【笔记】c++中opencv的使用

àì夳堔傛蜴生んèń 2022-10-23 11:28 235阅读 0赞

记录一下代码。

## 标准霍夫变换 HoughLines()![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70_pic_center] HoughLines(midImage, lines, 1, CV\_PI/180, 150, 0, 0 ); ##

函数输入图像为8位单通道二进制  
经过调用HoughLines函数后储存了霍夫线变换检测到线条的输出矢量。每一条线由具有两个元素的矢量（ρ，θ）表示，其中，ρ是离坐标原点（0，0）（也就是图像的左上角）的距离，θ是弧度线条旋转角度（0度表示垂直线，π/2度表示水平线）。 第三个参数，double类型的rho，以像素为单位的距离精度。另一种表述方式是直线搜索时的进步尺寸的单位半径。（Latex中/rho即表示ρ） 第四个参数，double类型的theta，以弧度为单位的角度精度。另一种表述方式是直线搜索时的进步尺寸的单位角度。 第五个参数，int类型的threshold，累加平面的阈值参数，即识别某部分为图中的一条直线时它在累加平面中必须达到的值。大于阈值threshold的线段才可以被检测通过并返回到结果中。

#include "opencv2/core.hpp"
    #include <opencv2/imgproc.hpp>
    #include "opencv2/video.hpp"
    #include "opencv2/videoio.hpp"
    #include "opencv2/highgui.hpp"
    #include"opencv2/imgproc/imgproc.hpp"
    #include<opencv2/opencv.hpp>
    #include <iostream>
    
    using namespace std;
    using namespace cv;
    
    int main()
    { 
        //【1】载入原始图和Mat变量定义
        Mat srcImage = imread("/home/heziyi/图片/6.jpg");  //工程目录下应该有一张名为1.jpg的素材图
        Mat midImage,dstImage;//临时变量和目标图的定义
    
        //【2】进行边缘检测和转化为灰度图
        Canny(srcImage, midImage, 50, 200, 3);//进行一此canny边缘检测
        cvtColor(midImage,dstImage, COLOR_GRAY2BGR);//转化边缘检测后的图为灰度图
    
        //【3】进行霍夫线变换
        vector<Vec2f> lines;//定义一个矢量结构lines用于存放得到的线段矢量集合
        HoughLines(midImage, lines, 1, CV_PI/180, 150, 0, 0 );
        for( size_t i = 0; i < lines.size(); i++ )
          { 
              float rho = lines[i][0], theta = lines[i][1];
              Point pt1, pt2;
              double a = cos(theta), b = sin(theta);
              double x0 = a*rho, y0 = b*rho;
              pt1.x = cvRound(x0 + 1000*(-b));
              pt1.y = cvRound(y0 + 1000*(a));
              pt2.x = cvRound(x0 - 1000*(-b));
              pt2.y = cvRound(y0 - 1000*(a));
                //此句代码的OpenCV2版为:
                //line( dstImage, pt1, pt2, Scalar(55,100,195), 1, CV_AA);
                //此句代码的OpenCV3版为:
                line( dstImage, pt1, pt2, Scalar(55,100,195), 1, LINE_AA);
          }
    
          //【5】显示原始图
          imshow("【原始图】", srcImage);
    
          //【6】边缘检测后的图
          imshow("【边缘检测后的图】", midImage);
    
          //【7】显示效果图
          imshow("【效果图】", dstImage);
    
