Image classification guide for iOS

The Image Classifier task lets you perform classification on images. You can use this task to identify what an image represents among a set of categories defined at training time. These instructions show you how to use the Image Classifier in iOS apps. The code sample described in these instructions is available on GitHub.

You can see this task in action by viewing this Web demo. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The MediaPipe Tasks example code is a basic implementation of an Image Classifier app for iOS. The example uses the camera on a physical iOS device to continuously classify objects, and can also use images and videos from the device gallery to statically classify objects.

You can use the app as a starting point for your own iOS app, or refer to it when modifying an existing app. The Image Classifier example code is hosted on GitHub.

Download the code

The following instructions show you how to create a local copy of the example code using the git command line tool.

To download the example code:

1. Clone the git repository using the following command:

   git clone https://github.com/google-ai-edge/mediapipe-samples
   

2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Image Classifier example app:

   cd mediapipe
   git sparse-checkout init --cone
   git sparse-checkout set examples/image_classification/ios/
   

After creating a local version of the example code, you can install the MediaPipe task library, open the project using Xcode and run the app. For instructions, see the Setup Guide for iOS.

Key components

The following files contain the crucial code for the Image Classifier example application:

• ImageClassifierService.swift: Initializes the Image Classifier, handles the model selection, and runs inference on the input data.
• CameraViewController.swift: Implements the UI for the live camera feed input mode and visualizes the results.
• MediaLibraryViewController.swift Implements the UI for the still image and video file input mode and visualizes the results.

Setup

This section describes key steps for setting up your development environment and code projects to use Image Classifier. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for iOS.

Dependencies

Image Classifier uses the MediaPipeTasksVision library, which must be installed using CocoaPods. The library is compatible with both Swift and Objective-C apps and does not require any additional language-specific setup.

For instructions to install CocoaPods on macOS, refer to the CocoaPods installation guide. For instructions on how to create a Podfile with the necessary pods for your app, refer to Using CocoaPods.

Add the MediaPipeTasksVision pod in the Podfile using the following code:

target 'MyImageClassifierApp' do
  use_frameworks!
  pod 'MediaPipeTasksVision'
end

If your app includes unit test targets, refer to the Set Up Guide for iOS for additional information on setting up your Podfile.

Model

The MediaPipe Image Classifier task requires a trained model that is compatible with this task. For more information about the available trained models for Image Classifier, see the task overview Models section.

Select and download a model, and add it to your project directory using Xcode. For instructions on how to add files to your Xcode project, refer to Managing files and folders in your Xcode project.

Use the BaseOptions.modelAssetPath property to specify the path to the model in your app bundle. For a code example, see the next section.

Create the task

You can create the Image Classifier task by calling one of its initializers. The ImageClassifier(options:) initializer sets values for configuration options including running mode, display names locale, max number of results, confidence threshold, category allowlist and denylist.

If you don't need an Image Classifier initialized with customized configuration options, you can use the ImageClassifier(modelPath:) initializer to create an Image Classifier with the default options. For more information about configuration options, see Configuration Overview.

The Image Classifier task supports 3 input data types: still images, video files and live video streams. By default, ImageClassifier(modelPath:) initializes a task for still images. If you want your task to be initialized to process video files or live video streams, use ImageClassifier(options:) to specify the video or livestream running mode. The livestream mode also requires the additional imageClassifierLiveStreamDelegate configuration option, which enables the Image Classifier to deliver image classification results to the delegate asynchronously.

Choose the tab corresponding to your running mode to see how to create the task and run inference.

Swift

import MediaPipeTasksVision

let modelPath = Bundle.main.path(forResource: "model",
                                      ofType: "tflite")

let options = ImageClassifierOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .image
options.maxResults = 5

let imageClassifier = try ImageClassifier(options: options)
    

import MediaPipeTasksVision

let modelPath = Bundle.main.path(forResource: "model",
                                      ofType: "tflite")

let options = ImageClassifierOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .video
options.maxResults = 5

let imageClassifier = try ImageClassifier(options: options)
    

import MediaPipeTasksVision

// Class that conforms to the `ImageClassifierLiveStreamDelegate` protocol and
// implements the method that the image classifier calls once it
// finishes performing classification on each input frame.
class ImageClassifierResultProcessor: NSObject, ImageClassifierLiveStreamDelegate {

   func imageClassifier(
    _ imageClassifier: ImageClassifier,
    didFinishClassification result: ImageClassifierResult?,
    timestampInMilliseconds: Int,
    error: Error?) {

    // Process the image classifier result or errors here.

