private void Init()
{
_traking = LocatorFactory.GetTrackingLocator(AppId, FtKey) as FaceTracking;
_detection = LocatorFactory.GetDetectionLocator(AppId, FdKey) as FaceDetection;
_recognize = new FaceRecognize(AppId, FrKey);
_processor = new FaceProcessor(_traking, _recognize);
//init cache
if (Directory.Exists(FaceLibraryPath))
{
var files = Directory.GetFiles(FaceLibraryPath);
foreach (var file in files)
{
var info = new FileInfo(file);
_cache.Add(info.Name.Replace(info.Extension, ""), File.ReadAllBytes(file));
}
}
CvInvoke.UseOpenCL = false;
_capture = GetWebCamera();
_capture.Start();
//Application.Idle += VideoCaptured; //可以采用此方式捕获视频，则无需单独开线程
//_capture.ImageGrabbed += VideoCaptured; //不要采用此方式
_run = new Task(VideoCaptured);
_run.Start();
}
/// <summary>
/// 获取面部检测
/// </summary>
/// <param name="appId">应用ID</param>
/// <param name="sdkKey">应用Key</param>
/// <param name="useTracking">如果设置为True，则启用面部跟踪，如果False，则启用面部检测</param>
/// <param name="preAllocMemSize">缓存区内存大小（byte）</param>
/// <param name="orientPriority">期望的脸部检测角度范围</param>
/// <param name="scale">
/// 用于数值表示的最小人脸尺寸有效值范围[2,50] 推荐值 16（面部跟踪的取值范围为[2,16]）。该尺寸是人脸相对于所在图片的长边的占比。例如，如果用户想检测到的最小人脸尺寸是图片长度的
/// 1/8，那么这个 nScale 就应该设置为 8
/// </param>
/// <param name="maxFaceNumber">用户期望引擎最多能检测出的人脸数有效值范围[1,50]（面部跟踪的取值范围为[1,20]）</param>
/// <returns></returns>
public static FaceLocator GetLocator(string appId, string sdkKey, bool useTracking = true,
int preAllocMemSize = 41943040,
OrientPriority orientPriority = OrientPriority.OrientHigherExt,
int scale = 16, int maxFaceNumber = 10)
{
FaceLocator locator;
if (useTracking)
{
locator = new FaceTracking(appId, sdkKey, false, preAllocMemSize);
}
else
{
locator = new FaceDetection(appId, sdkKey, false, preAllocMemSize);
}
var code = locator.Initialize(orientPriority, scale, maxFaceNumber);
if (code != ErrorCode.Ok)
{
throw new FaceException(code);
}
return(locator);
}
// -----------------------------------------------------------------------
// KEY SAMPLE CODE ENDS HERE
// -----------------------------------------------------------------------
/// <summary>
/// This method parses the JSON output, and converts to a sequence of time frames with highlight regions. One highlight region reprensents a tracked face in the frame.
