Jim Bennett

Senior Cloud Developer Advocate at Microsoft, Xamarin Certified Developer, author of Xamarin In Action, blogger, speaker, father and lover of beer, whisky and Thai food. Opinions are my own.

  Reading, UK

See me soon at:

Techorama NL
Expert Day For Xamarin
Caribbean Developers Conference

My book:

Xamarin In Action

My podcast:

The Jim And Tonic Show
The Jim And Tonic Show on iTunes

Contact me:

  Xamarin In Action
  Twitter
  The Jim And Tonic Show
  GitHub
  LinkedIn
  YouTube
  Email
  CV

In the first part of this series I used the Azure Custom Vision service to create an image classifier to allow me to easily identify my daughters cuddly toys. Once created I tested it by uploading an image and seeing what tags the classifier found for the image.

In the second part I accessed this model from a Xamarin app, so that I could use the camera to take a photo to run through the classifier using a NuGet package that talks to the Custom Vision service.

In the third part I showed how to download this model for iOS and run it locally, on device, using CoreML.

In the fourth part I showed how to download this model for Android and run it locally, on device, using Tensorflow.

To run these models locally is a lot of boilerplate code, so I decided to make it easier and create a NuGet package containing a Xamarin plugin that can be used from iOS, Android and Xamarin.Forms apps.

This plugin makes it easy to download and use these models offline from inside your mobile app, using CoreML on iOS or Tensorflow on Android. These models can then be called from a .NET standard library, using something like Xam.Plugins.Media to take photos for classification.

Setup

Platform Support

This requires iOS 11 and up to use models from CoreML. For Android with Tensorflow the minimum supported SDK version is 21.

Usage

Before you can use this API, you need to initialise it with the model file downloaded from CustomVision. Trying to classify an image without calling Init will result in a ImageClassifierException being thrown.

iOS

Download the Core ML model from Custom Vision.

Pre-compiled models

Models can be compiled before beiong used, or compiled on the device. To use a pre-compiled model, compile the downloaded model using:

xcrun coremlcompiler compile <model_file_name>.mlmodel <model_name>.mlmodelc  

This will spit out a folder called <model_name>.mlmodelc containing a number of files. Add this entire folder to the Resources folder in your iOS app. Once this has been added, add a call to Init to your app delegate, passing in the name of your compiled model without the extension (i.e. the name of the model folder without mlmodelc) and the type of model downloaded from the custom vision service:

public override bool FinishedLaunching(UIApplication uiApplication, NSDictionary launchOptions)  
{
   ...
   CrossImageClassifier.Current.Init("<model_name>", ModelType.General);
   return base.FinishedLaunching(uiApplication, launchOptions);
}
Uncompiled models

Add the downloaded model, called <model_name>.mlmodel, to the Resources folder in your iOS app.Once this has been added, add a call to Init to your app delegate, passing in the name of your model without the extension (i.e. the name of the model folder without mlmodel) and the type of model downloaded from the custom vision service:

public override bool FinishedLaunching(UIApplication uiApplication, NSDictionary launchOptions)  
{
   ...
   CrossImageClassifier.Current.Init("<model_name>", ModelType.General);
   return base.FinishedLaunching(uiApplication, launchOptions);
}

The call to Init will attempt to compile the model, throwning a ImageClassifierException if the compile fails.

Android

Download the tensorflow model from Custom Vision. This will be a folder containing two files.

  • labels.txt
  • model.pb

Add both these files to the Assets folder in your Android app. Once these are added, add a call to Init to your main activity passing in the name of the model file and the type of model downloaded from the custom vision service:

protected override void OnCreate(Bundle savedInstanceState)  
{
   ...
   CrossImageClassifier.Current.Init("model.pb", ModelType.General);
}

Note - the labels file must be present and called labels.txt.

Calling this from your .NET Standard library

To classify an image, call:

var tags = await CrossImageClassifier.Current.ClassifyImage(stream);  

Passing in an image as a stream. You can use a library like Xam.Plugins.Media to get an image as a stream from the camera or image library.

This will return a list of ImageClassification instances, one per tag in the model with the probabilty that the image matches that tag. Probabilities are doubles in the range of 0 - 1, with 1 being 100% probability that the image matches the tag. To find the most likely classification use:

tags.OrderByDescending(t => t.Probability)  
    .First().Tag;
Using with an IoC container

CrossImageClassifier.Current returns an instance of the IImageClassifier interface, and this can be stored inside your IoC container and injected where required.

