OpenCV-基于距离变换与分水岭的图像分割

图像分割 Image Segmentation

目的：将图像中的像素按照一定放规则分为若干个cluster集合，每个集合包含一类像素
根据算法分为监督学习方法跟无监督学习方法

距离变换与分水岭

API:

实现过程

代码：

#include
#include
#include
#include

using namespace std;
using namespace cv;

int main(int argc, char ** argv)
{
	Mat src;
	src = imread("E://VS-pro//images//text2.bmp");
	imshow("原图", src);

	//1.将色背景变为黑色
	Mat src_temp;
	src.copyTo(src_temp);
	for(int row = 0; row < src.rows; row++)
		for (int col = 0; col < src.cols; col++)
		{
			if (src_temp.at(row, col) == Vec3b(255, 255, 255))
			{
				src_temp.at(row, col) = Vec3b(0, 0, 0);
			}
		}

	imshow("黑色背景", src_temp);

	//2.锐化提高对比度
	Mat src_lapl;
	Mat kernel = (Mat_(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);
	filter2D(src_temp, src_lapl, CV_32F, kernel, Point(-1, -1), 0, BORDER_DEFAULT);
	
	src_temp.convertTo(src_temp, CV_32F);
	Mat resultImg = src_temp - src_lapl;
	src_temp.convertTo(src_temp, CV_8UC3);
	src_lapl.convertTo(src_lapl, CV_8UC3);
	resultImg.convertTo(resultImg, CV_8UC3);

	imshow("锐化后", resultImg);
	//src = resultImg;

	//转换为二值图像
	Mat src_bin;
	cvtColor(resultImg, resultImg, CV_BGR2GRAY);
	threshold(resultImg, src_bin, 40, 255, THRESH_BINARY | THRESH_OTSU);
	imshow("锐化后转二值", src_bin);


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

	//再次二值化
	threshold(distImg, distImg,0.4, 1, THRESH_BINARY);
	Mat k1 = Mat::ones(5, 5, CV_8UC1);
	imshow("距离变换后二值图像", distImg);
	//腐蚀
	erode(distImg, distImg, k1, Point(-1, -1));
	imshow("腐蚀distImg", distImg);

	// markers 找轮廓
	Mat dist_8u;
	distImg.convertTo(dist_8u, CV_8U);
	vector> contours;
	findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));

	// create makers 画轮廓markers标记
	Mat markers = Mat::zeros(src_temp.size(), CV_32SC1);
	
	for (size_t i = 0; i < contours.size(); i++) {
		drawContours(markers, contours, static_cast(i), Scalar::all(static_cast(i) + 1), -1);
	}
	circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);

	//imshow("my markers", markers);
	
   // perform watershed分水岭
	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);

	// 分配不同颜色
	vector 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));
	}

	// 填充颜色
	Mat dst = 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(row, col);
			
			if (index > 0 && index <= static_cast(contours.size())) {
				dst.at(row, col) = colors[index - 1];
				
			}
			else {
				dst.at(row, col) = Vec3b(0, 0, 0);
			}
		}
	}
	imshow("Final Result", dst);

	waitKey(0);
	return 0;
}