apple_vision_image_classification 0.0.2

A Flutter plugin to use Apple Vision Image Classification the classifies an image.

apple_vision_image_classification

Apple Vision Selfie Detection is a Flutter plugin that enables Flutter apps to use Apple Vision Image Classification.

• This plugin is not sponsor or maintained by Apple. The authors are developers who wanted to make a similar plugin to Google's ml kit for macos.

Requirements

MacOS

• Minimum osx Deployment Target: 10.13
• Xcode 13 or newer
• Swift 5
• ML Kit only supports 64-bit architectures (x86_64 and arm64).

iOS

• Minimum ios Deployment Target: 14.0
• Xcode 13 or newer
• Swift 5
• ML Kit only supports 64-bit architectures (x86_64 and arm64).

Getting Started

You need to first import 'package:apple_vision/apple_vision.dart';

  final GlobalKey cameraKey = GlobalKey(debugLabel: "cameraKey");
  AppleVisionImageClassificationController visionController = AppleVisionImageClassificationController();
  InsertCamera camera = InsertCamera();
  Size imageSize = const Size(640,640*9/16);
  String? deviceId;
  bool loading = true;

  List<Label>? labels;
  late double deviceWidth;
  late double deviceHeight;

  @override
  void initState() {
    camera.setupCameras().then((value){
      setState(() {
        loading = false;
      });
      camera.startLiveFeed((InputImage i){
        if(i.metadata?.size != null){
          imageSize = i.metadata!.size;
        }
        if(mounted) {
          Uint8List? image = i.bytes;
          visionController.processImage(image!, imageSize).then((data){
            labels = data;
            setState(() {
              
            });
          });
        }
      });
    });
    super.initState();
  }
  @override
  void dispose() {
    camera.dispose();
    super.dispose();
  }

  @override
  Widget build(BuildContext context) {
    deviceWidth = MediaQuery.of(context).size.width;
    deviceHeight = MediaQuery.of(context).size.height;
    return Stack(
      children:<Widget>[
        SizedBox(
          width: imageSize.width, 
          height: imageSize.height, 
          child: loading?Container():CameraSetup(camera: camera, size: imageSize)
        ),
        Column(children: showLabel(),)
      ]
    );
  }

  List<Widget> showLabel(){
    if(labels == null || labels!.isEmpty) return [];
    List<Widget> widgets = [];

    for(int i = 0; i < labels!.length; i++){
      widgets.add(
        Container(
          width: 320,
          height: 20,
          decoration: BoxDecoration(
            color: Colors.green,
            borderRadius: BorderRadius.circular(5)
          ),
          child: Text(
            '${labels![i].label}: ${labels![i].confidence}',
            style: const TextStyle(
              color: Colors.white,
              fontSize: 12
            ),
          ),
        )
      );
    }
    return widgets;
  }

  Widget loadingWidget(){
    return Container(
      width: deviceWidth,
      height:deviceHeight,
      color: Theme.of(context).canvasColor,
      alignment: Alignment.center,
      child: const CircularProgressIndicator(color: Colors.blue)
    );
  }

Example

Find the example for this API 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.