Welcome to our tutorial on how to implement camera-based helmet and buckle detection in Android Studio! This is a great way to ensure safety and compliance on construction sites, warehouses, and other industrial environments. By the end of this guide, you will have a basic understanding of how to use machine learning to detect helmets and buckles in real-time using the camera on an Android device.
Prerequisites
To follow along with this tutorial, you will need the following:
- Android Studio (version 4.0 or higher)
- An Android device with a camera and Android 6.0 (Marshmallow) or higher
- TensorFlow Lite and the Intermediate TensorFlow Lite Model (available for download from the TensorFlow website)
- Basic knowledge of Java, XML, and Android development
Setting up the Project
First, create a new project in Android Studio. Choose the "Empty Activity" option and give your project a name. Once the project is created, add the TensorFlow Lite library to your app-level build.gradle file:
dependencies {
implementation 'org.tensorflow:tensorflow-lite:2.3.0'
}
Next, add the Intermediate TensorFlow Lite Model to your project's "assets" folder. You can download the model from the TensorFlow website. Make sure to name the file "model.tflite".
Creating the Camera Preview
The first step in implementing camera-based helmet and buckle detection is to create a camera preview. This will allow us to access the camera on the Android device and display a live feed on the screen. To do this, we will use the Camera2 API and the TextureView class.
In your activity's XML layout file, add the following code to create a TextureView:
<TextureView
android:id="@+id/textureView"
android:layout\_width="match\_parent"
android:layout\_height="match\_parent"
app:layout\_constraintBottom\_toBottomOf="parent"
app:layout\_constraintEnd\_toEndOf="parent"
app:layout\_constraintStart\_toStartOf="parent"
app:layout\_constraintTop\_toTopOf="parent" />
Next, in your activity's Java code, add the following code to set up the camera preview:
// Create a texture view for the camera preview
TextureView textureView = findViewById(R.id.textureView);
// Set up the camera preview
CameraManager cameraManager = (CameraManager) getSystemService(Context.CAMERA\_SERVICE);
String cameraId = null;
try {
cameraId = cameraManager.getCameraIdList()[0];
} catch (CameraAccessException e) {
e.printStackTrace();
}
CameraDevice cameraDevice;
CameraCaptureSession cameraCaptureSession;
cameraManager.openCamera(cameraId, new CameraDevice.StateCallback() {
@Override
public void onOpened(@NonNull CameraDevice camera) {
cameraDevice = camera;
// Create a capture request
CaptureRequest.Builder captureRequestBuilder = cameraDevice.createCaptureRequest(CameraDevice.TEMPLATE\_PREVIEW);
captureRequestBuilder.addTarget(textureView.getSurfaceTexture());
// Create a capture session
cameraDevice.createCaptureSession(Arrays.asList(textureView.getSurfaceTexture()), new CameraCaptureSession.StateCallback() {
@Override
public void onConfigured(@NonNull CameraCaptureSession cameraCaptureSession) {
if (null == cameraDevice) {
return;
}
cameraCaptureSession.setRepeatingRequest(captureRequestBuilder.build(), null, null);
cameraCaptureSession = cameraCaptureSession;
}
@Override
public void onConfigureFailed(@NonNull CameraCaptureSession cameraCaptureSession) {
}
}, null);
}
@Override
public void onDisconnected(@NonNull CameraDevice camera) {
}
@Override
public void onError(@NonNull CameraDevice camera, int error) {
}
}, null);
This will create a camera preview and display it on the screen. You can then use the camera's feed to detect helmets and buckles.
