public static IEnumerable <FaceDetectionEntry> LoadDataset(string datasetPath, int width, int height, int n = -1)
{
var result = new ConcurrentBag <FaceDetectionEntry>();
var fileNames = Directory.EnumerateFiles(Path.Combine(datasetPath, "faces"), "*.pgm");
if (n != -1)
{
fileNames = fileNames.Take(n);
}
Parallel.ForEach(fileNames, filename =>
// foreach (var filename in fileNames)
{
Console.Write(".");
var data = new float[width * height];
var pgm = PgmImage.LoadImage(filename);
var image = PgmImage.MakeBitmap(pgm);
BitmapTool.ExtractData(width, height, data, image);
result.Add(new FaceDetectionEntry {
IsFace = true, ImageData = data, Filename = filename
});
}
);
return(result);
}
private void evaluate_Click(object sender, EventArgs e)
{
var bitmap = GetImage(this.textBox1.Text);
var output = (Bitmap)bitmap.Clone();
bitmap = BitmapTool.ResizeImage(bitmap, 300, 300);
bitmap.Save("input.jpg");
var boxes = new List <BoundingBox>();
int divisor = (int)this.divisorUpDown.Value;
var allExtracts = BitmapTool.SlideWindow(bitmap, 140, 140 / divisor)
.Concat(BitmapTool.SlideWindow(bitmap, 100, 100 / divisor))
.Concat(BitmapTool.SlideWindow(bitmap, 80, 80 / divisor))
.Concat(BitmapTool.SlideWindow(bitmap, 30, 30 / divisor));
foreach (var extracts in allExtracts.Batch(20))
{
boxes.AddRange(BitmapTool.Evaluate(extracts.ToList(), this._yhat, IMAGE_WIDTH, IMAGE_HEIGHT, (float)this.minProbaUpDown.Value / 100.0f));
}
this.currentBoundingBoxes.Clear();
var nms = new NonMaximumSuppresion();
var result = nms.Nms(boxes, (float)this.nmsUpDown.Value / 100.0f);
foreach (var boundingBox in result)
{
BitmapTool.DrawBoundingBox(output, boundingBox, Color.FromArgb(50, 0, 0, 255), false);
this.currentBoundingBoxes.Add(BitmapTool.Capture(bitmap, boundingBox));
}
this.pictureBox1.Image = output;
}
private void StartAI()
{
BuilderInstance <float> .Volume = new VolumeBuilder(); // Needed for GPU, must be done on AI thread
// Must be done after setting BuilderInstance<float>.Volume
this._yhat = SerializationExtensions.Load <float>("FaceDetection", false)[0];
while (true)
{
if (this._queueIn.TryDequeue(out var bitmap))
{
bitmap = BitmapTool.ResizeImage(bitmap, 300, 300);
var boxes = new List <BoundingBox>();
var allExtracts =
BitmapTool.SlideWindow(bitmap, 100, 100 / 9);
// .Concat(BitmapTool.SlideWindow(bitmap, 40, 40 / 3));
//.Concat(BitmapTool.SlideWindow(bitmap, 50, 50/3));
foreach (var extracts in allExtracts.Batch(10))
{
boxes.AddRange(BitmapTool.Evaluate(extracts.ToList(), this._yhat, IMAGE_WIDTH, IMAGE_HEIGHT, 0.8f));
}
this._queueOut.Enqueue(boxes);
}
}
}
public IEnumerable <FaceDetectionEntry> LoadDataset(string datasetPath, int width, int height, int n = -1)
{
var result = new ConcurrentBag <FaceDetectionEntry>();
var fileNames = Directory.EnumerateFiles(datasetPath, "*.jpg", SearchOption.AllDirectories);
if (n != -1)
{
fileNames = fileNames.Take(n);
}
Parallel.ForEach(fileNames, filename =>
//foreach (var filename in fileNames)
{
Console.Write(".");
var data = new float[width * height];
var image = Image.FromFile(filename) as Bitmap;
if (this._random)
{
for (var i = 0; i < this._randomPerImage; i++)
{
var x1 = Random.Value.Next((int)(image.Width * 0.8));
var x2 = Random.Value.Next(image.Width - x1) + this.minWidthPixel + x1;
var y1 = Random.Value.Next((int)(image.Height * 0.8));
var y2 = (int)((x2 - x1) / (float)image.Width * image.Height) + y1;
x2 = Math.Min(image.Width, x2);
y2 = Math.Min(image.Height, y2);
var subImage = image.Clone(new Rectangle(x1, y1, x2 - x1, y2 - y1), image.PixelFormat);
