#include <memory>
#include "fhog.hpp"
+#include "complexmat.hpp"
#ifdef CUFFT
-#include "complexmat.cuh"
-#include "cuda_functions.cuh"
#include "cuda_error_check.hpp"
#include <cuda_runtime.h>
-#else
-#include "complexmat.hpp"
#endif
#include "cnfeat.hpp"
struct BBox_c
{
- double cx, cy, w, h;
+ double cx, cy, w, h, a;
+
+ inline cv::Point2d center() const { return cv::Point2d(cx, cy); }
inline void scale(double factor)
{
h *= factor;
}
- inline void scale_x(double factor)
- {
- cx *= factor;
- w *= factor;
- }
-
- inline void scale_y(double factor)
- {
- cy *= factor;
- h *= factor;
- }
-
inline cv::Rect get_rect()
{
return cv::Rect(int(cx-w/2.), int(cy-h/2.), int(w), int(h));
class KCF_Tracker
{
friend ThreadCtx;
+ friend Kcf_Tracker_Private;
public:
- bool m_debug {false};
+ bool m_debug {false};
+ enum class vd {NONE, PATCH, RESPONSE} m_visual_debug {vd::NONE};
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 p_current_angle = 0.;
+
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;
const double p_floating_error = 0.0001;
const double p_padding = 1.5;
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
- const int p_cell_size = 4; //4 for hog (= bin_size)
- cv::Size p_windows_size;
- const uint p_num_scales {7};
- const double p_scale_step = 1.02;
- double p_current_scale = 1.;
+ cv::Size p_windows_size; // size of the patch to find the tracked object in
+ cv::Size fit_size; // size to which rescale the patch for better FFT performance
+
+ const uint p_num_scales = m_use_scale ? 5 : 1;
+ const double p_scale_step = 1.03;
double p_min_max_scale[2];
std::vector<double> p_scales;
+ const uint p_num_angles = 3;
+ const int p_angle_step = 10;
+ std::vector<double> p_angles;
+
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;
+ std::unique_ptr<Kcf_Tracker_Private> d;
+
+ class Model {
+ cv::Size feature_size;
+ uint height, width, n_feats;
+ public:
+ ComplexMat yf {height, width, 1};
+ ComplexMat model_alphaf {height, width, 1};
+ ComplexMat model_alphaf_num {height, width, 1};
+ ComplexMat model_alphaf_den {height, width, 1};
+ ComplexMat model_xf {height, width, n_feats};
+ ComplexMat xf {height, width, n_feats};
+
+ // Temporary variables for trainig
+ MatScaleFeats patch_feats{1, n_feats, feature_size};
+ MatScaleFeats temp{1, n_feats, feature_size};
+
+
+
+ Model(cv::Size feature_size, uint _n_feats)
+ : feature_size(feature_size)
+ , height(Fft::freq_size(feature_size).height)
+ , width(Fft::freq_size(feature_size).width)
+ , n_feats(_n_feats) {}
+ };
- //model
- ComplexMat p_yf;
- ComplexMat p_model_alphaf;
- ComplexMat p_model_alphaf_num;
- ComplexMat p_model_alphaf_den;
- ComplexMat p_model_xf;
- ComplexMat p_xf;
+ std::unique_ptr<Model> model;
class GaussianCorrelation {
public:
- GaussianCorrelation(uint num_scales, cv::Size size)
+ GaussianCorrelation(uint num_scales, uint num_feats, cv::Size size)
: xf_sqr_norm(num_scales)
- , xyf(Fft::freq_size(size), 1, num_scales)
+ , xyf(Fft::freq_size(size), num_feats, num_scales)
, ifft_res(num_scales, size)
, k(num_scales, size)
{}
//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) const;
+ cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y, double angle) const;
cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
std::unique_ptr<GaussianCorrelation> gaussian_correlation;
- cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot);
+ cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot) const;
cv::Mat cosine_window_function(int dim1, int dim2);
- 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::Mat get_features(cv::Mat &input_rgb, cv::Mat &input_gray, cv::Mat *dbg_patch, int cx, int cy, int size_x, int size_y, double scale, double angle) 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_rgb, cv::Mat input_gray, double interp_factor);
- double findMaxReponse(uint &max_idx, cv::Point2f &new_location) const;
+ double findMaxReponse(uint &max_idx, cv::Point2d &new_location) const;
};
#endif //KCF_HEADER_6565467831231
projects / hercules2020 / kcf.git / blobdiff