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/cuda_error_check.cuh"
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 bool m_use_scale {true};
62 bool m_use_color {true};
64 bool m_use_multithreading {true};
66 bool m_use_multithreading {false};
68 bool m_use_subpixel_localization {true};
69 bool m_use_subgrid_scale {true};
70 bool m_use_cnfeat {true};
71 bool m_use_linearkernel {false};
73 bool m_use_cuda {true};
75 bool m_use_cuda {false};
79 padding ... extra area surrounding the target (1.5)
80 kernel_sigma ... gaussian kernel bandwidth (0.5)
81 lambda ... regularization (1e-4)
82 interp_factor ... linear interpolation factor for adaptation (0.02)
83 output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
84 cell_size ... hog cell size (4)
86 KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
90 // Init/re-init methods
91 void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x, int fit_size_y);
92 void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x, int fit_size_y);
93 void updateTrackerPosition(BBox_c & bbox);
95 // frame-to-frame object tracking
96 void track(cv::Mat & img);
98 double getFilterResponse() const; // Measure of tracking accuracy
104 double max_response = -1.;
106 bool p_resize_image = false;
107 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;
112 const double p_floating_error = 0.0001;
114 double p_padding = 1.5;
115 double p_output_sigma_factor = 0.1;
116 double p_output_sigma;
117 double p_kernel_sigma = 0.5; //def = 0.5
118 double p_lambda = 1e-4; //regularization in learning step
119 double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
120 int p_cell_size = 4; //4 for hog (= bin_size)
121 cv::Size p_windows_size;
122 uint p_num_scales {7};
123 double p_scale_step = 1.02;
124 double p_current_scale = 1.;
125 double p_min_max_scale[2];
126 std::vector<double> p_scales;
128 const int p_num_of_feats = 31 + m_use_color ? 3 : 0 + m_use_cnfeat ? 10 : 0;
131 Kcf_Tracker_Private &d;
135 ComplexMat p_model_alphaf;
136 ComplexMat p_model_alphaf_num;
137 ComplexMat p_model_alphaf_den;
138 ComplexMat p_model_xf;
141 class GaussianCorrelation {
143 GaussianCorrelation(cv::Size size, uint num_scales, uint num_feats)
144 : xf_sqr_norm(num_scales)
145 , xyf(Fft::freq_size(size), num_scales)
146 , ifft_res({int(num_feats * num_scales), size.height, size.width}, CV_32F)
147 , k({int(num_scales), size.height, size.width}, CV_32F)
149 void operator()(const KCF_Tracker &kcf, ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation = false);
153 DynMem yf_sqr_norm{sizeof(float)};
160 void scale_track(ThreadCtx &vars, cv::Mat &input_rgb, cv::Mat &input_gray);
161 cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y);
162 MatDynMem gaussian_shaped_labels(double sigma, int dim1, int dim2);
163 std::unique_ptr<GaussianCorrelation> gaussian_correlation;
164 MatDynMem circshift(const cv::Mat &patch, int x_rot, int y_rot);
165 cv::Mat cosine_window_function(int dim1, int dim2);
166 void get_features(MatDynMem &feat_3d, cv::Mat &input_rgb, cv::Mat &input_gray, int cx, int cy, int size_x, int size_y, double scale);
167 cv::Point2f sub_pixel_peak(cv::Point &max_loc, cv::Mat &response);
168 double sub_grid_scale(uint index);
169 void resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray);
170 void train(cv::Mat input_gray, cv::Mat input_rgb, double interp_factor);
173 #endif //KCF_HEADER_6565467831231