图像处理方法（膨胀腐蚀，霍夫变换，滤波，去噪，图像增强，二值化，图片旋转，画直线）

OpenCV 基础，常用方法

导入头文件
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;
//读取图片
void fun_imread(Mat img) {
    Mat img1;
    imshow("原图", img);//图像显示
    waitKey();//读入图片显示时间waitKey(6000);显示6000毫秒，如果写，就一直显示
}
//腐蚀操作函数
void fun_fs(Mat img) {
    //进行腐蚀操作
    //卷积核所覆盖下的原图对应区域内的所有像素的最小值，用这个最小值替换当前像素值。图片通过这种局部颜色加深，
    //导致整体颜色加深
    Mat element = getStructuringElement(MORPH_RECT, Size(10, 10));// Size的参数是卷积核的大小，越大腐蚀越严重。
    //Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
    Mat dstImage;
    erode(img, dstImage, element);
    imshow("腐蚀操作后的图", dstImage);//腐蚀操作的函数
    waitKey();
}
//膨胀
void fun_pz(Mat img) {
    //腐蚀和膨胀区别是函数imshow("膨胀操作后的图", dstImage)//和池化操作类似
    //卷积核所覆盖下的原图对应区域内的所有像素的最大，用这个最小值替换当前像素值。图片通过这种局部颜色变浅，
    //导致整体颜色变浅
    Mat element = getStructuringElement(MORPH_RECT, Size(10, 10));// Size的参数是卷积核的大小，越大腐蚀越严重。
    //Mat element = getStructuringElement(MORPH_RECT, Size(15, 15)); //getStructuringElement获取结构元素
    Mat dstImage;
    dilate(img, dstImage, element);//第一个参数是输入图片，第二个参数是输出图片，第三个是
    imshow("膨胀操作后的图", dstImage); //膨胀操作的函数
    waitKey();
}
均值滤波
void fun_mh(Mat img) {
    //图片模糊的本质是对图片进行均值滤波，就是均值池化类似的操作
    Mat dstImage;
    blur(img, dstImage, Size(7, 7));//均值滤波
    imshow("均值滤波【效果图】", dstImage);
    waitKey();
}
    //中值滤波  中值滤波可以有效滤除椒盐噪声
    medianBlur(img1, img2, 3); //也会变模糊，但程度相对较小
    imshow("中值滤波后", img2);
    //均值滤波 均值滤波能够滤除白噪声，但会使原始图像丢掉一些细节(原图变得模糊)
    blur(img1, img2, Size(3,3)); 
    imshow("均值滤波后", img2);
    //高斯滤波 一般图像采取的都是高斯滤波
    //加权均值滤波(高斯滤波)也可以有效的滤除白噪声，同时不会丢掉原图中的细节(甚至原图更清晰)
    GaussianBlur(img1, img2, Size(3,3),5);//size的大小调节不好会报错size越大越模糊
    imshow("高斯滤波后", img2);

//转灰度图，进行边缘轮廓检测
void fun_hd(Mat img) {
    //将图片变成灰度图
    Mat dstImage,img1;
    blur(img, dstImage, Size(4, 4));//1、均值滤波
    cvtColor(dstImage, dstImage, COLOR_BGR2GRAY);//2、转成灰度图
    cout << dstImage.at<Vec3b>(2, 2)[1];
    imshow("灰度图", dstImage);
    Canny(dstImage, img1, 50, 120, 3); //3、使用边缘检测
    //canny 第一个数字越大
    imshow("边缘检测", img1);
    waitKey(0);
}

void fun(Mat img1) {
    Mat img;
    //创建相同大小相同类型的矩阵
    img.create(img1.size(),img1.type());
    img.create(img1.size(), CV_32FC3);
    img = img1.clone();
    img.rows;//行
    img.cols;//列
}

void fun_readvido() {
    //如果有视屏，则读取视屏，如果没有视屏参数写0，调用本地摄像头
    //VideoCapture cap("C:\Users\MH\Desktop\常用工具类\壁纸\1.avi");
    VideoCapture cap(0);
    while(1) {
        Mat frame;
        cap >> frame;
        Mat dstImage, img1;
        imshow("读取视屏", img1);
        waitKey(10);
    }
}