Getting the code

If you want to see the code for this, head to https://github.com/jimbobbennett/Xam.Plugins.OnDeviceCustomVision.




About the Author

Jim Bennett

Cloud Developer Advocate at Microsoft, Xamarin Certified Developer, blogger, author of Xamarin in Action, speaker, father and lover of beer, whisky and Thai food. Opinions are mine

 

In the first part of this series I used the Azure Custom Vision service to create an image classifier to allow me to easily identify my daughters cuddly toys. Once created I tested it by uploading an image and seeing what tags the classifier found for the image.

In the second part I accessed this model from a Xamarin app, so that I could use the camera to take a photo to run through the classifier using a NuGet package that talks to the Custom Vision service.

In the third part I showed how to download this model for iOS and run it locally, on device, using CoreML.

In the fourth part I showed how to download this model for Android and run it locally, on device, using Tensorflow.

To run these models locally is a lot of boilerplate code, so I decided to make it easier and create a NuGet package containing a Xamarin plugin that can be used from iOS, Android and Xamarin.Forms apps.

This plugin makes it easy to download and use these models offline from inside your mobile app, using CoreML on iOS or Tensorflow on Android. These models can then be called from a .NET standard library, using something like Xam.Plugins.Media to take photos for classification.

Setup

Platform Support

This requires iOS 11 and up to use models from CoreML. For Android with Tensorflow the minimum supported SDK version is 21.

Usage

Before you can use this API, you need to initialise it with the model file downloaded from CustomVision. Trying to classify an image without calling Init will result in a ImageClassifierException being thrown.

iOS

Download the Core ML model from Custom Vision.

Pre-compiled models

Models can be compiled before beiong used, or compiled on the device. To use a pre-compiled model, compile the downloaded model using:

xcrun coremlcompiler compile <model_file_name>.mlmodel <model_name>.mlmodelc  

This will spit out a folder called <model_name>.mlmodelc containing a number of files. Add this entire folder to the Resources folder in your iOS app. Once this has been added, add a call to Init to your app delegate, passing in the name of your compiled model without the extension (i.e. the name of the model folder without mlmodelc) and the type of model downloaded from the custom vision service:

public override bool FinishedLaunching(UIApplication uiApplication, NSDictionary launchOptions)  
{
   ...
   CrossImageClassifier.Current.Init("<model_name>", ModelType.General);
   return base.FinishedLaunching(uiApplication, launchOptions);
}
Uncompiled models

Add the downloaded model, called <model_name>.mlmodel, to the Resources folder in your iOS app.Once this has been added, add a call to Init to your app delegate, passing in the name of your model without the extension (i.e. the name of the model folder without mlmodel) and the type of model downloaded from the custom vision service:

public override bool FinishedLaunching(UIApplication uiApplication, NSDictionary launchOptions)  
{
   ...
   CrossImageClassifier.Current.Init("<model_name>", ModelType.General);
   return base.FinishedLaunching(uiApplication, launchOptions);
}

The call to Init will attempt to compile the model, throwning a ImageClassifierException if the compile fails.

Android

Download the tensorflow model from Custom Vision. This will be a folder containing two files.

  • labels.txt
  • model.pb

Add both these files to the Assets folder in your Android app. Once these are added, add a call to Init to your main activity passing in the name of the model file and the type of model downloaded from the custom vision service:

protected override void OnCreate(Bundle savedInstanceState)  
{
   ...
   CrossImageClassifier.Current.Init("model.pb", ModelType.General);
}

Note - the labels file must be present and called labels.txt.

Calling this from your .NET Standard library

To classify an image, call:

var tags = await CrossImageClassifier.Current.ClassifyImage(stream);  

Passing in an image as a stream. You can use a library like Xam.Plugins.Media to get an image as a stream from the camera or image library.

This will return a list of ImageClassification instances, one per tag in the model with the probabilty that the image matches that tag. Probabilities are doubles in the range of 0 - 1, with 1 being 100% probability that the image matches the tag. To find the most likely classification use:

tags.OrderByDescending(t => t.Probability)  
    .First().Tag;
Using with an IoC container

CrossImageClassifier.Current returns an instance of the IImageClassifier interface, and this can be stored inside your IoC container and injected where required.

Getting the code

If you want to see the code for this, head to https://github.com/jimbobbennett/Xam.Plugins.OnDeviceCustomVision.