安卓端实现实时图像识别源代码

安卓实时图像识别可以使用TensorFlow Lite和OpenCV库来实现。以下是一个简单的示例代码，用于从相机中捕获图像并将其传递给TensorFlow Lite模型进行分类。

首先，需要在Android Studio中导入TensorFlow Lite和OpenCV库。在app/build.gradle文件中添加以下依赖项：

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly'
    implementation 'org.opencv:opencv-android:3.4.3'
}

然后，在MainActivity.java文件中添加以下代码：

import android.Manifest;
import android.content.pm.PackageManager;
import android.graphics.Bitmap;
import android.os.Bundle;
import android.util.Log;
import android.view.SurfaceView;
import android.view.WindowManager;

import androidx.annotation.NonNull;
import androidx.appcompat.app.AppCompatActivity;
import androidx.core.app.ActivityCompat;
import androidx.core.content.ContextCompat;

import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;
import org.tensorflow.lite.Interpreter;
import org.tensorflow.lite.support.image.TensorImage;
import org.tensorflow.lite.support.label.TensorLabel;

import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.util.List;
import java.util.Map;

public class MainActivity extends AppCompatActivity implements CameraBridgeViewBase.CvCameraViewListener2 {

    private static final String TAG = "MainActivity";
    private static final int CAMERA_PERMISSION_REQUEST_CODE = 1000;
    private static final int CAMERA_WIDTH = 640;
    private static final int CAMERA_HEIGHT = 480;
    private static final int MODEL_INPUT_SIZE = 224;
    private static final String MODEL_FILE_NAME = "model.tflite";
    private static final String LABELS_FILE_NAME = "labels.txt";

    private CameraBridgeViewBase mCameraView;
    private Mat mRgba;
    private Mat mGray;
    private Interpreter mInterpreter;
    private List<String> mLabels;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override
        public void onManagerConnected(int status) {
            switch (status) {
                case LoaderCallbackInterface.SUCCESS:
                    Log.i(TAG, "OpenCV loaded successfully");
                    mCameraView.enableView();
                    break;
                default:
                    super.onManagerConnected(status);
                    break;
            }
        }
    };

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
        setContentView(R.layout.activity_main);

        mCameraView = (CameraBridgeViewBase) findViewById(R.id.camera_view);
        mCameraView.setVisibility(SurfaceView.VISIBLE);
        mCameraView.setCvCameraViewListener(this);

        if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) !=
                PackageManager.PERMISSION_GRANTED) {
            ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.CAMERA},
                    CAMERA_PERMISSION_REQUEST_CODE);
        } else {
            init();
        }
    }

    private void init() {
        try {
            mInterpreter = new Interpreter(loadModelFile());
            mLabels = Utils.loadLabels(this, LABELS_FILE_NAME);
        } catch (IOException e) {
            Log.e(TAG, "Error loading model or labels file", e);
        }
    }

    private ByteBuffer loadModelFile() throws IOException {
        ByteBuffer buffer = ByteBuffer.allocateDirect(4 * 1024 * 1024);
        buffer.order(ByteOrder.nativeOrder());

        Utils.copyInputStreamToFile(this.getAssets().open(MODEL_FILE_NAME),
                Utils.createTempFile(this, MODEL_FILE_NAME));

        buffer.put(Utils.readFileToByteArray(Utils.createTempFile(this, MODEL_FILE_NAME)));
        return buffer;
    }

    @Override
    public void onResume() {
        super.onResume();
        if (!OpenCVLoader.initDebug()) {
            Log.d(TAG, "Internal OpenCV library not found. Using OpenCV Manager for initialization");
            OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_3_4_0, this, mLoaderCallback);
        } else {
            mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
        }
    }

    @Override
    public void onPause() {
        super.onPause();
        if (mCameraView != null) {
            mCameraView.disableView();
        }
    }

    @Override
    public void onDestroy() {
        super.onDestroy();
        if (mCameraView != null) {
            mCameraView.disableView();
        }
    }

    @Override
    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
                                           @NonNull int[] grantResults) {
        super.onRequestPermissionsResult(requestCode, permissions, grantResults);

        if (requestCode == CAMERA_PERMISSION_REQUEST_CODE) {
            if (grantResults.length > 0 && grantResults[0] == PackageManager.PERMISSION_GRANTED) {
                init();
            } else {
                Log.e(TAG, "Camera permission not granted");
            }
        }
    }

    @Override
    public void onCameraViewStarted(int width, int height) {
        mRgba = new Mat(height, width, CvType.CV_8UC4);
        mGray = new Mat(height, width, CvType.CV_8UC1);
    }

    @Override
    public void onCameraViewStopped() {
        mRgba.release();
        mGray.release();
    }

    @Override
    public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
        mRgba = inputFrame.rgba();
        Imgproc.cvtColor(mRgba, mGray, Imgproc.COLOR_RGBA2GRAY);
        Imgproc.resize(mGray, mGray, new Size(MODEL_INPUT_SIZE, MODEL_INPUT_SIZE));

        TensorImage image = new TensorImage();
        image.load(mGray);

        ByteBuffer buffer = image.getBuffer();

        float[][] result = new float[1][mLabels.size()];
        mInterpreter.run(buffer, result);

        Map<String, Float> labeledProb = new TensorLabel(mLabels, result[0]).getMapWithFloatValue();
        String bestMatch = "";
        float bestConfidence = 0.0f;

        for (Map.Entry<String, Float> entry : labeledProb.entrySet()) {
            if (entry.getValue() > bestConfidence) {
                bestMatch = entry.getKey();
                bestConfidence = entry.getValue();
            }
        }

        Imgproc.putText(mRgba, bestMatch, new org.opencv.core.Point(10, 100),
                Imgproc.FONT_HERSHEY_SIMPLEX, 1.0, new org.opencv.core.Scalar(255, 255, 255), 2);

        return mRgba;
    }
}

该代码使用OpenCV库从相机中捕获图像，并将其转换为TensorFlow Lite模型的输入格式。模型返回一个包含每个类别概率的数组，然后在标签列表中寻找最高概率的类别，并在相机预览中显示它。

请注意，此示例仅适用于单个物体的分类。如果要检测多个物体，请考虑使用更高级的算法，如SSD或YOLO。