static void Main(string[] args)
{
// Read image in
var img = Dlib.LoadImage <RgbPixel>(imgFilePath);
// Let's detect faces and draw rectangles around them
FaceDetector faceDetector = new FaceDetector(facialLandmarksSerializedPredictor);
Rectangle[] facesBoundingBoxes = faceDetector.DetectFacesBoundingBoxes(img);
foreach (var bb in facesBoundingBoxes)
{
Dlib.DrawRectangle(img, bb, color: new RgbPixel(0, 0, 255), thickness: 3);
}
// Draw eyes bounding box for subject (i.e., largest) face
if (facesBoundingBoxes.Length > 0)
{
// Example code if you wish to do this only on the largest face
/*Rectangle subjectFaceBoundingBox = new Rectangle(0, 0);
* foreach (var bb in facesBoundingBoxes)
* {
* if (bb.Area > subjectFaceBoundingBox.Area)
* {
* subjectFaceBoundingBox = bb;
* }
* }*/
// Here we do it on all faces
foreach (var subjectFaceBoundingBox in facesBoundingBoxes)
{
// Next, obtain facial landmarks
var landmarks = faceDetector.DetectFacialLandmarks(img, subjectFaceBoundingBox);
// We also draw them
foreach (Point p in landmarks)
{
Dlib.DrawRectangle(img, new Rectangle(p), color: new RgbPixel(255, 0, 0), thickness: 3);
}
// Now draw bounding box around the eyes
var topLeft = new Point(landmarks[FacialLandmarks.RIGHT_EYEBROW].X, landmarks[FacialLandmarks.RIGHT_EYEBROW].Y);
var bottomRight = new Point(landmarks[FacialLandmarks.LEFT_EYEBROW].X, landmarks[FacialLandmarks.UPPER_NOSE].Y);
var eyesBoundingBox = new Rectangle(topLeft, bottomRight);
Dlib.DrawRectangle(img, eyesBoundingBox, color: new RgbPixel(0, 255, 0), thickness: 3);
}
}
// Create output file path (for later)
string outFilePath;
var tmpStrArray = imgFilePath.Split('/');
var extension = tmpStrArray[tmpStrArray.Length - 1].Split('.')[1];
outFilePath = String.Join('/', tmpStrArray.SkipLast(1).ToArray()) + '/' +
tmpStrArray[tmpStrArray.Length - 1].Replace("." + extension, "_out." + extension);
Console.WriteLine(outFilePath);
// Write img
faceDetector.WriteImageToFilePath(img, outFilePath);
}
public static void DetectFacesAsync(string inputFilePath, string subscriptionKey, string uriBase, IFaceClient client, string vocabularyPath)
{
// set up Dlib facedetector
DirectoryInfo dir = new DirectoryInfo(inputFilePath);
using (var fd = Dlib.GetFrontalFaceDetector())
{
foreach (FileInfo files in dir.GetFiles("*.jpg"))
{
string _inputFilePath = inputFilePath + files.Name;
// load input image
Array2D <RgbPixel> img = Dlib.LoadImage <RgbPixel>(_inputFilePath);
// find all faces in the image
Rectangle[] faces = fd.Operator(img);
if (faces.Length != 0)
{
Console.WriteLine("Picture " + files.Name + " have faces, sending data to Azure");
MakeAnalysisRequestAsync(_inputFilePath, subscriptionKey, uriBase, files.Name, client, vocabularyPath).Wait();
}
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
// export the modified image
Dlib.SaveJpeg(img, "./Results/" + files.Name);
}
}
/// <summary>
/// The main program entry point
/// </summary>
/// <param name="args">The command line arguments</param>
static void Main(string[] args)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// load input image
var img = Dlib.LoadImage <RgbPixel>(inputFilePath);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
// draw the landmark points on the image
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(255, 255, 0), thickness: 4);
}
}
// export the modified image
Dlib.SaveJpeg(img, "output.jpg");
}
}
public static void DrawPointsOfLandmarks(FileInfo image)
{
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize(GetFile(ShapePredictorFileName).FullName))
{
using (var img = Dlib.LoadImage <RgbPixel>(image.FullName))
{
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
foreach (var face in faces)
{
var shape = sp.Detect(img, face);
// draw the landmark points on the image
for (var i = 0; i < shape.Parts; i++)
{
var point = shape.GetPart((uint)i);
var rect = new Rectangle(point);
if (i == 0)
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 255), thickness: 8);
}
else if (i == 21 || i == 22 || i == 39 || i == 42 || i == 33 || i == 51 || i == 57 ||
i == 48 ||
i == 54)
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 0, 255), thickness: 4);
}
else if (i == 18 || i == 19 || i == 20 || i == 21) // left eye
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 0, 0), 6);
}
else if (i == 22 || i == 23 || i == 24 || i == 25) // right eye
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 128, 0), 6);
}
else if (i == 48 || i == 49 || i == 50) // left lip
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 0), 2);
