Shape module tests refactored
- common operations moved to separate class - debug console messages removed - test results are stored in memory instead of file
This commit is contained in:
Maksim Shabunin
parent
6e565ab4a4
commit
10639c9526
@ -1,263 +0,0 @@
|
|||||||
/*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 "test_precomp.hpp"
|
|
||||||
|
|
||||||
using namespace cv;
|
|
||||||
using namespace std;
|
|
||||||
|
|
||||||
const int angularBins=12;
|
|
||||||
const int radialBins=4;
|
|
||||||
const float minRad=0.2f;
|
|
||||||
const float maxRad=2;
|
|
||||||
const int NSN=5;//10;//20; //number of shapes per class
|
|
||||||
const int NP=100; //number of points sympliying the contour
|
|
||||||
const float CURRENT_MAX_ACCUR=95; //98% and 99% reached in several tests, 95 is fixed as minimum boundary
|
|
||||||
|
|
||||||
class CV_ShapeEMDTest : public cvtest::BaseTest
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
CV_ShapeEMDTest();
|
|
||||||
~CV_ShapeEMDTest();
|
|
||||||
protected:
|
|
||||||
void run(int);
|
|
||||||
|
|
||||||
private:
|
|
||||||
void mpegTest();
|
|
||||||
void listShapeNames(vector<string> &listHeaders);
|
|
||||||
vector<Point2f> convertContourType(const Mat &, int n=0 );
|
|
||||||
float computeShapeDistance(vector <Point2f>& queryNormal,
|
|
||||||
vector <Point2f>& queryFlipped1,
|
|
||||||
vector <Point2f>& queryFlipped2,
|
|
||||||
vector<Point2f>& testq);
|
|
||||||
void displayMPEGResults();
|
|
||||||
};
|
|
||||||
|
|
||||||
CV_ShapeEMDTest::CV_ShapeEMDTest()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
CV_ShapeEMDTest::~CV_ShapeEMDTest()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
vector <Point2f> CV_ShapeEMDTest::convertContourType(const Mat& currentQuery, int n)
|
|
||||||
{
|
|
||||||
vector<vector<Point> > _contoursQuery;
|
|
||||||
vector <Point2f> contoursQuery;
|
|
||||||
findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
|
|
||||||
for (size_t border=0; border<_contoursQuery.size(); border++)
|
|
||||||
{
|
|
||||||
for (size_t p=0; p<_contoursQuery[border].size(); p++)
|
|
||||||
{
|
|
||||||
contoursQuery.push_back(Point2f((float)_contoursQuery[border][p].x,
|
|
||||||
(float)_contoursQuery[border][p].y));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// In case actual number of points is less than n
|
|
||||||
int dum=0;
|
|
||||||
for (int add=(int)contoursQuery.size()-1; add<n; add++)
|
|
||||||
{
|
|
||||||
contoursQuery.push_back(contoursQuery[dum++]); //adding dummy values
|
|
||||||
}
|
|
||||||
|
|
||||||
// Uniformly sampling
|
|
||||||
random_shuffle(contoursQuery.begin(), contoursQuery.end());
|
|
||||||
int nStart=n;
|
|
||||||
vector<Point2f> cont;
|
|
||||||
for (int i=0; i<nStart; i++)
|
|
||||||
{
|
|
||||||
cont.push_back(contoursQuery[i]);
|
|
||||||
}
|
|
||||||
return cont;
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_ShapeEMDTest::listShapeNames( vector<string> &listHeaders)
|
|
||||||
{
|
|
||||||
listHeaders.push_back("apple"); //ok
|
|
||||||
listHeaders.push_back("children"); // ok
|
|
||||||
listHeaders.push_back("device7"); // ok
|
|
||||||
listHeaders.push_back("Heart"); // ok
|
|
||||||
listHeaders.push_back("teddy"); // ok
|
|
||||||
}
|
|
||||||
float CV_ShapeEMDTest::computeShapeDistance(vector <Point2f>& query1, vector <Point2f>& query2,
|
|
||||||
vector <Point2f>& query3, vector <Point2f>& testq)
|
|
||||||
{
|
|
||||||
//waitKey(0);
|
|
||||||
Ptr <ShapeContextDistanceExtractor> mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
|
|
||||||
//Ptr <HistogramCostExtractor> cost = createNormHistogramCostExtractor(cv::DIST_L1);
|
|
||||||
//Ptr <HistogramCostExtractor> cost = createChiHistogramCostExtractor(30,0.15);
|
|
||||||
//Ptr <HistogramCostExtractor> cost = createEMDHistogramCostExtractor();
|
|
||||||
// Ptr <HistogramCostExtractor> cost = createEMDL1HistogramCostExtractor();
|
|
||||||
mysc->setIterations(1); //(3)
|
|
||||||
mysc->setCostExtractor( createEMDL1HistogramCostExtractor() );
|
|
||||||
//mysc->setTransformAlgorithm(createAffineTransformer(true));
|
|
||||||
mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
|
|
||||||
//mysc->setImageAppearanceWeight(1.6);
|
|
||||||
//mysc->setImageAppearanceWeight(0.0);
|
|
||||||
//mysc->setImages(im1,imtest);
|
|
||||||
return ( std::min( mysc->computeDistance(query1, testq),
|
|
||||||
std::min(mysc->computeDistance(query2, testq), mysc->computeDistance(query3, testq) )));
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_ShapeEMDTest::mpegTest()
|
|
||||||
{
|
