Question

我已经将MLkit FaceDetection集成到了我的android应用程序中。我在下面引用了网址

https://firebase.google.com/docs/ml-kit/android/detect-faces

人脸检测处理器类别的代码为

import java.io.IOException;
import java.util.List;

/** Face Detector Demo. */
public class FaceDetectionProcessor extends VisionProcessorBase<List<FirebaseVisionFace>> {

  private static final String TAG = "FaceDetectionProcessor";

  private final FirebaseVisionFaceDetector detector;

  public FaceDetectionProcessor() {

    FirebaseVisionFaceDetectorOptions options =
        new FirebaseVisionFaceDetectorOptions.Builder()
            .setClassificationType(FirebaseVisionFaceDetectorOptions.ALL_CLASSIFICATIONS)
            .setLandmarkType(FirebaseVisionFaceDetectorOptions.ALL_LANDMARKS)
            .setTrackingEnabled(true)
            .build();

    detector = FirebaseVision.getInstance().getVisionFaceDetector(options);
  }

  @Override
  public void stop() {
    try {
      detector.close();
    } catch (IOException e) {
      Log.e(TAG, "Exception thrown while trying to close Face Detector: " + e);
    }
  }

  @Override
  protected Task<List<FirebaseVisionFace>> detectInImage(FirebaseVisionImage image) {
    return detector.detectInImage(image);
  }

  @Override
  protected void onSuccess(
      @NonNull List<FirebaseVisionFace> faces,
      @NonNull FrameMetadata frameMetadata,
      @NonNull GraphicOverlay graphicOverlay) {
      graphicOverlay.clear();

    for (int i = 0; i < faces.size(); ++i) {
      FirebaseVisionFace face = faces.get(i);
      FaceGraphic faceGraphic = new FaceGraphic(graphicOverlay);
      graphicOverlay.add(faceGraphic);
      faceGraphic.updateFace(face, frameMetadata.getCameraFacing());
    }




  }

  @Override
  protected void onFailure(@NonNull Exception e) {
    Log.e(TAG, "Face detection failed " + e);
  }
}

在“ onSuccess”侦听器中，我们将获得“ FirebaseVisionFace”类对象的数组，这些对象将具有“边界框”的脸。

@Override
      protected void onSuccess(
          @NonNull List<FirebaseVisionFace> faces,
          @NonNull FrameMetadata frameMetadata,
          @NonNull GraphicOverlay graphicOverlay) {
          graphicOverlay.clear();

        for (int i = 0; i < faces.size(); ++i) {
          FirebaseVisionFace face = faces.get(i);
          FaceGraphic faceGraphic = new FaceGraphic(graphicOverlay);
          graphicOverlay.add(faceGraphic);
          faceGraphic.updateFace(face, frameMetadata.getCameraFacing());
        }
      }

我想知道如何将此FirebaseVisionFace对象转换为Bitmap。我想提取面部图像并将其显示在ImageView中。谁能帮帮我吗。预先感谢。

注意：我已经从下面的URL下载了MLKit android的示例源代码

https://github.com/firebase/quickstart-android/tree/master/mlkit

Answer 1

您从位图创建了FirebaseVisionImage。返回检测结果后，每个FirebaseVisionFace都将边界框描述为Rect，您可以使用该边界框从原始位图中提取检测到的面部，例如使用Bitmap.createBitmap()。

Answer 2

如果您尝试使用ML Kit来检测面部并使用OpenCV对检测到的面部进行图像处理，这可能对您有所帮助。请注意，在此特定示例中，您需要在onSuccess中使用原始相机位图。

我没有找到一种方法来完成此操作，而没有位图，但实际上仍在搜索。

@Override
protected void onSuccess(@NonNull List<FirebaseVisionFace> faces, @NonNull FrameMetadata frameMetadata, @NonNull GraphicOverlay graphicOverlay) {
  graphicOverlay.clear();

  for (int i = 0; i < faces.size(); ++i) {
    FirebaseVisionFace face = faces.get(i);

    /* Original implementation has original image. Original Image represents the camera preview from the live camera */

    // Create Mat representing the live camera itself
    Mat rgba = new Mat(originalCameraImage.getHeight(), originalCameraImage.getWidth(), CvType.CV_8UC4);

    // The box with a Imgproc affect made by OpenCV
    Mat rgbaInnerWindow;
    Mat mIntermediateMat = new Mat();

