版权所有，翻版必究。
运行环境：WIN10，pycharm，相应的CUDA，CUDNN，tensorflow1.15.0，tensorflow-gpu-1.14.0，Anaconda3
https://blog.csdn.net/fightingxyz/article/details/107013155有空的先看看这篇文章，这边文章是在这个文章的基础上来做的。主要是前一篇文章是用来介绍怎么获取.pb文件和.pbtxt文件的。然后此篇博客是对此的改写，将其变成C#下的版本。如果有问题欢迎互相讨论，不惜勿喷！
第一步：获取.pb文件和.pbtxt文件（模型准备！）。详情请参见：https://blog.csdn.net/fightingxyz/article/details/107013155
主要测试的图像是这种类型：
上一篇文章中，有详细说明相关内容。主要是用来预测apple banana orange。我用C#的测试结果为：
第二步：配置C#的调用环境，看下图：
主要依赖库为这些！补充一句，必须要是opencv4.0及其以上才行，因为低版本还没有嵌入调用mask_rcnn的接口。
上面安装库中的TensorFlowSharp可用可不用，因为程序中没用(●'◡'●)
第三步：话不多说，详情看代码！
using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; using OpenCvSharp; using TensorFlow; using System.IO; namespace test10 { class Program { static void Main(string[] args) { string textGraph = "mask_rcnn.pbtxt"; string modelWeights = "frozen_inference_graph.pb"; string classesFile = "mscoco_labels.names"; string colorsFile = "colors.txt"; float confThreshold = 0.9f; //此处只是设置你的预测框阈值，判断是否显示 float maskThreshold = 0.3f; List<string> classes = new List<string>(); //用来存放类型数据在其中 List<Scalar> colors = new List<Scalar>(); //用来存放画图的颜色 Mat blob; float m_fWidthScale; float m_fHeighScale; //下面这段主要是用于读取文件，并将其中内容放置classes中。 //和上一篇文章中的void LoadLabelAndColor()函数一个意思，不懂可以去看看python版本或者C++版本 FileStream myfs = new FileStream(classesFile, FileMode.Open); StreamReader reader = new StreamReader(myfs); string con; con = reader.ReadToEnd(); var a = con.Split(new string[] { "\r\n" }, StringSplitOptions.None); foreach (var ai in a) { Console.WriteLine(ai); classes.Add(ai); } //这个地方也是读取文件，但是没用上 FileStream myfs1 = new FileStream(colorsFile, FileMode.OpenOrCreate); StreamReader reader1 = new StreamReader(myfs1); string con1; con1 = reader1.ReadToEnd(); var a1 = con1.Split(new string[] { "\r\n" }, StringSplitOptions.None); //因为我这里单独拿出来了 colors.Add(new Scalar(0, 255,0)); colors.Add(new Scalar(0, 0, 255)); colors.Add(new Scalar(255, 0, 0)); OpenCvSharp.Dnn.Net net = OpenCvSharp.Dnn.CvDnn.ReadNetFromTensorflow(modelWeights, textGraph); net.SetPreferableBackend(OpenCvSharp.Dnn.Net.Backend.OPENCV); //opencv是使用intel编译的 net.SetPreferableTarget(OpenCvSharp.Dnn.Net.Target.CPU); int ImgWidth = 224; int ImgHight = 224; Mat frame = Cv2.ImRead("1.jpg"); Mat m_DstMat = frame.Clone(); Size s2f = new Size(ImgWidth, ImgHight); Cv2.Resize(frame, frame, s2f); m_fWidthScale = (float)(m_DstMat.Cols * 1.0 / frame.Cols); m_fHeighScale = (float)(m_DstMat.Rows * 1.0 / frame.Rows); // Stop the program if reached end of video if (frame.Empty()) { return; } blob = OpenCvSharp.Dnn.CvDnn.BlobFromImage(frame, 1.0, new Size(frame.Cols, frame.Rows), new Scalar(), true, false); net.SetInput(blob); List<string> outNames = new List<string>(2); outNames.Add("detection_out_final"); //此处是预测的边框点 outNames.Add("detection_masks"); //这些名字设置和模型中的设置有关系，主要也是利用名称取索引相应的结果，此处是预测的掩模图 List<Mat> outs = new List<Mat>(); Mat outDetections; Mat outMasks ; //预测的图我选择了分开写，调用其他两种出不来，原因还在找，知道的可以交流交流，谢谢。