#ifdef CUFFT
#include "complexmat.cuh"
#include "cuda_functions.cuh"
-#include "cuda/cuda_error_check.cuh"
+#include "cuda_error_check.hpp"
#include <cuda_runtime.h>
#else
#include "complexmat.hpp"
{
double cx, cy, w, h;
+ inline cv::Point2d center() const { return cv::Point2d(cx, cy); }
+
inline void scale(double factor)
{
cx *= factor;
{
friend ThreadCtx;
public:
- bool m_debug {false};
- bool m_use_scale {true};
- bool m_use_color {true};
- bool m_use_subpixel_localization {true};
- bool m_use_subgrid_scale {true};
- bool m_use_cnfeat {true};
- bool m_use_linearkernel {false};
+ bool m_debug {false};
+ bool m_visual_debug {false};
+ const bool m_use_scale {true};
+ const bool m_use_color {true};
+ const bool m_use_subpixel_localization {true};
+ const bool m_use_subgrid_scale {true};
+ const bool m_use_cnfeat {true};
+ const bool m_use_linearkernel {false};
+ const int p_cell_size = 4; //4 for hog (= bin_size)
/*
padding ... extra area surrounding the target (1.5)
~KCF_Tracker();
// Init/re-init methods
- void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x, int fit_size_y);
- void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x, int fit_size_y);
+ void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x = -1, int fit_size_y = -1);
+ void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x = -1, int fit_size_y = -1);
void updateTrackerPosition(BBox_c & bbox);
// frame-to-frame object tracking
private:
Fft &fft;
- BBox_c p_pose;
+ // Initial pose of tracked object in internal image coordinates
+ // (scaled by p_downscale_factor if p_resize_image)
+ BBox_c p_init_pose;
+
+ // Information to calculate current pose of the tracked object
+ cv::Point2d p_current_center;
+ double p_current_scale = 1.;
+
double max_response = -1.;
bool p_resize_image = false;
bool p_fit_to_pw2 = false;
const double p_downscale_factor = 0.5;
- double p_scale_factor_x = 1;
- double p_scale_factor_y = 1;
+ double p_fit_factor_x = 1;
+ double p_fit_factor_y = 1;
const double p_floating_error = 0.0001;
- double p_padding = 1.5;
- double p_output_sigma_factor = 0.1;
+ const double p_padding = 1.5;
+ const double p_output_sigma_factor = 0.1;
double p_output_sigma;
- double p_kernel_sigma = 0.5; //def = 0.5
- double p_lambda = 1e-4; //regularization in learning step
- double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
- int p_cell_size = 4; //4 for hog (= bin_size)
+ const double p_kernel_sigma = 0.5; //def = 0.5
+ const double p_lambda = 1e-4; //regularization in learning step
+ const double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
cv::Size p_windows_size;
- uint p_num_scales {7};
- double p_scale_step = 1.02;
- double p_current_scale = 1.;
+
+ const uint p_num_scales = m_use_scale ? 7 : 1;
+ const double p_scale_step = 1.02;
double p_min_max_scale[2];
std::vector<double> p_scales;
+ const uint p_num_angles = 1;
+ const int p_angle_step = 10;
+ std::vector<double> p_angles = {0};
+
const int p_num_of_feats = 31 + (m_use_color ? 3 : 0) + (m_use_cnfeat ? 10 : 0);
- cv::Size p_roi;
+ cv::Size feature_size;
Kcf_Tracker_Private &d;
class GaussianCorrelation {
public:
- GaussianCorrelation(cv::Size size, uint num_scales, uint num_feats)
+ GaussianCorrelation(uint num_scales, cv::Size size)
: xf_sqr_norm(num_scales)
- , xyf(Fft::freq_size(size), num_scales)
- , ifft_res({{int(num_feats * num_scales), size.height, size.width}}, CV_32F)
- , k({{int(num_scales), size.height, size.width}}, CV_32F)
+ , xyf(Fft::freq_size(size), 1, num_scales)
+ , ifft_res(num_scales, size)
+ , k(num_scales, size)
{}
- void operator()(const KCF_Tracker &kcf, ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation = false);
+ void operator()(ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation, const KCF_Tracker &kcf);
private:
DynMem xf_sqr_norm;
DynMem yf_sqr_norm{1};
ComplexMat xyf;
- MatDynMem ifft_res;
- MatDynMem k;
+ MatScales ifft_res;
+ MatScales k;
};
//helping functions
void scale_track(ThreadCtx &vars, cv::Mat &input_rgb, cv::Mat &input_gray);
- cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y);
- MatDynMem gaussian_shaped_labels(double sigma, int dim1, int dim2);
+ cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y) const;
+ cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
std::unique_ptr<GaussianCorrelation> gaussian_correlation;
- MatDynMem circshift(const cv::Mat &patch, int x_rot, int y_rot);
+ cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot);
cv::Mat cosine_window_function(int dim1, int dim2);
- void get_features(MatDynMem &feat_3d, cv::Mat &input_rgb, cv::Mat &input_gray, int cx, int cy, int size_x, int size_y, double scale);
+ cv::Mat get_features(cv::Mat &input_rgb, cv::Mat &input_gray, int cx, int cy, int size_x, int size_y, double scale) const;
cv::Point2f sub_pixel_peak(cv::Point &max_loc, cv::Mat &response) const;
double sub_grid_scale(uint index);
void resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray);
- void train(cv::Mat input_gray, cv::Mat input_rgb, double interp_factor);
- void findMaxReponse(uint &max_idx, cv::Point2f &new_location) const;
+ void train(cv::Mat input_rgb, cv::Mat input_gray, double interp_factor);
+ double findMaxReponse(uint &max_idx, cv::Point2d &new_location) const;
};
#endif //KCF_HEADER_6565467831231