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 -;
     }
     imshow("input Image",src);

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

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

     //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(-, -), , 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,,,THRESH_BINARY|THRESH_OTSU);
     imshow("binary image",binary);

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

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

     //腐蚀(使得连在一起的部分分开)
     Mat k1 = Mat::ones(, , 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(, ));

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

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

     circle(marker, Point(, ), , Scalar(, , ), -);
     imshow("marker",marker*);

     //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 = ; i < contours.size(); i++) {
         int r = theRNG().uniform(, );
         int g = theRNG().uniform(, );
         int b = theRNG().uniform(, );
         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 = ; row < marker.rows; row++) {
         for (int col = ; col < marker.cols; col++) {
             int index = marker.at<int>(row, col);
             if (index >  && index <= static_cast<int>(contours.size())) {
                 dst.at<Vec3b>(row, col) = colors[index - ];
             }
             else {
                 dst.at<Vec3b>(row, col) = Vec3b(, , );
             }
         }
     }
     imshow("Final Result", dst);
     waitKey();
     return ;
 }