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266 lines
8.8 KiB
266 lines
8.8 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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namespace cv |
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{ |
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class AffineTransformerImpl : public AffineTransformer |
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{ |
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public: |
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/* Constructors */ |
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AffineTransformerImpl() |
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{ |
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fullAffine = true; |
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name_ = "ShapeTransformer.AFF"; |
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} |
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AffineTransformerImpl(bool _fullAffine) |
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{ |
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fullAffine = _fullAffine; |
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name_ = "ShapeTransformer.AFF"; |
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} |
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/* Destructor */ |
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~AffineTransformerImpl() |
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{ |
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} |
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virtual AlgorithmInfo* info() const { return 0; } |
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//! the main operator |
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virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, std::vector<DMatch> &matches); |
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virtual float applyTransformation(InputArray input, OutputArray output=noArray()); |
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virtual void warpImage(InputArray transformingImage, OutputArray output, |
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int flags, int borderMode, const Scalar& borderValue) const; |
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//! Setters/Getters |
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virtual void setFullAffine(bool _fullAffine) {fullAffine=_fullAffine;} |
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virtual bool getFullAffine() const {return fullAffine;} |
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//! write/read |
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virtual void write(FileStorage& fs) const |
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{ |
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fs << "name" << name_ |
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<< "affine_type" << int(fullAffine); |
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} |
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virtual void read(const FileNode& fn) |
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{ |
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CV_Assert( (String)fn["name"] == name_ ); |
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fullAffine = int(fn["affine_type"])?true:false; |
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} |
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private: |
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bool fullAffine; |
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Mat affineMat; |
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float transformCost; |
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protected: |
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String name_; |
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}; |
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void AffineTransformerImpl::warpImage(InputArray transformingImage, OutputArray output, |
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int flags, int borderMode, const Scalar& borderValue) const |
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{ |
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CV_Assert(!affineMat.empty()); |
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warpAffine(transformingImage, output, affineMat, transformingImage.getMat().size(), flags, borderMode, borderValue); |
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} |
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static Mat _localAffineEstimate(const std::vector<Point2f>& shape1, const std::vector<Point2f>& shape2, |
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bool fullAfine) |
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{ |
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Mat out(2,3,CV_32F); |
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int siz=2*(int)shape1.size(); |
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if (fullAfine) |
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{ |
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Mat matM(siz, 6, CV_32F); |
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Mat matP(siz,1,CV_32F); |
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int contPt=0; |
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for (int ii=0; ii<siz; ii++) |
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{ |
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Mat therow = Mat::zeros(1,6,CV_32F); |
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if (ii%2==0) |
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{ |
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therow.at<float>(0,0)=shape1[contPt].x; |
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therow.at<float>(0,1)=shape1[contPt].y; |
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therow.at<float>(0,2)=1; |
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therow.row(0).copyTo(matM.row(ii)); |
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matP.at<float>(ii,0) = shape2[contPt].x; |
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} |
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else |
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{ |
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therow.at<float>(0,3)=shape1[contPt].x; |
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therow.at<float>(0,4)=shape1[contPt].y; |
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therow.at<float>(0,5)=1; |
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therow.row(0).copyTo(matM.row(ii)); |
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matP.at<float>(ii,0) = shape2[contPt].y; |
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contPt++; |
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} |
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} |
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Mat sol; |
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solve(matM, matP, sol, DECOMP_SVD); |
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out = sol.reshape(0,2); |
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} |
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else |
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{ |
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Mat matM(siz, 4, CV_32F); |
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Mat matP(siz,1,CV_32F); |
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int contPt=0; |
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for (int ii=0; ii<siz; ii++) |
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{ |
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Mat therow = Mat::zeros(1,4,CV_32F); |
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if (ii%2==0) |
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{ |
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therow.at<float>(0,0)=shape1[contPt].x; |
