使用人脸客户端库快速实现对面部的分析---C#

本文主要是介绍使用人脸客户端库快速实现对面部的分析---C#，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

- - - 效果展示：
    - 步骤一：安装人脸客户端库和窗口相关的库
    - 步骤二：声明初始化相关变量
    - - 为资源的密钥和终结点创建变量
      - 客户端构造器
      - 图片相关变量
      - 窗口工具栏
    - 步骤三：MainWindiow窗口构造器
    - 步骤四：方法：BrowseButton_Click实现点击按钮以获取展示图片并调用UploadAndDetectFaces方法，在人脸周围显示矩形
    - 步骤五：方法FacePhoto_MouseMove：当鼠标移动到人脸矩形，显示相关的人脸描述信息
    - 步骤六：方法UploadAndDetectFaces:上传图片并且调用DetectWithStreamAsync
    - 步骤七：方法FaceDescription：处理人脸描述的字符串
    - 完整代码

效果展示：

在这里插入图片描述

步骤一：安装人脸客户端库和窗口相关的库

添加以下 using 指令：

///人脸客户端库
using System;
using System.Collections.Generic;
using System.IO;
using System.Text;
using System.Threading.Tasks;

using Microsoft.Azure.CognitiveServices.Vision.Face;
using Microsoft.Azure.CognitiveServices.Vision.Face.Models;

///窗口相关库
using System.Windows;
using System.Windows.Input;
using System.Windows.Media;
using System.Windows.Media.Imaging;

步骤二：声明初始化相关变量

为资源的密钥和终结点创建变量

// 添加你自己的密钥
private static string subscriptionKey = "xxxxxxxxxxxxxxxxxxxxxxxxxxx";
// 添加终结点
private static string faceEndpoint = "https://cnsoft.cognitiveservices.azure.cn/";

客户端构造器

private readonly IFaceClient faceClient = new FaceClient(
    new ApiKeyServiceClientCredentials(subscriptionKey),
    new System.Net.Http.DelegatingHandler[] { }
);

FaceClient:此类代表使用人脸服务的授权，使用所有人脸功能时都需要用到它。请使用你的订阅信息实例化此类，然后使用它来生成其他类的实例。

图片相关变量

// 检测到的人脸的列表
private IList<DetectedFace> faceList;
// 检测到的人脸描述 字符串数组
private string[] faceDescriptions;
// 显示图像的调整大小因子。
private double resizeFactor;

DectectedFace：此类代表已从图像中的单个人脸检测到的所有数据。可以使用它来检索有关人脸的详细信息。

窗口工具栏

private const string defaultStatusBarText =
    "Place the mouse pointer over a face to see the face description.";

步骤三：MainWindiow窗口构造器

public MainWindow()
        {
            //初始化窗口组件
            InitializeComponent();
            //如果字符串格式良好，为人脸客户端终结点赋值
            if (Uri.IsWellFormedUriString(faceEndpoint, UriKind.Absolute))
            {
                faceClient.Endpoint = faceEndpoint;
            }
            else//否则的话显示错误信息
            {
                MessageBox.Show(faceEndpoint,
                    "Invalid URI", MessageBoxButton.OK, MessageBoxImage.Error);
                Environment.Exit(0);
            }
            
        }

Uri.IsWellFormedUriString(String, UriKind):此方法通过尝试用字符串构造一个 URI 来指示字符串是否为格式良好的，并确保字符串不需要进一步转义。

步骤四：方法：BrowseButton_Click实现点击按钮以获取展示图片并调用UploadAndDetectFaces方法，在人脸周围显示矩形

        private async void BrowseButton_Click(object sender, RoutedEventArgs e)
        {
            //从用户获取图片文件
            var openDlg = new Microsoft.Win32.OpenFileDialog();
            //指定图片格式
            openDlg.Filter = "JPEG Image(*.jpg)|*.jpg";
            //判断是否获取成功
            bool? result = openDlg.ShowDialog(this);

            // Return if canceled.
            if (!(bool)result)
            {
                return;
            }