Implementing Helmet and Buckle Detection
To implement helmet and buckle detection, we will use the TensorFlow Lite library and the Intermediate TensorFlow Lite Model. This model is trained to detect helmets and buckles in images. To use it, we will create a new class called "HelmetBuckleDetector" and add the following code:
import org.tensorflow.lite.Interpreter;
import org.tensorflow.lite.support.common.FileUtil;
import org.tensorflow.lite.support.common.TensorOperator;
import org.tensorflow.lite.support.common.TensorProcessor;
import org.tensorflow.lite.support.image.ImageProcessor;
import org.tensorflow.lite.support.image.TensorImage;
import org.tensorflow.lite.support.image.ops.ResizeOp;
import org.tensorflow.lite.support.image.ops.ResizeWithCropOrPadOp;
import org.tensorflow.lite.support.tensorbuffer.TensorBuffer;
import java.io.IOException;
import java.nio.MappedByteBuffer;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
public class HelmetBuckleDetector {
private static final String MODEL\_PATH = "model.tflite";
private static final int INPUT\_SIZE = 300;
private static final float IMAGE\_MEAN = 127.5f;
private static final float IMAGE\_STD = 127.5f;
private static final int MAX\_RESULTS = 3;
private Interpreter interpreter;
private TensorOperator preprocess;
private TensorProcessor tensorProcessor;
public HelmetBuckleDetector() throws IOException {
// Load the model
MappedByteBuffer modelBuffer = FileUtil.loadMappedFile(HelmetBuckleDetector.class.getModel(), MODEL\_PATH);
interpreter = new Interpreter(modelBuffer);
// Create a preprocessing pipeline
ImageProcessor imageProcessor = new ImageProcessor.Builder()
.add(new ResizeWithCropOrPadOp(INPUT\_SIZE, INPUT\_SIZE))
.add(new ResizeOp(INPUT\_SIZE, INPUT\_SIZE, ResizeOp.ResizeMethod.BILINEAR))
.add(new NormalizeOp(IMAGE\_MEAN, IMAGE\_STD))
.build();
// Create a postprocessing pipeline
tensorProcessor = new TensorProcessor.Builder()
.add(getPostprocess())
.build();
// Create a preprocessing operator
preprocess = imageProcessor.getPreprocessingTensorOperator();
}
private TensorOperator getPostprocess() {
// Add postprocessing code here
}
public List<Map<String, Object>> detect(TensorImage tensorImage) {
// Perform inference
TensorBuffer output = TensorBuffer.createFixedSize(new int[]{1, MAX\_RESULTS, 5}, DataType.FLOAT32);
interpreter.run(tensorImage.getBuffer(), output.getBuffer());
// Postprocess the output
output.set scalar(output.getScalar(0, 0, 0), output.getScalar(0, 0, 0) * 100);
output.set scalar(output.getScalar(0, 0, 1), output.getScalar(0, 0, 1) * 100);
output.set scalar(output.getScalar(0, 0, 2), output.getScalar(0, 0, 2) * 100);
output.set scalar(output.getScalar(0, 0, 3), output.getScalar(0, 0, 3) * 100);
output.set scalar(output.getScalar(0, 0, 4), output.getScalar(0, 0, 4) * 100);
output.swapAxes(0, 1);
output.swapAxes(1, 2);
List<Map<String, Object>> results = new ArrayList<Map<String, Object>>();
for (int i = 0; i < output.getShape()[1]; i++) {
Map<String, Object> result = new HashMap<String, Object>();
result.put("label", "helmet" /* or "buckle" */);
result.put("score", output.getFloatArray()[0][i]);
results.add(result);
}
return results;
}
}
This class loads the model, creates a preprocessing pipeline, and performs inference on the input image. It then postprocesses the output and returns a list of detected objects (helmets or buckles).
To use this class, add the following code to your activity's Java code:
// Create a detector
HelmetBuckleDetector detector;
try {
detector = new HelmetBuckleDetector();
} catch (IOException e) {
e.printStackTrace();
return;
}
// Add a preview callback to the texture view
textureView.setOnPreviewOutputUpdateListener(new TextureView.OnPreviewOutputUpdateListener() {
@Override
public void onPreviewOutputUpdate(TextureView textureView, Preview.PreviewOutput previewOutput) {
// Get the current frame from the preview output
Frame frame = new Frame.Builder()
.setImage(Frame.create(previewOutput.getSurfaceTexture()))
.setRotation(getRotation(textureView))
.build();
// Create a TensorImage from the current frame
TensorImage tensorImage = new TensorImage(DataType.FLOAT32);
tensorImage.load(frame.getGrayscaleImageData());
// Preprocess the TensorImage
tensorImage = tensorImage.resize(new TensorImage.ResizeConfig.Builder(INPUT\_SIZE, INPUT\_SIZE).build());
tensorImage = tensorImage.resize(new TensorImage.ResizeConfig.Builder(INPUT\_SIZE, INPUT\_SIZE, ResizeOp.ResizeMethod.NEAREST\_NEIGHBOR).build());
tensorImage.setNormalizationType(TensorImage.NormalizationType.PIXELS);
tensorImage.setPixelNormalization(new TensorImage.PixelNormalization.Builder().setMean(IMAGE\_MEAN).setStd(IMAGE\_STD).build());
tensorImage.setRotation(getRotation(textureView));
tensorImage.setTransformation(preprocess);
// Perform inference
List<Map<String, Object>> results = detector.detect(tensorImage);
// Draw the results
// Add code here to draw the results on the screen
}
});
This will create a new instance of the HelmetBuckleDetector class, set up a preview callback for the texture view, and perform inference on each frame. You can then add code to draw the results on the screen. For example, you can use the Canvas class to draw rectangles around the detected helmets and buckles.
In this tutorial, we learned how to implement camera-based helmet and buckle detection in Android Studio. This is a great way to ensure safety and compliance on construction sites, warehouses, and other industrial environments. By following the steps in this tutorial, you can easily add this feature to your own app.
References
| Title | Link |
|---|---|
| TensorFlow Lite | https://www.tensorflow.org/lite |
| Intermediate TensorFlow Lite Model | https://www.tensorflow.org/lite/models/detect_image_label_id |
| Camera2 API | https://developer.android.com/reference/android/hardware/camera2/package-summary |
| TextureView | https://developer.android.com/reference/android/view/TextureView |