public void EmptyOptions()
{
CIDetectorOptions options = new CIDetectorOptions();
using (var dtor = CIDetector.CreateFaceDetector(null, options)) {
Assert.That(dtor.Description, Is.StringContaining("CIFaceCoreDetector").Or.StringContaining("FaceDetector"), "Description");
}
}
public override void ViewDidLoad()
{
base.ViewDidLoad();
this.Title = "IISS";
faceDetector = CIDetector.CreateFaceDetector(CIContext.FromOptions(null), false);
borderImage = UIImage.FromFile("square.png");
UIDevice.CurrentDevice.BeginGeneratingDeviceOrientationNotifications();
// Perform any additional setup after loading the view, typically from a nib.
}
/// <summary>
/// Creates an instance of FaceDetector. Once created, the two arguments cannot be changed for this instance.
/// If using high accuracy, the preprocessImageScale property is set to 1.0, otherwise defaults to 0.125.
/// </summary>
/// <param name="useHighAccuracy">Thether to use high accuracy for detection.</param>
/// <param name="trackFaces">Whether to track faces in live video or successive image detections; available ONLY in iOS 6.0 and later.</param>
public FaceDetector(bool useHighAccuracy = false, bool trackFaces = false)
{
// setup face detector
var context = CIContext.Context((Dictionary<object, object>)null);
var opts = new Dictionary<object, object>();
opts[CIDetector.Accuracy] = useHighAccuracy ? CIDetector.AccuracyHigh : CIDetector.AccuracyLow;
opts[CIDetector.Tracking] = trackFaces ? 1 : 0;
_detector = CIDetector.DetectorOfType(CIDetector.TypeFace, context, opts);
if (useHighAccuracy)
PreprocessImageScale = 1.0f;
}
public void NullContext()
{
using (var dtor = CIDetector.CreateFaceDetector(null, true)) {
}
using (var dtor = CIDetector.CreateFaceDetector(null, false)) {
}
using (var dtor = CIDetector.CreateFaceDetector(null, false, 2.0f)) {
}
using (var dtor = CIDetector.CreateFaceDetector(null, null, null, null)) {
}
}
public override void ViewDidLoad()
{
base.ViewDidLoad();
this.Title = "Intelligent Kiosk";
SetupSource();
var options = new CIContextOptions();
faceDetector = CIDetector.CreateFaceDetector(null, true);
borderImage = UIImage.FromFile("square.png");
UIDevice.CurrentDevice.BeginGeneratingDeviceOrientationNotifications();
}
/// <summary>
/// Creates an instance of FaceDetector. Once created, the two arguments cannot be changed for this instance.
/// If using high accuracy, the preprocessImageScale property is set to 1.0, otherwise defaults to 0.125.
/// </summary>
/// <param name="useHighAccuracy">Thether to use high accuracy for detection.</param>
/// <param name="trackFaces">Whether to track faces in live video or successive image detections; available ONLY in iOS 6.0 and later.</param>
public FaceDetector(bool useHighAccuracy = false, bool trackFaces = false)
{
// setup face detector
var context = CIContext.Context((Dictionary <object, object>)null);
var opts = new Dictionary <object, object>();
opts[CIDetector.Accuracy] = useHighAccuracy ? CIDetector.AccuracyHigh : CIDetector.AccuracyLow;
opts[CIDetector.Tracking] = trackFaces ? 1 : 0;
_detector = CIDetector.DetectorOfType(CIDetector.TypeFace, context, opts);
if (useHighAccuracy)
{
preprocessImageScale = 1.0f;
}
}
public void NullContext()
{
using (var dtor = CIDetector.CreateFaceDetector(null, true)) {
}
using (var dtor = CIDetector.CreateFaceDetector(null, false)) {
}
if (TestRuntime.CheckSystemAndSDKVersion(6, 0))
{
using (var dtor = CIDetector.CreateFaceDetector(null, false, 2.0f)) {
}
}
using (var dtor = CIDetector.CreateFaceDetector(null, null, null, null)) {
}
}
public PersonObserver(CameraService cameraService)
{
_cameraService = cameraService;
detector = CIDetector.CreateFaceDetector(null, true);
client = new FaceAccessClient();
speechService = new SpeechService();
timer = new Task(async() =>
{
while (true)
{
await Task.Delay(500);
var result = await CheckCurrentFrame();
if (result)
{
await Task.Delay(2000);
}
}
});
}
public override void ViewDidLoad()
{
base.ViewDidLoad();
this.View.BackgroundColor = UIColor.White;
NSError error;
// Setup detector options.
var options = new CIDetectorOptions {
Accuracy = FaceDetectorAccuracy.High,
// Can give a hint here about the rects to detect. 1.4 would be for A4 sheets of paper for instance.
