5 #include "threadctx.hpp"
13 #include "fft_cufft.h"
16 #include "fft_opencv.h"
24 DbgTracer __dbgTracer;
27 T clamp(const T& n, const T& lower, const T& upper)
29 return std::max(lower, std::min(n, upper));
33 void clamp2(T& n, const T& lower, const T& upper)
35 n = std::max(lower, std::min(n, upper));
38 #if CV_MAJOR_VERSION < 3
39 template<typename _Tp> static inline
40 cv::Size_<_Tp> operator / (const cv::Size_<_Tp>& a, _Tp b)
42 return cv::Size_<_Tp>(a.width / b, a.height / b);
46 class Kcf_Tracker_Private {
48 std::vector<ThreadCtx> threadctxs;
51 KCF_Tracker::KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor,
52 double output_sigma_factor, int cell_size)
53 : p_cell_size(cell_size), fft(*new FFT()), p_padding(padding), p_output_sigma_factor(output_sigma_factor), p_kernel_sigma(kernel_sigma),
54 p_lambda(lambda), p_interp_factor(interp_factor), d(*new Kcf_Tracker_Private)
58 KCF_Tracker::KCF_Tracker() : fft(*new FFT()), d(*new Kcf_Tracker_Private) {}
60 KCF_Tracker::~KCF_Tracker()
66 void KCF_Tracker::train(cv::Mat input_rgb, cv::Mat input_gray, double interp_factor)
70 // obtain a sub-window for training
71 // TODO: Move Mats outside from here
72 MatScaleFeats patch_feats(1, p_num_of_feats, feature_size);
73 MatScaleFeats temp(1, p_num_of_feats, feature_size);
74 get_features(input_rgb, input_gray, p_current_center.x, p_current_center.y,
75 p_windows_size.width, p_windows_size.height,
76 p_current_scale).copyTo(patch_feats.scale(0));
77 DEBUG_PRINT(patch_feats);
78 fft.forward_window(patch_feats, model->xf, temp);
79 DEBUG_PRINTM(model->xf);
80 model->model_xf = model->model_xf * (1. - interp_factor) + model->xf * interp_factor;
81 DEBUG_PRINTM(model->model_xf);
83 if (m_use_linearkernel) {
84 ComplexMat xfconj = model->xf.conj();
85 model->model_alphaf_num = xfconj.mul(model->yf);
86 model->model_alphaf_den = (model->xf * xfconj);
88 // Kernel Ridge Regression, calculate alphas (in Fourier domain)
89 cv::Size sz(Fft::freq_size(feature_size));
90 ComplexMat kf(sz.height, sz.width, 1);
91 (*gaussian_correlation)(kf, model->model_xf, model->model_xf, p_kernel_sigma, true, *this);
93 model->model_alphaf_num = model->yf * kf;
94 model->model_alphaf_den = kf * (kf + p_lambda);
96 model->model_alphaf = model->model_alphaf_num / model->model_alphaf_den;
97 DEBUG_PRINTM(model->model_alphaf);
98 // p_model_alphaf = p_yf / (kf + p_lambda); //equation for fast training
101 static int round_pw2_down(int x)
103 for (int i = 1; i < 32; i <<= 1)
110 void KCF_Tracker::init(cv::Mat &img, const cv::Rect &bbox, int fit_size_x, int fit_size_y)
112 __dbgTracer.debug = m_debug;
115 // check boundary, enforce min size
116 double x1 = bbox.x, x2 = bbox.x + bbox.width, y1 = bbox.y, y2 = bbox.y + bbox.height;
118 if (x2 > img.cols - 1) x2 = img.cols - 1;
120 if (y2 > img.rows - 1) y2 = img.rows - 1;
122 if (x2 - x1 < 2 * p_cell_size) {
123 double diff = (2 * p_cell_size - x2 + x1) / 2.;
124 if (x1 - diff >= 0 && x2 + diff < img.cols) {
127 } else if (x1 - 2 * diff >= 0) {
133 if (y2 - y1 < 2 * p_cell_size) {
134 double diff = (2 * p_cell_size - y2 + y1) / 2.;
135 if (y1 - diff >= 0 && y2 + diff < img.rows) {
138 } else if (y1 - 2 * diff >= 0) {
145 p_init_pose.w = x2 - x1;
146 p_init_pose.h = y2 - y1;
