ShapeModel smodel; //shape model
#endregion Fields
#region Constructors
public FaceTracker(string filepath)
{
points = new List<Point[]> ();
string jsonText = null;
using (System.IO.StreamReader reader = new System.IO.StreamReader(filepath)) {
jsonText = reader.ReadToEnd ();
reader.Close ();
}
// TextAsset textAsset = Resources.Load (filename) as TextAsset;
// string jsonText = textAsset.text;
IDictionary json = (IDictionary)Json.Deserialize (jsonText);
IDictionary ft = (IDictionary)json ["ft object"];
detector = new FaceDetector ();
detector.read (ft ["detector"]);
smodel = new ShapeModel ();
smodel.read (ft ["smodel"]);
pmodel = new PatchModels ();
pmodel.read (ft ["pmodel"]);
}
PatchModels pmodel; //feature detectors
public FaceTracker (string filepath)
{
points = new List<Point[]> ();
string jsonText = null;
#if UNITY_WSA
var data = File.ReadAllBytes(filepath);
jsonText = Encoding.UTF8.GetString(data, 0, data.Length);
#else
jsonText = File.ReadAllText(filepath);
#endif
// TextAsset textAsset = Resources.Load (filename) as TextAsset;
// string jsonText = textAsset.text;
IDictionary json = (IDictionary)Json.Deserialize (jsonText);
IDictionary ft = (IDictionary)json ["ft object"];
detector = new FaceDetector ();
detector.read (ft ["detector"]);
smodel = new ShapeModel ();
smodel.read (ft ["smodel"]);
pmodel = new PatchModels ();
pmodel.read (ft ["pmodel"]);
}
public void ControlLoad(object sender, EventArgs e)
{
var environmentService = new EnvironmentService();
var haarEyeFile = new FileInfo(environmentService.GetAbsolutePathFromAssemblyRelative("haarcascades/haarcascade_eye.xml"));
var haarFaceFile = new FileInfo(environmentService.GetAbsolutePathFromAssemblyRelative("haarcascades/haarcascade_frontalface_default.xml"));
_faceDetector = new FaceDetector(haarFaceFile.FullName, haarEyeFile.FullName);
_sunglassOverlay2 = new AccessoryOverlay(environmentService.GetAbsolutePathFromAssemblyRelative("Resources/Images/sunglasses2.png"));
_hatOverlay1 = new AccessoryOverlay(environmentService.GetAbsolutePathFromAssemblyRelative("Resources/Images/partyhat.png"));
_classiferParams = new ClassifierParameters();
classifierConfigControl.ConfigChanged += classifierConfigControl_ConfigChanged;
}
async Task OnProcessFrameAsync(SoftwareBitmap bitmap)
{
if (this.faceDetector == null)
{
this.faceDetector = await FaceDetector.CreateAsync();
}
var faces = await this.faceDetector.DetectFacesAsync(bitmap);
this.cameraDisplay.ShowCamera(faces.Count > 0);
if (faces.Count > 0)
{
foreach (var face in faces)
{
this.cameraDisplay.HighlightFace(face.FaceBox);
}
}
}
private static void FindManyFaces(FaceDetector faceDetector, string imagesFilePath, string outputDir, bool debug)
{
if (!Directory.Exists(outputDir))
{
Directory.CreateDirectory(outputDir);
}
var images = Directory.EnumerateFiles(imagesFilePath, "*.jpg");
int cnt = 0;
foreach (var image in images)
{
var outputFile = Path.Combine(outputDir, $"output_{cnt}.png");
FindFaces(faceDetector, image, outputFile, debug);
cnt++;
}
}
public int InitializeWithEyeList(BackgroundCanvas mainCanvas, string filename, List <Point> leftEyeList, List <Point> rightEyeList)
{
if (null == _detector)
{
_detector = new FaceDetector(
mainCanvas.OptionDialog.FaceDetectorDataPath,
true,
mainCanvas.OptionDialog.FaceDetectorThreshold);
}
// VIOLA: Removed face detection... since we have the eye are are relying on them
List <Rect> faceRects = new List <Rect>();
for (int i = 0; i < leftEyeList.Count; ++i)
{
Rect rect = new Rect();
Vector leye = new Vector(leftEyeList[i].X, leftEyeList[i].Y);
Vector reye = new Vector(rightEyeList[i].X, rightEyeList[i].Y);
Vector delta = reye - leye;
double eyeWidth = delta.Length;
Vector center = leye + 0.5 * delta;
rect.X = center.X - 1.5 * eyeWidth;
rect.Y = center.Y - 1.0 * eyeWidth;
rect.Width = 3.0 * eyeWidth;
rect.Height = 3.0 * eyeWidth;
faceRects.Add(rect);
}
_targetRect = new Rect();
_faceDisplayWidth = mainCanvas.OptionDialog.FaceDisplayWidth;
_defaultDPI = mainCanvas.OptionDialog.DefaultDPI;
_leftEyeList = leftEyeList;
_rightEyeList = rightEyeList;
return(InitializeInternal(mainCanvas, filename, faceRects, mainCanvas.OptionDialog.BorderWidth, null));
}
void AllocateObjects(ResourceSet resources)
{
_resources = resources;
_faceDetector = new FaceDetector(_resources.blazeFace);
_faceMesh = new FaceLandmarkDetector(_resources.faceMesh);
_irisMeshL = new EyeLandmarkDetector(_resources.iris);
_irisMeshR = new EyeLandmarkDetector(_resources.iris);
_cropMaterial = new Material(_resources.cropShader);
_faceCrop = new RenderTexture(192, 192, 0);
_irisCropL = new RenderTexture(64, 64, 0);
_irisCropR = new RenderTexture(64, 64, 0);
_refineBuffer = new ComputeBuffer(FaceLandmarkDetector.VertexCount,
sizeof(float) * 4);
_eyeToFace = IndexTable.CreateEyeToFaceLandmarkBuffer();
}
public async void ProcessCurrentVideoFrame(ThreadPoolTimer timer)
{
if (!frameProcessingSemaphore.Wait(0))
{
return;
}
IList <DetectedFace> detectedFaces = null;
try
{
const BitmapPixelFormat faceDetectionPixelFormat = BitmapPixelFormat.Nv12;