}
else if (i == 52 || i == 53 || i == 54) // right lip
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 0, 128), 2);
}
else
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(0, 0, 0), thickness: 4);
}
}
Dlib.SavePng(img, "output.jpg");
}
}
}
}
/// <summary>
/// 使用路径获取位置数据
/// </summary>
/// <param name="url"></param>
/// <returns></returns>
public static Rectangle[] GetResult(string url)
{
var dets = new Rectangle[0];
url = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, url);
using (var detector = Dlib.GetFrontalFaceDetector())
//using (var img = Dlib.LoadImage<RgbPixel>("png.png"))
using (var img = Dlib.LoadImage <RgbPixel>(url))
{
dets = detector.Operator(img);
}
return(dets);
}
public static void DetectFacesOnImage(string sourceImagePath, string destImagePath)
{
// set up Dlib facedetector
using (var fd = Dlib.GetFrontalFaceDetector())
{
// load input image
var image = Dlib.LoadImage <RgbPixel>(sourceImagePath);
DetectFacesOnImage(image);
// export the modified image
Dlib.SaveJpeg(image, destImagePath);
}
}
public void FindFaces()
{
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(path);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
Dlib.SaveJpeg(img, @"D:\output.png");
}
}
public static int Number(string file)
{
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(file);
int number = 0;
var faces = fd.Operator(img);
foreach (var face in faces)
{
number += 1;
}
return(number);
}
}
public static void Recognize(string file)
{
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(file);
//hola
var faces = fd.Operator(img);
foreach (var face in faces)
{
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
Dlib.SaveJpeg(img, file);
}
}
/// <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);
}
/// <summary>
/// The main program entry point
/// </summary>
/// <param name="args">The command line arguments</param>
static void Main(string[] args)
{
// set up Dlib facedetector
using (var fd = Dlib.GetFrontalFaceDetector())
{
// load input image
var img = Dlib.LoadImage <RgbPixel>(inputFilePath);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
// export the modified image
Dlib.SaveJpeg(img, "output.jpg");
}
}
public void GetImage(string imagePath)
{
Array2D <RgbPixel> image = Dlib.LoadImage <RgbPixel>(imagePath);
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);
}
}
Bitmap frame = image.ToBitmap <RgbPixel>();
MainWindow.main.Statusa = frame;
}
public static string TestCustomImage(string dir)
{
DataViewSchema predictionPipelineSchema;
ITransformer predictionPipeline = mlContext.Model.Load("model.zip", out predictionPipelineSchema);
PredictionEngine <FeatureInputData, ExpressionPrediction> predictionEngine = mlContext.Model.CreatePredictionEngine <FeatureInputData, ExpressionPrediction>(predictionPipeline);
var img = Dlib.LoadImage <RgbPixel>(dir);
// Set up Dlib Face Detector
using (var fd = Dlib.GetFrontalFaceDetector())
// ... and Dlib Shape Detector
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// find all faces in the image
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
FeatureInputData inputData = new FeatureInputData
{
leftEyebrow = CalculateLeftEyebrow(shape),
rightEyebrow = CalculateRightEyebrow(shape),
leftLip = CalculateLeftLip(shape),
rightLip = CalculateRightLip(shape),
lipWidth = CalculateLipWidth(shape),
lipHeight = CalculateLipHeight(shape)
};
ExpressionPrediction prediction = predictionEngine.Predict(inputData);
return(prediction.expression.ToString());
}
}
return("N/A");
}
// The main program entry point
static void Main(string[] args)
{
bool use_mirror = false;
// file paths
string[] files = Directory.GetFiles("images", "*.*", SearchOption.AllDirectories);
List <FullObjectDetection> shapes = new List <FullObjectDetection>();
List <string> emotions = new List <string>();
// Set up Dlib Face Detector
using (var fd = Dlib.GetFrontalFaceDetector())
// ... and Dlib Shape Detector
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// load input image
for (int i = 0; i < files.Length; i++)
{
var emotion = GetEmotion(files[i]);
var img = Dlib.LoadImage <RgbPixel>(files[i]);
// find all faces in the image
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
shapes.Add(shape);
emotions.Add(emotion);
// draw the landmark points on the image
for (var i2 = 0; i2 < shape.Parts; i2++)
{
var point = shape.GetPart((uint)i2);
var rect = new Rectangle(point);
if (point == GetPoint(shape, 40) || point == GetPoint(shape, 22))
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(0, 255, 0), thickness: 4);