          waitKey(0);
    }

## 累计概率霍夫变换：HoughLinesP（）函数 ##

此函数在HoughLines的基础上，在末尾加了一个代表Probabilistic（概率）的P，表明它可以采用累计概率霍夫变换（PPHT）来找出二值图像中的直线。 C++: void HoughLinesP(InputArray image, OutputArray lines, double rho, double theta, int threshold, double minLineLength=0, double maxLineGap=0 ) 第一个参数，InputArray类型的image，输入图像，即源图像。需为8位的单通道二进制图像，可以将任意的源图载入进来后由函数修改成此格式后，再填在这里。 第二个参数，InputArray类型的lines，经过调用HoughLinesP函数后存储了检测到的线条的输出矢量，每一条线由具有4个元素的矢量（x\_1，y\_1，x\_2，y\_2）表示，其中，（x\_1，y\_1）和（x\_2，y\_2）是是每个检测到的线段的结束点。 第三个参数，double类型的rho，以像素为单位的距离精度。另一种表述方式是直线搜索时的进步尺寸的单位半径。 第四个参数，double类型的theta，以弧度为单位的角度精度。另一种表述方式是直线搜索时的进步尺寸的单位角度。 第五个参数，int类型的threshold，累加平面的阈值参数，即识别某部分为图中的一条直线时它在累加平面中必须达到的值。大于阈值threshold的线段才可以被检测通过并返回到结果中。 第六个参数，double类型的minLineLength，有默认值0，表示最低线段的长度，比这个设定参数短的线段就不能被显现出来。 第七个参数，double类型的maxLineGap，有默认值0，允许将同一行点与点之间连接起来的最大的距离。

#include "opencv2/core.hpp"
    #include <opencv2/imgproc.hpp>
    #include "opencv2/video.hpp"
    #include "opencv2/videoio.hpp"
    #include "opencv2/highgui.hpp"
    #include"opencv2/imgproc/imgproc.hpp"
    #include<opencv2/opencv.hpp>
    #include <iostream>
    
    using namespace std;
    using namespace cv;
    
    int main()
    { 
        //【1】载入原始图和Mat变量定义
        Mat srcImage = imread("/home/heziyi/图片/6.jpg");  //工程目录下应该有一张名为1.jpg的素材图
        Mat midImage,dstImage;//临时变量和目标图的定义
    
        //【2】进行边缘检测和转化为灰度图
        Canny(srcImage, midImage, 50, 200, 3);//进行一此canny边缘检测
        cvtColor(midImage,dstImage, COLOR_GRAY2BGR);//转化边缘检测后的图为灰度图
    
        //【3】进行霍夫线变换
        vector<Vec4i> lines;//定义一个矢量结构lines用于存放得到的线段矢量集合
        HoughLinesP(midImage, lines, 1, CV_PI/180, 80, 50, 10 );
    
          //【4】依次在图中绘制出每条线段
          for( size_t i = 0; i < lines.size(); i++ )
          { 
              Vec4i l = lines[i];
           //此句代码的OpenCV2版为：
           //line( dstImage, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186,88,255), 1, CV_AA);
                //此句代码的OpenCV3版为：
           line( dstImage, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186,88,255), 1, LINE_AA);
          }
    
          //【5】显示原始图
          imshow("【原始图】", srcImage);
    
          //【6】边缘检测后的图
          imshow("【边缘检测后的图】", midImage);
    
          //【7】显示效果图
          imshow("【效果图】", dstImage);
    
          waitKey(0);
    }

原图没有什么直线，检测效果不明显。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70_pic_center 1]

findContours（）函数用于在二值图像中寻找轮廓。 C++: void findContours(InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point()) 第一个参数，InputArray类型的image，输入图像，即源图像，填Mat类的对象即可，且需为8位单通道图像。 第三个参数，OutputArray类型的hierarchy，可选的输出向量，包含图像的拓扑信息。其作为轮廓数量的表示，包含了许多元素。每个轮廓contours\[i\]对应4个hierarchy元素hierarchy\[i\]\[0\]～hierarchy\[i\]\[3\]，分别表示后一个轮廓、前一个轮廓、父轮廓、内嵌轮廓的索引编号。如果没有对应项，对应的hierarchy\[i\]值设置为负数。 第四个参数，int类型的mode，第五个参数，int类型的method，为轮廓的近似办法，

vector<vector> contours;  
findContours(image,  
contours, //轮廓数组  
CV\_RETR\_EXTERNAL, //获取外轮廓  
CV\_CHAIN\_APPROX\_NONE); // 获取每个轮廓的每个像素

例子：

#include "opencv2/core.hpp"
    #include <opencv2/imgproc.hpp>
    #include "opencv2/video.hpp"
    #include "opencv2/videoio.hpp"
    #include "opencv2/highgui.hpp"
    #include"opencv2/imgproc/imgproc.hpp"
    #include<opencv2/opencv.hpp>
    #include <iostream>
    
    using namespace std;
    using namespace cv;
    
    int main()
    { 
        //【1】载入原始图和Mat变量定义
        Mat srcImage = imread("/home/heziyi/图片/6.jpg");  //工程目录下应该有一张名为1.jpg的素材图
        imshow("原始图",srcImage);
    
