[OpenCV]代码整理

开发环境：Windows7, VS2010, OpenCV2.4.10

1.图像特征匹配

// AxFeatureExtract.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "opencv2/opencv.hpp"
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include "vector"

using namespace std;

int _tmain(int argc, _TCHAR* argv[])
{
    cv::Mat rgb1 = cv::imread( "G:\SLAMDatasets\一起做RGB-DSLAM数据\rgb_png\1.png");
    cv::Mat rgb2 = cv::imread( "G:\SLAMDatasets\一起做RGB-DSLAM数据\rgb_png\2.png");
    cv::Mat depth1 = cv::imread( "G:\SLAMDatasets\一起做RGB-DSLAM数据\depth_png\1.png", -1);
    cv::Mat depth2 = cv::imread( "G:\SLAMDatasets\一起做RGB-DSLAM数据\depth_png\2.png", -1);
     
    // 声明特征提取器与描述子提取器
    cv::Ptr<cv::FeatureDetector> _detector;
    cv::Ptr<cv::DescriptorExtractor> _descriptor;
    
    // 构建提取器，默认两者都为sift
    // 构建sift, surf之前要初始化nonfree模块
    cv::initModule_nonfree();
    _detector = cv::FeatureDetector::create( "GridSIFT" );
    _descriptor = cv::DescriptorExtractor::create( "SIFT" );
     
    vector< cv::KeyPoint > kp1, kp2; //关键点
    _detector->detect( rgb1, kp1 );  //提取关键点
    _detector->detect( rgb2, kp2 );
    
    cout<<"Key points of two images: "<<kp1.size()<<", "<<kp2.size()<<endl;
        
    // 可视化， 显示关键点
    cv::Mat imgShow;
    cv::drawKeypoints( rgb1, kp1, imgShow, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS );
    cv::imshow( "keypoints", imgShow );
    cv::imwrite( "data\keypoints.png", imgShow );
    cv::waitKey(0); //暂停等待一个按键
       
    // 计算描述子
    cv::Mat desp1, desp2;
    _descriptor->compute( rgb1, kp1, desp1 );
    _descriptor->compute( rgb2, kp2, desp2 );
    
    // 匹配描述子
    vector< cv::DMatch > matches; 
    cv::FlannBasedMatcher matcher;
    matcher.match( desp1, desp2, matches );
    cout<<"Find total "<<matches.size()<<" matches."<<endl;
    
    // 可视化：显示匹配的特征
    cv::Mat imgMatches;
    cv::drawMatches( rgb1, kp1, rgb2, kp2, matches, imgMatches );
    cv::imshow( "matches", imgMatches );
    cv::imwrite( "data\matches.png", imgMatches );
    cv::waitKey( 0 );
    
    // 筛选匹配，把距离太大的去掉
    // 这里使用的准则是去掉大于四倍最小距离的匹配
    vector< cv::DMatch > goodMatches;
    double minDis = 9999;
    for ( size_t i=0; i<matches.size(); i++ )
    {
            if ( matches[i].distance < minDis )
                minDis = matches[i].distance;
    }
    
    for ( size_t i=0; i<matches.size(); i++ )
    {
        if (matches[i].distance < 4*minDis)
            goodMatches.push_back( matches[i] );
    }

        // 显示 good matches
    cout<<"good matches="<<goodMatches.size()<<endl;
    cv::drawMatches( rgb1, kp1, rgb2, kp2, goodMatches, imgMatches );
    cv::imshow( "good matches", imgMatches );
    cv::imwrite( "data\good_matches.png", imgMatches );
    system("pause");
    return 0;
}

2.膨胀和侵蚀

形态学方法：读取图像，实现图像的膨胀和侵蚀操作。

// AxFeatureExtract.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "opencv2/opencv.hpp"
#include <opencv2/core/core.hpp>  
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/imgproc/imgproc.hpp> 
#include "vector"

using namespace std;
using namespace cv; 

int _tmain(int argc, _TCHAR* argv[])
{
    cv::Mat rgb1 = cv::imread( "G:\SLAMDatasets\一起做RGB-DSLAM数据\rgb_png\1.png");
    cv::Mat depth1 = cv::imread( "G:\SLAMDatasets\一起做RGB-DSLAM数据\depth_png\1.png", -1);

    cv::imshow( "matches", rgb1 );
    cv::Mat elementdilate = getStructuringElement(MORPH_RECT, Size(15, 15));  
    cv::Mat outdilate;  
    //进行膨胀操作  
    dilate(rgb1, outdilate, elementdilate); 
    cv::imshow( "dilate", outdilate );
    cv::imwrite( "data\dilate.png", outdilate );
    cv::waitKey( 0 );

    cv::Mat elementerode = getStructuringElement(MORPH_RECT, Size(15, 15));  
    cv::Mat outerode;  

