2013-09-24 01:00:49 +08:00
|
|
|
/*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.
|
|
|
|
//
|
|
|
|
//
|
|
|
|
// License Agreement
|
|
|
|
// For Open Source Computer Vision Library
|
|
|
|
//
|
|
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
|
|
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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 ThinPlateSplineShapeTransformerImpl : public ThinPlateSplineShapeTransformer
|
|
|
|
{
|
|
|
|
public:
|
|
|
|
/* Constructors */
|
|
|
|
ThinPlateSplineShapeTransformerImpl()
|
|
|
|
{
|
|
|
|
regularizationParameter=0;
|
|
|
|
name_ = "ShapeTransformer.TPS";
|
|
|
|
tpsComputed=false;
|
|
|
|
}
|
|
|
|
|
|
|
|
ThinPlateSplineShapeTransformerImpl(double _regularizationParameter)
|
|
|
|
{
|
|
|
|
regularizationParameter=_regularizationParameter;
|
|
|
|
name_ = "ShapeTransformer.TPS";
|
|
|
|
tpsComputed=false;
|
|
|
|
}
|
|
|
|
|
|
|
|
/* Destructor */
|
|
|
|
~ThinPlateSplineShapeTransformerImpl()
|
|
|
|
{
|
|
|
|
}
|
|
|
|
|
|
|
|
//! the main operators
|
|
|
|
virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, std::vector<DMatch> &matches);
|
|
|
|
virtual float applyTransformation(InputArray inPts, OutputArray output=noArray());
|
|
|
|
virtual void warpImage(InputArray transformingImage, OutputArray output,
|
|
|
|
int flags, int borderMode, const Scalar& borderValue) const;
|
|
|
|
|
|
|
|
//! Setters/Getters
|
|
|
|
virtual void setRegularizationParameter(double _regularizationParameter) {regularizationParameter=_regularizationParameter;}
|
|
|
|
virtual double getRegularizationParameter() const {return regularizationParameter;}
|
|
|
|
|
|
|
|
//! write/read
|
|
|
|
virtual void write(FileStorage& fs) const
|
|
|
|
{
|
|
|
|
fs << "name" << name_
|
|
|
|
<< "regularization" << regularizationParameter;
|
|
|
|
}
|
|
|
|
|
|
|
|
virtual void read(const FileNode& fn)
|
|
|
|
{
|
|
|
|
CV_Assert( (String)fn["name"] == name_ );
|
|
|
|
regularizationParameter = (int)fn["regularization"];
|
|
|
|
}
|
|
|
|
|
|
|
|
private:
|
|
|
|
bool tpsComputed;
|
|
|
|
double regularizationParameter;
|
|
|
|
float transformCost;
|
|
|
|
Mat tpsParameters;
|
|
|
|
Mat shapeReference;
|
|
|
|
|
|
|
|
protected:
|
|
|
|
String name_;
|
|
|
|
};
|
|
|
|
|
2013-09-24 03:24:27 +08:00
|
|
|
static float distance(Point2f p, Point2f q)
|
2013-09-24 01:00:49 +08:00
|
|
|
{
|
|
|
|
Point2f diff = p - q;
|
|
|
|
float norma = diff.x*diff.x + diff.y*diff.y;// - 2*diff.x*diff.y;
|
|
|
|
if (norma<0) norma=0;
|
|
|
|
//else norma = std::sqrt(norma);
|
|
|
|
norma = norma*std::log(norma+FLT_EPSILON);
|
|
|
|
return norma;
|
|
|
|
}
|
|
|
|
|
|
|
|
static Point2f _applyTransformation(const Mat &shapeRef, const Point2f point, const Mat &tpsParameters)
|
|
|
|
{
|
|
|
|
Point2f out;
|
|
|
|
for (int i=0; i<2; i++)
|
|
|
|
{
|
|
|
|
float a1=tpsParameters.at<float>(tpsParameters.rows-3,i);
|
|
|
|
float ax=tpsParameters.at<float>(tpsParameters.rows-2,i);
|
|
|
|
float ay=tpsParameters.at<float>(tpsParameters.rows-1,i);
|
|
|
|
|
|
|
|
float affine=a1+ax*point.x+ay*point.y;
|
|
|
|
float nonrigid=0;
|
|
|
|
for (int j=0; j<shapeRef.rows; j++)
|
|
|
|
{
|
|
|
|
nonrigid+=tpsParameters.at<float>(j,i)*