  }
}

let modelPath = Bundle.main.path(
  forResource: "model",
  ofType: "tflite")

let options = ImageClassifierOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .liveStream
options.maxResults = 5

// Assign an object of the class to the `imageClassifierLiveStreamDelegate`
// property.
let processor = ImageClassifierResultProcessor()
options.imageClassifierLiveStreamDelegate = processor

let imageClassifier = try ImageClassifier(options: options)
    

Objective-C

@import MediaPipeTasksVision;

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model"
                                                      ofType:@"tflite"];

MPPImageClassifierOptions *options = [[MPPImageClassifierOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeImage;
options.maxResults = 5;

MPPImageClassifier *imageClassifier =
      [[MPPImageClassifier alloc] initWithOptions:options error:nil];
    

@import MediaPipeTasksVision;

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model"
                                                      ofType:@"tflite"];

MPPImageClassifierOptions *options = [[MPPImageClassifierOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeVideo;
options.maxResults = 5;

MPPImageClassifier *imageClassifier =
      [[MPPImageClassifier alloc] initWithOptions:options error:nil];
    

@import MediaPipeTasksVision;

// Class that conforms to the `MPPImageClassifierLiveStreamDelegate` protocol
// and implements the method that the image classifier calls once it finishes
// performing classification on each input frame.

@interface APPImageClassifierResultProcessor : NSObject 

@end

@implementation APPImageClassifierResultProcessor

-   (void)imageClassifier:(MPPImageClassifier *)imageClassifier
    didFinishClassificationWithResult:(MPPImageClassifierResult *)imageClassifierResult
              timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                error:(NSError *)error {

    // Process the image classifier result or errors here.

}

@end

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model"
                                                      ofType:@"tflite"];

MPPImageClassifierOptions *options = [[MPPImageClassifierOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeLiveStream;
options.maxResults = 5;

// Assign an object of the class to the `imageClassifierLiveStreamDelegate`
// property.
APPImageClassifierResultProcessor *processor = [APPImageClassifierResultProcessor new];
options.imageClassifierLiveStreamDelegate = processor;

MPPImageClassifier *imageClassifier =
      [[MPPImageClassifier alloc] initWithOptions:options error:nil];
    

Configuration options

This task has the following configuration options for iOS apps:

Livestream configuration

When the running mode is set to livestream, the Image Classifier requires the additional imageClassifierLiveStreamDelegate configuration option, which enables the classifier to deliver classification results asynchronously. The delegate implements the imageClassifier(_:didFinishClassification:timestampInMilliseconds:error:) method, which the Image Classifier calls after processing the classification results for each frame.

Prepare data

You need to convert the input image or frame to an MPImage object before passing it to the Image Classifier. MPImage supports different types of iOS image formats, and can use them in any running mode for inference. For more information about MPImage, refer to the MPImage API.

Choose an iOS image format based on your use case and the running mode your application requires.MPImage accepts the UIImage, CVPixelBuffer, and CMSampleBuffer iOS image formats.

UIImage

The UIImage format is well-suited for the following running modes:

• Images: images from an app bundle, user gallery, or file system formatted as UIImage images can be converted to an MPImage object.

• Videos: use AVAssetImageGenerator to extract video frames to the CGImage format, then convert them to UIImage images.

// Load an image on the user's device as an iOS `UIImage` object.

// Convert the `UIImage` object to a MediaPipe's Image object having the default
// orientation `UIImage.Orientation.up`.
let image = try MPImage(uiImage: image)
    

// Load an image on the user's device as an iOS `UIImage` object.

// Convert the `UIImage` object to a MediaPipe's Image object having the default
// orientation `UIImageOrientationUp`.
MPImage *image = [[MPPImage alloc] initWithUIImage:image error:nil];
    

The example initializes an MPImage with the default UIImage.Orientation.Up orientation. You can initialize an MPImage with any of the supported UIImage.Orientation values. Image Classifier does not support mirrored orientations like .upMirrored, .downMirrored, .leftMirrored, .rightMirrored.

For more information about UIImage, refer to the UIImage Apple Developer Documentation.

CVPixelBuffer

The CVPixelBuffer format is well-suited for applications that generate frames and use the iOS CoreImage framework for processing.

The CVPixelBuffer format is well-suited for the following running modes:

• Images: apps that generate CVPixelBuffer images after some processing using iOS's CoreImage framework can be sent to the Image Classifier in the image running mode.