           //【2】初始化结果图
           Mat dstImage;
    Mat midImage;
           //【3】srcImage取大于阈值119的那部分
           srcImage = srcImage > 119;
           imshow( "取阈值后的原始图", srcImage );
    
           //【4】定义轮廓和层次结构
           vector<vector<Point> > contours;
           vector<Vec4i> hierarchy;
    
           //【5】查找轮廓
           //此句代码的OpenCV2版为：
           //findContours( srcImage, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
           //此句代码的OpenCV3版为：
          // Canny(srcImage, midImage, 50, 200, 3);//进行一此canny边缘检测
           cvtColor(srcImage,dstImage, COLOR_BGR2GRAY);//转化边缘检测后的图为灰度图
    
           findContours( dstImage, contours, hierarchy,RETR_TREE, CHAIN_APPROX_SIMPLE );
    
           // 【6】遍历所有顶层的轮廓， 以随机颜色绘制出每个连接组件颜色
           int index = 0;
           for( ; index >= 0; index = hierarchy[index][0] )
              { 
                   Scalar color( rand()&255, rand()&255, rand()&255 );
                   //此句代码的OpenCV2版为：
                   //drawContours( dstImage, contours, index, color, CV_FILLED, 8, hierarchy );
                   //此句代码的OpenCV3版为：
                   drawContours( dstImage, contours, index, color, FILLED, 8, hierarchy );     }
    
              //【7】显示最后的轮廓图
              imshow( "轮廓图", dstImage );
    
              waitKey(0);
    }

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70_pic_center 2]

## grabcut函数（官方 ##

#include "opencv2/imgcodecs.hpp"
    #include "opencv2/highgui.hpp"
    #include "opencv2/imgproc.hpp"
    
    #include <iostream>
    
    using namespace std;
    using namespace cv;
    
    static void help()
    { 
        cout << "\nThis program demonstrates GrabCut segmentation -- select an object in a region\n"
                "and then grabcut will attempt to segment it out.\n"
                "Call:\n"
                "./grabcut <image_name>\n"
            "\nSelect a rectangular area around the object you want to segment\n" <<
            "\nHot keys: \n"
            "\tESC - quit the program\n"
            "\tr - restore the original image\n"
            "\tn - next iteration\n"
            "\n"
            "\tleft mouse button - set rectangle\n"
            "\n"
            "\tCTRL+left mouse button - set GC_BGD pixels\n"
            "\tSHIFT+left mouse button - set GC_FGD pixels\n"
            "\n"
            "\tCTRL+right mouse button - set GC_PR_BGD pixels\n"
            "\tSHIFT+right mouse button - set GC_PR_FGD pixels\n" << endl;
    }
    
    const Scalar RED = Scalar(0,0,255);
    const Scalar PINK = Scalar(230,130,255);
    const Scalar BLUE = Scalar(255,0,0);
    const Scalar LIGHTBLUE = Scalar(255,255,160);
    const Scalar GREEN = Scalar(0,255,0);
    
    const int BGD_KEY = EVENT_FLAG_CTRLKEY;
    const int FGD_KEY = EVENT_FLAG_SHIFTKEY;
    
    static void getBinMask( const Mat& comMask, Mat& binMask )
    { 
        if( comMask.empty() || comMask.type()!=CV_8UC1 )
            CV_Error( Error::StsBadArg, "comMask is empty or has incorrect type (not CV_8UC1)" );
        if( binMask.empty() || binMask.rows!=comMask.rows || binMask.cols!=comMask.cols )
            binMask.create( comMask.size(), CV_8UC1 );
        binMask = comMask & 1;
    }
    
    class GCApplication
    { 
    public:
        enum{  NOT_SET = 0, IN_PROCESS = 1, SET = 2 };
        static const int radius = 2;
        static const int thickness = -1;
    
        void reset();
        void setImageAndWinName( const Mat& _image, const string& _winName );
        void showImage() const;
        void mouseClick( int event, int x, int y, int flags, void* param );
        int nextIter();
        int getIterCount() const {  return iterCount; }
    private:
        void setRectInMask();
        void setLblsInMask( int flags, Point p, bool isPr );
    
        const string* winName;
        const Mat* image;
        Mat mask;
        Mat bgdModel, fgdModel;
    
        uchar rectState, lblsState, prLblsState;
        bool isInitialized;
    
