Microsoft Azure和WPF实现人脸检测

在本文中，详解如何使用Microsoft Azure和WPF技术的帮助下使用实现人脸API应用程序。该应用程序检测人脸图像，显示每张脸周围的红框，以及通过将光标移动到框来显示每张脸的描述的状态栏。

先决条件

• 在 Azure 门户中创建人脸 API。
• 在 WPF 应用程序中访问和管理人脸 API 密钥。
• 在 Visual Studio 2019 中使用人脸 API 创建 WPF 应用程序。

按照步骤在 Azure 门户上创建人脸 API。

第1步

登录 Azure网站。

Step 2

按“+新建”，选择“AI+认知服务”，选择“人脸API”。

第 3 步

在“创建人脸 API”边栏选项卡中，输入您的API名称，选择所需的订阅，然后为API选择所需的位置。选择我们使用所需的适当定价层。为API创建资源组。然后，按“创建”。

复制端点并按“显示/管理密钥”以显示我们在WPF应用程序中使用的密钥。

第4步

“管理密钥”刀片显示将用作我们的人脸API访问密钥的密钥。单击“复制”按钮，然后将其粘贴到记事本中。

使用 C#在Visual Studio中实现人脸API

第 5 步

按照步骤使用 Visual Studio 2019创建基于Windows 的 WPF应用程序。

打开Visual Studio 2019。

• 从菜单栏中，单击文件>>新建>>新建项目。
• 在Visual Studio中，选择已安装-->模板-->Visual C#-->Windows经典桌面-->WPF应用程序（.NET 应用程序）。
• 为我们的应用程序输入名称为FaceApiDemo，然后按 OK 在解决方案资源管理器中打开它。

第 6 步

我们需要将两个包添加到我们的WPF应用程序中。右键单击解决方案资源管理器并按“NuGet 包管理器”添加两个包。

Newtonsoft.json是适用于.NET应用程序的流行JSON框架的框架。我们将使用它，因为我们的 Face API包含大量JSON文件来存储有关图像的各种详细信息。

在 NuGet 包中，浏览 Newtonsoft.json，然后按“安装”。

此外，浏览Microsoft.ProjectOxford.Face并按“安装” 。该包用于通过HTTPS请求配置人脸 API 和我们的应用程序。因此，它导入了一个封装 Face API REST 请求的 .NET 库。

