#include <opencv2/opencv.hpp>
#include <vector>
+#include <memory>
#include "fhog.hpp"
+#include "complexmat.hpp"
#ifdef CUFFT
- #include "complexmat.cuh"
- #include "cuda_functions.cuh"
- #include "cuda/cuda_error_check.cuh"
- #include <cuda_runtime.h>
-#else
- #include "complexmat.hpp"
+#include "cuda_error_check.hpp"
+#include <cuda_runtime.h>
#endif
#include "cnfeat.hpp"
#include "fft.h"
-#include "scale_vars.hpp"
+#include "pragmas.h"
+
+class Kcf_Tracker_Private;
+struct ThreadCtx;
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(cx-w/2., cy-h/2., w, h);
+ 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_use_scale {true};
- bool m_use_color {true};
-#ifdef ASYNC
- bool m_use_multithreading {true};
-#else
- bool m_use_multithreading {false};
-#endif //ASYNC
- bool m_use_subpixel_localization {true};
- bool m_use_subgrid_scale {true};
- bool m_use_cnfeat {true};
- bool m_use_linearkernel {false};
-#ifdef BIG_BATCH
- bool m_use_big_batch {true};
-#else
- bool m_use_big_batch {false};
-#endif
-#ifdef CUFFT
- bool m_use_cuda {true};
-#else
- bool m_use_cuda {false};
-#endif
+ 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_subgrid_angle {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 init_scale_vars();
- 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
void track(cv::Mat & img);
BBox_c getBBox();
+ double getFilterResponse() const; // Measure of tracking accuracy
private:
Fft &fft;
- BBox_c p_pose;
- bool p_resize_image = false;
- bool p_fit_to_pw2 = false;
+ // 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.;
- bool first = true;
+ double max_response = -1.;
+
+ bool p_resize_image = 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;
- 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)
- int p_windows_size[2];
- int p_num_scales {7};
- double p_scale_step = 1.02;
- double p_current_scale = 1.;
+ 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; // 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;
- //for big batch
- int p_num_of_feats;
- int p_roi_height, p_roi_width;
+ 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 feature_size;
+
+ 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) {}
+ };
+
+ std::unique_ptr<Model> model;
+
+ class GaussianCorrelation {
+ public:
+ GaussianCorrelation(uint num_scales, uint num_feats, cv::Size size)
+ : xf_sqr_norm(num_scales)
+ , xyf(Fft::freq_size(size), num_feats, num_scales)
+ , ifft_res(num_scales, size)
+ , k(num_scales, size)
+ {}
+ 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;
+ MatScales ifft_res;
+ MatScales k;
+ };
- std::vector<Scale_vars> p_scale_vars;
-
- //model
- ComplexMat p_yf;
- ComplexMat p_model_alphaf;
- ComplexMat p_model_alphaf_num;
- ComplexMat p_model_alphaf_den;
- ComplexMat p_model_xf;
//helping functions
- void scale_track(Scale_vars & vars, cv::Mat & input_rgb, cv::Mat & input_gray, double scale);
- cv::Mat get_subwindow(const cv::Mat & input, int cx, int cy, int size_x, int size_y);
+ 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, double angle) const;
cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
- void gaussian_correlation(struct Scale_vars &vars, const ComplexMat & xf, const ComplexMat & yf, double sigma, bool auto_correlation = false);
- cv::Mat circshift(const cv::Mat & patch, int x_rot, int y_rot);
+ std::unique_ptr<GaussianCorrelation> gaussian_correlation;
+ cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot) const;
cv::Mat cosine_window_function(int dim1, int dim2);
- void get_features(cv::Mat & input_rgb, cv::Mat & input_gray, int cx, int cy, int size_x, int size_y, Scale_vars & vars, double scale = 1.);
- cv::Point2f sub_pixel_peak(cv::Point & max_loc, cv::Mat & response);
- double sub_grid_scale(int index = -1);
-
+ 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::Point2d &new_location) const;
+ double sub_grid_angle(uint max_index);
};
#endif //KCF_HEADER_6565467831231
projects / hercules2020 / kcf.git / blobdiff