AspectRatio = 1.41f,
};
// Create a rectangle detector. Note that you can also create QR detector or a face detector.
// Most of this code will also work with other detectors (like streaming to a preview layer and grabbing images).
this.detector = CIDetector.CreateRectangleDetector(context: null, detectorOptions: options);
// Create the session. The AVCaptureSession is the managing instance of the whole video handling.
var captureSession = new AVCaptureSession()
{
// Defines what quality we want to use for the images we grab. Photo gives highest resolutions.
SessionPreset = AVCaptureSession.PresetPhoto
};
// Find a suitable AVCaptureDevice for video input.
var device = AVCaptureDevice.DefaultDeviceWithMediaType(AVMediaType.Video);
if (device == null)
{
// This will not work on the iOS Simulator - there is no camera. :-)
throw new InvalidProgramException("Failed to get AVCaptureDevice for video input!");
}
// Create a device input with the device and add it to the session.
var videoInput = AVCaptureDeviceInput.FromDevice(device, out error);
if (videoInput == null)
{
throw new InvalidProgramException("Failed to get AVCaptureDeviceInput from AVCaptureDevice!");
}
// Let session read from the input, this is our source.
captureSession.AddInput(videoInput);
// Create output for the video stream. This is the destination.
var videoOutput = new AVCaptureVideoDataOutput()
{
AlwaysDiscardsLateVideoFrames = true
};
// Define the video format we want to use. Note that Xamarin exposes the CompressedVideoSetting and UncompressedVideoSetting
// properties on AVCaptureVideoDataOutput un Unified API, but I could not get these to work. The VideoSettings property is deprecated,
// so I use the WeakVideoSettings instead which takes an NSDictionary as input.
this.videoSettingsDict = new NSMutableDictionary();
this.videoSettingsDict.Add(CVPixelBuffer.PixelFormatTypeKey, NSNumber.FromUInt32((uint)CVPixelFormatType.CV32BGRA));
videoOutput.WeakVideoSettings = this.videoSettingsDict;
// Create a delegate to report back to us when an image has been captured.
// We want to grab the camera stream and feed it through a AVCaptureVideoDataOutputSampleBufferDelegate
// which allows us to get notified if a new image is availeble. An implementation of that delegate is VideoFrameSampleDelegate in this project.
this.sampleBufferDelegate = new VideoFrameSamplerDelegate();
// Processing happens via Grand Central Dispatch (GCD), so we need to provide a queue.
// This is pretty much like a system managed thread (see: http://zeroheroblog.com/ios/concurrency-in-ios-grand-central-dispatch-gcd-dispatch-queues).
this.sessionQueue = new DispatchQueue("AVSessionQueue");
// Assign the queue and the delegate to the output. Now all output will go through the delegate.
videoOutput.SetSampleBufferDelegate(this.sampleBufferDelegate, this.sessionQueue);
// Add output to session.
captureSession.AddOutput(videoOutput);
// We also want to visualize the input stream. The raw stream can be fed into an AVCaptureVideoPreviewLayer, which is a subclass of CALayer.
// A CALayer can be added to a UIView. We add that layer to the controller's main view.
var layer = this.View.Layer;
this.videoLayer = AVCaptureVideoPreviewLayer.FromSession(captureSession);
this.videoLayer.Frame = layer.Bounds;
layer.AddSublayer(this.videoLayer);
// All setup! Start capturing!
captureSession.StartRunning();
// This is just for information and allows you to get valid values for the detection framerate.