|
|
|
|
distance(Point2f(shapeRef.at<float>(j,0),shapeRef.at<float>(j,1)),
|
|
|
|
point);
|
|
|
|
}
|
|
|
|
if (i==0)
|
|
|
|
{
|
|
|
|
out.x=affine+nonrigid;
|
|
|
|
}
|
|
|
|
if (i==1)
|
|
|
|
{
|
|
|
|
out.y=affine+nonrigid;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
return out;
|
|
|
|
}
|
|
|
|
|
|
|
|
/* public methods */
|
|
|
|
void ThinPlateSplineShapeTransformerImpl::warpImage(InputArray transformingImage, OutputArray output,
|
|
|
|
int flags, int borderMode, const Scalar& borderValue) const
|
|
|
|
{
|
|
|
|
CV_Assert(tpsComputed==true);
|
|
|
|
|
|
|
|
Mat theinput = transformingImage.getMat();
|
|
|
|
Mat mapX(theinput.rows, theinput.cols, CV_32FC1);
|
|
|
|
Mat mapY(theinput.rows, theinput.cols, CV_32FC1);
|
|
|
|
|
|
|
|
for (int row = 0; row < theinput.rows; row++)
|
|
|
|
{
|
|
|
|
for (int col = 0; col < theinput.cols; col++)
|
|
|
|
{
|
|
|
|
Point2f pt = _applyTransformation(shapeReference, Point2f(float(col), float(row)), tpsParameters);
|
|
|
|
mapX.at<float>(row, col) = pt.x;
|
|
|
|
mapY.at<float>(row, col) = pt.y;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
remap(transformingImage, output, mapX, mapY, flags, borderMode, borderValue);
|
|
|
|
}
|
|
|
|
|
|
|
|
float ThinPlateSplineShapeTransformerImpl::applyTransformation(InputArray inPts, OutputArray outPts)
|
|
|
|
{
|
|
|
|
CV_Assert(tpsComputed);
|
|
|
|
Mat pts1 = inPts.getMat();
|
2013-09-26 00:30:33 +08:00
|
|
|
CV_Assert((pts1.channels()==2) && (pts1.cols>0));
|
2013-09-24 01:00:49 +08:00
|
|
|
|
|
|
|
//Apply transformation in the complete set of points
|
|
|
|
// Ensambling output //
|
|
|
|
if (outPts.needed())
|
|
|
|
{
|
|
|
|
outPts.create(1,pts1.cols, CV_32FC2);
|
|
|
|
Mat outMat = outPts.getMat();
|
|
|
|
for (int i=0; i<pts1.cols; i++)
|
|
|
|
{
|
|
|
|
Point2f pt=pts1.at<Point2f>(0,i);
|
|
|
|
outMat.at<Point2f>(0,i)=_applyTransformation(shapeReference, pt, tpsParameters);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
return transformCost;
|
|
|
|
}
|
|
|
|
|
|
|
|
void ThinPlateSplineShapeTransformerImpl::estimateTransformation(InputArray _pts1, InputArray _pts2,
|
|
|
|
std::vector<DMatch>& _matches )
|
|
|
|
{
|
|
|
|
Mat pts1 = _pts1.getMat();
|
|
|
|
Mat pts2 = _pts2.getMat();
|
2013-09-26 00:30:33 +08:00
|
|
|
CV_Assert((pts1.channels()==2) && (pts1.cols>0) && (pts2.channels()==2) && (pts2.cols>0));
|
2013-09-24 01:00:49 +08:00
|
|
|
CV_Assert(_matches.size()>1);
|
|
|
|
|
|
|
|
if (pts1.type() != CV_32F)
|
|
|
|
pts1.convertTo(pts1, CV_32F);
|
|
|
|
if (pts2.type() != CV_32F)
|
|
|
|
pts2.convertTo(pts2, CV_32F);
|
|
|
|
|
|
|
|
// Use only valid matchings //
|
|
|
|
std::vector<DMatch> matches;
|
|
|
|
for (size_t i=0; i<_matches.size(); i++)
|
|
|
|
{
|
|
|
|
if (_matches[i].queryIdx<pts1.cols &&
|
|
|
|
_matches[i].trainIdx<pts2.cols)
|
|
|
|
{
|
|
|
|
matches.push_back(_matches[i]);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// Organizing the correspondent points in matrix style //
|
2013-10-04 21:03:15 +08:00
|
|
|
Mat shape1((int)matches.size(),2,CV_32F); // transforming shape
|
|
|
|
Mat shape2((int)matches.size(),2,CV_32F); // target shape
|
|
|
|
for (int i=0, end = (int)matches.size(); i<end; i++)
