static void Main(string[] args)
{
// Work with images folder configuration options
string imagesFolder = "D:\\My Work\\VR\\images";
string outputFilepath = "D:\\My Work\\VR\\test-data-norm.txt";
// Work with csv datasets configuration options
Size imageSize = new Size(350, 350);
string csvInput = "D:\\My Work\\VR\\dataset\\facial_emotions_2_ready.csv";
string csvOutput = "D:\\My Work\\VR\\dataset\\faceexpress_dataset_v2_2.csv";
// Models
string faceDetectorModel = "D:\\My Work\\VR\\resources\\haarcascade_frontalface_alt2.xml";
string facemarkModel = "D:\\My Work\\VR\\resources\\lbfmodel.yaml";
CascadeClassifier faceDetector = new CascadeClassifier(faceDetectorModel);
FacemarkLBFParams facemarkParams = new FacemarkLBFParams();
FacemarkLBF facemark = new FacemarkLBF(facemarkParams);
facemark.LoadModel(facemarkModel);
if (runWithCsvDataset)
{
RunWithCsv(faceDetector, facemark, csvInput, csvOutput, imageSize);
}
else
{
RunWithImagesFolder(imagesFolder, outputFilepath, faceDetector, facemark);
}
Console.WriteLine("Program finished successfully!");
}
void Start()
{
//cascadePath = Path.Combine (Directory.GetCurrentDirectory (), AssetDatabase.GetAssetPath (cascadeFile));
// We initialize webcam texture data
webcamTexture = new WebCamTexture();
webcamTexture.Play();
width = webcamTexture.width;
height = webcamTexture.height;
// We store settings internally for openCV after loading them in, these are the filepaths
filePath = Path.Combine(Application.persistentDataPath, cascadeModel.name + ".xml");
fmFilePath = Path.Combine(Application.persistentDataPath, "lbfmodel.yaml");
// We initialize the facemark system that will be used to recognize our face
fParams = new FacemarkLBFParams();
fParams.ModelFile = fmFilePath;
facemark = new FacemarkLBF(fParams);
facemark.LoadModel(fParams.ModelFile);
File.WriteAllBytes(filePath, cascadeModel.bytes);
convertedTexture = new Texture2D(width, height);
Debug.Log("Tracking Started! Recording with " + webcamTexture.deviceName + " at " + webcamTexture.width + "x" + webcamTexture.height);
InvokeRepeating("Track", trackingInterval, trackingInterval);
}
public FaceLandmarksDetector(string faceDetectorModel, string faceLandmarkerModel)
{
// Load face detector model
faceDetector = new CascadeClassifier(faceDetectorModel);
// Load facemark model (face landmarker)
FacemarkLBFParams facemarkParams = new FacemarkLBFParams();
facemark = new FacemarkLBF(facemarkParams);
facemark.LoadModel(faceLandmarkerModel);
}
private void FindFacialFeaturePoints()
{
string facePath;
try
{
// get face detect dataset
facePath = Path.GetFileName(@"data/haarcascade_frontalface_default.xml");
// get FFP dataset
facemarkParam = new FacemarkLBFParams();
facemark = new FacemarkLBF(facemarkParam);
facemark.LoadModel(@"data/lbfmodel.yaml");
}
catch (Exception ex)
{
throw new Exception(ex.Message);
}
// initialize imageMat
currImageMat = CurrImageI.Mat;
nextImageMat = NextImageI.Mat;
// Current Face
FacesListCurr = facesArrCurr.OfType <Rectangle>().ToList();
// Find facial feature points
VectorOfRect vrLeft = new VectorOfRect(facesArrCurr);
landmarksCurr = new VectorOfVectorOfPointF();
facemark.Fit(currImageMat, vrLeft, landmarksCurr);
ffpCurr = landmarksCurr[curr.SelectedFace];
// Next Face
FacesListNext = facesArrNext.OfType <Rectangle>().ToList();
// Find facial feature points
VectorOfRect vrRight = new VectorOfRect(facesArrNext);
landmarksNext = new VectorOfVectorOfPointF();
facemark.Fit(nextImageMat, vrRight, landmarksNext);
ffpNext = landmarksNext[next.SelectedFace];
// Add Corner points
ffpCurr = AddCornerPoints(ffpCurr, this.curr.ResizedImage.Mat);
ffpNext = AddCornerPoints(ffpNext, this.next.ResizedImage.Mat);
}
private void InitModel()
{
faceDetector = new CascadeClassifier(Constants.FACE_DETECTOR_PATH);
FacemarkLBFParams fParams = new FacemarkLBFParams();
fParams.ModelFile = Constants.LANDMARK_DETECTOR_PATH;
fParams.NLandmarks = 68; // number of landmark points
fParams.InitShapeN = 10; // number of multiplier for make data augmentation
fParams.StagesN = 5; // amount of refinement stages
fParams.TreeN = 6; // number of tree in the model for each landmark point
fParams.TreeDepth = 5; //he depth of decision tree
facemark = new FacemarkLBF(fParams);
facemark.LoadModel(fParams.ModelFile);
}
public Image <Bgr, Byte> GetFacePoints()
{
String facePath = Path.GetFullPath(@"../../data/haarcascade_frontalface_default.xml");
//CascadeClassifier faceDetector = new CascadeClassifier(@"..\..\Resource\EMGUCV\haarcascade_frontalface_default.xml");
CascadeClassifier faceDetector = new CascadeClassifier(facePath);
FacemarkLBFParams fParams = new FacemarkLBFParams();
//fParams.ModelFile = @"..\..\Resource\EMGUCV\lbfmodel.yaml";
fParams.ModelFile = @"lbfmodel.yaml";
fParams.NLandmarks = 68; // number of landmark points
fParams.InitShapeN = 10; // number of multiplier for make data augmentation
fParams.StagesN = 5; // amount of refinement stages
fParams.TreeN = 6; // number of tree in the model for each landmark point
fParams.TreeDepth = 5; //he depth of decision tree
FacemarkLBF facemark = new FacemarkLBF(fParams);
//facemark.SetFaceDetector(MyDetector);
Image <Bgr, Byte> image = new Image <Bgr, byte>("test.png");
Image <Gray, byte> grayImage = image.Convert <Gray, byte>();
grayImage._EqualizeHist();
VectorOfRect faces = new VectorOfRect(faceDetector.DetectMultiScale(grayImage));
VectorOfVectorOfPointF landmarks = new VectorOfVectorOfPointF();
facemark.LoadModel(fParams.ModelFile);
bool success = facemark.Fit(grayImage, faces, landmarks);
if (success)
{
Rectangle[] facesRect = faces.ToArray();
for (int i = 0; i < facesRect.Length; i++)
{
image.Draw(facesRect[i], new Bgr(Color.Blue), 2);
FaceInvoke.DrawFacemarks(image, landmarks[i], new Bgr(Color.Blue).MCvScalar);
}
return(image);
}
return(null);
}