Console.WriteLine("Available capture framerates:");
var rateRanges = device.ActiveFormat.VideoSupportedFrameRateRanges;
foreach (var r in rateRanges)
{
Console.WriteLine(r.MinFrameRate + "; " + r.MaxFrameRate + "; " + r.MinFrameDuration + "; " + r.MaxFrameDuration);
}
// Configure framerate. Kind of weird way of doing it but the only one that works.
device.LockForConfiguration(out error);
// CMTime constructor means: 1 = one second, DETECTION_FPS = how many samples per unit, which is 1 second in this case.
device.ActiveVideoMinFrameDuration = new CMTime(1, DETECTION_FPS);
device.ActiveVideoMaxFrameDuration = new CMTime(1, DETECTION_FPS);
device.UnlockForConfiguration();
// Put a small image view at the top left that shows the live image with the detected rectangle(s).
this.imageViewOverlay = new UIImageView
{
ContentMode = UIViewContentMode.ScaleAspectFit,
BackgroundColor = UIColor.Gray
};
this.imageViewOverlay.Layer.BorderColor = UIColor.Red.CGColor;
this.imageViewOverlay.Layer.BorderWidth = 3f;
this.Add(this.imageViewOverlay);
// Put another image view top right that shows the image with perspective correction.
this.imageViewPerspective = new UIImageView
{
ContentMode = UIViewContentMode.ScaleAspectFit,
BackgroundColor = UIColor.Gray
};
this.imageViewPerspective.Layer.BorderColor = UIColor.Red.CGColor;
this.imageViewPerspective.Layer.BorderWidth = 3f;
this.Add(this.imageViewPerspective);
// Add some lables for information.
this.mainWindowLbl = new UILabel
{
Text = "Live stream from camera. Point camera to a rectangular object.",
TextAlignment = UITextAlignment.Center
};
this.Add(this.mainWindowLbl);
this.detectionWindowLbl = new UILabel
{
Text = "Detected rectangle overlay",
TextAlignment = UITextAlignment.Center
};
this.Add(this.detectionWindowLbl);
this.perspectiveWindowLbl = new UILabel
{
Text = "Perspective corrected",
TextAlignment = UITextAlignment.Center
};
this.Add(this.perspectiveWindowLbl);
}
public void UseDetector()
{
var options = new CIDetectorOptions {
Accuracy = FaceDetectorAccuracy.High,
AspectRatio = 1.41f
};
detector = CIDetector.CreateRectangleDetector (context: null, detectorOptions: options);
using (CIImage ciImage = new CIImage (_parent.ImageView.Image))
{
InvokeOnMainThread (() =>
{
using (var dict = new NSMutableDictionary ())
{
var orient = GetExifOrientation (_parent.ImageView.Image);
var rectangles = detector.FeaturesInImage (ciImage, orient);
if (rectangles.Length > 0)
{
_currRect = (CIRectangleFeature)rectangles [0];
_markers [0].Location = ConvertImageToScreenCoords (_currRect.TopLeft);
_markers [1].Location = ConvertImageToScreenCoords (_currRect.TopRight);
_markers [2].Location = ConvertImageToScreenCoords (_currRect.BottomRight);
_markers [3].Location = ConvertImageToScreenCoords (_currRect.BottomLeft);
}
}
});
}
}
public override void ViewDidLoad ()
{
base.ViewDidLoad ();
this.View.BackgroundColor = UIColor.White;
NSError error;
// Setup detector options.
var options = new CIDetectorOptions {
Accuracy = FaceDetectorAccuracy.High,
// Can give a hint here about the rects to detect. 1.4 would be for A4 sheets of paper for instance.
AspectRatio = 1.41f,
};
// Create a rectangle detector. Note that you can also create QR detector or a face detector.
// Most of this code will also work with other detectors (like streaming to a preview layer and grabbing images).
this.detector = CIDetector.CreateRectangleDetector (context: null, detectorOptions: options);
// Create the session. The AVCaptureSession is the managing instance of the whole video handling.
var captureSession = new AVCaptureSession ()
{
// Defines what quality we want to use for the images we grab. Photo gives highest resolutions.
SessionPreset = AVCaptureSession.PresetPhoto
};
// Find a suitable AVCaptureDevice for video input.
var device = AVCaptureDevice.DefaultDeviceWithMediaType(AVMediaType.Video);
if (device == null)
{
// This will not work on the iOS Simulator - there is no camera. :-)
throw new InvalidProgramException ("Failed to get AVCaptureDevice for video input!");
}
// Create a device input with the device and add it to the session.
var videoInput = AVCaptureDeviceInput.FromDevice (device, out error);
if (videoInput == null)
{
throw new InvalidProgramException ("Failed to get AVCaptureDeviceInput from AVCaptureDevice!");
}
// Let session read from the input, this is our source.
captureSession.AddInput (videoInput);
// Create output for the video stream. This is the destination.
