mirror of
https://github.com/opencv/opencv.git
synced 2024-12-02 16:00:17 +08:00
281 lines
10 KiB
C++
281 lines
10 KiB
C++
/*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(); }
|