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"
22 double cx, cy, w, h, a;
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);
55 bool m_visual_debug {false};
56 bool m_use_scale {false};
57 bool m_use_angle {true}; //Currently only works when m_use_scale is off and m_use_subpixel_localization too and used on RotatingBox dataset.
58 bool m_use_color {true};
60 bool m_use_multithreading {true};
62 bool m_use_multithreading {false};
64 bool m_use_subpixel_localization {true};
65 bool m_use_subgrid_scale {true};
66 bool m_use_cnfeat {true};
67 bool m_use_linearkernel {false};
69 bool m_use_big_batch {true};
71 bool m_use_big_batch {false};
74 bool m_use_cuda {true};
76 bool m_use_cuda {false};
80 padding ... extra area surrounding the target (1.5)
81 kernel_sigma ... gaussian kernel bandwidth (0.5)
82 lambda ... regularization (1e-4)
83 interp_factor ... linear interpolation factor for adaptation (0.02)
84 output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
85 cell_size ... hog cell size (4)
87 KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
91 // Init/re-init methods
92 void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x, int fit_size_y);
93 void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x, int fit_size_y);
94 void updateTrackerPosition(BBox_c & bbox);
96 // frame-to-frame object tracking
97 void track(cv::Mat & img);
104 bool p_resize_image = false;
105 bool p_fit_to_pw2 = false;
109 const double p_downscale_factor = 0.5;
110 double p_scale_factor_x = 1;
111 double p_scale_factor_y = 1;
113 double p_padding = 1.5;
114 double p_output_sigma_factor = 0.1;
115 double p_output_sigma;
116 double p_kernel_sigma = 0.5; //def = 0.5
117 double p_lambda = 1e-4; //regularization in learning step
118 double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
119 int p_cell_size = 4; //4 for hog (= bin_size)
120 int p_windows_size[2];
121 int p_num_scales {5};
122 double p_scale_step = 1.02;
123 double p_current_scale = 1.;
124 double p_min_max_scale[2];
125 std::vector<double> p_scales;
126 int p_num_angles {5};
127 int p_current_angle = 0;
128 int p_angle_min = -20, p_angle_max = 20;
129 int p_angle_step = 10;
130 std::vector<double> p_angles;
133 int p_debug_image_size = 100;
135 std::vector<cv::Mat> p_debug_scale_responses;
136 std::vector<cv::Mat> p_debug_subwindows;
140 int p_roi_height, p_roi_width;
141 float *xf_sqr_norm = nullptr, *yf_sqr_norm = nullptr;
143 float *xf_sqr_norm_d = nullptr, *yf_sqr_norm_d = nullptr, *gauss_corr_res = nullptr;
148 ComplexMat p_model_alphaf;
149 ComplexMat p_model_alphaf_num;
150 ComplexMat p_model_alphaf_den;
151 ComplexMat p_model_xf;
153 cv::Mat get_subwindow(const cv::Mat & input, int cx, int cy, int size_x, int size_y);
154 cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
155 ComplexMat gaussian_correlation(const ComplexMat & xf, const ComplexMat & yf, double sigma, bool auto_correlation = false);
156 cv::Mat circshift(const cv::Mat & patch, int x_rot, int y_rot);
157 cv::Mat cosine_window_function(int dim1, int dim2);
158 std::vector<cv::Mat> get_features(cv::Mat & input_rgb, cv::Mat & input_gray);
159 void geometric_transformations(cv::Mat & patch, int size_x, int size_y, double scale = 1, int angle = 0, bool allow_debug = true);
160 cv::Point2f sub_pixel_peak(cv::Point & max_loc, cv::Mat & response);
161 double sub_grid_scale(std::vector<double> & responses, int index = -1);
165 #endif //KCF_HEADER_6565467831231