private void buscarrosto(Bitmap frame)
{
Image <Rgb, Byte> imageCV = new Image <Rgb, byte>(frame);
Emgu.CV.Mat mat = imageCV.Mat;
var array = new byte[mat.Width * mat.Height * mat.ElementSize];
mat.CopyTo(array);
using (Array2D <RgbPixel> image = Dlib.LoadImageData <RgbPixel>(array, (uint)mat.Height, (uint)mat.Width, (uint)(mat.Width * mat.ElementSize)))
{
using (FrontalFaceDetector fd = Dlib.GetFrontalFaceDetector())
{
var faces = fd.Operator(image);
foreach (DlibDotNet.Rectangle face in faces)
{
FullObjectDetection shape = _ShapePredictor.Detect(image, face);
ChipDetails faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
Array2D <RgbPixel> faceChip = Dlib.ExtractImageChip <RgbPixel>(image, faceChipDetail);
Bitmap bitmap1 = faceChip.ToBitmap <RgbPixel>();
MainWindow.main.Statusa1 = bitmap1;
Dlib.DrawRectangle(image, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
}
frame = image.ToBitmap <RgbPixel>();
MainWindow.main.Statusa = frame;
}
}
/// <summary>
/// Called when videoPlayer receives a new frame.
/// </summary>
/// <param name="sender"></param>
/// <param name="image"></param>
private void videoPlayer_NewFrameReceived(object sender, Accord.Video.NewFrameEventArgs eventArgs)
{
// convert image to dlib format
var img = eventArgs.Frame.ToArray2D <RgbPixel>();
// detect face every 4 frames
if (frameIndex % 4 == 0)
{
var faces = faceDetector.Detect(img);
if (faces.Length > 0)
{
currentFace = faces.First();
}
}
// abort if we don't have a face at this point
if (currentFace == default(DlibDotNet.Rectangle))
{
return;
}
// detect facial landmarks
var shape = shapePredictor.Detect(img, currentFace);
// detect head pose
if (shape.Parts == 68)
{
DetectHeadPose(eventArgs.Frame, shape);
}
// update frame counter
frameIndex++;
}
public void DetectEyes(Array2D <byte> image, ref System.Drawing.Point[][] eyes, ref bool IsDetected)
{
//ImageWindow win = new ImageWindow(image);
//win.Show();
var dets = detector.Operator(image);
eyes[0] = new Point[6];
eyes[1] = new Point[6];
if (dets.Length > 0)
{
var shape = sp.Detect(image, dets[0]);
if (shape.Parts > 60)
{
for (int ii = 0; ii < 6; ii++)
{
var temp = shape.GetPart(36 + (uint)ii);
eyes[0][ii] = new Point(temp.X, temp.Y);
temp = shape.GetPart(42 + (uint)ii);
eyes[1][ii] = new Point(temp.X, temp.Y);
}
IsDetected = true;
//var chipLocations = Dlib.GetFaceChipDetails(shapes);
}
else
{
IsDetected = false;
}
}
}
public void LandmarkTest()
{
using (var faceDetector = FrontalFaceDetector.GetFrontalFaceDetector())
using (var shapePredictor = new ShapePredictor(Configuration.SHAP_PREDICTOR_CONFIG))
{
// convert image to dlib format
var img = Utils.LoadImageAsBitmap("TestImage/pic01.jpg").ToArray2D <RgbPixel>();
// detect faces
var faces = faceDetector.Detect(img);
// detect facial landmarks
foreach (var rect in faces)
{
// detect facial landmarks
var shape = shapePredictor.Detect(img, rect);
//The left eye using landmark index[42, 47].
Landmarks landmarkLeftEye = new Landmarks(42, 47, shape);
Assert.AreEqual(landmarkLeftEye.LandMarkPointList.Count, 6);
//index range should be 0-67
Landmarks landmark2 = new Landmarks(42, 68, shape);
}
}
}
/// <summary>
/// Called when videoPlayer receives a new frame.
/// </summary>
/// <param name="sender"></param>
/// <param name="image"></param>
private void videoPlayer_NewFrame(object sender, ref System.Drawing.Bitmap image)
{
// conver frame to grayscale
var grayscale = new GrayscaleBT709();
var grayImage = grayscale.Apply(image);
// convert image to dlib format
var img = grayImage.ToArray2D <RgbPixel>();
// detect face every 4 frames
if (frameIndex % 4 == 0)
{
var faces = faceDetector.Detect(img);
if (faces.Length > 0)
{
currentFace = faces.First();
}
}
// abort if we don't have a face at this point
if (currentFace == default(DlibDotNet.Rectangle))
{
return;
}
// detect facial landmarks
var shape = shapePredictor.Detect(img, currentFace);
// detect eye state
DetectEyeState(image, shape);
// update frame counter
frameIndex++;
}
public double[] detectFaceLandmarks(Array2D <RgbPixel> frame)
{
var img = frame;
double[] headParams = new double[3];
var faces = fd.Operator(img);
foreach (var face in faces)
{
var shape = sp.Detect(img, face);
var eyesPoints =
(from i in new int[] { 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 }
let pt = shape.GetPart((uint)i)
select new OpenCvSharp.Point2d(pt.X, pt.Y)).ToArray();
headParams[2] = calculateEAR(eyesPoints);
var model = Utility.GetFaceModel();
var landmarks = new MatOfPoint2d(1, 6,
(from i in new int[] { 30, 8, 36, 45, 48, 54 }
let pt = shape.GetPart((uint)i)
select new OpenCvSharp.Point2d(pt.X, pt.Y)).ToArray());
var cameraMatrix = Utility.GetCameraMatrix((int)img.Rect.Width, (int)img.Rect.Height);
var coeffs = new MatOfDouble(4, 1);
coeffs.SetTo(0);
Mat rotation = new MatOfDouble();
Mat translation = new MatOfDouble();
Cv2.SolvePnP(model, landmarks, cameraMatrix, coeffs, rotation, translation);
/* var euler = Utility.GetEulerMatrix(rotation);
*
* var yaw = 180 * euler.At<double>(0, 2) / Math.PI;
* var pitch = 180 * euler.At<double>(0, 1) / Math.PI;
* var roll = 180 * euler.At<double>(0, 0) / Math.PI;
*
* pitch = Math.Sign(pitch) * 180 - pitch;
*/
var poseModel = new MatOfPoint3d(1, 1, new Point3d(0, 0, 1000));
var poseProjection = new MatOfPoint2d();
Cv2.ProjectPoints(poseModel, rotation, translation, cameraMatrix, coeffs, poseProjection);
var landmark = landmarks.At <Point2d>(0);
var p = poseProjection.At <Point2d>(0);
headParams[0] = (double)p.X;
headParams[1] = (double)p.Y;
}
return(headParams);
}
/// <summary>
/// Called when videoPlayer receives a new frame.
