ultralytics_yolo 0.0.4

Flutter plugin for YOLO (You Only Look Once) models, supporting object detection, segmentation, classification, pose estimation and oriented bounding boxes (OBB) on both Android and iOS.

Ultralytics YOLO Flutter Package

Flutter plugin for YOLO (You Only Look Once) models, supporting object detection, segmentation, classification, pose estimation and oriented bounding boxes (OBB) on both Android and iOS.

Features

• Object Detection: Identify and locate objects in images and camera feeds with bounding boxes
• Segmentation: Perform pixel-level segmentation of objects
• Classification: Classify objects in images
• Pose Estimation: Detect human poses and keypoints
• Oriented Bounding Boxes (OBB): Detect rotated or oriented bounding boxes for objects
• Cross-Platform: Works on both Android and iOS
• Real-time Processing: Optimized for real-time inference on mobile devices
• Camera Integration: Easy integration with device cameras

Installation

Add this to your package's pubspec.yaml file:

dependencies:
  ultralytics_yolo: ^0.0.4

Then run:

flutter pub get

Platform-Specific Setup

Android

Add the following permissions to your AndroidManifest.xml file:

<!-- For camera access -->
<uses-permission android:name="android.permission.CAMERA" />

<!-- For accessing images from storage -->
<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />

Set minimum SDK version in your android/app/build.gradle:

minSdkVersion 21

iOS

Add these entries to your Info.plist:

<key>NSCameraUsageDescription</key>
<string>This app needs camera access to detect objects</string>
<key>NSPhotoLibraryUsageDescription</key>
<string>This app needs photos access to get images for object detection</string>

Usage

Basic Example

import 'package:flutter/material.dart';
import 'package:ultralytics_yolo/yolo.dart';
import 'package:ultralytics_yolo/yolo_view.dart';
import 'package:ultralytics_yolo/yolo_task.dart';

class YoloDemo extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(title: Text('YOLO Object Detection')),
      body: Center(
        child: YoloView(
          task: YOLOTask.detect,
          modelPath: 'assets/models/yolo11n.tflite',
          threshold: 0.5,
          onResult: (results) {
            // Handle detection results
            print('Detected ${results.length} objects');
          },
        ),
      ),
    );
  }
}

Object Detection with Camera Feed

YoloView(
  task: YOLOTask.detect,
  modelPath: 'assets/models/yolo11n.tflite',
  useCamera: true,
  cameraResolution: '720p',
  threshold: 0.5,
  onResult: (results) {
    for (var result in results) {
      print('Detected: ${result.className}, Confidence: ${result.confidence}');
    }
  },
)

Image Segmentation

YoloView(
  task: YOLOTask.segment,
  modelPath: 'assets/models/yolo11n-seg.tflite',
  threshold: 0.5,
  onResult: (results) {
    // Process segmentation results
  },
)

Pose Estimation

YoloView(
  task: YOLOTask.pose,
  modelPath: 'assets/models/yolo11n-pose.tflite',
  threshold: 0.5,
  onResult: (results) {
    // Process pose keypoints
  },
)

API Reference

Classes

YOLO

Main class for YOLO operations.

YOLO({
  required String modelPath,
  required YOLOTask task,
  double threshold = 0.5,
});

YoloView

Flutter widget to display YOLO detection results.

YoloView({
  required YOLOTask task,
  required String modelPath,
  double threshold = 0.5,
  bool useCamera = false,
  String cameraResolution = '720p',
  Function(List<YOLOResult>)? onResult,
});

YOLOResult

Contains detection results.

class YOLOResult {
  final int classIndex;
  final String className;
  final double confidence;
  final Rect boundingBox;
  // For segmentation
  final List<List<double>>? mask;
  // For pose estimation
  final List<Point>? keypoints;
}

Enums

YOLOTask

enum YOLOTask {
  detect,   // Object detection
  segment,  // Image segmentation
  classify, // Image classification
  pose,     // Pose estimation
  obb,      // Oriented bounding boxes
}

Platform Support

Android	iOS	Web	macOS	Windows	Linux
✅	✅	❌	❌	❌	❌

Troubleshooting

Common Issues

1. Model loading fails

   • Make sure your model file is correctly placed in the assets directory
   • Verify that the model path is correctly specified
   • Check that the model format is compatible with TFLite

2. Low performance on older devices

   • Try using smaller models (e.g., YOLOv8n instead of YOLOv8l)
   • Reduce input image resolution
   • Adjust threshold values to reduce the number of detections

3. Camera permission issues

   • Ensure that your app has the proper permissions in the manifest or Info.plist
   • Handle runtime permissions properly in your app

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) - see the LICENSE file for details.