1 #ifndef KCF_HEADER_6565467831231
2 #define KCF_HEADER_6565467831231
4 #include <opencv2/opencv.hpp>
10 #include "complexmat.cuh"
11 #include "cuda_functions.cuh"
12 #include "cuda_error_check.hpp"
13 #include <cuda_runtime.h>
15 #include "complexmat.hpp"
22 class Kcf_Tracker_Private;
29 inline void scale(double factor)
37 inline void scale_x(double factor)
43 inline void scale_y(double factor)
49 inline cv::Rect get_rect()
51 return cv::Rect(int(cx-w/2.), int(cy-h/2.), int(w), int(h));
61 const bool m_use_scale {true};
62 const bool m_use_color {true};
63 const bool m_use_subpixel_localization {true};
64 const bool m_use_subgrid_scale {true};
65 const bool m_use_cnfeat {true};
66 const bool m_use_linearkernel {false};
67 const int p_cell_size = 4; //4 for hog (= bin_size)
70 padding ... extra area surrounding the target (1.5)
71 kernel_sigma ... gaussian kernel bandwidth (0.5)
72 lambda ... regularization (1e-4)
73 interp_factor ... linear interpolation factor for adaptation (0.02)
74 output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
75 cell_size ... hog cell size (4)
77 KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
81 // Init/re-init methods
82 void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x = -1, int fit_size_y = -1);
83 void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x = -1, int fit_size_y = -1);
84 void updateTrackerPosition(BBox_c & bbox);
86 // frame-to-frame object tracking
87 void track(cv::Mat & img);
89 double getFilterResponse() const; // Measure of tracking accuracy
95 double max_response = -1.;
97 bool p_resize_image = false;
98 bool p_fit_to_pw2 = false;
100 const double p_downscale_factor = 0.5;
101 double p_fit_factor_x = 1;
102 double p_fit_factor_y = 1;
103 const double p_floating_error = 0.0001;
105 const double p_padding = 1.5;
106 const double p_output_sigma_factor = 0.1;
107 double p_output_sigma;
108 const double p_kernel_sigma = 0.5; //def = 0.5
109 const double p_lambda = 1e-4; //regularization in learning step
110 const double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
111 cv::Size p_windows_size;
113 const uint p_num_scales = m_use_scale ? 7 : 1;
114 const double p_scale_step = 1.02;
115 double p_current_scale = 1.;
116 double p_min_max_scale[2];
117 std::vector<double> p_scales;
119 const uint p_num_angles = 1;
120 const int p_angle_step = 10;
121 std::vector<double> p_angles = {0};
123 const int p_num_of_feats = 31 + (m_use_color ? 3 : 0) + (m_use_cnfeat ? 10 : 0);
126 Kcf_Tracker_Private &d;
130 ComplexMat p_model_alphaf;
131 ComplexMat p_model_alphaf_num;
132 ComplexMat p_model_alphaf_den;
133 ComplexMat p_model_xf;
136 class GaussianCorrelation {
138 GaussianCorrelation(uint num_scales, cv::Size size)
139 : xf_sqr_norm(num_scales)
140 , xyf(Fft::freq_size(size), 1, num_scales)
141 , ifft_res(num_scales, size)
142 , k(num_scales, size)
144 void operator()(ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation, const KCF_Tracker &kcf);
148 DynMem yf_sqr_norm{1};
155 void scale_track(ThreadCtx &vars, cv::Mat &input_rgb, cv::Mat &input_gray);
156 cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y) const;
157 cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
158 std::unique_ptr<GaussianCorrelation> gaussian_correlation;
159 cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot);
160 cv::Mat cosine_window_function(int dim1, int dim2);
161 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;
162 cv::Point2f sub_pixel_peak(cv::Point &max_loc, cv::Mat &response) const;
163 double sub_grid_scale(uint index);
164 void resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray);
165 void train(cv::Mat input_rgb, cv::Mat input_gray, double interp_factor);
166 double findMaxReponse(uint &max_idx, cv::Point2f &new_location) const;
169 #endif //KCF_HEADER_6565467831231