/// </summary>
/// <param name="sender"></param>
/// <param name="image"></param>
private void videoPlayer_NewFrame(object sender, ref Bitmap image)
{
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// find the face
// note that we only detect faces every 4 frames
if (faceRect == default(DlibDotNet.Rectangle) || frameIndex++ % 4 == 0)
{
var faces = faceDetector.Detect(img);
faceRect = faces.FirstOrDefault();
}
// abort if we found no face
if (faceRect == default(DlibDotNet.Rectangle))
{
return;
}
// find face landmark points
var shape = shapePredictor.Detect(img, faceRect);
var landmarkPoints = BeardHelper.GetLandmarkPoints(shape);
// find beard landmark points
var beardPoints = BeardHelper.GetBeardPoints();
// calculate Delaunay triangles
var triangles = Utility.GetDelaunayTriangles(landmarkPoints);
// get transformations to warp the beard onto the face
var warps = Utility.GetWarps(beardPoints, landmarkPoints, triangles);
// split the beard image into an alpha mask and an RGB part
var beard = BitmapConverter.ToMat(beardImage);
BeardHelper.SplitChannels(beard, out var beardMask, out var beardRgb);
// warp the beard RGB image
var warpedBeard = Utility.ApplyWarps(BitmapConverter.ToBitmap(beardRgb), image.Width, image.Height, warps);
// warp the beard alpha mask
var warpedBeardMask = Utility.ApplyWarps(BitmapConverter.ToBitmap(beardMask), image.Width, image.Height, warps);
// blend the beard onto the camera frame by using the mask
var frame = BitmapConverter.ToMat(image);
var result = BeardHelper.Blend(warpedBeard, warpedBeardMask, frame);
// return result
image = BitmapConverter.ToBitmap(result);
}
private List <List <Point2f> > DetectFaces(FrontalFaceDetector detector, ShapePredictor predictor, Mat mat)
{
using (var image = ToArray(mat))
{
var points = detector.Operator(image)
.Select(rectangle => predictor.Detect(image, rectangle))
.Where(shape => shape.Parts > 2)
.Select(shape => Enumerable.Range(0, (int)shape.Parts)
.Select(i => shape.GetPart((uint)i))
.Select((p, i) => new Point2f(p.X, p.Y))
.ToList())
.ToList();
return(points);
}
}
private Bitmap ProcessImage(Bitmap image)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = FrontalFaceDetector.GetFrontalFaceDetector())
using (var sp = new ShapePredictor("shape_predictor_68_face_landmarks.dat"))
{
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// detect faces
var faces = fd.Detect(img);
// detect facial landmarks
foreach (var rect in faces)
{
// detect facial landmarks
var shape = sp.Detect(img, rect);
// extract face chip
var chip = Dlib.GetFaceChipDetails(shape);
var thumbnail = Dlib.ExtractImageChips <RgbPixel>(img, chip);
// add picturebox
var box = new PictureBox()
{
Image = thumbnail.ToBitmap <RgbPixel>(),
SizeMode = PictureBoxSizeMode.Zoom,
Width = 62,
Height = 62
};
imagesPanel.Controls.Add(box);
// draw landmarks on main image
var lines = Dlib.RenderFaceDetections(new FullObjectDetection[] { shape });
foreach (var line in lines)
{
Dlib.DrawRectangle(
img,
new DlibDotNet.Rectangle(line.Point1),
new RgbPixel {
Green = 255
},
8);
}
}
return(img.ToBitmap <RgbPixel>());
}
}
/// <summary>
/// Get the image with detected faces highlighted by the rectangle
/// </summary>
/// <param name="image"></param>
/// <param name="numOfFaceDetected"></param>
/// <returns></returns>
public Bitmap FaceDetectionFromImage(Bitmap image, out int numOfFaceDetected)
{
numOfFaceDetected = 0;
if (image != null)
{
// set up Dlib facedetectors and shapedetectors
using (var faceDetector = FrontalFaceDetector.GetFrontalFaceDetector())
using (var shapePredictor = new ShapePredictor(Configuration.SHAP_PREDICTOR_CONFIG))
{
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// detect faces
var faces = faceDetector.Detect(img);
// detect facial landmarks
foreach (var rect in faces)
{
// detect facial landmarks
var shape = shapePredictor.Detect(img, rect);
//The left eye using landmark index[42, 47].
Landmarks landmarkLeftEye = new Landmarks(42, 47, shape);
//The right eye using landmark index [36, 41].
Landmarks landmarkRightEye = new Landmarks(36, 41, shape);
//draw landmark rectangle
var leftEyeRect = Utils.RectangleAdjust(landmarkLeftEye.GetLandmarkRectangle(), img);
var rightEyeRect = Utils.RectangleAdjust(landmarkRightEye.GetLandmarkRectangle(), img);
var adjustedFaceRect = Utils.RectangleAdjust(rect, img);
Dlib.DrawRectangle(img, adjustedFaceRect, new RgbPixel {
Blue = 255
}, 5);
Dlib.DrawRectangle(img, leftEyeRect, new RgbPixel {
Green = 255
}, 2);
Dlib.DrawRectangle(img, rightEyeRect, new RgbPixel {
Green = 255
}, 2);
}
numOfFaceDetected = faces.Length;
return(img.ToBitmap <RgbPixel>());
}
}
return(image);
}
public static bool ExtractFaceDataFromImage(
Array2D <RgbPixel> rgb_array2d_img,
ref Array2D <RgbPixel> face_array2d_img,
ref Array2D <RgbPixel> left_eye_array2d_img,
ref Array2D <RgbPixel> right_eye_array2d_img,
ref float[] face_grid)
{
var ycbcr_array2d_img = RgbToYCbCr(rgb_array2d_img);
//Dlib.SaveJpeg(rgb_array2d_img, "face_rgb.jpg");
//Dlib.SaveJpeg(ycbcr_array2d_img, "face_ycbcr.jpg");
var face_rects = detector.Operator(rgb_array2d_img);
if (face_rects.Length != 1)
{
return(false);
}
foreach (var face_rect in face_rects)
{
var shape = sp.Detect(rgb_array2d_img, face_rect);
var left_eye_rect = GetRect(shape, LEFT_EYE_START, LEFT_EYE_END);
var right_eye_rect = GetRect(shape, RIGHT_EYE_START, RIGHT_EYE_END);
var left_eye_rect_normalized = GetEyeRectSizeNormalized(face_rect, left_eye_rect);
var right_eye_rect_normalized = GetEyeRectSizeNormalized(face_rect, right_eye_rect);
DrawDebugDataOnImage(rgb_array2d_img, shape, face_rect, left_eye_rect, right_eye_rect);
GenerateInputs(
ycbcr_array2d_img,
face_rect,
left_eye_rect_normalized,
right_eye_rect_normalized,
ref face_array2d_img,
ref left_eye_array2d_img,
ref right_eye_array2d_img,
ref face_grid);
}
return(true);
}
/// <summary>
/// Extract features from an image and store it in <see cref="FaceData1"/>.
/// </summary>
/// <param name="imageFileInfo">File info of the image.</param>
/// <param name="sp"></param>
/// <param name="fd"></param>
/// <param name="getLabel">>Whether to get the label or not. False if not using for prediction.</param>
/// <returns></returns>
/// <seealso cref="GetFaceDataPoints1"/>
static FaceData1 GetFaceData1FromImage(FileInfo imageFileInfo, ShapePredictor sp, FrontalFaceDetector fd, bool getLabel = true)
{
// load input image
using (var img = Dlib.LoadImage <RgbPixel>(imageFileInfo.FullName))
{
var faces = fd.Operator(img);
foreach (var face in faces)
{
var shape = sp.Detect(img, face);
return(GetFaceDataPoints1(ref shape,
getLabel
? GetLabel(imageFileInfo)
: "Not getting label, see argument this function was called with."));
}
}
Debug.WriteLine($"Unable to get facial feature from {imageFileInfo.Name} as no faces were found!");
return(null);
}
private void OnCameraFrame(object sender, EventArgs e)
{
img = capture.RetrieveMat();
Cv2.Flip(img, img, FlipMode.Y);
var array = new byte[img.Cols * img.Rows * img.ElemSize()];
Marshal.Copy(img.Data, array, 0, array.Length);
var image = Dlib.LoadImageData <RgbPixel>(array, (uint)img.Rows, (uint)img.Cols, (uint)(img.Cols * img.ElemSize()));
faces = detector.Operator(image);
shapes.Clear();
foreach (var rect in faces)
{
DlibDotNet.Rectangle face = rect;
shapes.Add(predictor.Detect(image, face));
}
Invalidate();
}
public async Task <MatrixFloatDto[]> GetFaceDescriptors(string filename, System.Drawing.Rectangle[] faces)
{
var inputFilename = filename;
var chips = new List <Matrix <RgbPixel> >();
using var img = await DlibHelpers.LoadRotatedImage(imageRotationService, inputFilename);
foreach (var face in faces.Select(x => new Rectangle(x.Left, x.Top, x.Right, x.Bottom)))
{
// 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);
}
if (!chips.Any())
{
return(Array.Empty <MatrixFloatDto>());
}
// put each fae in a 128D embedding space
// similar faces will be placed close together
var descriptors = dnn.Operator(chips);
return(descriptors
.Select(x => new MatrixFloatDto
{
Data = x.ToArray(),
Row = x.Rows,
Columns = x.Columns,
})
.ToArray());
}
private void GetLandmarks(OpenCvSharp.Mat frame, OpenCvSharp.Rect face, List <System.Drawing.Rectangle> rfaces)
{
EyePoints rightEye = new EyePoints(leftEye: true);
EyePoints leftEye = new EyePoints(leftEye: false);
ShapePredictor predictor = ShapePredictor.Deserialize(shapePredictorDataFile);
//Scalar eyecolor = new Scalar(0, 0, 255);
Array2D <byte> gray = ConvertMatToDlib2DArray(frame);
FullObjectDetection landmarks = predictor.Detect(gray, ConvertToDlib(face));
InitializeEyes(landmarks, leftEye, rightEye);
//DrawEye(que, landmarks, leftEye);
//DrawEye(que, landmarks, rightEye);
Rect leftboundingBox = BoundingBoxAroundEye(leftEye, 0);
rfaces.Add(FromOpenCvRect(leftboundingBox));
//DrawRect(frame, leftboundingBox);
OpenCvSharp.Point centerOfLeftEye = DetectCenterOfEye(frame, leftboundingBox);
centerOfLeftEye.X += leftboundingBox.X;
Rect rightboundingBox = BoundingBoxAroundEye(rightEye, 0);
rfaces.Add(FromOpenCvRect(rightboundingBox));
//DrawRect(frame, rightboundingBox);
OpenCvSharp.Point centerOfRightEye = DetectCenterOfEye(frame, rightboundingBox);
centerOfRightEye.X += rightboundingBox.X;
EyeDirection leftEyeDirection = leftEye.GetEyePosition(centerOfLeftEye);
EyeDirection rightEyeDirection = rightEye.GetEyePosition(centerOfRightEye);
//EyeDirection eyeDirection = EyeDirection.unknown;
//if (leftEyeDirection == EyeDirection.center || rightEyeDirection == EyeDirection.center) eyeDirection = EyeDirection.center;
//else if (leftEyeDirection == EyeDirection.left) eyeDirection = EyeDirection.left;
//else if (rightEyeDirection == EyeDirection.right) eyeDirection = EyeDirection.right;
