本文主要是介绍将开源虹膜识别算法OSIRIS4.1移植到Windows，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
开源虹膜识别算法OSIRIS是在Linux下运行的，为了介绍给众多windows平台下的开发者，这里简述一下如何把它移植到windows。
开发平台Windows XP + Visual Studio 2008 + OpenCV2.3.1。
1.新建一个对话框工程，将OSIRIS源码中的如下文件拷贝过去并添加到工程：
OsiCircle.cpp/.h
OsiEye.cpp/.h
OsiManager.cpp/.h
OsiProcessings.cpp/.h
OsiStringUtils.h2.在上述源码中，只需要对OsiManager.cpp/.h做少量修改即可使用了：
OsiManager.cpp#include <fstream>
#include <iterator>
#include <stdexcept>
#include "OsiManager.h"
#include "OsiStringUtils.h"
using namespace std ;
namespace osiris
{
OsiManager::OsiManager ( )
{
initConfiguration() ;
loadGaborFilters() ;
loadApplicationPoints() ;
}OsiManager::~OsiManager ( )
{if ( mpApplicationPoints ){cvReleaseMat(&mpApplicationPoints) ;}for ( int f = 0 ; f < mGaborFilters.size() ; f++ ){cvReleaseMat(&mGaborFilters[f]) ;}
}void OsiManager::initConfiguration ( )
{// Options of processingmProcessSegmentation = false ;mProcessNormalization = false ;mProcessEncoding = false ;mProcessMatching = false ;mUseMask = true ;// InputsmListOfImages.clear() ;mFilenameListOfImages = "" ;mInputDirOriginalImages = "./SourceImage/" ;mInputDirMasks = "" ;mInputDirParameters = "" ;mInputDirNormalizedImages = "" ;mInputDirNormalizedMasks = "./NormalizedMasks/" ;mInputDirIrisCodes = "./IrisCodes/" ;// OutputsmOutputDirSegmentedImages = "./SegmentedImages/" ;mOutputDirParameters = "./CircleParameters/" ;mOutputDirMasks = "./Masks/" ;mOutputDirNormalizedImages = "./NormalizedImages/" ;mOutputDirNormalizedMasks = "./NormalizedMasks/" ;mOutputDirIrisCodes = "./IrisCodes/" ;mOutputFileMatchingScores = "" ;// ParametersmMinPupilDiameter = 50 ;mMaxPupilDiameter = 160 ;mMinIrisDiameter = 160 ;mMaxIrisDiameter = 280 ;mWidthOfNormalizedIris = 512 ;mHeightOfNormalizedIris = 64 ;mFilenameGaborFilters = "./filters.txt" ;mFilenameApplicationPoints = "./points.txt" ;mGaborFilters.clear() ;mpApplicationPoints = 0 ;// Suffix for filenamesmSuffixSegmentedImages = "_segm.bmp" ;mSuffixParameters = "_para.txt" ;mSuffixMasks = "_mask.bmp" ;mSuffixNormalizedImages = "_imno.bmp" ;mSuffixNormalizedMasks = "_mano.bmp" ;mSuffixIrisCodes = "_code.bmp" ;
}void OsiManager::loadGaborFilters ( )
{
//不修改
}void OsiManager::loadApplicationPoints ( )
{
//不修改
}int OsiManager::processEye ( const string & rFileName , OsiEye & rEye )
{// Strings handleOsiStringUtils osu ;// Get eye namestring short_name = osu.extractFileName(rFileName);rEye.loadOriginalImage(mInputDirOriginalImages+rFileName); /// SEGMENTATION : process, loadrEye.segment(mMinIrisDiameter,mMinPupilDiameter,mMaxIrisDiameter,mMaxPupilDiameter) ;// Save segmented imagerEye.saveSegmentedImage(mOutputDirSegmentedImages+short_name+mSuffixSegmentedImages) ;/// NORMALIZATION : process, loadrEye.normalize(mWidthOfNormalizedIris,mHeightOfNormalizedIris) ;/// ENCODING : process, loadrEye.encode(mGaborFilters) ;/// SAVE// Save parametersrEye.saveParameters(mOutputDirParameters+short_name+mSuffixParameters) ;// Save maskrEye.saveMask(mOutputDirMasks+short_name+mSuffixMasks) ;// Save normalized imagerEye.saveNormalizedImage(mOutputDirNormalizedImages+short_name+mSuffixNormalizedImages) ;// Save normalized maskrEye.saveNormalizedMask(mOutputDirNormalizedMasks+short_name+mSuffixNormalizedMasks) ;// Save iris coderEye.saveIrisCode(mOutputDirIrisCodes+short_name+mSuffixIrisCodes) ; return 0;