// subImage.Save("test.jpg");
BitmapTool.ExtractData(width, height, data, subImage);
result.Add(new FaceDetectionEntry {
IsFace = false, ImageData = data, Filename = filename
});
subImage.Dispose();
}
}
else
{
BitmapTool.ExtractData(width, height, data, image);
result.Add(new FaceDetectionEntry {
IsFace = false, ImageData = data, Filename = filename
});
}
image.Dispose();
}
);
return(result);
}
private void bitmapToolButton_Click(object sender, EventArgs e)
{
//Prepare
BitmapTool bitmapTool = new BitmapTool();
bitmapTool.FormClosed += BitmapTool_FormClosed;
//Hide self, show bitmap tool
Hide();
bitmapTool.Show();
}
private void NewFrame_event(object send, NewFrameEventArgs e)
{
try
{
lock (this._locker)
{
var webcamFrame = e.Frame;
if (this._queueIn.Count == 0)
{
this._queueIn.Enqueue((Bitmap)webcamFrame.Clone());
}
if (this._queueOut.TryDequeue(out var boundingBox))
{
this._lastBoundingBoxes = boundingBox;
BeginInvoke(new Action(() => { this.Text = (++this._evaluationCount).ToString(); }));
}
if (this._lastBoundingBoxes != null)
{
foreach (var box in this._lastBoundingBoxes)
{
BitmapTool.DrawBoundingBox(webcamFrame, box, Color.FromArgb(50, 0, 0, 255), false);
}
var nms = new NonMaximumSuppresion();
var result = nms.Nms(this._lastBoundingBoxes, 0.20f);
foreach (var box in result)
{
BitmapTool.DrawBoundingBox(webcamFrame, box, Color.FromArgb(50, 0, 255, 0), false);
}
}
this.pictureBox.Image = (Image)webcamFrame.Clone();
}
}
catch
{
}
}
public static FaceLocalizationDataset LoadDataset(string datasetPath, int width, int height, int n = -1)
{
Console.Write("Loading Helen dataset");
var result = new FaceLocalizationDataset(width, height);
// Annotations
var annotations = new Dictionary <string, BoundingBox>();
foreach (var file in Directory.EnumerateFiles(Path.Combine(datasetPath, "annotation")))
{
using (var sr = new StreamReader(file))
{
var key = sr.ReadLine();
var boundingBox = new BoundingBox();
// Compute bounding box
while (!sr.EndOfStream)
{
var line = sr.ReadLine().Replace(" ", String.Empty).Split(',');
var x = Single.Parse(line[0]);
var y = Single.Parse(line[1]);
if (x < boundingBox.x1)
{
boundingBox.x1 = x;
}
if (x > boundingBox.x2)
{
boundingBox.x2 = x;
}
if (y < boundingBox.y1)
{
boundingBox.y1 = y;
}
if (y > boundingBox.y2)
{
boundingBox.y2 = y;
}
}
annotations[key] = boundingBox;
}
}
// Images
var fileNames = Directory.EnumerateFiles(Path.Combine(datasetPath, "helen_1"))
.Concat(Directory.EnumerateFiles(Path.Combine(datasetPath, "helen_2")))
.Concat(Directory.EnumerateFiles(Path.Combine(datasetPath, "helen_3")))
.Concat(Directory.EnumerateFiles(Path.Combine(datasetPath, "helen_4")))
.Concat(Directory.EnumerateFiles(Path.Combine(datasetPath, "helen_5")));
if (n != -1)
{
fileNames = fileNames.Take(n);
}
Parallel.ForEach(fileNames, filename =>
// foreach (var filename in fileNames)
{
Console.Write(".");
var data = new float[width * height];
// Load image
var image = (Bitmap)Image.FromFile(filename);
var originalWidth = image.Width;
var originalHeight = image.Height;
BitmapTool.ExtractData(width, height, data, image);
// normalize box
var boundingBox = annotations[Path.GetFileName(filename).Replace(".jpg", String.Empty)];
boundingBox.x1 = boundingBox.x1 / originalWidth;
boundingBox.y1 = boundingBox.y1 / originalHeight;
boundingBox.x2 = boundingBox.x2 / originalWidth;
boundingBox.y2 = boundingBox.y2 / originalHeight;
result.TrainSet.Add(new FaceLocalizationEntry {
ImageData = data, BoundingBox = boundingBox, Filename = filename
});
//}
});
return(result);
}
