using System; using System.Windows.Forms; using System.IO; using System.Drawing; using System.Drawing.Imaging; using System.ComponentModel; using System.Runtime.InteropServices; using System.Threading; using System.Diagnostics; using System.Linq; using System.Collections.Generic; using TensorFlow; using OpenCvSharp; namespace Tongue_extraction { public partial class Form1 : Form { //Use the RemoveSmallRegionDLL [DllImport(@"RemoveSmallRegionDLL.dll", EntryPoint = "RemoveSmallRegion", SetLastError = true, CharSet = CharSet.Ansi, ExactSpelling = false, CallingConvention = CallingConvention.StdCall)] extern static void RemoveSmallRegion(string name, string name2, int AreaLimit, int CheckMode, int NeihborMode); Mat mat_drawBox = new Mat(1024, 1280, MatType.CV_8UC3, 1); Mat mat_roi = new Mat(1024, 1280, MatType.CV_8UC3, 1); Mat mat_input = new Mat(1024, 1280, MatType.CV_8UC3, 1); Mat mat_roi256 = new Mat(256, 256, MatType.CV_8UC3, 1); Mat mat_roisize = new Mat(1024, 1280, MatType.CV_8UC3, 1); Mat mat_output = new Mat(1024, 1280, MatType.CV_8UC1, 1); Mat mat_outputNoBox = new Mat(1024, 1280, MatType.CV_8UC1, 1); Mat mat_outputChanged = new Mat(1024, 1280, MatType.CV_8UC1, 1); Mat mat_mask = new Mat(1024, 1280, MatType.CV_8UC1, 1); Mat mat_extraction = new Mat(1024, 1280, MatType.CV_8UC3, 1); Mat mat_cropped; Mat mat_outputSRGNoBox = new Mat(1024, 1280, MatType.CV_8UC1, 1); Mat mat_outputSRG = new Mat(1024, 1280, MatType.CV_8UC1, 1); Mat mat_maskSRG = new Mat(1024, 1280, MatType.CV_8UC1, 1); public static Bitmap bitmap_bitch; OpenCvSharp.Rect rectangle; byte[] byte_inputDetection; byte[] byte_inputSegmentation; OpenCvSharp.Point P1 = new OpenCvSharp.Point(); OpenCvSharp.Point P2 = new OpenCvSharp.Point(); byte[] mask = new byte[200000]; string basepath; string imageFile; string time; string modelFile; int ii = 0; int count; int areaCount; Rect roi = new Rect(); int mmp = 0; int pmm = 0; int check_detection = 0; float max_score = 0; private ManualResetEvent manualReset = new ManualResetEvent(true); string path_infocsv = Directory.GetCurrentDirectory() + "//info//" + DateTime.Now.ToLocalTime().ToString("yyyyMMddhhmmss") + ".csv"; string path_procList = @"ProcList.txt"; bool IsLastFileChanged = false; public Form1() { InitializeComponent(); } private void Form1_Load(object sender, EventArgs e) { comboBox_mode.SelectedIndex = 2; } private void Button_start_Click(object sender, EventArgs e) { button_start.Enabled = false; button_pause.Enabled = true; pictureBox_input.Image = null; pictureBox_input.Refresh(); pictureBox_detection.Image = null; pictureBox_detection.Refresh(); pictureBox_cropResized.Image = null; pictureBox_cropResized.Refresh(); pictureBox_output.Image = null; pictureBox_output.Refresh(); pictureBox_outputSRG.Image = null; pictureBox_outputSRG.Refresh(); pictureBox_maskSRG.Image = null; pictureBox_maskSRG.Refresh(); pictureBox_extraction.Image = null; pictureBox_extraction.Refresh(); pictureBox_last.Image = null; pictureBox_last.Refresh(); comboBox_mode.Enabled = false; if (comboBox_mode.SelectedIndex == 0) { // 画像のみ入力し，マスク等を得る this.backgroundWorker1.DoWork += new System.ComponentModel.DoWorkEventHandler(this.BackgroundWorker1_DoWork_Image); backgroundWorker1.RunWorkerAsync(); } else if (comboBox_mode.SelectedIndex == 1) { // 画像と校正画像を入力し，Lab色を得る this.backgroundWorker1.DoWork += new System.ComponentModel.DoWorkEventHandler(this.BackgroundWorker1_DoWork_ImageandCalib); backgroundWorker1.RunWorkerAsync(); } else if (comboBox_mode.SelectedIndex == 2) { // TIASフォルダを入力し，Lab色を得る this.backgroundWorker1.DoWork += new System.ComponentModel.DoWorkEventHandler(this.BackgroundWorker1_DoWork_TIASdata); backgroundWorker1.RunWorkerAsync(); } else { MessageBox.Show("Please select the mode on the combobox"); } } public static class ImageUtil { // Convert the image in filename to a Tensor suitable as input to the Inception model. public static TFTensor CreateTensorFromImageFile(byte[] contents, TFDataType destinationDataType = TFDataType.UInt8) { // DecodeJpeg uses a scalar String-valued tensor as input. var tensor = TFTensor.CreateString(contents); TFGraph graph; TFOutput input, output; // Construct a graph to normalize the image ConstructGraphToNormalizeImage(out graph, out input, out output, destinationDataType); // Execute that graph to normalize this one image using (var session = new TFSession(graph)) { var normalized = session.Run( inputs: new[] { input }, inputValues: new[] { tensor }, outputs: new[] { output }); if (session != null) { session.Dispose(); } if (tensor != null) { tensor.Dispose(); } if (graph != null) { graph.Dispose(); } GC.Collect(); GC.WaitForPendingFinalizers(); GC.Collect(); return normalized[0]; } } // The inception model takes as input the image described by a Tensor in a very // specific normalized format (a particular image size, shape of the input tensor, // normalized pixel values etc.). // // This function constructs a graph of TensorFlow operations which takes as // input a JPEG-encoded string and returns a tensor suitable as input to the // inception model. public static void ConstructGraphToNormalizeImage(out TFGraph graph, out TFOutput input, out TFOutput output, TFDataType destinationDataType = TFDataType.UInt8) { const int W = 256; const int H = 256; const float Mean = 0; const float Scale = 1; graph = new TFGraph(); input = graph.Placeholder(TFDataType.String); output = graph.Cast(graph.Div( x: graph.Sub( x: