/// <summary>
/// The main program entry point
/// </summary>
/// <param name="args">The command line arguments</param>
static void Main(string[] args)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// load input image
var img = Dlib.LoadImage <RgbPixel>(inputFilePath);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
// draw the landmark points on the image
for (var i = 0; i < shape.Parts; i++)
{
var point = shape.GetPart((uint)i);
var rect = new Rectangle(point);
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 0), thickness: 4);
}
}
// export the modified image
Dlib.SaveJpeg(img, "output.jpg");
}
}
private static void Load(string directory, out IList <Matrix <RgbPixel> > images, out IList <uint> labels)
{
var imageList = new List <Matrix <RgbPixel> >();
var labelList = new List <uint>();
foreach (var file in Directory.EnumerateFiles(directory))
{
var name = Path.GetFileName(file);
var s = name.Split('_');
if (s.Length != 4 || !uint.TryParse(s[1], out var gender))
{
continue;
}
using (var tmp = Dlib.LoadImageAsMatrix <RgbPixel>(file))
{
var m = new Matrix <RgbPixel>(Size, Size);
Dlib.ResizeImage(tmp, m);
imageList.Add(m);
labelList.Add(gender);
}
}
images = imageList;
labels = labelList;
}
public static void DetectFacesAsync(string inputFilePath, string subscriptionKey, string uriBase, IFaceClient client, string vocabularyPath)
{
// set up Dlib facedetector
DirectoryInfo dir = new DirectoryInfo(inputFilePath);
using (var fd = Dlib.GetFrontalFaceDetector())
{
foreach (FileInfo files in dir.GetFiles("*.jpg"))
{
string _inputFilePath = inputFilePath + files.Name;
// load input image
Array2D <RgbPixel> img = Dlib.LoadImage <RgbPixel>(_inputFilePath);
// find all faces in the image
Rectangle[] faces = fd.Operator(img);
if (faces.Length != 0)
{
Console.WriteLine("Picture " + files.Name + " have faces, sending data to Azure");
MakeAnalysisRequestAsync(_inputFilePath, subscriptionKey, uriBase, files.Name, client, vocabularyPath).Wait();
}
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
// export the modified image
Dlib.SaveJpeg(img, "./Results/" + files.Name);
}
}
private void buscarrosto(Bitmap frame)
{
Image <Rgb, Byte> imageCV = new Image <Rgb, byte>(frame);
Emgu.CV.Mat mat = imageCV.Mat;
var array = new byte[mat.Width * mat.Height * mat.ElementSize];
mat.CopyTo(array);
using (Array2D <RgbPixel> image = Dlib.LoadImageData <RgbPixel>(array, (uint)mat.Height, (uint)mat.Width, (uint)(mat.Width * mat.ElementSize)))
{
using (FrontalFaceDetector fd = Dlib.GetFrontalFaceDetector())
{
var faces = fd.Operator(image);
foreach (DlibDotNet.Rectangle face in faces)
{
FullObjectDetection shape = _ShapePredictor.Detect(image, face);
ChipDetails faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
Array2D <RgbPixel> faceChip = Dlib.ExtractImageChip <RgbPixel>(image, faceChipDetail);
Bitmap bitmap1 = faceChip.ToBitmap <RgbPixel>();
MainWindow.main.Statusa1 = bitmap1;
Dlib.DrawRectangle(image, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
}
frame = image.ToBitmap <RgbPixel>();
MainWindow.main.Statusa = frame;
}
}
private void BackgroundWorkerOnDoWork(object sender, DoWorkEventArgs doWorkEventArgs)
{
var path = doWorkEventArgs.Argument as string;
if (string.IsNullOrWhiteSpace(path) || !File.Exists(path))
{
return;
}
// DlibDotNet can create Array2D from file but this sample demonstrate
// converting managed image class to dlib class and vice versa.
