C# DlibDotNet 人脸识别、人脸68特征点识别、人脸5特征点识别、人脸对齐，三角剖分，人脸特征比对

先看效果，测尝试片来自网络，侵权联系删除。

人脸识别

人脸68特征点识别

人脸5特征点识别

人脸对齐

三角剖分

人脸特征比对

项目

VS2022+.net4.8+OpenCvSharp4+DlibDotNet

代码

using DlibDotNet.Extensions;using DlibDotNet;using System;using System.Collections.Generic;using System.ComponentModel;using System.Data;using System.Drawing;using System.IO;using System.Linq;using System.Text;using System.Windows.Forms;using static System.Windows.Forms.VisualStyles.VisualStyleElement;using OpenCvSharp;using System.Drawing.Imaging;using System.Globalization; namespace DlibDotNet_人脸识别_人脸68特征点识别_人脸5特征点识别{    public partial class Form1 : Form    {        public Form1()        {            InitializeComponent();        }         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";        string imgPath = "";        string imgPath2 = "";        string startupPath = "";          private void button1_Click(object sender, EventArgs e)        {            OpenFileDialog ofd = new OpenFileDialog();            ofd.Filter = fileFilter;            if (ofd.ShowDialog() != DialogResult.OK) return;             pictureBox1.Image = null;             imgPath = ofd.FileName;            pictureBox1.Image = new Bitmap(imgPath);         }         private void button2_Click(object sender, EventArgs e)        {            OpenFileDialog ofd = new OpenFileDialog();            ofd.Filter = fileFilter;            if (ofd.ShowDialog() != DialogResult.OK) return;             pictureBox2.Image = null;             imgPath2 = ofd.FileName;            pictureBox2.Image = new Bitmap(imgPath2);         }         private void Form1_Load(object sender, EventArgs e)        {            string startupPath = Application.StartupPath;            Dlib.Encoding = Environment.OSVersion.Platform == PlatformID.Win32NT ? Encoding.GetEncoding(CultureInfo.CurrentCulture.TextInfo.ANSICodePage) : Encoding.UTF8;         }         /// <summary>        /// 人脸识别        /// </summary>        /// <param name="sender"></param>        /// <param name="e"></param>        private void button3_Click(object sender, EventArgs e)        {             if (imgPath == "")            {                return;            }             using (var faceDetector = Dlib.GetFrontalFaceDetector())            using (var image = Dlib.LoadImage<RgbPixel>(imgPath))            {                var dets = faceDetector.Operator(image);                foreach (var r in dets)                    Dlib.DrawRectangle(image, r, new RgbPixel { Green = 255 });                 var result = image.ToBitmap();                this.pictureBox1.Invoke(new Action(() =>                {                    this.pictureBox1.Image?.Dispose();                    this.pictureBox1.Image = result;                }));            }         }         /// <summary>        /// 5特征点识别        /// </summary>        /// <param name="sender"></param>        /// <param name="e"></param>        private void button5_Click(object sender, EventArgs e)        {            if (imgPath == "")            {                return;            }             string faceDataPath = "shape_predictor_5_face_landmarks.dat";             Mat mat = new Mat(imgPath);             Bitmap bmp = new Bitmap(imgPath);            bmp = Get24bppRgb(bmp);             // 图像转换到Dlib的图像类中            Array2D<RgbPixel> img = DlibDotNet.Extensions.BitmapExtensions.ToArray2D<RgbPixel>(bmp);            var faceDetector = Dlib.GetFrontalFaceDetector();            var shapePredictor = ShapePredictor.Deserialize(faceDataPath);             // 检测人脸            var faces = faceDetector.Operator(img);             if (faces.Count() == 0) { return; }             // 人脸区域中识别脸部特征            var shape = shapePredictor.Detect(img, faces[0]);             var bradleyPoints = (from i in Enumerable.Range(0, (int)shape.Parts)                                 let p = shape.GetPart((uint)i)                                 select new OpenCvSharp.Point(p.X, p.Y)).ToArray();             foreach (var item in bradleyPoints)            {                Cv2.Circle(mat, item.X, item.Y, 2, Scalar.Green, 2);            }             pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);        }         // Convert to Format24bppRgb        private static Bitmap Get24bppRgb(System.Drawing.Image image)        {            var bitmap = new Bitmap(image);            var bitmap24 = new Bitmap(bitmap.Width, bitmap.Height, PixelFormat.Format24bppRgb);            using (var gr = Graphics.FromImage(bitmap24))            {                gr.DrawImage(bitmap, new System.Drawing.Rectangle(0, 0, bitmap24.Width, bitmap24.Height));            }            return bitmap24;        }         /// <summary>        /// 68特征点识别        /// </summary>        /// <param name="sender"></param>        /// <param name="e"></param>        private void button4_Click(object sender, EventArgs e)        {            if (imgPath == "")            {                return;            }             string faceDataPath = "shape_predictor_68_face_landmarks.dat";             Mat mat = new Mat(imgPath);             Bitmap bmp = new Bitmap(imgPath);            bmp = Get24bppRgb(bmp);             // 图像转换到Dlib的图像类中            Array2D<RgbPixel> img = DlibDotNet.Extensions.BitmapExtensions.ToArray2D<RgbPixel>(bmp);            var faceDetector = Dlib.GetFrontalFaceDetector();            var shapePredictor = ShapePredictor.Deserialize(faceDataPath);             // 检测人脸            var faces = faceDetector.Operator(img);             if (faces.Count() == 0) { return; }             // 人脸区域中识别脸部特征            var shape = shapePredictor.Detect(img, faces[0]);             var bradleyPoints = (from i in Enumerable.Range(0, (int)shape.Parts)                                 let p = shape.GetPart((uint)i)                                 select new OpenCvSharp.Point(p.X, p.Y)).ToArray();             foreach (var item in bradleyPoints)            {                Cv2.Circle(mat, item.X, item.Y, 2, Scalar.Green, 2);            }             pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);        }         /// <summary>        /// 人脸对齐        /// </summary>        /// <param name="sender"></param>        /// <param name="e"></param>        private void button6_Click(object sender, EventArgs e)        {            if (imgPath == "")            {                return;            }            var path = imgPath;            string faceDataPath = "shape_predictor_68_face_landmarks.dat";            using (var faceDetector = Dlib.GetFrontalFaceDetector())            using (var img = Dlib.LoadImage<RgbPixel>(path))            {                Dlib.PyramidUp(img);                var shapePredictor = ShapePredictor.Deserialize(faceDataPath);                var dets = faceDetector.Operator(img);                var shapes = new List<FullObjectDetection>();                foreach (var rect in dets)                {                    var shape = shapePredictor.Detect(img, rect);                    if (shape.Parts <= 2)                        continue;                    shapes.Add(shape);                }                 if (shapes.Any())                {                    var lines = Dlib.RenderFaceDetections(shapes);                    foreach (var line in lines)                        Dlib.DrawLine(img, line.Point1, line.Point2, new RgbPixel                        {                            Green = 255                        });                     var wb = img.ToBitmap();                    this.pictureBox1.Image?.Dispose();                    this.pictureBox1.Image = wb;                    foreach (var l in lines)                        l.Dispose();                    var chipLocations = Dlib.GetFaceChipDetails(shapes);                    using (var faceChips = Dlib.ExtractImageChips<RgbPixel>(img, chipLocations))                    using (var tileImage = Dlib.TileImages(faceChips))                    {                        // It is NOT necessary to re-convert WriteableBitmap to Matrix.                        // This sample demonstrate converting managed image class to                        // dlib class and vice versa.                        using (var tile = tileImage.ToBitmap())                        using (var mat = tile.ToMatrix<RgbPixel>())                        {                            var tile2 = mat.ToBitmap();                            this.pictureBox1.Image?.Dispose();                            this.pictureBox1.Image = tile2;                        }                    }                     foreach (var c in chipLocations)                        c.Dispose();                }                 foreach (var s in shapes)                    s.Dispose();            }        }         /// <summary>        /// 人脸特征比对        /// </summary>        /// <param name="sender"></param>        /// <param name="e"></param>        private void button7_Click(object sender, EventArgs e)        {            textBox1.Text = "";             if (imgPath == "" || imgPath2 == "")            {                return;            }             var detector = Dlib.GetFrontalFaceDetector();            var sp = ShapePredictor.Deserialize("shape_predictor_5_face_landmarks.dat");            var net = DlibDotNet.Dnn.LossMetric.Deserialize("dlib_face_recognition_resnet_model_v1.dat");              var img1 = Dlib.LoadImageAsMatrix<RgbPixel>(imgPath);            var img2 = Dlib.LoadImageAsMatrix<RgbPixel>(imgPath2);             var faces = new List<Matrix<RgbPixel>>();            foreach (var face in