Fixing Warnings #2

modules/tracking/src/tldDetector.cpp

@@ -53,7 +53,7 @@ namespace cv
 		}
 
 		// Calculate posterior probability, that the patch belongs to the current EC model
- 		double TLDDetector::ensembleClassifierNum(const uchar* data)
+		double TLDDetector::ensembleClassifierNum(const uchar* data)
 		{
 			double p = 0;
 			for (int k = 0; k < (int)classifiers.size(); k++)
@@ -106,10 +106,10 @@ namespace cv
 
 		double TLDDetector::ocl_Sr(const Mat_<uchar>& patch)
 		{
-			int64 e1, e2, e3, e4;
-			double t;
-			e1 = getTickCount();
-			e3 = getTickCount();
+			//int64 e1, e2, e3, e4;
+			//double t;
+			//e1 = getTickCount();
+			//e3 = getTickCount();
 			double splus = 0.0, sminus = 0.0;
 
 
@@ -134,23 +134,23 @@ namespace cv
 				posNum,
 				negNum);
 
-			e4 = getTickCount();
-			t = (e4 - e3) / getTickFrequency()*1000.0;
+			//e4 = getTickCount();
+			//t = (e4 - e3) / getTickFrequency()*1000.0;
 			//printf("Mem Cpy GPU: %f\n", t);
 
 			size_t globSize = 1000;
 			size_t localSize = 128;
-			e3 = getTickCount();
+			//e3 = getTickCount();
 			if (!k.run(1, &globSize, &localSize, true))
 				printf("Kernel Run Error!!!");
-			e4 = getTickCount();
-			t = (e4 - e3) / getTickFrequency()*1000.0;
+			//e4 = getTickCount();
+			//t = (e4 - e3) / getTickFrequency()*1000.0;
 			//printf("Kernel Run GPU: %f\n", t);
 
-			e3 = getTickCount();
+			//e3 = getTickCount();
 			Mat resNCC = devNCC.getMat(ACCESS_READ);
-			e4 = getTickCount();
-			t = (e4 - e3) / getTickFrequency()*1000.0;
+			//e4 = getTickCount();
+			//t = (e4 - e3) / getTickFrequency()*1000.0;
 			//printf("Read Mem GPU: %f\n", t);
 
 			////Compare
@@ -174,8 +174,8 @@ namespace cv
 			for (int i = 0; i < *negNum; i++)
 				sminus = std::max(sminus, 0.5 * (resNCC.at<float>(i+500) +1.0));
 
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Sr GPU: %f\n\n", t);
 
 			if (splus + sminus == 0.0)
@@ -185,10 +185,10 @@ namespace cv
 
 		void TLDDetector::ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches)
 		{
-			int64 e1, e2, e3, e4;
-			double t;
-			e1 = getTickCount();
-			e3 = getTickCount();
+			//int64 e1, e2, e3, e4;
+			//double t;
+			//e1 = getTickCount();
+			//e3 = getTickCount();
 
 			UMat devPatches = patches.getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
 			UMat devPositiveSamples = posExp->getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
@@ -213,25 +213,25 @@ namespace cv
 				negNum,
 				numOfPatches);
 
-			e4 = getTickCount();
-			t = (e4 - e3) / getTickFrequency()*1000.0;
+			//e4 = getTickCount();
+			//t = (e4 - e3) / getTickFrequency()*1000.0;
 			//printf("Mem Cpy GPU: %f\n", t);
 
 			// 2 -> Pos&Neg
 			size_t globSize = 2 * numOfPatches*MAX_EXAMPLES_IN_MODEL;
 			size_t localSize = 1024;
-			e3 = getTickCount();
+			//e3 = getTickCount();
 			if (!k.run(1, &globSize, &localSize, true))
 				printf("Kernel Run Error!!!");
-			e4 = getTickCount();
-			t = (e4 - e3) / getTickFrequency()*1000.0;
+			//e4 = getTickCount();
+			//t = (e4 - e3) / getTickFrequency()*1000.0;
 			//printf("Kernel Run GPU: %f\n", t);
 
-			e3 = getTickCount();
+			//e3 = getTickCount();
 			Mat posNCC = devPosNCC.getMat(ACCESS_READ);
 			Mat negNCC = devNegNCC.getMat(ACCESS_READ);
-			e4 = getTickCount();
-			t = (e4 - e3) / getTickFrequency()*1000.0;
+			//e4 = getTickCount();
+			//t = (e4 - e3) / getTickFrequency()*1000.0;
 			//printf("Read Mem GPU: %f\n", t);
 
 			//Calculate Srs
@@ -281,8 +281,8 @@ namespace cv
 
 
 
