android usb摄像头人脸识别代码

以下是一个简单的 Android USB 摄像头人脸识别的代码示例：

1. 添加依赖库

在你的 build.gradle 文件中添加以下依赖库：

implementation 'org.bytedeco:javacv-platform:1.5.3'
implementation 'org.bytedeco.javacpp-presets:opencv:4.5.1-1.5.3'

2. 打开 USB 摄像头

首先，你需要使用 Android 的 Camera2 API 打开 USB 摄像头。你可以使用以下代码来打开 USB 摄像头：

private CameraDevice mCameraDevice;
private HandlerThread mBackgroundThread;
private Handler mBackgroundHandler;
private String mCameraId = "1";
private ImageReader mImageReader;
private Size mPreviewSize;

private final CameraDevice.StateCallback mStateCallback = new CameraDevice.StateCallback() {
    @Override
    public void onOpened(@NonNull CameraDevice cameraDevice) {
        mCameraDevice = cameraDevice;
        startPreview();
    }

    @Override
    public void onDisconnected(@NonNull CameraDevice cameraDevice) {
        cameraDevice.close();
        mCameraDevice = null;
    }

    @Override
    public void onError(@NonNull CameraDevice cameraDevice, int error) {
        cameraDevice.close();
        mCameraDevice = null;
    }
};

private void openCamera() {
    CameraManager manager = (CameraManager) getSystemService(Context.CAMERA_SERVICE);
    try {
        CameraCharacteristics characteristics = manager.getCameraCharacteristics(mCameraId);
        StreamConfigurationMap map = characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
        mPreviewSize = map.getOutputSizes(SurfaceTexture.class)[0];

        if (ActivityCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {
            ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.CAMERA}, CAMERA_PERMISSION_REQUEST);
            return;
        }
        manager.openCamera(mCameraId, mStateCallback, mBackgroundHandler);
    } catch (CameraAccessException e) {
        e.printStackTrace();
    }
}

3. 实现人脸识别

一旦你打开了 USB 摄像头并开始预览，你就可以使用 OpenCV 库来实现人脸识别。以下是一个简单的示例代码：

private CascadeClassifier mFaceDetector;
private Mat mGray;
private Mat mRgba;

private void startPreview() {
    SurfaceTexture texture = mTextureView.getSurfaceTexture();
    assert texture != null;

    texture.setDefaultBufferSize(mPreviewSize.getWidth(), mPreviewSize.getHeight());
    Surface surface = new Surface(texture);

    try {
        mCaptureRequestBuilder = mCameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
        mCaptureRequestBuilder.addTarget(surface);

        mImageReader = ImageReader.newInstance(mPreviewSize.getWidth(), mPreviewSize.getHeight(), ImageFormat.YUV_420_888, 2);
        mImageReader.setOnImageAvailableListener(mOnImageAvailableListener, mBackgroundHandler);
        Surface readerSurface = mImageReader.getSurface();
        mCaptureRequestBuilder.addTarget(readerSurface);

        mCameraDevice.createCaptureSession(Arrays.asList(surface, readerSurface), mSessionStateCallback, mBackgroundHandler);
    } catch (CameraAccessException e) {
        e.printStackTrace();
    }

    mFaceDetector = new CascadeClassifier();
    mFaceDetector.load(getCascadeFile(R.raw.lbpcascade_frontalface));

    mGray = new Mat();
    mRgba = new Mat();
}

private File getCascadeFile(int id) {
    try {
        InputStream is = getResources().openRawResource(id);
        File cascadeDir = getDir("cascade", Context.MODE_PRIVATE);
        File cascadeFile = new File(cascadeDir, "cascade.xml");
        FileOutputStream os = new FileOutputStream(cascadeFile);

        byte[] buffer = new byte[4096];
        int bytesRead;
        while ((bytesRead = is.read(buffer)) != -1) {
            os.write(buffer, 0, bytesRead);
        }
        is.close();
        os.close();

        return cascadeFile;
    } catch (Exception e) {
        e.printStackTrace();
        return null;
    }
}

private final ImageReader.OnImageAvailableListener mOnImageAvailableListener = new ImageReader.OnImageAvailableListener() {
    @Override
    public void onImageAvailable(ImageReader reader) {
        Image image = reader.acquireLatestImage();

        if (image == null) {
            return;
        }

        Image.Plane[] planes = image.getPlanes();
        ByteBuffer buffer = planes[0].getBuffer();
        byte[] bytes = new byte[buffer.remaining()];
        buffer.get(bytes);

        Size size = new Size(mPreviewSize.getWidth(), mPreviewSize.getHeight());
        Mat yuv = new Mat(size, CvType.CV_8UC1);
        yuv.put(0, 0, bytes);

        Imgproc.cvtColor(yuv, mRgba, Imgproc.COLOR_YUV2RGBA_NV21);
        Imgproc.cvtColor(mRgba, mGray, Imgproc.COLOR_RGBA2GRAY);

        MatOfRect faces = new MatOfRect();
        mFaceDetector.detectMultiScale(mGray, faces, 1.3, 5, 0, new Size(30, 30), new Size());

        for (Rect rect : faces.toArray()) {
            Imgproc.rectangle(mRgba, rect.tl(), rect.br(), new Scalar(0, 255, 0, 255), 3);
        }

        image.close();
    }
};

这个示例代码使用了 OpenCV 库中的级联分类器来识别人脸。当你检测到人脸时，你可以在预览中显示一个矩形来标识出人脸的位置。

这只是一个简单的示例，你可以根据你的需求调整代码。