使用opencv中K-Means方法进行基于像素值的图像分割和背景替换_opencv和ncnn人像分割换背景

https://blog.csdn.net/ganwenbo2011/article/details/90577122

//图形分割
void segment(Mat img) {
	namedWindow("srcImg", 0);
	imshow("srcImg", img);
	int wid = img.cols;
	int hig = img.rows;
	int dim = img.channels();
	int sampleCount = wid*hig;  //像素点个数，即样本点数量
	int clusterCount = 3;  //分类数量
	//颜色索引表
	Scalar colorTab[5] = {
		Scalar(255,0,255),
		Scalar(0,0,255),
		Scalar(0,255,0),
		Scalar(255,0,0),
		Scalar(255,255,0),
 
	};
	Mat points(sampleCount, dim, CV_32F, Scalar(10)); //sampleCount行，dim列
 
	int index = 0;
	//图像转数据点
	for (int i=0;i<hig;i++)
	{
		for (int j=0;j<wid;j++)
		{
			index = i*wid + j;
			Vec3b bgr = img.at<Vec3b>(i, j);
			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]);
		}
	}
	//kmeans聚类
	Mat lables;
	Mat centers(clusterCount, 1, points.type());  //clusterCount行，1列
	kmeans(points, clusterCount, lables, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1), 3, KMEANS_PP_CENTERS, centers);
	//聚类结果点集转图像
	Mat segImg = Mat::zeros(img.size(), img.type());
	for (int i=0;i<hig;i++)
	{
		for (int j=0;j<wid;j++)
		{
			index= i*wid + j;
			int lable = lables.at<int>(index, 0);
			Vec3b vec(colorTab[lable][0], colorTab[lable][1], colorTab[lable][2]);
			segImg.at<Vec3b>(i, j) = vec;
		}
	}
	//输出类中心，注意，非坐标值，而是像素值
	for (int i=0;i<clusterCount;i++)
	{
		printf("Count%i:%f,%f,%f\n", i, centers.ptr<float>(i)[0], centers.ptr<float>(i)[1], centers.ptr<float>(i)[2]);
	}
	namedWindow("segmentation", 0);
	imshow("segmentation", segImg);
	//waitKey(0);
 
	//背景替换
	//Point p(100, 100);  //根据情况，背景中的一点
	//int lable = lables.at<int>(p.y*wid+p.x, 0);
	//for (int i = 0; i < hig; i++)
	//{
	//	for (int j = 0; j < wid; j++)
	//	{
	//		if (lable == lables.at<int>(i*wid + j, 0))
	//		{
	//			Vec3b vec(0, 0, 255);//替换为红色
	//			img.at<Vec3b>(i, j) = vec;
	//		}
	//	}
	//	
	//}
	//namedWindow("replace", 0);
	//imshow("replace", img);
	waitKey(0);
}
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