using (VideoFrame previewFrame = new VideoFrame(faceDetectionPixelFormat, (int)this.videoProperties.Width, (int)this.videoProperties.Height))
{
await this.mediaCapture.GetPreviewFrameAsync(previewFrame);
if (FaceDetector.IsBitmapPixelFormatSupported(previewFrame.SoftwareBitmap.BitmapPixelFormat))
{
detectedFaces = await this.faceTracker.ProcessNextFrameAsync(previewFrame);
}
else
{
frameProcessingSemaphore.Release();
return;
}
var previewFrameSize = new Size(previewFrame.SoftwareBitmap.PixelWidth, previewFrame.SoftwareBitmap.PixelHeight);
var ignored = this.Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () =>
{
this.SetupVisualization(previewFrameSize, detectedFaces);
});
}
}
catch (Exception e)
{
// Face tracking failed
}
finally
{
frameProcessingSemaphore.Release();
}
}
/// <summary>
///
/// </summary>
/// <param name="timer"></param>
private async void CurrentVideoFrame(ThreadPoolTimer timer)
{
if (!semaphore.Wait(0))
{
return;
}
try
{
IList <DetectedFace> faces = null;
const BitmapPixelFormat inputPixelFormat = BitmapPixelFormat.Nv12;
using (VideoFrame previewFrame = new VideoFrame(inputPixelFormat, 320, 240))
{
await capture.GetPreviewFrameAsync(previewFrame);
//顔検出実行
if (FaceDetector.IsBitmapPixelFormatSupported(previewFrame.SoftwareBitmap.BitmapPixelFormat))
{
faces = await faceTracker.ProcessNextFrameAsync(previewFrame);
}
else
{
throw new System.NotSupportedException("PixelFormat '" + inputPixelFormat.ToString() + "' is not supported by FaceDetector");
}
//顔が検出されたら録画スタート
if (faces.Count != 0)
{
Debug.WriteLine("Found Face");
await startRecoding();
}
}
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
}
finally
{
semaphore.Release();
}
}
public static IEnumerable <FaceModel> FaceAnalysis(FaceMaskDetector faceMaskDetector, byte[] imageData, IEnumerable <FaceRectangle> faceRectangles, bool mask)
{
if (mask)
{
IEnumerable <Rectangle> rectangles = faceRectangles.Select(faceRectangle => new Rectangle(faceRectangle.Top, faceRectangle.Left, faceRectangle.Width, faceRectangle.Height));
IEnumerable <Bitmap> faces = rectangles.Select(rectangle => FaceDetector.GetFaceImage(imageData, rectangle).ToBitmap());
IEnumerable <bool> isMasks = faceMaskDetector.Detect(faces);
return(faceRectangles.Zip(isMasks, (faceRectangle, isMask) => new FaceModel {
FaceRectangle = faceRectangle, FaceAttributes = new FaceAttributes {
IsMask = isMask
}
}));
}
return(faceRectangles.Select(faceRectangle => new FaceModel {
FaceRectangle = faceRectangle, FaceAttributes = new FaceAttributes()
}));
}
/// <summary>
/// Initializes a new MediaCapture instance and starts the Preview streaming to the CamPreview UI element.
/// </summary>
/// <returns>Async Task object returning true if initialization and streaming were successful and false if an exception occurred.</returns>
private async Task <bool> StartWebcamStreamingAsync()
{
bool successful = false;
this.faceDetector = await FaceDetector.CreateAsync();
try
{
this.mediaCapture = new MediaCapture();
// For this scenario, we only need Video (not microphone) so specify this in the initializer.
// NOTE: the appxmanifest only declares "webcam" under capabilities and if this is changed to include
// microphone (default constructor) you must add "microphone" to the manifest or initialization will fail.
MediaCaptureInitializationSettings settings = new MediaCaptureInitializationSettings();
settings.StreamingCaptureMode = StreamingCaptureMode.Video;
await this.mediaCapture.InitializeAsync(settings);
this.mediaCapture.CameraStreamStateChanged += this.MediaCapture_CameraStreamStateChanged;
// Cache the media properties as we'll need them later.
var deviceController = this.mediaCapture.VideoDeviceController;
this.videoProperties = deviceController.GetMediaStreamProperties(MediaStreamType.VideoPreview) as VideoEncodingProperties;
// Immediately start streaming to our CaptureElement UI.
// NOTE: CaptureElement's Source must be set before streaming is started.
this.CamPreview.Source = this.mediaCapture;
await this.mediaCapture.StartPreviewAsync();
successful = true;
}
catch (System.UnauthorizedAccessException)
{
// If the user has disabled their webcam this exception is thrown; provide a descriptive message to inform the user of this fact.
this.rootPage.NotifyUser("Webcam is disabled or access to the webcam is disabled for this app.\nEnsure Privacy Settings allow webcam usage.", NotifyType.ErrorMessage);
}
catch (Exception ex)
{
this.rootPage.NotifyUser(ex.ToString(), NotifyType.ErrorMessage);
}
return(successful);
}
/// <summary>
/// This method is invoked by a ThreadPoolTimer to execute the FaceTracker and Visualization logic at approximately 15 frames per second.
/// </summary>
/// <remarks>
/// Keep in mind this method is called from a Timer and not synchronized with the camera stream. Also, the processing time of FaceTracker
/// will vary depending on the size of each frame and the number of faces being tracked. That is, a large image with several tracked faces may
/// take longer to process.
