1.首先需要导入TensorFlow Lite和OpenCV for Android库

2.在layout中创建一个SurfaceView元素来呈现检测结果。

3.使用OpenCV加载图像，并将其传递到TensorFlow Lite分类器进行物体检测。检测完成后，将检测结果绘制到SurfaceView上。

代码示例：

public class MainActivity extends AppCompatActivity implements CameraBridgeViewBase.CvCameraViewListener2 {

 private JavaCameraView javaCameraView;
 private Mat mRGBA;
 private Mat mHSV;
 private Mat mIntermediate;
 private Mat circles;
 private Mat hierarchy;
 private Scalar CONTOUR_COLOR;
 private int iLineThickness = 5;
 private int iMinTrackingBallSize = 30;
 private int iMaxTrackingBallSize = 100;

 private Bitmap mBitmap;

 private TensorFlowImageClassifier mTensorFlowClassifier;

 private static final String MODEL_FILE = "detect.tflite";
 private static final String LABEL_FILE = "file:///android_asset/labelmap.txt";

 @Override
 protected void onCreate(Bundle savedInstanceState) {
     super.onCreate(savedInstanceState);
     setContentView(R.layout.activity_main);

     javaCameraView = (JavaCameraView) findViewById(R.id.camView);
     javaCameraView.setVisibility(SurfaceView.VISIBLE);
     javaCameraView.setCvCameraViewListener(this);

     mTensorFlowClassifier = new TensorFlowImageClassifier();
     mTensorFlowClassifier.initializeTensorFlow(
             getAssets(),
             MODEL_FILE,
             LABEL_FILE,
             224,
             224,
             3,
             0
     );
 }

 @Override
 public void onCameraViewStarted(int width, int height) {
     mRGBA = new Mat();
     mHSV = new Mat();
     mIntermediate = new Mat();
     circles = new Mat();
     hierarchy = new Mat();
 }

 @Override
 public void onCameraViewStopped() {
     mRGBA.release();
     mIntermediate.release();
     mHSV.release();
     circles.release();
     hierarchy.release();
 }

 @Override
 public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
     mRGBA = inputFrame.rgba();
     Imgproc.cvtColor(mRGBA, mHSV,