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>
19 class Kcf_Tracker_Private;
24 double cx, cy, w, h, a;
26 inline cv::Point2d center() const { return cv::Point2d(cx, cy); }
28 inline void scale(double factor)
36 inline cv::Rect get_rect()
38 return cv::Rect(int(cx-w/2.), int(cy-h/2.), int(w), int(h));
48 bool m_visual_debug {false};
49 const bool m_use_scale {true};
50 const bool m_use_color {true};
51 const bool m_use_subpixel_localization {true};
52 const bool m_use_subgrid_scale {true};
53 const bool m_use_cnfeat {true};
54 const bool m_use_linearkernel {false};
55 const int p_cell_size = 4; //4 for hog (= bin_size)
58 padding ... extra area surrounding the target (1.5)
59 kernel_sigma ... gaussian kernel bandwidth (0.5)
60 lambda ... regularization (1e-4)
61 interp_factor ... linear interpolation factor for adaptation (0.02)
62 output_sigma_factor ... spatial bandwidth (proportional to target) (0.1)
63 cell_size ... hog cell size (4)
65 KCF_Tracker(double padding, double kernel_sigma, double lambda, double interp_factor, double output_sigma_factor, int cell_size);
69 // Init/re-init methods
70 void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x = -1, int fit_size_y = -1);
71 void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x = -1, int fit_size_y = -1);
72 void updateTrackerPosition(BBox_c & bbox);
74 // frame-to-frame object tracking
75 void track(cv::Mat & img);
77 double getFilterResponse() const; // Measure of tracking accuracy
82 // Initial pose of tracked object in internal image coordinates
83 // (scaled by p_downscale_factor if p_resize_image)
86 // Information to calculate current pose of the tracked object
87 cv::Point2d p_current_center;
88 double p_current_scale = 1.;
90 double max_response = -1.;
92 bool p_resize_image = false;
94 const double p_downscale_factor = 0.5;
95 const double p_floating_error = 0.0001;
97 const double p_padding = 1.5;
98 const double p_output_sigma_factor = 0.1;
99 double p_output_sigma;
100 const double p_kernel_sigma = 0.5; //def = 0.5
101 const double p_lambda = 1e-4; //regularization in learning step
102 const double p_interp_factor = 0.02; //def = 0.02, linear interpolation factor for adaptation
103 cv::Size p_windows_size; // size of the patch to find the tracked object in
104 cv::Size fit_size; // size to which rescale the patch for better FFT performance
106 const uint p_num_scales = m_use_scale ? 7 : 1;
107 const double p_scale_step = 1.02;
108 double p_min_max_scale[2];
109 std::vector<double> p_scales;
111 const uint p_num_angles = 1;
112 const int p_angle_step = 10;
113 std::vector<double> p_angles;
115 const int p_num_of_feats = 31 + (m_use_color ? 3 : 0) + (m_use_cnfeat ? 10 : 0);
116 cv::Size feature_size;
118 Kcf_Tracker_Private &d;
121 cv::Size feature_size;
122 uint height, width, n_feats;
124 ComplexMat yf {height, width, 1};
125 ComplexMat model_alphaf {height, width, 1};
126 ComplexMat model_alphaf_num {height, width, 1};
127 ComplexMat model_alphaf_den {height, width, 1};
128 ComplexMat model_xf {height, width, n_feats};
129 ComplexMat xf {height, width, n_feats};
131 // Temporary variables for trainig
132 MatScaleFeats patch_feats{1, n_feats, feature_size};
133 MatScaleFeats temp{1, n_feats, feature_size};
137 Model(cv::Size feature_size, uint _n_feats)
138 : feature_size(feature_size)
139 , height(Fft::freq_size(feature_size).height)
140 , width(Fft::freq_size(feature_size).width)
141 , n_feats(_n_feats) {}
144 std::unique_ptr<Model> model;
146 class GaussianCorrelation {
148 GaussianCorrelation(uint num_scales, uint num_feats, cv::Size size)
149 : xf_sqr_norm(num_scales)
150 , xyf(Fft::freq_size(size), num_feats, num_scales)
151 , ifft_res(num_scales, size)
152 , k(num_scales, size)
154 void operator()(ComplexMat &result, const ComplexMat &xf, const ComplexMat &yf, double sigma, bool auto_correlation, const KCF_Tracker &kcf);
158 DynMem yf_sqr_norm{1};
165 void scale_track(ThreadCtx &vars, cv::Mat &input_rgb, cv::Mat &input_gray);
166 cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y) const;
167 cv::Mat gaussian_shaped_labels(double sigma, int dim1, int dim2);
168 std::unique_ptr<GaussianCorrelation> gaussian_correlation;
169 cv::Mat circshift(const cv::Mat &patch, int x_rot, int y_rot) const;
170 cv::Mat cosine_window_function(int dim1, int dim2);
171 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) const;
172 cv::Point2f sub_pixel_peak(cv::Point &max_loc, cv::Mat &response) const;
173 double sub_grid_scale(uint index);
174 void resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray);
175 void train(cv::Mat input_rgb, cv::Mat input_gray, double interp_factor);
176 double findMaxReponse(uint &max_idx, cv::Point2d &new_location) const;
179 #endif //KCF_HEADER_6565467831231