/// </remarks>
/// <param name="timer">Timer object invoking this call</param>
private async void ProcessCurrentVideoFrame(ThreadPoolTimer timer)
{
// If a lock is being held it means we're still waiting for processing work on the previous frame to complete.
// In this situation, don't wait on the semaphore but exit immediately.
if (!frameProcessingSemaphore.Wait(0))
{
return;
}
try {
IList <DetectedFace> faces = null;
// Create a VideoFrame object specifying the pixel format we want our capture image to be (NV12 bitmap in this case).
// GetPreviewFrame will convert the native webcam frame into this format.
const BitmapPixelFormat InputPixelFormat = BitmapPixelFormat.Nv12;
using (VideoFrame previewFrame = new VideoFrame(InputPixelFormat, (int)this.videoProperties.Width, (int)this.videoProperties.Height)) {
await this.camera.MediaCapture.GetPreviewFrameAsync(previewFrame);
// The returned VideoFrame should be in the supported NV12 format but we need to verify this.
if (FaceDetector.IsBitmapPixelFormatSupported(previewFrame.SoftwareBitmap.BitmapPixelFormat))
{
faces = await this.faceTracker.ProcessNextFrameAsync(previewFrame);
}
else
{
throw new System.NotSupportedException("PixelFormat '" + InputPixelFormat.ToString() + "' is not supported by FaceDetector");
}
// Create our visualization using the frame dimensions and face results but run it on the UI thread.
var previewFrameSize = new Windows.Foundation.Size(previewFrame.SoftwareBitmap.PixelWidth, previewFrame.SoftwareBitmap.PixelHeight);
var ignored = this.Dispatcher.RunAsync(Windows.UI.Core.CoreDispatcherPriority.Normal, () => {
this.SetupVisualization(previewFrameSize, faces);
});
}
}
catch (Exception ex) {
Debug.WriteLine(ex);
}
finally {
frameProcessingSemaphore.Release();
}
}
private void Initialize(object sender, PipelineRunEventArgs e)
{
string rootDirectory = AppDomain.CurrentDomain.BaseDirectory;
faceModelParameters = new FaceModelParameters(rootDirectory, true, false, false);
faceModelParameters.optimiseForVideo();
faceDetector = new FaceDetector(faceModelParameters.GetHaarLocation(), faceModelParameters.GetMTCNNLocation());
if (!faceDetector.IsMTCNNLoaded())
{
faceModelParameters.SetFaceDetector(false, true, false);
}
landmarkDetector = new CLNF(faceModelParameters);
faceAnalyser = new FaceAnalyser(rootDirectory, dynamic: true, output_width: 112, mask_aligned: true);
gazeAnalyser = new GazeAnalyser();
landmarkDetector.Reset();
faceAnalyser.Reset();
}
protected ExtractorBase(FaceModelParameters faceModelParameters)
{
if (_initialized)
{
return;
}
ModelParams = faceModelParameters;
GazeAnalyzer = new GazeAnalyserManaged();
var face_detector = new FaceDetector(ModelParams.GetHaarLocation(), ModelParams.GetMTCNNLocation());
if (!face_detector.IsMTCNNLoaded()) // If MTCNN model not available, use HOG
{
ModelParams.SetFaceDetector(false, true, false);
}
FaceModel = new CLNF(ModelParams);
_initialized = true;
}
public void StartDetection()
{
if (CoreXT.IsDevice)
{
if (_faceDetector == null)
{
// create detector with low accuracy and don't track faces
_faceDetector = new FaceDetector(false, false);
// shrink image to 12.5% first for faster detection
_faceDetector.preprocessImageScale = 0.125f;
}
_isDetecting = true;
}
else
{
Debug.Log("Not on device.");
}
}
IEnumerator Start()
{
Input.backButtonLeavesApp = true;
Screen.sleepTimeout = SleepTimeout.NeverSleep;
_detector = GetComponent <FaceDetector>();
_apiManager = GetComponent <FaceApiManager>();
_toMatHelperMgr = GetComponent <WebCamTextureToMatHelperManager>();
_toMatHelper = GetComponent <WebCamTextureToMatHelper>();
//カメラ等の初期化完了後，画像サイズを取得する
_toMatHelper.Initialize();
yield return(WaitInitialization());
var imgSize = new Size(_toMatHelper.GetWidth(), _toMatHelper.GetHeight());
_zeroMat = new ZeroMat(imgSize);
_volume.profile.TryGetSettings(out _distortion);
}
void Start() {
if (CoreXT.IsDevice)
webCam = new WebCamTexture("Front Camera");
else
webCam = new WebCamTexture();
RotatableGUIItem guiItem = ((RotatableGUIItem)(GameObject.Find ("CameraVideo").GetComponent("RotatableGUIItem")));
guiItem.texture = webCam;
guiItem.size = new Vector2(Screen.width / 8, Screen.height / 8);
guiItem.relativePosition = new Vector2(-Screen.width / 16, Screen.height / 16 + 75);
if (CoreXT.IsDevice) {
faceDetector = new FaceDetector(false, false);
faceDetector.IsMirrored = true;
}
StartDetect();
}
public async Task <Rect?> ProcessCameraFrameAsync(SoftwareBitmap bitmap)
{
if (this.faceDetector == null)
{
this.faceDetector = await FaceDetector.CreateAsync();
}
var result = await this.faceDetector.DetectFacesAsync(bitmap);
this.photoControl.Switch(result?.Count > 0);
Rect?returnValue = null;
if (result?.Count > 0)
{
returnValue = new Rect(
(double)result[0].FaceBox.X / bitmap.PixelWidth,
(double)result[0].FaceBox.Y / bitmap.PixelHeight,
(double)result[0].FaceBox.Width / bitmap.PixelWidth,
(double)result[0].FaceBox.Height / bitmap.PixelHeight);
}
return(returnValue);
}
private async void _timer_Tick(object sender, object e)
{
try
{
this.FaceCanvas.Children.Clear();
IEnumerable <DetectedFace> faces = null;
// Create a VideoFrame object specifying the pixel format we want our capture image to be (NV12 bitmap in this case).
