1 #ifndef KCF_HEADER_6565467831231
2 #define KCF_HEADER_6565467831231
4 #include <opencv2/opencv.hpp>
9 #include "complexmat.cuh"
10 #include "cuda_functions.cuh"
11 #include "cuda/cuda_error_check.cuh"
12 #include <cuda_runtime.h>
14 #include "complexmat.hpp"
24 inline void scale(double factor)
32 inline void scale_x(double factor)
38 inline void scale_y(double factor)
44 inline cv::Rect get_rect()
46 return cv::Rect(cx-w/2., cy-h/2., w, h);
53 float *xf_sqr_norm = nullptr, *yf_sqr_norm = nullptr;
55 float *xf_sqr_norm_d = nullptr, *yf_sqr_norm_d = nullptr, *gauss_corr_res = nullptr;
63 bool m_use_scale {true};
64 bool m_use_color {true};
66 bool m_use_multithreading {true};
68 bool m_use_multithreading {false};
70 bool m_use_subpixel_localization {true};
71 bool m_use_subgrid_scale {true};
72 bool m_use_cnfeat {true};
73 bool m_use_linearkernel {false};
75 bool m_use_big_batch {true};
77 bool m_use_big_batch {false};
80 bool m_use_cuda {true};
82 bool m_use_cuda {false};
86 padding ... extra area surrounding the target (1.5)
87 kernel_sigma ... gaussian kernel bandwidth (0.5)
88 lambda ... regularization (1e-4)
89 interp_factor ... linear interpolation factor for adaptation (0.02)
90 output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
91 cell_size ... hog cell size (4)
93 KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
97 // Init/re-init methods
98 void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x, int fit_size_y);
99 void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x, int fit_size_y);
100 void updateTrackerPosition(BBox_c & bbox);
102 // frame-to-frame object tracking
103 void track(cv::Mat & img);
110 bool p_resize_image = false;
111 bool p_fit_to_pw2 = false;
115 const double p_downscale_factor = 0.5;
116 double p_scale_factor_x = 1;
117 double p_scale_factor_y = 1;
119 double p_padding = 1.5;
120 double p_output_sigma_factor = 0.1;
121 double p_output_sigma;
122 double p_kernel_sigma = 0.5; //def = 0.5
123 double p_lambda = 1e-4; //regularization in learning step
124 double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
125 int p_cell_size = 4; //4 for hog (= bin_size)
126 int p_windows_size[2];
127 int p_num_scales {7};
128 double p_scale_step = 1.02;
129 double p_current_scale = 1.;
130 double p_min_max_scale[2];
131 std::vector<double> p_scales;
135 int p_roi_height, p_roi_width;
137 std::vector<Scale_var> scale_vars;
141 ComplexMat p_model_alphaf;
142 ComplexMat p_model_alphaf_num;
143 ComplexMat p_model_alphaf_den;
144 ComplexMat p_model_xf;
146 cv::Mat get_subwindow(const cv::Mat & input, int cx, int cy, int size_x, int size_y);
147 cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
148 ComplexMat gaussian_correlation(struct Scale_var &vars, const ComplexMat & xf, const ComplexMat & yf, double sigma, bool auto_correlation = false);
149 cv::Mat circshift(const cv::Mat & patch, int x_rot, int y_rot);
150 cv::Mat cosine_window_function(int dim1, int dim2);
151 std::vector<cv::Mat> get_features(cv::Mat & input_rgb, cv::Mat & input_gray, int cx, int cy, int size_x, int size_y, double scale = 1.);
152 cv::Point2f sub_pixel_peak(cv::Point & max_loc, cv::Mat & response);
153 double sub_grid_scale(std::vector<double> & responses, int index = -1);
157 #endif //KCF_HEADER_6565467831231