Image segmentation guide for iOS

The Image Segmenter task lets you divide images into regions based on predefined categories, and apply visual effects like background blurring. These instructions show you how to use the Image Segmenter with iOS apps.

The code sample described in these instructions is available on GitHub.

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

Code example

The MediaPipe Tasks code example contains a simple implementation of an Image Segmenter app for iOS.

The example implements an image segmenter that outputs category masks. It uses the camera on a physical iOS device to perform image segmentation on a live camera feed, or on images and videos from the device gallery.

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 Segmenter 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 Segmenter example app:

   cd mediapipe
   git sparse-checkout init --cone
   git sparse-checkout set examples/image_segmentation/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 Segmenter example application:

• ImageSegmenterService.swift: Initializes the Image Segmenter, 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 Segmenter. 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 Segmenter 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 'MyImageSegmenterApp' 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 Segmenter task requires a trained model that is compatible with this task. For more information about the available trained models for Image Segmenter, 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 Segmenter task by calling one of its initializers. The ImageSegmenter(options:) initializer accepts values for the configuration options.

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

The Image Segmenter task supports 3 input data types: still images, video files and live video streams. By default, ImageSegmenter(modelPath:) initializes a task for still images. If you want your task to be initialized to process video files or live video streams, use ImageSegmenter(options:) to specify the video or livestream running mode. The livestream mode also requires the additional imageSegmenterLiveStreamDelegate configuration option, which enables the Image Segmenter to deliver image segmentation 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 = ImageSegmenterOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .image
options.shouldOutputCategoryMask = true
options.shouldOutputConfidenceMasks = false

let imageSegmenter = try ImageSegmenter(options: options)
    

import MediaPipeTasksVision

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

let options = ImageSegmenterOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .video
options.shouldOutputCategoryMask = true
options.shouldOutputConfidenceMasks = false

let imageSegmenter = try ImageSegmenter(options: options)
    

import MediaPipeTasksVision

// Class that conforms to the `imageSegmenterLiveStreamDelegate` protocol and
// implements the method that the image segmenter calls once it finishes
// performing segmentation of each input frame.
class ImageSegmenterResultProcessor: NSObject, ImageSegmenterLiveStreamDelegate {

  func imageSegmenter(
    _ imageSegmenter: ImageSegmenter,
    didFinishSegmentation result: ImageSegmenterResult?,
    timestampInMilliseconds: Int,
    error: Error?) {

    // Process the image segmentation result or errors here.

  }
}

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

let options = ImageSegmenterOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .liveStream
options.shouldOutputCategoryMask = true
options.shouldOutputConfidenceMasks = false

// Set `imageSegmenterLiveStreamDelegate` to the object of the class that
// confirms to the `ImageSegmenterLiveStreamDelegate` protocol.
let processor = ImageSegmenterResultProcessor()
options.imageSegmenterLiveStreamDelegate = processor

let imageSegmenter = try ImageSegmenter(options: options)
    

Objective-C

@import MediaPipeTasksVision;

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

MPPImageSegmenterOptions *options = [[MPPImageSegmenterOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeImage;
options.shouldOutputCategoryMask = YES;
options.shouldOutputConfidenceMasks = NO;

MPPImageSegmenter *imageSegmenter =
  [[MPPImageSegmenter alloc] initWithOptions:options error:nil];
    

@import MediaPipeTasksVision;

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

MPPImageSegmenterOptions *options = [[MPPImageSegmenterOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeVideo;
options.shouldOutputCategoryMask = YES;
options.shouldOutputConfidenceMasks = NO;

MPPImageSegmenter *imageSegmenter =
  [[MPPImageSegmenter alloc] initWithOptions:options error:nil];
    

@import MediaPipeTasksVision;

// Class that conforms to the `MPPImageSegmenterLiveStreamDelegate` protocol
// and implements the method that the image segmenter calls once it finishes
// performing segmentation of each input frame.

