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实例5：GMM（高斯混合模型）图像分割

#include <opencv2/opencv.hpp>
#include <iostream>using namespace cv;
using namespace cv::ml;
using namespace std;int main(int argc, char** argv) {Mat src = imread("toux.jpg");if (src.empty()) {printf("could not load iamge...\n");return -1;}namedWindow("input image", CV_WINDOW_AUTOSIZE);imshow("input image", src);// 初始化int numCluster = 3;const Scalar colors[] = {Scalar(255, 0, 0),Scalar(0, 255, 0),Scalar(0, 0, 255),Scalar(255, 255, 0)};int width = src.cols;int height = src.rows;int dims = src.channels();int nsamples = width*height;Mat points(nsamples, dims, CV_64FC1);Mat labels;Mat result = Mat::zeros(src.size(), CV_8UC3);// 图像RGB像素数据转换为样本数据 int index = 0;for (int row = 0; row < height; row++) {for (int col = 0; col < width; col++) {index = row*width + col;Vec3b rgb = src.at<Vec3b>(row, col);points.at<double>(index, 0) = static_cast<int>(rgb[0]);points.at<double>(index, 1) = static_cast<int>(rgb[1]);points.at<double>(index, 2) = static_cast<int>(rgb[2]);}}// EM Cluster TrainPtr<EM> em_model = EM::create();em_model->setClustersNumber(numCluster);em_model->setCovarianceMatrixType(EM::COV_MAT_SPHERICAL);//设置协方差矩阵//设置停止条件，训练100次结束em_model->setTermCriteria(TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 100, 0.1));em_model->trainEM(points, noArray(), labels, noArray());// 对每个像素标记颜色与显示Mat sample(dims, 1, CV_64FC1);double time = getTickCount();int r = 0, g = 0, b = 0;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);Scalar c = colors[label];result.at<Vec3b>(row, col)[0] = c[0];result.at<Vec3b>(row, col)[1] = c[1];result.at<Vec3b>(row, col)[2] = c[2];/*b = src.at<Vec3b>(row, col)[0];g = src.at<Vec3b>(row, col)[1];r = src.at<Vec3b>(row, col)[2];sample.at<double>(0) = b;sample.at<double>(1) = g;sample.at<double>(2) = r;int response = cvRound(em_model->predict2(sample, noArray())[1]);Scalar c = colors[response];result.at<Vec3b>(row, col)[0] = c[0];result.at<Vec3b>(row, col)[1] = c[1];result.at<Vec3b>(row, col)[2] = c[2];*/}}printf("execution time(ms) : %.2f\n", (getTickCount() - time)/getTickFrequency()*1000);imshow("EM-Segmentation", result);waitKey(0);return 0;
}

           执行时间：

可见，GMM算法处理时间较长，并不适合工程实时图像处理。