OpenCV——距离变换与分水岭算法的（图像分割）

 C++: void distanceTransform(InputArray src, OutputArray dst, int distanceType, int maskSize)

参数详解：

InputArray src：输入的图像，一般为二值图像

 OutputArray dst：输出的图像

int distanceType：所用的求解距离的类型、

It can be CV_DIST_L1, CV_DIST_L2 , or CV_DIST_C

mask_size  距离变换掩模的大小，可以是 3 或 5. 对 CV_DIST_L1 或 CV_DIST_C 的情况，参数值被强制设定为 3, 因为 3×3 mask 给出 5×5 mask 一样的结果，而且速度还更快。 

mask

用户自定义距离情况下的 mask。 在 3×3 mask 下它由两个数(水平/垂直位量，对角线位移量）组成， 5×5 mask 下由三个数组成(水平/垂直位移量，对角位移和 国际象棋里的马步(马走日))

 

 

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

Mat src;

int main(int argc, char** argv)
{
    src = imread("分水岭.jpg");
    if (src.empty())
    {
        printf("Can not load Image...");
        return -1;
    }
    imshow("input Image",src);

    //白色背景变成黑色
    for (int row=0;row<src.rows;row++)
    {
        for (int col = 0; col < src.cols; col++) {
            if (src.at<Vec3b>(row, col) == Vec3b(255, 255, 255)) {
                src.at<Vec3b>(row, col)[0] = 0;
                src.at<Vec3b>(row, col)[1] = 0;
                src.at<Vec3b>(row, col)[2] = 0;
            }
        }
    }
    imshow("black backgroung", src);

    //sharpen(提高对比度)
    Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);

    //make it more sharp
    Mat imgLaplance;
    Mat sharpenImg = src;
    //拉普拉斯算子实现边缘提取
    filter2D(src, imgLaplance, CV_32F, kernel, Point(-1, -1), 0, BORDER_DEFAULT);//拉普拉斯有浮点数计算，位数要提高到32
    src.convertTo(sharpenImg, CV_32F);

    //原图减边缘（白色）实现边缘增强
    Mat resultImg = sharpenImg - imgLaplance;

    resultImg.convertTo(resultImg,CV_8UC3);
    imgLaplance.convertTo(imgLaplance, CV_8UC3);
    imshow("sharpen Image", resultImg);

    //转换成二值图
    Mat binary;
    cvtColor(resultImg, resultImg, CV_BGR2GRAY);
    threshold(resultImg, binary,40,255,THRESH_BINARY|THRESH_OTSU);
    imshow("binary image",binary);

    //距离变换
    Mat distImg;
    distanceTransform(binary,distImg,DIST_L1,3,5);
    normalize(distImg, distImg, 0, 1, NORM_MINMAX);    
    imshow("dist image",distImg);

    //二值化
    threshold(distImg, distImg, 0.4, 1, THRESH_BINARY);
    imshow("dist binary image", distImg);

    //腐蚀(使得连在一起的部分分开)
    Mat k1 = Mat::ones(3, 3, CV_8UC1);
    erode(distImg, distImg, k1);
    imshow("分开", distImg);

    //标记
    Mat dist_8u;
    distImg.convertTo(dist_8u,CV_8U);
    vector<vector<Point>> contours;
    findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));

    //创建标记
    Mat marker = Mat::zeros(src.size(),CV_32SC1);

    //画标记
    for (size_t i = 0; i < contours.size(); i++)
    {
        drawContours(marker,contours,static_cast<int>(i),Scalar(static_cast<int>(i)+1),-1);
    }

    circle(marker, Point(5, 5), 3, Scalar(255, 255, 255), -1);
    imshow("marker",marker*1000);

    //分水岭变换
    watershed(src,marker);//根据距离变换的标记，在原图上分离
    Mat water = Mat::zeros(marker.size(),CV_8UC1);
    marker.convertTo(water,CV_8UC1);
    bitwise_not(water, water,Mat());//取反操作
    //imshow("源 image", src);
    imshow("watershed Image", water);

    // generate random color
    vector<Vec3b> colors;
    for (size_t i = 0; i < contours.size(); i++) {
        int r = theRNG().uniform(0, 255);
        int g = theRNG().uniform(0, 255);
        int b = theRNG().uniform(0, 255);
        colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
    }

    // fill with color and display final result
    Mat dst = Mat::zeros(marker.size(), CV_8UC3);
    for (int row = 0; row < marker.rows; row++) {
        for (int col = 0; col < marker.cols; col++) {
            int index = marker.at<int>(row, col);
            if (index > 0 && index <= static_cast<int>(contours.size())) {
                dst.at<Vec3b>(row, col) = colors[index - 1];
            }
            else {
                dst.at<Vec3b>(row, col) = Vec3b(0, 0, 0);
            }
        }
    }
    imshow("Final Result", dst);
    waitKey(0);
    return 0;
}