3D object rendering in openCV using metaio in Android

3D Object Rendering in OpenCV using Metaio in Android

This article explores the integration of Metaio with OpenCV to render 3D objects in real-time on Android devices. Metaio is a powerful Augmented Reality (AR) framework that provides robust tracking and rendering capabilities, while OpenCV is a widely used computer vision library for image processing and analysis.

Prerequisites

• Android Studio
• Metaio SDK for Android
• OpenCV for Android (either pre-built or compiled from source)
• A 3D model in a compatible format (e.g., OBJ, 3DS)

Integrating Metaio with OpenCV

1. Project Setup

1. Create a new Android project in Android Studio.
2. Add the Metaio SDK library as a dependency to your project. You can find instructions on the Metaio website.
3. Include OpenCV library files in your project. You can either download pre-built binaries or compile OpenCV from source.

2. Creating an AR Scene

In your Android activity, initialize a Metaio Scene and set up the camera view. You can use the Metaio SDK to create a Scene object and configure its properties, such as the background image or video.

// Initialize a Metaio Scene
MetaioScene scene = new MetaioScene();

// Configure the camera view
CameraView cameraView = (CameraView) findViewById(R.id.cameraView);
scene.setCameraView(cameraView);

3. Loading the 3D Model

Load your 3D model into the scene using Metaio’s model loading functionality. You can specify the file path or use the model ID if it’s already registered with Metaio.

// Load the 3D model
Model model = new Model(scene, "path/to/your/model.obj");

// Add the model to the scene
scene.addChild(model);

4. Tracking and Rendering

Use Metaio’s tracking capabilities to position and orient the 3D model in the real world. You can track image markers, GPS coordinates, or even use face tracking to overlay the object on the user’s face.

// Start tracking using a predefined tracker
Tracker tracker = scene.getTrackerManager().getTracker("your_tracker_id");
tracker.startTracking();

// Attach the model to the tracker
model.attachToTracker(tracker);

// Render the scene
scene.startRendering();

5. Image Processing with OpenCV

You can leverage OpenCV’s image processing capabilities to manipulate the camera feed or the rendered scene. For example, you can use edge detection, filtering, or feature extraction to enhance the visual experience.

// Access the camera frame from the Metaio Scene
Mat frame = scene.getCameraFrame();

// Apply OpenCV image processing techniques
// ...

// Display the processed image
// ...

Example: Augmented Reality with Face Tracking

Let’s illustrate the integration with a basic example that uses face tracking to overlay a 3D object on a user’s face. We’ll use the Metaio SDK and OpenCV to achieve this.

Code Structure

• Activity: Handles the user interface, initializes the scene, loads the model, and starts the tracking process.
• Face Tracking: Implements face detection and tracking using OpenCV.
• Object Rendering: Loads the 3D model and renders it using Metaio.

Activity Code (MainActivity.java)

import android.app.Activity;
import android.os.Bundle;
import android.view.View;
import android.widget.TextView;
import android.widget.Toast;
import android.graphics.Bitmap;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.graphics.Rect;

import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.objdetect.Objdetect;

import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;

import io.meta.sdk.MetaioScene;
import io.meta.sdk.camera.CameraView;
import io.meta.sdk.model.Model;
import io.meta.sdk.tracker.Tracker;

public class MainActivity extends Activity implements CvCameraViewListener2 {

    private static final String TAG = "MainActivity";
    private MetaioScene scene;
    private CameraView cameraView;
    private Model model;
    private Tracker tracker;
    private TextView textView;

    private CascadeClassifier cascadeClassifier;
    private Mat mRgba;
    private Mat mGray;
    private int absoluteFaceSize = 0;

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

    @Override
    public void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);
        cameraView = (CameraView) findViewById(R.id.cameraView);
        cameraView.setCvCameraViewListener(this);
        textView = findViewById(R.id.textView);

        scene = new MetaioScene();
        scene.setCameraView(cameraView);
        
        model = new Model(scene, "path/to/your/model.obj");
        scene.addChild(model);
        
        tracker = scene.getTrackerManager().getTracker("face_tracker");
        tracker.startTracking();
        model.attachToTracker(tracker);
        scene.startRendering();

        // Load the cascade classifier
        try {
            // Load the cascade classifier from the assets folder
            InputStream is = getAssets().open("haarcascade_frontalface_alt.xml");
            File cascadeDir = getDir("cascade", MODE_PRIVATE);
            File mCascadeFile = new File(cascadeDir, "haarcascade_frontalface_alt.xml");
            FileOutputStream os = new FileOutputStream(mCascadeFile);
            byte[] buffer = new byte[4096];
            int bytesRead;
            while ((bytesRead = is.read(buffer)) != -1) {
                os.write(buffer, 0, bytesRead);
            }
            is.close();
            os.close();

            cascadeClassifier = new CascadeClassifier(mCascadeFile.getAbsolutePath());
            if (cascadeClassifier.empty()) {
                Toast.makeText(this, "Cascade not loaded!", Toast.LENGTH_SHORT).show();
            } else {
                Log.i(TAG, "Cascade loaded successfully");
            }
        } catch (Exception e) {
            Log.e(TAG, "Cascade load failed: " + e.getMessage());
            Toast.makeText(this, "Cascade load failed!", Toast.LENGTH_SHORT).show();
        }
    }

    @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 {
            Log.d(TAG, "OpenCV library found inside package. Using it!");
            mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
        }
    }

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

    public void onDestroy() {
        super.onDestroy();
        if (cameraView != null) {
            cameraView.disableView();
        }
    }

    @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();
        mGray = inputFrame.gray();

        if (absoluteFaceSize == 0) {
            int height = mGray.rows();
            if (Math.round(height * 0.2f) > 0) {
                absoluteFaceSize = Math.round(height * 0.2f);
            }
        }

        MatOfRect faces = new MatOfRect();
        if (cascadeClassifier != null) {
            cascadeClassifier.detectMultiScale(mGray, faces, 1.1, 2, 2, new Size(absoluteFaceSize, absoluteFaceSize), new Size());
        }

        Rect[] faceArray = faces.toArray();
        for (Rect face : faceArray) {
            Imgproc.rectangle(mRgba, new Point(face.x, face.y), new Point(face.x + face.width, face.y + face.height), new Scalar(0, 255, 0, 255), 3);
            // Update the tracker with the detected face
            tracker.updateTracker(face.x, face.y, face.width, face.height);
        }

        return mRgba;
    }

    private void initializeOpenCV() {
        // Initialize OpenCV with default settings
        // ...
    }
}

Conclusion

This article has demonstrated how to combine Metaio’s AR capabilities with OpenCV’s computer vision functionality in an Android application. This integration opens up a wide range of possibilities for creating innovative AR experiences, such as interactive 3D object overlays, face-based filters, and real-time image analysis.