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C++实现K-均值算法非监督分类（opencv2.4.9、gdal2.1.2）

1年前作者：金灯烛夜分类：Toy博客阅读(3)违法举报

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算法思想及流程：

C++实现K-均值算法非监督分类（opencv2.4.9、gdal2.1.2）,c++,k-means,opencv

代码：

#include <iostream>
#include <opencv2/opencv.hpp>
#include"gdal_priv.h"
#include<cmath>
using namespace std;
using namespace cv;

/// <summary>
/// 先通过GDAL库读入数据，再将数据其转为Mat类
/// </summary>
/// <param name="src"></param>
/// <param name="fileName"></param>
/// <returns></returns>
int readMat(Mat& src, string fileName)
{
	GDALDataset* poDataset;
	GDALAllRegister();//注册所有的驱动
	poDataset = (GDALDataset*)GDALOpen(fileName.c_str(), GA_ReadOnly);//读取影像
	if (poDataset == NULL)
	{
		cout << "read failed" << endl;
		return -1;
	}
	int width = poDataset->GetRasterXSize();//图像宽度（列数）
	int height = poDataset->GetRasterYSize();//图像高度（行数）
	Mat gdal_mat1(height, width, CV_8UC1, Scalar(0));
	Mat gdal_mat2(height, width, CV_8UC1, Scalar(0));
	Mat gdal_mat3(height, width, CV_8UC1, Scalar(0));
	Mat gdal_mat4(height, width, CV_8UC1, Scalar(0));
	Mat gdal_mat5(height, width, CV_8UC1, Scalar(0));
	Mat gdal_mat6(height, width, CV_8UC1, Scalar(0));
	Mat gdal_mat7(height, width, CV_8UC1, Scalar(0));
	poDataset->GetRasterBand(1)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat1.data, width, height, GDT_Byte, 0, 0);
	poDataset->GetRasterBand(2)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat2.data, width, height, GDT_Byte, 0, 0);
	poDataset->GetRasterBand(3)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat3.data, width, height, GDT_Byte, 0, 0);
	poDataset->GetRasterBand(4)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat4.data, width, height, GDT_Byte, 0, 0);
	poDataset->GetRasterBand(5)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat5.data, width, height, GDT_Byte, 0, 0);
	poDataset->GetRasterBand(6)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat6.data, width, height, GDT_Byte, 0, 0);
	poDataset->GetRasterBand(7)->RasterIO(GF_Read, 0, 0, width, height, gdal_mat7.data, width, height, GDT_Byte, 0, 0);
	merge(vector<Mat>{ gdal_mat1, gdal_mat2, gdal_mat3, gdal_mat4, gdal_mat5, gdal_mat6, gdal_mat7}, src);
	return 1;
}
int KMeans(Mat& src, Mat& classify, int K, double* band1Center, double* band2Center, double* band3Center,
	double* band4Center, double* band5Center, double* band6Center, double* band7Center)
{
	int f = 0;
	for (int i = 0; i < src.rows; i++)
		for (int j = 0; j < src.cols; j++)
		{
			double* dO = new double[K];
			for (int k = 0; k < K; k++)//求与第k类中心的距离
			{
				double d[7];
				d[0] = (int)src.at<Vec<uchar, 7>>(i, j)[0] - band1Center[k];
				d[1] = (int)src.at<Vec<uchar, 7>>(i, j)[1] - band2Center[k];
				d[2] = (int)src.at<Vec<uchar, 7>>(i, j)[2] - band3Center[k];
				d[3] = (int)src.at<Vec<uchar, 7>>(i, j)[3] - band4Center[k];
				d[4] = (int)src.at<Vec<uchar, 7>>(i, j)[4] - band5Center[k];
				d[5] = (int)src.at<Vec<uchar, 7>>(i, j)[5] - band6Center[k];
				d[6] = (int)src.at<Vec<uchar, 7>>(i, j)[6] - band7Center[k];
				dO[k] = sqrt(d[0] * d[0] + d[1] * d[1] + d[2] * d[2] + d[3] * d[3] + d[4] * d[4] + d[5] * d[5] + d[6] * d[6]);
				double dmin = 999999;
				int dminK;
				for (int l = 0; l < K; l++)
					if (dO[l] < dmin)
					{
						dmin = dO[l];
						dminK = l;
					}
				classify.at<int>(i, j) = dminK;//对像元分类
			}
			delete[] dO;
		}
	double* sum = new double[7];
	int classNum;//每个类像元个数
	for (int k = 0; k < K; k++)//求每个类的中心
	{