            / 显示图片
            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;
            

            
            / 检测所有的人脸
            Title = "Detecting...";
            faceList = await UploadAndDetectFaces(filePath);//上传并检测
            Title = String.Format(
                "Detection Finished. {0} face(s) detected", faceList.Count);

            if (faceList.Count > 0)
            {
                // 准备从人脸周围话矩形
                DrawingVisual visual = new DrawingVisual();
                DrawingContext drawingContext = visual.RenderOpen();
                drawingContext.DrawImage(bitmapSource,
                    new Rect(0, 0, bitmapSource.Width, bitmapSource.Height));
                double dpi = bitmapSource.DpiX;
                // 一些图片可能没有dpi
                resizeFactor = (dpi == 0) ? 1 : 96 / dpi;
                faceDescriptions = new String[faceList.Count];

                for (int i = 0; i < faceList.Count; ++i)
                {
                    DetectedFace face = faceList[i];

                    // 在人脸周围画矩形
                    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
                            )
                    );

                    // 将人脸的相关描述进行存储
                    faceDescriptions[i] = FaceDescription(face);
                }
                if (faceList.Count > 0)
                {
                    TheEmotion.Text = "复制下面全部信息：\n" + faceDescriptions[0];
                }
                drawingContext.Close();

                // 显示人脸周围的矩形
                RenderTargetBitmap faceWithRectBitmap = new RenderTargetBitmap(
                    (int)(bitmapSource.PixelWidth * resizeFactor),
                    (int)(bitmapSource.PixelHeight * resizeFactor),
                    96,
                    96,
                    PixelFormats.Pbgra32);

                faceWithRectBitmap.Render(visual);
                FacePhoto.Source = faceWithRectBitmap;

                // 设置显示人脸描述的工具栏
                faceDescriptionStatusBar.Text = defaultStatusBarText;
            }
        }

步骤五：方法FacePhoto_MouseMove：当鼠标移动到人脸矩形，显示相关的人脸描述信息

        private void FacePhoto_MouseMove(object sender, MouseEventArgs e)
        {
            // 如果这个REST还没有完成，直接 return
            if (faceList == null)
                return;

            // 找到图片相对于鼠标的位置
            Point mouseXY = e.GetPosition(FacePhoto);

            ImageSource imageSource = FacePhoto.Source;
            BitmapSource bitmapSource = (BitmapSource)imageSource;

            //调整比例
            var scale = FacePhoto.ActualWidth / (bitmapSource.PixelWidth / resizeFactor);

            // 检查是否在矩形内
            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;

                // 展示信息如果在矩形内
                if (mouseXY.X >= left && mouseXY.X <= left + width &&
                    mouseXY.Y >= top && mouseXY.Y <= top + height)
                {
                    faceDescriptionStatusBar.Text = faceDescriptions[i];
                    mouseOverFace = true;
                    break;
                }
            }

            // 如果不在显示默认信息
            if (!mouseOverFace) faceDescriptionStatusBar.Text = defaultStatusBarText;
        }

步骤六：方法UploadAndDetectFaces:上传图片并且调用DetectWithStreamAsync

        private async Task<IList<DetectedFace>> UploadAndDetectFaces(string imageFilePath)
        {
            // 人脸属性的返回列表
            IList<FaceAttributeType?> faceAttributes =
                new FaceAttributeType?[]
                {
                    FaceAttributeType.Gender, FaceAttributeType.Age,
                    FaceAttributeType.Smile, FaceAttributeType.Emotion,
                    FaceAttributeType.Glasses, FaceAttributeType.Hair
                };

            // 调用人脸API
            try
            {
                using (Stream imageFileStream = File.OpenRead(imageFilePath))
                {
                    IList<DetectedFace> faceList =
                        await faceClient.Face.DetectWithStreamAsync(
                            imageFileStream, true, false, faceAttributes);
                    return faceList;
                }
            }
            // 调用出错
            catch (APIErrorException f)
            {
                MessageBox.Show(f.Message);
                return new List<DetectedFace>();
            }
            // Catch 并且展示所有错误信息
            catch (Exception e)
            {
                MessageBox.Show(e.Message, "Error");
                return new List<DetectedFace>();
            }
        }