147 p_init_pose.cx = x1 + p_init_pose.w / 2.;
148 p_init_pose.cy = y1 + p_init_pose.h / 2.;
150 cv::Mat input_gray, input_rgb = img.clone();
151 if (img.channels() == 3) {
152 cv::cvtColor(img, input_gray, CV_BGR2GRAY);
153 input_gray.convertTo(input_gray, CV_32FC1);
155 img.convertTo(input_gray, CV_32FC1);
157 // don't need too large image
158 if (p_init_pose.w * p_init_pose.h > 100. * 100.) {
159 std::cout << "resizing image by factor of " << 1 / p_downscale_factor << std::endl;
160 p_resize_image = true;
161 p_init_pose.scale(p_downscale_factor);
162 cv::resize(input_gray, input_gray, cv::Size(0, 0), p_downscale_factor, p_downscale_factor, cv::INTER_AREA);
163 cv::resize(input_rgb, input_rgb, cv::Size(0, 0), p_downscale_factor, p_downscale_factor, cv::INTER_AREA);
166 // compute win size + fit to fhog cell size
167 p_windows_size.width = round(p_init_pose.w * (1. + p_padding) / p_cell_size) * p_cell_size;
168 p_windows_size.height = round(p_init_pose.h * (1. + p_padding) / p_cell_size) * p_cell_size;
170 if (fit_size_x == 0 || fit_size_y == 0) {
171 // Round down to the next highest power of 2
172 fit_size = cv::Size(round_pw2_down(p_windows_size.width),
173 round_pw2_down(p_windows_size.height));
174 } else if (fit_size_x == -1 || fit_size_y == -1) {
175 fit_size = p_windows_size;
177 fit_size = cv::Size(fit_size_x, fit_size_y);
180 feature_size = fit_size / p_cell_size;
183 for (int i = -int(p_num_scales) / 2; i <= int(p_num_scales) / 2; ++i)
184 p_scales.push_back(std::pow(p_scale_step, i));
187 if (m_use_linearkernel) {
188 std::cerr << "cuFFT supports only Gaussian kernel." << std::endl;
189 std::exit(EXIT_FAILURE);
193 model.reset(new Model(Fft::freq_size(feature_size), p_num_of_feats));
196 for (auto scale: p_scales)
197 d.threadctxs.emplace_back(feature_size, p_num_of_feats, scale);
199 d.threadctxs.emplace_back(feature_size, p_num_of_feats, p_num_scales);
202 gaussian_correlation.reset(new GaussianCorrelation(1, p_num_of_feats, feature_size));
204 p_current_center = p_init_pose.center();
205 p_current_scale = 1.;
207 double min_size_ratio = std::max(5. * p_cell_size / p_windows_size.width, 5. * p_cell_size / p_windows_size.height);
208 double max_size_ratio =
209 std::min(floor((img.cols + p_windows_size.width / 3) / p_cell_size) * p_cell_size / p_windows_size.width,
210 floor((img.rows + p_windows_size.height / 3) / p_cell_size) * p_cell_size / p_windows_size.height);
211 p_min_max_scale[0] = std::pow(p_scale_step, std::ceil(std::log(min_size_ratio) / log(p_scale_step)));
212 p_min_max_scale[1] = std::pow(p_scale_step, std::floor(std::log(max_size_ratio) / log(p_scale_step)));
214 std::cout << "init: img size " << img.size() << std::endl;
215 std::cout << "init: win size " << p_windows_size;
216 if (p_windows_size != fit_size)
217 std::cout << " resized to " << fit_size;
218 std::cout << std::endl;
219 std::cout << "init: FFT size " << feature_size << std::endl;
220 std::cout << "init: min max scales factors: " << p_min_max_scale[0] << " " << p_min_max_scale[1] << std::endl;
222 p_output_sigma = std::sqrt(p_init_pose.w * p_init_pose.h * double(fit_size.area()) / p_windows_size.area())
223 * p_output_sigma_factor / p_cell_size;
225 fft.init(feature_size.width, feature_size.height, p_num_of_feats, p_num_scales);
