Android人脸检测API-存储的视频文件 [英] Android Face Detection API - Stored video file

问题描述

我想使用Android Vision FaceDetector API对视频文件（例如，来自用户画廊的MP4）执行面部检测/跟踪。我可以看到许多有关使用 CameraSource 的示例>类，可在直接来自相机的流上执行面部跟踪（例如在android-vision github上），但在视频文件上没有。

I would like to perform face detection / tracking on a video file (e.g. an MP4 from the users gallery) using the Android Vision FaceDetector API. I can see many examples on using the CameraSource class to perform face tracking on the stream coming directly from the camera (e.g. on the android-vision github), but nothing on video files.

我尝试通过Android Studio查看 CameraSource 的源代码，但是它被混淆了，我看不到原始的线上。我认为使用相机和使用文件之间有许多共同点。大概我只是在 Surface 上播放视频文件，然后将其传递到管道。

I tried looking at the source code for CameraSource through Android Studio, but it is obfuscated, and I couldn't see the original online. I image there are many commonalities between using the camera and using a file. Presumably I just play the video file on a Surface, and then pass that to a pipeline.

我也可以看到 Frame.Builder 具有函数 setImageData 和 setTimestampMillis 。如果我能够以 ByteBuffer 的形式读取视频，该如何将其传递给 FaceDetector API？我猜这个问题很相似，但是没有答案。同样，将视频解码为 Bitmap 帧，然后将其传递给 setBitmap 。

Alternatively I can see that Frame.Builder has functions setImageData and setTimestampMillis. If I was able to read in the video as ByteBuffer, how would I pass that to the FaceDetector API? I guess this question is similar, but no answers. Similarly, decode the video into Bitmap frames and pass that to setBitmap.

理想情况下，我不想将视频呈现到屏幕上，并且处理应该以 FaceDetector API能够实现的速度进行。

Ideally I don't want to render the video to the screen, and the processing should happen as fast as the FaceDetector API is capable of.

推荐答案

或者，我可以看到Frame.Builder具有setImageData和setTimestampMillis函数。如果我能够以ByteBuffer的形式读取视频，该如何将其传递给FaceDetector API？

Alternatively I can see that Frame.Builder has functions setImageData and setTimestampMillis. If I was able to read in the video as ByteBuffer, how would I pass that to the FaceDetector API?

只需调用 SparseArray< Face> faces = detector.detect（frame）; 其中必须创建像这样的 detector ：

Simply call SparseArray<Face> faces = detector.detect(frame); where detector has to be created like this:

FaceDetector detector = new FaceDetector.Builder(context)
   .setProminentFaceOnly(true)
   .build();

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