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Merge pull request #1216 from LaurentBerger:FourierDescriptors
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‎modules/ximgproc/doc/ximgproc.bib

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@@ -76,9 +76,9 @@ @incollection{PFF2004
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publisher={Springer}
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}
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@article{deriche1987using,
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@article{deriche1987,
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title={Using Canny's criteria to derive a recursively implemented optimal edge detector},
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author={Deriche, Rachid},
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author={Deriche Rachid},
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journal={International journal of computer vision},
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volume={1},
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number={2},
@@ -259,3 +259,25 @@ @inproceedings{Khurshid2009
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year={2009},
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organization={International Society for Optics and Photonics}
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}
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@article{BergerRaghunathan1998,
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title={Coalescence in 2 dimensions: experiments on thin copolymer films and numerical simulations},
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author={Berger, L and Raghunathan, V A and Launay, C and Ausserré, D and Gallot, Y},
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journal={The European Physical Journal B - Condensed Matter and Complex Systems},
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volume={2},
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number={1},
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pages={93-99},
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year={1998},
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publisher={Springer}
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}
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@article{PersoonFu1977,
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title={Shape Discrimination Using Fourier Descriptors},
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author={E Persoon and King-Sun Fu},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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volume={7},
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number={3},
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pages={170-179},
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year={1977},
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publisher={IEEE Computer Society}
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}

‎modules/ximgproc/include/opencv2/ximgproc.hpp

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#include "ximgproc/fast_line_detector.hpp"
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#include "ximgproc/deriche_filter.hpp"
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#include "ximgproc/peilin.hpp"
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#include "ximgproc/fourier_descriptors.hpp"
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/** @defgroup ximgproc Extended Image Processing
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@{
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@defgroup ximgproc_segmentation Image segmentation
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@defgroup ximgproc_fast_line_detector Fast line detector
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@}
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@defgroup ximgproc_fourier Fourier descriptors
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@}
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*/
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namespace cv