// GetPreviewFrame will convert the native webcam frame into this format.
const BitmapPixelFormat InputPixelFormat = BitmapPixelFormat.Nv12;
using (VideoFrame previewFrame = new VideoFrame(InputPixelFormat, 1280, 720))
{
await this._mediaCapture.GetPreviewFrameAsync(previewFrame);
// The returned VideoFrame should be in the supported NV12 format but we need to verify this.
if (FaceDetector.IsBitmapPixelFormatSupported(previewFrame.SoftwareBitmap.BitmapPixelFormat))
{
faces = await this.faceTracker.ProcessNextFrameAsync(previewFrame);
}
}
if (faces != null)
{
foreach (DetectedFace face in faces)
{
Face.Margin = new Thickness(face.FaceBox.X, face.FaceBox.Y, 0, 0);
//faceBorder.ShowFaceRectangle(0, 0, (uint)(face.FaceBox.Width), (uint)(face.FaceBox.Height ));
FaceText.Text = face.FaceBox.X.ToString() + face.FaceBox.Y.ToString();
}
}
PicBtn.Content = DateTime.Now.ToString();
await Task.Delay(50);
}
catch (Exception)
{
}
}
/// <summary>
/// Creates and initializes a FaceSentimentAnalyzerSkill instance
/// </summary>
/// <param name="descriptor"></param>
/// <param name="device"></param>
/// <returns></returns>
internal static IAsyncOperation <FaceSentimentAnalyzerSkill> CreateAsync(
ISkillDescriptor descriptor,
ISkillExecutionDevice device)
{
return(AsyncInfo.Run(async(token) =>
{
// Create instance
var skillInstance = new FaceSentimentAnalyzerSkill(descriptor, device);
// Instantiate the FaceDetector
skillInstance.m_faceDetector = await FaceDetector.CreateAsync();
// Load WinML model
var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Contoso.FaceSentimentAnalyzer/{FaceSentimentAnalyzerConst.WINML_MODEL_FILENAME}"));
var winmlModel = LearningModel.LoadFromFilePath(modelFile.Path);
// Create WinML session
skillInstance.m_winmlSession = new LearningModelSession(winmlModel, GetWinMLDevice(device));
return skillInstance;
}));
}
public async Task <int> DetectFacesAsync(byte[] photoByteArray)
{
BitmapDecoder decoder = await BitmapDecoder.CreateAsync(photoByteArray.ToRandomAccessMemory());
BitmapTransform transform = new BitmapTransform();
const float sourceImageHeightLimit = 1280;
if (decoder.PixelHeight > sourceImageHeightLimit)
{
float scalingFactor = (float)sourceImageHeightLimit / (float)decoder.PixelHeight;
transform.ScaledWidth = (uint)Math.Floor(decoder.PixelWidth * scalingFactor);
transform.ScaledHeight = (uint)Math.Floor(decoder.PixelHeight * scalingFactor);
}
SoftwareBitmap sourceBitmap = await decoder.GetSoftwareBitmapAsync(decoder.BitmapPixelFormat, BitmapAlphaMode.Premultiplied, transform, ExifOrientationMode.IgnoreExifOrientation, ColorManagementMode.DoNotColorManage);
SoftwareBitmap convertedBitmap = sourceBitmap;
if (sourceBitmap.BitmapPixelFormat != faceDetectionPixelFormat)
{
convertedBitmap = SoftwareBitmap.Convert(sourceBitmap, faceDetectionPixelFormat);
}
FaceDetector detector = await FaceDetector.CreateAsync();
IList <DetectedFace> faces = null;
faces = await detector.DetectFacesAsync(convertedBitmap);
/* ICollection<System.Drawing.Rectangle> rectangles = new List<System.Drawing.Rectangle>();
*
* foreach(DetectedFace face in faces)
* rectangles.Add(new System.Drawing.Rectangle(Convert.ToInt32(face.FaceBox.X), Convert.ToInt32(face.FaceBox.Y), Convert.ToInt32(face.FaceBox.Width), Convert.ToInt32(face.FaceBox.Height)));
*/
sourceBitmap.Dispose();
convertedBitmap.Dispose();
return(faces.Count);
}
/// <summary>
/// Initialize a photo - run faceDetection
/// </summary>
/// <param name="mainCanvas">Main canvas reference</param>
/// <param name="filename">Full path name to image file</param>
public int InitializeWithFaceDetection(BackgroundCanvas mainCanvas, string filename)
{
if (null == _detector)
{
_detector = new FaceDetector(
mainCanvas.OptionDialog.FaceDetectorDataPath,
true,
mainCanvas.OptionDialog.FaceDetectorThreshold);
}
_detector.SetTargetDimension(mainCanvas.OptionDialog.FaceDetectTargetWidth,
mainCanvas.OptionDialog.FaceDetectTargetHeight);
DetectionResult detectionResult = _detector.DetectObject(filename);
List <ScoredRect> scoredResultList = detectionResult.GetMergedRectList(0.0F);
if (scoredResultList.Count < 0)
{
return(0);
}
List <Rect> faceRects = new List <Rect>();
foreach (ScoredRect scoredRect in scoredResultList)
{
Rect rect = new Rect();
rect.X = scoredRect.X;
rect.Y = scoredRect.Y;
rect.Width = scoredRect.Width;
rect.Height = scoredRect.Height;
faceRects.Add(rect);
}
_targetRect = new Rect();
_faceDisplayWidth = mainCanvas.OptionDialog.FaceDisplayWidth;
_defaultDPI = mainCanvas.OptionDialog.DefaultDPI;
return(InitializeInternal(mainCanvas, filename, faceRects, mainCanvas.OptionDialog.BorderWidth, null));
}
private async void ProcessCurrentVideoFrame(ThreadPoolTimer timer)
{
if (m_mediaCapture == null)
{
return;
}
if (m_mediaCapture.CameraStreamState != CameraStreamState.Streaming)
{
return;
}
if (!m_faceProcessingSemaphore.Wait(0))
{
return;
}
IList <DetectedFace> faces = null;
