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。
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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。
<Window x:Class="FaceApiDemo.MainWindow"
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
Title="MainWindow" Height="700" Width="960">
<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="20" VerticalAlignment="Bottom" HorizontalAlignment="Left"
Content="Browse..."
Click="BrowseButton_Click" />
<StatusBar VerticalAlignment="Bottom">
<StatusBarItem>
<TextBlock Name="faceDescriptionStatusBar" />
</StatusBarItem>
</StatusBar>
</DockPanel>
</Grid>
</Window> 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 < faces.Length; ++i)
{
FaceRectangle fr = faces[i].FaceRectangle;
double left = fr.Left * scale;
double top = fr.Top * scale;
double width = fr.Width * scale;
double height = fr.Height * scale;
// Display the face description for this face if the mouse is over this face rectangle.
if (mouseXY.X >= left && mouseXY.X <= left + width && mouseXY.Y >= 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<Face[]> UploadAndDetectFaces(string imageFilePath)
{
// The list of Face attributes to return.
IEnumerable<FaceAttributeType> 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。文章来源：https://www.toymoban.com/news/detail-494144.html
文章来源地址https://www.toymoban.com/news/detail-494144.html
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