|
||||||
string baseTestFolder="shape/mpeg_test/";
|
|
||||||
string path = cvtest::TS::ptr()->get_data_path() + baseTestFolder;
|
|
||||||
vector<string> namesHeaders;
|
|
||||||
listShapeNames(namesHeaders);
|
|
||||||
|
|
||||||
// distance matrix //
|
|
||||||
Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F);
|
|
||||||
|
|
||||||
// query contours (normal v flipped, h flipped) and testing contour //
|
|
||||||
vector<Point2f> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
|
|
||||||
|
|
||||||
// reading query and computing its properties //
|
|
||||||
int counter=0;
|
|
||||||
const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size();
|
|
||||||
for (size_t n=0; n<namesHeaders.size(); n++)
|
|
||||||
{
|
|
||||||
for (int i=1; i<=NSN; i++)
|
|
||||||
{
|
|
||||||
// read current image //
|
|
||||||
stringstream thepathandname;
|
|
||||||
thepathandname<<path+namesHeaders[n]<<"-"<<i<<".png";
|
|
||||||
Mat currentQuery, flippedHQuery, flippedVQuery;
|
|
||||||
currentQuery=imread(thepathandname.str(), IMREAD_GRAYSCALE);
|
|
||||||
flip(currentQuery, flippedHQuery, 0);
|
|
||||||
flip(currentQuery, flippedVQuery, 1);
|
|
||||||
// compute border of the query and its flipped versions //
|
|
||||||
vector<Point2f> origContour;
|
|
||||||
contoursQuery1=convertContourType(currentQuery, NP);
|
|
||||||
origContour=contoursQuery1;
|
|
||||||
contoursQuery2=convertContourType(flippedHQuery, NP);
|
|
||||||
contoursQuery3=convertContourType(flippedVQuery, NP);
|
|
||||||
|
|
||||||
// compare with all the rest of the images: testing //
|
|
||||||
for (size_t nt=0; nt<namesHeaders.size(); nt++)
|
|
||||||
{
|
|
||||||
for (int it=1; it<=NSN; it++)
|
|
||||||
{
|
|
||||||
// skip self-comparisson //
|
|
||||||
counter++;
|
|
||||||
if (nt==n && it==i)
|
|
||||||
{
|
|
||||||
distanceMat.at<float>(NSN*(int)n+i-1,
|
|
||||||
NSN*(int)nt+it-1)=0;
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
// read testing image //
|
|
||||||
stringstream thetestpathandname;
|
|
||||||
thetestpathandname<<path+namesHeaders[nt]<<"-"<<it<<".png";
|
|
||||||
Mat currentTest;
|
|
||||||
currentTest=imread(thetestpathandname.str().c_str(), 0);
|
|
||||||
// compute border of the testing //
|
|
||||||
contoursTesting=convertContourType(currentTest, NP);
|
|
||||||
|
|
||||||
// compute shape distance //
|
|
||||||
std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl;
|
|
||||||
std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<<
|
|
||||||
" and "<<namesHeaders[nt]<<it<<": ";
|
|
||||||
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)=
|
|
||||||
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting);
|
|
||||||
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
// save distance matrix //
|
|
||||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::WRITE);
|
|
||||||
fs << "distanceMat" << distanceMat;
|
|
||||||
}
|
|
||||||
|
|
||||||
const int FIRST_MANY=2*NSN;
|
|
||||||
void CV_ShapeEMDTest::displayMPEGResults()
|
|
||||||
{
|
|
||||||
string baseTestFolder="shape/mpeg_test/";
|
|
||||||
Mat distanceMat;
|
|
||||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::READ);
|
|
||||||
vector<string> namesHeaders;
|
|
||||||
listShapeNames(namesHeaders);
|
|
||||||
|
|
||||||
// Read generated MAT //
|
|
||||||
fs["distanceMat"]>>distanceMat;
|
|
||||||
|
|
||||||
int corrects=0;
|
|
||||||
int divi=0;
|
|
||||||
for (int row=0; row<distanceMat.rows; row++)
|
|
||||||
{
|
|
||||||
if (row%NSN==0) //another group
|
|
||||||
{
|
|
||||||
divi+=NSN;
|
|
||||||
}
|
|
||||||
for (int col=divi-NSN; col<divi; col++)
|
|
||||||
{
|
|
||||||
int nsmall=0;
|
|
||||||
for (int i=0; i<distanceMat.cols; i++)
|
|
||||||
{
|
|
||||||
if (distanceMat.at<float>(row,col)>distanceMat.at<float>(row,i))
|
|
||||||
{
|
|
||||||
nsmall++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
if (nsmall<=FIRST_MANY)
|
|
||||||
{
|
|
||||||
corrects++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
float porc = 100*float(corrects)/(NSN*distanceMat.rows);
|
|
||||||
std::cout<<"%="<<porc<<std::endl;
|
|
||||||
if (porc >= CURRENT_MAX_ACCUR)
|
|
||||||
ts->set_failed_test_info(cvtest::TS::OK);
|
|
||||||
else
|
|
||||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_ShapeEMDTest::run( int /*start_from*/ )
|
|
||||||
{
|
|
||||||
mpegTest();
|
|
||||||
displayMPEGResults();
|
|
||||||
}
|
|
||||||
|
|
||||||
TEST(ShapeEMD_SCD, regression) { CV_ShapeEMDTest test; test.safe_run(); }
|
|
||||||
@ -1,280 +0,0 @@
|
|||||||
/*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 "test_precomp.hpp"