}
else
{
Dlib.DrawRectangle(img, rect, color: new RgbPixel(255, 255, 0), thickness: 4);
}
}
}
// export the modified image
Console.WriteLine(files[i]);
Dlib.SaveJpeg(img, "output_" + files[i]);
}
string header = "leftEyebrow,rightEyebrow,leftLip,rightLip,lipHeight,lipWidth,emotion\n";
System.IO.File.WriteAllText(@"feature_vectors.csv", header);
for (var i = 0; i < shapes.Count; i++)
{
var shape = shapes[i];
var emotion = emotions[i];
using (System.IO.StreamWriter file = new System.IO.StreamWriter(@"feature_vectors.csv", true))
{
file.WriteLine(GetLeftEyebrow(shape) + "," + GetRightEyebrow(shape) + "," +
GetLeftLip(shape) + "," + GetRightLip(shape) + "," + GetLipWidth(shape) + "," + GetLipHeight(shape) +
"," + emotion);
if (use_mirror)
{
file.WriteLine(GetRightEyebrow(shape) + "," + GetLeftEyebrow(shape) + "," +
GetRightLip(shape) + "," + GetLeftLip(shape) + "," + GetLipWidth(shape) + "," + GetLipHeight(shape) +
"," + emotion);
}
}
}
}
}
/// <summary>
/// The main program entry point
/// </summary>
/// <param name="args">The command line arguments</param>
static void Main(string[] args)
{
// set up Dlib facedetectors and shapedetectors
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// load input image
var img = Dlib.LoadImage <RgbPixel>(inputFilePath);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
// build the 3d face model
var model = Utility.GetFaceModel();
// get the landmark point we need
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());
// build the camera matrix
var cameraMatrix = Utility.GetCameraMatrix((int)img.Rect.Width, (int)img.Rect.Height);
// build the coefficient matrix
var coeffs = new MatOfDouble(4, 1);
coeffs.SetTo(0);
// find head rotation and translation
Mat rotation = new MatOfDouble();
Mat translation = new MatOfDouble();
Cv2.SolvePnP(model, landmarks, cameraMatrix, coeffs, rotation, translation);
// find euler angles
var euler = Utility.GetEulerMatrix(rotation);
// calculate head rotation in degrees
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;
// looking straight ahead wraps at -180/180, so make the range smooth
pitch = Math.Sign(pitch) * 180 - pitch;
// calculate if the driver is facing forward
// the left/right angle must be in the -25..25 range
// the up/down angle must be in the -10..10 range
var facingForward =
yaw >= -25 && yaw <= 25 &&
pitch >= -10 && pitch <= 10;
// create a new model point in front of the nose, and project it into 2d
var poseModel = new MatOfPoint3d(1, 1, new Point3d(0, 0, 1000));
var poseProjection = new MatOfPoint2d();
Cv2.ProjectPoints(poseModel, rotation, translation, cameraMatrix, coeffs, poseProjection);
// draw the key landmark points in yellow on the image
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);
}
// draw a line from the tip of the nose pointing in the direction of head pose
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));
// draw a box around the face if it's facing forward
if (facingForward)
{
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
}
// export the modified image
Dlib.SaveJpeg(img, "output.jpg");
}
}
private static InputDataImages GetFeaturesValuesFromImage(string str)
{
var returnClass = new InputDataImages();
using (var fd = Dlib.GetFrontalFaceDetector())
// ... and Dlib Shape DetectorS
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// load input image
var img = Dlib.LoadImage <RgbPixel>(str);
// find all faces i n the image
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
// Create the CSV file and fill in the first line with the header
foreach (var face in faces)
{
// find the landmark points for this face
var shape = sp.Detect(img, face);
// draw the landmark points on the image
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(255, 255, 0), thickness: 4);
}
/////////////// WEEK 9 LAB ////////////////
double[] LeftEyebrowDistances = new double[4];
double[] RightEyebrowDistances = new double[4];
float LeftEyebrowSum = 0;
float RightEyebrowSum = 0;
//LIP VARIABLES
double[] LeftLipDistances = new double[4];
double[] RightLipDistances = new double[4];
float LeftLipSum = 0;
float RightLipSum = 0;
LeftEyebrowDistances[0] = (shape.GetPart(21) - shape.GetPart(39)).Length;
LeftEyebrowDistances[1] = (shape.GetPart(20) - shape.GetPart(39)).Length;
LeftEyebrowDistances[2] = (shape.GetPart(19) - shape.GetPart(39)).Length;
LeftEyebrowDistances[3] = (shape.GetPart(18) - shape.GetPart(39)).Length;
RightEyebrowDistances[0] = (shape.GetPart(22) - shape.GetPart(42)).Length;
