OpenCV根据面积筛选连通域学习示例_python

//src为二值图，minArea、maxArea为面积阈值，dest为结果图像
void connectionAreaSelect(Mat src, int minArea, int maxArea, Mat &dest)
{
	Mat labels, stats, centroids, img_color;
	//连通域计算
	int nccomps = connectedComponentsWithStats(
		src, //二值图像
		labels,
		stats,
		centroids
	);

	//去除过小区域，初始化颜色表
	vector<Vec3b> colors(nccomps);
	colors[0] = Vec3b(0, 0, 0); // background pixels remain black.
	for (int i = 1; i < nccomps; i++)
	{
		colors[i] = Vec3b(rand() % 256, rand() % 256, rand() % 256);

		//面积阈值筛选
		int holeArea = stats.at<int>(i, CC_STAT_AREA);
		if ((holeArea < minArea) || (holeArea > maxArea))
		{
			colors[i] = Vec3b(0, 0, 0);
		}
	}
	//按照label值，对不同的连通域进行着色
	img_color = Mat::zeros(src.size(), CV_8UC3);
	for (int y = 0; y < img_color.rows; y++)
	{
		for (int x = 0; x < img_color.cols; x++)
		{
			int label = labels.at<int>(y, x);
			CV_Assert(0 <= label && label <= nccomps);
			img_color.at<Vec3b>(y, x) = colors[label];
		}
	}
	//统计降噪后的连通区域
	Mat grayImg;
	cvtColor(img_color, grayImg, COLOR_BGR2GRAY);
	threshold(grayImg, grayImg, 1, 255, THRESH_BINARY);
	dest = grayImg.clone();

	labels.release();
	stats.release();
	centroids.release();
	img_color.release();
	grayImg.release();
}