} // end of functionint OsiManager::loadEye ( const string & rFileName , OsiEye & rEye )
{OsiStringUtils osu ;string short_name = osu.extractFileName(rFileName) ;// Load normalized maskrEye.loadNormalizedMask(mInputDirNormalizedMasks+short_name+mSuffixNormalizedMasks) ;// Load iris coderEye.loadIrisCode(mInputDirIrisCodes+short_name+mSuffixIrisCodes) ;return 0;
} // end of functionvoid OsiManager::process(string filename)
{try{OsiEye eye ;processEye(filename, eye) ; }catch ( exception & e ){cout << e.what() << endl ; }
}float OsiManager::match(string filename1, string filename2)
{float val = 0;try{OsiEye eye1, eye2 ;loadEye(filename1, eye1) ; loadEye(filename2, eye2) ; val = eye1.match(eye2, mpApplicationPoints);}catch ( exception & e ){cout << e.what() << endl ; }return val;
}
} // end of namespace
OsiManager.h
#ifndef OSI_MANAGER_H
#define OSI_MANAGER_H#include <iostream>
#include <vector>
#include “highgui.h”
#include “OsiEye.h”
namespace osiris
{
class OsiManager
{
public :
OsiManager ( ) ;
~OsiManager ( ) ;
void process(std::string filename);
float match(std::string filename1, std::string filename2);</code></pre><pre><code class="language-cpp"> private ://private变量不修改// Private methods//void initConfiguration ( ) ;void loadGaborFilters ( ) ;void loadApplicationPoints ( ) ;int processEye ( const std::string & rFileName , OsiEye & rEye );
int loadEye ( const std::string & rFileName , OsiEye & rEye );
} ; // End of class
} // End of namespace
#endif
可见，主要是增加了两个接口函数process()和match()，分别用于虹膜计算和虹膜对比；
3.在对话框程序中新建一个button：
void CosirismfcDlg::OnBnClickedButton1()
{
cvNamedWindow(“img”, 1);
cvNamedWindow(“segment”, 1);IplImage* img1 = cvLoadImage( "./SourceImage/S5000R00.jpg", CV_LOAD_IMAGE_GRAYSCALE );
cvShowImage( "img", img1 );
theManager.process("S5000R00.jpg");
IplImage* seg1 = cvLoadImage( "./SegmentedImages/S5000R00_segm.bmp", CV_LOAD_IMAGE_COLOR );
cvShowImage( "segment", seg1 );cvNamedWindow("img2", 1);
cvNamedWindow("segment2", 1);
IplImage* img2 = cvLoadImage( "./SourceImage/S5000R01.jpg", CV_LOAD_IMAGE_GRAYSCALE );
cvShowImage( "img2", img2 );
theManager.process("S5000R01.jpg");
IplImage* seg2 = cvLoadImage( "./SegmentedImages/S5000R01_segm.bmp", CV_LOAD_IMAGE_COLOR );
cvShowImage( "segment2", seg2 );if (theManager.match("S5000R00.jpg","S5000R01.jpg") < 0.32)MessageBox("S5000R00 and S5000R01 is same person");cvNamedWindow("img3", 1);
cvNamedWindow("segment3", 1);
IplImage* img3 = cvLoadImage( "./SourceImage/S5001R01.jpg", CV_LOAD_IMAGE_GRAYSCALE );
cvShowImage( "img3", img3 );
theManager.process("S5001R01.jpg");
IplImage* seg3 = cvLoadImage( "./SegmentedImages/S5001R01_segm.bmp", CV_LOAD_IMAGE_COLOR );
cvShowImage( "segment3", seg3 );if (theManager.match("S5000R00.jpg","S5001R01.jpg") > 0.32)MessageBox("S5000R00 and S5001R01 is not same person");
}
该button读取两张来自同一个人的虹膜图像，对比结果应当小于0.32；读取两张不同人的虹膜图像，对比结果应当大于0.32。
虹膜识别都要先进行process步骤，再进行match步骤。
4.把工程属性修改为不使用预编译头，编译；
5.在Release或Debug目录下新建如下几个文件夹：
CircleParameters
IrisCodes
Masks
NormalizedImages
NormalizedMasks
SegmentedImages
SourceImage向SourceImage目录中拷贝如下示例虹膜图像：
S5000R00.bmp
S5000R01.bmp
S5000R02.bmp
S5001R00.bmp
S5001R01.bmp
S5001R02.bmp并从OSIRIS源码中拷贝如下两个文件:
filters.txt
points.txt然后就可运行exe文件了，运行结果：
本文完整工程可在qq群里下载：
虹膜识别算法研究QQ群：422376177
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