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332 lines
12 KiB
332 lines
12 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "tldModel.hpp" |
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namespace cv |
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{ |
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namespace tld |
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{ |
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//Constructor |
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TrackerTLDModel::TrackerTLDModel(TrackerTLD::Params params, const Mat& image, const Rect2d& boundingBox, Size minSize): |
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timeStampPositiveNext(0), timeStampNegativeNext(0), minSize_(minSize), params_(params), boundingBox_(boundingBox) |
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{ |
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std::vector<Rect2d> closest, scanGrid; |
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Mat scaledImg, blurredImg, image_blurred; |
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//Create Detector |
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detector = Ptr<TLDDetector>(new TLDDetector()); |
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//Propagate data to Detector |
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posNum = 0; |
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negNum = 0; |
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posExp = Mat(Size(225, 500), CV_8UC1); |
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negExp = Mat(Size(225, 500), CV_8UC1); |
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detector->posNum = &posNum; |
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detector->negNum = &negNum; |
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detector->posExp = &posExp; |
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detector->negExp = &negExp; |
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detector->positiveExamples = &positiveExamples; |
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detector->negativeExamples = &negativeExamples; |
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detector->timeStampsPositive = &timeStampsPositive; |
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detector->timeStampsNegative = &timeStampsNegative; |
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detector->originalVariancePtr = &originalVariance_; |
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//Calculate the variance in initial BB |
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originalVariance_ = variance(image(boundingBox)); |
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//Find the scale |
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double scale = scaleAndBlur(image, cvRound(log(1.0 * boundingBox.width / (minSize.width)) / log(SCALE_STEP)), |
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scaledImg, blurredImg, GaussBlurKernelSize, SCALE_STEP); |
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GaussianBlur(image, image_blurred, GaussBlurKernelSize, 0.0); |
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TLDDetector::generateScanGrid(image.rows, image.cols, minSize_, scanGrid); |
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getClosestN(scanGrid, Rect2d(boundingBox.x / scale, boundingBox.y / scale, boundingBox.width / scale, boundingBox.height / scale), 10, closest); |
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Mat_<uchar> blurredPatch(minSize); |
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TLDEnsembleClassifier::makeClassifiers(minSize, MEASURES_PER_CLASSIFIER, GRIDSIZE, detector->classifiers); |
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//Generate initial positive samples and put them to the model |
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positiveExamples.reserve(200); |
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for (int i = 0; i < (int)closest.size(); i++) |
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{ |
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for (int j = 0; j < 20; j++) |
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{ |
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Point2f center; |
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Size2f size; |
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Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE); |
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center.x = (float)(closest[i].x + closest[i].width * (0.5 + rng.uniform(-0.01, 0.01))); |
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center.y = (float)(closest[i].y + closest[i].height * (0.5 + rng.uniform(-0.01, 0.01))); |
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size.width = (float)(closest[i].width * rng.uniform((double)0.99, (double)1.01)); |
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size.height = (float)(closest[i].height * rng.uniform((double)0.99, (double)1.01)); |
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float angle = (float)rng.uniform(-10.0, 10.0); |
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resample(scaledImg, RotatedRect(center, size, angle), standardPatch); |
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for (int y = 0; y < standardPatch.rows; y++) |
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{ |
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for (int x = 0; x < standardPatch.cols; x++) |
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{ |
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standardPatch(x, y) += (uchar)rng.gaussian(5.0); |
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} |
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} |
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#ifdef BLUR_AS_VADIM |
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GaussianBlur(standardPatch, blurredPatch, GaussBlurKernelSize, 0.0); |
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resize(blurredPatch, blurredPatch, minSize); |
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#else |
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resample(blurredImg, RotatedRect(center, size, angle), blurredPatch); |
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#endif |
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pushIntoModel(standardPatch, true); |
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for (int k = 0; k < (int)detector->classifiers.size(); k++) |
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detector->classifiers[k].integrate(blurredPatch, true); |
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} |
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} |
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//Generate initial negative samples and put them to the model |
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TLDDetector::generateScanGrid(image.rows, image.cols, minSize, scanGrid, true); |
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negativeExamples.clear(); |
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negativeExamples.reserve(NEG_EXAMPLES_IN_INIT_MODEL); |
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std::vector<int> indices; |
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indices.reserve(NEG_EXAMPLES_IN_INIT_MODEL); |
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while ((int)negativeExamples.size() < NEG_EXAMPLES_IN_INIT_MODEL) |
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{ |
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int i = rng.uniform((int)0, (int)scanGrid.size()); |
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if (std::find(indices.begin(), indices.end(), i) == indices.end() && overlap(boundingBox, scanGrid[i]) < NEXPERT_THRESHOLD) |
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{ |
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Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE); |
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resample(image, scanGrid[i], standardPatch); |
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pushIntoModel(standardPatch, false); |
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resample(image_blurred, scanGrid[i], blurredPatch); |
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for (int k = 0; k < (int)detector->classifiers.size(); k++) |
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detector->classifiers[k].integrate(blurredPatch, false); |
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} |
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} |
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} |
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void TrackerTLDModel::integrateRelabeled(Mat& img, Mat& imgBlurred, const std::vector<TLDDetector::LabeledPatch>& patches) |
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{ |
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Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE), blurredPatch(minSize_); |
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int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0; |
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for (int k = 0; k < (int)patches.size(); k++) |
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{ |
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if (patches[k].shouldBeIntegrated) |
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{ |
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resample(img, patches[k].rect, standardPatch); |
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if (patches[k].isObject) |
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{ |
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positiveIntoModel++; |
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pushIntoModel(standardPatch, true); |
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} |
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else |
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{ |
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negativeIntoModel++; |
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pushIntoModel(standardPatch, false); |
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} |
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} |
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#ifdef CLOSED_LOOP |
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if (patches[k].shouldBeIntegrated || !patches[k].isPositive) |
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#else |
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if (patches[k].shouldBeIntegrated) |
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#endif |
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{ |
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resample(imgBlurred, patches[k].rect, blurredPatch); |
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if (patches[k].isObject) |
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positiveIntoEnsemble++; |
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else |
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negativeIntoEnsemble++; |
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for (int i = 0; i < (int)detector->classifiers.size(); i++) |
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detector->classifiers[i].integrate(blurredPatch, patches[k].isObject); |
