/*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 : public GFTTDetector { public: GFTTDetector_Impl( int _nfeatures, double _qualityLevel, double _minDistance, int _blockSize, bool _useHarrisDetector, double _k ) : nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance), blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k) { } void setMaxFeatures(int maxFeatures) { nfeatures = maxFeatures; } int getMaxFeatures() const { return nfeatures; } void setQualityLevel(double qlevel) { qualityLevel = qlevel; } double getQualityLevel() const { return qualityLevel; } void setMinDistance(double minDistance_) { minDistance = minDistance_; } double getMinDistance() const { return minDistance; } void setBlockSize(int blockSize_) { blockSize = blockSize_; } int getBlockSize() const { return blockSize; } void setHarrisDetector(bool val) { useHarrisDetector = val; } bool getHarrisDetector() const { return useHarrisDetector; } void setK(double k_) { k = k_; } double getK() const { return k; } void detect( InputArray _image, std::vector& keypoints, InputArray _mask ) { std::vector 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, 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, useHarrisDetector, k ); } keypoints.resize(corners.size()); std::vector::const_iterator corner_it = corners.begin(); std::vector::iterator keypoint_it = keypoints.begin(); for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it ) *keypoint_it = KeyPoint( *corner_it, (float)blockSize ); } int nfeatures; double qualityLevel; double minDistance; int blockSize; bool useHarrisDetector; double k; }; Ptr GFTTDetector::create( int _nfeatures, double _qualityLevel, double _minDistance, int _blockSize, bool _useHarrisDetector, double _k ) { return makePtr(_nfeatures, _qualityLevel, _minDistance, _blockSize, _useHarrisDetector, _k); } }