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 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(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(y, x); CV_Assert(0 <= label && label <= nccomps); img_color.at(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(); }