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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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
class GFTTDetector_Impl CV_FINAL : public GFTTDetector
{
public:
GFTTDetector_Impl( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize, int _gradientSize,
bool _useHarrisDetector, double _k )
: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
blockSize(_blockSize), gradSize(_gradientSize), useHarrisDetector(_useHarrisDetector), k(_k)
{
}
void setMaxFeatures(int maxFeatures) CV_OVERRIDE { nfeatures = maxFeatures; }
int getMaxFeatures() const CV_OVERRIDE { return nfeatures; }
void setQualityLevel(double qlevel) CV_OVERRIDE { qualityLevel = qlevel; }
double getQualityLevel() const CV_OVERRIDE { return qualityLevel; }
void setMinDistance(double minDistance_) CV_OVERRIDE { minDistance = minDistance_; }
double getMinDistance() const CV_OVERRIDE { return minDistance; }
void setBlockSize(int blockSize_) CV_OVERRIDE { blockSize = blockSize_; }
int getBlockSize() const CV_OVERRIDE { return blockSize; }
//void setGradientSize(int gradientSize_) { gradSize = gradientSize_; }
//int getGradientSize() { return gradSize; }
void setHarrisDetector(bool val) CV_OVERRIDE { useHarrisDetector = val; }
bool getHarrisDetector() const CV_OVERRIDE { return useHarrisDetector; }
void setK(double k_) CV_OVERRIDE { k = k_; }
double getK() const CV_OVERRIDE { return k; }
void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
if(_image.empty())
{
keypoints.clear();
return;
}
std::vector<Point2f> corners;
if (_image.isUMat())
{
UMat ugrayImage;
if( _image.type() != CV_8U )
cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
else
ugrayImage = _image.getUMat();
goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
blockSize, gradSize, useHarrisDetector, k );
}
else
{
Mat image = _image.getMat(), grayImage = image;
if( image.type() != CV_8U )
cvtColor( image, grayImage, COLOR_BGR2GRAY );
goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
blockSize, gradSize, useHarrisDetector, k );
}
keypoints.resize(corners.size());
std::vector<Point2f>::const_iterator corner_it = corners.begin();
std::vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
for( ; corner_it != corners.end() && keypoint_it != keypoints.end(); ++corner_it, ++keypoint_it )
*keypoint_it = KeyPoint( *corner_it, (float)blockSize );
}
int nfeatures;
double qualityLevel;
double minDistance;
int blockSize;
int gradSize;
bool useHarrisDetector;
double k;
};
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize, int _gradientSize,
bool _useHarrisDetector, double _k )
{
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
_minDistance, _blockSize, _gradientSize, _useHarrisDetector, _k);
}
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
{
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
_minDistance, _blockSize, 3, _useHarrisDetector, _k);
}
String GFTTDetector::getDefaultName() const
{
return (Feature2D::getDefaultName() + ".GFTTDetector");
}
}