/// </summary>
/// <param name="json">JSON output of face tracking result.</param>
/// <returns>Sequence of time frames with highlight regions.</returns>
private static IEnumerable <FrameHighlight> GetHighlights(string json)
{
FaceTracking faceTrackingResult = Helpers.FromJson <FaceTracking>(json);
if (faceTrackingResult.FacesDetected == null)
{
yield break;
}
float timescale = (float)faceTrackingResult.Timescale;
Rect invisibleRect = new Rect(new Point(0, 0), new Size(0, 0)); // Uses this rectangle if a specific face is not showing in one frame
foreach (Fragment <FaceEvent> fragment in faceTrackingResult.Fragments)
{
FaceEvent[][] events = fragment.Events;
if (events == null || events.Length == 0)
{
// If 'Events' is empty, there isn't any face detected in this fragment
Rect[] rects = new Rect[faceTrackingResult.FacesDetected.Length];
for (int i = 0; i < rects.Length; i++)
{
rects[i] = invisibleRect;
}
yield return(new FrameHighlight()
{
Time = fragment.Start / timescale, HighlightRects = rects
});
}
else
{
long interval = fragment.Interval.GetValueOrDefault();
long start = fragment.Start;
int i = 0;
foreach (FaceEvent[] evt in events)
{
Rect[] rects = faceTrackingResult.FacesDetected.Select(face =>
{
FaceEvent faceRect = evt.FirstOrDefault(x => x.Id == face.FaceId);
if (faceRect == null)
{
return(invisibleRect);
}
// Creates highlight region at the location of the tracked face
return(new Rect(new Point(faceRect.X, faceRect.Y), new Size(faceRect.Width, faceRect.Height)));
}).ToArray();
yield return(new FrameHighlight()
{
Time = (start + interval * i) / timescale, HighlightRects = rects
});
i++;
}
}
}
}
void Start()
{
R_shoulder = Bip_R_UpperArm.GetComponentInParent <Transform>();
L_shoulder = Bip_L_UpperArm.GetComponentInParent <Transform>();
neck = Bip_C_Head.GetComponentInParent <Transform>();
body = GetComponent <BodyTracking>();
tracking = gameObject.GetComponent <FaceTrack.FaceTracking>();
}
private void Init()
{
_age = new FaceAge(AppId, AgeKey);
_gender = new FaceGender(AppId, GenderKey);
_traking = LocatorFactory.GetTrackingLocator(AppId, FtKey, _age, _gender) as FaceTracking;
_detection = LocatorFactory.GetDetectionLocator(AppId, FdKey) as FaceDetection;
_recognize = new FaceRecognize(AppId, FrKey);
_processor = new FaceProcessor(_traking, _recognize);
//init cache
if (Directory.Exists(FaceLibraryPath))
{
var files = Directory.GetFiles(FaceLibraryPath);
foreach (var file in files)
{
var info = new FileInfo(file);
_cache.Add(info.Name.Replace(info.Extension, ""), File.ReadAllBytes(file));
}
}
stride = width * pixelSize;
bufferSize = stride * height;
_pImage = Marshal.AllocHGlobal(bufferSize);
_image = new Bitmap(width, height, stride, PixelFormat.Format24bppRgb, _pImage);
var ffmpeg = new FFMpegConverter();
outputStream = new MemoryStream();
var setting =
new ConvertSettings
{
CustomOutputArgs = "-s 1920x1080", //根据业务需求-r参数可以调整，取决于摄像机的FPS
}; //-s 1920x1080 -q:v 2 -b:v 64k
//-an -r 15 -pix_fmt bgr24 -updatefirst 1
//task = ffmpeg.ConvertLiveMedia("rtsp://*****:*****@192.168.1.64:554/h264/ch1/main/av_stream", null,
// outputStream, Format.raw_video, setting);
/*
* USB摄像头捕获
* 通过ffmpeg可以捕获USB摄像，如下代码所示。
* 首先通过：ffmpeg -list_devices true -f dshow -i dummy命令，可以列出系统中存在的USB摄像设备（或通过控制面板的设备管理工具查看设备名称），例如在我电脑中叫USB2.0 PC CAMERA。
* 然后根据捕获的分辨率，修改视频图形信息，包括width和height，一般像素大小不用修改，如果要参考设备支持的分辨率，可以使用：
* ffmpeg -list_options true -f dshow -i video="USB2.0 PC CAMERA"命令
*/
task = ffmpeg.ConvertLiveMedia("video=Logitech HD Webcam C270", "dshow",
outputStream, Format.raw_video, setting);
task.OutputDataReceived += DataReceived;
task.Start();
_renderTask = new Task(Render);
_renderTask.Start();
}
public FaceDetectionService()
{
_age = new FaceAge(AppConfigurations.AppId, AppConfigurations.AgeKey); // 年龄识别
_gender = new FaceGender(AppConfigurations.AppId, AppConfigurations.GenderKey); // 性别识别
//// 图片检测人脸
_detection = LocatorFactory.GetDetectionLocator(AppConfigurations.AppId, AppConfigurations.FdKey, _age, _gender) as FaceDetection;
_traking = LocatorFactory.GetTrackingLocator(AppConfigurations.AppId, AppConfigurations.FtKey, _age, _gender) as FaceTracking;
_recognize = new FaceRecognize(AppConfigurations.AppId, AppConfigurations.FrKey);
_processor = new FaceProcessor(_detection, _recognize);
_personFaceRepository = new PersonFaceRepository();
}
/// <summary>
/// Constructs a new eyes object.