using (var faceDetector = FrontalFaceDetector.GetFrontalFaceDetector())
using (var ms = new MemoryStream(File.ReadAllBytes(path)))
using (var bitmap = (Bitmap)Image.FromStream(ms))
{
using (var image = bitmap.ToArray2D <RgbPixel>())
{
var dets = faceDetector.Detect(image);
foreach (var r in dets)
{
Dlib.DrawRectangle(image, r, new RgbPixel {
Green = 255
});
}
var result = image.ToBitmap();
this.pictureBox.Invoke(new Action(() =>
{
this.pictureBox.Image?.Dispose();
this.pictureBox.Image = result;
}));
}
}
}
/// <summary>
/// 人脸对比
/// </summary>
/// <param name="bitmap1"></param>
/// <param name="bitmap2"></param>
/// <returns></returns>
public static (float, List <Rectangle[]>) GetOutputLabels(Bitmap bitmap1, Bitmap bitmap2)
{
var data1 = GetData(new List <Bitmap>()
{
bitmap1
}, true);
var data2 = GetData(new List <Bitmap>()
{
bitmap2
}, true);
List <Rectangle[]> rectangles = new List <Rectangle[]>();
if (data1[0].Item1[0] == null)
{
return(-1, null);
}
if (data2[0].Item1[0] == null)
{
return(-2, null);
}
// 欧里几德距离
var diff = data1[0].Item1[0] - data2[0].Item1[0];
rectangles.Add(data1[0].Item2);
rectangles.Add(data2[0].Item2);
return(Dlib.Length(diff), rectangles);
}
/// <summary>
/// 人脸查找
/// </summary>
/// <param name="bitmap"></param>
/// <param name="bitmaps"></param>
/// <returns></returns>
public static List <(float, Rectangle[])> GetOutputLabels(Bitmap bitmap, List <Bitmap> bitmaps, bool isAFace = false)
{
List <(float, Rectangle[])> fNums = new List <(float, Rectangle[])>();
var data1 = GetData(new List <Bitmap>()
{
bitmap
}, isAFace);
var data2 = GetData(bitmaps, true);
if (data1[0].Item1[0] == null)
{
return(null);
}
foreach (var item in data2)
{
if (item.Item1[0] == null)
{
fNums.Add((99.99f, item.Item2));
}
else
{
// 欧里几德距离
var diff = data1[0].Item1[0] - data2[0].Item1[0];
fNums.Add((Dlib.Length(diff), item.Item2));
}
}
return(fNums);
}
static void Main(string[] args)
{
// Read image in
var img = Dlib.LoadImage <RgbPixel>(imgFilePath);
// Let's detect faces and draw rectangles around them
FaceDetector faceDetector = new FaceDetector(facialLandmarksSerializedPredictor);
Rectangle[] facesBoundingBoxes = faceDetector.DetectFacesBoundingBoxes(img);
foreach (var bb in facesBoundingBoxes)
{
Dlib.DrawRectangle(img, bb, color: new RgbPixel(0, 0, 255), thickness: 3);
}
// Draw eyes bounding box for subject (i.e., largest) face
if (facesBoundingBoxes.Length > 0)
{
// Example code if you wish to do this only on the largest face
/*Rectangle subjectFaceBoundingBox = new Rectangle(0, 0);
* foreach (var bb in facesBoundingBoxes)
* {
* if (bb.Area > subjectFaceBoundingBox.Area)
* {
* subjectFaceBoundingBox = bb;
* }
* }*/
// Here we do it on all faces
foreach (var subjectFaceBoundingBox in facesBoundingBoxes)
{
// Next, obtain facial landmarks
var landmarks = faceDetector.DetectFacialLandmarks(img, subjectFaceBoundingBox);
// We also draw them
foreach (Point p in landmarks)
{
Dlib.DrawRectangle(img, new Rectangle(p), color: new RgbPixel(255, 0, 0), thickness: 3);
}
// Now draw bounding box around the eyes
var topLeft = new Point(landmarks[FacialLandmarks.RIGHT_EYEBROW].X, landmarks[FacialLandmarks.RIGHT_EYEBROW].Y);
var bottomRight = new Point(landmarks[FacialLandmarks.LEFT_EYEBROW].X, landmarks[FacialLandmarks.UPPER_NOSE].Y);
var eyesBoundingBox = new Rectangle(topLeft, bottomRight);
Dlib.DrawRectangle(img, eyesBoundingBox, color: new RgbPixel(0, 255, 0), thickness: 3);
}
}
// Create output file path (for later)
string outFilePath;
var tmpStrArray = imgFilePath.Split('/');
var extension = tmpStrArray[tmpStrArray.Length - 1].Split('.')[1];
outFilePath = String.Join('/', tmpStrArray.SkipLast(1).ToArray()) + '/' +
tmpStrArray[tmpStrArray.Length - 1].Replace("." + extension, "_out." + extension);
Console.WriteLine(outFilePath);
// Write img
faceDetector.WriteImageToFilePath(img, outFilePath);
}
private static List <(OutputLabels <Matrix <float> >, Rectangle[])> GetData(List <Bitmap> bitmaps, bool isAFace = false)
{
var datas = new List <(OutputLabels <Matrix <float> >, Rectangle[])>();
try
{
foreach (var bitmap in bitmaps)
{
var faces = new List <Matrix <RgbPixel> >();
var dets = new Rectangle[0];
//在图像中寻找人脸我们需要一个人脸检测器:
using (var detector = Dlib.GetFrontalFaceDetector())
{
using (var img = bitmap.ToMatrix <RgbPixel>())
{
// 人脸 面积从大到小排序
dets = detector.Operator(img).OrderByDescending(x => x.Area).ToArray();
// 是否只检测面积最大的人脸
if (isAFace)
{
var shape = _SP.Detect(img, dets[0]);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(img, faceChipDetail);
faces.Add(faceChip);
}
else
{
foreach (var face in dets)
{
var shape = _SP.Detect(img, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(img, faceChipDetail);
faces.Add(faceChip);
}
}
if (!faces.Any())
{
datas.Add((null, null));
}
else
{
//此调用要求DNN将每个人脸图像转换为128D矢量。
//在这个128D的矢量空间中，来自同一个人的图像会彼此接近
//但是来自不同人的载体将会非常不同。所以我们可以用这些向量
//辨别一对图片是来自同一个人还是不同的人。
datas.Add((_NET.Operator(faces), dets));
}
}
}
}
}
catch (Exception ex)
{
LogHelperNLog.Error(ex);
}
return(datas);
}
/// <summary>
/// 连线
/// </summary>
/// <param name="shapes"></param>
/// <returns></returns>
public static ImageWindow.OverlayLine[] Getlines(List <FullObjectDetection> shapes)
{
//这个是用来连线的
ImageWindow.OverlayLine[] lines = null;
if (shapes.Any())
{
//就是这个
lines = Dlib.RenderFaceDetections(shapes);
}
return(lines);
}
public Rectangle[] DetectFacesBoundingBoxes(Array2D <RgbPixel> img)
{
Rectangle[] facesBoundingBoxes;
// Set up and apply face detector
using (var fd = Dlib.GetFrontalFaceDetector())
{
facesBoundingBoxes = fd.Operator(img);
}
return(facesBoundingBoxes);
}
/// <summary>
/// 具体计算
/// </summary>
/// <param name="bitmap"></param>
/// <returns></returns>
public Rectangle[] Face(Bitmap bitmap)
{
var dets = new Rectangle[0];
using (var detector = Dlib.GetFrontalFaceDetector())
//using (var img = Dlib.LoadImage<RgbPixel>("png.png"))
using (var img = bitmap.ToArray2D <RgbPixel>())
{
dets = detector.Operator(img);
}
return(dets);
}
public static void DrawPointsOfLandmarks(FileInfo image)
{
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize(GetFile(ShapePredictorFileName).FullName))
{
using (var img = Dlib.LoadImage <RgbPixel>(image.FullName))
{
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
foreach (var face in faces)
{
var shape = sp.Detect(img, face);
// draw the landmark points on the image
for (var i = 0; i < shape.Parts; i++)
{
var point = shape.GetPart((uint)i);
var rect = new Rectangle(point);
if (i == 0)
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 255), thickness: 8);
}
else if (i == 21 || i == 22 || i == 39 || i == 42 || i == 33 || i == 51 || i == 57 ||
i == 48 ||
i == 54)
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 0, 255), thickness: 4);
}
else if (i == 18 || i == 19 || i == 20 || i == 21) // left eye
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 0, 0), 6);
}