226 fft.set_window(MatDynMem(cosine_window_function(feature_size.width, feature_size.height)));
228 // window weights, i.e. labels
229 MatScales gsl(1, feature_size);
230 gaussian_shaped_labels(p_output_sigma, feature_size.width, feature_size.height).copyTo(gsl.plane(0));
231 fft.forward(gsl, model->yf);
232 DEBUG_PRINTM(model->yf);
234 // train initial model
235 train(input_rgb, input_gray, 1.0);
238 void KCF_Tracker::setTrackerPose(BBox_c &bbox, cv::Mat &img, int fit_size_x, int fit_size_y)
240 init(img, bbox.get_rect(), fit_size_x, fit_size_y);
243 void KCF_Tracker::updateTrackerPosition(BBox_c &bbox)
246 if (p_resize_image) {
247 tmp.scale(p_downscale_factor);
249 p_current_center = tmp.center();
252 BBox_c KCF_Tracker::getBBox()
255 tmp.cx = p_current_center.x;
256 tmp.cy = p_current_center.y;
257 tmp.w = p_init_pose.w * p_current_scale;
258 tmp.h = p_init_pose.h * p_current_scale;
262 tmp.scale(1 / p_downscale_factor);
267 double KCF_Tracker::getFilterResponse() const
269 return this->max_response;
272 void KCF_Tracker::resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray)
274 if (p_resize_image) {
275 cv::resize(input_gray, input_gray, cv::Size(0, 0), p_downscale_factor, p_downscale_factor, cv::INTER_AREA);
276 cv::resize(input_rgb, input_rgb, cv::Size(0, 0), p_downscale_factor, p_downscale_factor, cv::INTER_AREA);
280 double KCF_Tracker::findMaxReponse(uint &max_idx, cv::Point2d &new_location) const
283 max_idx = std::numeric_limits<uint>::max();
286 for (uint j = 0; j < d.threadctxs.size(); ++j) {
287 if (d.threadctxs[j].max.response > max) {
288 max = d.threadctxs[j].max.response;
293 for (uint j = 0; j < p_scales.size(); ++j) {
294 if (d.threadctxs[0].max[j].response > max) {
295 max = d.threadctxs[0].max[j].response;
300 assert(max_idx < IF_BIG_BATCH(p_scales.size(), d.threadctxs.size()));
302 if (m_visual_debug) {
303 int w = 100; //feature_size.width;
304 int h = 100; //feature_size.height;
305 cv::Mat all_responses(h * p_num_scales, w * p_num_angles,
306 d.threadctxs[0].response.type(), cv::Scalar::all(0));
307 for (size_t i = 0; i < p_num_scales; ++i) {
308 for (size_t j = 0; j < p_num_angles; ++j) {
309 cv::Mat tmp = d.threadctxs[IF_BIG_BATCH(0, p_num_angles * i + j)].response.plane(IF_BIG_BATCH(p_num_angles * i + j, 0));
310 tmp = circshift(tmp, -tmp.cols/2, -tmp.rows/2);
311 cv::resize(tmp, tmp, cv::Size(w, h));
312 cv::Mat resp_roi(all_responses, cv::Rect(j * w, i * h, w, h));
313 tmp.copyTo(resp_roi);
316 cv::namedWindow("All responses", CV_WINDOW_AUTOSIZE);
317 cv::imshow("All responses", all_responses);
320 cv::Point2i &max_response_pt = IF_BIG_BATCH(d.threadctxs[0].max[max_idx].loc, d.threadctxs[max_idx].max.loc);
321 cv::Mat max_response_map = IF_BIG_BATCH(d.threadctxs[0].response.plane(max_idx), d.threadctxs[max_idx].response.plane(0));
323 DEBUG_PRINTM(max_response_map);
324 DEBUG_PRINT(max_response_pt);
326 // sub pixel quadratic interpolation from neighbours
327 if (max_response_pt.y > max_response_map.rows / 2) // wrap around to negative half-space of vertical axis
328 max_response_pt.y = max_response_pt.y - max_response_map.rows;
329 if (max_response_pt.x > max_response_map.cols / 2) // same for horizontal axis
330 max_response_pt.x = max_response_pt.x - max_response_map.cols;
