环境准备

win10+VS2015+Opencv3.4

demo

opencv生成随机数据点，使用kmeans函数进行数据点分类

源码如下：

//随机数据点分类 void myKmeans( ) { Mat img(600, 600, CV_8UC3);//图像 RNG rng(12345);//随机数生成器，初始化可以传入一个64位的整型参数作为随机数产生器的初值 //颜色索引表，根据分类数量设定数组大小 Scalar colorTab[5] = { Scalar(255,0,255), Scalar(0,0,255), Scalar(0,255,0), Scalar(255,0,0), Scalar(255,255,0), }; int numCluster = rng.uniform(2, 5); //随机聚类数，2至5类 int sampleCount = rng.uniform(50, 1000); //随机样本数量50-1000个 Mat point(sampleCount, 1, CV_32FC2); //样本矩阵，sampleCount行，1列，通道2 //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 = point.rowRange(k*sampleCount / numCluster, (k == numCluster - 1 ? sampleCount:(k + 1)*sampleCount / numCluster)); //为指定的行跨度创建一个矩阵头 rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05)); //随机数填充矩阵 } //将原数组（矩阵）打乱 randShuffle(point, 1, &rng); Mat lables; //标签矩阵 Mat centers; //中心点矩阵 //使用kmeans kmeans(point, numCluster, lables, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1), 3, KMEANS_PP_CENTERS, centers); //不同颜色显示分类点 img = Scalar(255,255,255); for (int i=0;i<sampleCount;i++) { int index = lables.at<int>(i); Point p = point.at<Point2f>(i); circle(img, p, 2, colorTab[index], -1, 8); } //输出中心点坐标 for (int i=0;i<numCluster;i++) { printf("Center%d:(%f,%f)\n",i, centers.ptr<float>(i)[0], centers.ptr<float>(i)[1]); Point p(centers.ptr<float>(i)[0], centers.ptr<float>(i)[1]); circle(img, p, 5, Scalar(100,100,100), -1, 8); } namedWindow("K-Means demo",CV_WINDOW_AUTOSIZE); imshow("K-Means demo", img); waitKey(0); }