graph.ResizeBilinear( images: graph.ExpandDims( input: graph.Cast( graph.DecodeJpeg(contents: input, channels: 3), DstT: destinationDataType), dim: graph.Const(0, "make_batch")), size: graph.Const(new int[] { W, H }, "size")), y: graph.Const(Mean, "mean")), y: graph.Const(Scale, "scale")), destinationDataType); GC.Collect(); GC.WaitForPendingFinalizers(); GC.Collect(); } } public static class ImageUtil2 { // Convert the image in filename to a Tensor suitable as input to the Inception model. public static TFTensor CreateTensorFromImageFile(byte[] contents, TFDataType destinationDataType = TFDataType.Float) { // DecodeJpeg uses a scalar String-valued tensor as input. var tensor = TFTensor.CreateString(contents); TFGraph graph; TFOutput input, output; // Construct a graph to normalize the image ConstructGraphToNormalizeImage(out graph, out input, out output, destinationDataType); // Execute that graph to normalize this one image using (var session = new TFSession(graph)) { var normalized = session.Run( inputs: new[] { input }, inputValues: new[] { tensor }, outputs: new[] { output }); if (session != null) { session.Dispose(); } if (tensor != null) { tensor.Dispose(); } if (graph != null) { graph.Dispose(); } GC.Collect(); GC.WaitForPendingFinalizers(); GC.Collect(); return normalized[0]; } } // The inception model takes as input the image described by a Tensor in a very // specific normalized format (a particular image size, shape of the input tensor, // normalized pixel values etc.). // // This function constructs a graph of TensorFlow operations which takes as // input a JPEG-encoded string and returns a tensor suitable as input to the // inception model. public static void ConstructGraphToNormalizeImage(out TFGraph graph, out TFOutput input, out TFOutput output, TFDataType destinationDataType = TFDataType.Float) { const int W = 256; const int H = 256; //const int W = 512; //const int H = 512; const float Mean = 128; const float Scale = 128; graph = new TFGraph(); input = graph.Placeholder(TFDataType.String); output = graph.Cast(graph.Div( x: graph.Sub( x: graph.ResizeBilinear( images: graph.ExpandDims( input: graph.Cast( graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float), dim: graph.Const(0, "make_batch")), size: graph.Const(new int[] { W, H }, "size")), y: graph.Const(Mean, "mean")), y: graph.Const(Scale, "scale")), destinationDataType); GC.Collect(); GC.WaitForPendingFinalizers(); GC.Collect(); } } public static byte[] Bitmap2Byte(Bitmap bitmap) { using (MemoryStream stream = new MemoryStream()) { bitmap.Save(stream, ImageFormat.Jpeg); byte[] data = new byte[stream.Length]; stream.Seek(0, SeekOrigin.Begin); stream.Read(data, 0, Convert.ToInt32(stream.Length)); return data; } } private static string DownloadDefaultModel(string dir) { var modelFile = Path.Combine(dir, "pruning101_step11999.pb"); return modelFile; } private static string DownloadDefaultModel_noBoxPix2Pix(string dir) { var modelFile = Path.Combine(dir, "424_256_64_5999_scale300_enhancment_L1loss0.02001.pb"); return modelFile; } public static Bitmap ToGrayBitmap(byte[] rawValues, int width, int height) { //// Apply for a target bitmap variable and lock its memory area Bitmap bmp = new Bitmap(width, height, PixelFormat.Format8bppIndexed); BitmapData bmpData = bmp.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.WriteOnly, PixelFormat.Format8bppIndexed); //// Get image parameters int stride = bmpData.Stride; // Width of the scan line int offset = stride - width; // Show gap between width and scan line width IntPtr iptr = bmpData.Scan0; // Get the memory start position of bmpData int scanBytes = stride * height;// Use stride width to indicate that this is the size of the memory area //// The following is to convert the original display size byte array to the byte array actually stored in memory. int posScan = 0, posReal = 0;// Set two position pointers respectively, pointing to the source array and the target array byte[] pixelValues = new byte[scanBytes]; //Allocate memory for the target array for (int x = 0; x < height; x++) { //// The following loop section is a simulated line scan for (int y = 0; y < width; y++) { pixelValues[posScan++] = rawValues[posReal++]; } posScan += offset; //At the end of the line scan, move the target position pointer over that "gap" } //// Use Marshal's Copy method to copy the just obtained memory byte array into BitmapData. System.Runtime.InteropServices.Marshal.Copy(pixelValues, 0, iptr, scanBytes); bmp.UnlockBits(bmpData); // Unlock the memory area //// The following code is to modify the index table of the generated bitmap, from pseudo color to grayscale ColorPalette tempPalette; using (Bitmap tempBmp = new Bitmap(1, 1, PixelFormat.Format8bppIndexed)) { tempPalette = tempBmp.Palette; } for (int i = 0; i < 256; i++) { tempPalette.Entries[i] = Color.FromArgb(i, i, i); } bmp.Palette = tempPalette; //// The algorithm ends here and returns the result. return bmp; } private void BackgroundWorker1_DoWork_Image(object sender, DoWorkEventArgs e) { MessageBox.Show("this mode isn't implemented"); } private void BackgroundWorker1_DoWork_ImageandCalib(object sender, DoWorkEventArgs e) { // boundingboxなどのinfo出力用 var sw = new StreamWriter(path_infocsv, false, System.Text.Encoding.GetEncoding("shift_jis")); //　出力用csvの準備 sw.Write( "image" + "," + "L1" + "," + "a1" + "," + "b1" + "," + "L2" + "," + "a2" + "," + "b2" + "," + "L3" + "," + "a3" + "," + "b3" + "," + "L4" + "," + "a4" + "," + "b4" ); sw.WriteLine(); sw.Close(); using (MemoryStream ms = new MemoryStream()) { // ディレクトリglobの取得 var glob_dir = Directory.GetDirectories("input"); for (int a = 0; a < glob_dir.Length; a++) { manualReset.WaitOne(); // 画像と校正ファイルのパス var glob_file = Directory.GetFiles(glob_dir[a]); var path_calib = glob_file.Where(n => n.Contains("csv")).ToList()[0]; var path_image = glob_file.Where(n => n.Contains("bmp") || n.Contains("png")).ToList()[0]; var path_base = Directory.GetCurrentDirectory(); var name_image = Path.GetFileName(path_image); var name_dir = Path.GetFileName(glob_dir[a]); // ステータスの表示 Invoke((MethodInvoker)delegate { label_processingFileName.Text = "Processing File: " + "\\" + name_dir; count = a + 1; label_totalProgress.Text = "Total Progress: " + count + "/" + glob_dir.Length; }); // 入力画像読み込み・表示 mat_input = Cv2.ImRead(path_image, ImreadModes.Color); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_input); Invoke((MethodInvoker)delegate { pictureBox_input.Image = bitmap_bitch; pictureBox_input.Refresh(); }); // Detection label8.BackColor = Color.White; label1.BackColor = Color.Red; mat_drawBox = mat_input.Clone(); mat_cropped = new Mat(mat_input.Size(), MatType.CV_8UC3, 1); byte_inputDetection = Bitmap2Byte(bitmap_bitch); using (var graph = new TFGraph()) { var model = File.ReadAllBytes(path_base + "/Detection_Normal.pb"); graph.Import(model, ""); using (var session = new TFSession(graph)) { var tensor = ImageUtil.CreateTensorFromImageFile(byte_inputDetection, TFDataType.UInt8); var runner = session.GetRunner(); runner .AddInput(graph["image_tensor"][0], tensor) .Fetch("detection_boxes", "detection_scores", "detection_classes", "num_detections"); var output = runner.Run(); var boxes = (float[,,])output[0].GetValue(); var scores = (float[,])output[1].GetValue(); var classes = (float[,])output[2].GetValue(); var detections = (float[])output[3].GetValue(); check_detection = 0; max_score = 0; for (int i = 0; i < scores.Length; i++) { if ((scores[0, i] > 0.5) && (scores[0, i] > max_score)) { max_score = scores[0, i]; float y_min = boxes[0, i, 0] * (float)bitmap_bitch.Height; float x_min = boxes[0, i, 1] * (float)bitmap_bitch.Width; float y_max = boxes[0, i, 2] * (float)bitmap_bitch.Height; float x_max = boxes[0, i, 3] * (float)bitmap_bitch.Width; P1.X = (int)x_min; P1.Y = (int)y_min; P2.X = (int)x_max; P2.Y = (int)y_max; Cv2.Rectangle(mat_drawBox, P1, P2, new Scalar(0, 255, 0), 5); rectangle.X = (int)x_min; rectangle.Y = (int)y_min; rectangle.Width = (int)(x_max - x_min); rectangle.Height = (int)(y_max - y_min); check_detection = 1; } } } } // Detection結果表示・保存 mat_drawBox.SaveImage(path_base + "\\detection" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_drawBox); Invoke((MethodInvoker)delegate { pictureBox_detection.Image = bitmap_bitch; pictureBox_detection.Refresh(); }); label1.BackColor = Color.White; // 舌検出領域でcrop label2.BackColor = Color.Red; for (int i = P1.Y; i < P2.Y; i++) { for (int j = P1.X; j < P2.X; j++) { Vec3b pix = mat_input.At(i, j); mat_cropped.Set(i, j, pix); } } mat_cropped.SaveImage(path_base + "\\cropped" + "\\" + name_dir + ".bmp"); // 舌検出領域でresize OpenCvSharp.Size size_roi = new OpenCvSharp.Size(); size_roi.Height = rectangle.Height; size_roi.Width = rectangle.Width; roi = new Rect(P1, size_roi); // 20210201 var mat_input2 = mat_input.Clone(); mat_input2 = mat_input2 * 1.5; mat_roisize = mat_input2.Clone(roi); Cv2.Resize(mat_roisize, mat_roi, mat_roi256.Size()); mat_roi.SaveImage(path_base + "\\cropresized" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_roi); Invoke((MethodInvoker)delegate { pictureBox_cropResized.Image = bitmap_bitch; pictureBox_cropResized.Refresh(); }); label2.BackColor = Color.White; // Segmenation label3.BackColor = Color.Red; byte_inputSegmentation = Bitmap2Byte(bitmap_bitch); Thread.Sleep(1000); modelFile = DownloadDefaultModel(path_base); ii = 0; using (var graph = new TFGraph()) { var model = File.ReadAllBytes(modelFile); graph.Import(model, ""); using (var session = new TFSession(graph)) { var tensor = ImageUtil2.CreateTensorFromImageFile(byte_inputSegmentation); var runner = session.GetRunner(); runner //.AddInput(graph["generator/input_image"][0], tensor) //.Fetch(graph["generator/prediction"][0]); .AddInput(graph["input_image"][0], tensor) .Fetch(graph["generator1/decoder_1/Tanh"][0]); var output = runner.Run(); float[,,,] resultfloat = (float[,,,])output[0].GetValue(jagged: false); for (int p = 0; p < 256; p++) { for (int q = 0; q < 256; q++) { float check = resultfloat[0, p, q, 0]; if (check < 0) { mask[ii] = 0; } else { mask[ii] = 255; } ii++; } } } } GC.Collect(); Thread.Sleep(1000); // segmentation結果表示・保存 bitmap_bitch = ToGrayBitmap(mask, 256, 256); mat_output = OpenCvSharp.Extensions.BitmapConverter.ToMat(bitmap_bitch); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_output); Invoke((MethodInvoker)delegate { pictureBox_output.Image = bitmap_bitch; pictureBox_output.Refresh(); }); label3.BackColor = Color.White; label4.BackColor = Color.Red; mat_output.SaveImage(path_base + "\\output256" + "\\" + name_dir + ".bmp"); // 後処理（領域拡張法）でノイズ除去　iteration 2 try { RemoveSmallRegion(path_base + "\\output256" + "\\" + name_dir + ".bmp", path_base + "\\output_changed1" + "\\" + name_dir + ".bmp", 500, 1, 1); RemoveSmallRegion(path_base + "\\output_changed1" + "\\" + name_dir + ".bmp", path_base + "\\output_changed2" + "\\" + name_dir + ".bmp", 500, 0, 0); } catch { MessageBox.Show("Error: Unable to reprocess! Please check is there [RemoveSmallRegionDLL.dll] file in floder?", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error); break; } mat_outputSRG = new Mat(path_base + "\\output_changed2" + "\\" + name_dir + ".bmp", ImreadModes.GrayScale); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_outputSRG); Invoke((MethodInvoker)delegate { pictureBox_outputSRG.Image = bitmap_bitch; pictureBox_outputSRG.Refresh(); }); label4.BackColor = Color.White; label5.BackColor = Color.Red; // バウンディングボックスのサイズに戻す Cv2.Resize(mat_outputSRG, mat_outputChanged, mat_roisize.Size()); mat_outputChanged.SaveImage(path_base + "\\output_resized" + "\\" + name_dir + ".bmp"); ; // 入力と同じサイズでマスクを作成(なんかたまに不安定．