333 if (m_use_subpixel_localization) {
334 new_location = sub_pixel_peak(max_response_pt, max_response_map);
336 new_location = max_response_pt;
338 DEBUG_PRINT(new_location);
342 void KCF_Tracker::track(cv::Mat &img)
344 __dbgTracer.debug = m_debug;
347 cv::Mat input_gray, input_rgb = img.clone();
348 if (img.channels() == 3) {
349 cv::cvtColor(img, input_gray, CV_BGR2GRAY);
350 input_gray.convertTo(input_gray, CV_32FC1);
352 img.convertTo(input_gray, CV_32FC1);
354 // don't need too large image
355 resizeImgs(input_rgb, input_gray);
358 for (auto &it : d.threadctxs)
359 it.async_res = std::async(std::launch::async, [this, &input_gray, &input_rgb, &it]() -> void {
360 it.track(*this, input_rgb, input_gray);
362 for (auto const &it : d.threadctxs)
366 NORMAL_OMP_PARALLEL_FOR
367 for (uint i = 0; i < d.threadctxs.size(); ++i)
368 d.threadctxs[i].track(*this, input_rgb, input_gray);
371 cv::Point2d new_location;
373 max_response = findMaxReponse(max_idx, new_location);
375 new_location.x *= double(p_windows_size.width) / fit_size.width;
376 new_location.y *= double(p_windows_size.height) / fit_size.height;
378 p_current_center += p_current_scale * p_cell_size * new_location;
380 clamp2(p_current_center.x, 0.0, img.cols - 1.0);
381 clamp2(p_current_center.y, 0.0, img.rows - 1.0);
383 // sub grid scale interpolation
384 if (m_use_subgrid_scale) {
385 p_current_scale *= sub_grid_scale(max_idx);
387 p_current_scale *= p_scales[max_idx];
390 clamp2(p_current_scale, p_min_max_scale[0], p_min_max_scale[1]);
392 // train at newly estimated target position
393 train(input_rgb, input_gray, p_interp_factor);
396 void ThreadCtx::track(const KCF_Tracker &kcf, cv::Mat &input_rgb, cv::Mat &input_gray)
400 BIG_BATCH_OMP_PARALLEL_FOR
401 for (uint i = 0; i < IF_BIG_BATCH(kcf.p_num_scales, 1); ++i)
403 kcf.get_features(input_rgb, input_gray, kcf.p_current_center.x, kcf.p_current_center.y,
404 kcf.p_windows_size.width, kcf.p_windows_size.height,
405 kcf.p_current_scale * IF_BIG_BATCH(kcf.p_scales[i], scale))
406 .copyTo(patch_feats.scale(i));
407 DEBUG_PRINT(patch_feats.scale(i));
410 kcf.fft.forward_window(patch_feats, zf, temp);
413 if (kcf.m_use_linearkernel) {
414 kzf = zf.mul(kcf.model->model_alphaf).sum_over_channels();
416 gaussian_correlation(kzf, zf, kcf.model->model_xf, kcf.p_kernel_sigma, false, kcf);
418 kzf = kzf.mul(kcf.model->model_alphaf);
420 kcf.fft.inverse(kzf, response);
422 DEBUG_PRINTM(response);
424 /* target location is at the maximum response. we must take into
425 account the fact that, if the target doesn't move, the peak
426 will appear at the top-left corner, not at the center (this is
427 discussed in the paper). the responses wrap around cyclically. */
428 double min_val, max_val;
429 cv::Point2i min_loc, max_loc;
431 for (size_t i = 0; i < kcf.p_scales.size(); ++i) {
432 cv::minMaxLoc(response.plane(i), &min_val, &max_val, &min_loc, &max_loc);
433 DEBUG_PRINT(max_loc);
434 double weight = kcf.p_scales[i] < 1. ? kcf.p_scales[i] : 1. / kcf.p_scales[i];
435 max[i].response = max_val * weight;
436 max[i].loc = max_loc;
439 cv::minMaxLoc(response.plane(0), &min_val, &max_val, &min_loc, &max_loc);
441 DEBUG_PRINT(max_loc);
442 DEBUG_PRINT(max_val);
444 double weight = scale < 1. ? scale : 1. / scale;
