/// <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 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);
}
}
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 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>
/// 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 WriteImageToFilePath(Array2D <RgbPixel> img, string filepath)
{
Dlib.SaveJpeg(img, filepath);
}
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"));
}
}
/// <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);
}
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();
}
// 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);
}
}
}
}
}