@interface APPImageSegmenterResultProcessor : NSObject 

@end

@implementation APPImageSegmenterResultProcessor

-   (void)imageSegmenter:(MPPImageSegmenter *)imageSegmenter
    didFinishSegmentationWithResult:(MPPImageSegmenterResult *)imageSegmenterResult
         timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                           error:(NSError *)error {

    // Process the image segmentation result or errors here.

}

@end

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

MPPImageSegmenterOptions *options = [[MPPImageSegmenterOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeLiveStream;
options.shouldOutputCategoryMask = YES;
options.shouldOutputConfidenceMasks = NO;

// Set `imageSegmenterLiveStreamDelegate` to the object of the class that
// confirms to the `MPPImageSegmenterLiveStreamDelegate` protocol.
APPImageSegmenterResultProcessor *processor =
  [APPImageSegmenterResultProcessor new];
options.imageSegmenterLiveStreamDelegate = processor;

MPPImageSegmenter *imageSegmenter =
  [[MPPImageSegmenter alloc] initWithOptions:options error:nil];
    

The Image Segmenter example code implementation allows the user to switch between processing modes. The approach makes the task creation code more complicated and may not be appropriate for your use case.

Configuration options

This task has the following configuration options for iOS apps:

When the running mode is set to LIVE_STREAM, the Image Segmenter requires the additional imageSegmenterLiveStreamDelegate configuration option, which enables the Image Segmenter to deliver image segmentation results asynchronously. The delegate must implement the imageSegmenter(_:didFinishSegmentation:timestampInMilliseconds:error:) method, which the Image Segmenter calls after processing the results of performing segmentation on each frame.

Prepare data

You need to convert the input image or frame to an MPImage object before passing it to the Image Segmenter. 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 Segmenter 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 Segmenter in the image running mode.

• Videos: video frames can be converted to the CVPixelBuffer format for processing, and then sent to the Image Segmenter 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 Segmenter 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 Segmenter, use the segment() method specific to the assigned running mode:

• Still image: segment(image:)
• Video: segment(videoFrame:timestampInMilliseconds:)
• Livestream: segmentAsync(image:timestampInMilliseconds:)

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

Swift

let result = try imageSegmenter.segment(image: image)
    

let result = try imageSegmenter.segment(
  videoFrame: image,
  timestampInMilliseconds: timestamp)
    

try imageSegmenter.segmentAsync(
  image: image,
  timestampInMilliseconds: timestamp)
    

Objective-C

MPPImageSegmenterResult *result =
  [imageSegmenter segmentImage:image error:nil];
    

MPPImageSegmenterResult *result =
  [imageSegmenter segmentVideoFrame:image
            timestampInMilliseconds:timestamp
                              error:nil];
    

BOOL success =
  [imageSegmenter segmentAsyncImage:image
            timestampInMilliseconds:timestamp
                              error:nil];
    

The Image Segmenter code example shows the implementations of each of these modes in more detail segment(image:), segment(videoFrame:timestampInMilliseconds:), and segmentAsync(image:timestampInMilliseconds:).

Note the following:

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

• When running in image or video mode, the Image Segmenter 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 live stream mode, the Image Segmenter task returns immediately and doesn't block the current thread. It invokes the imageSegmenter(_:didFinishSegmentation:timestampInMilliseconds:error:) method with the image segmenter after processing each input frame. The Image Segmenter 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 segmentAsync function is called when the Image Segmenter task is busy processing another frame, the Image Segmenter ignores the new input frame.

Handle and display results

Upon running inference, the Image Segmenter task returns a ImageSegmenterResult object which contains the results of the segmentation task. The content of the output depends on the output type you set when you configured the task.

The following images show a visualization of the task output for a category value mask. The category mask range is [0, 255] and each pixel value represents the winning category index of the model output. The winning category index is has the highest score among the categories the model can recognize.

Original image and category mask output. Source image from the Pascal VOC 2012 dataset.

The Image Segmenter example code demonstrates how to display the Image Segmenter results, see the code example for details.