‎modules/ximgproc/include/opencv2/ximgproc/fourier_descriptors.hpp

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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef __OPENCV_FOURIERDESCRIPTORS_HPP__
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#define __OPENCV_FOURIERDESCRIPTORS_HPP__
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#include <opencv2/core.hpp>
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namespace cv {
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namespace ximgproc {
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//! @addtogroup ximgproc_fourier
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//! @{
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/** @brief Class for ContourFitting algorithms.
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ContourFitting match two contours \f$ z_a \f$ and \f$ z_b \f$ minimizing distance
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\f[ d(z_a,z_b)=\sum (a_n - s b_n e^{j(n \alpha +\phi )})^2 \f] where \f$ a_n \f$ and \f$ b_n \f$ are Fourier descriptors of \f$ z_a \f$ and \f$ z_b \f$ and s is a scaling factor and \f$ \phi \f$ is angle rotation and \f$ \alpha \f$ is starting point factor adjustement
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*/
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class CV_EXPORTS_W ContourFitting : public Algorithm
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{
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int ctrSize;
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int fdSize;
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std::vector<std::complex<double> > b;
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std::vector<std::complex<double> > a;
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std::vector<double> frequence;
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std::vector<double> rho, psi;
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void frequencyInit();
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void fAlpha(double x, double &fn, double &df);
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double distance(std::complex<double> r, double alpha);
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double newtonRaphson(double x1, double x2);
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public:
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/** @brief Fit two closed curves using fourier descriptors. More details in @cite PersoonFu1977 and @cite BergerRaghunathan1998
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* @param ctr number of Fourier descriptors equal to number of contour points after resampling.
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* @param fd Contour defining second shape (Target).
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*/
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ContourFitting(int ctr=1024,int fd=16):ctrSize(ctr),fdSize(fd){};
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/** @brief Fit two closed curves using fourier descriptors. More details in @cite PersoonFu1977 and @cite BergerRaghunathan1998
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@param src Contour defining first shape.
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@param dst Contour defining second shape (Target).
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@param alphaPhiST : \f$ \alpha \f$=alphaPhiST(0,0), \f$ \phi \f$=alphaPhiST(0,1) (in radian), s=alphaPhiST(0,2), Tx=alphaPhiST(0,3), Ty=alphaPhiST(0,4) rotation center
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@param dist distance between src and dst after matching.
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@param fdContour false then src and dst are contours and true src and dst are fourier descriptors.
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*/
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CV_WRAP void estimateTransformation(InputArray src, InputArray dst, OutputArray alphaPhiST, double *dist = 0, bool fdContour = false);
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/** @brief Fit two closed curves using fourier descriptors. More details in @cite PersoonFu1977 and @cite BergerRaghunathan1998
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@param src Contour defining first shape.
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@param dst Contour defining second shape (Target).
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@param alphaPhiST : \f$ \alpha \f$=alphaPhiST(0,0), \f$ \phi \f$=alphaPhiST(0,1) (in radian), s=alphaPhiST(0,2), Tx=alphaPhiST(0,3), Ty=alphaPhiST(0,4) rotation center
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@param dist distance between src and dst after matching.
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@param fdContour false then src and dst are contours and true src and dst are fourier descriptors.
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*/
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CV_WRAP void estimateTransformation(InputArray src, InputArray dst, OutputArray alphaPhiST, double &dist , bool fdContour = false);
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/** @brief set number of Fourier descriptors used in estimateTransformation
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@param n number of Fourier descriptors equal to number of contour points after resampling.
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*/
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CV_WRAP void setCtrSize(int n);
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/** @brief set number of Fourier descriptors when estimateTransformation used vector<Point>
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@param n number of fourier descriptors used for optimal curve matching.
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*/
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CV_WRAP void setFDSize(int n);
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/**
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@returns number of fourier descriptors
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*/
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CV_WRAP int getCtrSize() { return ctrSize; };
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/**
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@returns number of fourier descriptors used for optimal curve matching
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*/
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CV_WRAP int getFDSize() { return fdSize; };
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};
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/**
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* @brief Fourier descriptors for planed closed curves
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*
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* For more details about this implementation, please see @cite PersoonFu1977
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*
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* @param src contour type vector<Point> , vector<Point2f> or vector<Point2d>
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* @param dst Mat of type CV_64FC2 and nbElt rows A VERIFIER
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* @param nbElt number of rows in dst or getOptimalDFTSize rows if nbElt=-1
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* @param nbFD number of FD return in dst dst = [FD(1...nbFD/2) FD(nbFD/2-nbElt+1...:nbElt)]
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*
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*/
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CV_EXPORTS_W void fourierDescriptor(InputArray src, OutputArray dst, int nbElt=-1,int nbFD=-1);
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/**
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* @brief transform a contour
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*
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* @param src contour or Fourier Descriptors if fd is true
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* @param t transform Mat given by estimateTransformation
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* @param dst Mat of type CV_64FC2 and nbElt rows
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* @param fdContour true src are Fourier Descriptors. fdContour false src is a contour
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*
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*/
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CV_EXPORTS_W void transform(InputArray src, InputArray t,OutputArray dst, bool fdContour=true);
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/**
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* @brief Contour sampling .
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*
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* @param src contour type vector<Point> , vector<Point2f> or vector<Point2d>
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* @param out Mat of type CV_64FC2 and nbElt rows
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* @param nbElt number of points in out contour
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*
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*/
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CV_EXPORTS_W void contourSampling(InputArray src, OutputArray out, int nbElt);
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/**
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* @brief create
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* @param ctr number of Fourier descriptors equal to number of contour points after resampling.
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* @param fd Contour defining second shape (Target).
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*/
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CV_EXPORTS_W Ptr<ContourFitting> create(int ctr = 1024, int fd = 16);
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//! @} ximgproc_fourier
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}
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}
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#endif