RightEyebrowDistances[1] = (shape.GetPart(23) - shape.GetPart(42)).Length;
RightEyebrowDistances[2] = (shape.GetPart(24) - shape.GetPart(42)).Length;
RightEyebrowDistances[3] = (shape.GetPart(25) - shape.GetPart(42)).Length;
//LIP
LeftLipDistances[0] = (shape.GetPart(51) - shape.GetPart(33)).Length;
LeftLipDistances[1] = (shape.GetPart(50) - shape.GetPart(33)).Length;
LeftLipDistances[2] = (shape.GetPart(49) - shape.GetPart(33)).Length;
LeftLipDistances[3] = (shape.GetPart(48) - shape.GetPart(33)).Length;
RightLipDistances[0] = (shape.GetPart(51) - shape.GetPart(33)).Length;
RightLipDistances[1] = (shape.GetPart(52) - shape.GetPart(33)).Length;
RightLipDistances[2] = (shape.GetPart(53) - shape.GetPart(33)).Length;
RightLipDistances[3] = (shape.GetPart(54) - shape.GetPart(33)).Length;
for (int i = 0; i < 4; i++)
{
LeftEyebrowSum += (float)(LeftEyebrowDistances[i] / LeftEyebrowDistances[0]);
RightEyebrowSum += (float)(RightEyebrowDistances[i] / RightEyebrowDistances[0]);
}
LeftLipSum += (float)(LeftLipDistances[1] / LeftLipDistances[0]);
LeftLipSum += (float)(LeftLipDistances[2] / LeftLipDistances[0]);
LeftLipSum += (float)(LeftLipDistances[3] / LeftLipDistances[0]);
RightLipSum += (float)(RightLipDistances[1] / RightLipDistances[0]);
RightLipSum += (float)(RightLipDistances[2] / RightLipDistances[0]);
RightLipSum += (float)(RightLipDistances[3] / RightLipDistances[0]);
double LipWidth = (float)((shape.GetPart(48) - shape.GetPart(54)).Length / (shape.GetPart(33) - shape.GetPart(51)).Length);
double LipHeight = (float)((shape.GetPart(51) - shape.GetPart(57)).Length / (shape.GetPart(33) - shape.GetPart(51)).Length);
returnClass.LeftEyebrow = LeftEyebrowSum;
returnClass.RightEyebrow = RightLipSum;
returnClass.LeftLip = LeftLipSum;
returnClass.RightLip = RightLipSum;
returnClass.LipWidth = (float)LipWidth;
returnClass.LipHeight = (float)LipHeight;
// export the modified image
string filePath = "output" + ".jpg";
Dlib.SaveJpeg(img, filePath);
}
}
using (System.IO.StreamWriter file = new System.IO.StreamWriter(@"TestingFeatureVectorValues.csv", true))
{
DirectoryInfo dr = new DirectoryInfo(str);
//Console.WriteLine(dr.Parent.Name.ToString());
string ParentFolderName = dr.Parent.Name.ToString();
file.WriteLine(ParentFolderName + "," + returnClass.LeftEyebrow.ToString() + "," + returnClass.RightEyebrow.ToString()
+ "," + returnClass.LeftLip.ToString() + "," + returnClass.RightLip.ToString() + "," + returnClass.LipWidth.ToString()
+ "," + returnClass.LipHeight.ToString());
}
return(returnClass);
}
public static void DetectFacesAsync(string inputFilePath, string subscriptionKey, string uriBase, IFaceClient client, string databaseConnString)
{
//GetTheListOfPersonGroupsFromAPIAndDeleteThem(client); //temporarily
// Getting workers from web and from local db
List <List <WorkersForProcessing> > listOfWorkerLists = GetWorkersFromWebApplicationInDifferentListsDividedByClientID(databaseConnString);
List <Client> clientsInDB = GetClientListFromLocalDB();
List <DatabaseInfoAboutWorker> workersFromLocalDB = GetWorkersFromLocalDB();
// Change workersFromLocalDB format from List<DatabaseInfoAboutWorker> to List<List<WorkersForProcessing>>
List <List <WorkersForProcessing> > listOfWorkerListsFromLocalDB = GroupUpWorkersFromLocalDBInDifferentListsDividedByClientID(workersFromLocalDB);
// Prepare List, which will help update list or lists in API
ListForUpdatingWithGroupId listForUpdatingWithGroupId = new ListForUpdatingWithGroupId();
listForUpdatingWithGroupId.ListOfGroupId = new List <string>();
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating = new List <List <WorkersForProcessing> >();
listForUpdatingWithGroupId.UpdateSource = new List <List <WorkersForProcessing> >();
bool listsAreEqual = true;
Client clientForUpdate = new Client();
List <Client> listWithClientsFromWEB = new List <Client>();
foreach (var workerList in listOfWorkerLists)
{
Client newClient = new Client();
newClient.PersonGroupID = Guid.NewGuid().ToString();
newClient.ClientID = workerList[0].ClientID;
listWithClientsFromWEB.Add(newClient);
}
// Check if local DB have old Clients, and if it does,
// then thansfer PersonGroupID(API) to the Clients in the new list
if ((listWithClientsFromWEB.Count() != 0) && (clientsInDB.Count() != 0))
{
if (listWithClientsFromWEB.Count() >= clientsInDB.Count())
{
for (int i = 0; i < clientsInDB.Count(); i++)
{
if (clientsInDB[i].ClientID == listWithClientsFromWEB[i].ClientID)
{
listWithClientsFromWEB[i].PersonGroupID = clientsInDB[i].PersonGroupID;
