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OpenCV通过透视变换实现矫正图像详解

目录
  • 1、概述
  • 2、代码演示
  • 3、示例图片

1、概述

案例:使用OpenCV将一张折射的图片给矫正过来

实现步骤:

1.载入图像

2.图像灰度化

3.二值分割

4.形态学操作去除噪点

5.轮廓发现

6.使用霍夫直线检测,检测上下左右四条直线(有可能是多条,但是无所谓)

7.绘制出直线

8.寻找与定位上下左右是条直线

9.拟合四条直线方程

10.计算四条直线的交点,ps:这四个交点其实就是我们最终要寻找的,用于透视变换使用的

11.进行透视变换

12.输出透视变换的结果

说明:

解释一下为啥是上面那些步骤。

1.其实我们的最终目的是通过透视矩阵getPerspectiveTransform+透视变换warpPerspective来完成图像的矫正

2.但是getPerspectiveTransform需要两个参数,输入矩阵参数和目标矩阵参数。

3.由于输入矩阵www.devze.com参数就是原图像是个角的顶点,由于我们没有所以要求出来

4.所以我们以上的所有步骤都是为11、12步打基础的

ps:核心就是利用透视矩阵做透视变换

重点:

1.直线方程y=kx+c

2.如果两条直线有交点,则必有k1x1+c1=k2x2+c2

2、代码演示

//【1】载入图像
    Mat src = imread(filePath);
    if(src.empty()){
        qDebug()<<"图片为空";
        return;
    }
    imshow("src",src);
 
    //【2】图像灰度化
    Mat gray;
    cvtColor(src,gray,COLOR_BGR2GRAY);
    //【3】执行二值分割
    threshold(gray,gray,0,255,THRESH_BINARY_INV|THRESH_OTSU);
    imshow("threshold",gray);
    //【4】执行形态学开操作去除图像中的造点
    Mat kernel = getStructuringElement(MORPH_RECT,Size(5,5),Point(-1,-1));
    morphologyEx(gray,gray,MORPH_CLOSE,kernel,Point(-1,-1),3);
    imshow("morphologyEx",gray);
    //【5】轮廓发现
    bitwise_not(gray,gray);
    imshow("bitwise_not",gray);
 
    vector<vector<Point>> contours;
    vector<Vec4i> hier;
    RNG rng(12345);
    findContours(gray,contours,hier,RETR_TREE,CHAIN_APPROX_SIMPLE);
    Mat colorImage = Mat::zeros(gray.size(),CV_8UC3);
    for(size_t i = 0;i<contours.size();i++){
        Rect rect = boundingRect(contours[i]);
        //过滤目标轮廓
        if(rect.width<src.cols-5&&rect.height<src.rows-5&&rect.width>src.cols/2){
            drawContours(colorImage,contours,i,Scalar(rng.uniform(0,255),rng.uniform(0,255),rng.uniform(0,255)),1);
        }
 
    }
    imphpshow("findContours",colorImage);
 
    //【6】使用霍夫直线检测
    vector<Vec4i> lines;
    cvtColor(colorImage,colorImage,COLOR_BGR2GRAY);
    kernel = getStructuringElement(MORPH_RECT,Size(3,3),Point(-1,-1));
    dilate(colorImage,colorImage,kernel,Point(-1,-1),1);
    imshow("colorImage_gray",colorImage);
    int accu = min(src.cols*0.5, src.rows*0.5);
    HoughLinesP(colorImage,lines,1,CV_PI/180,accu,accu,0);
    //【7】绘制出直线
    Mat lineColorImage = Mat::zeros(gray.size(),CV_8UC3);
    qDebug()<<"line count:"<<lines.size();
    for(size_t i = 0;i<lines.size();i++){
        Vec4i ll = lines[i];
        line(lineColorImage,Point(ll[0],ll[1]),Point(ll[2],ll[3]),Scalar(rng.uniform(0,255),rng.uniform(0,255),rng.uniform(0,255)),2,LINE_8);
    }
    imshow("lines",lineColorImage);
 