|
|
||||||
#include <stdlib.h>
|
|
||||||
|
|
||||||
using namespace cv;
|
|
||||||
using namespace std;
|
|
||||||
|
|
||||||
const int NSN=5;//10;//20; //number of shapes per class
|
|
||||||
const float CURRENT_MAX_ACCUR=85; //90% and 91% reached in several tests, 85 is fixed as minimum boundary
|
|
||||||
|
|
||||||
class CV_HaussTest : public cvtest::BaseTest
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
CV_HaussTest();
|
|
||||||
~CV_HaussTest();
|
|
||||||
protected:
|
|
||||||
void run(int);
|
|
||||||
private:
|
|
||||||
float computeShapeDistance(vector<Point> &query1, vector<Point> &query2,
|
|
||||||
vector<Point> &query3, vector<Point> &testq);
|
|
||||||
vector <Point> convertContourType(const Mat& currentQuery, int n=180);
|
|
||||||
vector<Point2f> normalizeContour(const vector <Point>& contour);
|
|
||||||
void listShapeNames( vector<string> &listHeaders);
|
|
||||||
void mpegTest();
|
|
||||||
void displayMPEGResults();
|
|
||||||
};
|
|
||||||
|
|
||||||
CV_HaussTest::CV_HaussTest()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
CV_HaussTest::~CV_HaussTest()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
vector<Point2f> CV_HaussTest::normalizeContour(const vector<Point> &contour)
|
|
||||||
{
|
|
||||||
vector<Point2f> output(contour.size());
|
|
||||||
Mat disMat((int)contour.size(),(int)contour.size(),CV_32F);
|
|
||||||
Point2f meanpt(0,0);
|
|
||||||
float meanVal=1;
|
|
||||||
|
|
||||||
for (int ii=0, end1 = (int)contour.size(); ii<end1; ii++)
|
|
||||||
{
|
|
||||||
for (int jj=0, end2 = (int)contour.size(); end2; jj++)
|
|
||||||
{
|
|
||||||
if (ii==jj) disMat.at<float>(ii,jj)=0;
|
|
||||||
else
|
|
||||||
{
|
|
||||||
disMat.at<float>(ii,jj)=
|
|
||||||
float(fabs(double(contour[ii].x*contour[jj].x)))+float(fabs(double(contour[ii].y*contour[jj].y)));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
meanpt.x+=contour[ii].x;
|
|
||||||
meanpt.y+=contour[ii].y;
|
|
||||||
}
|
|
||||||
meanpt.x/=contour.size();
|
|
||||||
meanpt.y/=contour.size();
|
|
||||||
meanVal=float(cv::mean(disMat)[0]);
|
|
||||||
for (size_t ii=0; ii<contour.size(); ii++)
|
|
||||||
{
|
|
||||||
output[ii].x = (contour[ii].x-meanpt.x)/meanVal;
|
|
||||||
output[ii].y = (contour[ii].y-meanpt.y)/meanVal;
|
|
||||||
}
|
|
||||||
return output;
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_HaussTest::listShapeNames( vector<string> &listHeaders)
|
|
||||||
{
|
|
||||||
listHeaders.push_back("apple"); //ok
|
|
||||||
listHeaders.push_back("children"); // ok
|
|
||||||
listHeaders.push_back("device7"); // ok
|
|
||||||
listHeaders.push_back("Heart"); // ok
|
|
||||||
listHeaders.push_back("teddy"); // ok
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
vector <Point> CV_HaussTest::convertContourType(const Mat& currentQuery, int n)
|
|
||||||
{
|
|
||||||
vector<vector<Point> > _contoursQuery;
|
|
||||||
vector <Point> contoursQuery;
|
|
||||||
findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
|
|
||||||
for (size_t border=0; border<_contoursQuery.size(); border++)
|
|
||||||
{
|
|
||||||
for (size_t p=0; p<_contoursQuery[border].size(); p++)
|
|
||||||
{
|
|
||||||
contoursQuery.push_back(_contoursQuery[border][p]);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// In case actual number of points is less than n
|
|
||||||
for (int add=(int)contoursQuery.size()-1; add<n; add++)
|
|
||||||
{
|
|
||||||
contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
|
|
||||||
}
|
|
||||||
|
|
||||||
// Uniformly sampling
|
|
||||||
random_shuffle(contoursQuery.begin(), contoursQuery.end());
|
|
||||||
int nStart=n;
|
|
||||||
vector<Point> cont;
|
|
||||||
for (int i=0; i<nStart; i++)
|
|
||||||
{
|
|
||||||
cont.push_back(contoursQuery[i]);
|
|
||||||
}
|
|
||||||
return cont;
|
|
||||||
}
|
|
||||||
|
|
||||||
float CV_HaussTest::computeShapeDistance(vector <Point>& query1, vector <Point>& query2,
|
|
||||||
vector <Point>& query3, vector <Point>& testq)
|
|
||||||
{
|
|
||||||
Ptr <HausdorffDistanceExtractor> haus = createHausdorffDistanceExtractor();
|
|
||||||
return std::min(haus->computeDistance(query1,testq), std::min(haus->computeDistance(query2,testq),
|
|
||||||
haus->computeDistance(query3,testq)));
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_HaussTest::mpegTest()
|
|
||||||
{
|
|
||||||
string baseTestFolder="shape/mpeg_test/";
|
|
||||||
string path = cvtest::TS::ptr()->get_data_path() + baseTestFolder;
|
|
||||||
vector<string> namesHeaders;
|
|
||||||
listShapeNames(namesHeaders);