//随机产生椒盐噪声
Mat fun_1(Mat img1, int k) {
    Mat img = img1.clone();
    int i, j;
    for (int m = 0; m < k; m++) {//循环k次，随机产生k个点
        i = rand() % img.rows; //img.at<Vec3b>(i, j)[0] = 0; i代表行下标（高rows），j代表列下标写反位置会报错
        j = rand() % img.cols;//产生随机的下标点
        img.at<Vec3b>(i, j)[0] = 0;
        img.at<Vec3b>(i, j)[1] = 0;
        img.at<Vec3b>(i, j)[2] = 0;
    }
    for (int m = 0; m <int(k / 2); m++) {
        i = rand() % img.rows;
        j = rand() % img.cols;
        img.at<Vec3b>(i, j)[0] = 255;
        img.at<Vec3b>(i, j)[1] = 255;
        img.at<Vec3b>(i, j)[2] = 255;
    }
    return img;
}
//log图像增强
Mat fun_log(Mat img) {
    Mat img1;
    float C = 0.5;
    img1.create(img.size(), CV_32FC3);
    for (int i = 0; i < img.rows; i++) {
        for (int j = 0; j < img.cols; j++) {
            img1.at<Vec3f>(i, j)[0] = C * log(1+float(img.at<Vec3b>(i, j)[0]));
            img1.at<Vec3f>(i, j)[1] = C * log(1+float(img.at<Vec3b>(i, j)[1]));
            img1.at<Vec3f>(i, j)[2] = C * log(1+float(img.at<Vec3b>(i, j)[2]));
        }
    }
    //归一化到0~255  
    normalize(img1, img1, 0, 255, CV_MINMAX);
    //转换成8bit图像显示  
    convertScaleAbs(img1, img1);
    return img1;
}

//指数对图片进行放暗
Mat fun_3(Mat img) {
    int c = 3;
    float b = 0.1;
    Mat img1;
    //img1 = img.clone();
    img1.create(img.size(), CV_32FC3);//32位的图像像素灰度值在（0-1）之间的显示是正常显示，也可以将其转化成0-255，然后转乘8bit的图
    for (int m = 0; m < img.rows; m++) {
        for (int j = 0; j < img.cols; j++) {
            img1.at<Vec3f>(m, j)[0] = float(img.at<Vec3b>(m, j)[0]) / 255 * float(img.at<Vec3b>(m, j)[0]) / 255;
            img1.at<Vec3f>(m, j)[1] = float(img.at<Vec3b>(m, j)[1]) / 255 * float(img.at<Vec3b>(m, j)[1]) / 255;
            img1.at<Vec3f>(m, j)[2] = float(img.at<Vec3b>(m, j)[2]) / 255 * float(img.at<Vec3b>(m, j)[2]) / 255;
        }
    }
    return img1;
}



//霍夫变换+图像旋转校正+背景填充
//二值化
Mat fun_two(Mat img) {
    float a = 110;
    for (int i = 0; i < img.rows; i++) {
        for (int j = 0; j < img.cols; j++) {
            if (0.3*img.at<Vec3b>(i, j)[0] + 0.6*img.at<Vec3b>(i, j)[1] + 0.1*img.at<Vec3b>(i, j)[2] > a) {
                img.at<Vec3b>(i, j)[0] = 255;
                img.at<Vec3b>(i, j)[1] = 255;
                img.at<Vec3b>(i, j)[2] = 255;
            }
            else
            {
                img.at<Vec3b>(i, j)[0] = 0;
                img.at<Vec3b>(i, j)[1] = 0;
                img.at<Vec3b>(i, j)[2] = 0;
            }
        }
    }
    return img;
}