//OpenCvSharp.Point position = new OpenCvSharp.Point(50, 50);
//Cv2.PutText(img: frame, text: eyeDirection.ToDisplay(), org: position, fontFace: HersheyFonts.HersheySimplex, fontScale: 2, new Scalar(0, 0, 255));
}
/// <summary>
/// Detect all 68 landmarks on the face on camera
/// </summary>
/// <param name="image">The current camera frame to analyze</param>
/// <param name="frameIndex">The index number of the current camera frame</param>
/// <returns>A FullObjectDetection object containing all 68 facial landmark points</returns>
private FullObjectDetection DetectLandmarks(Bitmap image, int frameIndex)
{
// convert image to dlib format
var dlibImage = image.ToArray2D <RgbPixel>();
// detect faces every 5 frames
if (frameIndex % 5 == 0)
{
var faces = faceDetector.Detect(dlibImage);
if (faces.Length > 0)
{
// grab the first face
currentFace = faces.First();
}
}
// detect all 68 facial landmarks on the face
if (currentFace != default(DlibDotNet.Rectangle))
{
return(shapePredictor.Detect(dlibImage, currentFace));
}
return(null);
}
/// <summary>
/// 具体计算
/// </summary>
/// <param name="bitmap"></param>
/// <param name="sp"></param>
/// <returns></returns>
public List <FullObjectDetection> Face(Bitmap bitmap)
{
// 加载模型文件
if (sp == null)
{
var basePath = AppDomain.CurrentDomain.BaseDirectory;
sp = ShapePredictor.Deserialize(basePath + "ShapeModel/shape_predictor_68_face_landmarks.dat");
}
//var link = new ImageWindow.OverlayLine[0];
var shapes = new List <FullObjectDetection>();
using (var detector = Dlib.GetFrontalFaceDetector())
{
using (var img = bitmap.ToArray2D <RgbPixel>())
{
var dets = detector.Operator(img);
foreach (var rect in dets)
{
var shape = sp.Detect(img, rect);
if (shape.Parts > 2)
{
shapes.Add(shape);
}
}
//if (shapes.Any())
//{
// //就是这个
// var lines = Dlib.RenderFaceDetections(shapes);
// link = lines;
//}
}
}
return(shapes);
}
private void Timer1_Tick(object sender, EventArgs e)
{
capture.Read(frame);
this.point = new Point((frame.Width - size.Width) / 2, (frame.Height - size.Height) / 2);
this.rect = new Rect(point, size);
Cv2.Flip(frame, frame, FlipMode.Y);
if (!frame.Empty() && start)
{
var img = ConvertToArray2D(frame);
var faces = fd.Operator(img);
if (faces.Any(face => IsFaceInFrame(face)))
{
foreach (var face in faces)
{
if (IsFaceInFrame(face))
{
//Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
var shape = sp.Detect(img, face);
var landmarks = new MatOfPoint2d(1, 6,
(from i in new int[] { 30, 8, 36, 45, 48, 54 }
let pt = shape.GetPart((uint)i)
select new OpenCvSharp.Point2d(pt.X, pt.Y)).ToArray());
var cameraMatrix = Utility.GetCameraMatrix((int)img.Rect.Width, (int)img.Rect.Height);
Mat rotation = new MatOfDouble();
Mat translation = new MatOfDouble();
Cv2.SolvePnP(model, landmarks, cameraMatrix, coeffs, rotation, translation);
var euler = Utility.GetEulerMatrix(rotation);
var yaw = 180 * euler.At <double>(0, 2) / Math.PI;
var pitch = 180 * euler.At <double>(0, 1) / Math.PI;
pitch = Math.Sign(pitch) * 180 - pitch;
Cv2.ProjectPoints(poseModel, rotation, translation, cameraMatrix, coeffs, poseProjection);
//var landmark = landmarks.At<Point2d>(0);
//var p = poseProjection.At<Point2d>(0);
//Dlib.DrawLine(
// img,
// new DlibDotNet.Point((int)landmark.X, (int)landmark.Y),
// new DlibDotNet.Point((int)p.X, (int)p.Y),
// color: new RgbPixel(0, 255, 255));
//foreach (var i in new int[] { 30, 8, 36, 45, 48, 54 })
//{
// var point = shape.GetPart((uint)i);
// var rect = new Rectangle(point);
// Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 0), thickness: 4);
//}
for (var i = 0; i < shape.Parts; i++)
{
var point = shape.GetPart((uint)i);
var rect = new Rectangle(point);
Dlib.DrawRectangle(img, rect, color: new RgbPixel(0, 255, 255), thickness: 4);
}
CheckFace(pitch, frame, face, yaw, pitch);
frame = img.ToBitmap().ToMat();
}
}
}
else if (this.step > 0)
{
SetZero();
this.ErrorMsg.Visible = true;
}
}
Cv2.Rectangle(frame, rect, Scalar.Yellow, thickness: 2);
camera.Image = frame.ToBitmap();
}
private Bitmap ProcessImage(Bitmap image, Bitmap newImage)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = FrontalFaceDetector.GetFrontalFaceDetector())
using (var sp = new ShapePredictor("shape_predictor_68_face_landmarks.dat"))
{
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// find bradley's faces in image
var faces = fd.Detect(img);
var bradley = faces[0];
// get bradley's landmark points
var bradleyShape = sp.Detect(img, bradley);
var bradleyPoints = (from i in Enumerable.Range(0, (int)bradleyShape.Parts)
let p = bradleyShape.GetPart((uint)i)
select new OpenCvSharp.Point(p.X, p.Y)).ToArray();
// get convex hull of bradley's points
var hull = Cv2.ConvexHullIndices(bradleyPoints);
var bradleyHull = from i in hull
select bradleyPoints[i];
// find landmark points in face to swap
var imgMark = newImage.ToArray2D <RgbPixel>();
var faces2 = fd.Detect(imgMark);
var mark = faces2[0];
var markShape = sp.Detect(imgMark, mark);
var markPoints = (from i in Enumerable.Range(0, (int)markShape.Parts)
let p = markShape.GetPart((uint)i)
select new OpenCvSharp.Point(p.X, p.Y)).ToArray();
// get convex hull of mark's points
var hull2 = Cv2.ConvexHullIndices(bradleyPoints);
var markHull = from i in hull2
select markPoints[i];
// calculate Delaunay triangles
var triangles = Utility.GetDelaunayTriangles(bradleyHull);
// get transformations to warp the new face onto Bradley's face
var warps = Utility.GetWarps(markHull, bradleyHull, triangles);
// apply the warps to the new face to prep it for insertion into the main image
var warpedImg = Utility.ApplyWarps(newImage, image.Width, image.Height, warps);
// prepare a mask for the warped image
