人脸识别是人工智能机器学习比较成熟的一个领域。人脸识别已经应用到了很多生产场景。比如生物认证，人脸考勤，人流监控等场景。对于很多中小功能由于技术门槛问题很难自己实现人脸识别的算法。Azure人脸API对人脸识别机器学习算法进行封装提供REST API跟SDK方便用户进行自定义开发。
Azure人脸API可以对图像中的人脸进行识别，返回面部的坐标、性别、年龄、情感、愤怒还是高兴、是否微笑，是否带眼镜等等非常有意思的信息。
Azure人脸API也是一个免费服务，每个月30000次事务的免费额度。
创建人脸服务
填写实例名，选择一个区域，同样选离你近的。
获取秘钥跟终结点
选中侧边菜单“秘钥于终结点”，获取信息，这2个信息后面再sdk调用中需要用到。
新建WPF应用
新建一个WPF应用实现以下功能：
选择图片后把原图显示出来
选中后马上进行识别
识别成功后把脸部用红框描述出来
当鼠标移动到红框内的时候显示详细脸部信息
安装SDK
使用nuget安装对于的sdk包：
Install-Package Microsoft.Azure.CognitiveServices.Vision.Face -Version 2.5.0-preview.2
实现界面
编辑MainWindow.xml放置图像显示区域、文件选中、描述显示区域
<Window x:Class="FaceWpf.MainWindow"xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"xmlns:d="http://schemas.microsoft.com/expression/blend/2008"xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"xmlns:local="clr-namespace:FaceWpf"mc:Ignorable="d"Title="MainWindow" Height="600" Width="800"><Grid x:Name="BackPanel"><Image x:Name="FacePhoto" Stretch="Uniform" Margin="0,0,0,50" MouseMove="FacePhoto_MouseMove" /><DockPanel DockPanel.Dock="Bottom"><Button x:Name="BrowseButton" Width="72" Height="80" VerticalAlignment="Bottom" HorizontalAlignment="Left"Content="选择图片..."Click="BrowseButton_Click" /><StatusBar VerticalAlignment="Bottom"><StatusBarItem><TextBlock Name="faceDescriptionStatusBar" Height="80" FontSize="20" Text="" Width="500" TextWrapping="Wrap"/></StatusBarItem></StatusBar></DockPanel></Grid>
</Window>
构造函数
在编辑MainWindow类的构造函数初始化FaceClient等数据
private IFaceClient _faceClient;//检测到的人脸private IList<DetectedFace> _faceList;//人脸描述信息private string[] _faceDescriptions;private double _resizeFactor;private const string _defaultStatusBarText ="鼠标移动到面部显示描述信息.";public MainWindow(){InitializeComponent();//faceid的订阅keystring subscriptionKey = "";// faceid的终结的配置string faceEndpoint = "";_faceClient = new FaceClient(new ApiKeyServiceClientCredentials(subscriptionKey),new System.Net.Http.DelegatingHandler[] { });if (Uri.IsWellFormedUriString(faceEndpoint, UriKind.Absolute)){_faceClient.Endpoint = faceEndpoint;}else{MessageBox.Show(faceEndpoint,"Invalid URI", MessageBoxButton.OK, MessageBoxImage.Error);Environment.Exit(0);}}
图片选择并显示
// 选择图片并上传private async void BrowseButton_Click(object sender, RoutedEventArgs e){var openDlg = new Microsoft.Win32.OpenFileDialog();openDlg.Filter = "JPEG Image(*.jpg)|*.jpg";bool? result = openDlg.ShowDialog(this);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 = "识别中...";_faceList = await UploadAndDetectFaces(filePath);Title = String.Format("识别完成. {0}个人脸", _faceList.Count);if (_faceList.Count > 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;// Some images don't contain dpi info._resizeFactor = (dpi == 0) ? 1 : 96 / dpi;_faceDescriptions = new String[_faceList.Count];for (int i = 0; i < _faceList.Count; ++i){DetectedFace face = _faceList[i];//画方框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));_faceDescriptions[i] = FaceDescription(face);}drawingContext.Close();RenderTargetBitmap faceWithRectBitmap = new RenderTargetBitmap((int)(bitmapSource.PixelWidth * _resizeFactor),(int)(bitmapSource.PixelHeight * _resizeFactor),96,96,PixelFormats.Pbgra32);faceWithRectBitmap.Render(visual);FacePhoto.Source = faceWithRectBitmap;faceDescriptionStatusBar.Text = _defaultStatusBarText;}}
调用SDK进行识别
指定需要识别的要素，调用sdk进行图像识别