步骤七：方法FaceDescription：处理人脸描述的字符串

private string FaceDescription(DetectedFace face)
        {
            StringBuilder sb = new StringBuilder();

            sb.Append("Face: ");

            //添加性别年龄和微笑描述
            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));
            sb.Append("\n");

            // 添加情绪描述
          
            Emotion emotionScores = face.FaceAttributes.Emotion;

            sb.Append("anger: "+ emotionScores.Anger + "\n");
            sb.Append("contempt: " + emotionScores.Contempt + "\n");
            sb.Append("disgust: " + emotionScores.Disgust + "\n");
            sb.Append("fear: " + emotionScores.Fear + "\n");
            sb.Append("happiness: " + emotionScores.Happiness + "\n");
            sb.Append("neutral: " + emotionScores.Neutral + "\n");
            sb.Append("sadness: " + emotionScores.Sadness + "\n");
            sb.Append("surprise: " + emotionScores.Surprise + "\n");
            

            // 添加是否戴眼镜
            sb.Append("ifhasGlasses: "+face.FaceAttributes.Glasses);
            sb.Append("\n ");

            // 添加头发描述
            sb.Append("Hair: ");

            //秃头率
            if (face.FaceAttributes.Hair.Bald >= 0.01f)
                sb.Append(String.Format("bald {0:F1}% ", face.FaceAttributes.Hair.Bald * 100));

            // 头发颜色
            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));
                }
            }

            // R返回字符串
            return sb.ToString();
        }

完整代码

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows;
// using System.Windows.Controls;
// using System.Windows.Data;
// using System.Windows.Documents;
using System.Windows.Input;
using System.Windows.Media;
using System.Windows.Media.Imaging;
// using System.Windows.Navigation;
// using System.Windows.Shapes;
using Microsoft.Azure.CognitiveServices.Vision.Face;
using Microsoft.Azure.CognitiveServices.Vision.Face.Models;

namespace C14_WPF_EmotionAcuiring
{
    /// <summary>
    /// Interaction logic for MainWindow.xaml
    /// </summary>
    
    public partial class MainWindow : Window
    {
        // <snippet_mainwindow_fields>
        // Add your Face subscription key to your environment variables.
        private static string subscriptionKey = "xxxxxxxxxxxxxxxxxxxxxxxxxxxx";
        // Add your Face endpoint to your environment variables.
        private static string faceEndpoint = "https://cnsoft.cognitiveservices.azure.cn/";

        private readonly IFaceClient faceClient = new FaceClient(
            new ApiKeyServiceClientCredentials(subscriptionKey),
            new System.Net.Http.DelegatingHandler[] { });

        // The list of detected faces.
        private IList<DetectedFace> faceList;
        // The list of descriptions for the detected faces.
        private string[] faceDescriptions;
        // The resize factor for the displayed image.
        private double resizeFactor;

        private const string defaultStatusBarText =
            "Place the mouse pointer over a face to see the face description.";
        // </snippet_mainwindow_fields>

        // <snippet_mainwindow_constructor>
        public MainWindow()
        {
            InitializeComponent();

            if (Uri.IsWellFormedUriString(faceEndpoint, UriKind.Absolute))
            {
                faceClient.Endpoint = faceEndpoint;
            }
            else
            {
                MessageBox.Show(faceEndpoint,
                    "Invalid URI", MessageBoxButton.OK, MessageBoxImage.Error);
                Environment.Exit(0);
            }
            
        }
        // </snippet_mainwindow_constructor>

        // <snippet_browsebuttonclick_start>
        // Displays the image and calls UploadAndDetectFaces.
        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;
            // </snippet_browsebuttonclick_start>

            // <snippet_browsebuttonclick_mid>
            // Detect any faces in the image.
            Title = "Detecting...";
            faceList = await UploadAndDetectFaces(filePath);
            Title = String.Format(
                "Detection Finished. {0} face(s) detected", 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];