resizeで微妙にサイズ変わってrange overかも) // 気休め Thread.Sleep(100); GC.Collect(); mat_mask = new Mat(mat_input.Size(), MatType.CV_8UC1, 0); var y_mask = 0; for (int y = P1.Y; y < P2.Y; y++) { if (y_mask >= mat_outputChanged.Height) break; var x_mask = 0; for (int x = P1.X; x < P2.X; x++) { if (x_mask >= mat_outputChanged.Width) break; int pix = mat_outputChanged.At(y_mask, x_mask); mat_mask.Set(y, x, pix); x_mask++; } y_mask++; } mmp = 0; Cv2.Resize(mat_mask, mat_mask, mat_input.Size()); mat_mask.SaveImage(path_base + "\\mask" + "\\" + name_dir + ".bmp"); // ノイズ処理 try { RemoveSmallRegion(path_base + "\\mask" + "\\" + name_dir + ".bmp", path_base + "\\mask_changed1" + "\\" + name_dir + ".bmp", 500, 1, 1); RemoveSmallRegion(path_base + "\\mask_changed1" + "\\" + name_dir + ".bmp", path_base + "\\mask_changed2" + "\\" + name_dir + ".bmp", 500, 0, 0); } catch { MessageBox.Show("Error: Unable to reprocess! Please check is there [RemoveSmallRegionDLL.dll] file in floder?", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error); break; } mat_maskSRG = new Mat(path_base + "\\mask_changed2" + "\\" + name_dir + ".bmp", ImreadModes.GrayScale); Cv2.Threshold(mat_maskSRG, mat_maskSRG, 128, 255, ThresholdTypes.Binary); // 2値マスクの最終結果 mat_maskSRG.SaveImage(path_base + "\\mask_final" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_maskSRG); Invoke((MethodInvoker)delegate { pictureBox_maskSRG.Image = bitmap_bitch; pictureBox_maskSRG.Refresh(); }); label5.BackColor = Color.White; // 元画像とマスクを合わせて，舌領域を抜き出す　 // todo: opencv等によるマスク処理と領域計算へ label6.BackColor = Color.Red; mat_extraction = mat_input.Clone(); areaCount = 0; for (int i = 0; i < mat_input.Height; i++) { for (int j = 0; j < mat_input.Width; j++) { Vec3b pix = mat_extraction.At(i, j); if (mat_maskSRG.At(i, j) == 0) { pix[0] = (byte)(255); pix[1] = (byte)(255); pix[2] = (byte)(255); mat_extraction.Set(i, j, pix); } else { pix[0] = (byte)(mat_extraction.At(i, j).Item0); pix[1] = (byte)(mat_extraction.At(i, j).Item1); pix[2] = (byte)(mat_extraction.At(i, j).Item2); mat_extraction.Set(i, j, pix); areaCount++; } } } mat_extraction.SaveImage(path_base + "\\extraction" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_extraction); Invoke((MethodInvoker)delegate { pictureBox_extraction.Image = bitmap_bitch; pictureBox_extraction.Refresh(); }); label6.BackColor = Color.White; /// 5点クリックによる色抽出処理 var path_colorMatrixXYZ = "xyz.txt"; // 色抽出処理のクラス ColorExtractor ce = new ColorExtractor(); // マスクされた舌領域画像の作成 Mat mat_finalMask = mat_maskSRG.Clone(); Cv2.ImWrite(path_base + "\\final_mask" + "\\" + name_dir + ".bmp", mat_finalMask); Mat mat_maskedImg = new Mat(); mat_input.CopyTo(mat_maskedImg, mat_finalMask); // 5点クリック法（2010石川） List list_5points_3 = ce.Get5points(mat_finalMask, ColorExtractor.FivePointMethod.Method3); // 8領域の取得 List list_8area_3 = ce.Get8area(list_5points_3); // 領域の表示 var mat_areaDicision = ce.ShowResult(mat_input.Clone(), list_5points_3, list_8area_3); // 色抽出 List list_4Bgr = ce.Get4colors(mat_maskedImg, list_8area_3); // 色変換(RGB->XYZ->Lab) List list_4Lab = ce.Calc4Lab(list_4Bgr, path_calib, path_colorMatrixXYZ); // 保存 string CSVfilename = path_base + "\\color" + "\\" + name_dir + ".csv"; FileStream CSV_file = File.Open(CSVfilename, FileMode.OpenOrCreate, FileAccess.Write); CSV_file.Seek(0, SeekOrigin.Begin); CSV_file.SetLength(0); CSV_file.Close(); StreamWriter CSV_data = new StreamWriter(CSVfilename); CSV_data.WriteLine("Area,R,G,B,L,a,b"); for (int i = 0; i < list_4Bgr.Count(); i++) { string str = (i + 1).ToString() + ","; str += list_4Bgr[i].Val2.ToString("0.0000") + "," + list_4Bgr[i].Val1.ToString("0.0000") + "," + list_4Bgr[i].Val0.ToString("0.0000") + "," + list_4Lab[i].Val0.ToString("0.0000") + "," + list_4Lab[i].Val1.ToString("0.0000") + "," + list_4Lab[i].Val2.ToString("0.0000"); CSV_data.WriteLine(str); } CSV_data.Close(); // 廃棄 GC.Collect(); System.Threading.Thread.Sleep(100); mat_areaDicision.SaveImage(path_base + "\\autoAreaDecision" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_areaDicision); Invoke((MethodInvoker)delegate { pictureBox_last.Image = bitmap_bitch; pictureBox_last.Refresh(); }); label7.BackColor = Color.White; // 処理log label8.BackColor = Color.Red; time = DateTime.Now.ToLocalTime().ToString(); File.AppendAllText("Log.txt ", time + " " + "\\" + name_dir + ".bmp" + " Done!