Caffe 图像分类

                //当前程序的根目录
                string pathRoot = AppDomain.CurrentDomain.BaseDirectory;

                //demo项目的根目录
                string projectPath = Path.Combine(pathRoot, "DemoProject");

                //图像分类的训练器
                Trainer trainer = new Trainer(projectPath);

                //创建训练数据（首次必须创建，后续可以不再重新创建）
                bool bRebuildData = true;
                if (bRebuildData)
                {
                    //分类信息
                    List<LabelInfo> listLabel = new List<LabelInfo>();
                    listLabel.Add(new LabelInfo() { LabelValue = 0, LabelName = "b黑色" });
                    listLabel.Add(new LabelInfo() { LabelValue = 1, LabelName = "w白色" });
                    listLabel.Add(new LabelInfo() { LabelValue = 2, LabelName = "n无螺丝" });
                    //...

                    //存储样本图像的根目录
                    string imagePath = Path.Combine(pathRoot, "DemoProject", "Images");

                    //将用于训练的图像信息加入到列表中
                    List<ImageSampleInfo> listTrain = new List<ImageSampleInfo>();
                    listTrain.Add(new ImageSampleInfo() { LabelValue = 0, FileName = "4e1b2156a4d548b690b9630f6ca2f8aa.bmp" });
                    //...

                    //将用于测试的图像信息加入到列表中
                    List<ImageSampleInfo> listTest = new List<ImageSampleInfo>();
                    listTest.Add(new ImageSampleInfo() { LabelValue = 0, FileName = "0e48c295e2ab4eac85429ba94efa12c7.bmp" });
                    //...

                    //调用创建方法
                    trainer.CreateImageDataFile(
                        listLabel,
                        imagePath,
                        listTrain,
                        listTest,
                        false);
                }

                //设置基本参数
                trainer.ProjectSettings.resize_width = 71;
                trainer.ProjectSettings.resize_height = 71;
                trainer.ProjectSettings.gray = true;
                //...
                trainer.SaveProjectSettings();

                //设置训练参数
                trainer.solver_args.max_iter = 10;
                trainer.solver_args.snapshot = 10;
                trainer.solver_args.type = type.SGD;
                trainer.solver_args.solver_mode = solver_mode.CPU;
                //...
                trainer.SaveSolver();


                //调用训练方法
                trainer.Train();


                //等待训练进程
                Thread.Sleep(1000 * 3);
                string trainProcessName = "caffe";
                while (true)
                {
                    var array = Process.GetProcessesByName(trainProcessName);
                    if (array.Length > 0)
                    {
                        Debug.WriteLine($"进程还存在 {trainProcessName}");
                        //Application.DoEvents();
                        Thread.Sleep(500);
                    }
                    else
                    {
                        Debug.WriteLine($"进程已杀死 {trainProcessName}");
                        break;
                    }
                }//end while

                MessageBox.Show("训练完成！");

预测

当训练完成之后，根据训练得到的一些模型等文件，就可对后续的图像进行预测分类。

//当前程序的根目录
                string pathRoot = AppDomain.CurrentDomain.BaseDirectory;

                //demo项目的根目录
                string projectPath = Path.Combine(pathRoot, "DemoProject");

                string fileDeploy = Path.Combine(projectPath, "current_deploy.prototxt");
                string fileCaffeModel = Directory.GetFiles(projectPath, "*.caffemodel").FirstOrDefault();
                string fileLabel = Path.Combine(projectPath, "labels.txt");
                string filePredictionArgs = Path.Combine(projectPath, "prediction_args.json");


                //预测器
                Predictor predictor = new Predictor(fileDeploy, fileCaffeModel, fileLabel, filePredictionArgs);


                //待预测图像，图像数据需要转到Mat中，来源可以是：（1）图像文件（2）相机采集
                Mat matImage = null;

                //来源于图像文件
                string fileImage = Path.Combine(projectPath, "demo.bmp");
                matImage = Cv2.ImRead(fileImage, ImreadModes.Grayscale);

                //预测结果
                PredictResults results = null;

                results = predictor.Predict(matImage);
                //或者 results = predictor.Predict(fileImage);

                if (results.Highest != null)
                {
                    string show = $"LabelValue={results.Highest.LabelValue}, LabelName={results.Highest.LabelName}, Score={results.Highest.Score}";
                    Debug.WriteLine(show);
                }