OpenCV——距离变换与分水岭算法的（图像分割）

C++: void distanceTransformInputArray 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 ;

 }