OpenCV  4.9.0
Open Source Computer Vision
How to run deep networks on Android device

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See also
Deep Neural Networks (dnn module)
Original author Dmitry Kurtaev
Compatibility OpenCV >= 4.9

Introduction

In this tutorial you'll know how to run deep learning networks on Android device using OpenCV deep learning module. Tutorial was written for Android Studio 2022.2.1.

Requirements

Create an empty Android Studio project and add OpenCV dependency

Use Android Development with OpenCV tutorial to initialize your project and add OpenCV.

Make an app

Our sample will takes pictures from a camera, forwards it into a deep network and receives a set of rectangles, class identifiers and confidence values in range [0, 1].

package com.example.myapplication;
import android.content.Context;
import android.content.res.AssetManager;
import android.os.Bundle;
import android.util.Log;
import android.widget.Toast;
import org.opencv.android.CameraActivity;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.Net;
import org.opencv.dnn.Dnn;
import org.opencv.imgproc.Imgproc;
import java.io.InputStream;
import java.io.IOException;
import java.util.Collections;
import java.util.List;
public class MainActivity extends CameraActivity implements CvCameraViewListener2 {
@Override
public void onResume() {
super.onResume();
if (mOpenCvCameraView != null)
mOpenCvCameraView.enableView();
}
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
if (OpenCVLoader.initLocal()) {
Log.i(TAG, "OpenCV loaded successfully");
} else {
Log.e(TAG, "OpenCV initialization failed!");
(Toast.makeText(this, "OpenCV initialization failed!", Toast.LENGTH_LONG)).show();
return;
}
mModelBuffer = loadFileFromResource(R.raw.mobilenet_iter_73000);
mConfigBuffer = loadFileFromResource(R.raw.deploy);
if (mModelBuffer == null || mConfigBuffer == null) {
Log.e(TAG, "Failed to load model from resources");
} else
Log.i(TAG, "Model files loaded successfully");
net = Dnn.readNet("caffe", mModelBuffer, mConfigBuffer);
Log.i(TAG, "Network loaded successfully");
setContentView(R.layout.activity_main);
// Set up camera listener.
mOpenCvCameraView = (CameraBridgeViewBase)findViewById(R.id.CameraView);
mOpenCvCameraView.setVisibility(CameraBridgeViewBase.VISIBLE);
mOpenCvCameraView.setCvCameraViewListener(this);
}
@Override
public void onPause()
{
super.onPause();
if (mOpenCvCameraView != null)
mOpenCvCameraView.disableView();
}
@Override
protected List<? extends CameraBridgeViewBase> getCameraViewList() {
return Collections.singletonList(mOpenCvCameraView);
}
public void onDestroy() {
super.onDestroy();
if (mOpenCvCameraView != null)
mOpenCvCameraView.disableView();
mModelBuffer.release();
mConfigBuffer.release();
}
// Load a network.
public void onCameraViewStarted(int width, int height) {
}
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
final int IN_WIDTH = 300;
final int IN_HEIGHT = 300;
final float WH_RATIO = (float)IN_WIDTH / IN_HEIGHT;
final double IN_SCALE_FACTOR = 0.007843;
final double MEAN_VAL = 127.5;
final double THRESHOLD = 0.2;
// Get a new frame
Log.d(TAG, "handle new frame!");
Mat frame = inputFrame.rgba();
Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGBA2RGB);
// Forward image through network.
Mat blob = Dnn.blobFromImage(frame, IN_SCALE_FACTOR,
new Size(IN_WIDTH, IN_HEIGHT),
new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL), /*swapRB*/false, /*crop*/false);
net.setInput(blob);
Mat detections = net.forward();
int cols = frame.cols();
int rows = frame.rows();
detections = detections.reshape(1, (int)detections.total() / 7);
for (int i = 0; i < detections.rows(); ++i) {
double confidence = detections.get(i, 2)[0];
if (confidence > THRESHOLD) {
int classId = (int)detections.get(i, 1)[0];
int left = (int)(detections.get(i, 3)[0] * cols);
int top = (int)(detections.get(i, 4)[0] * rows);
int right = (int)(detections.get(i, 5)[0] * cols);
int bottom = (int)(detections.get(i, 6)[0] * rows);
// Draw rectangle around detected object.
Imgproc.rectangle(frame, new Point(left, top), new Point(right, bottom),
new Scalar(0, 255, 0));
String label = classNames[classId] + ": " + confidence;
int[] baseLine = new int[1];
Size labelSize = Imgproc.getTextSize(label, Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);
// Draw background for label.
Imgproc.rectangle(frame, new Point(left, top - labelSize.height),
new Point(left + labelSize.width, top + baseLine[0]),
new Scalar(255, 255, 255), Imgproc.FILLED);
// Write class name and confidence.
Imgproc.putText(frame, label, new Point(left, top),
Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0));
}
}
return frame;
}
public void onCameraViewStopped() {}
private MatOfByte loadFileFromResource(int id) {
byte[] buffer;
try {
// load cascade file from application resources
InputStream is = getResources().openRawResource(id);
int size = is.available();
buffer = new byte[size];
int bytesRead = is.read(buffer);
is.close();
} catch (IOException e) {
e.printStackTrace();
Log.e(TAG, "Failed to ONNX model from resources! Exception thrown: " + e);
(Toast.makeText(this, "Failed to ONNX model from resources!", Toast.LENGTH_LONG)).show();
return null;
}
return new MatOfByte(buffer);
}
private static final String TAG = "OpenCV-MobileNet";
private static final String[] classNames = {"background",
"aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"};
private MatOfByte mConfigBuffer;
private MatOfByte mModelBuffer;
private Net net;
private CameraBridgeViewBase mOpenCvCameraView;
}
private MatOfByte loadFileFromResource(int id) {
byte[] buffer;
try {
// load cascade file from application resources
InputStream is = getResources().openRawResource(id);
int size = is.available();
buffer = new byte[size];
int bytesRead = is.read(buffer);
is.close();
} catch (IOException e) {
e.printStackTrace();
Log.e(TAG, "Failed to ONNX model from resources! Exception thrown: " + e);
(Toast.makeText(this, "Failed to ONNX model from resources!", Toast.LENGTH_LONG)).show();
return null;
}
return new MatOfByte(buffer);
}