详情可以打开net.ForWard()看一看 //结果有三种： /*public Mat Forward([NullableAttribute(2)] string? outputName = null); public void Forward(IEnumerable<Mat> outputBlobs, [NullableAttribute(2)] string? outputName = null); public void Forward(IEnumerable<Mat> outputBlobs, IEnumerable<string> outBlobNames);*/ outMasks = net.Forward("detection_masks"); outDetections = net.Forward("detection_out_final"); int numDetections = outDetections.Size(2); int numClasses = outMasks.Size(1); // 预测框结果outDetections 掩膜结果outMasks outDetections = outDetections.Reshape(1, (int)outDetections.Total() / 7); for (int i = 0; i < numDetections; ++i) { float score = outDetections.At<float>(i, 2); if (score > confThreshold) //if (score > 0.2) { // Extract the bounding box int classId = (int)(outDetections.At<float>(i, 1)); int left = (int)(frame.Cols * outDetections.At<float>(i, 3)); int top = (int)(frame.Rows * outDetections.At<float>(i, 4)); int right = (int)(frame.Cols * outDetections.At<float>(i, 5)); int bottom = (int)(frame.Rows * outDetections.At<float>(i, 6)); left = Math.Max(0, Math.Min(left, frame.Cols - 1)); top = Math.Max(0, Math.Min(top, frame.Rows - 1)); right = Math.Max(0, Math.Min(right, frame.Cols - 1)); bottom = Math.Max(0, Math.Min(bottom, frame.Rows - 1)); Rect box = new Rect(left, top, right - left + 1, bottom - top + 1); /************************************************/ box.X = (int)Math.Round(box.X * m_fWidthScale); box.Y = (int)Math.Round(box.Y * m_fHeighScale); box.Width = (int)Math.Round(box.Width * m_fWidthScale); box.Height = (int)Math.Round(box.Height * m_fHeighScale); /************************************************/ // Extract the mask for the object Mat objectMask = new Mat(outMasks.Size(2), outMasks.Size(3), MatType.CV_32F, outMasks.Ptr(i, classId)); // Draw bounding box, colorize and show the mask on the image OpenCvSharp.Scalar s3 = new Scalar(255, 178, 50); //Draw a rectangle displaying the bounding box Cv2.Rectangle(m_DstMat, new Point(box.X, box.Y), new Point(box.X + box.Width, box.Y + box.Height), s3, 3); //Get the label for the class name and its confidence string label1; label1 = score.ToString("F4"); string labelasdfa; labelasdfa= classes[classId] + ":" + label1; //Display the label at the top of the bounding box int baseLine; Size labelSize = Cv2.GetTextSize(labelasdfa, 0, 0.5, 1, out baseLine); box.Y = Math.Max(box.Y, labelSize.Height); Cv2.Rectangle(m_DstMat, new Point(box.X, box.Y - Math.Round(1.5 * labelSize.Height)), new Point(box.X + Math.Round(1.5 * labelSize.Width), box.Y + baseLine), new Scalar(255, 255, 255), Cv2.FILLED); Cv2.PutText(m_DstMat, labelasdfa, new Point(box.X, box.Y), 0, 0.75, new Scalar(0, 0, 0), 1); Scalar color = colors[classId % colors.Capacity]; // Resize the mask, threshold, color and apply it on the image Cv2.Resize(objectMask, objectMask, new Size(box.Width, box.Height)); Mat mask = objectMask; Mat pos1 = new Mat(m_DstMat, box); //Mat coloredRoi = (0.3 * color + 0.7 * m_DstMat(box)); Mat coloredRoi = new Mat(m_DstMat, box); coloredRoi.ConvertTo(coloredRoi, MatType.CV_8UC3); mask.ConvertTo(mask, MatType.CV_8U); Cv2.FindContours(mask, out Point[][] contours,out HierarchyIndex[] hierarchy, RetrievalModes.CComp, ContourApproximationModes.ApproxSimple); Cv2.DrawContours(coloredRoi, contours, -1, color, 5, LineTypes.Link8, hierarchy, 100); Mat pos = new Mat(m_DstMat, box); coloredRoi.CopyTo(pos, mask); } } double[] layersTimes = new double[] { }; double freq = Cv2.GetTickFrequency() / 1000; double t = net.GetPerfProfile(out layersTimes) / freq; string label = "Inference time for a frame "+t.ToString("F4")+"ms"; OpenCvSharp.Scalar s2 = new Scalar(0, 0, 0); Point xy = new Point(0, 15); Cv2.PutText(m_DstMat, label, xy, 0, 0.5, s2); Cv2.ImShow("Result", m_DstMat); Cv2.WaitKey(0); } } }至此结束！本来想分分让其更好看一些，太忙了，小伙伴们有空可以封装一下。欢迎互相交流。附送一张图(●'◡'●)！！！