 
    //【8】寻找与定位上下左右四条直线
    int deltah  = 0;
    int width = src.cols;
    int height = src.rows;
    Vec4i topLine, bottomLpythonine;
    Vec4i leftLine, rightLine;
    for(size_t i=0;i<lines.size();i++){
        Vec4i ln = lines[i];
        deltah  = abs(ln[3]-ln[1]);//直线高度
        if (ln[3] < height / 2.0 && ln[1]js < height / 2.0 && deltah < accu - 1) {
            if 开发者_Python培训(topLine[3] > ln[3] && topLine[3]>0) {
                topLine = lines[i];
            } else {
                topLine = lines[i];
            }
        }
        if (ln[3] > height / 2.0 && ln[1] > height / 2.0 && deltah < accu - 1) {
            bottomLine = lines[i];
        }
        if (ln[0] < width / 2.0 && ln[2] < width/2.0) {
            leftLine = lines[i];
        }
        if (ln[0] > width / 2.0 && ln[2] > width / 2.0) {
            rightLine = lines[i];
        }
    }
 
    //直线方程y=kx+c
    // 【9】拟合四条直线方程
    float k1, c1;
    k1 = float(topLine[3] - topLine[1]) / float(topLine[2] - topLine[0]);
    c1 = topLine[1] - k1*topLine[0];
    float k2, c2;
    k2 = float(bottomLine[3] - bottomLine[1]) / float(bottomLine[2] - bottomLinephp[0]);
    c2 = bottomLine[1] - k2*bottomLine[0];
    float k3, c3;
    k3 = float(leftLine[3] - leftLine[1]) / float(leftLine[2] - leftLine[0]);
    c3 = leftLine[1] - k3*leftLine[0];
    float k4, c4;
    k4 = float(rightLine[3] - rightLine[1]) / float(rightLine[2] - rightLine[0]);
    c4 = rightLine[1] - k4*rightLine[0];
 
    // 【10】四条直线交点,其实最终的目的就是找这是条直线的交点
    Point p1; // 左上角
    p1.x = static_cast<int>((c1 - c3) / (k3 - k1));
    p1.y = static_cast<int>(k1*p1.x + c1);
    Point p2; // 右上角
    p2.x = static_cast<int>((c1 - c4) / (k4 - k1));
    p2.y = static_cast<int>(k1*p2.x + c1);
    Point p3; // 左下角
    p3.x = static_cast<int>((c2 - c3) / (k3 - k2));
    p3.y = static_cast<int>(k2*p3.x + c2);
    Point p4; // 右下角
    p4.x = static_cast<int>((c2 - c4) / (k4 - k2));
    p4.y = static_cast<int>(k2*p4.x + c2);
 
    // 显示四个点坐标
    circle(lineColorImage, p1, 2, Scalar(255, 0, 0), 2, 8, 0);
    circle(lineColorImage, p2, 2, Scalar(255, 0, 0), 2, 8, 0);
    circle(lineColorImage, p3, 2, Scalar(255, 0, 0), 2, 8, 0);
    circle(lineColorImage, p4, 2, Scalar(255, 0, 0), 2, 8, 0);
    line(lineColorImage, Point(topLine[0], topLine[1]), Point(topLine[2], topLine[3]), Scalar(0, 255, 0), 2, 8, 0);
    imshow("four corners", lineColorImage);
 
    // 【11】透视变换
    vector<Point2f> src_corners(4);
    src_corners[0] = p1;
    src_corners[1] = p2;
    src_corners[2] = p3;
    src_corners[3] = p4;
 
    vector<Point2f> dst_corners(4);
    dst_corners[0] = Point(0, 0);
    dst_corners[1] = Point(width, 0);
    dst_corners[2] = Point(0, height);
    dst_corners[3] = Point(width, height);
 
    // 【12】获取透视变换矩阵,并最终显示变换后的结果
    Mat resultImage;
    Mat warpmatrix = getPerspectiveTransform(src_corners, dst_corners);
    warpPerspective(src, resultImage, warpmatrix, resultImage.size(), INTER_LINEAR);
    imshow("Final Result", resultImage);

3、示例图片

OpenCV通过透视变换实现矫正图像详解

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