    //进行腐蚀操作  
    erode(depth1,outerode, elementerode);  
    cv::imshow( "erode", outerode );
    cv::imwrite( "data\outerode.png", outerode );
    system("pause");
    return 0;
}

3.图像二值化

4.图像边缘提取

// AxFeatureExtract.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "opencv2/opencv.hpp"
#include <opencv2/core/core.hpp>  
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/imgproc/imgproc.hpp> 
#include "vector"

using namespace std;
using namespace cv; 
void RemoveSmallRegion(Mat& Src, Mat& Dst, int AreaLimit=50, int CheckMode=1, int NeihborMode=0);

int _tmain(int argc, _TCHAR* argv[])
{
    cv::Mat rgb1 = cv::imread( "data\dump-00050.png");
    cv::imshow( "dump", rgb1 );
    
    

    cv::waitKey( 0 );
    //提取边缘
    cv::Mat src=rgb1.clone();
    cv::Mat dstSobel;
    Sobel(src,dstSobel,src.depth(),1,1);
    cv::imshow( "Sobel", dstSobel );
    imwrite("sobel.jpg",dstSobel);

    cv::Mat dstLaplacian;
    Laplacian(src,dstLaplacian,src.depth());
    cv::imshow("Laplacian", dstLaplacian );
    imwrite("laplacian.jpg",dstLaplacian);

    cv::Mat g_grayImage;
    cv::Mat dstCanny;
    cvtColor(src,g_grayImage,CV_BGR2GRAY);  //canny只处理灰度图
    Canny(g_grayImage,dstCanny,50,150,3);
    cv::imshow("Canny", dstCanny );
    imwrite("canny.jpg",dstCanny);


    cv::Mat g_dstImage;
    threshold(g_grayImage,g_dstImage,125,255,CV_THRESH_BINARY_INV);  //大津阈值  
    imshow("threshold",g_dstImage); 
    imwrite("threshold.jpg",g_dstImage);

    cv::Mat elementdilate = getStructuringElement(MORPH_RECT, Size(5, 5));  
    cv::Mat outdilate;  
    //进行膨胀操作  
    dilate(g_dstImage, outdilate, elementdilate); 
    cv::imshow( "dilate", outdilate );
    //cv::imwrite( "data\dilate.png", outdilate );

    cv::Mat outresult;  
    cv::Mat image=dstLaplacian.clone();
    cv::subtract(dstCanny,  outdilate,  outresult);//注意保证两个图像的格式一致
    cv::imshow( "frontier", outresult );

    Mat rgb2 = Mat::zeros(outresult.size(), CV_8UC1);  
    /*blur(outresult,rgb2,Size(3,3),Point(-1,-1));
    cv::imshow( "blur", rgb2 );*/
    RemoveSmallRegion(outresult,rgb2,10,1, 1);
    cv::imshow( "blur", rgb2 );
    cv::waitKey( 0 );
    system("pause");
    return 0;
}

void RemoveSmallRegion(Mat& Src, Mat& Dst, int AreaLimit, int CheckMode, int NeihborMode)  
{     
    int RemoveCount=0;       //记录除去的个数  
    //记录每个像素点检验状态的标签，0代表未检查，1代表正在检查,2代表检查不合格（需要反转颜色），3代表检查合格或不需检查  
    Mat Pointlabel = Mat::zeros( Src.size(), CV_8UC1 );  
      