}
}
}
else
{
for (int i = 0; i < listWithClientsFromWEB.Count(); i++)
{
if (listWithClientsFromWEB[i].ClientID == clientsInDB[i].ClientID)
{
listWithClientsFromWEB[i].PersonGroupID = clientsInDB[i].PersonGroupID;
}
}
}
}
// Check if worker lists from WEB(aka Clients) and from local DB(aka API) are equal
if (listOfWorkerLists.Count() == listOfWorkerListsFromLocalDB.Count())
{
for (int j = 0; j < listOfWorkerLists.Count(); j++)
{
if (listOfWorkerLists[j][0].ClientID != listOfWorkerListsFromLocalDB[j][0].ClientID)
{
listsAreEqual = false;
listForUpdatingWithGroupId.ListOfGroupId.Clear();
listForUpdatingWithGroupId.UpdateSource.Clear();
listForUpdatingWithGroupId.ListOfGroupId.Clear();
break;
}
if (listOfWorkerLists[j].Count() == listOfWorkerListsFromLocalDB[j].Count())
{
for (int i = 0; i < listOfWorkerLists[j].Count(); i++)
{
if (listOfWorkerLists[j][i].FullName != listOfWorkerListsFromLocalDB[j][i].FullName)
{
if (!listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Contains(listOfWorkerListsFromLocalDB[j]))
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerListsFromLocalDB[j]);
listForUpdatingWithGroupId.UpdateSource.Add(listOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
if (!listOfWorkerLists[j][i].Avatar.SequenceEqual(listOfWorkerListsFromLocalDB[j][i].Avatar))
{
if (!listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Contains(listOfWorkerListsFromLocalDB[j]))
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerListsFromLocalDB[j]);
listForUpdatingWithGroupId.UpdateSource.Add(listOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
if (listOfWorkerLists[j][i].ClientID != listOfWorkerListsFromLocalDB[j][i].ClientID)
{
if (!listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Contains(listOfWorkerListsFromLocalDB[j]))
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerListsFromLocalDB[j]);
listForUpdatingWithGroupId.UpdateSource.Add(listOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
}
}
else
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerListsFromLocalDB[j]);
listForUpdatingWithGroupId.UpdateSource.Add(listOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
}
else
{
listsAreEqual = false;
}
if (listsAreEqual)
{ //case when groups on the API are equal to the lists from WEB
listWithClientsFromWEB.Clear();
goto BeginningOfInfiniteLoop;
}
else
{ //case when client count was equal but one or many clients have been changed in
//WEB and needs to change them in the local DB(aka API)
if (listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Count() != 0)
{
for (int i = 0; i < listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Count(); i++)
{
DeletePersonGroup(client, listForUpdatingWithGroupId.ListOfGroupId[i]).Wait();
CreateAndTrainWorkersPersonGroup(client, listForUpdatingWithGroupId.UpdateSource[i], listForUpdatingWithGroupId.ListOfGroupId[i]).Wait();
}
listForUpdatingWithGroupId.UpdateSource.Clear();
listForUpdatingWithGroupId.ListOfGroupId.Clear();
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Clear();
listOfWorkerListsFromLocalDB = listOfWorkerLists;
clientsInDB.Clear();
for (int i = 0; i < listWithClientsFromWEB.Count(); i++)
{
clientsInDB.Add(listWithClientsFromWEB[i]);
}
DeleteClientsAndWorkersFromLocalDB();
AddClientsToLocalDB(listWithClientsFromWEB).Wait();
AddWorkersToLocalDB(listOfWorkerLists).Wait();
listWithClientsFromWEB.Clear();
listsAreEqual = true;
}
else
{ //case when client count wasn't equal
DeletePersonGroups(client, clientsInDB).Wait();
CreateAndTrainWorkersPersonGroups(client, listOfWorkerLists, listWithClientsFromWEB).Wait();
listOfWorkerListsFromLocalDB = listOfWorkerLists;
clientsInDB.Clear();
for (int i = 0; i < listWithClientsFromWEB.Count(); i++)
{
clientsInDB.Add(listWithClientsFromWEB[i]);
}
DeleteClientsAndWorkersFromLocalDB();
AddClientsToLocalDB(listWithClientsFromWEB).Wait();
AddWorkersToLocalDB(listOfWorkerLists).Wait();
listWithClientsFromWEB.Clear();
listsAreEqual = true;
}
}
//Here ends part, which check Local DB and WEB DB and update Local DB And API if needed
BeginningOfInfiniteLoop:
List <List <WorkersForProcessing> > templistOfWorkerLists = new List <List <WorkersForProcessing> >();
// Variable is needed for infinite loop
bool enabledInfiniteLoop = true;
// Infinite loop, which search for new images in the target folder and if search is succesfull,
// then use the local library to find the faces in the image. If faces are founded, then starting work with
// Microsot Azure API to identify faces and save results to the database.
