基于摄像机的人脸检测 [英] Face Detection with Camera
本文介绍了基于摄像机的人脸检测的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
如何才能像"摄像头"一样实时进行人脸检测？
我注意到AVCaptureStillImageOutput在10.0之后已弃用，因此我使用 改为AVCapturePhotoOutput。但是，我发现我为面部检测保存的图像并不是那么令人满意？有什么想法吗？
更新
在尝试了@Shravya Boggarapu之后提到。目前，我使用AVCaptureMetadataOutput检测人脸，没有使用CIFaceDetector。它按照预期工作。然而，当我试图画出脸部的边界时，它似乎放错了位置。有什么想法吗？
let metaDataOutput = AVCaptureMetadataOutput()
captureSession.sessionPreset = AVCaptureSessionPresetPhoto
let backCamera = AVCaptureDevice.defaultDevice(withDeviceType: .builtInWideAngleCamera, mediaType: AVMediaTypeVideo, position: .back)
do {
let input = try AVCaptureDeviceInput(device: backCamera)
if (captureSession.canAddInput(input)) {
captureSession.addInput(input)
// MetadataOutput instead
if(captureSession.canAddOutput(metaDataOutput)) {
captureSession.addOutput(metaDataOutput)
metaDataOutput.setMetadataObjectsDelegate(self, queue: DispatchQueue.main)
metaDataOutput.metadataObjectTypes = [AVMetadataObjectTypeFace]
previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
previewLayer?.frame = cameraView.bounds
previewLayer?.videoGravity = AVLayerVideoGravityResizeAspectFill
cameraView.layer.addSublayer(previewLayer!)
captureSession.startRunning()
}
}
} catch {
print(error.localizedDescription)
}
和
extension CameraViewController: AVCaptureMetadataOutputObjectsDelegate {
func captureOutput(_ captureOutput: AVCaptureOutput!, didOutputMetadataObjects metadataObjects: [Any]!, from connection: AVCaptureConnection!) {
if findFaceControl {
findFaceControl = false
for metadataObject in metadataObjects {
if (metadataObject as AnyObject).type == AVMetadataObjectTypeFace {
print("😇😍😎")
print(metadataObject)
let bounds = (metadataObject as! AVMetadataFaceObject).bounds
print("origin x: (bounds.origin.x)")
print("origin y: (bounds.origin.y)")
print("size width: (bounds.size.width)")
print("size height: (bounds.size.height)")
print("cameraView width: (self.cameraView.frame.width)")
print("cameraView height: (self.cameraView.frame.height)")
var face = CGRect()
face.origin.x = bounds.origin.x * self.cameraView.frame.width
face.origin.y = bounds.origin.y * self.cameraView.frame.height
face.size.width = bounds.size.width * self.cameraView.frame.width
face.size.height = bounds.size.height * self.cameraView.frame.height
print(face)
showBounds(at: face)
}
}
}
}
}
原始
var captureSession = AVCaptureSession()
var photoOutput = AVCapturePhotoOutput()
var previewLayer: AVCaptureVideoPreviewLayer?
override func viewWillAppear(_ animated: Bool) {
super.viewWillAppear(true)
captureSession.sessionPreset = AVCaptureSessionPresetHigh
let backCamera = AVCaptureDevice.defaultDevice(withMediaType: AVMediaTypeVideo)
do {
let input = try AVCaptureDeviceInput(device: backCamera)
if (captureSession.canAddInput(input)) {
captureSession.addInput(input)
if(captureSession.canAddOutput(photoOutput)){
captureSession.addOutput(photoOutput)
captureSession.startRunning()
previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
previewLayer?.videoGravity = AVLayerVideoGravityResizeAspectFill
previewLayer?.frame = cameraView.bounds
cameraView.layer.addSublayer(previewLayer!)
}
}
} catch {
print(error.localizedDescription)
}
}
func captureImage() {
let settings = AVCapturePhotoSettings()
let previewPixelType = settings.availablePreviewPhotoPixelFormatTypes.first!
let previewFormat = [kCVPixelBufferPixelFormatTypeKey as String: previewPixelType
]
settings.previewPhotoFormat = previewFormat
photoOutput.capturePhoto(with: settings, delegate: self)
}
func capture(_ captureOutput: AVCapturePhotoOutput, didFinishProcessingPhotoSampleBuffer photoSampleBuffer: CMSampleBuffer?, previewPhotoSampleBuffer: CMSampleBuffer?, resolvedSettings: AVCaptureResolvedPhotoSettings, bracketSettings: AVCaptureBracketedStillImageSettings?, error: Error?) {
if let error = error {
print(error.localizedDescription)
}
// Not include previewPhotoSampleBuffer
if let sampleBuffer = photoSampleBuffer,
let dataImage = AVCapturePhotoOutput.jpegPhotoDataRepresentation(forJPEGSampleBuffer: sampleBuffer, previewPhotoSampleBuffer: nil) {
self.imageView.image = UIImage(data: dataImage)
self.imageView.isHidden = false
self.previewLayer?.isHidden = true
self.findFace(img: self.imageView.image!)