		for (int i = 0; i < 7; i++)//重置sum
			sum[i] = 0;
		classNum = 0;//重置classNum
		for (int i = 0; i < src.rows; i++)
			for (int j = 0; j < src.cols; j++)
				if (classify.at<int>(i, j) == k)
				{
					for (int l = 0; l < 7; l++)
						sum[l] = sum[l] + (int)src.at<Vec<uchar, 7>>(i, j)[l];
					classNum++;
				}
		double bandCenterD;//聚类中心移动距离
		bandCenterD = fabs(sum[0] / classNum - band1Center[k]);
		if (bandCenterD > 0)//若聚类中心移动距离大于阈值
			f = 1;
		bandCenterD = fabs(sum[1] / classNum - band2Center[k]);
		if (bandCenterD > 0)
			f = 1;
		bandCenterD = fabs(sum[2] / classNum - band3Center[k]);
		if (bandCenterD > 0)
			f = 1;
		bandCenterD = fabs(sum[3] / classNum - band4Center[k]);
		if (bandCenterD > 0)
			f = 1;
		bandCenterD = fabs(sum[4] / classNum - band5Center[k]);
		if (bandCenterD > 0)
			f = 1;
		bandCenterD = fabs(sum[5] / classNum - band6Center[k]);
		if (bandCenterD > 0)
			f = 1;
		bandCenterD = fabs(sum[6] / classNum - band7Center[k]);
		if (bandCenterD > 0)
			f = 1;
		band1Center[k] = sum[0] / classNum;//计算新的聚类中心
		band2Center[k] = sum[1] / classNum;
		band3Center[k] = sum[2] / classNum;
		band4Center[k] = sum[3] / classNum;
		band5Center[k] = sum[4] / classNum;
		band6Center[k] = sum[5] / classNum;
		band7Center[k] = sum[6] / classNum;
	}
	return f;
}
int main()
{
	Mat src;
	string fileName = "E:\\show\\4\\before.img";
	if (readMat(src, fileName) == -1)//读入数据
		return -1;
	int K;
	cout << "请输入要分为几类" << endl;
	cin >> K;
	Mat classify = Mat_<int>(src.rows, src.cols);//(Size(src.cols, src.rows), CV_8U);
	double* band1Center = new double[K];
	double* band2Center = new double[K];
	double* band3Center = new double[K];
	double* band4Center = new double[K];
	double* band5Center = new double[K];
	double* band6Center = new double[K];
	double* band7Center = new double[K];
	RNG rng1;
	for (int k = 0; k < K; k++)//初始聚类中心为随机均匀分布
	{
		band1Center[k] = rng1.uniform(int(0), int(255));
		band2Center[k] = rng1.uniform(int(0), int(255));
		band3Center[k] = rng1.uniform(int(0), int(255));
		band4Center[k] = rng1.uniform(int(0), int(255));
		band5Center[k] = rng1.uniform(int(0), int(255));
		band6Center[k] = rng1.uniform(int(0), int(255));
		band7Center[k] = rng1.uniform(int(0), int(255));
	}
	int I = 1;
	while (KMeans(src, classify, K, band1Center, band2Center, band3Center, band4Center, band5Center, band6Center, band7Center))
	{
		cout << "第" << I << "次迭代" << endl;
		I++;
	}
	Mat classifyed(Size(src.cols, src.rows), CV_8UC3);
	int* rR = new int[K];
	int* rG = new int[K];
	int* rB = new int[K];
	RNG rng2;
	for (int k = 0; k < K; k++)
	{
		rR[k] = rng2.uniform(int(0), int(255));
		rG[k] = rng2.uniform(int(0), int(255));
		rB[k] = rng2.uniform(int(0), int(255));
	}
	for (int i = 0; i < src.rows; i++)//设置每一类的颜色
		for (int j = 0; j < src.cols; j++)
			for (int k = 0; k < K; k++)
				if (classify.at<int>(i, j) == k)
					classifyed.at<Vec3b>(i, j) = { (uchar)rB[k],(uchar)rG[k],(uchar)rR[k] };
	namedWindow("Classification", CV_WINDOW_FREERATIO);
	imshow("Classification", classifyed);
	cout << "Classification complete" << endl;
	waitKey(0);
	return 0;
}

原图像（3、2、1波段）：

C++实现K-均值算法非监督分类（opencv2.4.9、gdal2.1.2）,c++,k-means,opencv 

分类后（输入分类数K=4）：

C++实现K-均值算法非监督分类（opencv2.4.9、gdal2.1.2）,c++,k-means,opencv

C++实现K-均值算法非监督分类（opencv2.4.9、gdal2.1.2）,c++,k-means,opencv

代码已能实现简单的分类。但受限于本人水平，该代码仍有许多不足，初始聚类中心的选择尚需改进，存储聚类中心的方式不够简洁

注：当前代码只能读入img格式图像文章来源地址https://www.toymoban.com/news/detail-799420.html

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