OpenCv案例（九）：基于OpenCvSharp图像分割提取目标区域和定位

以下原图中，物体连靠在一起，目的是将其分割开，再提取轮廓和定位

原图：

最终效果：

麻烦的地方是，分割开右下角部分，两个连在一起的目标物体，下图所示：

基本方法：BoxFilter滤波、二值化、轮廓提取，凸包检测，图像的矩

代码如下：

/// /// 获取分割点/// 
/// /// /// /// /// public List GetSplitPoints(Point[][] contours, List contourCount, int arcLength, int farDistance){#region 凸包检测List lArc = new List();//Mat src = srcImage.Clone();List lpContours = new List();List hulls = new List();Point lastP = new Point();Point firstP = new Point();Point farLastP = new Point();List lps = new List();int dot = 1;List depth = new List();for (int i = 0; i < contourCount.Count; i++){InputArray inputArray = InputArray.Create(contours[contourCount[i]]);OutputArray outputArray = OutputArray.Create(hulls);Cv2.ConvexHull(inputArray, outputArray, false, false);if (Cv2.ArcLength(inputArray, true) < arcLength){//lArc.Add(Cv2.ArcLength(inputArray, true));continue;}//前三个值得含义分别为：凸缺陷的起始点，凸缺陷的终点，凸缺陷的最深点（即边缘点到凸包距离最大点）。var defects = Cv2.ConvexityDefects(contours[contourCount[i]], hulls);for (int j = 0; j  farDistance) //(4500 < defects[j].Item3 && defects[j].Item3 < 300000){lps.Add(contours[contourCount[i]][defects[j].Item2]);depth.Add(defects[j].Item3);}}}#endregionreturn lps;}/// /// 获取最小内接矩形/// 
/// /// /// public List GetMinRects(Point[][] contours, List contourCount){//Cv2.ImShow(",mmmm", morphImage);//double rotateAngel = 0;Point2f[] vertices = new Point2f[4];//Point2f minRectcenterPoint = new Point2f();List minRects = new List();for (int i = 0; i < contourCount.Count; i++){//获取轮廓点的矩形区域//绘制Rio区域最小矩形#region 绘制Rio区域最小矩形RotatedRect minRect = Cv2.MinAreaRect(contours[contourCount[i]]);minRects.Add(minRect);#endregion}return minRects;}/// /// 返回设置范围内的轮廓/// 
/// /// /// /// /// public Point[][] GetImageContours(Mat mat, int length, out List contourCount){List arclength = new List();OpenCvSharp.Point[][] contours;HierarchyIndex[] hierarchies;//Cv2.ImShow(",mmmm", mat);Cv2.FindContours(mat, out contours, out hierarchies, RetrievalModes.External, ContourApproximationModes.ApproxSimple, new Point());Mat connImg = Mat.Zeros(mat.Size(), MatType.CV_8UC3);Point2f[] vertices = new Point2f[4];Mat drawOutline = Mat.Zeros(mat.Size(), mat.Type());int sum = 0;contourCount = new List();for (int i = 0; i  length)//(rect1.Width > range1 && rect1.Height < range2){Cv2.DrawContours(drawOutline, contours, i, new Scalar(255, 0, 255), 2, LineTypes.Link8, hierarchies);contourCount.Add(i);arclength.Add(Cv2.ArcLength(contours[i], true));sum++;}}Cv2.ImShow("contours", drawOutline);return contours;}/// /// 图像灰度/// 盒子滤波 保留边缘信息/// 自适应阈值 效果不错 无需形态学降噪/// 取反操作 /// 过滤不需要轮廓信息(面积 边长)/// 轮廓提取 /// (以上每一步都很重要,否则,无法获取良好的轮廓)/// 凸包检测/// 根据轮廓信息,查找大凸包,获取分割点/// 重新操作图像/// 在二值化图像时,分割连接点位置/// 绘制轮廓/// 绘制最小内接矩形和质心点/// 识别目标位置完成/// 注意:不同大小的图像处理时,需要修改自适应阈值参数、轮廓过滤面积、凸包检测的分割点过滤/// 
