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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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@@ -52,6 +52,8 @@
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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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@{
@@ -67,7 +69,9 @@ i.e. algorithms which somehow takes into account pixel affinities in natural ima
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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_shape 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_shape
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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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*
96+
*/
97+
CV_EXPORTS_W void transform(InputArray _src, InputArray _t,OutputArray _dst, bool fdContour=true);
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/**
99+
* @brief Contour sampling .
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*
101+
* @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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*
105+
*/
106+
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).
113+
*/
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CV_EXPORTS_W Ptr<ContourFitting> create(int ctr = 1024, int fd = 16);
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//! @} ximgproc_shape
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}
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}
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#endif

‎modules/ximgproc/samples/fourier_descriptors_demo.cpp

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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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#include <opencv2/core.hpp>
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#include <opencv2/core/utility.hpp>
7+
#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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12+
using namespace cv;
13+
using namespace std;
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struct ThParameters {
16+
int levelNoise;
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int angle;
18+
int scale10;
19+
int origin;
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int xg;
21+
int yg;
22+
bool update;
23+
} ;
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25+
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);
28+
29+
int main(void)
30+
{
31+
vector<Point> ctrRef;
32+
vector<Point> ctrRotate, ctrNoisy, ctrNoisyRotate, ctrNoisyRotateShift;
33+
// build a shape with 5 vertex
34+
ctrRef.push_back(Point(250,250)); ctrRef.push_back(Point(400, 250));
35+
ctrRef.push_back(Point(400, 300)); ctrRef.push_back(Point(250, 300));ctrRef.push_back(Point(180, 270));
36+
Point cg(0,0);
37+
for (int i=0;i<static_cast<int>(ctrRef.size());i++)
38+
cg+=ctrRef[i];
39+
cg.x /= static_cast<int>(ctrRef.size());
40+
cg.y /= static_cast<int>(ctrRef.size());
41+
ThParameters p;
42+
p.levelNoise=6;
43+
p.angle=45;
44+
p.scale10=5;
45+
p.origin=10;
46+
p.xg=150;
47+
p.yg=150;
48+
p.update=true;
49+
namedWindow("FD Curve matching");
50+
// A rotation with center at (150,150) of angle 45 degrees and a scaling of 5/10
51+
AddSlider("Noise", "FD Curve matching", 0, 20, p.levelNoise, &p.levelNoise, UpdateShape, &p);
52+
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);
57+
int code=0;
58+
double dist;
59+
vector<vector<Point> > c;
60+
Mat img;
61+
cout << "******************** PRESS G TO MATCH CURVES *************\n";
62+
do
63+
{
64+
code = waitKey(30)&0xFF;
65+
if (p.update)
66+
{
67+
Mat r = getRotationMatrix2D(Point(p.xg, p.yg), p.angle, 10.0/ p.scale10);
68+
ctrNoisy= NoisyPolygon(ctrRef,static_cast<double>(p.levelNoise));
69+
transform(ctrNoisy, ctrNoisyRotate,r);
70+
ctrNoisyRotateShift.clear();
71+
for (int i=0;i<static_cast<int>(ctrNoisy.size());i++)