}
}
findFace适用于普通图像。但是，我通过相机捕获的图像不起作用，或者有时只能识别一张脸。
正常图像
捕获图像
func findFace(img: UIImage) {
guard let faceImage = CIImage(image: img) else { return }
let accuracy = [CIDetectorAccuracy: CIDetectorAccuracyHigh]
let faceDetector = CIDetector(ofType: CIDetectorTypeFace, context: nil, options: accuracy)
// For converting the Core Image Coordinates to UIView Coordinates
let detectedImageSize = faceImage.extent.size
var transform = CGAffineTransform(scaleX: 1, y: -1)
transform = transform.translatedBy(x: 0, y: -detectedImageSize.height)
if let faces = faceDetector?.features(in: faceImage, options: [CIDetectorSmile: true, CIDetectorEyeBlink: true]) {
for face in faces as! [CIFaceFeature] {
// Apply the transform to convert the coordinates
var faceViewBounds = face.bounds.applying(transform)
// Calculate the actual position and size of the rectangle in the image view
let viewSize = imageView.bounds.size
let scale = min(viewSize.width / detectedImageSize.width,
viewSize.height / detectedImageSize.height)
let offsetX = (viewSize.width - detectedImageSize.width * scale) / 2
let offsetY = (viewSize.height - detectedImageSize.height * scale) / 2
faceViewBounds = faceViewBounds.applying(CGAffineTransform(scaleX: scale, y: scale))
print("faceBounds = (faceViewBounds)")
faceViewBounds.origin.x += offsetX
faceViewBounds.origin.y += offsetY
showBounds(at: faceViewBounds)
}
if faces.count != 0 {
print("Number of faces: (faces.count)")
} else {
print("No faces 😢")
}
}
}
func showBounds(at bounds: CGRect) {
let indicator = UIView(frame: bounds)
indicator.frame = bounds
indicator.layer.borderWidth = 3
indicator.layer.borderColor = UIColor.red.cgColor
indicator.backgroundColor = .clear
self.imageView.addSubview(indicator)
faceBoxes.append(indicator)
}
推荐答案
检测人脸有两种方式：CIFaceDetector和AVCaptureMetadataOutput。根据您的要求，选择与您相关的内容。
CIFaceDetector有更多功能，它可以为您提供眼睛和嘴巴的位置、微笑检测器等。
另一方面，AVCaptureMetadataOutput是在帧上计算的，检测到的人脸被跟踪，我们不需要添加额外的代码。我发现了这一点，因为追踪。在这个过程中，人脸检测更加可靠。这样做的缺点是，您只需检测人脸，而不能检测眼睛或嘴巴的位置。
此方法的另一个优点是方向问题较小，因为您可以在设备方向更改时使用videoOrientation，并且面的方向将相对于该方向。
在我的示例中，我的应用程序使用YUV420作为所需的格式，因此实时使用CIDetector(使用RGB)是不可行的。由于连续跟踪，使用AVCaptureMetadataOutput可以节省大量工作，并且执行起来更可靠。
获得面部的边界框后，我编码了额外的功能，如皮肤检测，并将其应用于静止图像。
注意：捕获静止图像时，面框信息将随元数据一起添加，因此不会出现同步问题。
您也可以将两者结合使用以获得更好的结果。
根据您的应用程序了解并评估利弊。
面矩形是WRT图像的原点。所以，对于屏幕来说，可能会有所不同。 使用：
for (AVMetadataFaceObject *faceFeatures in metadataObjects) {
CGRect face = faceFeatures.bounds;
CGRect facePreviewBounds = CGRectMake(face.origin.y * previewLayerRect.size.width,
face.origin.x * previewLayerRect.size.height,
face.size.width * previewLayerRect.size.height,
face.size.height * previewLayerRect.size.width);
/* Draw rectangle facePreviewBounds on screen */
}
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