while (enabledInfiniteLoop)
{
Thread.Sleep((int)(3000));
DirectoryInfo dir = new DirectoryInfo(inputFilePath);
// Dlib library using for local face search
using (var fd = Dlib.GetFrontalFaceDetector())
{
foreach (FileInfo file in dir.GetFiles("*.jpeg"))
{
Thread.Sleep((int)(60000));
string _inputFilePath = inputFilePath + file.Name;
// load input image
Array2D <RgbPixel> img = Dlib.LoadImage <RgbPixel>(_inputFilePath);
// find all faces in the image
Rectangle[] faces = fd.Operator(img);
// if search was succesfull then starting work with Microsoft Azure API
if (faces.Length != 0)
{
Console.WriteLine();
Console.WriteLine("Picture " + file.Name + " have faces(according to the DlibDotNetNative library), sending data to Microsoft Azure API");
templistOfWorkerLists = GetWorkersFromWebApplicationInDifferentListsDividedByClientID(databaseConnString);
foreach (var workerList in templistOfWorkerLists)
{
Client newClient = new Client();
newClient.PersonGroupID = Guid.NewGuid().ToString();
newClient.ClientID = workerList[0].ClientID;
listWithClientsFromWEB.Add(newClient);
}
// Check if local DB have old Clients, and if it does,
// then thansfer PersonGroupID(API) to the Clients in the new list
if ((listWithClientsFromWEB.Count() != 0) && (clientsInDB.Count() != 0))
{
if (listWithClientsFromWEB.Count() >= clientsInDB.Count())
{
for (int i = 0; i < clientsInDB.Count(); i++)
{
if (clientsInDB[i].ClientID == listWithClientsFromWEB[i].ClientID)
{
listWithClientsFromWEB[i].PersonGroupID = clientsInDB[i].PersonGroupID;
}
}
}
else
{
for (int i = 0; i < listWithClientsFromWEB.Count(); i++)
{
if (listWithClientsFromWEB[i].ClientID == clientsInDB[i].ClientID)
{
listWithClientsFromWEB[i].PersonGroupID = clientsInDB[i].PersonGroupID;
}
}
}
}
listsAreEqual = true;
if (listOfWorkerLists.Count() == templistOfWorkerLists.Count())
{
for (int j = 0; j < listOfWorkerLists.Count(); j++)
{
if (listOfWorkerLists[j][0].ClientID != templistOfWorkerLists[j][0].ClientID)
{
listsAreEqual = false;
listForUpdatingWithGroupId.ListOfGroupId.Clear();
listForUpdatingWithGroupId.UpdateSource.Clear();
listForUpdatingWithGroupId.ListOfGroupId.Clear();
break;
}
if (listOfWorkerLists[j].Count() == templistOfWorkerLists[j].Count())
{
for (int i = 0; i < listOfWorkerLists[j].Count(); i++)
{
if (listOfWorkerLists[j][i].FullName != templistOfWorkerLists[j][i].FullName)
{
if (!listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Contains(listOfWorkerLists[j]))
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerLists[j]);
listForUpdatingWithGroupId.UpdateSource.Add(templistOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
if (!listOfWorkerLists[j][i].Avatar.SequenceEqual(templistOfWorkerLists[j][i].Avatar))
{
if (!listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Contains(listOfWorkerLists[j]))
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerLists[j]);
listForUpdatingWithGroupId.UpdateSource.Add(templistOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
if (listOfWorkerLists[j][i].ClientID != templistOfWorkerLists[j][i].ClientID)
{
if (!listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Contains(listOfWorkerLists[j]))
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerLists[j]);
listForUpdatingWithGroupId.UpdateSource.Add(templistOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
}
}
else
{
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Add(listOfWorkerLists[j]);
listForUpdatingWithGroupId.UpdateSource.Add(templistOfWorkerLists[j]);
clientForUpdate = listWithClientsFromWEB.FirstOrDefault(x => x.ClientID == listOfWorkerListsFromLocalDB[j][0].ClientID);
listForUpdatingWithGroupId.ListOfGroupId.Add(clientForUpdate.PersonGroupID);
listsAreEqual = false;
}
}
}
else
{
listsAreEqual = false;
}
CheckIfPersonGroupsIsOutdated:
if (listsAreEqual)
{ //case when groups on the API are equal to the lists from local and WEB DB
// Get ClientID and CameraID from XML
(string, string)infoFromXML = GetInfoFromXML(file.Name, inputFilePath);
for (int i = 0; i < listOfWorkerLists.Count(); i++)
{
if (listOfWorkerLists[i][0].ClientID == infoFromXML.Item1)
{
FindFacesWithAPIIdentifyThemAndAddInDB(_inputFilePath, subscriptionKey, uriBase, file.Name, client, inputFilePath, clientsInDB[i].PersonGroupID).Wait();
file.Delete();
}
}
listWithClientsFromWEB.Clear();
}
else
{ //case when client count was equal but one or many clients have been changed
if (listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Count() != 0)
{
for (int i = 0; i < listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Count(); i++)
{
DeletePersonGroup(client, listForUpdatingWithGroupId.ListOfGroupId[i]).Wait();
CreateAndTrainWorkersPersonGroup(client, listForUpdatingWithGroupId.UpdateSource[i], listForUpdatingWithGroupId.ListOfGroupId[i]).Wait();
}
listForUpdatingWithGroupId.UpdateSource.Clear();
listForUpdatingWithGroupId.ListOfGroupId.Clear();
listForUpdatingWithGroupId.ListOfWorkerGroupsForUpdating.Clear();
listOfWorkerListsFromLocalDB = templistOfWorkerLists;
listOfWorkerLists = templistOfWorkerLists;
clientsInDB.Clear();
for (int i = 0; i < listWithClientsFromWEB.Count(); i++)
{
clientsInDB.Add(listWithClientsFromWEB[i]);
}
DeleteClientsAndWorkersFromLocalDB();
AddClientsToLocalDB(clientsInDB).Wait();
AddWorkersToLocalDB(listOfWorkerListsFromLocalDB).Wait();
listWithClientsFromWEB.Clear();
listsAreEqual = true;
goto CheckIfPersonGroupsIsOutdated;
}
else
{ //case when client count wasn't equal
DeletePersonGroups(client, clientsInDB).Wait();
CreateAndTrainWorkersPersonGroups(client, templistOfWorkerLists, listWithClientsFromWEB).Wait();
listOfWorkerListsFromLocalDB = templistOfWorkerLists;
listOfWorkerLists = templistOfWorkerLists;
clientsInDB.Clear();
for (int i = 0; i < listWithClientsFromWEB.Count(); i++)
{
clientsInDB.Add(listWithClientsFromWEB[i]);
}
DeleteClientsAndWorkersFromLocalDB();
AddClientsToLocalDB(clientsInDB).Wait();
AddWorkersToLocalDB(listOfWorkerListsFromLocalDB).Wait();
listWithClientsFromWEB.Clear();
listsAreEqual = true;
goto CheckIfPersonGroupsIsOutdated;
}
}
}
else
{
file.Delete();
}
// After all work is done delete XML file with info about image set, received from client
string xmlName = file.Name.Substring(0, file.Name.LastIndexOf('_')) + ".xml";
if (dir.GetFiles(file.Name.Substring(0, file.Name.LastIndexOf('_')) + "*.jpeg").Length <= 0)
{
foreach (FileInfo xmlFile in dir.GetFiles(xmlName))
{
xmlFile.Delete();
}
}
}
}
}
private void BackgroundWorkerOnDoWork(object sender, DoWorkEventArgs doWorkEventArgs)
{
var path = doWorkEventArgs.Argument as string;
if (string.IsNullOrWhiteSpace(path) || !File.Exists(path))
{
return;
}
using (var faceDetector = Dlib.GetFrontalFaceDetector())
using (var img = Dlib.LoadImage <RgbPixel>(path))
{
Dlib.PyramidUp(img);
var dets = faceDetector.Operator(img);
var shapes = new List <FullObjectDetection>();
foreach (var rect in dets)
{
var shape = this._ShapePredictor.Detect(img, rect);
if (shape.Parts <= 2)
{
continue;
}
shapes.Add(shape);
}
if (shapes.Any())
{
var lines = Dlib.RenderFaceDetections(shapes);
foreach (var line in lines)
{
Dlib.DrawLine(img, line.Point1, line.Point2, new RgbPixel
{
Green = 255
});
}
var wb = img.ToBitmap();
this.pictureBoxImage.Image?.Dispose();
this.pictureBoxImage.Image = wb;
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))
{
// It is NOT necessary to re-convert WriteableBitmap to Matrix.
