/*

Tracker based on Kernelized Correlation Filter (KCF) [1] and Circulant Structure with Kernels (CSK) [2].
CSK is implemented by using raw gray level features, since it is a single-channel filter.
KCF is implemented by using HOG features (the default), since it extends CSK to multiple channels.

[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
"High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.

[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
"Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012.

Authors: Joao Faro, Christian Bailer, Joao F. Henriques
Contacts: joaopfaro@gmail.com, Christian.Bailer@dfki.de, henriques@isr.uc.pt
Institute of Systems and Robotics - University of Coimbra / Department Augmented Vision DFKI


Constructor parameters, all boolean:
    hog: use HOG features (default), otherwise use raw pixels
    fixed_window: fix window size (default), otherwise use ROI size (slower but more accurate)
    multiscale: use multi-scale tracking (default; cannot be used with fixed_window = true)

Default values are set for all properties of the tracker depending on the above choices.
Their values can be customized further before calling init():
    interp_factor: linear interpolation factor for adaptation
    sigma: gaussian kernel bandwidth
    lambda: regularization
    cell_size: HOG cell size
    padding: horizontal area surrounding the target, relative to its size
    output_sigma_factor: bandwidth of gaussian target
    template_size: template size in pixels, 0 to use ROI size
    scale_step: scale step for multi-scale estimation, 1 to disable it
    scale_weight: to downweight detection scores of other scales for added stability

For speed, the value (template_size/cell_size) should be a power of 2 or a product of small prime numbers.

Inputs to init():
   image is the initial frame.
   roi is a cv::Rect with the target positions in the initial frame

Inputs to update():
   image is the current frame.

Outputs of update():
   cv::Rect with target positions for the current frame


By downloading, copying, installing or using the software you agree to this license.
If you do not agree to this license, do not download, install,
copy or use the software.


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               For Open Source Computer Vision Library
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This software is provided by the copyright holders and contributors "as is" and
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In no event shall copyright holders or contributors be liable for any direct,
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 */

#pragma once


#include "tracker.h"
#ifndef _OPENCV_KCFTRACKER_HPP_
#define _OPENCV_KCFTRACKER_HPP_

#endif

class KCFTracker : public Tracker
{
public:
    // Constructor
    KCFTracker(bool hog = true, bool fixed_window = true, bool multiscale = true, bool lab = true);

    // Initialize tracker 
    virtual void init(const cv::Rect &roi, cv::Mat image);
    
    // Update position based on the new frame
    virtual cv::Rect update(cv::Mat image);

    float interp_factor; // linear interpolation factor for adaptation
    float sigma; // gaussian kernel bandwidth
    float lambda; // regularization
    int cell_size; // HOG cell size
    int cell_sizeQ; // cell size^2, to avoid repeated operations
    float padding; // extra area surrounding the target
    float output_sigma_factor; // bandwidth of gaussian target
    int template_size; // template size
    float scale_step; // scale step for multi-scale estimation
    float scale_weight;  // to downweight detection scores of other scales for added stability

protected:
    // Detect object in the current frame.
    cv::Point2f detect(cv::Mat z, cv::Mat x, float &peak_value);

    // train tracker with a single image
    void train(cv::Mat x, float train_interp_factor);

    // Evaluates a Gaussian kernel with bandwidth SIGMA for all relative shifts between input images X and Y, which must both be MxN. They must    also be periodic (ie., pre-processed with a cosine window).
    cv::Mat gaussianCorrelation(cv::Mat x1, cv::Mat x2);

    // Create Gaussian Peak. Function called only in the first frame.
    cv::Mat createGaussianPeak(int sizey, int sizex);

    // Obtain sub-window from image, with replication-padding and extract features
    cv::Mat getFeatures(const cv::Mat & image, bool inithann, float scale_adjust = 1.0f);

    // Initialize Hanning window. Function called only in the first frame.
    void createHanningMats();

    // Calculate sub-pixel peak for one dimension
    float subPixelPeak(float left, float center, float right);

    cv::Mat _alphaf;
    cv::Mat _prob; //fft2(y)
    cv::Mat _tmpl; //提取的特征
    cv::Mat _num;
    cv::Mat _den;
    cv::Mat _labCentroids;

private:
    int size_patch[3];
    cv::Mat hann; //汉宁窗
    cv::Size _tmpl_sz;
    float _scale;
    int _gaussian_size;
    bool _hogfeatures;
    bool _labfeatures;
};