var mask = new Mat(image.Height, image.Width, MatType.CV_8UC3);
mask.SetTo(0);
Cv2.FillConvexPoly(mask, bradleyHull, new Scalar(255, 255, 255), LineTypes.Link8);
// find the center of the warped face
var r = Cv2.BoundingRect(bradleyHull);
var center = new OpenCvSharp.Point(r.Left + r.Width / 2, r.Top + r.Height / 2);
// blend the warped face into the main image
var selfie = BitmapConverter.ToMat(image);
var blend = new Mat(selfie.Size(), selfie.Type());
Cv2.SeamlessClone(warpedImg, selfie, mask, center, blend, SeamlessCloneMethods.NormalClone);
// return the modified main image
return(BitmapConverter.ToBitmap(blend));
}
}
private static void Main(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine("Run this example by invoking it like this: ");
Console.WriteLine(" ./DnnFaceRecognition faces/bald_guys.jpg");
Console.WriteLine("You will also need to get the face landmarking model file as well as ");
Console.WriteLine("the face recognition model file. Download and then decompress these files from: ");
Console.WriteLine("http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2");
Console.WriteLine("http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2");
return;
}
// The first thing we are going to do is load all our models. First, since we need to
// find faces in the image we will need a face detector:
using (var detector = FrontalFaceDetector.GetFrontalFaceDetector())
// We will also use a face landmarking model to align faces to a standard pose: (see face_landmark_detection_ex.cpp for an introduction)
using (var sp = new ShapePredictor("shape_predictor_5_face_landmarks.dat"))
// And finally we load the DNN responsible for face recognition.
using (var net = DlibDotNet.Dnn.LossMetric.Deserialize("dlib_face_recognition_resnet_model_v1.dat"))
using (var img = Dlib.LoadImage <RgbPixel>(args[0]))
using (var mat = new Matrix <RgbPixel>(img))
// Display the raw image on the screen
using (var win = new ImageWindow(img))
{
// Run the face detector on the image of our action heroes, and for each face extract a
// copy that has been normalized to 150x150 pixels in size and appropriately rotated
// and centered.
var faces = new List <Matrix <RgbPixel> >();
foreach (var face in detector.Detect(img))
{
var shape = sp.Detect(img, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(mat, faceChipDetail);
//faces.Add(move(face_chip));
faces.Add(faceChip);
// Also put some boxes on the faces so we can see that the detector is finding
// them.
win.AddOverlay(face);
}
if (!faces.Any())
{
Console.WriteLine("No faces found in image!");
return;
}
// This call asks the DNN to convert each face image in faces into a 128D vector.
// In this 128D vector space, images from the same person will be close to each other
// but vectors from different people will be far apart. So we can use these vectors to
// identify if a pair of images are from the same person or from different people.
var faceDescriptors = net.Operator(faces);
// In particular, one simple thing we can do is face clustering. This next bit of code
// creates a graph of connected faces and then uses the Chinese whispers graph clustering
// algorithm to identify how many people there are and which faces belong to whom.
var edges = new List <SamplePair>();
for (uint i = 0; i < faceDescriptors.Count; ++i)
{
for (var j = i; j < faceDescriptors.Count; ++j)
{
// Faces are connected in the graph if they are close enough. Here we check if
// the distance between two face descriptors is less than 0.6, which is the
// decision threshold the network was trained to use. Although you can
// certainly use any other threshold you find useful.
var diff = faceDescriptors[i] - faceDescriptors[j];
if (Dlib.Length(diff) < 0.6)
{
edges.Add(new SamplePair(i, j));
}
}
}
Dlib.ChineseWhispers(edges, 100, out var numClusters, out var labels);
// This will correctly indicate that there are 4 people in the image.
Console.WriteLine($"number of people found in the image: {numClusters}");
// Now let's display the face clustering results on the screen. You will see that it
// correctly grouped all the faces.
var winClusters = new List <ImageWindow>();
for (var i = 0; i < numClusters; i++)
{
winClusters.Add(new ImageWindow());
}
var tileImages = new List <Matrix <RgbPixel> >();
for (var clusterId = 0ul; clusterId < numClusters; ++clusterId)
{
var temp = new List <Matrix <RgbPixel> >();
for (var j = 0; j < labels.Length; ++j)
{
if (clusterId == labels[j])
{
temp.Add(faces[j]);
}
}
winClusters[(int)clusterId].Title = $"face cluster {clusterId}";
var tileImage = Dlib.TileImages(temp);
tileImages.Add(tileImage);
winClusters[(int)clusterId].SetImage(tileImage);
}
// Finally, let's print one of the face descriptors to the screen.
using (var trans = Dlib.Trans(faceDescriptors[0]))
{
Console.WriteLine($"face descriptor for one face: {trans}");
// It should also be noted that face recognition accuracy can be improved if jittering
// is used when creating face descriptors. In particular, to get 99.38% on the LFW
// benchmark you need to use the jitter_image() routine to compute the descriptors,
// like so:
var jitterImages = JitterImage(faces[0]).ToArray();
var ret = net.Operator(jitterImages);
using (var m = Dlib.Mat(ret))
using (var faceDescriptor = Dlib.Mean <float>(m))
using (var t = Dlib.Trans(faceDescriptor))
{
Console.WriteLine($"jittered face descriptor for one face: {t}");
// If you use the model without jittering, as we did when clustering the bald guys, it
// gets an accuracy of 99.13% on the LFW benchmark. So jittering makes the whole
// procedure a little more accurate but makes face descriptor calculation slower.