‎modules/ximgproc/samples/fourier_descriptors_demo.cpp

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#include <opencv2/core.hpp>
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#include <opencv2/core/utility.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/ximgproc.hpp>
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#include <iostream>
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using namespace cv;
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using namespace std;
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struct ThParameters {
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int levelNoise;
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int angle;
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int scale10;
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int origin;
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int xg;
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int yg;
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bool update;
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} ;
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static vector<Point> NoisyPolygon(vector<Point> pRef, double n);
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static void UpdateShape(int , void *r);
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static void AddSlider(String sliderName, String windowName, int minSlider, int maxSlider, int valDefault, int *valSlider, void(*f)(int, void *), void *r);
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int main(void)
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{
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vector<Point> ctrRef;
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vector<Point> ctrRotate, ctrNoisy, ctrNoisyRotate, ctrNoisyRotateShift;
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// build a shape with 5 vertex
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ctrRef.push_back(Point(250,250)); ctrRef.push_back(Point(400, 250));
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ctrRef.push_back(Point(400, 300)); ctrRef.push_back(Point(250, 300));ctrRef.push_back(Point(180, 270));
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Point cg(0,0);
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for (int i=0;i<static_cast<int>(ctrRef.size());i++)
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cg+=ctrRef[i];
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cg.x /= static_cast<int>(ctrRef.size());
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cg.y /= static_cast<int>(ctrRef.size());
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ThParameters p;
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p.levelNoise=6;
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p.angle=45;
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p.scale10=5;
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p.origin=10;
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p.xg=150;
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p.yg=150;
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p.update=true;
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namedWindow("FD Curve matching");
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// A rotation with center at (150,150) of angle 45 degrees and a scaling of 5/10
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AddSlider("Noise", "FD Curve matching", 0, 20, p.levelNoise, &p.levelNoise, UpdateShape, &p);
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AddSlider("Angle", "FD Curve matching", 0, 359, p.angle, &p.angle, UpdateShape, &p);
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AddSlider("Scale", "FD Curve matching", 5, 100, p.scale10, &p.scale10, UpdateShape, &p);
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AddSlider("Origin%%", "FD Curve matching", 0, 100, p.origin, &p.origin, UpdateShape, &p);
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AddSlider("Xg", "FD Curve matching", 150, 450, p.xg, &p.xg, UpdateShape, &p);
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AddSlider("Yg", "FD Curve matching", 150, 450, p.yg, &p.yg, UpdateShape, &p);
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int code=0;
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double dist;
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vector<vector<Point> > c;
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Mat img;
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cout << "******************** PRESS g TO MATCH CURVES *************\n";
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do
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{
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code = waitKey(30);
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if (p.update)
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{
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Mat r = getRotationMatrix2D(Point(p.xg, p.yg), p.angle, 10.0/ p.scale10);
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ctrNoisy= NoisyPolygon(ctrRef,static_cast<double>(p.levelNoise));
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transform(ctrNoisy, ctrNoisyRotate,r);
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ctrNoisyRotateShift.clear();
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for (int i=0;i<static_cast<int>(ctrNoisy.size());i++)
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ctrNoisyRotateShift.push_back(ctrNoisyRotate[(i+(p.origin*ctrNoisy.size())/100)% ctrNoisy.size()]);
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// To draw contour using drawcontours
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c.clear();
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c.push_back(ctrRef);
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c.push_back(ctrNoisyRotateShift);
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p.update = false;
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Rect rglobal;
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for (int i = 0; i < static_cast<int>(c.size()); i++)
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{
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rglobal = boundingRect(c[i]) | rglobal;
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}
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rglobal.width += 10;
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rglobal.height += 10;
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img = Mat::zeros(2 * rglobal.height, 2 * rglobal.width, CV_8UC(3));
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drawContours(img, c, 0, Scalar(255,0,0));
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drawContours(img, c, 1, Scalar(0, 255, 0));
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circle(img, c[0][0], 5, Scalar(255, 0, 0));
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circle(img, c[1][0], 5, Scalar(0, 255, 0));
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imshow("FD Curve matching", img);
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}
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if (code == 'd')
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{
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destroyWindow("FD Curve matching");
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namedWindow("FD Curve matching");
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// A rotation with center at (150,150) of angle 45 degrees and a scaling of 5/10