else if (i == 22 || i == 23 || i == 24 || i == 25) // right eye
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 128, 0), 6);
}
else if (i == 48 || i == 49 || i == 50) // left lip
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 0), 2);
}
else if (i == 52 || i == 53 || i == 54) // right lip
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 0, 128), 2);
}
else
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(0, 0, 0), thickness: 4);
}
}
Dlib.SavePng(img, "output.jpg");
}
}
}
}
/// <summary>
/// 使用路径获取位置数据
/// </summary>
/// <param name="url"></param>
/// <returns></returns>
public static Rectangle[] GetResult(string url)
{
var dets = new Rectangle[0];
url = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, url);
using (var detector = Dlib.GetFrontalFaceDetector())
//using (var img = Dlib.LoadImage<RgbPixel>("png.png"))
using (var img = Dlib.LoadImage <RgbPixel>(url))
{
dets = detector.Operator(img);
}
return(dets);
}
private static void DetectFacesOnImage(Array2D <RgbPixel> image)
{
// set up Dlib facedetector
using (var fd = Dlib.GetFrontalFaceDetector())
{
// find all faces in the image
var faces = fd.Operator(image);
foreach (Rectangle face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(image, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
}
}
public static void DetectFacesOnImage(string sourceImagePath, string destImagePath)
{
// set up Dlib facedetector
using (var fd = Dlib.GetFrontalFaceDetector())
{
// load input image
var image = Dlib.LoadImage <RgbPixel>(sourceImagePath);
DetectFacesOnImage(image);
// export the modified image
Dlib.SaveJpeg(image, destImagePath);
}
}
private Bitmap ProcessImage(Bitmap image)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = FrontalFaceDetector.GetFrontalFaceDetector())
using (var sp = new ShapePredictor("shape_predictor_68_face_landmarks.dat"))
{
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// detect faces
var faces = fd.Detect(img);
// detect facial landmarks
foreach (var rect in faces)
{
// detect facial landmarks
var shape = sp.Detect(img, rect);
// extract face chip
var chip = Dlib.GetFaceChipDetails(shape);
var thumbnail = Dlib.ExtractImageChips <RgbPixel>(img, chip);
// add picturebox
var box = new PictureBox()
{
Image = thumbnail.ToBitmap <RgbPixel>(),
SizeMode = PictureBoxSizeMode.Zoom,
Width = 62,
Height = 62
};
imagesPanel.Controls.Add(box);
// draw landmarks on main image
var lines = Dlib.RenderFaceDetections(new FullObjectDetection[] { shape });
foreach (var line in lines)
{
Dlib.DrawRectangle(
img,
new DlibDotNet.Rectangle(line.Point1),
new RgbPixel {
Green = 255
},
8);
}
}
return(img.ToBitmap <RgbPixel>());
}
}
private static IEnumerable <Matrix <RgbPixel> > JitterImage(Matrix <RgbPixel> img)
{
// All this function does is make 100 copies of img, all slightly jittered by being
// zoomed, rotated, and translated a little bit differently. They are also randomly
// mirrored left to right.
var rnd = new Rand();
var crops = new List <Matrix <RgbPixel> >();
for (var i = 0; i < 100; ++i)
{
crops.Add(Dlib.JitterImage(img, rnd));
}
return(crops);
}
public void FindFaces()
{
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(path);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
Dlib.SaveJpeg(img, @"D:\output.png");
}
}
/// <summary>
/// 具体计算
/// </summary>
/// <param name="bitmaps"></param>
/// <returns></returns>
private static List <List <Landmark68ViewModler> > GetDate(List <Bitmap> bitmaps)