|
|
||||||
|
|
||||||
// distance matrix //
|
|
||||||
Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F);
|
|
||||||
|
|
||||||
// query contours (normal v flipped, h flipped) and testing contour //
|
|
||||||
vector<Point> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
|
|
||||||
|
|
||||||
// reading query and computing its properties //
|
|
||||||
int counter=0;
|
|
||||||
const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size();
|
|
||||||
for (size_t n=0; n<namesHeaders.size(); n++)
|
|
||||||
{
|
|
||||||
for (int i=1; i<=NSN; i++)
|
|
||||||
{
|
|
||||||
// read current image //
|
|
||||||
stringstream thepathandname;
|
|
||||||
thepathandname<<path+namesHeaders[n]<<"-"<<i<<".png";
|
|
||||||
Mat currentQuery, flippedHQuery, flippedVQuery;
|
|
||||||
currentQuery=imread(thepathandname.str(), IMREAD_GRAYSCALE);
|
|
||||||
flip(currentQuery, flippedHQuery, 0);
|
|
||||||
flip(currentQuery, flippedVQuery, 1);
|
|
||||||
// compute border of the query and its flipped versions //
|
|
||||||
vector<Point> origContour;
|
|
||||||
contoursQuery1=convertContourType(currentQuery);
|
|
||||||
origContour=contoursQuery1;
|
|
||||||
contoursQuery2=convertContourType(flippedHQuery);
|
|
||||||
contoursQuery3=convertContourType(flippedVQuery);
|
|
||||||
|
|
||||||
// compare with all the rest of the images: testing //
|
|
||||||
for (size_t nt=0; nt<namesHeaders.size(); nt++)
|
|
||||||
{
|
|
||||||
for (int it=1; it<=NSN; it++)
|
|
||||||
{
|
|
||||||
/* skip self-comparisson */
|
|
||||||
counter++;
|
|
||||||
if (nt==n && it==i)
|
|
||||||
{
|
|
||||||
distanceMat.at<float>(NSN*(int)n+i-1,
|
|
||||||
NSN*(int)nt+it-1)=0;
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
// read testing image //
|
|
||||||
stringstream thetestpathandname;
|
|
||||||
thetestpathandname<<path+namesHeaders[nt]<<"-"<<it<<".png";
|
|
||||||
Mat currentTest;
|
|
||||||
currentTest=imread(thetestpathandname.str().c_str(), 0);
|
|
||||||
|
|
||||||
// compute border of the testing //
|
|
||||||
contoursTesting=convertContourType(currentTest);
|
|
||||||
|
|
||||||
// compute shape distance //
|
|
||||||
std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl;
|
|
||||||
std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<<
|
|
||||||
" and "<<namesHeaders[nt]<<it<<": ";
|
|
||||||
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)=
|
|
||||||
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting);
|
|
||||||
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
// save distance matrix //
|
|
||||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::WRITE);
|
|
||||||
fs << "distanceMat" << distanceMat;
|
|
||||||
}
|
|
||||||
|
|
||||||
const int FIRST_MANY=2*NSN;
|
|
||||||
void CV_HaussTest::displayMPEGResults()
|
|
||||||
{
|
|
||||||
string baseTestFolder="shape/mpeg_test/";
|
|
||||||
Mat distanceMat;
|
|
||||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::READ);
|
|
||||||
vector<string> namesHeaders;
|
|
||||||
listShapeNames(namesHeaders);
|
|
||||||
|
|
||||||
// Read generated MAT //
|
|
||||||
fs["distanceMat"]>>distanceMat;
|
|
||||||
|
|
||||||
int corrects=0;
|
|
||||||
int divi=0;
|
|
||||||
for (int row=0; row<distanceMat.rows; row++)
|
|
||||||
{
|
|
||||||
if (row%NSN==0) //another group
|
|
||||||
{
|
|
||||||
divi+=NSN;
|
|
||||||
}
|
|
||||||
for (int col=divi-NSN; col<divi; col++)
|
|
||||||
{
|
|
||||||
int nsmall=0;
|
|
||||||
for (int i=0; i<distanceMat.cols; i++)
|
|
||||||
{
|
|
||||||
if (distanceMat.at<float>(row,col)>distanceMat.at<float>(row,i))
|
|
||||||
{
|
|
||||||
nsmall++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
if (nsmall<=FIRST_MANY)
|
|
||||||
{
|
|
||||||
corrects++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
float porc = 100*float(corrects)/(NSN*distanceMat.rows);
|
|
||||||
std::cout<<"%="<<porc<<std::endl;
|
|
||||||
if (porc >= CURRENT_MAX_ACCUR)
|
|
||||||
ts->set_failed_test_info(cvtest::TS::OK);
|
|
||||||
else
|
|
||||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
void CV_HaussTest::run(int /* */)
|
|
||||||
{
|
|
||||||
mpegTest();
|
|
||||||
displayMPEGResults();
|
|
||||||
ts->set_failed_test_info(cvtest::TS::OK);
|
|
||||||
}
|
|
||||||
|
|
||||||
TEST(Hauss, regression) { CV_HaussTest test; test.safe_run(); }
|
|
||||||
@ -1 +0,0 @@
|
|||||||
#include "test_precomp.hpp"
|
|
||||||
@ -16,6 +16,4 @@
|
|||||||
#include "opencv2/imgcodecs.hpp"
|
#include "opencv2/imgcodecs.hpp"
|
||||||
#include "opencv2/shape.hpp"
|
#include "opencv2/shape.hpp"
|
||||||
|
|
