在 iOS 上使用 ML Kit 偵測臉部

你可以使用 ML Kit 偵測圖片和影片中的臉孔。

立即試用

• 使用範例應用程式試試 請查看此 API 的使用範例。
• 自行嘗試透過 編寫程式碼 程式碼研究室。

事前準備

1. 在 Podfile 中加入下列 ML Kit Pod：

   pod 'GoogleMLKit/FaceDetection', '15.5.0'
   

2. 安裝或更新專案的 Pod 後，請使用 .xcworkspace。Xcode 12.4 以上版本支援 ML Kit。

輸入圖片規範

如要使用臉部辨識功能，圖片尺寸應至少設為 480x360 像素。 為了讓 ML Kit 準確偵測臉孔，輸入圖片必須包含臉孔 以充足的像素資料表示基本上， 至少需要 100x100 像素如要偵測 臉部輪廓線，則 ML Kit 需要較高的解析度輸入： 至少應為 200 x 200 像素。

如果您在即時應用程式中偵測到臉孔，您可能還需要 將輸入圖片的整體尺寸納入考量較小的圖片 加快處理速度，因此為了縮短延遲時間，擷取解析度較低的圖片 著重於上述準確率規定，並確保 拍攝主體的臉孔會盡量佔滿圖片。另請參閱 即時效能改善訣竅。

圖像對焦品質不佳也可能會影響準確度。如果沒有接受 結果，請要求使用者重新擷取圖片。

臉部與相機相對的方向也會影響臉部表情 ML Kit 偵測到的特徵詳情請見 臉部偵測概念。

1. 設定臉部偵測工具

例如，建構 FaceDetectorOptions 物件，如下所示：

// High-accuracy landmark detection and face classification
let options = FaceDetectorOptions()
options.performanceMode = .accurate
options.landmarkMode = .all
options.classificationMode = .all

// Real-time contour detection of multiple faces
// options.contourMode = .all

// High-accuracy landmark detection and face classification
MLKFaceDetectorOptions *options = [[MLKFaceDetectorOptions alloc] init];
options.performanceMode = MLKFaceDetectorPerformanceModeAccurate;
options.landmarkMode = MLKFaceDetectorLandmarkModeAll;
options.classificationMode = MLKFaceDetectorClassificationModeAll;

// Real-time contour detection of multiple faces
// options.contourMode = MLKFaceDetectorContourModeAll;

2. 準備輸入圖片

使用 UIImage 或VisionImage CMSampleBuffer。

如果您使用 UIImage，請按照下列步驟操作：

• 使用 UIImage 建立 VisionImage 物件。請務必指定正確的 .orientation。

  Swift

  let image = VisionImage(image: UIImage)
  visionImage.orientation = image.imageOrientation

  Objective-C

  MLKVisionImage *visionImage = [[MLKVisionImage alloc] initWithImage:image];
  visionImage.orientation = image.imageOrientation;

如果您使用 CMSampleBuffer，請按照下列步驟操作：

• 指定 CMSampleBuffer。

  如何取得圖片方向：

  Swift

  func imageOrientation(
    deviceOrientation: UIDeviceOrientation,
    cameraPosition: AVCaptureDevice.Position
  ) -> UIImage.Orientation {
    switch deviceOrientation {
    case .portrait:
      return cameraPosition == .front ? .leftMirrored : .right
    case .landscapeLeft:
      return cameraPosition == .front ? .downMirrored : .up
    case .portraitUpsideDown:
      return cameraPosition == .front ? .rightMirrored : .left
    case .landscapeRight:
      return cameraPosition == .front ? .upMirrored : .down
    case .faceDown, .faceUp, .unknown:
      return .up
    }
  }
        

  Objective-C

  - (UIImageOrientation)
    imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation
                           cameraPosition:(AVCaptureDevicePosition)cameraPosition {
    switch (deviceOrientation) {
      case UIDeviceOrientationPortrait:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationLeftMirrored
                                                              : UIImageOrientationRight;
  
      case UIDeviceOrientationLandscapeLeft:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationDownMirrored
                                                              : UIImageOrientationUp;
      case UIDeviceOrientationPortraitUpsideDown:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationRightMirrored
                                                              : UIImageOrientationLeft;
      case UIDeviceOrientationLandscapeRight:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationUpMirrored
                                                              : UIImageOrientationDown;
      case UIDeviceOrientationUnknown:
      case UIDeviceOrientationFaceUp:
      case UIDeviceOrientationFaceDown:
        return UIImageOrientationUp;
    }
  }
        

