OpenCV Blob分析-基于FindContours联通区域分析本文是一个较完整的Blob分析思路解说，是用OpenCvSharp联合c#编写的。
思路如下：1、对图像进行二值化2、设定ROI3、腐蚀、膨胀（可选）4、边缘提取（有区分是否需填充孔洞）5、进行筛选看一组使用效果图1 原图图2 提取黑色对象图3 提取黑色对象并填充孔洞部分源代码解说：InputImage来着输入的要处理的图片。
gray_min、gray_max二值化的上下阈值1、对图像进行二值化（根据二值化的上下阈值来判定二值化方式）Mat binaryImage = new Mat(InputImage.Size(), MatType.CV_8UC1, Scalar.Black);if (gray_min != 0 && gray_max != 255){binaryImage = InputImage.Threshold(gray_min, 255, ThresholdTypes.Binary);Mat binaryImage2 = InputImage.Threshold(gray_max, 255, ThresholdTypes.BinaryInv);Mat binaryImage3 = new Mat(InputImage.Size(), MatType.CV_8UC1,Scalar.Black);binaryImage.CopyTo(binaryImage3, binaryImage2);binaryImage = binaryImage3.Clone();binaryImage2.Dispose();binaryImage3.Dispose();}else{if (gray_min == 0) binaryImage = InputImage.Threshold(gray_max, 255, ThresholdTypes.BinaryInv);if (gray_max == 255) binaryImage = InputImage.Threshold(gray_min, 255, ThresholdTypes.Binary);}Mat binaryImage1 = new Mat(InputImage.Size(), MatType.CV_8UC1, Scalar.Black);2、设定ROI（Mask就是ROI感兴趣区域）binaryImage.CopyTo(binaryImage1, Mask);3、腐蚀、膨胀（可选）if (erosion_dilation == "腐蚀"){Cv2.Erode(binaryImage1, binaryImage1, element);}else{Cv2.Dilate(binaryImage1, binaryImage1, element);}4、边缘提取（有区分是否需填充孔洞）if (fill_up_flag == true){Cv2.FindContours(binaryImage1, out contours, hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxSimple);}else{Cv2.FindContours(binaryImage1, out contours, hierarchy, omp, ContourApproximationModes.ApproxSimple);}5、进行筛选Mat uu = hierarchy.GetMat();// 筛选那些面积小的List<MyCenter> centers = new List<MyCenter>();List<RotatedRect> regions = new List<RotatedRect>();var detectorParams = new SimpleBlobDetector.Params{MinDistBetweenBlobs = 10, // 10 pixels between blobsMinRepeatability = 1,MinThreshold = 0,MaxThreshold = 128,ThresholdStep = 2,FilterByArea = area_bool,MinArea = area_min, // 10 pixels squaredMaxArea = area_max,//圆度FilterByCircularity = circularity_bool,MinCircularity = circularity_min,MaxCircularity = circularity_max,// 凸包形状分析 - 过滤凹包FilterByConvexity = false,//FilterByConvexity = true,MinConvexity = 0.001f,MaxConvexity = 10,FilterByInertia = false,//FilterByInertia = true,MinInertiaRatio = 0.001f,FilterByColor = false,//FilterByColor = true,BlobColor = 255 // to extract light blobs};MyCenter center;int j = 0;Vec4i value;Mat cnt = new Mat();foreach (Mat cnt0 in contours){value = uu.Get<Vec4i>(0, j);j++;//int i = hierarchy;// 根据轮廓面积进行筛选if (value[3] != -1){cnt = contours[value[3]];}else{cnt = cnt0;}center.confidence = 1;Moments moms = Cv2.Moments(cnt);if (detectorParams.FilterByArea) //斑点面积的限制{double area = moms.M00; //零阶矩即为二值图像的面积//如果面积超出了设定的范围，则不再考虑该斑点if (area < detectorParams.MinArea || area >= detectorParams.MaxArea)continue;}if (detectorParams.FilterByCircularity) //斑点圆度的限制{double area = moms.M00; //得到斑点的面积//计算斑点的周长double perimeter = Cv2.ArcLength(cnt, true);//由公式3得到斑点的圆度double ratio = 4 * Cv2.PI * area / (perimeter * perimeter);//如果圆度超出了设定的范围，则不再考虑该斑点if (ratio < detectorParams.MinCircularity || ratio >= detectorParams.MaxCircularity)continue;}if (detectorParams.FilterByInertia) //斑点惯性率的限制{//计算公式13中最右侧等式中的开根号的值double denominator = Math.Sqrt(Math.Pow(2 * moms.Mu11, 2) +Math.Pow(moms.Mu20 - moms.Mu02, 2));const double eps = 1e-2; //定义一个极小值double ratio;if (denominator > eps){//cosmin和sinmin用于计算图像协方差矩阵中较小的那个特征值λ2double cosmin = (moms.Mu20 - moms.Mu02) / denominator;double sinmin = 2 * moms.Mu11 / denominator;//cosmin和sinmin用于计算图像协方差矩阵中较大的那个特征值λ1double cosmax = -cosmin;double sinmax = -sinmin;//imin为λ2乘以零阶中心矩μ00double imin = 0.5 * (moms.Mu20 + moms.Mu02) - 0.5 * (moms.Mu20 - moms.Mu02) * cosmin - moms.Mu11 * sinmin;//imax为λ1乘以零阶中心矩μ00double imax = 0.5 * (moms.Mu20 + moms.Mu02) - 0.5 * (moms.Mu20 - moms.Mu02) * cosmax - moms.Mu11 * sinmax;ratio = imin / imax; //得到斑点的惯性率}else{ratio = 1; //直接设置为1，即为圆}//如果惯性率超出了设定的范围，则不再考虑该斑点if (ratio < detectorParams.MinInertiaRatio || ratio >= detectorParams.MaxInertiaRatio)continue;//斑点中心的权值定义为惯性率的平方center.confidence = ratio * ratio;}if (detectorParams.FilterByConvexity) //斑点凸度的限制{Mat hull = new Mat(); //定义凸壳变量//调用convexHull函数，得到该斑点的凸壳Cv2.ConvexHull(cnt, hull);//分别得到斑点和凸壳的面积，contourArea函数本质上也是求图像的零阶矩double area = Cv2.ContourArea(cnt);double hullArea = Cv2.ContourArea(hull);double ratio = area / hullArea; //公式5，计算斑点的凸度//如果凸度超出了设定的范围，则不再考虑该斑点if (ratio < detectorParams.MinConvexity || ratio >= detectorParams.MaxConvexity)continue;}//根据公式7，计算斑点的质心center.location = new Point2d(moms.M10 / moms.M00, moms.M01 / moms.M00);// 根据轮廓近似进行筛选，作用很小double epsilon = 0.001 * Cv2.ArcLength(cnt, true);Mat approx = new Mat();Cv2.ApproxPolyDP(cnt, approx, epsilon, true);// 根据最小矩形进行筛选，该矩形可能有方向RotatedRect rect = Cv2.MinAreaRect(approx);//int row = rect.BoundingRect().X;//int col = rect.BoundingRect().Y;if (width_bool){int width = rect.BoundingRect().Width;if (width < width_min || width > width_max) continue;}if (height_bool){int height = rect.BoundingRect().Height;if (height < height_min || height > height_max) continue;}//根据质心来判断颜色不合理，但是我觉得这个不合理就把它禁用了//if (detectorParams.FilterByColor) //斑点颜色的限制//{// //判断一下斑点的颜色是否正确// if (binaryImage.At(int) (cvRound(center.location.Y),cvRound(center.location.X)) != detectorParams.BlobColor)// continue;// }////compute blob radius//{// Mat dists; //定义距离队列// //遍历该斑点边界上的所有像素// for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)// {// Point2d pt = contours[contourIdx][pointIdx]; //得到边界像素坐标// //计算该点坐标与斑点中心的距离，并放入距离队列中// dists.push_back(norm(center.location - pt));// }// std::sort(dists.begin(), dists.end()); //距离队列排序// //计算斑点的半径，它等于距离队列中中间两个距离的平均值// center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;//}centers.Add(center);outcontours.Add(cnt0);}。