445 max.response = max_val * weight;
450 // ****************************************************************************
452 cv::Mat KCF_Tracker::get_features(cv::Mat &input_rgb, cv::Mat &input_gray, int cx, int cy,
453 int size_x, int size_y, double scale) const
455 cv::Size scaled = cv::Size(floor(size_x * scale), floor(size_y * scale));
457 cv::Mat patch_gray = get_subwindow(input_gray, cx, cy, scaled.width, scaled.height);
458 cv::Mat patch_rgb = get_subwindow(input_rgb, cx, cy, scaled.width, scaled.height);
460 // resize to default size
461 if (scaled.area() > fit_size.area()) {
462 // if we downsample use INTER_AREA interpolation
463 // note: this is just a guess - we may downsample in X and upsample in Y (or vice versa)
464 cv::resize(patch_gray, patch_gray, fit_size, 0., 0., cv::INTER_AREA);
466 cv::resize(patch_gray, patch_gray, fit_size, 0., 0., cv::INTER_LINEAR);
469 // get hog(Histogram of Oriented Gradients) features
470 std::vector<cv::Mat> hog_feat = FHoG::extract(patch_gray, 2, p_cell_size, 9);
472 // get color rgb features (simple r,g,b channels)
473 std::vector<cv::Mat> color_feat;
474 if ((m_use_color || m_use_cnfeat) && input_rgb.channels() == 3) {
475 // resize to default size
476 if (scaled.area() > (fit_size / p_cell_size).area()) {
477 // if we downsample use INTER_AREA interpolation
478 cv::resize(patch_rgb, patch_rgb, fit_size / p_cell_size, 0., 0., cv::INTER_AREA);
480 cv::resize(patch_rgb, patch_rgb, fit_size / p_cell_size, 0., 0., cv::INTER_LINEAR);
484 if (m_use_color && input_rgb.channels() == 3) {
485 // use rgb color space
486 cv::Mat patch_rgb_norm;
487 patch_rgb.convertTo(patch_rgb_norm, CV_32F, 1. / 255., -0.5);
488 cv::Mat ch1(patch_rgb_norm.size(), CV_32FC1);
489 cv::Mat ch2(patch_rgb_norm.size(), CV_32FC1);
490 cv::Mat ch3(patch_rgb_norm.size(), CV_32FC1);
491 std::vector<cv::Mat> rgb = {ch1, ch2, ch3};
492 cv::split(patch_rgb_norm, rgb);
493 color_feat.insert(color_feat.end(), rgb.begin(), rgb.end());
496 if (m_use_cnfeat && input_rgb.channels() == 3) {
497 std::vector<cv::Mat> cn_feat = CNFeat::extract(patch_rgb);
498 color_feat.insert(color_feat.end(), cn_feat.begin(), cn_feat.end());
501 hog_feat.insert(hog_feat.end(), color_feat.begin(), color_feat.end());
503 int size[] = {p_num_of_feats, feature_size.height, feature_size.width};
504 cv::Mat result(3, size, CV_32F);
505 for (uint i = 0; i < hog_feat.size(); ++i)
506 hog_feat[i].copyTo(cv::Mat(size[1], size[2], CV_32FC1, result.ptr(i)));
511 cv::Mat KCF_Tracker::gaussian_shaped_labels(double sigma, int dim1, int dim2)
513 cv::Mat labels(dim2, dim1, CV_32FC1);
514 int range_y[2] = {-dim2 / 2, dim2 - dim2 / 2};
515 int range_x[2] = {-dim1 / 2, dim1 - dim1 / 2};
517 double sigma_s = sigma * sigma;
519 for (int y = range_y[0], j = 0; y < range_y[1]; ++y, ++j) {
520 float *row_ptr = labels.ptr<float>(j);
522 for (int x = range_x[0], i = 0; x < range_x[1]; ++x, ++i) {
523 row_ptr[i] = std::exp(-0.5 * (y_s + x * x) / sigma_s); //-1/2*e^((y^2+x^2)/sigma^2)
527 // rotate so that 1 is at top-left corner (see KCF paper for explanation)
528 MatDynMem rot_labels = circshift(labels, range_x[0], range_y[0]);
529 // sanity check, 1 at top left corner
530 assert(rot_labels.at<float>(0, 0) >= 1.f - 1e-10f);