/// </summary>
/// <param name="visionPreview">A capture element that is placed on a canvas used for capturing what Robbie sees.</param>
/// <param name="previewCanvas">A canvas element used for rendering the image preview showing what Robbie sees.</param>
public Eyes(CaptureElement visionPreview, Canvas previewCanvas)
{
Camera.Instance.Initialize(visionPreview);
this.previewCanvas = previewCanvas;
faceTracking = new FaceTracking();
faceDetection = new FaceDetection();
computerVision = new ComputerVision();
identityInterpolation = new IdentityInterpolation();
visualization = new Vision();
panTilt = new PanTilt();
eyesDisplay = new EyesDisplay();
identityInterpolation.LargestFaceChanged += IdentityInterpolation_LargestFaceChanged;
// fire up the continuous tasks of processing video and controlling the servos
ThreadPoolTimer.CreatePeriodicTimer(ProcessCurrentVideoFrame_Delegate, TimeSpan.FromMilliseconds(125)); // 8 fps
ThreadPoolTimer.CreatePeriodicTimer(UpdatePanTiltPosition_Delegate, TimeSpan.FromMilliseconds(25)); // 40 fps
ThreadPoolTimer.CreatePeriodicTimer(Blink_Delegate, TimeSpan.FromMilliseconds(5000)); // 12 fpm
}
private void Init()
{
_traking = LocatorFactory.GetTrackingLocator(AppId, FtKey) as FaceTracking;
_detection = LocatorFactory.GetDetectionLocator(AppId, FdKey) as FaceDetection;
_recognize = new FaceRecognize(AppId, FrKey);
_processor = new FaceProcessor(_traking, _recognize);
//init cache
if (Directory.Exists(FaceLibraryPath))
{
var files = Directory.GetFiles(FaceLibraryPath);
foreach (var file in files)
{
var info = new FileInfo(file);
_cache.Add(info.Name.Replace(info.Extension, ""), File.ReadAllBytes(file));
}
}
_pImage = Marshal.AllocHGlobal(1920 * 1080 * 3);
_image = new Bitmap(1920, 1080, 1920 * 3, PixelFormat.Format24bppRgb, _pImage);
var ffmpeg = new FFMpegConverter();
outputStream = new MemoryStream();
var setting =
new ConvertSettings
{
CustomOutputArgs = "-an -r 15 -pix_fmt bgr24 -updatefirst 1" //根据业务需求-r参数可以调整，取决于摄像机的FPS
}; //-s 1920x1080 -q:v 2 -b:v 64k
task = ffmpeg.ConvertLiveMedia("rtsp://*****:*****@192.168.1.64:554/h264/ch1/main/av_stream", null,
outputStream, Format.raw_video, setting);
task.OutputDataReceived += DataReceived;
task.Start();
_renderTask = new Task(Render);
_renderTask.Start();
}
public SendFace2VMCProtocolToPerformer(FaceTracking tracker, uOscClient client)
{
cli = client;
face = tracker;
}
public SendBoth2VMCProtocolToPerformer(BodyTracking BodyTracker, FaceTracking faceTracker, uOscClient client)
{
body = BodyTracker;
face = faceTracker;
cli = client;
}
private void DoTracking()
{
var ft = new FaceTracking(this);
ft.SimplePipeline();
Invoke(new DoTrackingCompleted(() =>
{
foreach (CheckBox moduleCheckBox in m_modulesCheckBoxes)
{
moduleCheckBox.Enabled = true;
}
Start.Enabled = true;
Stop.Enabled = false;
MainMenu.Enabled = true;
Mirror.Enabled = true;
NumDetectionText.Enabled = true;
NumLandmarksText.Enabled = true;
NumPoseText.Enabled = true;
NumExpressionsText.Enabled = true;
RegisterUser.Enabled = false;
UnregisterUser.Enabled = false;
if (m_closing) Close();
}));
}