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Sr GPU: %f\n\n", t);
 		}
 
@@ -312,9 +312,9 @@ namespace cv
 			return splus / (sminus + splus);
 			*/
 
-			int64 e1, e2;
-			float t;
-			e1 = getTickCount();
+			//int64 e1, e2;
+			//double t;
+			//e1 = getTickCount();
 			double splus = 0.0, sminus = 0.0;
 			Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
 			int med = getMedian((*timeStampsPositive));
@@ -331,8 +331,8 @@ namespace cv
 				modelSample.data = &(negExp->data[i * 225]);
 				sminus = std::max(sminus, 0.5 * (NCC(modelSample, patch) + 1.0));
 			}
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Sc: %f\n", t);
 			if (splus + sminus == 0.0)
 				return 0.0;
@@ -473,10 +473,10 @@ namespace cv
 			std::vector <Mat> resized_imgs, blurred_imgs;
 			std::vector <Point> varBuffer, ensBuffer;
 			std::vector <double> varScaleIDs, ensScaleIDs;
-			int64 e1, e2;
-			double t;
+			//int64 e1, e2;
+			//double t;
 
-			e1 = getTickCount();
+			//e1 = getTickCount();
 			//Detection part
 			//Generate windows and filter by variance
 			scaleID = 0;
@@ -505,12 +505,12 @@ namespace cv
 				GaussianBlur(resized_imgs[scaleID], tmp, GaussBlurKernelSize, 0.0f);
 				blurred_imgs.push_back(tmp);
 			} while (size.width >= initSize.width && size.height >= initSize.height);
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Variance: %d\t%f\n", varBuffer.size(), t);
 
 			//Encsemble classification
-			e1 = getTickCount();
+			//e1 = getTickCount();
 			for (int i = 0; i < (int)varBuffer.size(); i++)
 			{
 				prepareClassifiers((int)blurred_imgs[varScaleIDs[i]].step[0]);
@@ -519,12 +519,12 @@ namespace cv
 				ensBuffer.push_back(varBuffer[i]);
 				ensScaleIDs.push_back(varScaleIDs[i]);
 			}
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Ensemble: %d\t%f\n", ensBuffer.size(), t);
 
 			//NN classification
-			e1 = getTickCount();
+			//e1 = getTickCount();
 			for (int i = 0; i < (int)ensBuffer.size(); i++)
 			{
 				LabeledPatch labPatch;
@@ -558,8 +558,8 @@ namespace cv
 					maxScRect = labPatch.rect;
 				}
 			}
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("NN: %d\t%f\n", patches.size(), t);
 
 			if (maxSc < 0)
@@ -583,10 +583,10 @@ namespace cv
 			std::vector <Mat> resized_imgs, blurred_imgs;
 			std::vector <Point> varBuffer, ensBuffer;
 			std::vector <double> varScaleIDs, ensScaleIDs;
-			int64 e1, e2;
-			double t;
+			//int64 e1, e2;
+			//double t;
 
-			e1 = getTickCount();
+			//e1 = getTickCount();
 			//Detection part
 			//Generate windows and filter by variance
 			scaleID = 0;
@@ -615,12 +615,12 @@ namespace cv
 				GaussianBlur(resized_imgs[scaleID], tmp, GaussBlurKernelSize, 0.0f);
 				blurred_imgs.push_back(tmp);
 			} while (size.width >= initSize.width && size.height >= initSize.height);
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Variance: %d\t%f\n", varBuffer.size(), t);
 
 			//Encsemble classification
-			e1 = getTickCount();
+			//e1 = getTickCount();
 			for (int i = 0; i < (int)varBuffer.size(); i++)
 			{
 				prepareClassifiers((int)blurred_imgs[varScaleIDs[i]].step[0]);
@@ -629,12 +629,12 @@ namespace cv
 				ensBuffer.push_back(varBuffer[i]);
 				ensScaleIDs.push_back(varScaleIDs[i]);
 			}
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("Ensemble: %d\t%f\n", ensBuffer.size(), t);
 
 			//NN classification
-			e1 = getTickCount();
+			//e1 = getTickCount();
 			//Prepare batch of patches
 			int numOfPatches = ensBuffer.size();
 			Mat_<uchar> stdPatches(numOfPatches, 225);
@@ -690,8 +690,8 @@ namespace cv
 					maxScRect = labPatch.rect;
 				}
 			}
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency()*1000.0;
 			//printf("NN: %d\t%f\n", patches.size(), t);
 
 			if (maxSc < 0)

modules/tracking/src/tldEnsembleClassifier.cpp

@@ -55,15 +55,14 @@ namespace cv
 			measurements.assign(meas.begin() + beg, meas.begin() + end);
 			offset.assign(mpc, Point2i(0, 0));
 		}
-		
-		// Calculate measure locations from 15x15 grid on minSize patches 
+		// Calculate measure locations from 15x15 grid on minSize patches
 		void TLDEnsembleClassifier::stepPrefSuff(std::vector<Vec4b>& arr, int pos, int len, int gridSize)
 		{
-#if 0
+		#if 0
 			int step = len / (gridSize - 1), pref = (len - step * (gridSize - 1)) / 2;
 			for (int i = 0; i < (int)(sizeof(x1) / sizeof(x1[0])); i++)
 				arr[i] = pref + arr[i] * step;
-#else
+		#else
 			int total = len - gridSize;
 			int quo = total / (gridSize - 1), rem = total % (gridSize - 1);
 			int smallStep = quo, bigStep = quo + 1;
@@ -92,7 +91,7 @@ namespace cv
 			}
 #endif
 		}
-		
+
 		// Calculate offsets for classifier
 		void TLDEnsembleClassifier::prepareClassifier(int rowstep)
 		{
@@ -106,7 +105,7 @@ namespace cv
 				}
 			}
 		}
-		
+
 		// Integrate patch into the Ensemble Classifier model
 		void TLDEnsembleClassifier::integrate(const Mat_<uchar>& patch, bool isPositive)
 		{
@@ -137,7 +136,7 @@ namespace cv
 				return posNum / (posNum + negNum);
 		}
 
