欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

图像分割系列5_GMM(高斯混合模型)对图像进行分割

程序员文章站 2024-03-25 08:09:22
...

OpenCV图像分割资料分享:贾志刚的OpenCV图像分割实战****全套资料(包含配套视频、配套PPT的PDF文件、源码和用到的图片素材等)

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 Train
	Ptr<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;
}

图像分割系列5_GMM(高斯混合模型)对图像进行分割       图像分割系列5_GMM(高斯混合模型)对图像进行分割    执行时间:图像分割系列5_GMM(高斯混合模型)对图像进行分割

可见,GMM算法处理时间较长,并不适合工程实时图像处理。