const BitmapPixelFormat inputPixelFormat = BitmapPixelFormat.Nv12;
using (VideoFrame previewFrame = new VideoFrame(inputPixelFormat, (int)m_videoProperties.Width, (int)m_videoProperties.Height))
{
await m_mediaCapture.GetPreviewFrameAsync(previewFrame);
if (FaceDetector.IsBitmapPixelFormatSupported(previewFrame.SoftwareBitmap.BitmapPixelFormat))
{
faces = await m_faceTracker.ProcessNextFrameAsync(previewFrame);
}
};
foreach (DetectedFace face in faces)
{
Debug.Log(string.Format("x={0}, y={1}, w={2}, h={3}", face.FaceBox.X, face.FaceBox.Y, face.FaceBox.Width, face.FaceBox.Height));
}
m_faceProcessingSemaphore.Release();
}
private void StartFaceDetection(MediaCapture capture)
{
if (capture == null)
{
throw new ArgumentNullException();
}
if (_faceDetectionCancellationTokenSource != null)
{
return;
}
_faceDetectionCancellationTokenSource = new CancellationTokenSource();
var token = _faceDetectionCancellationTokenSource.Token;
_faceDetectionTask = Task.Factory.StartNew(async() =>
{
try
{
FaceDetector detector = new FaceDetector();
detector.Initialize(new FaceDetectorInitializationData()
{
FaceData = Package.Current.InstalledLocation.Path + "\\" + FacePredictorFileName
});
while (!token.IsCancellationRequested)
{
await FaceDetectAsync(detector, capture, token);
}
}
catch (OperationCanceledException) { }
catch (Exception e)
{
Debug.WriteLine("Face detection failed.");
Debug.WriteLine(e.Message);
}
}, token);
}
private async Task <string> DetectEmotionWithWinML()
{
var videoFrame = lastFrame;
if (faceDetector == null)
{
faceDetector = await FaceDetector.CreateAsync();
}
var detectedFaces = await faceDetector.DetectFacesAsync(videoFrame.SoftwareBitmap);
if (detectedFaces != null && detectedFaces.Any())
{
var face = detectedFaces.OrderByDescending(s => s.FaceBox.Height * s.FaceBox.Width).First();
var randomAccessStream = new InMemoryRandomAccessStream();
var decoder = await BitmapDecoder.CreateAsync(randomAccessStream);
var croppedImage = await decoder.GetSoftwareBitmapAsync(decoder.BitmapPixelFormat, BitmapAlphaMode.Ignore, new BitmapTransform()
{
Bounds = new BitmapBounds()
{
X = face.FaceBox.X, Y = face.FaceBox.Y, Width = face.FaceBox.Width, Height = face.FaceBox.Height
}
}, ExifOrientationMode.IgnoreExifOrientation, ColorManagementMode.DoNotColorManage);
videoFrame = VideoFrame.CreateWithSoftwareBitmap(croppedImage);
}
var emotion = await model.EvaluateAsync(new CNTKGraphModelInput()
{
Input338 = videoFrame
});
var index = emotion.Plus692_Output_0.IndexOf(emotion.Plus692_Output_0.Max());
string label = labels[index];
return(label);
}
private async Task InitializeCamera()
{
_requestStopCancellationToken = new CancellationTokenSource();
_captureElement = new CaptureElement();
var videoCaptureDevices = await DeviceInformation.FindAllAsync(DeviceClass.VideoCapture);
var camera = videoCaptureDevices.FirstOrDefault();
MediaCaptureInitializationSettings initialisationSettings = new MediaCaptureInitializationSettings()
{
StreamingCaptureMode = StreamingCaptureMode.Video,
VideoDeviceId = camera.Id
};
_mediaCapture = new MediaCapture();
await _mediaCapture.InitializeAsync(initialisationSettings);
_captureElement.Source = _mediaCapture;
await _mediaCapture.StartPreviewAsync();
var videoProperties = (_mediaCapture.VideoDeviceController.GetMediaStreamProperties(MediaStreamType.VideoPreview) as VideoEncodingProperties);
var videoSize = new Rect(0, 0, videoProperties.Width, videoProperties.Height);
var detector = await FaceDetector.CreateAsync();
var bitmap = FaceDetector.GetSupportedBitmapPixelFormats().First();
try
{
await Task.Run(async() =>
{
VideoFrame frame = new VideoFrame(bitmap, (int)videoSize.Width, (int)videoSize.Height);
TimeSpan?lastFrameTime = null;
while (true)
{
if (!_requestStopCancellationToken.Token.IsCancellationRequested)
{
await _mediaCapture.GetPreviewFrameAsync(frame);
if ((!lastFrameTime.HasValue) || (lastFrameTime != frame.RelativeTime))
{
var detectedFaces = await detector.DetectFacesAsync(frame.SoftwareBitmap);
if (detectedFaces.Count == 1)
{
var convertedRgba16Bitmap = SoftwareBitmap.Convert(frame.SoftwareBitmap, BitmapPixelFormat.Rgba16);
InMemoryRandomAccessStream stream = new InMemoryRandomAccessStream();
BitmapEncoder encoder = await BitmapEncoder.CreateAsync(BitmapEncoder.JpegEncoderId, stream);
encoder.SetSoftwareBitmap(convertedRgba16Bitmap);
await encoder.FlushAsync();
var detectedPerson = await _faceService.DetectFace(stream.AsStream());
if (detectedPerson != null && detectedPerson.PersonId.HasValue)
{
_userService.PersonId = detectedPerson.PersonId.Value;
var user = await _userService.GetModelAsync();
if (user == null)
{
user = new UserProfileModel().RandomData(detectedPerson.Gender);
user.PersonId = detectedPerson.PersonId.Value;
user.FaceIds.Add(detectedPerson.FaceId.Value);
user = await _userService.AddUserAsync(user);
}
await UserViewModel.SetValuesAsync(User, user);
}
else
{
// bug: when a person was not detected, the stream gets disposed
