/// <summary>
/// Processes scors and boxes and generate a list of face rectangles.
/// </summary>
/// <param name="landmarkTensors">landmark output of Onnx model.</param>
/// <param name="imageX">X start position of the image.</param>
/// <param name="imageY">Y start position of the image.</param>
/// <param name="imageWidth">width of the image.</param>
/// <param name="imageHeight">height of the image.</param>
public static FaceLandmarks Predict(TensorFloat landmarkTensors, int imageX, int imageY, int imageWidth, int imageHeight)
{
var faceLandmarks = new FaceLandmarks();
IReadOnlyList <float> vectorLandmarks = landmarkTensors.GetAsVectorView();
IList <float> landmarkFloatList = vectorLandmarks.ToList();
long numAnchors = (long)Math.Ceiling(landmarkTensors.Shape[1] * 0.5);
for (var i = 0; i < numAnchors; i++)
{
var mark = new FaceLandmark
{
X = landmarkFloatList[i * 2] * imageWidth + imageX,
Y = landmarkFloatList[i * 2 + 1] * imageHeight + imageY
};
faceLandmarks.landmarkList.Add(mark);
}
return(faceLandmarks);
}
/// <summary>
/// Load the picked image and preprocessed it as the model input.
/// The function should excute after the image is loaded.
/// </summary>
private async void DetectFaces()
{
// Detect face using Onnx models
rfbInput.input = FaceDetectionHelper.SoftwareBitmapToTensorFloat(imageInputData);
rfbOutput = await rfbModelGen.EvaluateAsync(rfbInput);
List <FaceDetectionRectangle> faceRects = (List <FaceDetectionRectangle>)FaceDetectionHelper.Predict(rfbOutput.scores, rfbOutput.boxes);
// Detect facial landmarks using Onnx models
List <FaceLandmarks> faceLandmarksList = new List <FaceLandmarks>();
if (ShowDetail)
{
closestDistance = 10000.0f;
System.Diagnostics.Debug.WriteLine("Total: " + faceRects.Count);
foreach (FaceDetectionRectangle faceRect in faceRects)
{
int rectX = (int)faceRect.X1;
int rectY = (int)faceRect.Y1;
int rectWidth = (int)(faceRect.X2 - faceRect.X1) + 1;
int rectHeight = (int)(faceRect.Y2 - faceRect.Y1) + 1;
// Crop only the image region with faces
SoftwareBitmap croppedBitmap = new SoftwareBitmap(
imageInputData.BitmapPixelFormat,
FaceLandmarkHelper.inputImageDataSize,
FaceLandmarkHelper.inputImageDataSize,
BitmapAlphaMode.Ignore);
System.Diagnostics.Debug.WriteLine("Crop");
bool cropped = openCVHelper.CropResize(imageInputData, croppedBitmap, rectX, rectY, rectWidth, rectHeight);
if (!cropped)
{
continue;
}
// Model Processing
landmarkInput.input = FaceDetectionHelper.SoftwareBitmapToTensorFloat(croppedBitmap);
landmarkOutput = await landmarkModelGen.EvaluateAsync(landmarkInput);
FaceLandmarks faceLandmarks =
(FaceLandmarks)FaceLandmarkHelper.Predict(landmarkOutput.output, rectX, rectY, rectWidth, rectHeight);
// Calculate camera distance
if (faceLandmarks.IsValid)
{
System.Diagnostics.Debug.WriteLine("Valid: " + faceLandmarks.landmarkList.Count);
float distance = ImageHelper.CalculateCameraDistance(cameraFocalLength, faceLandmarks.EyeDistance);
closestDistance = distance < closestDistance ? distance : closestDistance;
faceLandmarksList.Add(faceLandmarks);
}
croppedBitmap.Dispose();
}
closestDistance = closestDistance == 10000.0f ? 0.0f : closestDistance;
if (CameraMode)
{
detailText.Text = $"Distance: {(int)closestDistance} cm";
}
else
{
detailText.Text = "";
}
}
else
{
detailText.Text = "";
}
// Draw rectangles or facial landmarks of detected faces on top of image
ClearPreviousFaceRectangles();
if (ShowDetail)
{
drawingFace.DrawFaceAll(faceRects, faceLandmarksList);
}
else
{
drawingFace.DrawFaceRetangles(faceRects);
}
foreach (Path path in drawingFace.pathes)
{
imageGrid.Children.Add(path);
}
faceLandmarksList.Clear();
}