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);
}