535 cv::Mat KCF_Tracker::circshift(const cv::Mat &patch, int x_rot, int y_rot) const
537 cv::Mat rot_patch(patch.size(), CV_32FC1);
538 cv::Mat tmp_x_rot(patch.size(), CV_32FC1);
540 // circular rotate x-axis
542 // move part that does not rotate over the edge
543 cv::Range orig_range(-x_rot, patch.cols);
544 cv::Range rot_range(0, patch.cols - (-x_rot));
545 patch(cv::Range::all(), orig_range).copyTo(tmp_x_rot(cv::Range::all(), rot_range));
548 orig_range = cv::Range(0, -x_rot);
549 rot_range = cv::Range(patch.cols - (-x_rot), patch.cols);
550 patch(cv::Range::all(), orig_range).copyTo(tmp_x_rot(cv::Range::all(), rot_range));
551 } else if (x_rot > 0) {
552 // move part that does not rotate over the edge
553 cv::Range orig_range(0, patch.cols - x_rot);
554 cv::Range rot_range(x_rot, patch.cols);
555 patch(cv::Range::all(), orig_range).copyTo(tmp_x_rot(cv::Range::all(), rot_range));
558 orig_range = cv::Range(patch.cols - x_rot, patch.cols);
559 rot_range = cv::Range(0, x_rot);
560 patch(cv::Range::all(), orig_range).copyTo(tmp_x_rot(cv::Range::all(), rot_range));
561 } else { // zero rotation
562 // move part that does not rotate over the edge
563 cv::Range orig_range(0, patch.cols);
564 cv::Range rot_range(0, patch.cols);
565 patch(cv::Range::all(), orig_range).copyTo(tmp_x_rot(cv::Range::all(), rot_range));
568 // circular rotate y-axis
570 // move part that does not rotate over the edge
571 cv::Range orig_range(-y_rot, patch.rows);
572 cv::Range rot_range(0, patch.rows - (-y_rot));
573 tmp_x_rot(orig_range, cv::Range::all()).copyTo(rot_patch(rot_range, cv::Range::all()));
576 orig_range = cv::Range(0, -y_rot);
577 rot_range = cv::Range(patch.rows - (-y_rot), patch.rows);
578 tmp_x_rot(orig_range, cv::Range::all()).copyTo(rot_patch(rot_range, cv::Range::all()));
579 } else if (y_rot > 0) {
580 // move part that does not rotate over the edge
581 cv::Range orig_range(0, patch.rows - y_rot);
582 cv::Range rot_range(y_rot, patch.rows);
583 tmp_x_rot(orig_range, cv::Range::all()).copyTo(rot_patch(rot_range, cv::Range::all()));
586 orig_range = cv::Range(patch.rows - y_rot, patch.rows);
587 rot_range = cv::Range(0, y_rot);
588 tmp_x_rot(orig_range, cv::Range::all()).copyTo(rot_patch(rot_range, cv::Range::all()));
589 } else { // zero rotation
590 // move part that does not rotate over the edge
591 cv::Range orig_range(0, patch.rows);
592 cv::Range rot_range(0, patch.rows);
593 tmp_x_rot(orig_range, cv::Range::all()).copyTo(rot_patch(rot_range, cv::Range::all()));
599 // hann window actually (Power-of-cosine windows)
600 cv::Mat KCF_Tracker::cosine_window_function(int dim1, int dim2)
602 cv::Mat m1(1, dim1, CV_32FC1), m2(dim2, 1, CV_32FC1);
603 double N_inv = 1. / (static_cast<double>(dim1) - 1.);
604 for (int i = 0; i < dim1; ++i)
605 m1.at<float>(i) = float(0.5 * (1. - std::cos(2. * CV_PI * static_cast<double>(i) * N_inv)));
606 N_inv = 1. / (static_cast<double>(dim2) - 1.);
607 for (int i = 0; i < dim2; ++i)
608 m2.at<float>(i) = float(0.5 * (1. - std::cos(2. * CV_PI * static_cast<double>(i) * N_inv)));
609 cv::Mat ret = m2 * m1;
613 // Returns sub-window of image input centered at [cx, cy] coordinates),
614 // with size [width, height]. If any pixels are outside of the image,
615 // they will replicate the values at the borders.