72+
ctrNoisyRotateShift.push_back(ctrNoisyRotate[(i+(p.origin*ctrNoisy.size())/100)% ctrNoisy.size()]);
73+
// To draw contour using drawcontours
74+
c.clear();
75+
c.push_back(ctrRef);
76+
c.push_back(ctrNoisyRotateShift);
77+
p.update = false;
78+
Rect rglobal;
79+
for (int i = 0; i < static_cast<int>(c.size()); i++)
80+
{
81+
rglobal = boundingRect(c[i]) | rglobal;
82+
}
83+
rglobal.width += 10;
84+
rglobal.height += 10;
85+
img = Mat::zeros(2 * rglobal.height, 2 * rglobal.width, CV_8UC(3));
86+
drawContours(img, c, 0, Scalar(255,0,0));
87+
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));
90+
imshow("FD Curve matching", img);
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}
92+
if (code == 'd')
93+
{
94+
destroyWindow("FD Curve matching");
95+
namedWindow("FD Curve matching");
96+
// A rotation with center at (150,150) of angle 45 degrees and a scaling of 5/10
97+
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);
102+
AddSlider("Yg", "FD Curve matching", 150, 450, p.yg, &p.yg, UpdateShape, &p);
103+
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}
105+
if (code == 'g')
106+
{
107+
ximgproc::ContourFitting fit;
108+
vector<Point2f> ctrRef2d, ctrRot2d;
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// sampling contour we want 256 points
110+
ximgproc::contourSampling(ctrRef, ctrRef2d, 256); // use a mat
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ximgproc::contourSampling(ctrNoisyRotateShift, ctrRot2d, 256); // use a vector og point
112+
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";
117+
cout << "Scale = " << t.at<double>(0, 2) << " expected " << p.scale10 / 10.0 << "\n";
118+
Mat dst;
119+
ximgproc::transform(ctrRot2d, t, dst, false);
120+
c.push_back(dst);
121+
drawContours(img, c, 2, Scalar(0,255,255));
122+
circle(img, c[2][0], 5, Scalar(0, 255, 255));
123+
imshow("FD Curve matching", img);
124+
}
125+
}
126+
while (code!=27);
127+
128+
return 0;
129+
}
130+
131+
vector<Point> NoisyPolygon(vector<Point> pRef, double n)
132+
{
133+
RNG rng;
134+
vector<Point> c;
135+
vector<Point> p = pRef;
136+
vector<vector<Point> > contour;
137+
for (int i = 0; i<static_cast<int>(p.size()); i++)
138+
p[i] += Point(Point2d(n*rng.uniform((double)-1, (double)1), n*rng.uniform((double)-1, (double)1)));
139+
if (n==0)
140+
return p;
141+
c.push_back(p[0]);
142+
int minX = p[0].x, maxX = p[0].x, minY = p[0].y, maxY = p[0].y;
143+
for (int i = 0; i <static_cast<int>(p.size()); i++)
144+
{
145+
int next = i + 1;
146+
if (next == static_cast<int>(p.size()))
147+
next = 0;
148+
Point2d u = p[next] - p[i];
149+
int d = static_cast<int>(norm(u));
150+
double a = atan2(u.y, u.x);
151+
int step = 1;
152+
if (n != 0)
153+
step = static_cast<int>(d / n);
154+
for (int j = 1; j<d; j += max(step, 1))
155+
{
156+
Point pNew;
157+
do
158+
{
159+
160+
Point2d pAct = (u*j) / static_cast<double>(d);
161+
double r = n*rng.uniform((double)0, (double)1);
162+
double theta = a + rng.uniform(0., 2 * CV_PI);
163+
pNew = Point(Point2d(r*cos(theta) + pAct.x + p[i].x, r*sin(theta) + pAct.y + p[i].y));
164+
} while (pNew.x<0 || pNew.y<0);
165+
if (pNew.x<minX)
166+
minX = pNew.x;
167+
if (pNew.x>maxX)
168+
maxX = pNew.x;
169+
if (pNew.y<minY)
170+
minY = pNew.y;
171+
if (pNew.y>maxY)
172+
maxY = pNew.y;
173+
c.push_back(pNew);
174+
}
175+
}
176+
return c;
177+
}
178+
179+
void UpdateShape(int , void *r)
180+
{
181+
((ThParameters *)r)->update = true;
182+
}
183+
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void AddSlider(String sliderName, String windowName, int minSlider, int maxSlider, int valDefault, int *valSlider, void(*f)(int, void *), void *r)
185+
{
186+
createTrackbar(sliderName, windowName, valSlider, 1, f, r);
187+
setTrackbarMin(sliderName, windowName, minSlider);
188+
setTrackbarMax(sliderName, windowName, maxSlider);
189+
setTrackbarPos(sliderName, windowName, valDefault);
190+
}

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