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Merge pull request #1552 from amuhic:master
Implementation of CSR-DCF tracker (#1552) * Initial commit for CSR-DCF tracker implementation * Fixes for automatic build * General code fixes * Removed unused parameters. Added CSRT to automatic tests. * Fixed VS build warnings. Fixed a bug with gray sequences. * Fixed VS build errors for samples file.
This commit is contained in:
Andrej Muhič
committed by
Alexander Alekhin
Alexander Alekhin
parent
295dce52b1
commit
ced5aa7606
211
modules/tracking/src/trackerCSRTScaleEstimation.cpp
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211
modules/tracking/src/trackerCSRTScaleEstimation.cpp
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "precomp.hpp"
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#include "trackerCSRTScaleEstimation.hpp"
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#include "trackerCSRTUtils.hpp"
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//Discriminative Scale Space Tracking
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namespace cv
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{
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class ParallelGetScaleFeatures : public ParallelLoopBody
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{
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public:
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ParallelGetScaleFeatures(
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Mat img,
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Point2f pos,
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Size2f base_target_sz,
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float current_scale,
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std::vector<float> &scale_factors,
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Mat scale_window,
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Size scale_model_sz,
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int col_len,
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Mat &result)
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{
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this->img = img;
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this->pos = pos;
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this->base_target_sz = base_target_sz;
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this->current_scale = current_scale;
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this->scale_factors = scale_factors;
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this->scale_window = scale_window;
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this->scale_model_sz = scale_model_sz;
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this->col_len = col_len;
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this->result = result;
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}
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virtual void operator ()(const Range& range) const
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{
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for (int s = range.start; s < range.end; s++) {
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Size patch_sz = Size(static_cast<int>(current_scale * scale_factors[s] * base_target_sz.width),
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static_cast<int>(current_scale * scale_factors[s] * base_target_sz.height));
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Mat img_patch = get_subwindow(img, pos, patch_sz.width, patch_sz.height);
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img_patch.convertTo(img_patch, CV_32FC3);
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resize(img_patch, img_patch, Size(scale_model_sz.width, scale_model_sz.height),0,0,INTER_LINEAR);
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std::vector<Mat> hog;
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hog = get_features_hog(img_patch, 4);
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for (int i = 0; i < static_cast<int>(hog.size()); ++i) {
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hog[i] = hog[i].t();
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hog[i] = scale_window.at<float>(0,s) * hog[i].reshape(0, col_len);
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hog[i].copyTo(result(Rect(Point(s, i*col_len), hog[i].size())));
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}
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}
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}
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ParallelGetScaleFeatures& operator=(const ParallelGetScaleFeatures &) {
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return *this;
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}
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private:
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Mat img;
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Point2f pos;
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Size2f base_target_sz;
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float current_scale;
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std::vector<float> scale_factors;
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Mat scale_window;
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Size scale_model_sz;
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int col_len;
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Mat result;
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};
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DSST::DSST(const Mat &image,
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Rect2f bounding_box,
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Size2f template_size,
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int numberOfScales,
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float scaleStep,
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float maxModelArea,
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float sigmaFactor,
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float scaleLearnRate):
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scales_count(numberOfScales), scale_step(scaleStep), max_model_area(maxModelArea),
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sigma_factor(sigmaFactor), learn_rate(scaleLearnRate)
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{
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original_targ_sz = bounding_box.size();
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Point2f object_center = Point2f(bounding_box.x + original_targ_sz.width / 2,
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bounding_box.y + original_targ_sz.height / 2);
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current_scale_factor = 1.0;
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if(scales_count % 2 == 0)
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scales_count++;
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scale_sigma = static_cast<float>(sqrt(scales_count) * sigma_factor);
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min_scale_factor = pow(scale_step,
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cvCeil(log(max(5.0 / template_size.width, 5.0 / template_size.height)) / log(scale_step)));
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max_scale_factor = powf(scale_step,
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static_cast<float>(cvFloor(log(min((float)image.rows / (float)bounding_box.width,
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(float)image.cols / (float)bounding_box.height)) / log(scale_step))));
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ys = Mat(1, scales_count, CV_32FC1);
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float ss, sf;
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for(int i = 0; i < ys.cols; ++i) {
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ss = (float)(i+1) - cvCeil((float)scales_count / 2.0f);
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ys.at<float>(0,i) = static_cast<float>(exp(-0.5 * pow(ss,2) / pow(scale_sigma,2)));
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sf = static_cast<float>(i + 1);
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scale_factors.push_back(pow(scale_step, cvCeil((float)scales_count / 2.0f) - sf));
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}
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scale_window = get_hann_win(Size(scales_count, 1));
