1 #ifndef KCF_HEADER_6565467831231
2 #define KCF_HEADER_6565467831231
4 #include <opencv2/opencv.hpp>
9 #include "complexmat.hpp"
11 #include "cuda_error_check.hpp"
12 #include <cuda_runtime.h>
20 #include "fft_cufft.h"
23 #include "fft_opencv.h"
28 class Kcf_Tracker_Private;
33 double cx, cy, w, h, a;
35 inline cv::Point2d center() const { return cv::Point2d(cx, cy); }
37 inline void scale(double factor)
45 inline cv::Rect get_rect()
47 return cv::Rect(int(cx-w/2.), int(cy-h/2.), int(w), int(h));
55 friend Kcf_Tracker_Private;
58 enum class vd {NONE, PATCH, RESPONSE} m_visual_debug {vd::NONE};
59 constexpr static bool m_use_scale {true};
60 constexpr static bool m_use_color {true};
61 constexpr static bool m_use_subpixel_localization {true};
62 constexpr static bool m_use_subgrid_scale {true};
63 constexpr static bool m_use_subgrid_angle {true};
64 constexpr static bool m_use_cnfeat {true};
65 constexpr static bool m_use_linearkernel {false};
66 const int p_cell_size = 4; //4 for hog (= bin_size)
69 padding ... extra area surrounding the target (1.5)
70 kernel_sigma ... gaussian kernel bandwidth (0.5)
71 lambda ... regularization (1e-4)
72 interp_factor ... linear interpolation factor for adaptation (0.02)
73 output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
74 cell_size ... hog cell size (4)
76 KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
80 // Init/re-init methods
81 void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x = -1, int fit_size_y = -1);
82 void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x = -1, int fit_size_y = -1);
83 void updateTrackerPosition(BBox_c & bbox);
85 // frame-to-frame object tracking
86 void track(cv::Mat & img);
88 double getFilterResponse() const; // Measure of tracking accuracy
93 // Initial pose of tracked object in internal image coordinates
94 // (scaled by p_downscale_factor if p_resize_image)
97 // Information to calculate current pose of the tracked object
98 cv::Point2d p_current_center;
99 double p_current_scale = 1.;
100 double p_current_angle = 0.;
102 double max_response = -1.;
104 bool p_resize_image = false;
106 constexpr static double p_downscale_factor = 0.5;
107 constexpr static double p_floating_error = 0.0001;
109 const double p_padding = 1.5;
110 const double p_output_sigma_factor = 0.1;
111 double p_output_sigma;
112 const double p_kernel_sigma = 0.5; //def = 0.5
113 const double p_lambda = 1e-4; //regularization in learning step
114 const double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
115 cv::Size p_windows_size; // size of the patch to find the tracked object in
116 cv::Size fit_size; // size to which rescale the patch for better FFT performance
118 constexpr static uint p_num_scales = m_use_scale ? 5 : 1;
119 constexpr static double p_scale_step = 1.03;
120 double p_min_max_scale[2];
121 std::vector<double> p_scales;
123 constexpr static uint p_num_angles = 3;
124 constexpr static int p_angle_step = 10;
125 std::vector<double> p_angles;
127 constexpr static int p_num_of_feats = 31 + (m_use_color ? 3 : 0) + (m_use_cnfeat ? 10 : 0);
128 cv::Size feature_size;
130 std::unique_ptr<Kcf_Tracker_Private> d;
133 cv::Size feature_size;
134 uint height, width, n_feats;
136 ComplexMat yf {height, width, 1};
137 ComplexMat model_alphaf {height, width, 1};
138 ComplexMat model_alphaf_num {height, width, 1};
139 ComplexMat model_alphaf_den {height, width, 1};
140 ComplexMat model_xf {height, width, n_feats};
141 ComplexMat xf {height, width, n_feats};
143 // Temporary variables for trainig
144 MatScaleFeats patch_feats{1, n_feats, feature_size};
145 MatScaleFeats temp{1, n_feats, feature_size};
149 Model(cv::Size feature_size, uint _n_feats)
150 : feature_size(feature_size)
151 , height(Fft::freq_size(feature_size).height)
152 , width(Fft::freq_size(feature_size).width)
153 , n_feats(_n_feats) {}
156 std::unique_ptr<Model> model;
158 class GaussianCorrelation {
160 GaussianCorrelation(uint num_scales, uint num_feats, cv::Size size)
161 : xf_sqr_norm(num_scales)
162 , xyf(Fft::freq_size(size), num_feats, num_scales)
163 , ifft_res(num_scales, size)
164 , k(num_scales, size)
166 void operator()(ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation, const KCF_Tracker &kcf);
170 DynMem yf_sqr_norm{1};
177 void scale_track(ThreadCtx &vars, cv::Mat &input_rgb, cv::Mat &input_gray);
178 cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y, double angle) const;
179 cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
180 std::unique_ptr<GaussianCorrelation> gaussian_correlation;
181 cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot) const;
182 cv::Mat cosine_window_function(int dim1, int dim2);
183 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;
184 cv::Point2f sub_pixel_peak(cv::Point &max_loc, cv::Mat &response) const;
185 double sub_grid_scale(uint index);
186 void resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray);
187 void train(cv::Mat input_rgb, cv::Mat input_gray, double interp_factor);
188 double findMaxReponse(uint &max_idx, cv::Point2d &new_location) const;
189 double sub_grid_angle(uint max_index);
192 #endif //KCF_HEADER_6565467831231