                    // 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);
                }
                if (faceList.Count > 0)
                {
                    TheEmotion.Text = "复制下面全部信息：\n" + faceDescriptions[0];
                }
                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 = defaultStatusBarText;
            }
            // </snippet_browsebuttonclick_mid>
            // <snippet_browsebuttonclick_end>
        }
        // </snippet_browsebuttonclick_end>

        // <snippet_mousemove_start>
        // Displays the face description when the mouse is over a face rectangle.
        private void FacePhoto_MouseMove(object sender, MouseEventArgs e)
        {
            // </snippet_mousemove_start>

            // <snippet_mousemove_mid>
            // If the REST call has not completed, return.
            if (faceList == 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 < 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;

                // Display the face description 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;
                }
            }

            // String to display when the mouse is not over a face rectangle.
            if (!mouseOverFace) faceDescriptionStatusBar.Text = defaultStatusBarText;
            // </snippet_mousemove_mid>
            // <snippet_mousemove_end>
        }
        // </snippet_mousemove_end>

        // <snippet_uploaddetect>
        // Uploads the image file and calls DetectWithStreamAsync.
        private async Task<IList<DetectedFace>> UploadAndDetectFaces(string imageFilePath)
        {
            // The list of Face attributes to return.
            IList<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))
                {
                    // The second argument specifies to return the faceId, while
                    // the third argument specifies not to return face landmarks.
                    IList<DetectedFace> faceList =
                        await faceClient.Face.DetectWithStreamAsync(
                            imageFileStream, true, false, faceAttributes);
                    return faceList;
                }
            }
            // Catch and display Face API errors.
            catch (APIErrorException f)
            {
                MessageBox.Show(f.Message);
                return new List<DetectedFace>();
            }
            // Catch and display all other errors.
            catch (Exception e)
            {
                MessageBox.Show(e.Message, "Error");
                return new List<DetectedFace>();
            }
        }
        // </snippet_uploaddetect>

        // <snippet_facedesc>
        // Creates a string out of the attributes describing the face.
        private string FaceDescription(DetectedFace 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));
            sb.Append("\n");

            // Add the emotions. Display all emotions over 10%.
          
            Emotion emotionScores = face.FaceAttributes.Emotion;

            sb.Append("anger: "+ emotionScores.Anger + "\n");
            sb.Append("contempt: " + emotionScores.Contempt + "\n");
            sb.Append("disgust: " + emotionScores.Disgust + "\n");
            sb.Append("fear: " + emotionScores.Fear + "\n");
            sb.Append("happiness: " + emotionScores.Happiness + "\n");
            sb.Append("neutral: " + emotionScores.Neutral + "\n");
            sb.Append("sadness: " + emotionScores.Sadness + "\n");
            sb.Append("surprise: " + emotionScores.Surprise + "\n");
            

            // Add glasses.
            sb.Append("ifhasGlasses: "+face.FaceAttributes.Glasses);
            sb.Append("\n ");

            // 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%.
            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 the built string.
            return sb.ToString();
        }
        // </snippet_facedesc>
    }
}

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Net Core教程

使用人脸客户端库快速实现对面部的分析---C#

目录

效果展示：

步骤一：安装人脸客户端库和窗口相关的库

步骤二：声明初始化相关变量

为资源的密钥和终结点创建变量

客户端构造器

图片相关变量

窗口工具栏

步骤三：MainWindiow窗口构造器

步骤四：方法：BrowseButton_Click实现点击按钮以获取展示图片并调用UploadAndDetectFaces方法，在人脸周围显示矩形

步骤五：方法FacePhoto_MouseMove：当鼠标移动到人脸矩形，显示相关的人脸描述信息

步骤六：方法UploadAndDetectFaces:上传图片并且调用DetectWithStreamAsync

步骤七：方法FaceDescription：处理人脸描述的字符串

完整代码

前端开发

后端开发

移动端开发

数据库

服务器运维

人工智能

区块链

游戏开发

网站运营

大数据/云计算

软件工程

软件/开发工具使用

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