// This sample demonstrate converting managed image class to
// dlib class and vice versa.
using (var tile = tileImage.ToBitmap())
using (var mat = tile.ToMatrix <RgbPixel>())
{
var tile2 = mat.ToBitmap();
this.pictureBoxTileImage.Image?.Dispose();
this.pictureBoxTileImage.Image = tile2;
}
}
foreach (var c in chipLocations)
{
c.Dispose();
}
}
foreach (var s in shapes)
{
s.Dispose();
}
}
}
static void Main(string[] args)
{
/// FaceDetectionWith_API
Location[] coord = TestImage(fileName, Model.Hog);
/// Face DetectionWith_DLIB
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(fileName);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
Dlib.SaveJpeg(img, outputName);
}
// 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 = Dlib.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 = ShapePredictor.Deserialize("shape_predictor_68_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.LoadImageAsMatrix <RgbPixel>(fileName))
using (var win = new ImageWindow(img))
{
var faces = new List <Matrix <RgbPixel> >();
foreach (var face in detector.Operator(img))
{
var shape = sp.Detect(img, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(img, faceChipDetail);
//faces.Add(move(face_chip));
faces.Add(faceChip);
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}");
// Отобразим результат в ImageList
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();
}
}
}
}
System.Console.ReadLine();
}
public static void CreateFeatureVectors()
{
int faceCount = 0;
float leftEyebrow, rightEyebrow, leftLip, rightLip, lipHeight, lipWidth;
string output;
if (currentDataType == Datatype.Testing)
{
output = testingOutput;
}
else
{
output = trainingOutput;
}
string[] dirs = Directory.GetFiles(currentFilePath, "*.*", SearchOption.AllDirectories);
// Set up Dlib Face Detector
using (var fd = Dlib.GetFrontalFaceDetector())
// ... and Dlib Shape Detector
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
string header = "leftEyebrow,rightEyebrow,leftLip,rightLip,lipWidth,lipHeight,label\n";
// Create the CSV file and fill in the first line with the header
System.IO.File.WriteAllText(output, header);
foreach (string dir in dirs)
{
// call function that sets the label based on what the filename contains
string label = DetermineLabel(dir);
// load input image
if (!(dir.EndsWith("png") || dir.EndsWith("jpg")))
{
continue;
}
var img = Dlib.LoadImage <RgbPixel>(dir);
// find all faces in the image
var faces = fd.Operator(img);
// for each face draw over the facial landmarks
foreach (var face in faces)
{
// Write to the console displaying the progress and current emotion
Form1.SetProgress(faceCount, dirs.Length - 1);
// find the landmark points for this face
var shape = sp.Detect(img, face);
for (var i = 0; i < shape.Parts; i++)
{
RgbPixel colour = new RgbPixel(255, 255, 255);
var point = shape.GetPart((uint)i);
var rect = new DlibDotNet.Rectangle(point);
Dlib.DrawRectangle(img, rect, color: colour, thickness: 2);
}
SetFormImage(img);
leftEyebrow = CalculateLeftEyebrow(shape);
rightEyebrow = CalculateRightEyebrow(shape);
leftLip = CalculateLeftLip(shape);
rightLip = CalculateRightLip(shape);
lipWidth = CalculateLipWidth(shape);
lipHeight = CalculateLipHeight(shape);
using (System.IO.StreamWriter file = new System.IO.StreamWriter(output, true))
{
file.WriteLine(leftEyebrow + "," + rightEyebrow + "," + leftLip + "," + rightLip + "," + lipWidth + "," + lipHeight + "," + label);
}
// Increment count used for console output
faceCount++;
}
}
if (currentDataType == Datatype.Testing)
{
var testDataView = mlContext.Data.LoadFromTextFile <FeatureInputData>(output, hasHeader: true, separatorChar: ',');
GenerateMetrics(testDataView);
}
Form1.HideImage();
}
}