    // Make box for Imgproc the size of the detected face
    int rows = (int) face.getBoundingBox().height();
    int cols = (int) face.getBoundingBox().width();

    int left = cols / 8;
    int top = rows / 8;

    int width = cols * 3 / 4;
    int height = rows * 3 / 4;

    // Create a new bitmap based on live preview
    // which will show the actual image processing
    Bitmap newBitmap = Bitmap.createBitmap(originalCameraImage);

    // Bit map to Mat
    Utils.bitmapToMat(newBitmap, rgba);

    // Imgproc stuff. In this examply I'm doing edge detection.
    rgbaInnerWindow = rgba.submat(top, top + height, left, left + width);
    Imgproc.Canny(rgbaInnerWindow, mIntermediateMat, 80, 90);
    Imgproc.cvtColor(mIntermediateMat, rgbaInnerWindow, Imgproc.COLOR_GRAY2BGRA, 4);
    rgbaInnerWindow.release();

    // After processing image, back to bitmap
    Utils.matToBitmap(rgba, newBitmap);

    // Load the bitmap
    CameraImageGraphic imageGraphic = new CameraImageGraphic(graphicOverlay, newBitmap);
    graphicOverlay.add(imageGraphic);

    FaceGraphic faceGraphic;
    faceGraphic = new FaceGraphic(graphicOverlay, face, null);
    graphicOverlay.add(faceGraphic);


    FaceGraphic faceGraphic = new FaceGraphic(graphicOverlay);
    graphicOverlay.add(faceGraphic);


    // I can't speak for this
    faceGraphic.updateFace(face, frameMetadata.getCameraFacing());
  }

}

Answer 3

由于接受的答案不够具体，我将尝试解释我的所作所为。

1.-像这样在LivePreviewActivity上创建一个ImageView：

private ImageView imageViewTest;

2.-在Activity xml上创建它，并将其链接到java文件。我将其放在示例代码之前，因此可以在照相机源顶部看到它。

3.-在创建FaceDetectionProcessor时，传递imageView的实例以能够在对象内部设置源图像。

FaceDetectionProcessor processor = new FaceDetectionProcessor(imageViewTest);

4.-更改FaceDetectionProcessor的构造函数，使其能够接收ImageView作为参数，并创建一个保存该实例的全局变量。

public FaceDetectionProcessor(ImageView imageView) {
    FirebaseVisionFaceDetectorOptions options =
            new FirebaseVisionFaceDetectorOptions.Builder()
                    .setClassificationType(FirebaseVisionFaceDetectorOptions.ALL_CLASSIFICATIONS)
                    .setTrackingEnabled(true)
                    .build();

    detector = FirebaseVision.getInstance().getVisionFaceDetector(options);
    this.imageView  = imageView;
}

5.-我创建了一个裁剪方法，该方法采用位图和Rect来仅聚焦于面部。因此，继续执行相同的操作。

    public static Bitmap cropBitmap(Bitmap bitmap, Rect rect) {
    int w = rect.right - rect.left;
    int h = rect.bottom - rect.top;
    Bitmap ret = Bitmap.createBitmap(w, h, bitmap.getConfig());
    Canvas canvas = new Canvas(ret);
    canvas.drawBitmap(bitmap, -rect.left, -rect.top, null);
    return ret;
}

6.-修改detectInImage方法以保存要检测的位图实例并将其保存在全局变量中。

    @Override
protected Task<List<FirebaseVisionFace>> detectInImage(FirebaseVisionImage image) {
    imageBitmap = image.getBitmapForDebugging();
    return detector.detectInImage(image);
}

7.-最后，通过调用裁剪方法修改OnSuccess方法并将结果分配给imageView。

    @Override
protected void onSuccess(
        @NonNull List<FirebaseVisionFace> faces,
        @NonNull FrameMetadata frameMetadata,
        @NonNull GraphicOverlay graphicOverlay) {
    graphicOverlay.clear();
    for (int i = 0; i < faces.size(); ++i) {
        FirebaseVisionFace face = faces.get(i);
        FaceGraphic faceGraphic = new FaceGraphic(graphicOverlay);
        graphicOverlay.add(faceGraphic);
        faceGraphic.updateFace(face, frameMetadata.getCameraFacing());
        croppedImage = cropBitmap(imageBitmap, face.getBoundingBox());
    }
    imageView.setImageBitmap(croppedImage);
}

Answer 4

实际上，您可以只读取ByteBuffer，然后可以使用OutputStream获取要写入目标文件的数组。当然，您也可以从getBoundingBox()获得它。

MLKit Firebase android-如何将FirebaseVisionFace转换为图像对象（如位图）？

4 个答案: