google_mlkit_object_detection 1.0.0

A Flutter plugin to use Google's ML Kit Object Detection and Tracking to detect and track objects in an image or live camera feed.

Google's ML Kit Object Detection and Tracking for Flutter

A Flutter plugin to use Google's ML Kit Object Detection and Tracking to detect and track objects in an image or live camera feed.

PLEASE READ THIS before continuing or posting a new issue:

• Google's ML Kit was built only for mobile platforms: iOS and Android apps. Web or any other platform is not supported, you can request support for those platforms to Google in their repo.

• This plugin is not sponsored or maintained by Google. The authors are developers excited about Machine Learning that wanted to expose Google's native APIs to Flutter.

• Google's ML Kit APIs are only developed natively for iOS and Android. This plugin uses Flutter Platform Channels as explained here.

  Messages are passed between the client (the app/plugin) and host (platform) using platform channels as illustrated in this diagram:

  

  Messages and responses are passed asynchronously, to ensure the user interface remains responsive. To read more about platform channels go here.

  Because this plugin uses platform channels, no Machine Learning processing is done in Flutter/Dart, all the calls are passed to the native platform using MethodChannel in Android and FlutterMethodChannel in iOS, and executed using Google's native APIs. Think of this plugin as a bridge between your app and Google's native ML Kit APIs. This plugin only passes the call to the native API and the processing is done by Google's API. It is important that you understand this concept when it comes to debugging errors for your ML model and/or app.

• Since the plugin uses platform channels, you may encounter issues with the native API. Before submitting a new issue, identify the source of the issue. You can run both iOS and/or Android native example apps by Google and make sure that the issue is not reproducible with their native examples. If you can reproduce the issue in their apps then report the issue to Google. The authors do not have access to the source code of their native APIs, so you need to report the issue to them. If you find that their example apps are okay and still you have an issue using this plugin, then look at our closed and open issues. If you cannot find anything that can help you then report the issue and provide enough details. Be patient, someone from the community will eventually help you.

Requirements

iOS

• Minimum iOS Deployment Target: 15.5
• Xcode 15.3.0 or newer
• Swift 5.9 (Swift Package Manager)
• ML Kit does not support 32-bit architectures (i386 and armv7). ML Kit does support 64-bit architectures (x86_64 and arm64). Check this list to see if your device has the required device capabilities. More info here.

Swift Package Manager Setup

This plugin uses Swift Package Manager (SwiftPM) via d-date/google-mlkit-swiftpm (9.0.2), which also supports Apple Silicon iOS Simulators out of the box.

1. Linker Flags: In Xcode, go to Runner > Build Settings > Linking - General > Other Linker Flags (OTHER_LDFLAGS) and add:

   • -ObjC
   • -all_load

2. ML Kit Resource Bundles: SwiftPM cannot bundle model files inside .binaryTarget frameworks into the main app bundle automatically. Download MLKitObjectDetectionResources.bundle and MLKitObjectDetectionCommonResources.bundle from the d-date/google-mlkit-swiftpm v9.0.2 release and add them to your Xcode Runner target (Build Phases > Copy Bundle Resources). See packages/example/ios/download_mlkit_bundles.sh for an example download script.

Android

• minSdkVersion: 21
• targetSdkVersion: 36
• compileSdkVersion: 36

⚠️ Production Disclaimer: Using this plugin in production is the responsibility of the developers consuming the plugin, not the authors. The authors provide this plugin as-is and are not responsible for any issues, failures, or compatibility problems that may arise from using this plugin in production environments.

Usage

Object Detection and Tracking

Create an instance of InputImage

Create an instance of InputImage as explained here.

final InputImage inputImage;

Create an instance of ObjectDetector

// Use DetectionMode.stream when processing camera feed.
// Use DetectionMode.single when processing a single image.
final mode = DetectionMode.stream; // or DetectionMode.single

// Options to configure the detector while using with base model.
final options = ObjectDetectorOptions(...);

// Options to configure the detector while using a local custom model.
final options = LocalObjectDetectorOptions(...);

// Options to configure the detector while using a Firebase model.
final options = FirebaseObjectDetectorOptions(...);

final objectDetector = ObjectDetector(options: options);

Process image

final List<DetectedObject> objects = await objectDetector.processImage(inputImage);

for (DetectedObject detectedObject in objects) {
  final rect = detectedObject.boundingBox;
  final trackingId = detectedObject.trackingId;

  for (Label label in detectedObject.labels) {
    print('${label.index} ${label.text} ${label.confidence}');
  }
}

Release resources with close()

objectDetector.close();

Models

Object Detection and Tracking can be used with either the Base Model or a Custom Model. The base model is the default model bundled in the SDK, and a custom model can either be bundled with the app as an asset or downloaded from Firebase.

Base model

To use the base model:

final options = ObjectDetectorOptions(
  mode: DetectionMode.stream,
  classifyObjects: classifyObjects,
  multipleObjects: multipleObjects,
);
final objectDetector = ObjectDetector(options: options);

Local custom model

Before using a custom model make sure you read and understand the ML Kit's compatibility requirements for TensorFlow Lite models here. To learn how to create a custom model that is compatible with ML Kit go here.

To use a local custom model add the tflite model to your pubspec.yaml:

assets:
  - assets/ml/

Add this method:

import 'dart:io';
import 'package:flutter/services.dart';
import 'package:path/path.dart';
import 'package:path_provider/path_provider.dart';

Future<String> getModelPath(String asset) async {
  final path = '${(await getApplicationSupportDirectory()).path}/$asset';
  await Directory(dirname(path)).create(recursive: true);
  final file = File(path);
  if (!await file.exists()) {
    final byteData = await rootBundle.load(asset);
    await file.writeAsBytes(byteData.buffer
            .asUint8List(byteData.offsetInBytes, byteData.lengthInBytes));
  }
  return file.path;
}

Create an instance of ObjectDetector:

final modelPath = await getModelPath('assets/ml/object_labeler.tflite');
final options = LocalObjectDetectorOptions(
  mode: DetectionMode.stream,
  modelPath: modelPath,
  classifyObjects: classifyObjects,
  multipleObjects: multipleObjects,
  confidenceThreshold: 0.5, // optional, defaults to 0.5
  maximumLabelsPerObject: 10, // optional, defaults to 10
);
final objectDetector = ObjectDetector(options: options);

Android Additional Setup

Add the following to your app's android/app/build.gradle (or android/app/build.gradle.kts) file to ensure Gradle doesn't compress the model file when building the app:

Groovy (build.gradle):

android {
    // ...
    androidResources {
        noCompress "tflite"
        // or noCompress "lite"
    }
}

Kotlin DSL (build.gradle.kts):

android {
    // ...
    androidResources {
        noCompress += "tflite"
        // or noCompress += "lite"
    }
}

Firebase model

Google's standalone ML Kit library does NOT have any direct dependency with Firebase. As designed by Google, you do NOT need to include Firebase in your project in order to use ML Kit. However, to use a remote model hosted in Firebase, you must set up Firebase in your project following these steps:

• Android
• iOS

iOS Additional Setup

Note: The iOS implementation uses Swift’s #if canImport(MLKitLinkFirebase) to conditionally enable Firebase-hosted model code. Because the Swift Package Manager distribution (d-date/google-mlkit-swiftpm) does not currently expose an MLKitLinkFirebase product, the plugin compiles without Firebase remote model support by default on iOS and returns an error (ERROR_MISSING_MLKIT_FIREBASE_MODELS) when FirebaseObjectDetectorOptions is used unless MLKitLinkFirebase is linked into the build.

Usage

To use a Firebase model, make sure you download the remote model first using FirebaseObjectDetectorModelManager().downloadModel(modelName) (calling processImage() before the model is downloaded returns a "Model has not been downloaded yet" error):

final options = FirebaseObjectDetectorOptions(
  mode: DetectionMode.stream,
  modelName: modelName,
  classifyObjects: classifyObjects,
  multipleObjects: multipleObjects,
  confidenceThreshold: 0.5, // optional, defaults to 0.5
  maximumLabelsPerObject: 10, // optional, defaults to 10
);
final objectDetector = ObjectDetector(options: options);

Managing Firebase models

Create an instance of model manager:

final modelManager = FirebaseObjectDetectorModelManager();

To check if a model is downloaded:

final bool response = await modelManager.isModelDownloaded(modelName);

To download a model:

final bool response = await modelManager.downloadModel(
  modelName,
  isWifiRequired: true, // optional, defaults to true (enforced on Android only)
);

To delete a model:

final bool response = await modelManager.deleteModel(modelName);

Example app

Find the example app here.

Contributing

Contributions are welcome. In case of any problems look at existing issues, if you cannot find anything related to your problem then open an issue. Create an issue before opening a pull request for non-trivial fixes. In case of trivial fixes open a pull request directly.