||||||
#include "opencv2/opencv_modules.hpp"
|
|
||||||
|
|
||||||
#endif
|
#endif
|
||||||
|
|||||||
@ -44,222 +44,258 @@
|
|||||||
using namespace cv;
|
using namespace cv;
|
||||||
using namespace std;
|
using namespace std;
|
||||||
|
|
||||||
const int angularBins=12;
|
template <typename T, typename compute>
|
||||||
const int radialBins=4;
|
class ShapeBaseTest : public cvtest::BaseTest
|
||||||
const float minRad=0.2f;
|
|
||||||
const float maxRad=2;
|
|
||||||
const int NSN=5;//10;//20; //number of shapes per class
|
|
||||||
const int NP=120; //number of points sympliying the contour
|
|
||||||
const float CURRENT_MAX_ACCUR=95; //99% and 100% reached in several tests, 95 is fixed as minimum boundary
|
|
||||||
|
|
||||||
class CV_ShapeTest : public cvtest::BaseTest
|
|
||||||
{
|
{
|
||||||
public:
|
public:
|
||||||
CV_ShapeTest();
|
typedef Point_<T> PointType;
|
||||||
~CV_ShapeTest();
|
ShapeBaseTest(int _NSN, int _NP, float _CURRENT_MAX_ACCUR)
|
||||||
protected:
|
: NSN(_NSN), NP(_NP), CURRENT_MAX_ACCUR(_CURRENT_MAX_ACCUR)
|
||||||
void run(int);
|
|
||||||
|
|
||||||
private:
|
|
||||||
void mpegTest();
|
|
||||||
void listShapeNames(vector<string> &listHeaders);
|
|
||||||
vector<Point2f> convertContourType(const Mat &, int n=0 );
|
|
||||||
float computeShapeDistance(vector <Point2f>& queryNormal,
|
|
||||||
vector <Point2f>& queryFlipped1,
|
|
||||||
vector <Point2f>& queryFlipped2,
|
|
||||||
vector<Point2f>& testq);
|
|
||||||
void displayMPEGResults();
|
|
||||||
};
|
|
||||||
|
|
||||||
CV_ShapeTest::CV_ShapeTest()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
CV_ShapeTest::~CV_ShapeTest()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
vector <Point2f> CV_ShapeTest::convertContourType(const Mat& currentQuery, int n)
|
|
||||||
{
|
|
||||||
vector<vector<Point> > _contoursQuery;
|
|
||||||
vector <Point2f> contoursQuery;
|
|
||||||
findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
|
|
||||||
for (size_t border=0; border<_contoursQuery.size(); border++)
|
|
||||||
{
|
{
|
||||||
for (size_t p=0; p<_contoursQuery[border].size(); p++)
|
// generate file list
|
||||||
|
vector<string> shapeNames;
|
||||||
|
shapeNames.push_back("apple"); //ok
|
||||||
|
shapeNames.push_back("children"); // ok
|
||||||
|
shapeNames.push_back("device7"); // ok
|
||||||
|
shapeNames.push_back("Heart"); // ok
|
||||||
|
shapeNames.push_back("teddy"); // ok
|
||||||
|
for (vector<string>::const_iterator i = shapeNames.begin(); i != shapeNames.end(); ++i)
|
||||||
{
|
{
|
||||||
contoursQuery.push_back(Point2f((float)_contoursQuery[border][p].x,
|
for (int j = 0; j < NSN; ++j)
|
||||||
(float)_contoursQuery[border][p].y));
|
{
|
||||||
|
stringstream filename;
|
||||||
|
filename << cvtest::TS::ptr()->get_data_path()
|
||||||
|
<< "shape/mpeg_test/" << *i << "-" << j + 1 << ".png";
|
||||||
|
filenames.push_back(filename.str());
|
||||||
|
}
|
||||||
}
|
}
|
||||||
|
// distance matrix
|
||||||
|
const int totalCount = (int)filenames.size();
|
||||||
|
distanceMat = Mat::zeros(totalCount, totalCount, CV_32F);
|
||||||
}
|
}
|
||||||
|
|
||||||
// In case actual number of points is less than n
|
protected:
|
||||||
for (int add=(int)contoursQuery.size()-1; add<n; add++)
|
void run(int)
|
||||||
{
|
{
|
||||||
contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
|
mpegTest();
|
||||||
|
displayMPEGResults();
|
||||||
}
|
}
|
||||||
|
|
||||||
// Uniformly sampling
|
vector<PointType> convertContourType(const Mat& currentQuery) const
|
||||||
random_shuffle(contoursQuery.begin(), contoursQuery.end());
|
|
||||||
int nStart=n;
|
|
||||||
vector<Point2f> cont;
|
|
||||||
for (int i=0; i<nStart; i++)
|
|
||||||
{
|
{
|
||||||
cont.push_back(contoursQuery[i]);
|
vector<vector<Point> > _contoursQuery;
|
||||||
}
|
findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
|
||||||
return cont;
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_ShapeTest::listShapeNames( vector<string> &listHeaders)
|
vector <PointType> contoursQuery;
|
||||||
{
|
for (size_t border=0; border<_contoursQuery.size(); border++)
|
||||||
listHeaders.push_back("apple"); //ok
|
|
||||||
listHeaders.push_back("children"); // ok
|
|
||||||
listHeaders.push_back("device7"); // ok
|
|
||||||
listHeaders.push_back("Heart"); // ok
|
|
||||||
listHeaders.push_back("teddy"); // ok
|
|
||||||
}
|
|
||||||
|
|
||||||
float CV_ShapeTest::computeShapeDistance(vector <Point2f>& query1, vector <Point2f>& query2,
|
|
||||||
vector <Point2f>& query3, vector <Point2f>& testq)
|
|
||||||
{
|
