2021-08-29图像分割_kmean

Kmeans方法

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

using namespace cv;
using namespace std;

int main() {
	Mat src, dst, gray_src;
	src = imread("D:/b.jpeg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}

	//namedWindow("input_image", WINDOW_AUTOSIZE);
	//imshow("input_image",src);

	Mat img(500, 500, CV_8UC3);
	RNG rng(12345);

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

	int numCluster = rng.uniform(2, 5);
	printf("number of clusters:%d\n",numCluster);
	int sampelCount = rng.uniform(5, 1000);
	Mat points(sampelCount,1,CV_32FC2);
	Mat labels;
	Mat centers;

	//生成随机数
	for (int k = 0; k < numCluster; k++) {
		Point center;
		center.x = rng.uniform(0, img.cols);
		center.y = rng.uniform(0, img.rows);
		Mat pointChunk = points.rowRange(k * sampelCount / numCluster, k == numCluster - 1 ?
			sampelCount:(k + 1) * sampelCount / numCluster);
		rng.fill(pointChunk,RNG::NORMAL,Scalar(center.x,center.y),Scalar(img.cols*0.05,img.rows*0.05));
	}

	randShuffle(points, 1, &rng);
	//使用kmean
	kmeans(points,numCluster,labels,TermCriteria(TermCriteria::EPS+TermCriteria::COUNT,10,0.1),
		3,KMEANS_PP_CENTERS,centers);
	//用不同颜色显示分类
	for (int i = 0; i < sampelCount; i++) {
		int index = labels.at<int>(i);
		Point p = points.at<Point2f>(i);
		circle(img, p, 2, colorTab[index], -1, 8);
	}
	//绘制每个聚类的中心
	for (int i = 0; i < centers.rows; i++) {
		int x = centers.at<float>(i, 0);
		int y = centers.at<float>(i, 1);
		printf("c.x=%d,c.y=%d",x,y);
		circle(img, Point(x, y), 40, colorTab[i], 1, LINE_AA);
	
	}
	imshow("Kmean_data",img);
	waitKey(0);
	return 0;
}

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

using namespace cv;
using namespace std;

int main(int argc, char** argv) {
	Mat src = imread("D:/b.jpeg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input image", 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;
	int height = src.rows;
	int dims = src.channels();

	// 初始化定义
	int sampleCount = width * height;
	int clusterCount = 4;
	Mat points(sampleCount, dims, CV_32F, Scalar(10));
	Mat labels;
	Mat centers(clusterCount, 1, points.type());

	// 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]);
			points.at<float>(index, 1) = static_cast<int>(bgr[1]);
			points.at<float>(index, 2) = static_cast<int>(bgr[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);

	// 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;
}