        Rect rect;
        vector<Point> fgdPxls, bgdPxls, prFgdPxls, prBgdPxls;
        int iterCount;
    };
    
    void GCApplication::reset()
    { 
        if( !mask.empty() )
            mask.setTo(Scalar::all(GC_BGD));
        bgdPxls.clear(); fgdPxls.clear();
        prBgdPxls.clear();  prFgdPxls.clear();
    
        isInitialized = false;
        rectState = NOT_SET;
        lblsState = NOT_SET;
        prLblsState = NOT_SET;
        iterCount = 0;
    }
    
    void GCApplication::setImageAndWinName( const Mat& _image, const string& _winName  )
    { 
        if( _image.empty() || _winName.empty() )
            return;
        image = &_image;
        winName = &_winName;
        mask.create( image->size(), CV_8UC1);
        reset();
    }
    
    void GCApplication::showImage() const
    { 
        if( image->empty() || winName->empty() )
            return;
    
        Mat res;
        Mat binMask;
        if( !isInitialized )
            image->copyTo( res );
        else
        { 
            getBinMask( mask, binMask );
            image->copyTo( res, binMask );
        }
    
        vector<Point>::const_iterator it;
        for( it = bgdPxls.begin(); it != bgdPxls.end(); ++it )
            circle( res, *it, radius, BLUE, thickness );
        for( it = fgdPxls.begin(); it != fgdPxls.end(); ++it )
            circle( res, *it, radius, RED, thickness );
        for( it = prBgdPxls.begin(); it != prBgdPxls.end(); ++it )
            circle( res, *it, radius, LIGHTBLUE, thickness );
        for( it = prFgdPxls.begin(); it != prFgdPxls.end(); ++it )
            circle( res, *it, radius, PINK, thickness );
    
        if( rectState == IN_PROCESS || rectState == SET )
            rectangle( res, Point( rect.x, rect.y ), Point(rect.x + rect.width, rect.y + rect.height ), GREEN, 2);
    
        imshow( *winName, res );
    }
    
    void GCApplication::setRectInMask()
    { 
        CV_Assert( !mask.empty() );
        mask.setTo( GC_BGD );
        rect.x = max(0, rect.x);
        rect.y = max(0, rect.y);
        rect.width = min(rect.width, image->cols-rect.x);
        rect.height = min(rect.height, image->rows-rect.y);
        (mask(rect)).setTo( Scalar(GC_PR_FGD) );
    }
    
    void GCApplication::setLblsInMask( int flags, Point p, bool isPr )
    { 
        vector<Point> *bpxls, *fpxls;
        uchar bvalue, fvalue;
        if( !isPr )
        { 
            bpxls = &bgdPxls;
            fpxls = &fgdPxls;
            bvalue = GC_BGD;
            fvalue = GC_FGD;
        }
        else
        { 
            bpxls = &prBgdPxls;
            fpxls = &prFgdPxls;
            bvalue = GC_PR_BGD;
            fvalue = GC_PR_FGD;
        }
        if( flags & BGD_KEY )
        { 
            bpxls->push_back(p);
            circle( mask, p, radius, bvalue, thickness );
        }
        if( flags & FGD_KEY )
        { 
            fpxls->push_back(p);
            circle( mask, p, radius, fvalue, thickness );
        }
    }
    
