Refactor p_pose and related variables

[hercules2020/kcf.git] / src / kcf.h

diff --git a/src/kcf.h b/src/kcf.h
index 88798d1ec56e5b9b5c3d074e5a0d0025c401f29f..76068c165401bf02d736d169eddc65475c0ad29b 100644 (file)
--- a/src/kcf.h
+++ b/src/kcf.h
@@ -26,6 +26,8 @@ struct BBox_c
 {
     double cx, cy, w, h;
 
+    inline cv::Point2d center() const { return cv::Point2d(cx, cy); }
+
     inline void scale(double factor)
     {
         cx *= factor;
@@ -64,6 +66,7 @@ public:
     const bool m_use_subgrid_scale {true};
     const bool m_use_cnfeat {true};
     const bool m_use_linearkernel {false};
+    const int p_cell_size = 4;            //4 for hog (= bin_size)
 
     /*
     padding             ... extra area surrounding the target           (1.5)
@@ -78,8 +81,8 @@ public:
     ~KCF_Tracker();
 
     // Init/re-init methods
-    void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x, int fit_size_y);
-    void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x, int fit_size_y);
+    void init(cv::Mat & img, const cv::Rect & bbox, int fit_size_x = -1, int fit_size_y = -1);
+    void setTrackerPose(BBox_c & bbox, cv::Mat & img, int fit_size_x = -1, int fit_size_y = -1);
     void updateTrackerPosition(BBox_c & bbox);
 
     // frame-to-frame object tracking
@@ -90,15 +93,22 @@ public:
 private:
     Fft &fft;
 
-    BBox_c p_pose;
+    // Initial pose of tracked object in internal image coordinates
+    // (scaled by p_downscale_factor if p_resize_image)
+    BBox_c p_init_pose;
+
+    // Information to calculate current pose of the tracked object
+    cv::Point2d p_current_center;
+    double p_current_scale = 1.;
+
     double max_response = -1.;
 
     bool p_resize_image = false;
     bool p_fit_to_pw2 = false;
 
     const double p_downscale_factor = 0.5;
-    double p_scale_factor_x = 1;
-    double p_scale_factor_y = 1;
+    double p_fit_factor_x = 1;
+    double p_fit_factor_y = 1;
     const double p_floating_error = 0.0001;
 
     const double p_padding = 1.5;
@@ -107,14 +117,17 @@ private:
     const double p_kernel_sigma = 0.5;    //def = 0.5
     const double p_lambda = 1e-4;         //regularization in learning step
     const double p_interp_factor = 0.02;  //def = 0.02, linear interpolation factor for adaptation
-    const int p_cell_size = 4;            //4 for hog (= bin_size)
     cv::Size p_windows_size;
-    const uint p_num_scales {7};
+
+    const uint p_num_scales = m_use_scale ? 7 : 1;
     const double p_scale_step = 1.02;
-    double p_current_scale = 1.;
     double p_min_max_scale[2];
     std::vector<double> p_scales;
 
+    const uint p_num_angles = 1;
+    const int p_angle_step = 10;
+    std::vector<double> p_angles = {0};
+
     const int p_num_of_feats = 31 + (m_use_color ? 3 : 0) + (m_use_cnfeat ? 10 : 0);
     cv::Size p_roi;
 
@@ -158,7 +171,7 @@ private:
     double sub_grid_scale(uint index);
     void resizeImgs(cv::Mat &input_rgb, cv::Mat &input_gray);
     void train(cv::Mat input_rgb, cv::Mat input_gray, double interp_factor);
-    double findMaxReponse(uint &max_idx, cv::Point2f &new_location) const;
+    double findMaxReponse(uint &max_idx, cv::Point2d &new_location) const;
 };
 
 #endif //KCF_HEADER_6565467831231