#include <future>
#include "dynmem.hpp"
#include "kcf.h"
-
-#ifdef CUFFT
-#include "complexmat.cuh"
-#else
#include "complexmat.hpp"
-#endif
class KCF_Tracker;
struct ThreadCtx {
public:
- ThreadCtx(cv::Size roi, uint num_of_feats, double scale, uint num_of_scales)
- : scale(scale)
- , gc(num_of_scales)
- {
- uint cells_size = roi.width * roi.height * sizeof(float);
-
-#if defined(CUFFT) || defined(FFTW)
- this->gauss_corr_res = DynMem(cells_size * num_of_scales);
- this->data_features = DynMem(cells_size * num_of_feats);
-
- uint width_freq = roi.width / 2 + 1;
-
- this->in_all = cv::Mat(roi.height * num_of_scales, roi.width, CV_32F, this->gauss_corr_res.hostMem());
- this->fw_all = cv::Mat(roi.height * num_of_feats, roi.width, CV_32F, this->data_features.hostMem());
+ ThreadCtx(cv::Size roi, uint num_features
+#ifdef BIG_BATCH
+ , uint num_scales
#else
- uint width_freq = roi.width;
-
- this->in_all = cv::Mat(roi, CV_32F);
+ , double scale
#endif
-
- this->data_i_features = DynMem(cells_size * num_of_feats);
- this->data_i_1ch = DynMem(cells_size * num_of_scales);
-
- this->ifft2_res = cv::Mat(roi, CV_32FC(num_of_feats), this->data_i_features.hostMem());
- this->response = cv::Mat(roi, CV_32FC(num_of_scales), this->data_i_1ch.hostMem());
-
-#ifdef CUFFT
- this->zf.create(roi.height, width_freq, num_of_feats, num_of_scales);
- this->kzf.create(roi.height, width_freq, num_of_scales);
- this->kf.create(roi.height, width_freq, num_of_scales);
-#else
- this->zf.create(roi.height, width_freq, num_of_feats, num_of_scales);
- this->kzf.create(roi.height, width_freq, num_of_scales);
- this->kf.create(roi.height, width_freq, num_of_scales);
+ )
+ : roi(roi)
+ , num_features(num_features)
+ , num_scales(IF_BIG_BATCH(num_scales, 1))
+#ifndef BIG_BATCH
+ , scale(scale)
#endif
+ {}
-#ifdef BIG_BATCH
- if (num_of_scales > 1) {
- this->max_responses.reserve(num_of_scales);
- this->max_locs.reserve(num_of_scales);
- this->response_maps.reserve(num_of_scales);
- }
-#endif
- }
ThreadCtx(ThreadCtx &&) = default;
- const double scale;
-#ifdef ASYNC
- std::future<void> async_res;
-#endif
+ void track(const KCF_Tracker &kcf, cv::Mat &input_rgb, cv::Mat &input_gray);
+private:
+ cv::Size roi;
+ uint num_features;
+ uint num_scales;
+ cv::Size freq_size = Fft::freq_size(roi);
- class gaussian_correlation_data {
- friend void KCF_Tracker::gaussian_correlation(struct ThreadCtx &vars, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation);
- DynMem xf_sqr_norm;
- DynMem yf_sqr_norm{sizeof(float)};
+ MatScaleFeats patch_feats{num_scales, num_features, roi};
+ MatScaleFeats temp{num_scales, num_features, roi};
- public:
- gaussian_correlation_data(uint num_of_scales) : xf_sqr_norm(num_of_scales * sizeof(float)) {}
- } gc;
+ KCF_Tracker::GaussianCorrelation gaussian_correlation{num_scales, num_features, roi};
- cv::Mat in_all, fw_all, ifft2_res, response;
- ComplexMat zf, kzf, kf, xyf;
+ MatScales ifft2_res{num_scales, roi};
- DynMem data_i_features, data_i_1ch;
- // CuFFT and FFTW variables
- DynMem gauss_corr_res, data_features;
+ ComplexMat zf{uint(freq_size.height), uint(freq_size.width), num_features, num_scales};
+ ComplexMat kzf{uint(freq_size.height), uint(freq_size.width), 1, num_scales};
- // CuFFT variables
- ComplexMat model_alphaf, model_xf;
+public:
+#ifdef ASYNC
+ std::future<void> async_res;
+#endif
- // Variables used during non big batch mode and in big batch mode with ThreadCtx in p_threadctxs in kcf on zero index.
- cv::Point2i max_loc;
- double max_val, max_response;
+ MatScales response{num_scales, roi};
+
+ struct Max {
+ cv::Point2i loc;
+ double response;
+ };
#ifdef BIG_BATCH
- // Stores value of responses, location of maximal response and response maps for each scale
- std::vector<double> max_responses;
- std::vector<cv::Point2i> max_locs;
- std::vector<cv::Mat> response_maps;
+ std::vector<Max> max = std::vector<Max>(num_scales);
+#else
+ Max max;
+ const double scale;
#endif
};