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/*M///////////////////////////////////////////////////////////////////////////////////////
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 //  If you do not agree to this license, do not download, install,
 //  copy or use the software.
 //
 //
 //                           License Agreement
 //                For Open Source Computer Vision Library
 //
 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
 // Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
 // Third party copyrights are property of their respective owners.
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 // Redistribution and use in source and binary forms, with or without modification,
 // are permitted provided that the following conditions are met:
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#include "precomp.hpp"
namespace cv
{

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DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector(const Ptr<AdjusterAdapter>& a,
                                         int min_features, int max_features, int max_iters ) :
        escape_iters_(max_iters), min_features_(min_features), max_features_(max_features), adjuster_(a)
{}

bool DynamicAdaptedFeatureDetector::empty() const
{
    return adjuster_.empty() || adjuster_->empty();
}

void DynamicAdaptedFeatureDetector::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
    //for oscillation testing
    bool down = false;
    bool up = false;

    //flag for whether the correct threshhold has been reached
    bool thresh_good = false;

    Ptr<AdjusterAdapter> adjuster = adjuster_->clone();

    //break if the desired number hasn't been reached.
    int iter_count = escape_iters_;

    while( iter_count > 0 && !(down && up) && !thresh_good && adjuster->good() )
    {
        keypoints.clear();

        //the adjuster takes care of calling the detector with updated parameters
        adjuster->detect(image, keypoints,mask);

        if( int(keypoints.size()) < min_features_ )
        {
            down = true;
            adjuster->tooFew(min_features_, (int)keypoints.size());
        }
        else if( int(keypoints.size()) > max_features_ )
        {
            up = true;
            adjuster->tooMany(max_features_, (int)keypoints.size());
        }
        else
            thresh_good = true;

        iter_count--;
    }

}

FastAdjuster::FastAdjuster( int init_thresh, bool nonmax, int min_thresh, int max_thresh ) :
    thresh_(init_thresh), nonmax_(nonmax), init_thresh_(init_thresh),
    min_thresh_(min_thresh), max_thresh_(max_thresh)
{}

void FastAdjuster::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
    FastFeatureDetector(thresh_, nonmax_).detect(image, keypoints, mask);
}

void FastAdjuster::tooFew(int, int)
{
    //fast is easy to adjust
    thresh_--;
}

void FastAdjuster::tooMany(int, int)
{
    //fast is easy to adjust
    thresh_++;
}

//return whether or not the threshhold is beyond
//a useful point
bool FastAdjuster::good() const
{
    return (thresh_ > min_thresh_) && (thresh_ < max_thresh_);
}

Ptr<AdjusterAdapter> FastAdjuster::clone() const
{
    Ptr<AdjusterAdapter> cloned_obj = new FastAdjuster( init_thresh_, nonmax_, min_thresh_, max_thresh_ );
    return cloned_obj;
}

StarAdjuster::StarAdjuster(double initial_thresh, double min_thresh, double max_thresh) :
    thresh_(initial_thresh), init_thresh_(initial_thresh),
    min_thresh_(min_thresh), max_thresh_(max_thresh)
{}

void StarAdjuster::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
    StarFeatureDetector detector_tmp(16, cvRound(thresh_), 10, 8, 3);
    detector_tmp.detect(image, keypoints, mask);
}

void StarAdjuster::tooFew(int, int)
{
    thresh_ *= 0.9;
    if (thresh_ < 1.1)
            thresh_ = 1.1;
}

void StarAdjuster::tooMany(int, int)
{
    thresh_ *= 1.1;
}

bool StarAdjuster::good() const
{
    return (thresh_ > min_thresh_) && (thresh_ < max_thresh_);
}

Ptr<AdjusterAdapter> StarAdjuster::clone() const
{
    Ptr<AdjusterAdapter> cloned_obj = new StarAdjuster( init_thresh_, min_thresh_, max_thresh_ );
    return cloned_obj;
}

SurfAdjuster::SurfAdjuster( double initial_thresh, double min_thresh, double max_thresh ) :
    thresh_(initial_thresh), init_thresh_(initial_thresh),
    min_thresh_(min_thresh), max_thresh_(max_thresh)
{}

void SurfAdjuster::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const cv::Mat& mask) const
{
    Ptr<FeatureDetector> surf = FeatureDetector::create("SURF");
    surf->set("hessianThreshold", thresh_);
    surf->detect(image, keypoints, mask);
}

void SurfAdjuster::tooFew(int, int)
{
    thresh_ *= 0.9;
    if (thresh_ < 1.1)
            thresh_ = 1.1;
}

void SurfAdjuster::tooMany(int, int)
{
    thresh_ *= 1.1;
}

//return whether or not the threshhold is beyond
//a useful point
bool SurfAdjuster::good() const
{
    return (thresh_ > min_thresh_) && (thresh_ < max_thresh_);
}

Ptr<AdjusterAdapter> SurfAdjuster::clone() const
{
    Ptr<AdjusterAdapter> cloned_obj = new SurfAdjuster( init_thresh_, min_thresh_, max_thresh_ );
    return cloned_obj;
}

Ptr<AdjusterAdapter> AdjusterAdapter::create( const string& detectorType )
{
    Ptr<AdjusterAdapter> adapter;

    if( !detectorType.compare( "FAST" ) )
    {
        adapter = new FastAdjuster();
    }
    else if( !detectorType.compare( "STAR" ) )
    {
        adapter = new StarAdjuster();
    }
    else if( !detectorType.compare( "SURF" ) )
    {
        adapter = new SurfAdjuster();
    }

    return adapter;
}

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}