616 cv::Mat KCF_Tracker::get_subwindow(const cv::Mat &input, int cx, int cy, int width, int height) const
620 int x1 = cx - width / 2;
621 int y1 = cy - height / 2;
622 int x2 = cx + width / 2;
623 int y2 = cy + height / 2;
626 if (x1 >= input.cols || y1 >= input.rows || x2 < 0 || y2 < 0) {
627 patch.create(height, width, input.type());
628 patch.setTo(double(0.f));
632 int top = 0, bottom = 0, left = 0, right = 0;
634 // fit to image coordinates, set border extensions;
643 if (x2 >= input.cols) {
644 right = x2 - input.cols + width % 2;
649 if (y2 >= input.rows) {
650 bottom = y2 - input.rows + height % 2;
655 if (x2 - x1 == 0 || y2 - y1 == 0)
656 patch = cv::Mat::zeros(height, width, CV_32FC1);
658 cv::copyMakeBorder(input(cv::Range(y1, y2), cv::Range(x1, x2)), patch, top, bottom, left, right,
659 cv::BORDER_REPLICATE);
660 // imshow( "copyMakeBorder", patch);
665 assert(patch.cols == width && patch.rows == height);
670 void KCF_Tracker::GaussianCorrelation::operator()(ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf,
671 double sigma, bool auto_correlation, const KCF_Tracker &kcf)
674 xf.sqr_norm(xf_sqr_norm);
675 DEBUG_PRINTM(xf_sqr_norm[0]);
676 if (auto_correlation) {
677 yf_sqr_norm = xf_sqr_norm;
679 yf.sqr_norm(yf_sqr_norm);
681 DEBUG_PRINTM(yf_sqr_norm[0]);
682 xyf = auto_correlation ? xf.sqr_mag() : xf * yf.conj(); // xf.muln(yf.conj());
685 // ifft2 and sum over 3rd dimension, we dont care about individual channels
686 ComplexMat xyf_sum = xyf.sum_over_channels();
687 DEBUG_PRINTM(xyf_sum);
688 kcf.fft.inverse(xyf_sum, ifft_res);
689 DEBUG_PRINTM(ifft_res);
690 #if 0 && defined(CUFFT)
692 cuda_gaussian_correlation(ifft_res.deviceMem(), k.deviceMem(), xf_sqr_norm.deviceMem(),
693 auto_correlation ? xf_sqr_norm.deviceMem() : yf_sqr_norm.deviceMem(), sigma,
694 xf.n_channels, xf.n_scales, kcf.feature_size.height, kcf.feature_size.width);
697 float numel_xf_inv = 1.f / (xf.cols * xf.rows * (xf.channels() / xf.n_scales));
698 for (uint i = 0; i < xf.n_scales; ++i) {
699 cv::Mat plane = ifft_res.plane(i);
700 DEBUG_PRINT(ifft_res.plane(i));
701 cv::exp(-1. / (sigma * sigma) * cv::max((xf_sqr_norm[i] + yf_sqr_norm[0] - 2 * ifft_res.plane(i))
702 * numel_xf_inv, 0), plane);
706 kcf.fft.forward(ifft_res, result);
709 float get_response_circular(cv::Point2i &pt, cv::Mat &response)
713 assert(response.dims == 2); // ensure .cols and .rows are valid
714 if (x < 0) x = response.cols + x;
715 if (y < 0) y = response.rows + y;
716 if (x >= response.cols) x = x - response.cols;
717 if (y >= response.rows) y = y - response.rows;
719 return response.at<float>(y, x);
722 cv::Point2f KCF_Tracker::sub_pixel_peak(cv::Point &max_loc, cv::Mat &response) const
724 // find neighbourhood of max_loc (response is circular)
728 cv::Point2i p1(max_loc.x - 1, max_loc.y - 1), p2(max_loc.x, max_loc.y - 1), p3(max_loc.x + 1, max_loc.y - 1);
729 cv::Point2i p4(max_loc.x - 1, max_loc.y), p5(max_loc.x + 1, max_loc.y);