    void GCApplication::mouseClick( int event, int x, int y, int flags, void* )
    { 
        // TODO add bad args check
        switch( event )
        { 
        case EVENT_LBUTTONDOWN: // set rect or GC_BGD(GC_FGD) labels
            { 
                bool isb = (flags & BGD_KEY) != 0,
                     isf = (flags & FGD_KEY) != 0;
                if( rectState == NOT_SET && !isb && !isf )
                { 
                    rectState = IN_PROCESS;
                    rect = Rect( x, y, 1, 1 );
                }
                if ( (isb || isf) && rectState == SET )
                    lblsState = IN_PROCESS;
            }
            break;
        case EVENT_RBUTTONDOWN: // set GC_PR_BGD(GC_PR_FGD) labels
            { 
                bool isb = (flags & BGD_KEY) != 0,
                     isf = (flags & FGD_KEY) != 0;
                if ( (isb || isf) && rectState == SET )
                    prLblsState = IN_PROCESS;
            }
            break;
        case EVENT_LBUTTONUP:
            if( rectState == IN_PROCESS )
            { 
                rect = Rect( Point(rect.x, rect.y), Point(x,y) );
                rectState = SET;
                setRectInMask();
                CV_Assert( bgdPxls.empty() && fgdPxls.empty() && prBgdPxls.empty() && prFgdPxls.empty() );
                showImage();
            }
            if( lblsState == IN_PROCESS )
            { 
                setLblsInMask(flags, Point(x,y), false);
                lblsState = SET;
                showImage();
            }
            break;
        case EVENT_RBUTTONUP:
            if( prLblsState == IN_PROCESS )
            { 
                setLblsInMask(flags, Point(x,y), true);
                prLblsState = SET;
                showImage();
            }
            break;
        case EVENT_MOUSEMOVE:
            if( rectState == IN_PROCESS )
            { 
                rect = Rect( Point(rect.x, rect.y), Point(x,y) );
                CV_Assert( bgdPxls.empty() && fgdPxls.empty() && prBgdPxls.empty() && prFgdPxls.empty() );
                showImage();
            }
            else if( lblsState == IN_PROCESS )
            { 
                setLblsInMask(flags, Point(x,y), false);
                showImage();
            }
            else if( prLblsState == IN_PROCESS )
            { 
                setLblsInMask(flags, Point(x,y), true);
                showImage();
            }
            break;
        }
    }
    
    int GCApplication::nextIter()
    { 
        if( isInitialized )
            grabCut( *image, mask, rect, bgdModel, fgdModel, 1 );
        else
        { 
            if( rectState != SET )
                return iterCount;
    
            if( lblsState == SET || prLblsState == SET )
                grabCut( *image, mask, rect, bgdModel, fgdModel, 1, GC_INIT_WITH_MASK );
            else
                grabCut( *image, mask, rect, bgdModel, fgdModel, 1, GC_INIT_WITH_RECT );
    
            isInitialized = true;
        }
        iterCount++;
    
        bgdPxls.clear(); fgdPxls.clear();
        prBgdPxls.clear(); prFgdPxls.clear();
    
        return iterCount;
    }
    
    GCApplication gcapp;
    
    static void on_mouse( int event, int x, int y, int flags, void* param )
    { 
        gcapp.mouseClick( event, x, y, flags, param );
    }
    
    int main( int argc, char** argv )
    { 
        cv::CommandLineParser parser(argc, argv, "{@input| /home/heziyi/图片/6.jpg |}");
        help();
    
        string filename = parser.get<string>("@input");
        if( filename.empty() )
        { 
            cout << "\nDurn, empty filename" << endl;
            return 1;
        }
        Mat image = imread(samples::findFile(filename), IMREAD_COLOR);
        if( image.empty() )
        { 
            cout << "\n Durn, couldn't read image filename " << filename << endl;
            return 1;
        }
    
        const string winName = "image";
        namedWindow( winName, WINDOW_AUTOSIZE );
        setMouseCallback( winName, on_mouse, 0 );
    
        gcapp.setImageAndWinName( image, winName );
        gcapp.showImage();
    
        for(;;)
        { 
            char c = (char)waitKey(0);
            switch( c )
            { 
            case '\x1b':
                cout << "Exiting ..." << endl;
                goto exit_main;
            case 'r':
                cout << endl;
                gcapp.reset();
                gcapp.showImage();
                break;
            case 'n':
                int iterCount = gcapp.getIterCount();
                cout << "<" << iterCount << "... ";
                int newIterCount = gcapp.nextIter();
                if( newIterCount > iterCount )
                { 
                    gcapp.showImage();
                    cout << iterCount << ">" << endl;
                }
                else
                    cout << "rect must be determined>" << endl;
                break;
            }
        }
    
    exit_main:
        destroyWindow( winName );
        return 0;
    }

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