And then the network initialization is done with the following lines:

mModelBuffer = loadFileFromResource(R.raw.mobilenet_iter_73000);
mConfigBuffer = loadFileFromResource(R.raw.deploy);
if (mModelBuffer == null || mConfigBuffer == null) {
Log.e(TAG, "Failed to load model from resources");
} else
Log.i(TAG, "Model files loaded successfully");
net = Dnn.readNet("caffe", mModelBuffer, mConfigBuffer);
Log.i(TAG, "Network loaded successfully");

See also Android documentation on resources

Mat blob = Dnn.blobFromImage(frame, IN_SCALE_FACTOR,
new Size(IN_WIDTH, IN_HEIGHT),
new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL), /*swapRB*/false, /*crop*/false);
net.setInput(blob);
Mat detections = net.forward();
int cols = frame.cols();
int rows = frame.rows();
detections = detections.reshape(1, (int)detections.total() / 7);
for (int i = 0; i < detections.rows(); ++i) {
double confidence = detections.get(i, 2)[0];
if (confidence > THRESHOLD) {
int classId = (int)detections.get(i, 1)[0];
int left = (int)(detections.get(i, 3)[0] * cols);
int top = (int)(detections.get(i, 4)[0] * rows);
int right = (int)(detections.get(i, 5)[0] * cols);
int bottom = (int)(detections.get(i, 6)[0] * rows);
// Draw rectangle around detected object.
Imgproc.rectangle(frame, new Point(left, top), new Point(right, bottom),
new Scalar(0, 255, 0));
String label = classNames[classId] + ": " + confidence;
int[] baseLine = new int[1];
Size labelSize = Imgproc.getTextSize(label, Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);
// Draw background for label.
Imgproc.rectangle(frame, new Point(left, top - labelSize.height),
new Point(left + labelSize.width, top + baseLine[0]),
new Scalar(255, 255, 255), Imgproc.FILLED);
// Write class name and confidence.
Imgproc.putText(frame, label, new Point(left, top),
Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0));
}
}

Dnn.blobFromImage converts camera frame to neural network input tensor. Resize and statistical normalization are applied. Each line of network output tensor contains information on one detected object in the following order: confidence in range [0, 1], class id, left, top, right, bottom box coordinates. All coordinates are in range [0, 1] and should be scaled to image size before rendering.