图像分割系列3_KMeans对图像进行分割

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实例3：KMeans图像分割

#include <opencv2/opencv.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
int main(int argc, char** argv) {
	Mat src = imread("toux.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input image", CV_WINDOW_AUTOSIZE);
	imshow("input image", src);
 
	Scalar colorTab[] = {
		Scalar(0, 0, 255),
		Scalar(0, 255, 0),
		Scalar(255, 0, 0),
		Scalar(0, 255, 255),
		Scalar(255, 0, 255)
	};
 
	int width = src.cols;//图像的列
	int height = src.rows;//图像的行
	int dims = src.channels();//图像的通道数
 
	// 初始化定义
	int sampleCount = width*height;//获取图像的像素点数
	int clusterCount = 4;//要分类块数
	Mat points(sampleCount, dims, CV_32F, Scalar(10));//定义样本数据
	Mat labels;
	Mat centers(clusterCount, 1, points.type());//定义中心点(浮点数)
 
	// RGB 数据转换到样本数据
	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]);//通道0
			points.at<float>(index, 1) = static_cast<int>(bgr[1]);//通道1
			points.at<float>(index, 2) = static_cast<int>(bgr[2]);//通道2
		}
	}
 
	// 运行K-Means
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
	kmeans(points, clusterCount, labels, criteria, 3, KMEANS_PP_CENTERS, centers);//尝试3次
 
	// 显示图像分割结果
	Mat result = Mat::zeros(src.size(), src.type());
	for (int row = 0; row < height; row++) {
		for (int col = 0; col < width; col++) {
			index = row*width + col;
			int label = labels.at<int>(index, 0);
			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];
		}
	}
 
	for (int i = 0; i < centers.rows; i++) {
		int x = centers.at<float>(i, 0);
		int y = centers.at<float>(i, 1);
		printf("center %d = c.x : %d, c.y : %d\n", i, x, y);
	}
	
	imshow("KMeans Image Segmentation Demo", result);
	waitKey(0);
	return 0;
}

注：中心点是相对于图像RGB像素值而求定的位置。