OpenCv-C++-基于距离变换与分水岭的图像分割

在这里，先感谢贾志刚老师的教学，我今天学习了图像分水岭分割，什么是图像分割呢？借用贾志刚老师的课件，如下图所示：

其实大致就是将下面图1变成图2的样子：
图1：

图2：

或：

具体操作有什么步骤？看下图：

下面附上代码（具体解释代码已注释）：

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

using namespace cv;
using namespace std;

Mat src,dst;
int main(int argc, char** argv)
{
	src = imread("D:/test/pukepai.png");
	if (!src.data)
	{
		cout << "图片未找到" << endl;
		return -1;
	}
	imshow("input title", src);
	//把白色背景变成黑色背景
	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)) //3个255是白色
			{
				src.at<Vec3b>(row, col)[0] = 0;
				src.at<Vec3b>(row, col)[1] = 0;
				src.at<Vec3b>(row, col)[2] = 0;
			}
		}
	}
	//imshow("black background", src);

	/*--------sharpen(使用filter2D与拉普拉斯算子提高图像对比度)------------*/
	Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);
	Mat LaplanceImg;
	Mat sharpImg = src;
	src.convertTo(sharpImg, CV_32F);//将src转成cv_32f类型的矩阵，计算下面减法时类型要一致
	/*为什么用CV_32F，因为拉普拉斯计算的是浮点数，有正值有负值，可能会超0~255范围*/
	filter2D(src, LaplanceImg, CV_32F, kernel, Point(-1, -1),0,BORDER_DEFAULT);
	Mat resultImg = sharpImg - LaplanceImg;
	resultImg.convertTo(resultImg, CV_8UC3);
	LaplanceImg.convertTo(LaplanceImg, CV_8UC3);
	imshow("black background sharpen", resultImg);
	//src = resultImg;
	/*---------------------------------------------------------*/
	/*------------------转为二值图像（threshold）---------------*/
	//先转为灰度图像，再转为二值图像
	cvtColor(resultImg, resultImg, CV_BGR2GRAY);
	Mat binaryImg;
	threshold(resultImg, binaryImg, 40, 255, THRESH_BINARY | THRESH_OTSU);//自动确定阈值
	imshow("binaryImg", binaryImg);
	/*---------------距离变换---------------------------------------*/
	Mat distImg;
	distanceTransform(binaryImg, distImg, DIST_L1, 3, 5);
	normalize(distImg, distImg, 0, 1, NORM_MINMAX);
	imshow("distance Image",distImg);
	/*--------------将距离变换之后的结果再进行二值化-------------------------*/
	Mat thres_againImg;
	threshold(distImg, thres_againImg, 0.4, 0.8, THRESH_BINARY);

	imshow("binaryImg again", thres_againImg);

	/*----------------------腐蚀操作（二值图像）---------------------------*/
	Mat k = Mat::ones(5,5,CV_8UC1);  //结构元素
	erode(thres_againImg, dst, k,Point(-1,-1));
	imshow("erode Image", dst);

	/*-----------------标记（给每一个小山头（白色块）编号）--------------------*/
	//这里主要使用发现轮廓和绘制轮廓
	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));
	Mat markers = Mat::zeros(src.size(),CV_32SC1);
	for (size_t i = 0; i < contours.size(); i++)
	{
		drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i) + 1), -1);

	}
	circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);
	imshow("makers", markers*1000);  //因为makers的值很低很低

	/*----------------------------分水岭变换------------------*/
	watershed(src,markers);
	Mat mark = Mat::zeros(markers.size(), CV_8UC1);
	markers.convertTo(mark, CV_8UC1);
	bitwise_not(mark, mark, Mat());
	imshow("watershed Image", mark);

	/*-------------------------着色--------------------------------*/
	vector<Vec3b> colors;
	for (size_t i = 0; i < contours.size(); i++) {
		int r = theRNG().uniform(0, 255);//theRNG()，自带的函数，随机数生成器
		int g = theRNG().uniform(0, 255);
		int b = theRNG().uniform(0, 255);
		colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
	}

	// 填充颜色并显示
	Mat colorImg = Mat::zeros(markers.size(), CV_8UC3);
	for (int row = 0; row < markers.rows; row++) {
		for (int col = 0; col < markers.cols; col++) {
			int index = markers.at<int>(row, col);
			if (index > 0 && index <= static_cast<int>(contours.size())) {
				colorImg.at<Vec3b>(row, col) = colors[index - 1];
			}
			else {
				colorImg.at<Vec3b>(row, col) = Vec3b(0, 0, 0);
			}
		}
	}
	imshow("Finally Image", colorImg);
	waitKey(0);
	return 0;

}

在此特别感谢贾志刚老师的教学！！！