\n"); // info出力 sw = new StreamWriter(path_infocsv, true, System.Text.Encoding.GetEncoding("shift_jis")); string stra = ""; for (int i = 0; i < list_4Bgr.Count(); i++) { stra += list_4Lab[i].Val0.ToString("0.0000") + "," + list_4Lab[i].Val1.ToString("0.0000") + "," + list_4Lab[i].Val2.ToString("0.0000") + ","; } sw.Write( name_dir.ToString() + "," + stra ); sw.Write("\n"); sw.Close(); // 破棄 } GC.Collect(); MessageBox.Show("Finished!"); Invoke((MethodInvoker)delegate { button_start.Enabled = true; button_pause.Enabled = false; label_processingFileName.Text = "Processing File: None"; }); } } // Debugs private void BackgroundWorker1_DoWork_TIASdata(object sender, DoWorkEventArgs e) { // boundingboxなどのinfo出力用 var sw = new StreamWriter(path_infocsv, false, System.Text.Encoding.GetEncoding("shift_jis")); //　出力用csvの準備 sw.WriteLine( "image" + "," + "top left X" + "," + "top left Y" + "," + "bottom right X" + "," + "bottom right Y" + "," + "Width" + "," + "Height" + "," + "Area"); sw.Close(); // TIASフォルダのパスを取得 var path_procBase = ""; Invoke((MethodInvoker)delegate { //ダイアログを表示する if (folderBrowserDialog1.ShowDialog() == DialogResult.OK) { path_procBase = folderBrowserDialog1.SelectedPath; label_processingFileName.Text = "処理フォルダ: " + path_procBase; Console.WriteLine("処理フォルダ: " + path_procBase); } else { MessageBox.Show("プログラムを再起動してください。"); return; } }); // 色抽出（TongueAnalysisbin2.3）のため，Proclistを初期化 FileStream fs = File.Open(path_procList, FileMode.OpenOrCreate, FileAccess.Write); fs.Seek(0, SeekOrigin.Begin); fs.SetLength(0); StreamWriter sw_ = new StreamWriter(fs); sw_.Write("BASEDIR=" + Directory.GetCurrentDirectory() + "\\input"); sw_.WriteLine(); sw_.Close(); fs.Close(); try { // inputとoutputフォルダを空にする ToDo: 過去に処理したデータがあれば再利用 var _target = new DirectoryInfo("input"); foreach (FileInfo file in _target.GetFiles()) file.Delete(); foreach (DirectoryInfo dir in _target.GetDirectories()) dir.Delete(true); _target = new DirectoryInfo("output"); foreach (FileInfo file in _target.GetFiles()) file.Delete(); foreach (DirectoryInfo dir in _target.GetDirectories()) dir.Delete(true); // 選択されたTIASdirの全日付dir var glob_tias_dirs = Directory.GetDirectories(path_procBase); for (int a = 0; a < glob_tias_dirs.Length; a++) { // 全日付dirに入っている全dir var glob_date_dirs = Directory.GetDirectories(glob_tias_dirs[a]); // 日付dirをコピー var path_data_dst = @"input\" + Path.GetFileName(glob_tias_dirs[a]); if (!Directory.Exists(path_data_dst)) { Directory.CreateDirectory(path_data_dst); } else { Console.WriteLine(path_data_dst + "は既に存在します"); } // 画像dirと校正dir var path_calib_dirs = glob_date_dirs.Where(n => n.Contains("校正")).ToList(); var path_notCalib_dirs = glob_date_dirs.Where(n => !n.Contains("校正")).ToList(); var path_img_dirs = path_notCalib_dirs.Where(n => File.Exists(n + @"\Shot0001.bmp")).ToList(); // yyyy-MM-dd var st_time_tiasDir = Path.GetFileName(glob_tias_dirs[a]).ToString(); // 色抽出（TongueAnalysisbin2.3）のため，Proclistを記述 sw_ = new StreamWriter(path_procList, true); sw_.WriteLine(st_time_tiasDir); sw_.Close(); // 画像dirに対して処理 for (int b = 0; b < path_img_dirs.Count(); b++) { Console.WriteLine("img dir:"); Console.WriteLine(path_img_dirs[b]); // もし舌裏撮影なら飛ばす if (File.Exists(path_img_dirs[b] + @"\NShot0001.bmp")) continue; // もし舌画像がなければ飛ばす if (!File.Exists(path_img_dirs[b] + @"\Shot0001.bmp")) continue; // HH_mm_ss-患者ID-来院回数 var st_time_imgDir = Path.GetFileName(path_img_dirs[b]).ToString().Substring(0, 8); Console.WriteLine(st_time_imgDir); // 日付時刻文字列をDatetimeフォーマットへ変換 string strTime = st_time_tiasDir + "-" + st_time_imgDir; string format = "yyyy-MM-dd-HH_mm_ss"; DateTime time_img_dir = DateTime.ParseExact(strTime, format, null); // 舌画像dirから見て最新calib_dirを探索 var path_newCalibDir = path_calib_dirs[0]; foreach (var path_calibDir in path_calib_dirs) { string target = st_time_tiasDir + "-" + Path.GetFileName(path_calibDir).ToString().Substring(0, 8); DateTime time_target = DateTime.ParseExact(target, format, null); string newone = st_time_tiasDir + "-" + Path.GetFileName(path_newCalibDir).ToString().Substring(0, 8); DateTime time_newone = DateTime.ParseExact(newone, format, null); // imgDirより前に作成され，かつその中で最新 if (time_img_dir > time_target) if (time_newone < time_target) path_newCalibDir = path_calibDir; } Console.WriteLine("入力画像dir"); Console.WriteLine(path_img_dirs[b]); Console.WriteLine("入力校正dir"); Console.WriteLine(path_newCalibDir); // それぞれのファイルとフォルダーをコピー // (条件　処理が二回目以降でinfo.csvが既に存在するなら，info.csvを生成しない) const string DEFAULT_FILENAME = "Shot0001.bmp"; var path_imgdir_dst = path_data_dst + "\\" + Path.GetFileName(path_img_dirs[b]); if (!Directory.Exists(path_imgdir_dst)) { Directory.CreateDirectory(path_imgdir_dst); File.Copy(path_img_dirs[b] + "\\" + DEFAULT_FILENAME, path_imgdir_dst + "\\" + DEFAULT_FILENAME); } else { Console.WriteLine(path_imgdir_dst + "は存在します"); } var path_calibdir_dst = path_data_dst + "\\" + Path.GetFileName(path_newCalibDir); if (!Directory.Exists(path_calibdir_dst)) { Directory.CreateDirectory(path_calibdir_dst); File.Copy(path_newCalibDir + "\\" + DEFAULT_FILENAME, path_calibdir_dst + "\\" + DEFAULT_FILENAME); } else { Console.WriteLine(path_imgdir_dst + "は存在します"); } } } GC.Collect(); } catch { Console.WriteLine("フォルダの読み込み，校正ファイルの検索における例外"); MessageBox.Show("選択されたフォルダはTIASデータフォルダではありません\nプログラムを再起動してください。"); return; } using (MemoryStream ms = new MemoryStream()) { // ディレクトリglobの取得 var glob_tias_dirs_ = Directory.GetDirectories("input"); for (int a = 0; a < glob_tias_dirs_.Length; a++) { // tiasDirに入っている全dir var glob_date_dirs = Directory.GetDirectories(glob_tias_dirs_[a]); // 画像dirと校正dir var path_calib_dirs = glob_date_dirs.Where(n => n.Contains("校正")).ToList(); var path_img_dirs = glob_date_dirs.Where(n => !n.Contains("校正")).ToList(); // yyyy-MM-dd var st_time_tiasDir = Path.GetFileName(glob_tias_dirs_[a]).ToString(); Console.WriteLine(st_time_tiasDir.ToString()); // 