From d39fbb7608050632ea28ca7eb302faea43e5c6ce Mon Sep 17 00:00:00 2001 From: Vladimir Date: Tue, 30 Jun 2015 22:24:13 +0900 Subject: [PATCH] Fixing Warnings #2 --- modules/tracking/src/tldDetector.cpp | 112 +++++++++--------- .../tracking/src/tldEnsembleClassifier.cpp | 15 ++- .../tracking/src/tldEnsembleClassifier.hpp | 2 - modules/tracking/src/tldModel.cpp | 13 +- modules/tracking/src/tldModel.hpp | 8 +- modules/tracking/src/tldTracker.hpp | 4 +- 6 files changed, 68 insertions(+), 86 deletions(-) diff --git a/modules/tracking/src/tldDetector.cpp b/modules/tracking/src/tldDetector.cpp index 0b510ad5f..9f9bdb627 100644 --- a/modules/tracking/src/tldDetector.cpp +++ b/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_& 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(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_& 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_ 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 resized_imgs, blurred_imgs; std::vector varBuffer, ensBuffer; std::vector 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 resized_imgs, blurred_imgs; std::vector varBuffer, ensBuffer; std::vector 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_ 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) diff --git a/modules/tracking/src/tldEnsembleClassifier.cpp b/modules/tracking/src/tldEnsembleClassifier.cpp index 32286928e..1d72b3e81 100644 --- a/modules/tracking/src/tldEnsembleClassifier.cpp +++ b/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& 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_& 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)); diff --git a/modules/tracking/src/tldEnsembleClassifier.hpp b/modules/tracking/src/tldEnsembleClassifier.hpp index 2725700e6..97221372d 100644 --- a/modules/tracking/src/tldEnsembleClassifier.hpp +++ b/modules/tracking/src/tldEnsembleClassifier.hpp @@ -64,7 +64,5 @@ namespace cv std::vector offset; int lastStep_; }; - - } } \ No newline at end of file diff --git a/modules/tracking/src/tldModel.cpp b/modules/tracking/src/tldModel.cpp index 6c926d706..59b621845 100644 --- a/modules/tracking/src/tldModel.cpp +++ b/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_ 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 diff --git a/modules/tracking/src/tldModel.hpp b/modules/tracking/src/tldModel.hpp index 67746745b..ea11d17d8 100644 --- a/modules/tracking/src/tldModel.hpp +++ b/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_& example, bool positive); void modelEstimationImpl(const std::vector& /*responses*/){} void modelUpdateImpl(){} - Rect2d boundingBox_; + Rect2d boundingBox_; RNG rng; - }; } diff --git a/modules/tracking/src/tldTracker.hpp b/modules/tracking/src/tldTracker.hpp index de2622383..e04926d5d 100644 --- a/modules/tracking/src/tldTracker.hpp +++ b/modules/tracking/src/tldTracker.hpp @@ -49,7 +49,7 @@ #include #include -namespace cv +namespace cv { TrackerTLD::Params::Params(){} @@ -60,8 +60,6 @@ void TrackerTLD::Params::write(cv::FileStorage& /*fs*/) const {} namespace tld { - - class TrackerProxy { public: