google_ml_kit 0.0.1

flutter plugin for google ml kit

Google Ml kit Plugin

Flutter plugin to use google's standalone ml kit for Android .

[screenshots/pose.png] [screenshots/imagelabeling.png] [screenshots/barcode.png]

Currently supported api's

• Pose Detection
• Image Labelling
• Barcode Scanning

Support for other api's will be shortly added

Usage

Add this plugin as dependency in your pubspec.yaml.

• In your project-level build.gradle file, make sure to include Google's Maven repository in both your buildscript and allprojects sections(for all api's).
• Add the dependencies for the ML Kit Android libraries to your module's app-level gradle file.
• For api's which require models choose either to bundle model with your app or using model within google play service.

First Create an InputImage

Prepare Input Image(image you want to process)

import 'package:google_ml_kit/google_ml_kit.dart';

final inputImage = InputImage.fromFilePath(filePath);

// Or you can prepare image form
//final inputImage = InputImage.fromFile(file);

// final inputImageData = InputImageData(
//     size: size of image,
//     rotation: roatation degree(0,90,180,270 supported),
//     inputImageFormat:InputImageFormat.NV21 (default format of image);
//         
//
// var inputImage = InputImage.fromBytes(
//     bytes: await pickedFile.readAsBytes(), 
//     inputImageData: inputImageData);

To know more about formats of image.

Create an instance of detector

Call processImage() to obtain the result

An example covering all the api's usage

Pose Detection

-In you app-level build.gradle. For latest version check Pose Detection

dependencies {
  // If you want to use the base sdk
  implementation 'com.google.mlkit:pose-detection:latest version'
  // If you want to use the accurate sdk
  implementation 'com.google.mlkit:pose-detection-accurate:latest version'
}

If you choose google service way.In app level buil.gradle add

<application ...>
        ...
      <meta-data
          android:name="com.google.mlkit.vision.DEPENDENCIES"
          android:value="ica" />
      <!-- To use multiple models: android:value="ica,model2,model3" -->
      </application>

The same implies for all other models as well

-Create PoseDetectorOptions

final options = PoseDetectorOptions(
        poseDetectionModel: PoseDetectionModel.BasePoseDetector,
        selectionType : LandmarkSelectionType.all,
        poseLandmarks:(list of poseaLndmarks you want); 
//or PoseDetectionModel.AccuratePoseDetector to use accurate pose detector
        

-Obtain [PoseDetector] instance.

Note: To obtain default poseDetector no options need to be specied. It gives all available landmarks using BasePoseDetector Model.

The same implies to other detectors as well

PoseDetector poseDetector = GoogleMlKit.instance
                               .poseDetector([PoseDetectorOptions options]);

-Call processImage(InputImage inputImage) to obtain the result. It returns

Map<int,PoseLandMark>

final landMarksMap = await poseDetector.processImage(inputImage);

Use the map to extract data. See this example to get better idea.

Image Labeling

In you app-level build.gradle. For latest version check Image Labeling

dependencies {
       // ...
// Use this dependency to bundle the model with your app
      implementation 'com.google.mlkit:image-labeling:17.0.0'
// Use this dependency to use dynamically downloaded model in Google Play Service
      implementation 'com.google.android.gms:play-services-mlkit-image-labeling:16.0.0'
    }

Create ImageLabelerOptions. This uses google's base model

final options =ImageLabelerOptions( confidenceThreshold = confidenceThreshold);
// Default =0.5
//lies between 0.0 to 1.0
        

To use custom tflite models

CustomImageLabelerOptions options = CustomImageLabelerOptions(
        customModel: CustomTrainedModel.asset 
       (or CustomTrainedModel.file),// To use files stored in device
        customModelPath: "file path");

To use autoMl vision models models

final options = AutoMlImageLabelerOptions(
      customTrainedModel:, 
      customModelPath:);

Obtain ImageLabeler instance.

ImageLabeler imageLabeler = GoogleMlKit.instance.imageLabeler([options]);

call processImage() It returns List<ImageLabel>

final labels = await imageLabeler.processImage(inputImage);

To know more see this example

Barcode Scanner

In you app-level build.gradle. For latest version check Barcode Scanning

dependencies {
      // ...
// Use this dependency to bundle the model with your app
      implementation 'com.google.mlkit:barcode-scanning:16.0.3'
// Use this dependency to use the dynamically downloaded model in Google Play Services
      implementation 'com.google.android.gms:play-services-mlkit-barcode-scanning:16.1.2'
    }

Obtain BarcodeScanner instance.

BarcodeScanner barcodeScanner = GoogleMlKit.instance
                                           .barcodeScanner(
                                           formats:(List of BarcodeFormats);

Supported BarcodeFormats .Access them using

Barcode.FORMAT_Default

Barcode.FORMAT_Code_128

etc..

call processImage() It returns List<Barcode>

final result = await barcodeScanner.processImage(inputImage);

To know more see this example

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT