■ 텐서플로우를 사용해 물체를 인식하는 방법을 보여준다.
▶ MainForm.cs
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using System; using System.Drawing; using System.Drawing.Imaging; using System.IO; using System.Windows.Forms; using AForge.Video.DirectShow; using TensorFlow; namespace TestProject { /// <summary> /// 메인 폼 /// </summary> public partial class MainForm : Form { //////////////////////////////////////////////////////////////////////////////////////////////////// Field ////////////////////////////////////////////////////////////////////////////////////////// Private #region Field /// <summary> /// 타이머 /// </summary> private Timer timer = null; /// <summary> /// 필터 정보 컬렉션 /// </summary> private FilterInfoCollection filterInfoCollection = null; /// <summary> /// 모델 배열 /// </summary> private byte[] modelArray; /// <summary> /// 레이블 배열 /// </summary> private string[] labelArray = null; /// <summary> /// 텐서플로우 세션 /// </summary> private TFSession session = null; /// <summary> /// 텐서플로우 그래프 /// </summary> private TFGraph graph = null; #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Constructor ////////////////////////////////////////////////////////////////////////////////////////// Public #region 생성자 - MainForm() /// <summary> /// 생성자 /// </summary> public MainForm() { InitializeComponent(); #region 타이머를 설정한다. this.timer = new Timer(); this.timer.Interval = 1000; #endregion #region 이벤트를 설정한다. Load += Form_Load; FormClosing += Form_FormClosing; this.startButton.Click += startButton_Click; this.timer.Tick += timer_Tick; #endregion } #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Method ////////////////////////////////////////////////////////////////////////////////////////// Private //////////////////////////////////////////////////////////////////////////////// Event #region 폼 로드시 처리하기 - Form_Load(sender, e) /// <summary> /// 폼 로드시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_Load(object sender, EventArgs e) { #region 카메라 장치 콤보 박스를 설정한다. this.filterInfoCollection = new FilterInfoCollection(FilterCategory.VideoInputDevice); for(int i = 0; i < this.filterInfoCollection.Count; i++) { this.cameraDeviceComboBox.Items.Add(this.filterInfoCollection[i].Name); } if(this.cameraDeviceComboBox.Items.Count > 0) { this.cameraDeviceComboBox.SelectedIndex = 0; } #endregion #region 시작 버튼을 설정한다. this.startButton.Enabled = (this.cameraDeviceComboBox.Items.Count > 0); #endregion #region 모델 배열을 설정한다. this.modelArray = File.ReadAllBytes("DATA\\tensorflow_inception_graph.pb"); #endregion #region 레이블 배열을 설정한다. this.labelArray = File.ReadAllLines("DATA\\imagenet_comp_graph_label_strings.txt"); #endregion this.graph = new TFGraph(); this.session = new TFSession(graph); this.graph.Import(this.modelArray, ""); } #endregion #region 폼 닫을 경우 처리하기 - Form_FormClosing(sender, e) /// <summary> /// 폼 닫을 경우 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_FormClosing(object sender, FormClosingEventArgs e) { this.timer.Stop(); StopCameraCaoture(); } #endregion #region 시작 버튼 클릭시 처리하기 - startButton_Click(sender, e) /// <summary> /// 시작 버튼 클릭시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void startButton_Click(object sender, EventArgs e) { if(this.startButton.Text == "시작") { this.startButton.Text = "중지"; StartCameraCapture(); this.timer.Start(); } else { this.timer.Stop(); StopCameraCaoture(); this.startButton.Text = "시작"; } } #endregion #region 타이머 틱 처리하기 - timer_Tick(sender, e) /// <summary> /// 타이머 틱 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void timer_Tick(object sender, EventArgs e) { Bitmap