opencv/modules/features2d/src/dynamic.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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.
//
//
// 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.
//
// 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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// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
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//M*/
#include "precomp.hpp"
namespace cv
{
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)
{}
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bool DynamicAdaptedFeatureDetector::empty() const
{
return adjuster_.empty() || adjuster_->empty();
}
void DynamicAdaptedFeatureDetector::detectImpl(const Mat& image, std::vector<KeyPoint>& keypoints, const Mat& mask) const
{
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//for oscillation testing
bool down = false;
bool up = false;
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//flag for whether the correct threshhold has been reached
bool thresh_good = false;
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Ptr<AdjusterAdapter> adjuster = adjuster_->clone();
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//break if the desired number hasn't been reached.
int iter_count = escape_iters_;
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while( iter_count > 0 && !(down && up) && !thresh_good && adjuster->good() )
{
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keypoints.clear();
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//the adjuster takes care of calling the detector with updated parameters
adjuster->detect(image, keypoints,mask);
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if( int(keypoints.size()) < min_features_ )
{
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down = true;
adjuster->tooFew(min_features_, (int)keypoints.size());
}
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else if( int(keypoints.size()) > max_features_ )
{
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up = true;
adjuster->tooMany(max_features_, (int)keypoints.size());
}
else
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thresh_good = true;
iter_count--;
}
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}
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, std::vector<KeyPoint>& keypoints, const Mat& mask) const
{
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FastFeatureDetector(thresh_, nonmax_).detect(image, keypoints, mask);
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}
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void FastAdjuster::tooFew(int, int)
{
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//fast is easy to adjust
thresh_--;
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}
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void FastAdjuster::tooMany(int, int)
{
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//fast is easy to adjust
thresh_++;
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}
//return whether or not the threshhold is beyond
//a useful point
bool FastAdjuster::good() const
{
return (thresh_ > min_thresh_) && (thresh_ < max_thresh_);
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}
Ptr<AdjusterAdapter> FastAdjuster::clone() const
{
Ptr<AdjusterAdapter> cloned_obj = new FastAdjuster( init_thresh_, nonmax_, min_thresh_, max_thresh_ );
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return cloned_obj;
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}
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, std::vector<KeyPoint>& keypoints, const Mat& mask) const
{
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StarFeatureDetector detector_tmp(16, cvRound(thresh_), 10, 8, 3);
detector_tmp.detect(image, keypoints, mask);
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}
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void StarAdjuster::tooFew(int, int)
{
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thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
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}
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void StarAdjuster::tooMany(int, int)
{
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thresh_ *= 1.1;
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}
bool StarAdjuster::good() const
{
return (thresh_ > min_thresh_) && (thresh_ < max_thresh_);
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}
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Ptr<AdjusterAdapter> StarAdjuster::clone() const
{
Ptr<AdjusterAdapter> cloned_obj = new StarAdjuster( init_thresh_, min_thresh_, max_thresh_ );
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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, std::vector<KeyPoint>& keypoints, const cv::Mat& mask) const
{
Ptr<FeatureDetector> surf = FeatureDetector::create("SURF");
surf->set("hessianThreshold", thresh_);
surf->detect(image, keypoints, mask);
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}
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void SurfAdjuster::tooFew(int, int)
{
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thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
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}
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void SurfAdjuster::tooMany(int, int)
{
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thresh_ *= 1.1;
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}
//return whether or not the threshhold is beyond
//a useful point
bool SurfAdjuster::good() const
{
return (thresh_ > min_thresh_) && (thresh_ < max_thresh_);
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}
Ptr<AdjusterAdapter> SurfAdjuster::clone() const
{
Ptr<AdjusterAdapter> cloned_obj = new SurfAdjuster( init_thresh_, min_thresh_, max_thresh_ );
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return cloned_obj;
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}
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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}