【笔记】c++中opencv的使用

记录一下代码。

标准霍夫变换 HoughLines() 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 
#include "opencv2/video.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include"opencv2/imgproc/imgproc.hpp"
#include
#include 

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 
#include "opencv2/video.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include"opencv2/imgproc/imgproc.hpp"
#include
#include 

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。

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 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 
#include "opencv2/video.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include"opencv2/imgproc/imgproc.hpp"
#include
#include 

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);
}

grabcut函数（官方

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"

#include 

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 \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;
}