//stream.Seek(0);
stream = new InMemoryRandomAccessStream();
encoder = await BitmapEncoder.CreateAsync(BitmapEncoder.JpegEncoderId, stream);
encoder.SetSoftwareBitmap(convertedRgba16Bitmap);
await encoder.FlushAsync();
// TODO: ask new user for initial profile data
var user = new UserProfileModel().RandomData(detectedPerson.Gender);
user.PersonId = await _faceService.CreatePersonAsync(user.FullName);
var faceIds = new List <Guid>();
faceIds.Add(await _faceService.AddFaceAsync(user.PersonId, stream.AsStream()));
user.FaceIds.AddRange(faceIds);
user = await _userService.AddUserAsync(user);
await UserViewModel.SetValuesAsync(User, user);
}
await Task.Delay(CHECK_INTERVAL * 1000, _requestStopCancellationToken.Token);
}
}
lastFrameTime = frame.RelativeTime;
}
}
}, _requestStopCancellationToken.Token);
}
catch (Microsoft.ProjectOxford.Face.FaceAPIException fex)
{
Debug.WriteLine(fex.ErrorMessage);
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
}
if (_requestStopCancellationToken.IsCancellationRequested)
{
await _mediaCapture.StopPreviewAsync();
_captureElement.Source = null;
_requestStopCancellationToken.Dispose();
}
}
void Start()
{
if (CoreXT.IsDevice) {
// subscribes to events
GUIXT.MediaPicked += OnMediaPicked;
_faceDetector = new FaceDetector(true, false);
_imageFilter = new ImageFilter();
}
}
private void ProcessIndividualImages(ImageReader reader)
{
// Make sure the GUI is setup appropriately
SetupFeatureExtractionMode();
// Indicate we will start running the thread
thread_running = true;
// Reload the face landmark detector if needed
ReloadLandmarkDetector();
if (!landmark_detector.isLoaded())
{
DetectorNotFoundWarning();
EndMode();
thread_running = false;
return;
}
// Setup the parameters optimized for working on individual images rather than sequences
face_model_params.optimiseForImages();
// Setup the visualization
Visualizer visualizer_of = new Visualizer(ShowTrackedVideo || RecordTracked, ShowAppearance, ShowAppearance, false);
// Initialize the face detector if it has not been initialized yet
if (face_detector == null)
{
face_detector = new FaceDetector(face_model_params.GetHaarLocation(), face_model_params.GetMTCNNLocation());
}
// Initialize the face analyser
face_analyser = new FaceAnalyserManaged(AppDomain.CurrentDomain.BaseDirectory, false, image_output_size, MaskAligned);
// Loading an image file
var frame = new RawImage(reader.GetNextImage());
var gray_frame = new RawImage(reader.GetCurrentFrameGray());
// For FPS tracking
DateTime?startTime = CurrentTime;
var lastFrameTime = CurrentTime;
// This will be false when the image is not available
while (reader.isOpened())
{
if (!thread_running)
{
break;
}
// Setup recording
RecorderOpenFaceParameters rec_params = new RecorderOpenFaceParameters(false, false,
Record2DLandmarks, Record3DLandmarks, RecordModelParameters, RecordPose, RecordAUs,
RecordGaze, RecordHOG, RecordTracked, RecordAligned, true,
reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy(), 0);
RecorderOpenFace recorder = new RecorderOpenFace(reader.GetName(), rec_params, record_root);
// Detect faces here and return bounding boxes
List <Rect> face_detections = new List <Rect>();
List <float> confidences = new List <float>();
if (DetectorHOG)
{
face_detector.DetectFacesHOG(face_detections, gray_frame, confidences);
}
else if (DetectorCNN)
{
face_detector.DetectFacesMTCNN(face_detections, frame, confidences);
}
else if (DetectorHaar)
{
face_detector.DetectFacesHaar(face_detections, gray_frame, confidences);
}
// For visualization
double progress = reader.GetProgress();
for (int i = 0; i < face_detections.Count; ++i)
{
bool detection_succeeding = landmark_detector.DetectFaceLandmarksInImage(frame, face_detections[i], face_model_params, gray_frame);
var landmarks = landmark_detector.CalculateAllLandmarks();
// Predict action units
var au_preds = face_analyser.PredictStaticAUsAndComputeFeatures(frame, landmarks);
// Predic eye gaze
gaze_analyser.AddNextFrame(landmark_detector, detection_succeeding, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
// Only the final face will contain the details
VisualizeFeatures(frame, visualizer_of, landmarks, landmark_detector.GetVisibilities(), detection_succeeding, i == 0, true, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy(), progress);
// Record an observation
RecordObservation(recorder, visualizer_of.GetVisImage(), i, detection_succeeding, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy(), 0, 0);
}
frame = new RawImage(reader.GetNextImage());
gray_frame = new RawImage(reader.GetCurrentFrameGray());
// Write out the tracked image
if (RecordTracked)
{
recorder.WriteObservationTracked();
}
// Do not cary state accross images
landmark_detector.Reset();
face_analyser.Reset();
recorder.Close();
lastFrameTime = CurrentTime;
processing_fps.AddFrame();
// TODO how to report errors from the reader here? exceptions? logging? Problem for future versions?