730 cv::Point2i p6(max_loc.x - 1, max_loc.y + 1), p7(max_loc.x, max_loc.y + 1), p8(max_loc.x + 1, max_loc.y + 1);
733 // fit 2d quadratic function f(x, y) = a*x^2 + b*x*y + c*y^2 + d*x + e*y + f
734 cv::Mat A = (cv::Mat_<float>(9, 6) <<
735 p1.x*p1.x, p1.x*p1.y, p1.y*p1.y, p1.x, p1.y, 1.f,
736 p2.x*p2.x, p2.x*p2.y, p2.y*p2.y, p2.x, p2.y, 1.f,
737 p3.x*p3.x, p3.x*p3.y, p3.y*p3.y, p3.x, p3.y, 1.f,
738 p4.x*p4.x, p4.x*p4.y, p4.y*p4.y, p4.x, p4.y, 1.f,
739 p5.x*p5.x, p5.x*p5.y, p5.y*p5.y, p5.x, p5.y, 1.f,
740 p6.x*p6.x, p6.x*p6.y, p6.y*p6.y, p6.x, p6.y, 1.f,
741 p7.x*p7.x, p7.x*p7.y, p7.y*p7.y, p7.x, p7.y, 1.f,
742 p8.x*p8.x, p8.x*p8.y, p8.y*p8.y, p8.x, p8.y, 1.f,
743 max_loc.x*max_loc.x, max_loc.x*max_loc.y, max_loc.y*max_loc.y, max_loc.x, max_loc.y, 1.f);
744 cv::Mat fval = (cv::Mat_<float>(9, 1) <<
745 get_response_circular(p1, response),
746 get_response_circular(p2, response),
747 get_response_circular(p3, response),
748 get_response_circular(p4, response),
749 get_response_circular(p5, response),
750 get_response_circular(p6, response),
751 get_response_circular(p7, response),
752 get_response_circular(p8, response),
753 get_response_circular(max_loc, response));
756 cv::solve(A, fval, x, cv::DECOMP_SVD);
758 float a = x.at<float>(0), b = x.at<float>(1), c = x.at<float>(2), d = x.at<float>(3), e = x.at<float>(4);
760 cv::Point2f sub_peak(max_loc.x, max_loc.y);
761 if (b > 0 || b < 0) {
762 sub_peak.y = ((2.f * a * e) / b - d) / (b - (4 * a * c) / b);
763 sub_peak.x = (-2 * c * sub_peak.y - e) / b;
769 double KCF_Tracker::sub_grid_scale(uint index)
772 if (index >= p_scales.size()) {
773 // interpolate from all values
774 // fit 1d quadratic function f(x) = a*x^2 + b*x + c
775 A.create(p_scales.size(), 3, CV_32FC1);
776 fval.create(p_scales.size(), 1, CV_32FC1);
777 for (size_t i = 0; i < p_scales.size(); ++i) {
778 A.at<float>(i, 0) = float(p_scales[i] * p_scales[i]);
779 A.at<float>(i, 1) = float(p_scales[i]);
780 A.at<float>(i, 2) = 1;
781 fval.at<float>(i) = d.threadctxs.back().IF_BIG_BATCH(max[i].response, max.response);
784 // only from neighbours
785 if (index == 0 || index == p_scales.size() - 1)
786 return p_scales[index];
788 A = (cv::Mat_<float>(3, 3) <<
789 p_scales[index - 1] * p_scales[index - 1], p_scales[index - 1], 1,
790 p_scales[index + 0] * p_scales[index + 0], p_scales[index + 0], 1,
791 p_scales[index + 1] * p_scales[index + 1], p_scales[index + 1], 1);
793 fval = (cv::Mat_<float>(3, 1) <<
794 d.threadctxs.back().max[index - 1].response,
795 d.threadctxs.back().max[index + 0].response,
796 d.threadctxs.back().max[index + 1].response);
798 fval = (cv::Mat_<float>(3, 1) <<
799 d.threadctxs[index - 1].max.response,
800 d.threadctxs[index + 0].max.response,
801 d.threadctxs[index + 1].max.response);
806 cv::solve(A, fval, x, cv::DECOMP_SVD);
807 float a = x.at<float>(0), b = x.at<float>(1);
808 double scale = p_scales[index];
810 scale = -b / (2 * a);