// 上传图片使用faceclient识别private async Task<IList<DetectedFace>> UploadAndDetectFaces(string imageFilePath){IList<FaceAttributeType> faceAttributes =new FaceAttributeType[]{FaceAttributeType.Gender, FaceAttributeType.Age,FaceAttributeType.Smile, FaceAttributeType.Emotion,FaceAttributeType.Glasses, FaceAttributeType.Hair};using (Stream imageFileStream = File.OpenRead(imageFilePath)){IList<DetectedFace> faceList =await _faceClient.Face.DetectWithStreamAsync(imageFileStream, true, false, faceAttributes);return faceList;}}
显示脸部的描述
对人脸识别后的结果信息组装成字符串，当鼠标移动到人脸上的时候显示这些信息。
/// <summary>/// 鼠标移动显示脸部描述/// </summary>/// <param name="sender"></param>/// <param name="e"></param>private void FacePhoto_MouseMove(object sender, MouseEventArgs e){if (_faceList == null)return;Point mouseXY = e.GetPosition(FacePhoto);ImageSource imageSource = FacePhoto.Source;BitmapSource bitmapSource = (BitmapSource)imageSource;var scale = FacePhoto.ActualWidth / (bitmapSource.PixelWidth / _resizeFactor);bool mouseOverFace = false;for (int i = 0; i < _faceList.Count; ++i){FaceRectangle fr = _faceList[i].FaceRectangle;double left = fr.Left * scale;double top = fr.Top * scale;double width = fr.Width * scale;double height = fr.Height * scale;if (mouseXY.X >= left && mouseXY.X <= left + width &&mouseXY.Y >= top && mouseXY.Y <= top + height){faceDescriptionStatusBar.Text = _faceDescriptions[i];mouseOverFace = true;break;}}if (!mouseOverFace) faceDescriptionStatusBar.Text = _defaultStatusBarText;}
/// <summary>/// 脸部描述/// </summary>/// <param name="face"></param>/// <returns></returns>private string FaceDescription(DetectedFace face){StringBuilder sb = new StringBuilder();sb.Append("人脸: ");// 性别年龄sb.Append(face.FaceAttributes.Gender.Value == Gender.Female ? "女性" : "男性");sb.Append(", ");sb.Append(face.FaceAttributes.Age.ToString() + "岁");sb.Append(", ");sb.Append(String.Format("微笑 {0:F1}%, ", face.FaceAttributes.Smile * 100));// 显示超过0.1的表情sb.Append("表情: ");Emotion emotionScores = face.FaceAttributes.Emotion;if (emotionScores.Anger >= 0.1f) sb.Append(String.Format("生气 {0:F1}%, ", emotionScores.Anger * 100));if (emotionScores.Contempt >= 0.1f) sb.Append(String.Format("蔑视 {0:F1}%, ", emotionScores.Contempt * 100));if (emotionScores.Disgust >= 0.1f) sb.Append(String.Format("厌恶 {0:F1}%, ", emotionScores.Disgust * 100));if (emotionScores.Fear >= 0.1f) sb.Append(String.Format("恐惧 {0:F1}%, ", emotionScores.Fear * 100));if (emotionScores.Happiness >= 0.1f) sb.Append(String.Format("高兴 {0:F1}%, ", emotionScores.Happiness * 100));if (emotionScores.Neutral >= 0.1f) sb.Append(String.Format("自然 {0:F1}%, ", emotionScores.Neutral * 100));if (emotionScores.Sadness >= 0.1f) sb.Append(String.Format("悲伤 {0:F1}%, ", emotionScores.Sadness * 100));if (emotionScores.Surprise >= 0.1f) sb.Append(String.Format("惊喜 {0:F1}%, ", emotionScores.Surprise * 100));sb.Append(face.FaceAttributes.Glasses);sb.Append(", ");sb.Append("头发: ");if (face.FaceAttributes.Hair.Bald >= 0.01f)sb.Append(String.Format("秃头 {0:F1}% ", face.FaceAttributes.Hair.Bald * 100));IList<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 sb.ToString();}
运行
到此我们的应用打造完成了。先让我们选择一张结衣的图片试试：
看看我们的结衣微笑率97.9%。
再选一张杰伦的图片试试：
嗨，杰伦就是不喜欢笑，微笑率0% 。。。
总结
通过简单的一个wpf的应用我们演示了如果使用Azure人脸API进行图片中的人脸检测，真的非常方便，识别代码只有1行而已。如果不用C# sdk还可以使用更加通用的rest api来调用，这样可以适配任何开发语言。Azure人脸API除了能对图片中的人脸进行检测，还可以对多个人脸进行比对，检测是否是同一个人，这样就可以实现人脸考勤等功能了，这个下次再说吧。
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