毕设分享基于Kmeans的图像分割算法软件设计

文章目录

[0 简介](#0 简介)
[1 Kmeans聚类算法基本原理](#1 Kmeans聚类算法基本原理)
[2 基于Kmeans图像分割算法流程](#2 基于Kmeans图像分割算法流程)
[4 代码运行结果及评价](#4 代码运行结果及评价)
[5 最后](#5 最后)

0 简介

今天学长向大家分享一个毕业设计项目

毕业设计基于Kmeans的图像分割算法软件设计

项目运行效果：

毕业设计基于kmean的图像分割

🧿 项目分享:见文末!

1 Kmeans聚类算法基本原理

K-Means算法的思想很简单，对于给定的样本集，按照样本之间的距离大小，将样本集划分为K个簇。让簇内的点尽量紧密的连在一起，而让簇间的距离尽量的大。

以彩色图像为例：基于彩色图像的RGB三通道为xyz轴建立空间直角坐标系，那么一副图像上的每个像素点与该空间直角坐标系建立了一一映射（双射）的关系。

从空间直角坐标系中随机取 k 个点，作为 k个簇的各自的中心。计算所有像素点到k个簇心的距离，并将所有像素点划分至与其距离最小的簇类。自此聚类完成。其中，距离定义为欧氏距离：

其中r，g，b分别表示红绿蓝三通道，r1，g1，b1为彩色图片中某像素点；r0，g0，b0表示某簇类的簇心。

2 基于Kmeans图像分割算法流程

Note：彩色图像的操作是基于一个三维空间

1、加载图像，获取图像的所有像素点并将其转换为样本数据。

2、开始迭代

a)、初始化簇心坐标。

a)、更新簇心坐标，遍历样本数据中的数据点并计算数据点与所有簇心的距离。对于某数据点计算得到的与所有簇类的欧氏距离中，取欧氏距离最小所对应的簇类作为该数据点对应的类。

c)、计算更新后的簇心坐标与更新前的簇心坐标的均方误差。若均方误差仍大于某阈值，则重复b)，反之结束迭代。

Kmeans的详细算法流程参考博文：
https://www.cnblogs.com/pinard/p/6164214.html

三、代码主函数部分

1、加载图片

	Mat src, dst;
	src = imread("J20.jpg");
	namedWindow("输入图像", WINDOW_AUTOSIZE);
	imshow("输入图像", src);

2、初始化颜色（图像分割后需要上色）

	Scalar colorTab[] = {
		Scalar(0,0,255),
		Scalar(0,255,0),
		Scalar(255,0,0),
		Scalar(0,255,255),
		Scalar(255,0,255),
	};

3、初始化簇类数，并将所有的像素点全部转换为样本数据

	int sampleCount = width*height;
	int clusterCount = 5;
	Mat points(sampleCount, dims, CV_32F, Scalar(10));
	//将彩色图像的像素点转换为样本数据
	int index = 0;
	for (int row = 0; row < height; row++)
	{
		for (int col = 0; col < width; col++)
		{
			index = row*width + col;
			Vec3b bgr = src.at<Vec3b>(row, col);
			points.at<float>(index, 0) = static_cast<int>(bgr[0]);//b
			points.at<float>(index, 1) = static_cast<int>(bgr[1]);//g
			points.at<float>(index, 2) = static_cast<int>(bgr[2]);//r
		}
	}

4、利用Kmeans算法对样本数据分类

	centers, feature = Kmeans(points, clusterCount, sampleCount);

5、

显示图像分割后的结果

	//显示图像分割结果
	Mat result = Mat::zeros(src.size(), src.type());
	int index1 = 0;
	for (int row = 0; row < height; row++)
	{
		for (int col = 0; col < width; col++)
		{
			index1 = row*width + col;
			int label = feature.at<float>(index1, 3);
			result.at<Vec3b>(row, col)[0] = colorTab[label][0];
			result.at<Vec3b>(row, col)[1] = colorTab[label][1];
			result.at<Vec3b>(row, col)[2] = colorTab[label][2];
		}
	}
	imshow("基于Kmeans聚类的图像分割", result);

四、代码子函数部分

1、初始化聚类中心

	//初始化簇心
	Mat centers = Mat::zeros(clusterCount, 1, CV_32FC3);//4行3列
	centers.at<float>(0, 0) = 150;
	centers.at<float>(0, 1) = 180;
	centers.at<float>(0, 2) = 200;

	centers.at<float>(1, 0) = 20;
	centers.at<float>(1, 1) = 25;
	centers.at<float>(1, 2) = 37;

	centers.at<float>(2, 0) = 80;
	centers.at<float>(2, 1) = 100;
	centers.at<float>(2, 2) = 140;

	centers.at<float>(3, 0) = 226;
	centers.at<float>(3, 1) = 234;
	centers.at<float>(3, 2) = 235;

2、利用Kmeans算法进行迭代

	Mat feature = Mat::zeros(sampleCount, 1, CV_32FC4);
	float *distance = new float[sampleCount];
	int epoch = 0;
	while (true)
	{
		for (int row = 0; row < points.rows; row++)
		{
			for (int i = 0; i < centers.rows; i++)
			{
				distance[i] = pow((points.at<float>(row, 0) - centers.at<float>(i, 0)), 2) + pow((points.at<float>(row, 1) - centers.at<float>(i, 1)), 2)
					+ pow((points.at<float>(row, 2) - centers.at<float>(i, 2)), 2);
			}
			float min = distance[0];
			int flag = 0;
			for (int i = 0; i < clusterCount; i++)
			{
				if (min > distance[i])
				{
					min = distance[i];
					flag = i;
				}
			}
			feature.at<float>(row, 0) = points.at<float>(row, 0);
			feature.at<float>(row, 1) = points.at<float>(row, 1);
			feature.at<float>(row, 2) = points.at<float>(row, 2);
			feature.at<float>(row, 3) = flag;
		}
		float new_center_r = 0, new_center_g = 0, new_center_b = 0;
		float *temp = new float[clusterCount];
		for (int i = 0; i < clusterCount; i++)
		{
			int num = 0;
			float sum_center_r = 0, sum_center_g = 0, sum_center_b = 0;
			for (int row = 0; row < sampleCount; row++)
			{
				if (feature.at<float>(row, 3) == i)
				{
					sum_center_b = sum_center_b + feature.at<float>(row, 0);
					sum_center_g = sum_center_g + feature.at<float>(row, 1);
					sum_center_r = sum_center_r + feature.at<float>(row, 2);
					num++;
				}
			}
			new_center_b = sum_center_b / num;
			new_center_g = sum_center_g / num;
			new_center_r = sum_center_r / num;

			temp[i] = pow((new_center_b - centers.at<float>(i, 0)), 2) + pow((new_center_g - centers.at<float>(i, 1)), 2)
				+ pow((new_center_r - centers.at<float>(i, 2)), 2);

			centers.at<float>(i, 0) = new_center_b;
			centers.at<float>(i, 1) = new_center_g;
			centers.at<float>(i, 2) = new_center_r;
		}
		float total = 0;
		float mean_square_error = 0;
		for (int i = 0; i < clusterCount; i++)
		{
			total = total + temp[i];
		}
		mean_square_error = total / 4;
		if (epoch % 1 == 0)
			cout << "epoch：" << epoch << "\terror of mean square：" << mean_square_error << endl;
		if (mean_square_error < 0.01)
			break;
		epoch++;
	}