bitmap = this.videoSourcePlayer.GetCurrentVideoFrame(); if(bitmap == null) { return; } MemoryStream memoryStream = new MemoryStream(); bitmap.Save(memoryStream, ImageFormat.Jpeg); TFTensor tensor = GetTensor(memoryStream.GetBuffer()); TFSession.Runner runner = this.session.GetRunner(); runner.AddInput(graph["input"][0], tensor).Fetch(graph["output"][0]); TFTensor[] outputTensorArray = runner.Run(); TFTensor outputTensor = outputTensorArray[0]; long[] shapeArray = outputTensor.Shape; if(outputTensor.NumDims != 2 || shapeArray[0] != 1) { string shapeString = string.Empty; foreach(long shape in shapeArray) { shapeString += $"{shape} "; } shapeString = shapeString.Trim(); Console.WriteLine($"에러 : [1 N] 차원 텐서의 생성을 기대했는데(여기서 N은 라벨의 개수), [{shapeString}] 차원 텐서를 생성했습니다."); Environment.Exit(1); } bool jagged = true; int bestIndex = 0; float best = 0; if(jagged) { float[] probabilityArray = ((float[][])outputTensor.GetValue(jagged : true))[0]; for(int i = 0; i < probabilityArray.Length; i++) { if(probabilityArray[i] > best) { bestIndex = i; best = probabilityArray[i]; } } } else { float[,] valueArray = (float[,])outputTensor.GetValue(jagged : false); for(int i = 0; i < valueArray.GetLength(1); i++) { if(valueArray[0, i] > best) { bestIndex = i; best = valueArray[0, i]; } } } this.informationLabel.Text = $"판정 : [{bestIndex}] {best * 100.0}% {this.labelArray[bestIndex]}"; Console.WriteLine($"판정 : [{bestIndex}] {best * 100.0}% {this.labelArray[bestIndex]}"); } #endregion //////////////////////////////////////////////////////////////////////////////// Function #region 카메라 캡처 시작하기 - StartCameraCapture() /// <summary> /// 카메라 캡처 시작하기 /// </summary> private void StartCameraCapture() { FilterInfo filterInfo; filterInfo = this.filterInfoCollection[this.cameraDeviceComboBox.SelectedIndex]; VideoCaptureDevice videoCaptureDevice = new VideoCaptureDevice(filterInfo.MonikerString); this.videoSourcePlayer.VideoSource = videoCaptureDevice; this.videoSourcePlayer.AutoSizeControl = true; this.videoSourcePlayer.Start(); } #endregion #region 카메라 캡처 중단하기 - StopCameraCaoture() /// <summary> /// 카메라 캡처 중단하기 /// </summary> private void StopCameraCaoture() { this.videoSourcePlayer.SignalToStop(); this.videoSourcePlayer.WaitForStop(); } #endregion #region 값 설정하기 - SetValue(graph, input, output) /// <summary> /// 값 설정하기 /// </summary> /// <param name="graph">그래프</param> /// <param name="input">입력</param> /// <param name="output">출력</param> private void SetValue(out TFGraph graph, out TFOutput input, out TFOutput output) { const int IMAGE_WIDTH = 224; const int IMAGE_HEIGHT = 224; const float MEAN = 117; const float SCALE = 1; graph = new TFGraph(); input = graph.Placeholder(TFDataType.String); output = 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[] { IMAGE_WIDTH, IMAGE_HEIGHT }, "size")), y : graph.Const(MEAN, "mean") ), y : graph.Const(SCALE, "scale") ); } #endregion #region 텐서 구하기 - GetTensor(sourceArray) /// <summary> /// 텐서 구하기 /// </summary> /// <param name="sourceArray">소스 배열</param> /// <returns>텐서</returns> private TFTensor GetTensor(byte[] sourceArray) { TFTensor tensor = TFTensor.CreateString(sourceArray); TFGraph graph; TFOutput input; TFOutput output; SetValue(out graph, out input, out output); using(TFSession session = new TFSession(graph)) { TFTensor[] normalizedTensorArray = session.Run ( inputs : new[] { input }, inputValues : new[] { tensor }, outputs : new[] { output } ); return normalizedTensorArray[0]; } } #endregion } } |
※ 닷넷 프레임워크 4.7.1 버전을 사용해야 한다.
※ 프로젝트 속성의 빌드 탭에서 “32비트 기본 사용”을 체크 해제해야 한다.
