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实例5：GMM（高斯混合模型）图像分割

#include 
   
    #include 
    
     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
     
      (row, col);			points.at
      
       (index, 0) = static_cast
       
        (rgb[0]);			points.at
        
         (index, 1) = static_cast
         
          (rgb[1]); points.at
          
           (index, 2) = static_cast
           
            (rgb[2]); } } // EM Cluster Train Ptr
             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
             
              (index, 0); Scalar c = colors[label]; result.at
              
               (row, col)[0] = c[0]; result.at
               
                (row, col)[1] = c[1]; result.at
                
                 (row, col)[2] = c[2]; /*b = src.at
                 
                  (row, col)[0]; g = src.at
                  
                   (row, col)[1]; r = src.at
                   
                    (row, col)[2]; sample.at
                    
                     (0) = b; sample.at
                     
                      (1) = g; sample.at
                      
                       (2) = r; int response = cvRound(em_model->predict2(sample, noArray())[1]); Scalar c = colors[response]; result.at
                       
                        (row, col)[0] = c[0]; result.at
                        
                         (row, col)[1] = c[1]; result.at
                         
                          (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。

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