第 7 步

将以下代码复制并粘贴到MainWindow.Xaml。这是我们的Windows应用程序的布局代码。

步骤8

打开MainWindow.xaml.cs，用下面的代码替换代码。

using System;  using System.Collections.Generic;  using System.IO;  using System.Text;  using System.Threading.Tasks;  using System.Windows;  using System.Windows.Input;  using System.Windows.Media;  using System.Windows.Media.Imaging;  using Microsoft.ProjectOxford.Common.Contract;  using Microsoft.ProjectOxford.Face;  using Microsoft.ProjectOxford.Face.Contract;    namespace FaceApiDemo    {      public partial class MainWindow : Window      {   // Replace the first parameter with your valid subscription key.   //   // Replace or verify the region in the second parameter.   //   // You must use the same region in your REST API call as you used to obtain your subscription keys.   // For example, if you obtained your subscription keys from the westus region, replace   // "westcentralus" in the URI below with "westus".   //   // NOTE: Free trial subscription keys are generated in the westcentralus region, so if you are using   // a free trial subscription key, you should not need to change this region.   private readonly IFaceServiceClient faceServiceClient =new FaceServiceClient("keys", "Endpoints");     Face[] faces;     // The list of detected faces.   String[] faceDescriptions;      // The list of descriptions for the detected faces.   double resizeFactor;     // The resize factor for the displayed image.   //private object faceDescriptionStatusBar;     public MainWindow()   {InitializeComponent();   }     // Displays the image and calls Detect Faces.     private async void BrowseButton_Click(object sender, RoutedEventArgs e)   {// Get the image file to scan from the user.var openDlg = new Microsoft.Win32.OpenFileDialog();  openDlg.Filter = "JPEG Image(*.jpg)|*.jpg";bool? result = openDlg.ShowDialog(this);  // Return if canceled.if (!(bool)result){    return;}  // Display the image file.string filePath = openDlg.FileName;  Uri fileUri = new Uri(filePath);BitmapImage bitmapSource = new BitmapImage();  bitmapSource.BeginInit();bitmapSource.CacheOption = BitmapCacheOption.None;bitmapSource.UriSource = fileUri;bitmapSource.EndInit();  FacePhoto.Source = bitmapSource;  // Detect any faces in the image.Title = "Detecting...";faces = await UploadAndDetectFaces(filePath);Title = String.Format("Detection Finished. {0} face(s) detected", faces.Length);  if (faces.Length > 0){    // Prepare to draw rectangles around the faces.    DrawingVisual visual = new DrawingVisual();    DrawingContext drawingContext = visual.RenderOpen();    drawingContext.DrawImage(bitmapSource, new Rect(0, 0, bitmapSource.Width, bitmapSource.Height));    double dpi = bitmapSource.DpiX;    resizeFactor = 96 / dpi;    faceDescriptions = new String[faces.Length];      for (int i = 0; i < faces.Length; ++i)    { Face face = faces[i];   // Draw a rectangle on the face. drawingContext.DrawRectangle(     Brushes.Transparent,     new Pen(Brushes.Red, 2),     new Rect(  face.FaceRectangle.Left * resizeFactor,  face.FaceRectangle.Top * resizeFactor,  face.FaceRectangle.Width * resizeFactor,  face.FaceRectangle.Height * resizeFactor  ) );   // Store the face description. faceDescriptions[i] = FaceDescription(face);    }      drawingContext.Close();      // Display the image with the rectangle around the face.    RenderTargetBitmap faceWithRectBitmap = new RenderTargetBitmap( (int)(bitmapSource.PixelWidth * resizeFactor), (int)(bitmapSource.PixelHeight * resizeFactor), 96, 96, PixelFormats.Pbgra32);      faceWithRectBitmap.Render(visual);    FacePhoto.Source = faceWithRectBitmap;      // Set the status bar text.    faceDescriptionStatusBar.Text = "Place the mouse pointer over a face to see the face description.";}   }     // Displays the face description when the mouse is over a face rectangle.     private void FacePhoto_MouseMove(object sender, MouseEventArgs e)   {// If the REST call has not completed, return from this method.if (faces == null)    return;  // Find the mouse position relative to the image.Point mouseXY = e.GetPosition(FacePhoto);  ImageSource imageSource = FacePhoto.Source;BitmapSource bitmapSource = (BitmapSource)imageSource;  // Scale adjustment between the actual size and displayed size.var scale = FacePhoto.ActualWidth / (bitmapSource.PixelWidth / resizeFactor);  // Check if this mouse position is over a face rectangle.bool mouseOverFace = false;  for (int i = 0; i = left && mouseXY.X = top && mouseXY.Y <= top + height)    { faceDescriptionStatusBar.Text = faceDescriptions[i]; mouseOverFace = true; break;    }}  // If the mouse is not over a face rectangle.if (!mouseOverFace)    faceDescriptionStatusBar.Text = "Place the mouse pointer over a face to see the face description.";   }     // Uploads the image file and calls Detect Faces.     private async Task UploadAndDetectFaces(string imageFilePath)   {// The list of Face attributes to return.IEnumerable faceAttributes =    new FaceAttributeType[] { FaceAttributeType.Gender, FaceAttributeType.Age, FaceAttributeType.Smile, FaceAttributeType.Emotion, FaceAttributeType.Glasses, FaceAttributeType.Hair };  // Call the Face API.try{    using (Stream imageFileStream = File.OpenRead(imageFilePath))    { Face[] faces = await faceServiceClient.DetectAsync(imageFileStream, returnFaceId: true, returnFaceLandmarks: false, returnFaceAttributes: faceAttributes); return faces;    }}// Catch and display Face API errors.catch (FaceAPIException f){    MessageBox.Show(f.ErrorMessage, f.ErrorCode);    return new Face[0];}// Catch and display all other errors.catch (Exception e){    MessageBox.Show(e.Message, "Error");    return new Face[0];}   }     // Returns a string that describes the given face.     private string FaceDescription(Face face)   {StringBuilder sb = new StringBuilder();  sb.Append("Face: ");  // Add the gender, age, and smile.sb.Append(face.FaceAttributes.Gender);sb.Append(", ");sb.Append(face.FaceAttributes.Age);sb.Append(", ");sb.Append(String.Format("smile {0:F1}%, ", face.FaceAttributes.Smile * 100));  // Add the emotions. Display all emotions over 10%.sb.Append("Emotion: ");EmotionScores emotionScores = face.FaceAttributes.Emotion;if (emotionScores.Anger >= 0.1f) sb.Append(String.Format("anger {0:F1}%, ", emotionScores.Anger * 100));if (emotionScores.Contempt >= 0.1f) sb.Append(String.Format("contempt {0:F1}%, ", emotionScores.Contempt * 100));if (emotionScores.Disgust >= 0.1f) sb.Append(String.Format("disgust {0:F1}%, ", emotionScores.Disgust * 100));if (emotionScores.Fear >= 0.1f) sb.Append(String.Format("fear {0:F1}%, ", emotionScores.Fear * 100));if (emotionScores.Happiness >= 0.1f) sb.Append(String.Format("happiness {0:F1}%, ", emotionScores.Happiness * 100));if (emotionScores.Neutral >= 0.1f) sb.Append(String.Format("neutral {0:F1}%, ", emotionScores.Neutral * 100));if (emotionScores.Sadness >= 0.1f) sb.Append(String.Format("sadness {0:F1}%, ", emotionScores.Sadness * 100));if (emotionScores.Surprise >= 0.1f) sb.Append(String.Format("surprise {0:F1}%, ", emotionScores.Surprise * 100));  // Add glasses.sb.Append(face.FaceAttributes.Glasses);sb.Append(", ");  // Add hair.sb.Append("Hair: ");  // Display baldness confidence if over 1%.if (face.FaceAttributes.Hair.Bald >= 0.01f)    sb.Append(String.Format("bald {0:F1}% ", face.FaceAttributes.Hair.Bald * 100));  // Display all hair color attributes over 10%.HairColor[] hairColors = face.FaceAttributes.Hair.HairColor;foreach (HairColor hairColor in hairColors){    if (hairColor.Confidence >= 0.1f)    { sb.Append(hairColor.Color.ToString()); sb.Append(String.Format(" {0:F1}% ", hairColor.Confidence * 100));    }}  // Return the built string.return sb.ToString();   }      }  }  

在代码的第30行，输入我们从Azure Face API获取的密钥和端点 URL。

private readonly IFaceServiceClient faceServiceClient =   new FaceServiceClient("_key_", "End-point URL");  

在 Azure 门户上使用人脸API替换密钥和端点。

步骤 9

保存并启动程序。然后，单击“浏览”按钮导入图像以检测人脸。

第 10 步

等待几秒钟。云API想要响应我们的图像，以使用图像中人脸周围的红色框来检测人脸。将光标移到面部矩形上，该图像的描述会显示在状态栏中。