static void Main()
{
Console.WriteLine("FaceONNX: Race and age classification");
var files = Directory.GetFiles(@"..\..\..\images");
var path = @"..\..\..\results";
Directory.CreateDirectory(path);
_faceDetectorLight = new FaceDetectorLight();
_faceLandmarksExtractor = new FaceLandmarksExtractor();
_faceRaceClassifier = new FaceRaceClassifier();
_faceAgeClassifier = new FaceAgeClassifier();
var painter = new Painter()
{
PointPen = new Pen(Color.Yellow, 4),
Transparency = 0,
TextFont = new Font("Arial", 24)
};
Console.WriteLine($"Processing {files.Length} images");
foreach (var file in files)
{
using var bitmap = new Bitmap(file);
var filename = Path.GetFileName(file);
var faces = _faceDetectorLight.Forward(bitmap);
int i = 1;
Console.WriteLine($"Image: [{filename}] --> detected [{faces.Length}] faces");
foreach (var face in faces)
{
Console.Write($"\t[Face #{i++}]: ");
var labels = GetRaceAndAge(bitmap, face);
var paintData = new PaintData()
{
Rectangle = face,
Labels = labels
};
painter.Draw(bitmap, paintData);
}
bitmap.Save(Path.Combine(path, filename));
}
_faceDetectorLight.Dispose();
_faceLandmarksExtractor.Dispose();
_faceRaceClassifier.Dispose();
_faceAgeClassifier.Dispose();
Console.WriteLine("Done.");
Console.ReadKey();
}
static float[] GetEmbedding(Bitmap image)
{
var faces = _faceDetectorLight.Forward(image);
using var cropped = Imaging.Crop(image, faces.First());
var points = _faceLandmarksExtractor.Forward(cropped);
using var aligned = FaceLandmarksExtractor.Align(cropped, points);
return(_faceEmbedder.Forward(aligned));
}
static string[] GetEmotionAndBeauty(Bitmap image, Rectangle face)
{
using var cropped = Imaging.Crop(image, face);
var points = _faceLandmarksExtractor.Forward(cropped);
using var aligned = FaceLandmarksExtractor.Align(cropped, points);
var emotion = _faceEmotionClassifier.Forward(aligned);
var emotionLabel = FaceEmotionClassifier.Labels[emotion.Argmax()];
var beauty = _faceBautyClassifier.Forward(aligned);
var beautyLabel = $"{Math.Round(2 * beauty.Max(), 1)}/10.0";
Console.WriteLine($"--> classified as [{emotionLabel}] emotion and [{beautyLabel}] beauty");
return(new string[] { emotionLabel, beautyLabel });
}
static string[] GetRaceAndAge(Bitmap image, Rectangle face)
{
using var cropped = Imaging.Crop(image, face);
var points = _faceLandmarksExtractor.Forward(cropped);
using var aligned = FaceLandmarksExtractor.Align(cropped, points);
var race = _faceRaceClassifier.Forward(aligned);
var raceLabel = FaceRaceClassifier.Labels[race.Argmax()];
var age = _faceAgeClassifier.Forward(aligned);
var ageLabel = FaceAgeClassifier.Labels[age.Argmax()];
Console.WriteLine($"--> classified as [{raceLabel}] race and [{ageLabel}] age");
return(new string[] { raceLabel, ageLabel });
}
static void Main()
{
Console.WriteLine("FaceONNX: Face landmarks extraction");
var files = Directory.GetFiles(@"..\..\..\images");
var path = @"..\..\..\results";
Directory.CreateDirectory(path);
using var faceDetector = new FaceDetector();
using var faceLandmarksExtractor = new FaceLandmarksExtractor();
var painter = new Painter()
{
PointPen = new Pen(Color.Yellow, 4),
Transparency = 0,
};
Console.WriteLine($"Processing {files.Length} images");
foreach (var file in files)
{
using var bitmap = new Bitmap(file);
var filename = Path.GetFileName(file);
var faces = faceDetector.Forward(bitmap);
Console.WriteLine($"Image: [{filename}] --> detected [{faces.Length}] faces");
foreach (var face in faces)
{
var points = faceLandmarksExtractor.Forward(bitmap, face);
foreach (var point in points)
{
var paintData = new PaintData()
{
Points = point,
Title = string.Empty,
};
painter.Draw(bitmap, paintData);
bitmap.Save(Path.Combine(path, filename));
}
}
}
Console.WriteLine("Done.");
Console.ReadKey();
}
static void Main()
{
Console.WriteLine("FaceONNX: Face embeddings classification");
var fits = Directory.GetFiles(@"..\..\..\images\fit");
_faceDetectorLight = new FaceDetectorLight();
_faceLandmarksExtractor = new FaceLandmarksExtractor();
_faceEmbedder = new FaceEmbedder();
var embeddings = new Embeddings();
foreach (var fit in fits)
{
using var bitmap = new Bitmap(fit);
var embedding = GetEmbedding(bitmap);
var name = Path.GetFileNameWithoutExtension(fit);
embeddings.Add(embedding, name);
}
Console.WriteLine($"Embeddings count: {embeddings.Count}");
var scores = Directory.GetFiles(@"..\..\..\images\score");
Console.WriteLine($"Processing {scores.Length} images");
foreach (var score in scores)
{
using var bitmap = new Bitmap(score);
var embedding = GetEmbedding(bitmap);
var proto = embeddings.FromSimilarity(embedding);
var label = proto.Item1;
var similarity = proto.Item2;
var filename = Path.GetFileName(score);
Console.WriteLine($"Image: [{filename}] --> classified as [{label}] with similarity [{similarity}]");
}
_faceDetectorLight.Dispose();
_faceLandmarksExtractor.Dispose();
_faceEmbedder.Dispose();
Console.WriteLine("Done.");
Console.ReadKey();
}
static void Main()
{
Console.WriteLine("FaceONNX: Emotion and beauty estimation");
var files = Directory.GetFiles(@"..\..\..\images");
var path = @"..\..\..\results";
Directory.CreateDirectory(path);
_faceDetectorLight = new FaceDetectorLight();
_faceLandmarksExtractor = new FaceLandmarksExtractor();
_faceEmotionClassifier = new FaceEmotionClassifier();
_faceBautyClassifier = new FaceBautyClassifier();
Console.WriteLine($"Processing {files.Length} images");
foreach (var file in files)
{
using var bitmap = new Bitmap(file);
var filename = Path.GetFileName(file);
var faces = _faceDetectorLight.Forward(bitmap);
int i = 1;
Console.WriteLine($"Image: [{filename}] --> detected [{faces.Length}] faces");
foreach (var face in faces)
{
Console.Write($"\t[Face #{i++}]: ");
var labels = GetEmotionAndBeauty(bitmap, face);
}
}
_faceDetectorLight.Dispose();
_faceLandmarksExtractor.Dispose();
_faceEmotionClassifier.Dispose();
_faceBautyClassifier.Dispose();
Console.WriteLine("Done.");
Console.ReadKey();
}
