Attention: This MediaPipe Solutions Preview is an early release. Learn more

Object detection guide for iOS

The Object Detector task lets you detect the presence and location of multiple classes of objects. For example, an Object Detector can locate dogs within an image. These instructions show you how to use the Object Detector task in iOS. 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 Object Detector app for iOS. The example uses the camera on a physical iOS device to continuously detect objects, and can also use images and videos from the device gallery to statically detect 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 Object Detector 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:

Clone the git repository using the following command:

git clone https://github.com/googlesamples/mediapipe

Optionally, configure your git instance to use sparse checkout, so you have only the files for the Object Detector example app:
```
cd mediapipe
git sparse-checkout init --cone
git sparse-checkout set examples/object_detection/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 Object Detector example application:

ObjectDetectorService.swift: Initializes the detector, 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 detection results.
MediaLibraryViewController.swift: Implements the UI for the still image and video file input mode and visualizes the detection results.

Setup

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

Dependencies

Object Detector 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 'MyObjectDetectorApp' 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 Object Detector task requires a trained model that is compatible with this task. For more information about the available trained models for Object Detector, 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 Object Detector task by calling one of its initializers. The ObjectDetector(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 Object Detector initialized with customized configuration options, you can use the ObjectDetector(modelPath:) initializer to create an Object Detector with the default options. For more information about configuration options, see Configuration Overview.

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

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

Swift

Image

import MediaPipeTasksVision

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

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

let objectDetector = try ObjectDetector(options: options)

Video

import MediaPipeTasksVision

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

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

let objectDetector = try ObjectDetector(options: options)

livestream

import MediaPipeTasksVision

// Class that conforms to the `ObjectDetectorLiveStreamDelegate` protocol and
// implements the method that the object detector calls once it
// finishes performing detection on each input frame.
class ObjectDetectorResultProcessor: NSObject, ObjectDetectorLiveStreamDelegate {

  func objectDetector(
    _ objectDetector: ObjectDetector,
    didFinishDetection objectDetectionResult: ObjectDetectorResult?,
    timestampInMilliseconds: Int,
    error: Error?) {
    // Process the detection result or errors here.
  }
}

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

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

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

let objectDetector = try ObjectDetector(options: options)

Objective-C

Image

@import MediaPipeTasksVision;

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

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

MPPObjectDetector *objectDetector =
      [[MPPObjectDetector alloc] initWithOptions:options error:nil];

Video

@import MediaPipeTasksVision;

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

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

MPPObjectDetector *objectDetector =
      [[MPPObjectDetector alloc] initWithOptions:options error:nil];

livestream

@import MediaPipeTasksVision;

// Class that conforms to the `ObjectDetectorLiveStreamDelegate` protocol and
// implements the method that the object detector calls once it
// finishes performing detection on each input frame.

@interface APPObjectDetectorResultProcessor : NSObject 

@end

@implementation MPPObjectDetectorResultProcessor

-   (void)objectDetector:(MPPObjectDetector *)objectDetector
    didFinishDetectionWithResult:(MPPObjectDetectorResult *)ObjectDetectorResult
         timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                           error:(NSError *)error {

    // Process the detection result or errors here.

}

@end

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

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

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

MPPObjectDetector *objectDetector =
      [[MPPObjectDetector alloc] initWithOptions:options error:nil];

Configuration options

This task has the following configuration options for iOS apps:

Option Name	Description	Value Range	Default Value
`runningMode`	Sets the running mode for the task. There are three modes: IMAGE: The mode for single image inputs. VIDEO: The mode for decoded frames of a video. LIVE_STREAM: The mode for a livestream of input data, such as from a camera. In this mode, resultListener must be called to set up a listener to receive results asynchronously.	{`RunningMode.image, RunningMode.video, RunningMode.liveStream`}	`RunningMode.image`