|
2013-09-24 01:00:49 +08:00
|
|
|
{
|
|
|
|
Point2f pt1=pts1.at<Point2f>(0,matches[i].queryIdx);
|
|
|
|
shape1.at<float>(i,0) = pt1.x;
|
|
|
|
shape1.at<float>(i,1) = pt1.y;
|
|
|
|
|
|
|
|
Point2f pt2=pts2.at<Point2f>(0,matches[i].trainIdx);
|
|
|
|
shape2.at<float>(i,0) = pt2.x;
|
|
|
|
shape2.at<float>(i,1) = pt2.y;
|
|
|
|
}
|
|
|
|
shape1.copyTo(shapeReference);
|
|
|
|
|
|
|
|
// Building the matrices for solving the L*(w|a)=(v|0) problem with L={[K|P];[P'|0]}
|
|
|
|
|
|
|
|
//Building K and P (Neede to buil L)
|
2013-10-04 21:03:15 +08:00
|
|
|
Mat matK((int)matches.size(),(int)matches.size(),CV_32F);
|
|
|
|
Mat matP((int)matches.size(),3,CV_32F);
|
|
|
|
for (int i=0, end=(int)matches.size(); i<end; i++)
|
2013-09-24 01:00:49 +08:00
|
|
|
{
|
2013-10-04 21:03:15 +08:00
|
|
|
for (int j=0; j<end; j++)
|
2013-09-24 01:00:49 +08:00
|
|
|
{
|
|
|
|
if (i==j)
|
|
|
|
{
|
2013-09-24 03:24:27 +08:00
|
|
|
matK.at<float>(i,j)=float(regularizationParameter);
|
2013-09-24 01:00:49 +08:00
|
|
|
}
|
|
|
|
else
|
|
|
|
{
|
|
|
|
matK.at<float>(i,j) = distance(Point2f(shape1.at<float>(i,0),shape1.at<float>(i,1)),
|
|
|
|
Point2f(shape1.at<float>(j,0),shape1.at<float>(j,1)));
|
|
|
|
}
|
|
|
|
}
|
|
|
|
matP.at<float>(i,0) = 1;
|
|
|
|
matP.at<float>(i,1) = shape1.at<float>(i,0);
|
|
|
|
matP.at<float>(i,2) = shape1.at<float>(i,1);
|
|
|
|
}
|
|
|
|
|
|
|
|
//Building L
|
2013-10-04 21:03:15 +08:00
|
|
|
Mat matL=Mat::zeros((int)matches.size()+3,(int)matches.size()+3,CV_32F);
|
|
|
|
Mat matLroi(matL, Rect(0,0,(int)matches.size(),(int)matches.size())); //roi for K
|
2013-09-24 01:00:49 +08:00
|
|
|
matK.copyTo(matLroi);
|
2013-10-04 21:03:15 +08:00
|
|
|
matLroi = Mat(matL,Rect((int)matches.size(),0,3,(int)matches.size())); //roi for P
|
2013-09-24 01:00:49 +08:00
|
|
|
matP.copyTo(matLroi);
|
|
|
|
Mat matPt;
|
|
|
|
transpose(matP,matPt);
|
2013-10-04 21:03:15 +08:00
|
|
|
matLroi = Mat(matL,Rect(0,(int)matches.size(),(int)matches.size(),3)); //roi for P'
|
2013-09-24 01:00:49 +08:00
|
|
|
matPt.copyTo(matLroi);
|
|
|
|
|
|
|
|
//Building B (v|0)
|
2013-10-04 21:03:15 +08:00
|
|
|
Mat matB = Mat::zeros((int)matches.size()+3,2,CV_32F);
|
|
|
|
for (int i=0, end = (int)matches.size(); i<end; i++)
|
2013-09-24 01:00:49 +08:00
|
|
|
{
|
|
|
|
matB.at<float>(i,0) = shape2.at<float>(i,0); //x's
|
|
|
|
matB.at<float>(i,1) = shape2.at<float>(i,1); //y's
|
|
|
|
}
|
|
|
|
|
|
|
|
//Obtaining transformation params (w|a)
|
|
|
|
solve(matL, matB, tpsParameters, DECOMP_LU);
|
|
|
|
//tpsParameters = matL.inv()*matB;
|
|
|
|
|
|
|
|
//Setting transform Cost and Shape reference
|
|
|
|
Mat w(tpsParameters, Rect(0,0,2,tpsParameters.rows-3));
|
|
|
|
Mat Q=w.t()*matK*w;
|
|
|
|
transformCost=fabs(Q.at<float>(0,0)*Q.at<float>(1,1));//fabs(mean(Q.diag(0))[0]);//std::max(Q.at<float>(0,0),Q.at<float>(1,1));
|
|
|
|
tpsComputed=true;
|
|
|
|
}
|
|
|
|
|
|
|
|
Ptr <ThinPlateSplineShapeTransformer> createThinPlateSplineShapeTransformer(double regularizationParameter)
|
|
|
|
{
|
|
|
|
return Ptr<ThinPlateSplineShapeTransformer>( new ThinPlateSplineShapeTransformerImpl(regularizationParameter) );
|
|
|
|
}
|
|
|
|
|
|
|
|
} // cv
|