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AddSlider("Noise", "FD Curve matching", 0, 20, p.levelNoise, &p.levelNoise, UpdateShape, &p);
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AddSlider("Angle", "FD Curve matching", 0, 359, p.angle, &p.angle, UpdateShape, &p);
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AddSlider("Scale", "FD Curve matching", 5, 100, p.scale10, &p.scale10, UpdateShape, &p);
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AddSlider("Origin%%", "FD Curve matching", 0, 100, p.origin, &p.origin, UpdateShape, &p);
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AddSlider("Xg", "FD Curve matching", 150, 450, p.xg, &p.xg, UpdateShape, &p);
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AddSlider("Yg", "FD Curve matching", 150, 450, p.yg, &p.yg, UpdateShape, &p);
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}
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if (code == 'g')
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{
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ximgproc::ContourFitting fit;
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vector<Point2f> ctrRef2d, ctrRot2d;
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// sampling contour we want 256 points
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ximgproc::contourSampling(ctrRef, ctrRef2d, 256); // use a mat
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ximgproc::contourSampling(ctrNoisyRotateShift, ctrRot2d, 256); // use a vector og point
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fit.setFDSize(16);
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Mat t;
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fit.estimateTransformation(ctrRot2d, ctrRef2d, t, &dist, false);
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cout << "Transform *********\n "<<"Origin = "<< t.at<double>(0,0)*ctrNoisy.size() <<" expected "<< (p.origin*ctrNoisy.size()) / 100 <<" ("<< ctrNoisy.size()<<")\n";
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cout << "Angle = " << t.at<double>(0, 1) * 180 / M_PI << " expected " << p.angle <<"\n";
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cout << "Scale = " << t.at<double>(0, 2) << " expected " << p.scale10 / 10.0 << "\n";
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Mat dst;
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ximgproc::transform(ctrRot2d, t, dst, false);
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c.push_back(dst);
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drawContours(img, c, 2, Scalar(0,255,255));
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circle(img, c[2][0], 5, Scalar(0, 255, 255));
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imshow("FD Curve matching", img);
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}
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}
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while (code!=27);
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return 0;
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}
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vector<Point> NoisyPolygon(vector<Point> pRef, double n)
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{
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RNG rng;
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vector<Point> c;
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vector<Point> p = pRef;
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vector<vector<Point> > contour;
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for (int i = 0; i<static_cast<int>(p.size()); i++)
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p[i] += Point(Point2d(n*rng.uniform((double)-1, (double)1), n*rng.uniform((double)-1, (double)1)));
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if (n==0)
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return p;
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c.push_back(p[0]);
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int minX = p[0].x, maxX = p[0].x, minY = p[0].y, maxY = p[0].y;
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for (int i = 0; i <static_cast<int>(p.size()); i++)
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{
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int next = i + 1;
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if (next == static_cast<int>(p.size()))
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next = 0;
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Point2d u = p[next] - p[i];
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int d = static_cast<int>(norm(u));
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double a = atan2(u.y, u.x);
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int step = 1;
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if (n != 0)
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step = static_cast<int>(d / n);
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for (int j = 1; j<d; j += max(step, 1))
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{
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Point pNew;
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do
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{
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Point2d pAct = (u*j) / static_cast<double>(d);
157+
double r = n*rng.uniform((double)0, (double)1);
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double theta = a + rng.uniform(0., 2 * CV_PI);
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pNew = Point(Point2d(r*cos(theta) + pAct.x + p[i].x, r*sin(theta) + pAct.y + p[i].y));
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} while (pNew.x<0 || pNew.y<0);
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if (pNew.x<minX)
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minX = pNew.x;
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if (pNew.x>maxX)
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maxX = pNew.x;
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if (pNew.y<minY)
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minY = pNew.y;
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if (pNew.y>maxY)
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maxY = pNew.y;
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c.push_back(pNew);
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}
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}
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return c;
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}
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void UpdateShape(int , void *r)
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{
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((ThParameters *)r)->update = true;
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}
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void AddSlider(String sliderName, String windowName, int minSlider, int maxSlider, int valDefault, int *valSlider, void(*f)(int, void *), void *r)
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{
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createTrackbar(sliderName, windowName, valSlider, 1, f, r);
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setTrackbarMin(sliderName, windowName, minSlider);
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setTrackbarMax(sliderName, windowName, maxSlider);
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setTrackbarPos(sliderName, windowName, valDefault);
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}

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