/// /// public Mat PreProcess(Mat srcImage){Mat grayMat = new Mat();Cv2.CvtColor(srcImage, grayMat, ColorConversionCodes.BGRA2GRAY);//Cv2.ImShow("grayMat", grayMat);Mat blurImg = BoxFilter(grayMat);//Cv2.ImShow("blurImg", blurImg);// 注意:不同大小的图像处理时,需要修改参数Mat threshold = new Mat();Cv2.AdaptiveThreshold(blurImg, threshold, 255, AdaptiveThresholdTypes.MeanC, ThresholdTypes.Binary, 15, 2);//Cv2.Threshold(threshold, threshold, 0, 255, ThresholdTypes.BinaryInv);Cv2.ImShow("threshold", threshold);//Mat morphImg = MorphImage(threshold, MorphShapes.Ellipse, MorphTypes.Dilate, 1, new OpenCvSharp.Size(3, 3));//Cv2.ImShow("morphImg", morphImg);//Mat cannyImg = new Mat();//Cv2.Laplacian(morphImg2, cannyImg, MatType.CV_8UC3, 5, 1);//Cv2.Canny(morphImg, cannyImg, 30, 90);//3和4参数的 最佳比例在1/3和1/2之间//Cv2.ImShow("cannyImg", cannyImg);Mat bitwiseMat = new Mat();Cv2.BitwiseNot(threshold, bitwiseMat);Cv2.ImShow("bitwiseMat", bitwiseMat);List contourCount;//轮廓提取Point[][] contours = GetImageContours(bitwiseMat, 600, out contourCount);//凸包检测List lps = GetSplitPoints(contours, contourCount, 800, 4500);// 注意:不同大小的图像处理时,需要修改参数//重新处理Cv2.AdaptiveThreshold(blurImg, threshold, 255.0, AdaptiveThresholdTypes.MeanC, ThresholdTypes.Binary, 13, 2);Cv2.ImShow("threshold1", threshold);//MorphImage(threshold, MorphShapes.Ellipse, MorphTypes.Close, 1, new OpenCvSharp.Size(3, 3));//Cv2.ImShow("morphImg1", morphImg);Cv2.BitwiseNot(threshold, bitwiseMat);Cv2.ImShow("bitwiseMat1", bitwiseMat);//提取凸显点坐标if (lps.Count > 1){Cv2.Line(bitwiseMat, lps[0], lps[1], Scalar.Black, 2, LineTypes.Link8);}Cv2.ImShow("bitwiseMat2", bitwiseMat);//轮廓提取 contourCount.Clear();// 注意:不同大小的图像处理时,需要修改length参数Point[][] newContours = GetImageContours(bitwiseMat, 550, out contourCount);List rotatedRects = GetMinRects(newContours, contourCount);for (int i = 0; i < rotatedRects.Count; i++){#region 绘制Rio区域最小矩形Point2f[] vertices = rotatedRects[i].Points();#endregion//绘制最小矩形#region 绘制最小矩形Cv2.Line(srcImage, Convert.ToInt32(vertices[0].X), Convert.ToInt32(vertices[0].Y), Convert.ToInt32(vertices[1].X), Convert.ToInt32(vertices[1].Y), new Scalar(0, 0, 255), 2);Cv2.Line(srcImage, Convert.ToInt32(vertices[0].X), Convert.ToInt32(vertices[0].Y), Convert.ToInt32(vertices[3].X), Convert.ToInt32(vertices[3].Y), new Scalar(0, 0, 255), 2);Cv2.Line(srcImage, Convert.ToInt32(vertices[1].X), Convert.ToInt32(vertices[1].Y), Convert.ToInt32(vertices[2].X), Convert.ToInt32(vertices[2].Y), new Scalar(0, 0, 255), 2);Cv2.Line(srcImage, Convert.ToInt32(vertices[2].X), Convert.ToInt32(vertices[2].Y), Convert.ToInt32(vertices[3].X), Convert.ToInt32(vertices[3].Y), new Scalar(0, 0, 255), 2);//获取重心点Moments M;M = Cv2.Moments(vertices);double cX = M.M10 / M.M00;double cY = M.M01 / M.M00;//显示目标中心并提取坐标点Cv2.Circle(srcImage, (int)cX, (int)cY, 2, Scalar.Yellow, 2);//Console.WriteLine("AngleRect_angle: {0}", minRect.Angle);#endregion}Cv2.ImShow("srcImage", srcImage);return null;}

灰度图像后图像二值化：