detector.Operator(img1))            {                var shape = sp.Detect(img1, face);                var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);                var faceChip = Dlib.ExtractImageChip<RgbPixel>(img1, faceChipDetail);                faces.Add(faceChip);            }             foreach (var face in detector.Operator(img2))            {                var shape = sp.Detect(img2, face);                var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);                var faceChip = Dlib.ExtractImageChip<RgbPixel>(img2, faceChipDetail);                faces.Add(faceChip);            }             if (faces.Count != 2)            {                return;            }             var faceDescriptors = net.Operator(faces);             // Faces are connected in the graph if they are close enough.  Here we check if            // the distance between two face descriptors is less than 0.6, which is the            // decision threshold the network was trained to use.  Although you can            // certainly use any other threshold you find useful.            var diff = faceDescriptors[1] - faceDescriptors[0];            float len = Dlib.Length(diff);             String str = "";            if (len < 0.6)            {                str += "图片1和图片2距离:" + len.ToString() + "   是一个人" + "
";            }            else            {                str += "图片1和图片2距离:" + len.ToString() + "   不是一个人" + "
";            }             str += "图片1特征值:[" + faceDescriptors[0].ToString() + "]
";            str += "图片2特征值:[" + faceDescriptors[1].ToString() + "]
";             textBox1.Text = str;         }         /// <summary>        /// 三角剖分        /// </summary>        /// <param name="sender"></param>        /// <param name="e"></param>        private void button8_Click(object sender, EventArgs e)        {             if (imgPath == "")            {                return;            }             string faceDataPath = "shape_predictor_68_face_landmarks.dat";            Mat mat = new Mat(imgPath);            Bitmap bmp = new Bitmap(imgPath);            bmp = Get24bppRgb(bmp);            // 图像转换到Dlib的图像类中            Array2D<RgbPixel> img = DlibDotNet.Extensions.BitmapExtensions.ToArray2D<RgbPixel>(bmp);            var faceDetector = Dlib.GetFrontalFaceDetector();            var shapePredictor = ShapePredictor.Deserialize(faceDataPath);            // 检测人脸            var faces = faceDetector.Operator(img);            if (faces.Count() == 0)            {                return;            }            // 人脸区域中识别脸部特征            var shape = shapePredictor.Detect(img, faces[0]);             var bradleyPoints = (from i in Enumerable.Range(0, (int)shape.Parts)                                 let p = shape.GetPart((uint)i)                                 select new OpenCvSharp.Point(p.X, p.Y)).ToArray();             pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);             //凸包提取            var hull = Cv2.ConvexHullIndices(bradleyPoints);            var bradleyHull = (from i in hull                               select bradleyPoints[i]).ToArray();             for (int i = 0; i < bradleyHull.Length - 1; i++)            {                Cv2.Line(mat, bradleyHull[i].X, bradleyHull[i].Y, bradleyHull[i + 1].X, bradleyHull[i + 1].Y, Scalar.Red);            }            Cv2.Line(mat, bradleyHull[bradleyHull.Length - 1].X, bradleyHull[bradleyHull.Length - 1].Y, bradleyHull[0].X, bradleyHull[0].Y, Scalar.Red);             pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);             RotatedRect minAreaRect = Cv2.MinAreaRect(bradleyPoints);            //三角剖分            Subdiv2D subdiv = new Subdiv2D();            Rect rect = new Rect(0, 0, pictureBox1.Image.Width, pictureBox1.Image.Height);            subdiv.InitDelaunay(rect);             // 添加与绘制特征点            for (int i = 0; i < bradleyPoints.Length; i++)            {                subdiv.Insert(new Point2f(bradleyPoints[i].X, bradleyPoints[i].Y));            }            // 生成剖分三角形            Vec6f[] triangleList = subdiv.GetTriangleList();            OpenCvSharp.Point[] pt = new OpenCvSharp.Point[3];            // 绘制剖分三角形            for (int i = 0; i < triangleList.Length; i++)            {                Vec6f t = triangleList[i];                pt[0] = new OpenCvSharp.Point((int)t[0], (int)t[1]);                pt[1] = new OpenCvSharp.Point((int)t[2], (int)t[3]);                pt[2] = new OpenCvSharp.Point((int)t[4], (int)t[5]);                 Cv2.Line(mat, pt[0], pt[1], Scalar.Green, 1);                Cv2.Line(mat, pt[1], pt[2], Scalar.Green, 1);                Cv2.Line(mat, pt[2], pt[1], Scalar.Green, 1);            }             pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);         }    }}}