|
||||||
//waitKey(0);
|
|
||||||
Ptr <ShapeContextDistanceExtractor> mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
|
|
||||||
//Ptr <HistogramCostExtractor> cost = createNormHistogramCostExtractor(cv::DIST_L1);
|
|
||||||
Ptr <HistogramCostExtractor> cost = createChiHistogramCostExtractor(30,0.15f);
|
|
||||||
//Ptr <HistogramCostExtractor> cost = createEMDHistogramCostExtractor();
|
|
||||||
//Ptr <HistogramCostExtractor> cost = createEMDL1HistogramCostExtractor();
|
|
||||||
mysc->setIterations(1);
|
|
||||||
mysc->setCostExtractor( cost );
|
|
||||||
//mysc->setTransformAlgorithm(createAffineTransformer(true));
|
|
||||||
mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
|
|
||||||
//mysc->setImageAppearanceWeight(1.6);
|
|
||||||
//mysc->setImageAppearanceWeight(0.0);
|
|
||||||
//mysc->setImages(im1,imtest);
|
|
||||||
return ( std::min( mysc->computeDistance(query1, testq),
|
|
||||||
std::min(mysc->computeDistance(query2, testq), mysc->computeDistance(query3, testq) )));
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_ShapeTest::mpegTest()
|
|
||||||
{
|
|
||||||
string baseTestFolder="shape/mpeg_test/";
|
|
||||||
string path = cvtest::TS::ptr()->get_data_path() + baseTestFolder;
|
|
||||||
vector<string> namesHeaders;
|
|
||||||
listShapeNames(namesHeaders);
|
|
||||||
|
|
||||||
// distance matrix //
|
|
||||||
Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F);
|
|
||||||
|
|
||||||
// query contours (normal v flipped, h flipped) and testing contour //
|
|
||||||
vector<Point2f> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
|
|
||||||
|
|
||||||
// reading query and computing its properties //
|
|
||||||
int counter=0;
|
|
||||||
const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size();
|
|
||||||
for (size_t n=0; n<namesHeaders.size(); n++)
|
|
||||||
{
|
|
||||||
for (int i=1; i<=NSN; i++)
|
|
||||||
{
|
{
|
||||||
// read current image //
|
for (size_t p=0; p<_contoursQuery[border].size(); p++)
|
||||||
stringstream thepathandname;
|
{
|
||||||
thepathandname<<path+namesHeaders[n]<<"-"<<i<<".png";
|
contoursQuery.push_back(PointType((T)_contoursQuery[border][p].x,
|
||||||
Mat currentQuery, flippedHQuery, flippedVQuery;
|
(T)_contoursQuery[border][p].y));
|
||||||
currentQuery=imread(thepathandname.str(), IMREAD_GRAYSCALE);
|
}
|
||||||
Mat currentQueryBuf=currentQuery.clone();
|
}
|
||||||
|
|
||||||
|
// In case actual number of points is less than n
|
||||||
|
for (int add=(int)contoursQuery.size()-1; add<NP; add++)
|
||||||
|
{
|
||||||
|
contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
|
||||||
|
}
|
||||||
|
|
||||||
|
// Uniformly sampling
|
||||||
|
random_shuffle(contoursQuery.begin(), contoursQuery.end());
|
||||||
|
int nStart=NP;
|
||||||
|
vector<PointType> cont;
|
||||||
|
for (int i=0; i<nStart; i++)
|
||||||
|
{
|
||||||
|
cont.push_back(contoursQuery[i]);
|
||||||
|
}
|
||||||
|
return cont;
|
||||||
|
}
|
||||||
|
|
||||||
|
void mpegTest()
|
||||||
|
{
|
||||||
|
// query contours (normal v flipped, h flipped) and testing contour
|
||||||
|
vector<PointType> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
|
||||||
|
// reading query and computing its properties
|
||||||
|
for (vector<string>::const_iterator a = filenames.begin(); a != filenames.end(); ++a)
|
||||||
|
{
|
||||||
|
// read current image
|
||||||
|
int aIndex = a - filenames.begin();
|
||||||
|
Mat currentQuery = imread(*a, IMREAD_GRAYSCALE);
|
||||||
|
Mat flippedHQuery, flippedVQuery;
|
||||||
flip(currentQuery, flippedHQuery, 0);
|
flip(currentQuery, flippedHQuery, 0);
|
||||||
flip(currentQuery, flippedVQuery, 1);
|
flip(currentQuery, flippedVQuery, 1);
|
||||||
// compute border of the query and its flipped versions //
|
// compute border of the query and its flipped versions
|
||||||
vector<Point2f> origContour;
|
contoursQuery1=convertContourType(currentQuery);
|
||||||
contoursQuery1=convertContourType(currentQuery, NP);
|
contoursQuery2=convertContourType(flippedHQuery);
|
||||||
origContour=contoursQuery1;
|
contoursQuery3=convertContourType(flippedVQuery);
|
||||||
contoursQuery2=convertContourType(flippedHQuery, NP);
|
// compare with all the rest of the images: testing
|
||||||
contoursQuery3=convertContourType(flippedVQuery, NP);
|
for (vector<string>::const_iterator b = filenames.begin(); b != filenames.end(); ++b)
|
||||||
|
|
||||||
// compare with all the rest of the images: testing //
|
|
||||||
for (size_t nt=0; nt<namesHeaders.size(); nt++)
|
|
||||||
{
|
{
|
||||||
for (int it=1; it<=NSN; it++)