-		// Calculate the 13-bit fern index 
+		// Calculate the 13-bit fern index
 		int TLDEnsembleClassifier::codeFast(const uchar* data) const
 		{
 			int position = 0;
@@ -188,7 +187,7 @@ namespace cv
 			stepPrefSuff(measurements, 2, size.height, gridSize);
 			stepPrefSuff(measurements, 3, size.height, gridSize);
 
-			//Compile fern classifiers 
+			//Compile fern classifiers
 			for (int i = 0, howMany = (int)measurements.size() / measurePerClassifier; i < howMany; i++)
 				classifiers.push_back(TLDEnsembleClassifier(measurements, i * measurePerClassifier, (i + 1) * measurePerClassifier));
 

modules/tracking/src/tldEnsembleClassifier.hpp

@@ -64,7 +64,5 @@ namespace cv
 			std::vector<Point2i> offset;
 			int lastStep_;
 		};
-
-	
 	}
 }
\ No newline at end of file

modules/tracking/src/tldModel.cpp

@@ -65,7 +65,7 @@ namespace cv
 			detector->posExp = &posExp;
 			detector->negExp = &negExp;
 
-			detector->positiveExamples = &positiveExamples;			
+			detector->positiveExamples = &positiveExamples;
 			detector->negativeExamples = &negativeExamples;
 			detector->timeStampsPositive = &timeStampsPositive;
 			detector->timeStampsNegative = &timeStampsNegative;
@@ -73,21 +73,18 @@ namespace cv
 
 			//Calculate the variance in initial BB
 			originalVariance_ = variance(image(boundingBox));
-			
 			//Find the scale
 			double scale = scaleAndBlur(image, cvRound(log(1.0 * boundingBox.width / (minSize.width)) / log(SCALE_STEP)),
 				scaledImg, blurredImg, GaussBlurKernelSize, SCALE_STEP);
 			GaussianBlur(image, image_blurred, GaussBlurKernelSize, 0.0);
 			TLDDetector::generateScanGrid(image.rows, image.cols, minSize_, scanGrid);
-			getClosestN(scanGrid, Rect2d(boundingBox.x / scale, boundingBox.y / scale, boundingBox.width / scale, 
-				boundingBox.height / scale), 10, closest);
-
+			getClosestN(scanGrid, Rect2d(boundingBox.x / scale, boundingBox.y / scale, boundingBox.width / scale, boundingBox.height / scale), 10, closest);
 			Mat_<uchar> blurredPatch(minSize);
 			TLDEnsembleClassifier::makeClassifiers(minSize, MEASURES_PER_CLASSIFIER, GRIDSIZE, detector->classifiers);
 
 			//Generate initial positive samples and put them to the model
 			positiveExamples.reserve(200);
-			
+
 			for (int i = 0; i < (int)closest.size(); i++)
 			{
 				for (int j = 0; j < 20; j++)
@@ -297,9 +294,5 @@ namespace cv
 			dfprintf((port, "\tpositiveExamples.size() = %d\n", (int)positiveExamples.size()));
 			dfprintf((port, "\tnegativeExamples.size() = %d\n", (int)negativeExamples.size()));
 		}
-
-
-		
-
 	}
 }
\ No newline at end of file

modules/tracking/src/tldModel.hpp

@@ -50,10 +50,6 @@ namespace cv
 {
 	namespace tld
 	{
-
-		
-
-
 		class TrackerTLDModel : public TrackerModel
 		{
 		public:
@@ -77,14 +73,12 @@ namespace cv
 
 		protected:
 			Size minSize_;
-			
 			TrackerTLD::Params params_;
 			void pushIntoModel(const Mat_<uchar>& example, bool positive);
 			void modelEstimationImpl(const std::vector<Mat>& /*responses*/){}
 			void modelUpdateImpl(){}
-			Rect2d boundingBox_;			
+			Rect2d boundingBox_;
 			RNG rng;
-			
 		};
 
 	}

modules/tracking/src/tldTracker.hpp

@@ -49,7 +49,7 @@
 #include<algorithm>
 #include<limits.h>
 
-namespace cv 
+namespace cv
 {
 
 TrackerTLD::Params::Params(){}
@@ -60,8 +60,6 @@ void TrackerTLD::Params::write(cv::FileStorage& /*fs*/) const {}
 
 namespace tld
 {
-		
-
 class TrackerProxy
 {
 public: