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 bool Contrast(OutputLabels <Matrix <float> > faceDescriptors) { if (_faceDescriptors == null || _faceDescriptors.Count <= 0) { return(false); } foreach (var item in faceDescriptors) { foreach (var item1 in _faceDescriptors) { var diff = item - item1; if (Dlib.Length(diff) < 0.4) { return(true); } } } return(false); }
public static MatrixOp Mat <T>(OutputLabels <Matrix <T> > collection) where T : struct { if (collection == null) { throw new ArgumentNullException(nameof(collection)); } if (!collection.Any()) { throw new ArgumentException(); } if (collection.Any(obj => obj == null)) { throw new ArgumentException(); } collection.ThrowIfDisposed(); Matrix <T> .TryParse <T>(out var elementType); var first = collection.First(); var templateRows = first.TemplateRows; var templateColumns = first.TemplateColumns; var ret = NativeMethods.mat_mat_OpStdVectToMat(elementType.ToNativeMatrixElementType(), collection.NativePtr, templateRows, templateColumns, out var matrix); switch (ret) { case NativeMethods.ErrorType.MatrixElementTypeNotSupport: throw new ArgumentException($"{elementType} is not supported."); } return(new MatrixOp(NativeMethods.ElementType.OpStdVectToMat, elementType, matrix, templateRows, templateColumns)); }
public async Task ProcessAsync(string[] inputFilenames) { var chips = new List <Matrix <RgbPixel> >(); var faces = new List <Rectangle>(); var filename = new List <string>(); var jsonFilename = inputFilenames.First() + ".json"; foreach (var inputFilename in inputFilenames) { if (!File.Exists(inputFilename)) { break; } if (File.Exists(jsonFilename)) { continue; } // load the image using var img = await DlibHelpers.LoadRotatedImage(imageRotationService, inputFilename); // Dlib.SaveJpeg(img, inputFilename + "__1.jpg", 25); // Dlib.SaveJpeg(img, inputFilename + "__2.jpg", 25); // detect all faces foreach (var face in detector.Operator(img)) { // detect landmarks var shape = predictor.Detect(img, face); // extract normalized and rotated 150x150 face chip var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25); var faceChip = Dlib.ExtractImageChip <RgbPixel>(img, faceChipDetail); // convert the chip to a matrix and store var matrix = new Matrix <RgbPixel>(faceChip); chips.Add(matrix); faces.Add(face); filename.Add(inputFilename); } } if (!File.Exists(jsonFilename)) { var ffd = new FoundFacesData { // Chips = chips, Faces = faces, Filenames = filename, }; OutputLabels <Matrix <float> > descriptors = null; if (chips.Any()) { // put each fae in a 128D embedding space // similar faces will be placed close together // Console.WriteLine("Recognizing faces..."); descriptors = dnn.Operator(chips); ffd.Descriptors = descriptors.ToList(); } else { ffd.Descriptors = new List <Matrix <float> >(0); } var dto = new FoundFacesDataDto { Faces = ffd.Faces .Select(f => new RectangleDto { Bottom = f.Bottom, Left = f.Left, Top = f.Top, Right = f.Right, }) .ToList(), Filenames = ffd.Filenames, Descriptors = ffd.Descriptors .Select(x => new MatrixFloatDto { Data = x.ToArray(), Row = x.Rows, Columns = x.Columns, }) .ToList() }; var x = JsonConvert.SerializeObject(dto); File.WriteAllText(jsonFilename, JsonConvert.SerializeObject(dto)); } FoundFacesData items; using (var r = new StreamReader(jsonFilename)) { var json = r.ReadToEnd(); var itemsdto = JsonConvert.DeserializeObject <FoundFacesDataDto>(json); items = new FoundFacesData { Faces = itemsdto.Faces.Select(f => new Rectangle(f.Left, f.Top, f.Right, f.Bottom)).ToList(), Filenames = itemsdto.Filenames.ToList(), Descriptors = itemsdto.Descriptors.Select(d => new Matrix <float>(d.Data, d.Row, d.Columns)).ToList(), }; } if (items.Faces.Count <= 0) { return; } // // compare each face with all other faces var edges = new List <SamplePair>(); // for (uint i = 0; i < descriptors.Count; ++i) // for (var j = i; j < descriptors.Count; ++j) // // // record every pair of two similar faces // // faces are similar if they are less than 0.6 apart in the 128D embedding space // if (Dlib.Length(descriptors[i] - descriptors[j]) < 0.5) // edges.Add(new SamplePair(i, j)); // // // use the chinese whispers algorithm to find all face clusters // Dlib.ChineseWhispers(edges, 100, out var clusters, out var labels); // // Console.WriteLine($" Found {clusters} unique person(s) in the image"); // // // draw rectangles on each face using the cluster color // for (var i = 0; i < faces.Count; i++) // { // var color = new RgbPixel(255, 255, 255); // if (labels[i] < palette.Length) // color = palette[labels[i]]; // // using var img = Dlib.LoadImage<RgbPixel>(filename[i] + "__1.jpg"); // Dlib.DrawRectangle(img, faces[i], color: color, thickness: 4); // Dlib.SaveJpeg(img, filename[i] + "__1.jpg", 25); // } // // Console.WriteLine("end 1"); // compare each face with all other faces edges = new List <SamplePair>(); for (var i = 0; i < items.Descriptors.Count; ++i) { for (var j = i; j < items.Descriptors.Count; ++j) { // record every pair of two similar faces // faces are similar if they are less than 0.6 apart in the 128D embedding space if (Dlib.Length(items.Descriptors[i] - items.Descriptors[j]) < 0.4) { edges.Add(new SamplePair((uint)i, (uint)j)); } } } // use the chinese whispers algorithm to find all face clusters Dlib.ChineseWhispers(edges, 100, out var clusters2, out var labels2); // Console.WriteLine($" Found {clusters} unique person(s) in the image"); // draw rectangles on each face using the cluster color for (var i = 0; i < items.Faces.Count; i++) { var color = palette[0]; if (labels2[i] < palette.Length) { color = palette[labels2[i]]; } if (!File.Exists(items.Filenames[i] + $"_x{labels2[i]}.jpg")) { using var img2 = await DlibHelpers.LoadRotatedImage(imageRotationService, items.Filenames[i]); Dlib.SaveJpeg(img2, items.Filenames[i] + $"_x{labels2[i]}.jpg", 25); } using var img = Dlib.LoadImage <RgbPixel>(items.Filenames[i] + $"_x{labels2[i]}.jpg"); Dlib.DrawRectangle(img, items.Faces[i], color: color, thickness: 4); Dlib.SaveJpeg(img, items.Filenames[i] + $"_x{labels2[i]}.jpg", 25); } // var origFilename = new FileInfo(inputFilename).Name; // var outputFilename = Path.Combine(outputDirectory, $"{origFilename}_Identification.jpg"); // Dlib.SaveJpeg(img, inputFilename, 75); }