public void Deserialize2()
{
var path = Path.Combine(this.ModelDirectory, "dlib_face_recognition_resnet_model_v1.dat");
using (var loss = LossMetric.Deserialize(File.ReadAllBytes(path)))
Assert.Equal(132, loss.NumLayers);
}
/// <summary>
/// Initializes a new instance of the <see cref="FaceRecognition"/> class with the directory path that stores model files.
/// </summary>
/// <param name="directory">The directory path that stores model files.</param>
/// <exception cref="FileNotFoundException">The model file is not found.</exception>
/// <exception cref="DirectoryNotFoundException">The specified directory path is not found.</exception>
private FaceRecognition(string directory)
{
if (!Directory.Exists(directory))
{
throw new DirectoryNotFoundException(directory);
}
var predictor68PointModel = Path.Combine(directory, FaceRecognitionModels.GetPosePredictorModelLocation());
if (!File.Exists(predictor68PointModel))
{
throw new FileNotFoundException(predictor68PointModel);
}
var predictor5PointModel = Path.Combine(directory, FaceRecognitionModels.GetPosePredictorFivePointModelLocation());
if (!File.Exists(predictor5PointModel))
{
throw new FileNotFoundException(predictor5PointModel);
}
var cnnFaceDetectionModel = Path.Combine(directory, FaceRecognitionModels.GetCnnFaceDetectorModelLocation());
if (!File.Exists(cnnFaceDetectionModel))
{
throw new FileNotFoundException(cnnFaceDetectionModel);
}
var faceRecognitionModel = Path.Combine(directory, FaceRecognitionModels.GetFaceRecognitionModelLocation());
if (!File.Exists(faceRecognitionModel))
{
throw new FileNotFoundException(faceRecognitionModel);
}
this._FaceDetector?.Dispose();
this._FaceDetector = DlibDotNet.Dlib.GetFrontalFaceDetector();
this._PosePredictor68Point?.Dispose();
this._PosePredictor68Point = ShapePredictor.Deserialize(predictor68PointModel);
this._PosePredictor5Point?.Dispose();
this._PosePredictor5Point = ShapePredictor.Deserialize(predictor5PointModel);
this._CnnFaceDetector?.Dispose();
this._CnnFaceDetector = LossMmod.Deserialize(cnnFaceDetectionModel);
this._FaceEncoder?.Dispose();
this._FaceEncoder = LossMetric.Deserialize(faceRecognitionModel);
var predictor194PointModel = Path.Combine(directory, FaceRecognitionModels.GetPosePredictor194PointModelLocation());
if (File.Exists(predictor194PointModel))
{
this._PosePredictor194Point?.Dispose();
this._PosePredictor194Point = ShapePredictor.Deserialize(predictor194PointModel);
}
}
public void Operator()
{
var image = this.GetDataFile("Lenna.jpg");
var path1 = Path.Combine(this.ModelDirectory, "dlib_face_recognition_resnet_model_v1.dat");
var path2 = Path.Combine(this.ModelDirectory, "shape_predictor_5_face_landmarks.dat");
using (var net1 = LossMetric.Deserialize(path1))
using (var net2 = LossMetric.Deserialize(File.ReadAllBytes(path1)))
using (var sp = ShapePredictor.Deserialize(path2))
using (var matrix = Dlib.LoadImageAsMatrix <RgbPixel>(image.FullName))
using (var detector = Dlib.GetFrontalFaceDetector())
{
var faces = new List <Matrix <RgbPixel> >();
foreach (var face in detector.Operator(matrix))
{
var shape = sp.Detect(matrix, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(matrix, faceChipDetail);
faces.Add(faceChip);
}
foreach (var face in faces)
{
using (var ret1 = net1.Operator(face))
using (var ret2 = net2.Operator(face))
{
Assert.Equal(1, ret1.Count);
Assert.Equal(1, ret2.Count);
var r1 = ret1[0];
var r2 = ret2[0];
Assert.Equal(r1.Columns, r2.Columns);
Assert.Equal(r1.Rows, r2.Rows);
for (var c = 0; c < r1.Columns; c++)
{
for (var r = 0; r < r1.Rows; r++)
{
Assert.Equal(r1[r, c], r2[r, c]);
}
}
}
face.Dispose();
}
}
}
public FaceContrast(string path)
{
var files = Directory.GetFiles(path, "*.jpg");
if (files.Length > 0)
{
using (var net = LossMetric.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "dlib_face_recognition_resnet_model_v1.dat")))
{
List <Matrix <RgbPixel> > facesList = new List <Matrix <RgbPixel> >();
foreach (var item in files)
{
facesList.Add(Dlib.LoadImageAsMatrix <RgbPixel>(item));
}
_faceDescriptors = net.Operator(facesList);
}
}
}
public async Task <ActionResult> Login([FromBody] InputFaceModel model)
{
RequestFaceModel request = new RequestFaceModel()
{
Status = 500,
Message = null
};
var filePath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "FaceImages", model.user_name);
if (!Directory.Exists(filePath))
{
request.Enum = RequestEnum.Failed;
Console.WriteLine(request.Message);
Thread.Sleep(5000);
return(Ok(request));
}
FaceContrast faceContrast = new FaceContrast(filePath);
VideoCapture cap = null;
try
{
if (model.rmtp_url == "0")
{
cap = new VideoCapture(0);
}
else
{
cap = new VideoCapture(model.rmtp_url);
}
var flag = false;
var faceFlag = false;
var bioFlag = false;
QueueFixedLength <double> leftEarQueue = new QueueFixedLength <double>(10);
QueueFixedLength <double> rightEarQueue = new QueueFixedLength <double>(10);
QueueFixedLength <double> mouthQueue = new QueueFixedLength <double>(20);
bool leftEarFlag = false;
bool rightEarFlag = false;
bool mouthFlag = false;
using (var sp = ShapePredictor.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "shape_predictor_5_face_landmarks.dat")))
using (var win = new ImageWindow())
{
// Load face detection and pose estimation models.
