model

modules/tracking/src/tldTracker.cpp

@@ -151,12 +151,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
     }
     std::vector<double>::iterator it = std::max_element(candidatesRes.begin(), candidatesRes.end());
 
-    //dfprintf((stdout, "scale = %f\n", log(1.0 * boundingBox.width / (data->getMinSize()).width) / log(SCALE_STEP)));
-    //for( int i = 0; i < (int)candidatesRes.size(); i++ )
-        //dprintf(("\tcandidatesRes[%d] = %f\n", i, candidatesRes[i]));
-    //data->printme();
-    //tldModel->printme(stdout);
-
     if( it == candidatesRes.end() )
     {
         data->confident = false;
@@ -173,16 +167,7 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
 
 #if 1
     if( it != candidatesRes.end() )
-    {
         resample(imageForDetector, candidates[it - candidatesRes.begin()], standardPatch);
-        //dfprintf((stderr, "%d %f %f\n", data->frameNum, tldModel->Sc(standardPatch), tldModel->Sr(standardPatch)));
-        //if( candidatesRes.size() == 2 &&  it == (candidatesRes.begin() + 1) )
-            //dfprintf((stderr, "detector WON\n"));
-    }
-    else
-    {
-        //dfprintf((stderr, "%d x x\n", data->frameNum));
-    }
 #endif
 
     if( *it > CORE_THRESHOLD )
@@ -213,7 +198,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
             detectorResults[i].isObject = expertResult;
         }
         tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
-        //dprintf(("%d relabeled by nExpert\n", negRelabeled));
         pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
 		if (ocl::haveOpenCL())
 			tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true);
@@ -234,16 +218,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
 #endif
     }
 
-
-	//Debug display candidates after Variance Filter
-	////////////////////////////////////////////////
-	Mat tmpImg = image;
-	for (int i = 0; i < (int)tldModel->detector->debugStack[0].size(); i++)
-		//rectangle(tmpImg, tldModel->detector->debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
-	tldModel->detector->debugStack[0].clear();
-	tmpImg.copyTo(image);
-
-	////////////////////////////////////////////////
     return true;
 }
 
@@ -310,7 +284,6 @@ Data::Data(Rect2d initBox)
     minSize.width = (int)(initBox.width * 20.0 / minDim);
     minSize.height = (int)(initBox.height * 20.0 / minDim);
     frameNum = 0;
-    //dprintf(("minSize = %dx%d\n", minSize.width, minSize.height));
 }
 
 void Data::printme(FILE*  port)