var videoOutput = new AVCaptureVideoDataOutput () {
AlwaysDiscardsLateVideoFrames = true
};
// Define the video format we want to use. Note that Xamarin exposes the CompressedVideoSetting and UncompressedVideoSetting
// properties on AVCaptureVideoDataOutput un Unified API, but I could not get these to work. The VideoSettings property is deprecated,
// so I use the WeakVideoSettings instead which takes an NSDictionary as input.
this.videoSettingsDict = new NSMutableDictionary ();
this.videoSettingsDict.Add (CVPixelBuffer.PixelFormatTypeKey, NSNumber.FromUInt32((uint)CVPixelFormatType.CV32BGRA));
videoOutput.WeakVideoSettings = this.videoSettingsDict;
// Create a delegate to report back to us when an image has been captured.
// We want to grab the camera stream and feed it through a AVCaptureVideoDataOutputSampleBufferDelegate
// which allows us to get notified if a new image is availeble. An implementation of that delegate is VideoFrameSampleDelegate in this project.
this.sampleBufferDelegate = new VideoFrameSamplerDelegate ();
// Processing happens via Grand Central Dispatch (GCD), so we need to provide a queue.
// This is pretty much like a system managed thread (see: http://zeroheroblog.com/ios/concurrency-in-ios-grand-central-dispatch-gcd-dispatch-queues).
this.sessionQueue = new DispatchQueue ("AVSessionQueue");
// Assign the queue and the delegate to the output. Now all output will go through the delegate.
videoOutput.SetSampleBufferDelegate(this.sampleBufferDelegate, this.sessionQueue);
// Add output to session.
captureSession.AddOutput(videoOutput);
// We also want to visualize the input stream. The raw stream can be fed into an AVCaptureVideoPreviewLayer, which is a subclass of CALayer.
// A CALayer can be added to a UIView. We add that layer to the controller's main view.
var layer = this.View.Layer;
this.videoLayer = AVCaptureVideoPreviewLayer.FromSession (captureSession);
this.videoLayer.Frame = layer.Bounds;
layer.AddSublayer (this.videoLayer);
// All setup! Start capturing!
captureSession.StartRunning ();
// This is just for information and allows you to get valid values for the detection framerate.
Console.WriteLine ("Available capture framerates:");
var rateRanges = device.ActiveFormat.VideoSupportedFrameRateRanges;
foreach (var r in rateRanges)
{
Console.WriteLine (r.MinFrameRate + "; " + r.MaxFrameRate + "; " + r.MinFrameDuration + "; " + r.MaxFrameDuration);
}
// Configure framerate. Kind of weird way of doing it but the only one that works.
device.LockForConfiguration (out error);
// CMTime constructor means: 1 = one second, DETECTION_FPS = how many samples per unit, which is 1 second in this case.
device.ActiveVideoMinFrameDuration = new CMTime(1, DETECTION_FPS);
device.ActiveVideoMaxFrameDuration = new CMTime(1, DETECTION_FPS);
device.UnlockForConfiguration ();
// Put a small image view at the top left that shows the live image with the detected rectangle(s).
this.imageViewOverlay = new UIImageView
{
ContentMode = UIViewContentMode.ScaleAspectFit,
BackgroundColor = UIColor.Gray
};
this.imageViewOverlay.Layer.BorderColor = UIColor.Red.CGColor;
this.imageViewOverlay.Layer.BorderWidth = 3f;
this.Add (this.imageViewOverlay);
// Put another image view top right that shows the image with perspective correction.
this.imageViewPerspective = new UIImageView
{
ContentMode = UIViewContentMode.ScaleAspectFit,
BackgroundColor = UIColor.Gray
};
this.imageViewPerspective.Layer.BorderColor = UIColor.Red.CGColor;
this.imageViewPerspective.Layer.BorderWidth = 3f;
this.Add (this.imageViewPerspective);
// Add some lables for information.
this.mainWindowLbl = new UILabel
{
Text = "Live stream from camera. Point camera to a rectangular object.",
TextAlignment = UITextAlignment.Center
};
this.Add (this.mainWindowLbl);
this.detectionWindowLbl = new UILabel
{
Text = "Detected rectangle overlay",
TextAlignment = UITextAlignment.Center
};
this.Add (this.detectionWindowLbl);
this.perspectiveWindowLbl = new UILabel
{
Text = "Perspective corrected",
TextAlignment = UITextAlignment.Center
};
this.Add (this.perspectiveWindowLbl);
}