{
List <List <FullObjectDetection> > Detection = new List <List <FullObjectDetection> >();
List <List <Landmark68ViewModler> > landmark68s = new List <List <Landmark68ViewModler> >();
//人脸检测器
using (var detector = Dlib.GetFrontalFaceDetector())
{
foreach (var bitmap in bitmaps)
{
var shapes = new List <FullObjectDetection>();
// 图片转换
using (var img = bitmap.ToArray2D <RgbPixel>())
{
//获取位置数据
var dets = detector.Operator(img);
// 循环人脸数据
foreach (var rect in dets)
{
// 特征点检测
var shape = _SP.Detect(img, rect);
if (shape.Parts > 2)
{
shapes.Add(shape);
}
List <Landmark68ViewModler> landmark68 = new List <Landmark68ViewModler>();
for (uint i = 0; i < shape.Parts; i++)
{
var item = shape.GetPart(i);
landmark68.Add(new Landmark68ViewModler()
{
X = item.X,
Y = item.Y,
});
}
landmark68s.Add(landmark68);
}
}
Detection.Add(shapes);
}
}
return(landmark68s);
}
public static int Number(string file)
{
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(file);
int number = 0;
var faces = fd.Operator(img);
foreach (var face in faces)
{
number += 1;
}
return(number);
}
}
/// <summary>
/// Get the image with detected faces highlighted by the rectangle
/// </summary>
/// <param name="image"></param>
/// <param name="numOfFaceDetected"></param>
/// <returns></returns>
public Bitmap FaceDetectionFromImage(Bitmap image, out int numOfFaceDetected)
{
numOfFaceDetected = 0;
if (image != null)
{
// set up Dlib facedetectors and shapedetectors
using (var faceDetector = FrontalFaceDetector.GetFrontalFaceDetector())
using (var shapePredictor = new ShapePredictor(Configuration.SHAP_PREDICTOR_CONFIG))
{
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// detect faces
var faces = faceDetector.Detect(img);
// detect facial landmarks
foreach (var rect in faces)
{
// detect facial landmarks
var shape = shapePredictor.Detect(img, rect);
//The left eye using landmark index[42, 47].
Landmarks landmarkLeftEye = new Landmarks(42, 47, shape);
//The right eye using landmark index [36, 41].
Landmarks landmarkRightEye = new Landmarks(36, 41, shape);
//draw landmark rectangle
var leftEyeRect = Utils.RectangleAdjust(landmarkLeftEye.GetLandmarkRectangle(), img);
var rightEyeRect = Utils.RectangleAdjust(landmarkRightEye.GetLandmarkRectangle(), img);
var adjustedFaceRect = Utils.RectangleAdjust(rect, img);
Dlib.DrawRectangle(img, adjustedFaceRect, new RgbPixel {
Blue = 255
}, 5);
Dlib.DrawRectangle(img, leftEyeRect, new RgbPixel {
Green = 255
}, 2);
Dlib.DrawRectangle(img, rightEyeRect, new RgbPixel {
Green = 255
}, 2);
}
numOfFaceDetected = faces.Length;
return(img.ToBitmap <RgbPixel>());
}
}
return(image);
}
public static void Recognize(string file)
{
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(file);
//hola
var faces = fd.Operator(img);
foreach (var face in faces)
{
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
Dlib.SaveJpeg(img, file);
}
}
public static void SetFormImage(Array2D <RgbPixel> img)
{
Dlib.ResizeImage(img, 0.4);
Bitmap image = new Bitmap(img.Columns, img.Rows, System.Drawing.Imaging.PixelFormat.Format32bppArgb);
for (int i = 0; i < img.Columns; i++)
{
for (int j = 0; j < img.Rows; j++)
{
//Color c = Color.FromArgb(img[i][j].Red, img[i][j].Green, img[i][j].Blue);
//image.SetPixel(i, j, c);
}
}
image.RotateFlip(RotateFlipType.Rotate90FlipNone);
Form1.SetImage(image);
}
/// <summary>
/// Extract features from an image and store it in <see cref="FaceData1"/>.