Console.WriteLine("hit enter to terminate");
Console.ReadKey();
foreach (var jitterImage in jitterImages)
{
jitterImage.Dispose();
}
foreach (var tileImage in tileImages)
{
tileImage.Dispose();
}
foreach (var edge in edges)
{
edge.Dispose();
}
foreach (var descriptor in faceDescriptors)
{
descriptor.Dispose();
}
foreach (var face in faces)
{
face.Dispose();
}
}
}
}
}
private static void Main(string[] args)
{
if (args.Length == 0)
{
Console.WriteLine("Give some image files as arguments to this program.");
Console.WriteLine("Call this program like this:");
Console.WriteLine("./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg");
Console.WriteLine("You can get the shape_predictor_68_face_landmarks.dat file from:");
Console.WriteLine("http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2");
return;
}
using (var win = new ImageWindow())
using (var winFaces = new ImageWindow())
using (var detector = FrontalFaceDetector.GetFrontalFaceDetector())
using (var sp = new ShapePredictor(args[0]))
foreach (var file in args.ToList().GetRange(1, args.Length - 1))
{
Console.WriteLine($"processing image {file}");
using (var img = Dlib.LoadImage <RgbPixel>(file))
{
Dlib.PyramidUp(img);
var dets = detector.Detect(img);
Console.WriteLine($"Number of faces detected: {dets.Length}");
var shapes = new List <FullObjectDetection>();
foreach (var rect in dets)
{
var shape = sp.Detect(img, rect);
Console.WriteLine($"number of parts: {shape.Parts}");
if (shape.Parts > 2)
{
Console.WriteLine($"pixel position of first part: {shape.GetPart(0)}");
Console.WriteLine($"pixel position of second part: {shape.GetPart(1)}");
shapes.Add(shape);
}
}
win.ClearOverlay();
win.SetImage(img);
if (shapes.Any())
{
var lines = Dlib.RenderFaceDetections(shapes);
win.AddOverlay(lines);
foreach (var l in lines)
{
l.Dispose();
}
var chipLocations = Dlib.GetFaceChipDetails(shapes);
using (var faceChips = Dlib.ExtractImageChips <RgbPixel>(img, chipLocations))
using (var tileImage = Dlib.TileImages(faceChips))
winFaces.SetImage(tileImage);
foreach (var c in chipLocations)
{
c.Dispose();
}
}
Console.WriteLine("hit enter to process next frame");
Console.ReadKey();
foreach (var s in shapes)
{
s.Dispose();
}
foreach (var r in dets)
{
r.Dispose();
}
}
}
}
private static void Preprocess(string type, string input, FrontalFaceDetector faceDetector, ShapePredictor posePredictor, string output)
{
var imageCount = 0;
var r = new ulong[Size * Size];
var g = new ulong[Size * Size];
var b = new ulong[Size * Size];
var csv = ReadCsv(Path.Combine(input, $"{type}.csv"));
var outputDir = Path.Combine(output, type);
foreach (var kvp in csv)
{
using (var tmp = Dlib.LoadImageAsMatrix <RgbPixel>(Path.Combine(input, type, kvp.Key)))
{
var dets = faceDetector.Operator(tmp);
if (!dets.Any())
{
Console.WriteLine($"Warning: Failed to detect face from '{kvp}'");
continue;
}
// Get max size rectangle. It could be better face.
var det = dets.Select((val, idx) => new { V = val, I = idx }).Aggregate((max, working) => (max.V.Area > working.V.Area) ? max : working).V;
using (var ret = posePredictor.Detect(tmp, det))
using (var chip = Dlib.GetFaceChipDetails(ret, Size, 0d))
using (var faceChips = Dlib.ExtractImageChip <RgbPixel>(tmp, chip))
{
var dst = Path.Combine(outputDir, kvp.Key);
var dstDir = Path.GetDirectoryName(dst);
Directory.CreateDirectory(dstDir);
Dlib.SaveJpeg(faceChips, Path.Combine(outputDir, kvp.Key), 100);
var index = 0;
for (var row = 0; row < Size; row++)
{
for (var col = 0; col < Size; col++)
{
var rgb = faceChips[row, col];
r[index] += rgb.Red;
g[index] += rgb.Green;
b[index] += rgb.Blue;
index++;
}
}
}
imageCount++;
}
}
using (var mean = new Matrix <RgbPixel>(Size, Size))
{
var index = 0;
for (var row = 0; row < Size; row++)
{
for (var col = 0; col < Size; col++)
{
var red = (double)r[index] / imageCount;
var green = (double)g[index] / imageCount;
var blue = (double)b[index] / imageCount;
var newRed = (byte)Math.Round(red);
var newGreen = (byte)Math.Round(green);
var newBlue = (byte)Math.Round(blue);
mean[row, col] = new RgbPixel(newRed, newGreen, newBlue);
index++;
}
}
Dlib.SaveBmp(mean, Path.Combine(output, $"{type}.mean.bmp"));
}
}
private void StartWebCam(BackgroundWorker worker = null)
{
if (cap == null)
{
cap = new VideoCapture(0);
}
if (!cap.Open(0))
{
return;
}
OpenCvSharp.Cv2.NamedWindow("Video", WindowMode.AutoSize);
int cnt = 0;
Mat frame = new Mat();
EyePoints rightEye = new EyePoints(true);
EyePoints leftEye = new EyePoints(false);
IsRunning = true;
while (IsRunning)
{
bool result = cap.Read(frame);
if (!result)
{
worker.CancelAsync();
IsRunning = false;
}
if (frame != null && (frame.Rows * frame.Cols > 0))