//背景
Mat fun_bj(Mat img, float a, float b, float c) {
    for (int i = 0; i < img.rows; i++) {
        for (int j = 0; j < img.cols; j++) {
            if (0.3*img.at<Vec3b>(i, j)[0] + 0.6*img.at<Vec3b>(i, j)[1] + 0.1*img.at<Vec3b>(i, j)[2] == 255) {
                img.at<Vec3b>(i, j)[0] = a;
                img.at<Vec3b>(i, j)[1] = b;
                img.at<Vec3b>(i, j)[2] = c;
            }
            else
            {
                img.at<Vec3b>(i, j)[0] = 0;
                img.at<Vec3b>(i, j)[1] = 0;
                img.at<Vec3b>(i, j)[2] = 0;
            }
        }
    }
    return img;
}

//画线的函数
void fun_line(vector<Vec2f>  lines, Mat img) {
    for (size_t i = 0; i < lines.size(); i++)
    {
        float rho = lines[i][0];
        float theta = lines[i][1];
        double a = cos(theta), b = sin(theta);
        double x0 = a * rho, y0 = b * rho;
        Point pt1(cvRound(x0 + 1000 * (-b)),
            cvRound(y0 + 1000 * (a)));
        Point pt2(cvRound(x0 - 1000 * (-b)),
            cvRound(y0 - 1000 * (a)));
        line(img, pt1, pt2, Scalar(0, 0, 255), 3, 8);
        
    }
    imshow("线性图", img);
}

//图片旋转//放射变换
Mat rotateImage(Mat img, double jd)
{
    Mat img1;
    //旋转中心为图像中心    
    Point2f center;
    center.x = float(img.cols / 2.0);
    center.y = float(img.rows / 2.0);
    int length = 0;
    length = sqrt(img.cols*img.cols + img.rows*img.rows);
    //计算二维旋转的仿射变换矩阵  
    Mat M = getRotationMatrix2D(center, jd, 1);
    warpAffine(img, img1, M, Size(length, length), 1, 0, Scalar(255, 255, 255));//仿射变换，背景色填充为白色
    return img1;
}


int main()
{
    Mat imroa, img3;
    float a, b, c;//
    imroa = imread("C:\Users\MH\Desktop\pInFileName.jpg");
    a = imroa.at<Vec3b>(int(imroa.rows*0.9), int(imroa.rows*0.6))[0];
    b = imroa.at<Vec3b>(int(imroa.rows*0.9), int(imroa.rows*0.6))[1];
    c = imroa.at<Vec3b>(int(imroa.rows*0.9), int(imroa.rows*0.6))[2];
    imshow("原图", imroa);
    for (int i = 0; i < 10; i++)
    {
        medianBlur(imroa, imroa, 7);//第三个参数一般设为奇数

    }
    blur(imroa, imroa, Size(5, 5));

    img3 = imroa.clone();

    cvtColor(imroa, imroa, COLOR_BGR2GRAY);

    Canny(imroa, imroa, 50, 200, 3);

    vector<Vec2f> lines;
    //霍夫变换，获取直线对象
    HoughLines(imroa, lines, 1, CV_PI / 180, 300, 0, 0);
    //// 输入，线条对象，极径的步长，角度的步长，阈值(阈值越大对直线要求越高，提取的直线数量越少)
    float sum = 0;
    for (size_t i = 0; i < lines.size(); i++) {
        sum += lines[i][1];
    }
    float jd = sum / lines.size() / CV_PI * 180;
    cout << lines.size() << endl;
    cout << jd;
    //二值化
    img3 = fun_two(img3);

    fun_line(lines,img3);

    Mat img2;
    //旋转
    img2 = rotateImage(img3, jd);
    //imshow("中值滤波后", imroa);
    //imroa = fun_two(imroa);
    //imshow("二值化", imroa);

    //背景颜色填充裁剪
    Mat img4;
    fun_bj(img2, a, b, c);

    imshow("填充", img2);

    waitKey();
    return 0;
}

 
图像处理的基本思路：