This small ios sample application uses Google's MLKit to analyze your face and put some nice hearts when you are smiling by using Machine Learning algorithms.
To fetch images from the camera it uses OpenTok library
You will need to create a Firebase project, download your GoogleService-info.plist file containing your project credentials and replace the empty placeholder file which lives in SmileDetector folder.
When you have your firebase dep solved, please run:
$ pod install
$ open SmileDetector.xcworkspaceWhen Xcode opens the project, click on run and see the cute hearts raining when you smile at the camera.
In order to get camera frames from the device when using OpenTok, you need to build a CustomCapturer, in this sample that is achieved by ExampleVideoCapture class.
That capturer will call its delegate whenever it has a frame from the camera passing the CVPixelBuffer content of the frame.
For MLKit to recognize the image, we need to convert that CVPixelBuffer to a UIImage, we do that with this code:
extension UIImage {
convenience init(pixelBuffer: CVPixelBuffer, withRotation rotation: OTVideoOrientation) {
let ciImage = CIImage(cvPixelBuffer: pixelBuffer)
let width = CGFloat(CVPixelBufferGetWidth(pixelBuffer))
let height = CGFloat(CVPixelBufferGetHeight(pixelBuffer))
let imgRotation: Double = {
switch (rotation)
{
case .up: return 0
case .right: return .pi / 2
case .down: return .pi
case .left: return -.pi / 2
}
}()
var tx = CGAffineTransform(translationX: width/2.0, y: height/2.0)
tx = tx.rotated(by: CGFloat(imgRotation))
tx = tx.translatedBy(x: -width/2, y: -height/2)
let transformed = ciImage.transformed(by: tx)
let context = CIContext(options: nil)
let cgImage = context.createCGImage(transformed, from: CGRect(x: 0, y: 0, width: width, height: height))
self.init(cgImage: cgImage!, scale: 1.0, orientation: .up)
}
}It creates the UIImage rotating the Pixel buffer accordingly to the device orientation
Once we have a UIImage, we feed MLKit with it by using:
let img = UIImage(pixelBuffer: frame, withRotation: orientation)
if let detector = self.detector {
let visionImage = VisionImage(image: img)
detector.detect(in: visionImage) { (faces, err) in
...
}
}We will have in faces variable the outcome of the detection.
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