{
cnt++;
if (cnt % frameskip == 0)
{
FrameQueue.Enqueue(frame);
cnt = 0;
}
}
while (FrameQueue.Count > 0)
{
Mat que = FrameQueue.Dequeue();
Rect[] faces = GetFaces(que, 1);
for (int i = 0; i < faces.Length; i++)
{
//GetFaceInRect(faces[i], que, i);
Scalar eyecolor = new Scalar(0, 0, 255);
Array2D <byte> gray = ConvertMatToDlib2DArray(que);
FullObjectDetection landmarks = predictor.Detect(gray, ConvertToDlib(faces[i]));
InitializeEyes(landmarks, leftEye, rightEye);
//DrawEye(que, landmarks, leftEye);
//DrawEye(que, landmarks, rightEye);
Rect leftboundingBox = BoundingBoxAroundEye(leftEye, 0);
DrawRect(que, leftboundingBox);
OpenCvSharp.Point centerOfLeftEye = DetectCenterOfEye(que, leftboundingBox);
centerOfLeftEye.X += leftboundingBox.X;
Rect rightboundingBox = BoundingBoxAroundEye(rightEye, 0);
DrawRect(que, rightboundingBox);
OpenCvSharp.Point centerOfRightEye = DetectCenterOfEye(que, rightboundingBox);
centerOfRightEye.X += rightboundingBox.X;
EyeDirection leftEyeDirection = leftEye.GetEyePosition(centerOfLeftEye);
EyeDirection rightEyeDirection = rightEye.GetEyePosition(centerOfRightEye);
EyeDirection eyeDirection = EyeDirection.unknown;
if (leftEyeDirection == EyeDirection.center || rightEyeDirection == EyeDirection.center)
{
eyeDirection = EyeDirection.center;
}
else if (leftEyeDirection == EyeDirection.left)
{
eyeDirection = EyeDirection.left;
}
else if (rightEyeDirection == EyeDirection.right)
{
eyeDirection = EyeDirection.right;
}
OpenCvSharp.Point position = new OpenCvSharp.Point(50, 50);
Cv2.PutText(img: que, text: eyeDirection.ToDisplay(), org: position, fontFace: HersheyFonts.HersheySimplex, fontScale: 2, new Scalar(0, 0, 255));
}
//BitmapImage bmi = ConvertToBMI(frame, cnt, "D:/junk/TestCamImages");
if (worker != null)
{
//worker.ReportProgress(cnt, bmi);
try
{
OpenCvSharp.Cv2.ImShow("Video", que);
int key = Cv2.WaitKey(10); // as in 10 milliseconds
if (key == 27)
{
worker.CancelAsync();
IsRunning = false;
}
}
catch (Exception ex)
{
string msg = ex.Message;
}
}
if (worker.CancellationPending)
{
Cv2.DestroyWindow("Video");
break;
}
}
}
}
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);
}
private void StartWebCam()
{
bool useHaarcascade = true;
if (cap == null)
{
cap = new VideoCapture(0);
}
if (!cap.Open(0))
{
return;
}
OpenCvSharp.Cv2.NamedWindow("Video", WindowMode.AutoSize);
EyePoints rightEye = new EyePoints(true);
EyePoints leftEye = new EyePoints(false);
IsRunning = true;
while (IsRunning)
{
frame = new Mat();
bool result = cap.Read(frame);
if (!result)
{
IsRunning = false;
break;
}
Rect[] faces = useHaarcascade ? GetHaarcascadeFaces(frame, 1) : GetDnnFaces(frame, 1);
if (faces == null)
{
continue;
}
for (int i = 0; i < faces.Length; i++)
{
//GetFaceInRect(faces[i], que, i);
Scalar eyecolor = new Scalar(0, 0, 255);
Array2D <byte> gray = ConvertMatToDlib2DArray(frame);
FullObjectDetection landmarks = predictor.Detect(gray, ConvertToDlib(faces[i]));
InitializeEyes(landmarks, leftEye, rightEye);
//DrawEye(que, landmarks, leftEye);
//DrawEye(que, landmarks, rightEye);
Rect leftboundingBox = BoundingBoxAroundEye(leftEye, 0);
DrawRect(frame, leftboundingBox);
OpenCvSharp.Point centerOfLeftEye = DetectCenterOfEye(frame, leftboundingBox);
centerOfLeftEye.X += leftboundingBox.X;
Rect rightboundingBox = BoundingBoxAroundEye(rightEye, 0);
DrawRect(frame, rightboundingBox);
OpenCvSharp.Point centerOfRightEye = DetectCenterOfEye(frame, rightboundingBox);
centerOfRightEye.X += rightboundingBox.X;
EyeDirection leftEyeDirection = leftEye.GetEyePosition(centerOfLeftEye);
EyeDirection rightEyeDirection = rightEye.GetEyePosition(centerOfRightEye);
EyeDirection eyeDirection = EyeDirection.unknown;
if (leftEyeDirection == EyeDirection.center || rightEyeDirection == EyeDirection.center)
{
eyeDirection = EyeDirection.center;
}
else if (leftEyeDirection == EyeDirection.left)
{
eyeDirection = EyeDirection.left;
}
else if (rightEyeDirection == EyeDirection.right)
{
eyeDirection = EyeDirection.right;
}
OpenCvSharp.Point position = new OpenCvSharp.Point(50, 50);
Cv2.PutText(img: frame, text: eyeDirection.ToDisplay(), org: position, fontFace: HersheyFonts.HersheySimplex, fontScale: 2, new Scalar(0, 0, 255));
}
try
{
OpenCvSharp.Cv2.ImShow("Video", frame);
int key = Cv2.WaitKey(10); // as in 10 milliseconds
if (key == 27)
{
IsRunning = false;
}
}
catch (Exception ex)
{
string msg = ex.Message;
}
frame.Dispose();
}
}
private void ProcessFrame(object sender, EventArgs e)
{
Stopwatch SW = new Stopwatch();
SW.Start();
try
{
Mat temp = new Mat();
_capture.Read(temp);
var array = new byte[temp.Width * temp.Height * temp.ElementSize];
temp.CopyTo(array);
