#ifndef SCALE_VARS_HPP
#define SCALE_VARS_HPP
+#include <future>
#include "dynmem.hpp"
-
-#ifdef CUFFT
-#include "complexmat.cuh"
-#else
+#include "kcf.h"
#include "complexmat.hpp"
-#ifndef CUFFTW
-// For compatibility reasons between CuFFT and FFTW, OpenCVfft versions.
-typedef int *cudaStream_t;
-#endif
-#endif
+
+class KCF_Tracker;
struct ThreadCtx {
public:
- ThreadCtx(cv::Size windows_size, uint cell_size, uint num_of_feats, uint num_of_scales = 1,
- ComplexMat *model_xf = nullptr, ComplexMat *yf = nullptr, bool zero_index = false)
- {
-#ifdef CUFFT
- if (zero_index) {
- cudaSetDeviceFlags(cudaDeviceMapHost);
- this->zero_index = true;
- }
-
-#if defined(ASYNC) || defined(OPENMP)
- CudaSafeCall(cudaStreamCreate(&this->stream));
+ ThreadCtx(cv::Size roi, uint num_features
+#ifdef BIG_BATCH
+ , uint num_scales
+#else
+ , double scale
#endif
+ )
+ : roi(roi)
+ , num_features(num_features)
+ , num_scales(IF_BIG_BATCH(num_scales, 1))
+#ifndef BIG_BATCH
+ , scale(scale)
+#endif
+ {}
- this->patch_feats.reserve(uint(num_of_feats));
- // Size of cufftReal == float
- uint cells_size =
- ((uint(windows_size.width) / cell_size) * (uint(windows_size.height) / cell_size)) * sizeof(float);
-
- this->data_i_1ch = DynMem(cells_size * num_of_scales);
- this->data_i_features = DynMem(cells_size * num_of_feats);
-
- this->ifft2_res = cv::Mat(windows_size.height / int(cell_size), windows_size.width / int(cell_size),
- CV_32FC(int(num_of_feats)), this->data_i_features.hostMem());
- this->response = cv::Mat(windows_size.height / int(cell_size), windows_size.width / int(cell_size),
- CV_32FC(int(num_of_scales)), this->data_i_1ch.hostMem());
-
- this->zf.create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1,
- num_of_feats, num_of_scales, this->stream);
- this->kzf.create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1,
- num_of_scales, this->stream);
- this->kf.create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1,
- num_of_scales, this->stream);
-
- this->xf_sqr_norm = DynMem(num_of_scales * sizeof(float));
- this->yf_sqr_norm = DynMem(sizeof(float));
+ ThreadCtx(ThreadCtx &&) = default;
- this->gauss_corr_res = DynMem(cells_size * num_of_scales);
- this->in_all = cv::Mat(windows_size.height / int(cell_size) * int(num_of_scales),
- windows_size.width / int(cell_size), CV_32F, this->gauss_corr_res.hostMem());
+ 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);
- if (zero_index) {
- this->rot_labels_data = DynMem(cells_size);
- this->rot_labels = cv::Mat(windows_size.height / int(cell_size), windows_size.width / int(cell_size),
- CV_32FC1, this->rot_labels_data.hostMem());
- }
+ MatScaleFeats patch_feats{num_scales, num_features, roi};
+ MatScaleFeats temp{num_scales, num_features, roi};
- this->data_features = DynMem(cells_size * num_of_feats);
- this->fw_all = cv::Mat((windows_size.height / int(cell_size)) * int(num_of_feats),
- windows_size.width / int(cell_size), CV_32F, this->data_features.hostMem());
-#else
+ KCF_Tracker::GaussianCorrelation gaussian_correlation{num_scales, num_features, roi};
- this->xf_sqr_norm = DynMem(num_of_scales * sizeof(float));
- this->yf_sqr_norm = DynMem(sizeof (float));
+ MatScales ifft2_res{num_scales, roi};
- this->patch_feats.reserve(num_of_feats);
+ 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};
- uint height = uint(windows_size.height) / cell_size;
-#ifdef FFTW
- uint width = (uint(windows_size.width) / cell_size) / 2 + 1;
-#else
- int width = windows_size.width / cell_size;
+public:
+#ifdef ASYNC
+ std::future<void> async_res;
#endif
- this->ifft2_res = cv::Mat(int(height), windows_size.width / int(cell_size), CV_32FC(int(num_of_feats)));
- this->response = cv::Mat(int(height), windows_size.width / int(cell_size), CV_32FC(int(num_of_scales)));
+ MatScales response{num_scales, roi};
- this->zf = ComplexMat(height, width, num_of_feats, num_of_scales);
- this->kzf = ComplexMat(height, width, num_of_scales);
- this->kf = ComplexMat(height, width, num_of_scales);
-#ifdef FFTW
- this->in_all = cv::Mat((windows_size.height / int(cell_size)) * int(num_of_scales),
- windows_size.width / int(cell_size), CV_32F);
- this->fw_all = cv::Mat((windows_size.height / int(cell_size)) * int(num_of_feats),
- windows_size.width / int(cell_size), CV_32F);
-#else
- this->in_all = cv::Mat((windows_size.height / int(cell_size)), windows_size.width / int(cell_size), CV_32F);
-#endif
-#endif
-#if defined(FFTW) || defined(CUFFT)
- if (zero_index) {
- model_xf->create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1,
- num_of_feats);
- yf->create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1, 1);
- // We use scale_vars[0] for updating the tracker, so we only allocate memory for its xf only.
-#ifdef CUFFT
- this->xf.create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1,
- num_of_feats, this->stream);
-#else
- this->xf.create(uint(windows_size.height) / cell_size, (uint(windows_size.width) / cell_size) / 2 + 1,
- num_of_feats);
-#endif
- } else if (num_of_scales > 1) {
- this->max_responses.reserve(uint(num_of_scales));
- this->max_locs.reserve(uint(num_of_scales));
- this->response_maps.reserve(uint(num_of_scales));
- }
-#else
- if (zero_index) {
- model_xf->create(windows_size.height / cell_size, windows_size.width / cell_size, num_of_feats);
- yf->create(windows_size.height / cell_size, windows_size.width / cell_size, 1);
- this->xf.create(windows_size.height / cell_size, windows_size.width / cell_size, num_of_feats);
- }
-#endif
- }
+ struct Max {
+ cv::Point2i loc;
+ double response;
+ };
- ~ThreadCtx()
- {
-#if defined(CUFFT) && (defined(ASYNC) || defined(OPENMP))
- CudaSafeCall(cudaStreamDestroy(this->stream));
+#ifdef BIG_BATCH
+ std::vector<Max> max = std::vector<Max>(num_scales);
+#else
+ Max max;
+ const double scale;
#endif
- }
-
- DynMem xf_sqr_norm, yf_sqr_norm;
- std::vector<cv::Mat> patch_feats;
-
- cv::Mat in_all, fw_all, ifft2_res, response;
- ComplexMat zf, kzf, kf, xyf, xf;
-
- // CuFFT variables
- cv::Mat rot_labels;
- DynMem gauss_corr_res, rot_labels_data, data_features, data_f, data_i_features, data_i_1ch;
-
- cudaStream_t stream = nullptr;
- ComplexMat model_alphaf, model_xf;
-
- // Big batch variables
- cv::Point2i max_loc;
- double max_val, max_response;
-
- std::vector<double> max_responses;
- std::vector<cv::Point2i> max_locs;
- std::vector<cv::Mat> response_maps;
- bool zero_index = false;
};
#endif // SCALE_VARS_HPP