画像dir for (int b = 0; b < path_img_dirs.Count(); b++) { Console.WriteLine(path_img_dirs.Count()); Console.WriteLine(path_img_dirs[b]); // HH_mm_ss-患者ID-来院回数 var st_time_imgDir = Path.GetFileName(path_img_dirs[b]).ToString().Substring(0, 8); Console.WriteLine(st_time_imgDir); // 日付時刻文字列をDatetimeフォーマットへ変換 string strTime = st_time_tiasDir + "-" + st_time_imgDir; string format = "yyyy-MM-dd-HH_mm_ss"; DateTime time_img_dir = DateTime.ParseExact(strTime, format, null); // 舌画像dirから見て最新calib_dirを探索 var path_newCalibDir = path_calib_dirs[0]; foreach (var path_calibDir in path_calib_dirs) { string target = st_time_tiasDir + "-" + Path.GetFileName(path_calibDir).ToString().Substring(0, 8); DateTime time_target = DateTime.ParseExact(target, format, null); string newone = st_time_tiasDir + "-" + Path.GetFileName(path_newCalibDir).ToString().Substring(0, 8); DateTime time_newone = DateTime.ParseExact(newone, format, null); // imgDirより前に作成され，かつその中で最新 if (time_img_dir > time_target) if (time_newone < time_target) path_newCalibDir = path_calibDir; } Console.WriteLine("入力画像dir"); Console.WriteLine(path_img_dirs[b]); Console.WriteLine("入力校正dir"); Console.WriteLine(path_newCalibDir); // 画像に対する繰り返し(今は最も古い画像(0001.bmp)のみを処理，ゆくゆくは全画像(0001-0200)を処理) var glob_image_path = Directory.GetFiles(path_img_dirs[b]).Where(n => n.Contains(".bmp")); ; foreach (var path_img in glob_image_path) { // DeepTIAS処理 manualReset.WaitOne(); // 画像と校正ファイルのパス var path_calib = Directory.GetFiles(path_newCalibDir).Where(n => n.Contains("Shot0001.bmp")).ToList()[0]; var path_base = Directory.GetCurrentDirectory(); var name_image = Path.GetFileName(path_img); var name_dir = Path.GetFileName(st_time_tiasDir); // ステータスの表示 Invoke((MethodInvoker)delegate { label_processingFileName.Text = "Processing File: " + name_dir + "\\" + Path.GetFileName(path_img_dirs[b]); count = a + 1; label_totalProgress.Text = "Total Progress: " + count + "/" + glob_tias_dirs_.Length; }); // 入力画像読み込み・表示 mat_input = Cv2.ImRead(path_img, ImreadModes.Color); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_input); Invoke((MethodInvoker)delegate { pictureBox_input.Image = bitmap_bitch; pictureBox_input.Refresh(); }); // Detection label8.BackColor = Color.White; label1.BackColor = Color.Red; mat_drawBox = mat_input.Clone(); mat_cropped = new Mat(mat_input.Size(), MatType.CV_8UC3, 1); byte_inputDetection = Bitmap2Byte(bitmap_bitch); using (var graph = new TFGraph()) { var model = File.ReadAllBytes(path_base + "/Detection_Normal.pb"); graph.Import(model, ""); using (var session = new TFSession(graph)) { var tensor = ImageUtil.CreateTensorFromImageFile(byte_inputDetection, TFDataType.UInt8); var runner = session.GetRunner(); runner .AddInput(graph["image_tensor"][0], tensor) .Fetch("detection_boxes", "detection_scores", "detection_classes", "num_detections"); var output = runner.Run(); var boxes = (float[,,])output[0].GetValue(); var scores = (float[,])output[1].GetValue(); var classes = (float[,])output[2].GetValue(); var detections = (float[])output[3].GetValue(); check_detection = 0; max_score = 0; for (int i = 0; i < scores.Length; i++) { if ((scores[0, i] > 0.5) && (scores[0, i] > max_score)) { max_score = scores[0, i]; float y_min = boxes[0, i, 0] * (float)bitmap_bitch.Height; float x_min = boxes[0, i, 1] * (float)bitmap_bitch.Width; float y_max = boxes[0, i, 2] * (float)bitmap_bitch.Height; float x_max = boxes[0, i, 3] * (float)bitmap_bitch.Width; P1.X = (int)x_min; P1.Y = (int)y_min; P2.X = (int)x_max; P2.Y = (int)y_max; Cv2.Rectangle(mat_drawBox, P1, P2, new Scalar(0, 255, 0), 5); rectangle.X = (int)x_min; rectangle.Y = (int)y_min; rectangle.Width = (int)(x_max - x_min); rectangle.Height = (int)(y_max - y_min); check_detection = 1; } } } } // Detection結果表示・保存 mat_drawBox.SaveImage(path_base + "\\detection" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_drawBox); Invoke((MethodInvoker)delegate { pictureBox_detection.Image = bitmap_bitch; pictureBox_detection.Refresh(); }); label1.BackColor = Color.White; // 舌検出領域でcrop label2.BackColor = Color.Red; for (int i = P1.Y; i < P2.Y; i++) { for (int j = P1.X; j < P2.X; j++) { Vec3b pix = mat_input.At(i, j); mat_cropped.Set(i, j, pix); } } mat_cropped.SaveImage(path_base + "\\cropped" + "\\" + name_dir + ".bmp"); // 舌検出領域でresize OpenCvSharp.Size size_roi = new OpenCvSharp.Size(); size_roi.Height = rectangle.Height; size_roi.Width = rectangle.Width; roi = new Rect(P1, size_roi); // 20210201 //var mat_input2 = mat_input.Clone(); //mat_input2 = mat_input2 * 1.5; mat_roisize = mat_input.Clone(roi); Cv2.Resize(mat_roisize, mat_roi, mat_roi256.Size()); mat_roi.SaveImage(path_base + "\\cropresized" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_roi); Invoke((MethodInvoker)delegate { pictureBox_cropResized.Image = bitmap_bitch; pictureBox_cropResized.Refresh(); }); label2.BackColor = Color.White; // Segmenation label3.BackColor = Color.Red; byte_inputSegmentation = Bitmap2Byte(bitmap_bitch); Thread.Sleep(1000); modelFile = DownloadDefaultModel(path_base); ii = 0; using (var graph = new TFGraph()) { var model = File.ReadAllBytes(modelFile); graph.Import(model, ""); using (var session = new TFSession(graph)) { var tensor = ImageUtil2.CreateTensorFromImageFile(byte_inputSegmentation); var runner = session.GetRunner(); runner //.AddInput(graph["generator/input_image"][0], tensor) //.Fetch(graph["generator/prediction"][0]); .AddInput(graph["input_image"][0], tensor) .Fetch(graph["generator1/decoder_1/Tanh"][0]); var