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therow.at<float>(0,1)=shape1[contPt].y; |
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therow.at<float>(0,2)=1; |
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therow.row(0).copyTo(matM.row(ii)); |
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matP.at<float>(ii,0) = shape2[contPt].x; |
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} |
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else |
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{ |
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therow.at<float>(0,0)=-shape1[contPt].y; |
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therow.at<float>(0,1)=shape1[contPt].x; |
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therow.at<float>(0,3)=1; |
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therow.row(0).copyTo(matM.row(ii)); |
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matP.at<float>(ii,0) = shape2[contPt].y; |
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contPt++; |
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} |
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} |
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Mat sol; |
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solve(matM, matP, sol, DECOMP_SVD); |
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out.at<float>(0,0)=sol.at<float>(0,0); |
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out.at<float>(0,1)=sol.at<float>(1,0); |
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out.at<float>(0,2)=sol.at<float>(2,0); |
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out.at<float>(1,0)=-sol.at<float>(1,0); |
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out.at<float>(1,1)=sol.at<float>(0,0); |
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out.at<float>(1,2)=sol.at<float>(3,0); |
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} |
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return out; |
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} |
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void AffineTransformerImpl::estimateTransformation(InputArray _pts1, InputArray _pts2, std::vector<DMatch>& _matches) |
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{ |
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Mat pts1 = _pts1.getMat(); |
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Mat pts2 = _pts2.getMat(); |
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CV_Assert((pts1.channels()==2) && (pts1.cols>0) && (pts2.channels()==2) && (pts2.cols>0)); |
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CV_Assert(_matches.size()>1); |
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if (pts1.type() != CV_32F) |
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pts1.convertTo(pts1, CV_32F); |
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if (pts2.type() != CV_32F) |
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pts2.convertTo(pts2, CV_32F); |
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// Use only valid matchings // |
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std::vector<DMatch> matches; |
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for (size_t i=0; i<_matches.size(); i++) |
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{ |
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if (_matches[i].queryIdx<pts1.cols && |
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_matches[i].trainIdx<pts2.cols) |
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{ |
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matches.push_back(_matches[i]); |
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} |
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} |
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// Organizing the correspondent points in vector style // |
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std::vector<Point2f> shape1; // transforming shape |
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std::vector<Point2f> shape2; // target shape |
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for (size_t i=0; i<matches.size(); i++) |
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{ |
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Point2f pt1=pts1.at<Point2f>(0,matches[i].queryIdx); |
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shape1.push_back(pt1); |
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Point2f pt2=pts2.at<Point2f>(0,matches[i].trainIdx); |
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shape2.push_back(pt2); |
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} |
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// estimateRigidTransform // |
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Mat affine; |
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estimateRigidTransform(shape1, shape2, fullAffine).convertTo(affine, CV_32F); |
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if (affine.empty()) |
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affine=_localAffineEstimate(shape1, shape2, fullAffine); //In case there is not good solution, just give a LLS based one |
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affineMat = affine; |
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} |
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float AffineTransformerImpl::applyTransformation(InputArray inPts, OutputArray outPts) |
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{ |
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Mat pts1 = inPts.getMat(); |
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CV_Assert((pts1.channels()==2) && (pts1.cols>0)); |
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//Apply transformation in the complete set of points |
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Mat fAffine; |
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transform(pts1, fAffine, affineMat); |
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// Ensambling output // |
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if (outPts.needed()) |
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{ |
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outPts.create(1,fAffine.cols, CV_32FC2); |
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Mat outMat = outPts.getMat(); |
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for (int i=0; i<fAffine.cols; i++) |
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outMat.at<Point2f>(0,i)=fAffine.at<Point2f>(0,i); |
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} |
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// Updating Transform Cost // |
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Mat Af(2, 2, CV_32F); |
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Af.at<float>(0,0)=affineMat.at<float>(0,0); |
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Af.at<float>(0,1)=affineMat.at<float>(1,0); |
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Af.at<float>(1,0)=affineMat.at<float>(0,1); |
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Af.at<float>(1,1)=affineMat.at<float>(1,1); |
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SVD mysvd(Af, SVD::NO_UV); |
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Mat singVals=mysvd.w; |
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transformCost=std::log((singVals.at<float>(0,0)+FLT_MIN)/(singVals.at<float>(1,0)+FLT_MIN)); |
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return transformCost; |
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} |
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Ptr <AffineTransformer> createAffineTransformer(bool fullAffine) |
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{ |
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return Ptr<AffineTransformer>( new AffineTransformerImpl(fullAffine) ); |
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} |
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} // cv
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