how to use TensorFlow Lite (TFLite) model in Flutter for basic object detection | TFlite model in android devices | onDevice machine learning |

source: Google

 TensorFlow Lite is a lightweight machine learning framework developed by Google for running inference on mobile and embedded devices. With Flutter, you can integrate TensorFlow Lite models into your mobile apps to perform various tasks, including object detection. In this tutorial, we will explore how to use a TensorFlow Lite model for basic object detection in a Flutter app.

1. Set up a Flutter Project: Start by creating a new Flutter project or using an existing one. Open your project in your favorite code editor and ensure that you have the necessary dependencies set up, including the tflite_flutter package.

2. Prepare the Model: Obtain a pre-trained TensorFlow Lite model for object detection. You can either train your own model or find a pre-trained model from reliable sources like the TensorFlow Lite Model Zoo. Ensure that the model is compatible with object detection tasks.

3. Convert the Model: To use the TensorFlow Lite model in your Flutter app, you need to convert it to the .tflite format. Use the TensorFlow Lite Converter tool, which is typically a Python script, to convert your model. Follow the official TensorFlow Lite documentation or specific conversion instructions provided with the model to perform the conversion.

4. Integrate the Model: Once you have the converted .tflite model, add it to your Flutter project. Create a folder, such as assets, in your project directory and place the model file inside it. Update the pubspec.yaml file to include the model as an asset. For example:

flutter:

  assets:

    - assets/model.tflite

5. Load the Model: In your Flutter app, load the TensorFlow Lite model using the Interpreter class provided by the tflite_flutter package. Initialize an instance of the Interpreter and load the model from the asset file. This step prepares the model for inference.

6. Preprocess the Input: Before running inference, you need to preprocess the input data. For object detection, you typically preprocess images by resizing them to the input size required by the model and normalizing the pixel values. Use the image package in Flutter to perform image preprocessing tasks.

7. Run Inference: With the model loaded and input preprocessed, you can now run inference to perform object detection. Pass the preprocessed input to the Interpreter and obtain the output. The output will contain information about the detected objects, such as their classes, bounding boxes, and confidence scores.

8. Post-process the Output: The output of the inference step may need further processing to extract meaningful information. For object detection, you can filter the detected objects based on a confidence threshold, decode the bounding box coordinates, and map the class indices to human-readable labels. This step helps in visualizing and presenting the detected objects accurately.

9. Display the Results: Finally, display the results of the object detection in your Flutter app's user interface. You can draw bounding boxes around the detected objects, overlay labels with their class names and confidence scores, or provide any other visual representation as per your app's requirements.

import 'dart:io';

import 'package:flutter/material.dart';

import 'package:flutter/services.dart';

import 'package:tflite_flutter/tflite_flutter.dart';

import 'package:image/image.dart' as img;

class ObjectDetectionScreen extends StatefulWidget {

  @override

  _ObjectDetectionScreenState createState() => _ObjectDetectionScreenState();

}

class _ObjectDetectionScreenState extends State<ObjectDetectionScreen> {

  Interpreter? _interpreter;

  late List<String> _labels;

  late img.Image _inputImage;

  @override

  void initState() {

    super.initState();

    loadModel();

  }

  Future<void> loadModel() async {

    try {

      _interpreter = await Interpreter.fromAsset('assets/model.tflite');

      await loadLabels();

    } catch (e) {

      print('Error loading model: $e');

    }

  }

  Future<void> loadLabels() async {

    final labelData = await rootBundle.loadString('assets/labels.txt');

    _labels = labelData.split('\n');

  }

  Future<void> runObjectDetection() async {

    final preprocessedImage = preprocessImage(_inputImage);

    final input = preprocessedImage.reshape([1, preprocessedImage.length]);

    final output = List.filled(_labels.length, 0.0).reshape([1, _labels.length]);

    _interpreter!.run(input, output);

    final detectionResults = output[0];

    // Process the detection results and display them as desired

    for (int i = 0; i < detectionResults.length; i++) {

      final label = _labels[i];

      final confidence = detectionResults[i];

      print('Label: $label, Confidence: $confidence');

    }

  }

  img.Image preprocessImage(img.Image image) {

    final resizedImage = img.copyResize(image, width: 300, height: 300);

    final normalizedImage = resizedImage.convert(format: img.Format.rgb);

    return normalizedImage;

  }

  void selectImage() async {

    final imagePicker = ImagePicker();

    final pickedImage = await imagePicker.getImage(source: ImageSource.gallery);

    if (pickedImage != null) {

      final file = File(pickedImage.path);

      final bytes = await file.readAsBytes();

      setState(() {

        _inputImage = img.decodeImage(bytes)!;

      });

      runObjectDetection();

    }

  }

  @override

  Widget build(BuildContext context) {

    return Scaffold(

      appBar: AppBar(

        title: Text('Object Detection'),

      ),

      body: Center(

        child: _inputImage != null

            ? Image.memory(img.encodePng(_inputImage))

            : Text('No image selected'),

      ),

      floatingActionButton: FloatingActionButton(

        onPressed: selectImage,

        child: Icon(Icons.image),

      ),

    );

  }

}

Conclusion: In this tutorial, we explored how to use TensorFlow Lite for basic object detection in a Flutter app. We learned about converting the model to the .tflite format, integrating it into a Flutter project, running inference, and displaying the results. By leveraging the power of TensorFlow Lite and Flutter, you can create mobile apps with real-time object detection capabilities.

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