• 使用VisionImage CMSampleBuffer 物件和方向：

  Swift

  let image = VisionImage(buffer: sampleBuffer)
  image.orientation = imageOrientation(
    deviceOrientation: UIDevice.current.orientation,
    cameraPosition: cameraPosition)

  Objective-C

   MLKVisionImage *image = [[MLKVisionImage alloc] initWithBuffer:sampleBuffer];
   image.orientation =
     [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation
                                  cameraPosition:cameraPosition];

3. 取得 FaceDetector 執行個體

取得 FaceDetector 的執行個體：

let faceDetector = FaceDetector.faceDetector(options: options)
ViewController.swift

MLKFaceDetector *faceDetector = [MLKFaceDetector faceDetectorWithOptions:options];
      

4. 處理圖片

weak var weakSelf = self
faceDetector.process(visionImage) { faces, error in
  guard let strongSelf = weakSelf else {
    print("Self is nil!")
    return
  }
  guard error == nil, let faces = faces, !faces.isEmpty else {
    // ...
    return
  }

  // Faces detected
  // ...
}
ViewController.swift

[faceDetector processImage:image
                completion:^(NSArray<MLKFace *> *faces,
                             NSError *error) {
  if (error != nil) {
    return;
  }
  if (faces.count > 0) {
    // Recognized faces
  }
}];

5. 取得系統偵測到的臉孔資訊

for face in faces {
  let frame = face.frame
  if face.hasHeadEulerAngleX {
    let rotX = face.headEulerAngleX  // Head is rotated to the uptoward rotX degrees
  }
  if face.hasHeadEulerAngleY {
    let rotY = face.headEulerAngleY  // Head is rotated to the right rotY degrees
  }
  if face.hasHeadEulerAngleZ {
    let rotZ = face.headEulerAngleZ  // Head is tilted sideways rotZ degrees
  }

  // If landmark detection was enabled (mouth, ears, eyes, cheeks, and
  // nose available):
  if let leftEye = face.landmark(ofType: .leftEye) {
    let leftEyePosition = leftEye.position
  }

  // If contour detection was enabled:
  if let leftEyeContour = face.contour(ofType: .leftEye) {
    let leftEyePoints = leftEyeContour.points
  }
  if let upperLipBottomContour = face.contour(ofType: .upperLipBottom) {
    let upperLipBottomPoints = upperLipBottomContour.points
  }

  // If classification was enabled:
  if face.hasSmilingProbability {
    let smileProb = face.smilingProbability
  }
  if face.hasRightEyeOpenProbability {
    let rightEyeOpenProb = face.rightEyeOpenProbability
  }

  // If face tracking was enabled:
  if face.hasTrackingID {
    let trackingId = face.trackingID
  }
}

for (MLKFace *face in faces) {
  // Boundaries of face in image
  CGRect frame = face.frame;
  if (face.hasHeadEulerAngleX) {
    CGFloat rotX = face.headEulerAngleX;  // Head is rotated to the upward rotX degrees
  }
  if (face.hasHeadEulerAngleY) {
    CGFloat rotY = face.headEulerAngleY;  // Head is rotated to the right rotY degrees
  }
  if (face.hasHeadEulerAngleZ) {
    CGFloat rotZ = face.headEulerAngleZ;  // Head is tilted sideways rotZ degrees
  }

  // If landmark detection was enabled (mouth, ears, eyes, cheeks, and
  // nose available):
  MLKFaceLandmark *leftEar = [face landmarkOfType:FIRFaceLandmarkTypeLeftEar];
  if (leftEar != nil) {
    MLKVisionPoint *leftEarPosition = leftEar.position;
  }

  // If contour detection was enabled:
  MLKFaceContour *upperLipBottomContour = [face contourOfType:FIRFaceContourTypeUpperLipBottom];
  if (upperLipBottomContour != nil) {
    NSArray<MLKVisionPoint *> *upperLipBottomPoints = upperLipBottomContour.points;
    if (upperLipBottomPoints.count > 0) {
      NSLog("Detected the bottom contour of the subject's upper lip.")
    }
  }

  // If classification was enabled:
  if (face.hasSmilingProbability) {
    CGFloat smileProb = face.smilingProbability;
  }
  if (face.hasRightEyeOpenProbability) {
    CGFloat rightEyeOpenProb = face.rightEyeOpenProbability;
  }

  // If face tracking was enabled:
  if (face.hasTrackingID) {
    NSInteger trackingID = face.trackingID;
  }
}

臉部輪廓範例

啟用臉部輪廓偵測功能後，畫面上會列出 偵測到的臉部特徵這些點代表 而不是每個特徵的分數查看臉孔 偵測概念，進一步瞭解輪廓係數 。

下圖說明這些點如何對應到某個臉孔， 放大圖片：