}
EndMode();
}
private async void OpenImg_Click(object sender, RoutedEventArgs e)
{
IList <DetectedFace> faces = null;
SoftwareBitmap detectorInput = null;
WriteableBitmap displaySource = null;
try
{
FileOpenPicker photoPicker = new FileOpenPicker();
photoPicker.ViewMode = PickerViewMode.Thumbnail;
photoPicker.SuggestedStartLocation = PickerLocationId.PicturesLibrary;
photoPicker.FileTypeFilter.Add(".jpg");
photoPicker.FileTypeFilter.Add(".jpeg");
photoPicker.FileTypeFilter.Add(".png");
photoPicker.FileTypeFilter.Add(".bmp");
photoFile = await photoPicker.PickSingleFileAsync();
if (photoFile == null)
{
return;
}
using (IRandomAccessStream fileStream = await photoFile.OpenAsync(Windows.Storage.FileAccessMode.Read))
{
BitmapImage bitmapImage = new BitmapImage();
bitmapImage.SetSource(fileStream);
sourceImg.Source = bitmapImage;
BitmapDecoder decoder = await BitmapDecoder.CreateAsync(fileStream);
BitmapTransform transform = this.ComputeScalingTransformForSourceImage(decoder);
using (SoftwareBitmap originalBitmap = await decoder.GetSoftwareBitmapAsync(decoder.BitmapPixelFormat, BitmapAlphaMode.Ignore, transform, ExifOrientationMode.IgnoreExifOrientation, ColorManagementMode.DoNotColorManage))
{
// face can detect Gray8 file
const BitmapPixelFormat InputPixelFormat = BitmapPixelFormat.Gray8;
if (FaceDetector.IsBitmapPixelFormatSupported(InputPixelFormat))
{
using (detectorInput = SoftwareBitmap.Convert(originalBitmap, InputPixelFormat))
{
// Create a WritableBitmap for our visualization display; copy the original bitmap pixels to wb's buffer.
displaySource = new WriteableBitmap(originalBitmap.PixelWidth, originalBitmap.PixelHeight);
originalBitmap.CopyToBuffer(displaySource.PixelBuffer);
FaceDetector detector = await FaceDetector.CreateAsync(); // should reuse the detect obj
faces = await detector.DetectFacesAsync(detectorInput);
// Create our display using the available image and face results.
this.SetupVisualization(displaySource, faces);
}
}
else
{
}
}
}
}
catch (Exception ex)
{
this.ClearVisualization();
}
}
/// <summary>
/// Responds when we navigate to this page.
/// </summary>
/// <param name="e">Event data</param>
protected override async void OnNavigatedTo(NavigationEventArgs e)
{
this.rootPage = MainPage.Current;
// The 'await' operation can only be used from within an async method but class constructors
// cannot be labeled as async, and so we'll initialize FaceDetector here.
if (this.faceDetector == null)
{
this.faceDetector = await FaceDetector.CreateAsync();
}
}
public FaceDetectionBenchmark()
{
faceDetector = new FaceDetector();
}
public LocalFaceDetector(FaceDetector faceDetector, IImagePersiter imagePersiter)
{
_faceDetector = faceDetector;
_imagePersiter = imagePersiter;
}
public async Task <ImageAnalyzer> CaptureFrameAsync(CurrentFrameModel currentFrame)
{
try
{
using (Stream stream = currentFrame.DataCurrent.AsBuffer().AsStream())
{
stream.Position = 0;
var decoder = await BitmapDecoder.CreateAsync(stream.AsRandomAccessStream());
var softwareBitmap = await decoder.GetSoftwareBitmapAsync();
var detector = await FaceDetector.CreateAsync();
using (SoftwareBitmap convertedBitmap = SoftwareBitmap.Convert(softwareBitmap, BitmapPixelFormat.Gray8))
{
faces = await detector.DetectFacesAsync(convertedBitmap, SearchArea);
convertedBitmap.Dispose();
}
this.NumFacesOnLastFrame = faces.Count();
var previewFrameSize = new Windows.Foundation.Size(softwareBitmap.PixelWidth, softwareBitmap.PixelHeight);
this.ShowFaceTrackingVisualization(previewFrameSize, faces);
softwareBitmap.Dispose();
stream.Dispose();
}
//Không có face thì không phân tích
if (this.NumFacesOnLastFrame == 0)
{
faces = null;
CoreUtil.FreeMemory();
return(null);
}
//Hai khung hình có số lượng khung mật giống nhau quá nửa thì không phân tích nữa
if (this.AreFacesStill(this.detectedFacesFromPreviousFrame, faces))
{
faces = null;
CoreUtil.FreeMemory();
return(null);
}
this.detectedFacesFromPreviousFrame = faces;
imageWithFace = new ImageAnalyzer(currentFrame.DataCurrent);
imageWithFace.CameraIPAdres = CameraIPAdres;
imageWithFace.imageWidth = InfoSettingFix.FixImageWidth;
imageWithFace.imageHeight = InfoSettingFix.FixImageHeight;
imageWithFace.CaptureTime = currentFrame.CaptureTime;
imageWithFace.ListDetectedFaceJson = JsonConvert.SerializeObject(faces.Select(r => r.FaceBox).ToList());
faces = null;
CoreUtil.FreeMemory();
return(imageWithFace);
}
catch (Exception ex)
{
CoreUtil.FreeMemory();
return(null);
}
}
/// <summary>
/// detect faces on a picture and draw a square in each face
/// </summary>
private void detectFaces()
{
//first check if picture has been taken
if (null != cameraBitmap)
{
//get width of a picture