output = runner.Run(); float[,,,] resultfloat = (float[,,,])output[0].GetValue(jagged: false); for (int p = 0; p < 256; p++) { for (int q = 0; q < 256; q++) { float check = resultfloat[0, p, q, 0]; if (check < 0) { mask[ii] = 0; } else { mask[ii] = 255; } ii++; } } } } GC.Collect(); Thread.Sleep(1000); // segmentation結果表示・保存 bitmap_bitch = ToGrayBitmap(mask, 256, 256); mat_output = OpenCvSharp.Extensions.BitmapConverter.ToMat(bitmap_bitch); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_output); Invoke((MethodInvoker)delegate { pictureBox_output.Image = bitmap_bitch; pictureBox_output.Refresh(); }); label3.BackColor = Color.White; label4.BackColor = Color.Red; mat_output.SaveImage(path_base + "\\output256" + "\\" + name_dir + ".bmp"); // 後処理（領域拡張法）でノイズ除去　iteration 2 try { RemoveSmallRegion(path_base + "\\output256" + "\\" + name_dir + ".bmp", path_base + "\\output_changed1" + "\\" + name_dir + ".bmp", 500, 1, 1); RemoveSmallRegion(path_base + "\\output_changed1" + "\\" + name_dir + ".bmp", path_base + "\\output_changed2" + "\\" + name_dir + ".bmp", 500, 0, 0); } catch { MessageBox.Show("Error: Unable to reprocess! Please check is there [RemoveSmallRegionDLL.dll] file in floder?", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error); break; } mat_outputSRG = new Mat(path_base + "\\output_changed2" + "\\" + name_dir + ".bmp", ImreadModes.GrayScale); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_outputSRG); Invoke((MethodInvoker)delegate { pictureBox_outputSRG.Image = bitmap_bitch; pictureBox_outputSRG.Refresh(); }); label4.BackColor = Color.White; label5.BackColor = Color.Red; // バウンディングボックスのサイズに戻す Cv2.Resize(mat_outputSRG, mat_outputChanged, mat_roisize.Size()); mat_outputChanged.SaveImage(path_base + "\\output_resized" + "\\" + name_dir + ".bmp"); // 入力と同じサイズでマスクを作成(なんかたまに不安定．resizeで微妙にサイズ変わってrange overかも) // 気休め Thread.Sleep(100); GC.Collect(); mat_mask = new Mat(mat_input.Size(), MatType.CV_8UC1, 0); var y_mask = 0; for (int y = P1.Y; y < P2.Y; y++) { if (y_mask >= mat_outputChanged.Height) break; var x_mask = 0; for (int x = P1.X; x < P2.X; x++) { if (x_mask >= mat_outputChanged.Width) break; int pix = mat_outputChanged.At(y_mask, x_mask); mat_mask.Set(y, x, pix); x_mask++; } y_mask++; } mmp = 0; Cv2.Resize(mat_mask, mat_mask, mat_input.Size()); mat_mask.SaveImage(path_base + "\\mask" + "\\" + name_dir + ".bmp"); // ノイズ処理 try { RemoveSmallRegion(path_base + "\\mask" + "\\" + name_dir + ".bmp", path_base + "\\mask_changed1" + "\\" + name_dir + ".bmp", 500, 1, 1); RemoveSmallRegion(path_base + "\\mask_changed1" + "\\" + name_dir + ".bmp", path_base + "\\mask_changed2" + "\\" + name_dir + ".bmp", 500, 0, 0); } catch { MessageBox.Show("Error: Unable to reprocess! Please check is there [RemoveSmallRegionDLL.dll] file in floder?", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error); break; } mat_maskSRG = new Mat(path_base + "\\mask_changed2" + "\\" + name_dir + ".bmp", ImreadModes.GrayScale); Cv2.Threshold(mat_maskSRG, mat_maskSRG, 128, 255, ThresholdTypes.Binary); // 2値マスクの最終結果 mat_maskSRG.SaveImage(path_base + "\\mask_final" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_maskSRG); Invoke((MethodInvoker)delegate { pictureBox_maskSRG.Image = bitmap_bitch; pictureBox_maskSRG.Refresh(); }); label5.BackColor = Color.White; // 元画像とマスクを合わせて，舌領域を抜き出す　 // todo: opencv等によるマスク処理と領域計算へ label6.BackColor = Color.Red; mat_extraction = mat_input.Clone(); areaCount = 0; for (int i = 0; i < mat_input.Height; i++) { for (int j = 0; j < mat_input.Width; j++) { Vec3b pix = mat_extraction.At(i, j); if (mat_maskSRG.At(i, j) == 0) { pix[0] = (byte)(255); pix[1] = (byte)(255); pix[2] = (byte)(255); mat_extraction.Set(i, j, pix); } else { pix[0] = (byte)(mat_extraction.At(i, j).Item0); pix[1] = (byte)(mat_extraction.At(i, j).Item1); pix[2] = (byte)(mat_extraction.At(i, j).Item2); mat_extraction.Set(i, j, pix); areaCount++; } } } mat_extraction.SaveImage(path_base + "\\extraction" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_extraction); Invoke((MethodInvoker)delegate { pictureBox_extraction.Image = bitmap_bitch; pictureBox_extraction.Refresh(); }); label6.BackColor = Color.White; /// 5点クリックによる色抽出処理 var path_colorMatrixXYZ = "xyz.txt"; // 色抽出処理のクラス ColorExtractor ce = new ColorExtractor(); // マスクされた舌領域画像の作成 Mat mat_finalMask = mat_maskSRG.Clone(); Cv2.ImWrite(path_base + "\\final_mask" + "\\" + name_dir + ".bmp", mat_finalMask); Mat mat_maskedImg = new Mat(); mat_input.CopyTo(mat_maskedImg, mat_finalMask); // 5点クリック法（2010石川） List list_5points_3 = ce.Get5points(mat_finalMask, ColorExtractor.FivePointMethod.Method3); // 5点座標を入力画像，校正画像と同じディレクトリに保存 var path_infocsv = path_img_dirs[b] + "\\Info.csv"; FileStream CSV_file = File.Open(path_infocsv, FileMode.OpenOrCreate, FileAccess.Write); CSV_file.Seek(0, SeekOrigin.Begin); CSV_file.SetLength(0); CSV_file.Close(); Console.WriteLine(path_infocsv); StreamWriter CSV_data = new StreamWriter(path_infocsv); CSV_data.WriteLine(Path.GetFileName(path_img)); foreach (var item in list_5points_3) { string str = item.X + "," + item.Y; CSV_data.WriteLine(str); } CSV_data.Close(); // 8領域の取得 List list_8area_3 = ce.Get8area(list_5points_3); // 領域の表示 var mat_areaDicision = ce.ShowResult(mat_input.Clone(), list_5points_3, list_8area_3); mat_areaDicision.SaveImage(path_base + "\\autoAreaDecision" + "\\" + name_dir + ".bmp"); bitmap_bitch = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat_areaDicision); Invoke((MethodInvoker)delegate { pictureBox_last.Image = bitmap_bitch; pictureBox_last.Refresh(); }); label7.BackColor = Color.White; //csvにboundingbox情報を保存 sw = new StreamWriter(this.path_infocsv, true, System.Text.Encoding.GetEncoding("shift_jis")); sw.WriteLine( path_img.Substring(6) + "," + P1.X.ToString() + "," + P1.Y.ToString() + "," + P2.X.ToString() + "," + P2.Y.ToString() + "," + Math.Abs(P1.X - P2.X).ToString() + "," + Math.Abs(P1.Y - P2.Y).ToString() + "," + areaCount.ToString() ); sw.Close(); // 処理log label8.BackColor = Color.Red; time = DateTime.Now.ToLocalTime().ToString(); File.AppendAllText("Log.txt ", time + " " + "\\" + name_dir + ".bmp" + " Done!