/// </summary>
/// <param name="imageFileInfo">File info of the image.</param>
/// <param name="sp"></param>
/// <param name="fd"></param>
/// <param name="getLabel">>Whether to get the label or not. False if not using for prediction.</param>
/// <returns></returns>
/// <seealso cref="GetFaceDataPoints1"/>
static FaceData1 GetFaceData1FromImage(FileInfo imageFileInfo, ShapePredictor sp, FrontalFaceDetector fd, bool getLabel = true)
{
// load input image
using (var img = Dlib.LoadImage <RgbPixel>(imageFileInfo.FullName))
{
var faces = fd.Operator(img);
foreach (var face in faces)
{
var shape = sp.Detect(img, face);
return(GetFaceDataPoints1(ref shape,
getLabel
? GetLabel(imageFileInfo)
: "Not getting label, see argument this function was called with."));
}
}
Debug.WriteLine($"Unable to get facial feature from {imageFileInfo.Name} as no faces were found!");
return(null);
}
/// <summary>
/// The main program entry point
/// </summary>
/// <param name="args">The command line arguments</param>
static void Main(string[] args)
{
// set up Dlib facedetector
using (var fd = Dlib.GetFrontalFaceDetector())
{
// load input image
var img = Dlib.LoadImage <RgbPixel>(inputFilePath);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
// export the modified image
Dlib.SaveJpeg(img, "output.jpg");
}
}
/// <summary>
/// 具体计算
/// </summary>
/// <param name="bitmaps"></param>
/// <returns></returns>
private static List <Rectangle[]> GetDate(List <Bitmap> bitmaps)
{
List <Rectangle[]> rectangles = new List <Rectangle[]>();
// 检测器
using (var detector = Dlib.GetFrontalFaceDetector())
{
// 循环所有的图片
foreach (var bitmap in bitmaps)
{
// 图片格式转化
using (var img = bitmap.ToArray2D <RgbPixel>())
{
// 获取位置数据
var dets = detector.Operator(img);
rectangles.Add(dets);
}
}
}
return(rectangles);
}
public void GetImage(string imagePath)
{
Array2D <RgbPixel> image = Dlib.LoadImage <RgbPixel>(imagePath);
using (FrontalFaceDetector fd = Dlib.GetFrontalFaceDetector())
{
var faces = fd.Operator(image);
foreach (DlibDotNet.Rectangle face in faces)
{
FullObjectDetection shape = _ShapePredictor.Detect(image, face);
ChipDetails faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
Array2D <RgbPixel> faceChip = Dlib.ExtractImageChip <RgbPixel>(image, faceChipDetail);
Bitmap bitmap1 = faceChip.ToBitmap <RgbPixel>();
MainWindow.main.Statusa1 = bitmap1;
Dlib.DrawRectangle(image, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
}
Bitmap frame = image.ToBitmap <RgbPixel>();
MainWindow.main.Statusa = frame;
}
public static string TestCustomImage(string dir)
{
DataViewSchema predictionPipelineSchema;
ITransformer predictionPipeline = mlContext.Model.Load("model.zip", out predictionPipelineSchema);
PredictionEngine <FeatureInputData, ExpressionPrediction> predictionEngine = mlContext.Model.CreatePredictionEngine <FeatureInputData, ExpressionPrediction>(predictionPipeline);
var img = Dlib.LoadImage <RgbPixel>(dir);
// Set up Dlib Face Detector
using (var fd = Dlib.GetFrontalFaceDetector())
// ... and Dlib Shape Detector
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// find all faces in the image
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
FeatureInputData inputData = new FeatureInputData
{
leftEyebrow = CalculateLeftEyebrow(shape),
rightEyebrow = CalculateRightEyebrow(shape),
leftLip = CalculateLeftLip(shape),
rightLip = CalculateRightLip(shape),
lipWidth = CalculateLipWidth(shape),
lipHeight = CalculateLipHeight(shape)
};
ExpressionPrediction prediction = predictionEngine.Predict(inputData);
return(prediction.expression.ToString());
}
}
return("N/A");
}
/// <summary>
/// 具体计算
/// </summary>
/// <param name="bitmap"></param>
/// <param name="sp"></param>
/// <returns></returns>
public List <FullObjectDetection> Face(Bitmap bitmap)
{
// 加载模型文件
if (sp == null)
{
var basePath = AppDomain.CurrentDomain.BaseDirectory;
sp = ShapePredictor.Deserialize(basePath + "ShapeModel/shape_predictor_68_face_landmarks.dat");
}
//var link = new ImageWindow.OverlayLine[0];
var shapes = new List <FullObjectDetection>();
using (var detector = Dlib.GetFrontalFaceDetector())
{
using (var img = bitmap.ToArray2D <RgbPixel>())
{
var dets = detector.Operator(img);
foreach (var rect in dets)
{
var shape = sp.Detect(img, rect);
if (shape.Parts > 2)
{
shapes.Add(shape);
}
}
//if (shapes.Any())
//{
// //就是这个
// var lines = Dlib.RenderFaceDetections(shapes);
// link = lines;
//}
}
}
return(shapes);
}