using (var detector = Dlib.GetFrontalFaceDetector())
using (var net = LossMetric.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "dlib_face_recognition_resnet_model_v1.dat")))
using (var poseModel = ShapePredictor.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "shape_predictor_68_face_landmarks.dat")))
{
var ti = true;
System.Timers.Timer t = new System.Timers.Timer(30000);
t.Elapsed += new System.Timers.ElapsedEventHandler((object source, System.Timers.ElapsedEventArgs e) =>
{
ti = false;
});
t.AutoReset = false;
t.Enabled = true;
//抓取和处理帧，直到用户关闭主窗口。
while (/*!win.IsClosed() &&*/ ti)
{
try
{
// Grab a frame
var temp = new Mat();
if (!cap.Read(temp))
{
break;
}
//把OpenCV的Mat变成dlib可以处理的东西。注意
//包装Mat对象，它不复制任何东西。所以cimg只对as有效
//只要温度是有效的。也不要做任何可能导致它的临时工作
//重新分配存储图像的内存，因为这将使cimg
//包含悬空指针。这基本上意味着您不应该修改temp
//使用cimg时。
var array = new byte[temp.Width * temp.Height * temp.ElemSize()];
Marshal.Copy(temp.Data, array, 0, array.Length);
using (var cimg = Dlib.LoadImageData <RgbPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElemSize())))
{
// Detect faces
var faces = detector.Operator(cimg);
// Find the pose of each face.
var shapes = new List <FullObjectDetection>();
for (var i = 0; i < faces.Length; ++i)
{
var det = poseModel.Detect(cimg, faces[i]);
shapes.Add(det);
}
if (shapes.Count > 0)
{
// 活体检测
if (!bioFlag)
{
bioFlag = BioAssay(shapes[0], ref leftEarQueue, ref rightEarQueue, ref mouthQueue, ref leftEarFlag, ref rightEarFlag, ref mouthFlag);
}
}
if (!faceFlag)
{
foreach (var face in faces)
{
var shape = sp.Detect(cimg, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
Matrix <RgbPixel> rgbPixels = new Matrix <RgbPixel>(cimg);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(rgbPixels, faceChipDetail);
var faceDescriptors = net.Operator(faceChip);
faceFlag = faceContrast.Contrast(faceDescriptors);
}
}
Console.WriteLine(model.user_name + ":" + faceFlag);
if (bioFlag && faceFlag)
{
flag = bioFlag && faceFlag;
if (flag)
{
break;
}
}
//在屏幕上显示
win.ClearOverlay();
win.SetImage(cimg);
var lines = Dlib.RenderFaceDetections(shapes);
win.AddOverlay(faces, new RgbPixel {
Red = 72, Green = 118, Blue = 255
});
win.AddOverlay(lines);
foreach (var line in lines)
{
line.Dispose();
}
}
}
catch (Exception ex)
{
request.Message = ex.ToString();
break;
}
}
}
}
if (flag)
{
request.Enum = RequestEnum.Succeed;
}
else
{
request.Enum = RequestEnum.Failed;
}
}
catch (Exception ex)
{
request.Message = ex.ToString();
}
finally
{
if (cap != null)
{
cap.Dispose();
}
}
Console.WriteLine(request.Message);
return(Ok(request));
}