/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ /* * This class implements an algorithm described in "Visual Tracking of Human Visitors under * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. * * Prepared and integrated by Andrew B. Godbehere. */ #include "precomp.hpp" cv::BackgroundSubtractorGMG::BackgroundSubtractorGMG() { /* * Default Parameter Values. Override with algorithm "set" method. */ maxFeatures = 64; learningRate = 0.025; numInitializationFrames = 120; quantizationLevels = 16; backgroundPrior = 0.8; decisionThreshold = 0.8; smoothingRadius = 7; } cv::BackgroundSubtractorGMG::~BackgroundSubtractorGMG() { } void cv::BackgroundSubtractorGMG::initialize(cv::Size frameSize, double min, double max) { CV_Assert(min < max); CV_Assert(maxFeatures > 0); CV_Assert(learningRate >= 0.0 && learningRate <= 1.0); CV_Assert(numInitializationFrames >= 1); CV_Assert(quantizationLevels >= 1 && quantizationLevels <= 255); CV_Assert(backgroundPrior >= 0.0 && backgroundPrior <= 1.0); minVal_ = min; maxVal_ = max; frameSize_ = frameSize; frameNum_ = 0; nfeatures_.create(frameSize_); colors_.create(frameSize_.area(), maxFeatures); weights_.create(frameSize_.area(), maxFeatures); nfeatures_.setTo(cv::Scalar::all(0)); } namespace { float findFeature(int color, const int* colors, const float* weights, int nfeatures) { for (int i = 0; i < nfeatures; ++i) { if (color == colors[i]) return weights[i]; } // not in histogram, so return 0. return 0.0f; } void normalizeHistogram(float* weights, int nfeatures) { float total = 0.0f; for (int i = 0; i < nfeatures; ++i) total += weights[i]; if (total != 0.0f) { for (int i = 0; i < nfeatures; ++i) weights[i] /= total; } } bool insertFeature(int color, float weight, int* colors, float* weights, int& nfeatures, int maxFeatures) { int idx = -1; for (int i = 0; i < nfeatures; ++i) { if (color == colors[i]) { // feature in histogram weight += weights[i]; idx = i; break; } } if (idx >= 0) { // move feature to beginning of list ::memmove(colors + 1, colors, idx * sizeof(int)); ::memmove(weights + 1, weights, idx * sizeof(float)); colors[0] = color; weights[0] = weight; } else if (nfeatures == maxFeatures) { // discard oldest feature ::memmove(colors + 1, colors, (nfeatures - 1) * sizeof(int)); ::memmove(weights + 1, weights, (nfeatures - 1) * sizeof(float)); colors[0] = color; weights[0] = weight; } else { colors[nfeatures] = color; weights[nfeatures] = weight; ++nfeatures; return true; } return false; } } namespace { template struct Quantization_ { template static inline int apply(T val, double minVal, double maxVal, int quantizationLevels) { int res = 0; res |= static_cast((val[0] - minVal) * quantizationLevels / (maxVal - minVal)); res |= static_cast((val[1] - minVal) * quantizationLevels / (maxVal - minVal)) << 8; res |= static_cast((val[2] - minVal) * quantizationLevels / (maxVal - minVal)) << 16; return res; } }; template <> struct Quantization_<1> { template static inline int apply(T val, double minVal, double maxVal, int quantizationLevels) { return static_cast((val - minVal) * quantizationLevels / (maxVal - minVal)); } }; template struct Quantization { static int apply(const void* src_, int x, double minVal, double maxVal, int quantizationLevels) { const T* src = static_cast(src_); return Quantization_::channels>::apply(src[x], minVal, maxVal, quantizationLevels); } }; class GMG_LoopBody : public cv::ParallelLoopBody { public: GMG_LoopBody(const cv::Mat& frame, const cv::Mat& fgmask, const cv::Mat_& nfeatures, const cv::Mat_& colors, const cv::Mat_& weights, int maxFeatures, double learningRate, int numInitializationFrames, int quantizationLevels, double backgroundPrior, double decisionThreshold, double maxVal, double minVal, int