|
int bIndex = b - filenames.begin();
|
||||||
|
float distance = 0;
|
||||||
|
// skip self-comparisson
|
||||||
|
if (a != b)
|
||||||
{
|
{
|
||||||
// skip self-comparisson //
|
// read testing image
|
||||||
counter++;
|
Mat currentTest = imread(*b, IMREAD_GRAYSCALE);
|
||||||
if (nt==n && it==i)
|
// compute border of the testing
|
||||||
{
|
contoursTesting=convertContourType(currentTest);
|
||||||
distanceMat.at<float>(NSN*(int)n+i-1,
|
// compute shape distance
|
||||||
NSN*(int)nt+it-1)=0;
|
distance = cmp(contoursQuery1, contoursQuery2,
|
||||||
continue;
|
contoursQuery3, contoursTesting);
|
||||||
}
|
|
||||||
// read testing image //
|
|
||||||
stringstream thetestpathandname;
|
|
||||||
thetestpathandname<<path+namesHeaders[nt]<<"-"<<it<<".png";
|
|
||||||
Mat currentTest;
|
|
||||||
currentTest=imread(thetestpathandname.str().c_str(), 0);
|
|
||||||
// compute border of the testing //
|
|
||||||
contoursTesting=convertContourType(currentTest, NP);
|
|
||||||
|
|
||||||
// compute shape distance //
|
|
||||||
std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl;
|
|
||||||
std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<<
|
|
||||||
" and "<<namesHeaders[nt]<<it<<": ";
|
|
||||||
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)=
|
|
||||||
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting);
|
|
||||||
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl;
|
|
||||||
}
|
}
|
||||||
|
distanceMat.at<float>(aIndex, bIndex) = distance;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
// save distance matrix //
|
|
||||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::WRITE);
|
|
||||||
fs << "distanceMat" << distanceMat;
|
|
||||||
}
|
|
||||||
|
|
||||||
const int FIRST_MANY=2*NSN;
|
void displayMPEGResults()
|
||||||
void CV_ShapeTest::displayMPEGResults()
|
|
||||||
{
|
|
||||||
string baseTestFolder="shape/mpeg_test/";
|
|
||||||
Mat distanceMat;
|
|
||||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::READ);
|
|
||||||
vector<string> namesHeaders;
|
|
||||||
listShapeNames(namesHeaders);
|
|
||||||
|
|
||||||
// Read generated MAT //
|
|
||||||
fs["distanceMat"]>>distanceMat;
|
|
||||||
|
|
||||||
int corrects=0;
|
|
||||||
int divi=0;
|
|
||||||
for (int row=0; row<distanceMat.rows; row++)
|
|
||||||
{
|
{
|
||||||
if (row%NSN==0) //another group
|
const int FIRST_MANY=2*NSN;
|
||||||
|
|
||||||
|
int corrects=0;
|
||||||
|
int divi=0;
|
||||||
|
for (int row=0; row<distanceMat.rows; row++)
|
||||||
{
|
{
|
||||||
divi+=NSN;
|
if (row%NSN==0) //another group
|
||||||
}
|
|
||||||
for (int col=divi-NSN; col<divi; col++)
|
|
||||||
{
|
|
||||||
int nsmall=0;
|
|
||||||
for (int i=0; i<distanceMat.cols; i++)
|
|
||||||
{
|
{
|
||||||
if (distanceMat.at<float>(row,col)>distanceMat.at<float>(row,i))
|
divi+=NSN;
|
||||||
|
}
|
||||||
|
for (int col=divi-NSN; col<divi; col++)
|
||||||
|
{
|
||||||
|
int nsmall=0;
|
||||||
|
for (int i=0; i<distanceMat.cols; i++)
|
||||||
{
|
{
|
||||||
nsmall++;
|
if (distanceMat.at<float>(row,col) > distanceMat.at<float>(row,i))
|
||||||
|
{
|
||||||
|
nsmall++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (nsmall<=FIRST_MANY)
|
||||||
|
{
|
||||||
|
corrects++;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
if (nsmall<=FIRST_MANY)
|
|
||||||
{
|
|
||||||
corrects++;
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
|
float porc = 100*float(corrects)/(NSN*distanceMat.rows);
|
||||||
|
std::cout << "Test result: " << porc << "%" << std::endl;
|
||||||
|
if (porc >= CURRENT_MAX_ACCUR)
|
||||||
|
ts->set_failed_test_info(cvtest::TS::OK);
|
||||||
|
else
|
||||||
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||||
}
|
}
|
||||||
float porc = 100*float(corrects)/(NSN*distanceMat.rows);
|
|
||||||
std::cout<<"%="<<porc<<std::endl;
|
|
||||||
if (porc >= CURRENT_MAX_ACCUR)
|
|
||||||
ts->set_failed_test_info(cvtest::TS::OK);
|
|
||||||
else
|
|
||||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
||||||
//done
|
|
||||||
}
|
|
||||||
|
|
||||||
void CV_ShapeTest::run( int /*start_from*/ )
|
protected:
|
||||||
|
int NSN;
|
||||||
|
int NP;
|
||||||
|
float CURRENT_MAX_ACCUR;
|
||||||
|
vector<string> filenames;
|
||||||
|
Mat distanceMat;
|
||||||
|
compute cmp;
|
||||||
|
};
|
||||||
|
|
||||||
|
//------------------------------------------------------------------------
|
||||||
|
// Test Shape_SCD.regression
|
||||||
|
//------------------------------------------------------------------------
|
||||||
|
|
||||||
|
class computeShapeDistance_Chi
|
||||||