`displayNamesLocales`	Sets the language of labels to use for display names provided in the metadata of the task's model, if available. Default is `en` for English. You can add localized labels to the metadata of a custom model using the TensorFlow Lite Metadata Writer API	Locale code	en
`maxResults`	Sets the optional maximum number of top-scored detection results to return.	Any positive numbers	-1 (all results are returned)
`scoreThreshold`	Sets the prediction score threshold that overrides the one provided in the model metadata (if any). Results below this value are rejected.	Any float	Not set
`categoryAllowlist`	Sets the optional list of allowed category names. If non-empty, detection results whose category name is not in this set will be filtered out. Duplicate or unknown category names are ignored. This option is mutually exclusive with `categoryDenylist` and using both results in an error.	Any strings	Not set
`categoryDenylist`	Sets the optional list of category names that are not allowed. If non-empty, detection results whose category name is in this set will be filtered out. Duplicate or unknown category names are ignored. This option is mutually exclusive with `categoryAllowlist` and using both results in an error.	Any strings	Not set

Livestream configuration

When the running mode is set to livestream, the Object Detector requires the additional objectDetectorLiveStreamDelegate configuration option, which enables the detector to deliver detection results asynchronously. The delegate implements the objectDetector(_objectDetector:didFinishDetection:timestampInMilliseconds:error:) method, which the Object Detector calls after processing the detection result for each frame.

Option name	Description	Value Range	Default Value
`objectDetectorLiveStreamDelegate`	Enables Object Detector to receive detection results asynchronously in livestream mode. The class whose instance is set to this property must implement the `objectDetector(_:didFinishDetection:timestampInMilliseconds:error:)` method.	Not applicable	Not set

Prepare data

You need to convert the input image or frame to an MPImage object before passing it to the Object Detector. 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.

Swift

// 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)

Objective-C

// 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. Object Detector 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 Object Detector in the image running mode.
Videos: video frames can be converted to the CVPixelBuffer format for processing, and then sent to the Object Detector 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 Object Detector in livestream mode.

Swift

// 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)

Objective-C

// 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.

Swift

// 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)

Objective-C

// 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 Object Detector, use the detect() method specific to the assigned running mode:

Still image: detect(image:)
Video: detect(videoFrame:timestampInMilliseconds:)
livestream: detectAsync(image:)

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

Swift

Image

let objectDetector.detect(image:image)

Video

let objectDetector.detect(videoFrame:image)

livestream

let objectDetector.detectAsync(image:image)

Objective-C

Image

MPPObjectDetectorResult *result = [objectDetector detectInImage:image error:nil];

Video

MPPObjectDetectorResult *result = [objectDetector detectInVideoFrame:image          timestampInMilliseconds:timestamp error:nil];

livestream

BOOL success = [objectDetector detectAsyncInImage:image
                          timestampInMilliseconds:timestamp
                                            error:nil];

The Object Detector code example shows the implementations of each of these modes in more detail detect(image:), detect(videoFrame:), and detectAsync(image:). 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 Object Detector task.
When running in image or video mode, the Object Detector 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 Object Detector task returns immediately and doesn't block the current thread. It invokes the objectDetector(_objectDetector:didFinishDetection:timestampInMilliseconds:error:) method with the detection result after processing each input frame. The Object Detector invokes this method asynchronously on a dedicated serial dispatch queue. For displaying results on the user interface, dispatch results to the main queue after processing the results. If the detectAsync function is called when the Object Detector task is busy processing another frame, the Object Detector ignores the new input frame.

Handle and display results

Upon running inference, the Object Detector task returns an ObjectDetectorResult object which describes the objects that it has found in the input image.

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

ObjectDetectorResult:
 Detection #0:
  Box: (x: 355, y: 133, w: 190, h: 206)
  Categories:
   index       : 17
   score       : 0.73828
   class name  : dog
 Detection #1:
  Box: (x: 103, y: 15, w: 138, h: 369)
  Categories:
   index       : 17
   score       : 0.73047
   class name  : dog

The following image shows a visualization of the task output:

The Object Detector example code demonstrates how to display the detection results returned from the task, see the code example for details.