frameNum) : frame_(frame), fgmask_(fgmask), nfeatures_(nfeatures), colors_(colors), weights_(weights), maxFeatures_(maxFeatures), learningRate_(learningRate), numInitializationFrames_(numInitializationFrames), quantizationLevels_(quantizationLevels), backgroundPrior_(backgroundPrior), decisionThreshold_(decisionThreshold), maxVal_(maxVal), minVal_(minVal), frameNum_(frameNum) { } void operator() (const cv::Range& range) const; private: const cv::Mat frame_; mutable cv::Mat_ fgmask_; mutable cv::Mat_ nfeatures_; mutable cv::Mat_ colors_; mutable cv::Mat_ weights_; int maxFeatures_; double learningRate_; int numInitializationFrames_; int quantizationLevels_; double backgroundPrior_; double decisionThreshold_; double maxVal_; double minVal_; int frameNum_; }; void GMG_LoopBody::operator() (const cv::Range& range) const { typedef int (*func_t)(const void* src_, int x, double minVal, double maxVal, int quantizationLevels); static const func_t funcs[6][4] = { {Quantization::apply, 0, Quantization::apply, Quantization::apply}, {0,0,0,0}, {Quantization::apply, 0, Quantization::apply, Quantization::apply}, {0,0,0,0}, {0,0,0,0}, {Quantization::apply, 0, Quantization::apply, Quantization::apply}, }; const func_t func = funcs[frame_.depth()][frame_.channels() - 1]; CV_Assert(func != 0); for (int y = range.start, featureIdx = y * frame_.cols; y < range.end; ++y) { const uchar* frame_row = frame_.ptr(y); int* nfeatures_row = nfeatures_[y]; uchar* fgmask_row = fgmask_[y]; for (int x = 0; x < frame_.cols; ++x, ++featureIdx) { int nfeatures = nfeatures_row[x]; int* colors = colors_[featureIdx]; float* weights = weights_[featureIdx]; int newFeatureColor = func(frame_row, x, minVal_, maxVal_, quantizationLevels_); bool isForeground = false; if (frameNum_ >= numInitializationFrames_) { // typical operation const double weight = findFeature(newFeatureColor, colors, weights, nfeatures); // see Godbehere, Matsukawa, Goldberg (2012) for reasoning behind this implementation of Bayes rule const double posterior = (weight * backgroundPrior_) / (weight * backgroundPrior_ + (1.0 - weight) * (1.0 - backgroundPrior_)); isForeground = ((1.0 - posterior) > decisionThreshold_); // update histogram. for (int i = 0; i < nfeatures; ++i) weights[i] *= 1.0f - learningRate_; bool inserted = insertFeature(newFeatureColor, learningRate_, colors, weights, nfeatures, maxFeatures_); if (inserted) { normalizeHistogram(weights, nfeatures); nfeatures_row[x] = nfeatures; } } else { // training-mode update insertFeature(newFeatureColor, 1.0f, colors, weights, nfeatures, maxFeatures_); if (frameNum_ == numInitializationFrames_ - 1) normalizeHistogram(weights, nfeatures); } fgmask_row[x] = (uchar)(-isForeground); } } } } void cv::BackgroundSubtractorGMG::operator ()(InputArray _frame, OutputArray _fgmask, double newLearningRate) { cv::Mat frame = _frame.getMat(); CV_Assert(frame.depth() == CV_8U || frame.depth() == CV_16U || frame.depth() == CV_32F); CV_Assert(frame.channels() == 1 || frame.channels() == 3 || frame.channels() == 4); if (newLearningRate != -1.0) { CV_Assert(newLearningRate >= 0.0 && newLearningRate <= 1.0); learningRate = newLearningRate; } if (frame.size() != frameSize_) initialize(frame.size(), 0.0, frame.depth() == CV_8U ? 255.0 : frame.depth() == CV_16U ? std::numeric_limits::max() : 1.0); _fgmask.create(frameSize_, CV_8UC1); cv::Mat fgmask = _fgmask.getMat(); GMG_LoopBody body(frame, fgmask, nfeatures_, colors_, weights_, maxFeatures, learningRate, numInitializationFrames, quantizationLevels, backgroundPrior, decisionThreshold, maxVal_, minVal_, frameNum_); cv::parallel_for_(cv::Range(0, frame.rows), body); cv::medianBlur(fgmask, buf_, smoothingRadius); cv::swap(fgmask, buf_); // keep track of how many frames we have processed ++frameNum_; }