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} |
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} |
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} |
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void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive) |
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{ |
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int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0; |
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if ((int)eForModel.size() == 0) return; |
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for (int k = 0; k < (int)eForModel.size(); k++) |
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{ |
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double sr = detector->Sr(eForModel[k]); |
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if ((sr > THETA_NN) != isPositive) |
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{ |
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if (isPositive) |
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{ |
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positiveIntoModel++; |
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pushIntoModel(eForModel[k], true); |
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} |
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else |
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{ |
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negativeIntoModel++; |
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pushIntoModel(eForModel[k], false); |
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} |
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} |
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double p = 0; |
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for (int i = 0; i < (int)detector->classifiers.size(); i++) |
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p += detector->classifiers[i].posteriorProbability(eForEnsemble[k].data, (int)eForEnsemble[k].step[0]); |
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p /= detector->classifiers.size(); |
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if ((p > ENSEMBLE_THRESHOLD) != isPositive) |
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{ |
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if (isPositive) |
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positiveIntoEnsemble++; |
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else |
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negativeIntoEnsemble++; |
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for (int i = 0; i < (int)detector->classifiers.size(); i++) |
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detector->classifiers[i].integrate(eForEnsemble[k], isPositive); |
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} |
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} |
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} |
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void TrackerTLDModel::ocl_integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive) |
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{ |
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int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0; |
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if ((int)eForModel.size() == 0) return; |
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//Prepare batch of patches |
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int numOfPatches = (int)eForModel.size(); |
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Mat_<uchar> stdPatches(numOfPatches, 225); |
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double *resultSr = new double[numOfPatches]; |
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double *resultSc = new double[numOfPatches]; |
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uchar *patchesData = stdPatches.data; |
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for (int i = 0; i < numOfPatches; i++) |
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{ |
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uchar *stdPatchData = eForModel[i].data; |
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for (int j = 0; j < 225; j++) |
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patchesData[225 * i + j] = stdPatchData[j]; |
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} |
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//Calculate Sr and Sc batches |
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detector->ocl_batchSrSc(stdPatches, resultSr, resultSc, numOfPatches); |
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for (int k = 0; k < (int)eForModel.size(); k++) |
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{ |
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double sr = resultSr[k]; |
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if ((sr > THETA_NN) != isPositive) |
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{ |
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if (isPositive) |
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{ |
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positiveIntoModel++; |
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pushIntoModel(eForModel[k], true); |
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} |
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else |
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{ |
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negativeIntoModel++; |
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pushIntoModel(eForModel[k], false); |
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} |
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} |
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double p = 0; |
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for (int i = 0; i < (int)detector->classifiers.size(); i++) |
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p += detector->classifiers[i].posteriorProbability(eForEnsemble[k].data, (int)eForEnsemble[k].step[0]); |
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p /= detector->classifiers.size(); |
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if ((p > ENSEMBLE_THRESHOLD) != isPositive) |
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{ |
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if (isPositive) |
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positiveIntoEnsemble++; |
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else |
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negativeIntoEnsemble++; |
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for (int i = 0; i < (int)detector->classifiers.size(); i++) |
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detector->classifiers[i].integrate(eForEnsemble[k], isPositive); |
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} |
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} |
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} |
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//Push the patch to the model |
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void TrackerTLDModel::pushIntoModel(const Mat_<uchar>& example, bool positive) |
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{ |
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std::vector<Mat_<uchar> >* proxyV; |
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int* proxyN; |
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std::vector<int>* proxyT; |
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if (positive) |
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{ |
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if (posNum < 500) |
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{ |
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uchar *patchPtr = example.data; |
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uchar *modelPtr = posExp.data; |
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for (int i = 0; i < STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE; i++) |
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modelPtr[posNum*STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE + i] = patchPtr[i]; |
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posNum++; |
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} |
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proxyV = &positiveExamples; |
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proxyN = &timeStampPositiveNext; |
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proxyT = &timeStampsPositive; |
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} |
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else |
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{ |
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if (negNum < 500) |
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{ |
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uchar *patchPtr = example.data; |
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uchar *modelPtr = negExp.data; |
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for (int i = 0; i < STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE; i++) |
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modelPtr[negNum*STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE + i] = patchPtr[i]; |
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negNum++; |
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} |
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proxyV = &negativeExamples; |
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proxyN = &timeStampNegativeNext; |
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proxyT = &timeStampsNegative; |
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} |
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if ((int)proxyV->size() < MAX_EXAMPLES_IN_MODEL) |
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{ |
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proxyV->push_back(example); |
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proxyT->push_back(*proxyN); |
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} |
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else |
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{ |
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int index = rng.uniform((int)0, (int)proxyV->size()); |
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(*proxyV)[index] = example; |
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(*proxyT)[index] = (*proxyN); |
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} |
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(*proxyN)++; |
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} |
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void TrackerTLDModel::printme(FILE* port) |
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{ |
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dfprintf((port, "TrackerTLDModel:\n")); |
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dfprintf((port, "\tpositiveExamples.size() = %d\n", (int)positiveExamples.size())); |
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dfprintf((port, "\tnegativeExamples.size() = %d\n", (int)negativeExamples.size())); |
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} |
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} |
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} |