    if(CheckMode==1)  
    {  
        cout<<"Mode: 去除小区域. ";  
        for(int i = 0; i < Src.rows; ++i)    
        {    
            uchar* iData = Src.ptr<uchar>(i);  
            uchar* iLabel = Pointlabel.ptr<uchar>(i);  
            for(int j = 0; j < Src.cols; ++j)    
            {    
                if (iData[j] < 10)    
                {    
                    iLabel[j] = 3;   
                }    
            }    
        }    
    }  
    else  
    {  
        cout<<"Mode: 去除孔洞. ";  
        for(int i = 0; i < Src.rows; ++i)    
        {    
            uchar* iData = Src.ptr<uchar>(i);  
            uchar* iLabel = Pointlabel.ptr<uchar>(i);  
            for(int j = 0; j < Src.cols; ++j)    
            {    
                if (iData[j] > 10)    
                {    
                    iLabel[j] = 3;   
                }    
            }    
        }    
    }  
  
    vector<Point2i> NeihborPos;  //记录邻域点位置  
    NeihborPos.push_back(Point2i(-1, 0));  
    NeihborPos.push_back(Point2i(1, 0));  
    NeihborPos.push_back(Point2i(0, -1));  
    NeihborPos.push_back(Point2i(0, 1));  
    if (NeihborMode==1)  
    {  
        cout<<"Neighbor mode: 8邻域."<<endl;  
        NeihborPos.push_back(Point2i(-1, -1));  
        NeihborPos.push_back(Point2i(-1, 1));  
        NeihborPos.push_back(Point2i(1, -1));  
        NeihborPos.push_back(Point2i(1, 1));  
    }  
    else cout<<"Neighbor mode: 4邻域."<<endl;  
    int NeihborCount=4+4*NeihborMode;  
    int CurrX=0, CurrY=0;  
    //开始检测  
    for(int i = 0; i < Src.rows; ++i)    
    {    
        uchar* iLabel = Pointlabel.ptr<uchar>(i);  
        for(int j = 0; j < Src.cols; ++j)    
        {    
            if (iLabel[j] == 0)    
            {    
                //********开始该点处的检查**********  
                vector<Point2i> GrowBuffer;                                      //堆栈，用于存储生长点  
                GrowBuffer.push_back( Point2i(j, i) );  
                Pointlabel.at<uchar>(i, j)=1;  
                int CheckResult=0;                                               //用于判断结果（是否超出大小），0为未超出，1为超出  
  
                for ( int z=0; z<GrowBuffer.size(); z++ )  
                {  
  
                    for (int q=0; q<NeihborCount; q++)                                      //检查四个邻域点  
                    {  
                        CurrX=GrowBuffer.at(z).x+NeihborPos.at(q).x;  
                        CurrY=GrowBuffer.at(z).y+NeihborPos.at(q).y;  
                        if (CurrX>=0&&CurrX<Src.cols&&CurrY>=0&&CurrY<Src.rows)  //防止越界  
                        {  
                            if ( Pointlabel.at<uchar>(CurrY, CurrX)==0 )  
                            {  
                                GrowBuffer.push_back( Point2i(CurrX, CurrY) );  //邻域点加入buffer  
                                Pointlabel.at<uchar>(CurrY, CurrX)=1;           //更新邻域点的检查标签，避免重复检查  
                            }  
                        }  
                    }  
  
                }  
                if (GrowBuffer.size()>AreaLimit) CheckResult=2;                 //判断结果（是否超出限定的大小），1为未超出，2为超出  
                else {CheckResult=1;   RemoveCount++;}  
                for (int z=0; z<GrowBuffer.size(); z++)                         //更新Label记录  
                {  
                    CurrX=GrowBuffer.at(z).x;   
                    CurrY=GrowBuffer.at(z).y;  
                    Pointlabel.at<uchar>(CurrY, CurrX) += CheckResult;  
                }  
                //********结束该点处的检查**********  
  
  
            }    
        }    
    }    
  
    CheckMode=255*(1-CheckMode);  
    //开始反转面积过小的区域  
    for(int i = 0; i < Src.rows; ++i)    
    {    
        uchar* iData = Src.ptr<uchar>(i);  
        uchar* iDstData = Dst.ptr<uchar>(i);  
        uchar* iLabel = Pointlabel.ptr<uchar>(i);  
        for(int j = 0; j < Src.cols; ++j)    
        {    
            if (iLabel[j] == 2)    
            {    
                iDstData[j] = CheckMode;   
            }    
            else if(iLabel[j] == 3)  
            {  
                iDstData[j] = iData[j];  
            }  
        }    
    }   
      
    cout<<RemoveCount<<" objects removed."<<endl;  
}