int width = cameraBitmap.Width;
//get height of a picture
int height = cameraBitmap.Height;
//Initialize a facedetector with the picture dimensions and the max number of faces to check
FaceDetector detector = new FaceDetector(width, height, MainActivity.MAX_FACES);
//Create an array of faces with the number of max faces to check
Android.Media.FaceDetector.Face[] faces = new Android.Media.FaceDetector.Face[MainActivity.MAX_FACES];
//create a main bitmap
Bitmap bitmap565 = Bitmap.CreateBitmap(width, height, Bitmap.Config.Rgb565);
//create a dither paint
Paint ditherPaint = new Paint();
//create a draw paint
Paint drawPaint = new Paint();
//set true dither to dither paint variable
ditherPaint.Dither = true;
//set color red for the square
drawPaint.Color = Color.Red;
//set stroke to style
drawPaint.SetStyle(Paint.Style.Stroke);
//set stroke width
drawPaint.StrokeWidth = 2;
//Create a canvas
Canvas canvas = new Canvas();
//set bitmap to canvas
canvas.SetBitmap(bitmap565);
//draw bitmap to canvas
canvas.DrawBitmap(cameraBitmap, 0, 0, ditherPaint);
//get a number of faces detected
int facesFound = detector.FindFaces(bitmap565, faces);
//mid face point
PointF midPoint = new PointF();
//eye distance variable
float eyeDistance = 0.0f;
//confidence variable
float confidence = 0.0f;
//print numbre of faces found
System.Console.WriteLine("Number of faces found: " + facesFound);
//check if found at least one face
if (facesFound > 0)
{
//for each face draw a red squeare
for (int index = 0; index < facesFound; ++index)
{
// get midpoint of a face
faces[index].GetMidPoint(midPoint);
//get eye distance
eyeDistance = faces[index].EyesDistance();
//get confidence
confidence = faces [index].Confidence();
//print all parameters
System.Console.WriteLine("Confidence: " + confidence +
", Eye distance: " + eyeDistance +
", Mid Point: (" + midPoint.X + ", " + midPoint.Y + ")");
//draw a square in the picture
canvas.DrawRect((int)midPoint.X - eyeDistance,
(int)midPoint.Y - eyeDistance,
(int)midPoint.X + eyeDistance,
(int)midPoint.Y + eyeDistance, drawPaint);
}
}
//get imageview from layout
ImageView imageView = (ImageView)FindViewById(Resource.Id.image_view);
//set image with the red squares to imageview
imageView.SetImageBitmap(bitmap565);
}
}
public FaceDetectorController(FaceDetector faceDetector, FaceMaskDetector faceMaskDetector)
{
this.faceDetector = faceDetector;
this.faceMaskDetector = faceMaskDetector;
}
//Start the process
private async void button_Click(object sender, RoutedEventArgs e)
{
FolderPicker folderPicker = new FolderPicker();
folderPicker.FileTypeFilter.Add(".jpg");
folderPicker.FileTypeFilter.Add(".jpeg");
folderPicker.FileTypeFilter.Add(".png");
folderPicker.FileTypeFilter.Add(".bmp");
folderPicker.ViewMode = PickerViewMode.Thumbnail;
StorageFolder photoFolder = await folderPicker.PickSingleFolderAsync();
if (photoFolder == null)
{
return;
}
var files = await photoFolder.GetFilesAsync();
List <Scores> E = new List <Scores>();
int[] num = new int[files.Count];
for (int i = 0; i < files.Count; i++)
{
IRandomAccessStream fileStream = await files[i].OpenAsync(FileAccessMode.Read);
BitmapDecoder decoder = await BitmapDecoder.CreateAsync(fileStream);
BitmapTransform transform = new BitmapTransform();
const float sourceImageHeightLimit = 1280;
if (decoder.PixelHeight > sourceImageHeightLimit)
{
float scalingFactor = (float)sourceImageHeightLimit / (float)decoder.PixelHeight;
transform.ScaledWidth = (uint)Math.Floor(decoder.PixelWidth * scalingFactor);
transform.ScaledHeight = (uint)Math.Floor(decoder.PixelHeight * scalingFactor);
}
SoftwareBitmap sourceBitmap = await decoder.GetSoftwareBitmapAsync(decoder.BitmapPixelFormat, BitmapAlphaMode.Premultiplied, transform, ExifOrientationMode.IgnoreExifOrientation, ColorManagementMode.DoNotColorManage);
const BitmapPixelFormat faceDetectionPixelFormat = BitmapPixelFormat.Gray8;
SoftwareBitmap convertedBitmap;
if (sourceBitmap.BitmapPixelFormat != faceDetectionPixelFormat)
{
convertedBitmap = SoftwareBitmap.Convert(sourceBitmap, faceDetectionPixelFormat);
}
else
{
convertedBitmap = sourceBitmap;
}
if (faceDetector == null)
{
faceDetector = await FaceDetector.CreateAsync();
}
detectedFaces = await faceDetector.DetectFacesAsync(convertedBitmap);
Scores sc = null;
if (detectedFaces.Count > 0)
{
num[i] = detectedFaces.Count;
FaceRectangle rectID = new FaceRectangle();
rectID = await UploadAndDetectFaces(files[i].Path);
if (rectID != null)
{
sc = await EstimateEmotions(files[i].Path, rectID);
}
}
E.Add(sc);
if (sc != null)
{
Items.Add(new DataItem(i.ToString(), (int)(sc.Happiness * 100)));
}
sourceBitmap.Dispose();
fileStream.Dispose();
convertedBitmap.Dispose();
}
}