• Videos: video frames can be converted to the CVPixelBuffer format for processing, and then sent to the Image Classifier in video mode.

• livestream: apps using an iOS camera to generate frames may be converted into the CVPixelBuffer format for processing before being sent to the Image Classifier in livestream mode.

// Obtain a CVPixelBuffer.

// Convert the `CVPixelBuffer` object to a MediaPipe's Image object having the default
// orientation `UIImage.Orientation.up`.
let image = try MPImage(pixelBuffer: pixelBuffer)
    

// Obtain a CVPixelBuffer.

// Convert the `CVPixelBuffer` object to a MediaPipe's Image object having the
// default orientation `UIImageOrientationUp`.
MPImage *image = [[MPPImage alloc] initWithUIImage:image error:nil];
    

For more information about CVPixelBuffer, refer to the CVPixelBuffer Apple Developer Documentation.

CMSampleBuffer

The CMSampleBuffer format stores media samples of a uniform media type, and is well-suited for the livestream running mode. Live frames from iOS cameras are asynchronously delivered in the CMSampleBuffer format by iOS AVCaptureVideoDataOutput.

// Obtain a CMSampleBuffer.

// Convert the `CMSampleBuffer` object to a MediaPipe's Image object having the default
// orientation `UIImage.Orientation.up`.
let image = try MPImage(sampleBuffer: sampleBuffer)
    

// Obtain a `CMSampleBuffer`.

// Convert the `CMSampleBuffer` object to a MediaPipe's Image object having the
// default orientation `UIImageOrientationUp`.
MPImage *image = [[MPPImage alloc] initWithSampleBuffer:sampleBuffer error:nil];
    

For more information about CMSampleBuffer, refer to the CMSampleBuffer Apple Developer Documentation.

Run the task

To run the Image Classifier, use the classify() method specific to the assigned running mode:

• Still image: classify(image:)
• Video: classify(videoFrame:timestampInMilliseconds:)
• livestream: classifyAsync(image:timestampInMilliseconds:)

The Image Classifier returns the possible categories for the object within the input image or frame.

The following code samples show basic examples of how to run Image Classifier in these different running modes:

Swift

let result = try imageClassifier.classify(image: image)
    

let result = try imageClassifier.classify(
  videoFrame: image,
  timestampInMilliseconds: timestamp)
    

try imageClassifier.classifyAsync(
  image: image,
  timestampInMilliseconds: timestamp)
    

Objective-C

MPPImageClassifierResult *result = [imageClassifier classifyImage:image
                                                            error:nil];
    

MPPImageClassifierResult *result = [imageClassifier classifyVideoFrame:image
                                               timestampInMilliseconds:timestamp
                                                                 error:nil];
    

BOOL success = [imageClassifier classifyAsyncImage:image
                          timestampInMilliseconds:timestamp
                                            error:nil];
    

The Image Classifier code example shows the implementations of each of these modes in more detail classify(image:), classify(videoFrame:timestampInMilliseconds:), and classifyAsync(image:timestampInMilliseconds:). The example code allows the user to switch between processing modes which may not be required for your use case.

Note the following:

• When running in video mode or livestream mode, you must also provide the timestamp of the input frame to the Image Classifier task.

• When running in image or video mode, the Image Classifier task blocks the current thread until it finishes processing the input image or frame. To avoid blocking the current thread, execute the processing in a background thread using iOS Dispatch or NSOperation frameworks.

• When running in livestream mode, the Image Classifier task returns immediately and doesn't block the current thread. It invokes the imageClassifier(_:didFinishClassification:timestampInMilliseconds:error:) method with the classification result after processing each input frame. The Image Classifier invokes this method asynchronously on a dedicated serial dispatch queue. For displaying results on the user interface, dispatch the results to the main queue after processing the results. If the classifyAsync function is called when the Image Classifier task is busy processing another frame, the Image Classifier ignores the new input frame.

Handle and display results

Upon running inference, the Image Classifier task returns an ImageClassifierResult object which contains the list of possible categories for the objects within the input image or frame.

The following shows an example of the output data from this task:

ImageClassifierResult:
 Classifications #0 (single classification head):
  head index: 0
  category #0:
   category name: "/m/01bwb9"
   display name: "Passer domesticus"
   score: 0.91406
   index: 671
  category #1:
   category name: "/m/01bwbt"
   display name: "Passer montanus"
   score: 0.00391
   index: 670

This result has been obtained by running the Bird Classifier on:

The Image Classifier example code demonstrates how to display the classification results returned from the task, see the code example for details.