Array2D <RgbPixel> cimg = Dlib.LoadImageData <RgbPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElementSize));
Rectangle[] faces = detector.Operator(cimg);
if (faces.Any())
{
FullObjectDetection det = poseModel.Detect(cimg, faces[0]);
List <FullObjectDetection> shapes = new List <FullObjectDetection>();
shapes.Add(det);
FullObjectDetection shape = shapes[0];
ImageWindow.OverlayLine[] lines = Dlib.RenderFaceDetections(shapes);
if (chbShowLineOnly.Checked)
{
cimg = new Array2D <RgbPixel>(cimg.Rows, cimg.Columns);
}
foreach (var line in lines)
{
Dlib.DrawLine(cimg, line.Point1, line.Point2, new RgbPixel {
Green = 255
});
}
pictureBoxImage.Image?.Dispose();
pictureBoxImage.Image = cimg.ToBitmap();
foreach (var line in lines)
{
line.Dispose();
}
for (uint i = 0; i < shape.Parts; i++)
{
landmarkPoint.Insert((int)i, new Point(shape.GetPart(i).X, shape.GetPart(i).Y));
}
GetFacialBlendShape();
foreach (var s in shapes)
{
s.Dispose();
}
}
}
catch (Exception exception)
{
Console.WriteLine(exception.StackTrace);
}
SW.Stop();
Debug.WriteLine(string.Format("FPS: {0}", 1000 / SW.ElapsedMilliseconds));
}
private Bitmap ProcessImage(Bitmap image, Bitmap newImage)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = FrontalFaceDetector.GetFrontalFaceDetector())
using (var sp = new ShapePredictor("shape_predictor_68_face_landmarks.dat"))
{
// convert images to opencv format
var selfie = BitmapConverter.ToMat(image);
var mark = BitmapConverter.ToMat(newImage);
// convert image to dlib format
var img = image.ToArray2D <RgbPixel>();
// find all faces in image
var faces = fd.Detect(img);
// get bradley's landmark points
var bradleyShape = sp.Detect(img, faces[1]);
var bradleyPoints = (from i in Enumerable.Range(0, (int)bradleyShape.Parts)
let p = bradleyShape.GetPart((uint)i)
select new OpenCvSharp.Point(p.X, p.Y)).ToArray();
// *** WEBINAR STEP 1: draw landmarks on bradley's face
// Utility.DrawLandmarks(selfie, bradleyPoints);
// return BitmapConverter.ToBitmap(selfie);
// get convex hull of bradley's points
var hull = Cv2.ConvexHullIndices(bradleyPoints);
var bradleyHull = from i in hull
select bradleyPoints[i];
// *** WEBINAR STEP 2a: draw convex hull for bradley
// Utility.DrawLandmarks(selfie, bradleyHull);
// Utility.DrawHull(selfie, bradleyHull);
// return BitmapConverter.ToBitmap(selfie);
// find landmark points in face to swap
var imgMark = newImage.ToArray2D <RgbPixel>();
var faces2 = fd.Detect(imgMark);
var markShape = sp.Detect(imgMark, faces2[0]);
var markPoints = (from i in Enumerable.Range(0, (int)markShape.Parts)
let p = markShape.GetPart((uint)i)
select new OpenCvSharp.Point(p.X, p.Y)).ToArray();
// get convex hull of mark's points
var hull2 = Cv2.ConvexHullIndices(bradleyPoints);
var markHull = from i in hull2
select markPoints[i];
// *** WEBINAR STEP 2b: draw convex hull for mark
// Utility.DrawLandmarks(mark, markHull);
// Utility.DrawHull(mark, markHull);
// return BitmapConverter.ToBitmap(mark);
// calculate Delaunay triangles
var triangles = Utility.GetDelaunayTriangles(bradleyHull);
// *** WEBINAR STEP 3: draw delaunay triangles for bradley
// Utility.DrawTriangles(selfie, triangles);
// return BitmapConverter.ToBitmap(selfie);
// get transformations to warp the new face onto Bradley's face
var warps = Utility.GetWarps(markHull, bradleyHull, triangles);
// *** WEBINAR STEP 6: demonstrate triangulation with all landmark points
// triangles = Utility.GetDelaunayTriangles(bradleyPoints);
// warps = Utility.GetWarps(markPoints, bradleyPoints, triangles);
// apply the warps to the new face to prep it for insertion into the main image
var warpedImg = Utility.ApplyWarps(newImage, image.Width, image.Height, warps);
// *** WEBINAR STEP 4: warp triangles from mark to bradley
// return BitmapConverter.ToBitmap(warpedImg);
// prepare a mask for the warped image
var mask = new Mat(image.Height, image.Width, MatType.CV_8UC3);
mask.SetTo(0);
Cv2.FillConvexPoly(mask, bradleyHull, new Scalar(255, 255, 255), LineTypes.Link8);
// *** WEBINAR STEP 5a: show blend without seamless clone
// Cv2.Multiply(selfie, new Scalar(1, 1, 1) - mask, selfie);
// Cv2.Add(selfie, warpedImg, selfie);
// return BitmapConverter.ToBitmap(selfie);
// find the center of the warped face
var r = Cv2.BoundingRect(bradleyHull);
var center = new OpenCvSharp.Point(r.Left + r.Width / 2, r.Top + r.Height / 2);
// blend the warped face into the main image
var blend = new Mat(selfie.Size(), selfie.Type());
Cv2.SeamlessClone(warpedImg, selfie, mask, center, blend, SeamlessCloneMethods.NormalClone);
// *** WEBINAR STEP 5b: show blend with seamless clone
return(BitmapConverter.ToBitmap(blend));
}
}