{
|
{
|
||||||
mpegTest();
|
Ptr <ShapeContextDistanceExtractor> mysc;
|
||||||
displayMPEGResults();
|
public:
|
||||||
ts->set_failed_test_info(cvtest::TS::OK);
|
computeShapeDistance_Chi()
|
||||||
|
{
|
||||||
|
const int angularBins=12;
|
||||||
|
const int radialBins=4;
|
||||||
|
const float minRad=0.2f;
|
||||||
|
const float maxRad=2;
|
||||||
|
mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
|
||||||
|
mysc->setIterations(1);
|
||||||
|
mysc->setCostExtractor(createChiHistogramCostExtractor(30,0.15f));
|
||||||
|
mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
|
||||||
|
}
|
||||||
|
float operator()(vector <Point2f>& query1, vector <Point2f>& query2,
|
||||||
|
vector <Point2f>& query3, vector <Point2f>& testq)
|
||||||
|
{
|
||||||
|
return std::min(mysc->computeDistance(query1, testq),
|
||||||
|
std::min(mysc->computeDistance(query2, testq),
|
||||||
|
mysc->computeDistance(query3, testq)));
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
TEST(Shape_SCD, regression)
|
||||||
|
{
|
||||||
|
const int NSN_val=5;//10;//20; //number of shapes per class
|
||||||
|
const int NP_val=120; //number of points simplifying the contour
|
||||||
|
const float CURRENT_MAX_ACCUR_val=95; //99% and 100% reached in several tests, 95 is fixed as minimum boundary
|
||||||
|
ShapeBaseTest<float, computeShapeDistance_Chi> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
|
||||||
|
test.safe_run();
|
||||||
}
|
}
|
||||||
|
|
||||||
TEST(Shape_SCD, regression) { CV_ShapeTest test; test.safe_run(); }
|
//------------------------------------------------------------------------
|
||||||
|
// Test ShapeEMD_SCD.regression
|
||||||
|
//------------------------------------------------------------------------
|
||||||
|
|
||||||
|
class computeShapeDistance_EMD
|
||||||
|
{
|
||||||
|
Ptr <ShapeContextDistanceExtractor> mysc;
|
||||||
|
public:
|
||||||
|
computeShapeDistance_EMD()
|
||||||
|
{
|
||||||
|
const int angularBins=12;
|
||||||
|
const int radialBins=4;
|
||||||
|
const float minRad=0.2f;
|
||||||
|
const float maxRad=2;
|
||||||
|
mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
|
||||||
|
mysc->setIterations(1);
|
||||||
|
mysc->setCostExtractor( createEMDL1HistogramCostExtractor() );
|
||||||
|
mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
|
||||||
|
}
|
||||||
|
float operator()(vector <Point2f>& query1, vector <Point2f>& query2,
|
||||||
|
vector <Point2f>& query3, vector <Point2f>& testq)
|
||||||
|
{
|
||||||
|
return std::min(mysc->computeDistance(query1, testq),
|
||||||
|
std::min(mysc->computeDistance(query2, testq),
|
||||||
|
mysc->computeDistance(query3, testq)));
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
TEST(ShapeEMD_SCD, regression)
|
||||||
|
{
|
||||||
|
const int NSN_val=5;//10;//20; //number of shapes per class
|
||||||
|
const int NP_val=100; //number of points simplifying the contour
|
||||||
|
const float CURRENT_MAX_ACCUR_val=95; //98% and 99% reached in several tests, 95 is fixed as minimum boundary
|
||||||
|
ShapeBaseTest<float, computeShapeDistance_EMD> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
|
||||||
|
test.safe_run();
|
||||||
|
}
|
||||||
|
|
||||||
|
//------------------------------------------------------------------------
|
||||||
|
// Test Hauss.regression
|
||||||
|
//------------------------------------------------------------------------
|
||||||
|
|
||||||
|
class computeShapeDistance_Haussdorf
|
||||||
|
{
|
||||||
|
Ptr <HausdorffDistanceExtractor> haus;
|
||||||
|
public:
|
||||||
|
computeShapeDistance_Haussdorf()
|
||||||
|
{
|
||||||
|
haus = createHausdorffDistanceExtractor();
|
||||||
|
}
|
||||||
|
float operator()(vector<Point> &query1, vector<Point> &query2,
|
||||||
|
vector<Point> &query3, vector<Point> &testq)
|
||||||
|
{
|
||||||
|
return std::min(haus->computeDistance(query1,testq),
|
||||||
|
std::min(haus->computeDistance(query2,testq),
|
||||||
|
haus->computeDistance(query3,testq)));
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
TEST(Hauss, regression)
|
||||||
|
{
|
||||||
|
const int NSN_val=5;//10;//20; //number of shapes per class
|
||||||
|
const int NP_val = 180; //number of points simplifying the contour
|
||||||
|
const float CURRENT_MAX_ACCUR_val=85; //90% and 91% reached in several tests, 85 is fixed as minimum boundary
|
||||||
|
ShapeBaseTest<int, computeShapeDistance_Haussdorf> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
|
||||||
|
test.safe_run();
|
||||||
|
}
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user