\n"); Thread.Sleep(100); // 破棄 GC.Collect(); } } } // 色抽出のためproclist sw_ = new StreamWriter(path_procList, true); sw_.WriteLine("END"); sw_.Close(); // 最後の日付のデータを監視し，更新され次第，tongueanalysisを閉じる var fs2 = File.Open(path_procList, FileMode.Open, FileAccess.Read); var sr = new StreamReader(fs2); fs2.Seek(0, SeekOrigin.Begin); string line = ""; string line_l = ""; var path_watchedFile = ""; while ((line = sr.ReadLine()) != null) { if (line != "END") { line_l = line; continue; } Console.WriteLine("Last folder:"); var path_watchedFolder = Directory.GetCurrentDirectory() + "\\input\\" + line_l; var path_img_dirs = Directory.GetDirectories(path_watchedFolder).Where(n => !n.Contains("校正")).ToList(); path_watchedFolder = path_img_dirs[path_img_dirs.Count() - 1]; path_watchedFile = path_watchedFolder + "\\" + "analysis.csv"; Console.WriteLine("Watching:" + path_watchedFile); } sr.Close(); fs2.Close(); // proclist最後のファイルについて，analysis.csvの更新を非同期監視 System.IO.FileSystemWatcher watcher = new System.IO.FileSystemWatcher(); watcher.Path = Path.GetDirectoryName(path_watchedFile); watcher.NotifyFilter = (System.IO.NotifyFilters.LastAccess | System.IO.NotifyFilters.LastWrite | System.IO.NotifyFilters.FileName | System.IO.NotifyFilters.DirectoryName); watcher.Filter = "analysis.csv"; //UIのスレッドにマーシャリングする //コンソールアプリケーションでの使用では必要ない watcher.SynchronizingObject = this; //イベントハンドラの追加 watcher.Changed += new System.IO.FileSystemEventHandler(watcher_Changed); watcher.Created += new System.IO.FileSystemEventHandler(watcher_Changed); watcher.Deleted += new System.IO.FileSystemEventHandler(watcher_Changed); watcher.Renamed += new System.IO.RenamedEventHandler(watcher_Renamed); //監視を開始する watcher.EnableRaisingEvents = true; Console.WriteLine("監視を開始しました。"); // TongueAnalysis_bin2.3を動かす var proc = Process.Start(@"TongueAnalysis_bin2.3\TongueAnalysis.exe"); while(true) { if (IsLastFileChanged) { watcher.EnableRaisingEvents = false; watcher.Dispose(); proc.Kill(); break; } else { proc.WaitForExit(100); } } // 生成されたファイルをわかりやすい場所(exeの親dir)にコピー var path_finaloutputDir = @"..\Output"; if (!Directory.Exists(path_finaloutputDir)) Directory.CreateDirectory(path_finaloutputDir); else { Console.WriteLine(path_finaloutputDir + "は既に存在します"); Directory.Delete(path_finaloutputDir, true); Directory.CreateDirectory(path_finaloutputDir); } var _target = new DirectoryInfo("output"); foreach (FileInfo file in _target.GetFiles()) { if (file.Name != "analysis.csv") file.CopyTo(path_finaloutputDir + @"\" + file.Name); } // 形状に関する出力csvと色に関するcsvを合わせる var path_infocsv_ = path_infocsv; var path_colorcsv_ = @"output\analysis.csv"; var path_result_ = path_finaloutputDir + @"\AnalysisResult.csv"; var isFirst = true; using (var sr_info = new StreamReader(path_infocsv_)) using (var sr_color = new StreamReader(path_colorcsv_)) using (var sw_result = new StreamWriter(path_result_)) { while ((sr_info.EndOfStream == false) && (sr_color.EndOfStream == false)) { string line_info = sr_info.ReadLine(); line_info = line_info.Substring(line_info.IndexOf(",") + 1); Console.WriteLine(line_info); string line_color = sr_color.ReadLine(); Console.WriteLine(line_color); string line_result = line_color + line_info; if (isFirst) { var header = "date,time-case,image,L1,a1,b1,L2,a2,b2,L3,a3,b3,L4,a4,b4," + "top left X,top left Y,bottom right X,bottom right Y,Width,Height,Area"; sw_result.WriteLine(header); isFirst = false; continue; } sw_result.WriteLine(line_result); } sw_result.Close(); sr_info.Close(); sr_color.Close(); } // 終了処理 GC.Collect(); MessageBox.Show("Finished!"); Invoke((MethodInvoker)delegate { button_start.Enabled = true; button_pause.Enabled = false; label_processingFileName.Text = "Processing File: None"; }); } } //イベントハンドラ private void watcher_Changed(System.Object source, System.IO.FileSystemEventArgs e) { switch (e.ChangeType) { case System.IO.WatcherChangeTypes.Changed: Console.WriteLine( "ファイル 「" + e.FullPath + "」が変更されました。"); IsLastFileChanged = true; break; case System.IO.WatcherChangeTypes.Created: Console.WriteLine( "ファイル 「" + e.FullPath + "」が作成されました。"); break; case System.IO.WatcherChangeTypes.Deleted: Console.WriteLine( "ファイル 「" + e.FullPath + "」が削除されました。"); break; } } private void watcher_Renamed(System.Object source, System.IO.RenamedEventArgs e) { Console.WriteLine( "ファイル 「" + e.FullPath + "」の名前が変更されました。"); } private void Button_pause_Click(object sender, EventArgs e) { if (button_pause.Text == "Pause") { manualReset.Reset(); button_pause.Text = "Continue"; } else { manualReset.Set(); button_pause.Text = "Pause"; } } private void Form1_FormClosing(object sender, FormClosingEventArgs e) { } private void comboBox1_SelectedIndexChanged(object sender, EventArgs e) { } } }