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float scale_model_factor = 1.0;
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if(template_size.width * template_size.height * pow(scale_model_factor, 2) > max_model_area)
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{
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scale_model_factor = sqrt(max_model_area /
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(template_size.width * template_size.height));
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}
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scale_model_sz = Size(cvFloor(template_size.width * scale_model_factor),
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cvFloor(template_size.height * scale_model_factor));
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Mat scale_resp = get_scale_features(image, object_center, original_targ_sz,
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current_scale_factor, scale_factors, scale_window, scale_model_sz);
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Mat ysf_row = Mat(ys.size(), CV_32FC2);
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dft(ys, ysf_row, DFT_ROWS | DFT_COMPLEX_OUTPUT, 0);
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ysf = repeat(ysf_row, scale_resp.rows, 1);
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Mat Fscale_resp;
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dft(scale_resp, Fscale_resp, DFT_ROWS | DFT_COMPLEX_OUTPUT);
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mulSpectrums(ysf, Fscale_resp, sf_num, 0 , true);
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Mat sf_den_all;
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mulSpectrums(Fscale_resp, Fscale_resp, sf_den_all, 0, true);
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reduce(sf_den_all, sf_den, 0, CV_REDUCE_SUM, -1);
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}
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DSST::~DSST()
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{
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}
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Mat DSST::get_scale_features(
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Mat img,
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Point2f pos,
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Size2f base_target_sz,
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float current_scale,
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std::vector<float> &scale_factors,
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Mat scale_window,
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Size scale_model_sz)
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{
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Mat result;
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int col_len = 0;
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Size patch_sz = Size(cvFloor(current_scale * scale_factors[0] * base_target_sz.width),
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cvFloor(current_scale * scale_factors[0] * base_target_sz.height));
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Mat img_patch = get_subwindow(img, pos, patch_sz.width, patch_sz.height);
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img_patch.convertTo(img_patch, CV_32FC3);
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resize(img_patch, img_patch, Size(scale_model_sz.width, scale_model_sz.height),0,0,INTER_LINEAR);
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std::vector<Mat> hog;
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hog = get_features_hog(img_patch, 4);
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result = Mat(Size((int)scale_factors.size(), hog[0].cols * hog[0].rows * (int)hog.size()), CV_32F);
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col_len = hog[0].cols * hog[0].rows;
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for (int i = 0; i < static_cast<int>(hog.size()); ++i) {
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hog[i] = hog[i].t();
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hog[i] = scale_window.at<float>(0,0) * hog[i].reshape(0, col_len);
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hog[i].copyTo(result(Rect(Point(0, i*col_len), hog[i].size())));
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}
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ParallelGetScaleFeatures parallelGetScaleFeatures(img, pos, base_target_sz,
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current_scale, scale_factors, scale_window, scale_model_sz, col_len, result);
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parallel_for_(Range(1, static_cast<int>(scale_factors.size())), parallelGetScaleFeatures);
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return result;
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}
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void DSST::update(const Mat &image, const Point2f object_center)
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{
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Mat scale_features = get_scale_features(image, object_center, original_targ_sz,
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current_scale_factor, scale_factors, scale_window, scale_model_sz);
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Mat Fscale_features;
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dft(scale_features, Fscale_features, DFT_ROWS | DFT_COMPLEX_OUTPUT);
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Mat new_sf_num;
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Mat new_sf_den;
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Mat new_sf_den_all;
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mulSpectrums(ysf, Fscale_features, new_sf_num, DFT_ROWS, true);
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Mat sf_den_all;
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mulSpectrums(Fscale_features, Fscale_features, new_sf_den_all, DFT_ROWS, true);
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reduce(new_sf_den_all, new_sf_den, 0, CV_REDUCE_SUM, -1);
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sf_num = (1 - learn_rate) * sf_num + learn_rate * new_sf_num;
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sf_den = (1 - learn_rate) * sf_den + learn_rate * new_sf_den;
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}
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float DSST::getScale(const Mat &image, const Point2f object_center)
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{
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Mat scale_features = get_scale_features(image, object_center, original_targ_sz,
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current_scale_factor, scale_factors, scale_window, scale_model_sz);
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Mat Fscale_features;
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dft(scale_features, Fscale_features, DFT_ROWS | DFT_COMPLEX_OUTPUT);
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mulSpectrums(Fscale_features, sf_num, Fscale_features, 0, false);
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Mat scale_resp;
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reduce(Fscale_features, scale_resp, 0, CV_REDUCE_SUM, -1);
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scale_resp = divide_complex_matrices(scale_resp, sf_den + 0.01f);
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idft(scale_resp, scale_resp, DFT_REAL_OUTPUT|DFT_SCALE);
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Point max_loc;
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minMaxLoc(scale_resp, NULL, NULL, NULL, &max_loc);
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current_scale_factor *= scale_factors[max_loc.x];
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if(current_scale_factor < min_scale_factor)
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current_scale_factor = min_scale_factor;
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else if(current_scale_factor > max_scale_factor)
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current_scale_factor = max_scale_factor;
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return current_scale_factor;
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}
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} /* namespace cv */
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