Image Labeler

Label images for computer vision applications

Description

The Image Labeler app enables you to label ground truth data in a collection of images. Using the app, you can:

• Define rectangular regions of interest (ROI) labels, polyline ROI labels, pixel ROI labels, and scene labels. Use these labels to interactively label your ground truth data.

• Use built-in detection or tracking algorithms to label your ground truth data.

• Write, import, and use your own custom automation algorithm to automatically label ground truth. See Create Automation Algorithm for Labeling.

• Evaluate the performance of your label automation algorithms using a visual summary. See View Summary of Ground Truth Labels.

• Export the labeled ground truth as a groundTruth object. You can use this object for system verification or for training an object detector or semantic segmentation network. See Training Data for Object Detection and Semantic Segmentation.

The Image Labeler app supports all image file formats supported by imread. To add additional file formats to imread, use imformats.

To learn more about this app, see Get Started with the Image Labeler.

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Open the Image Labeler App

• MATLAB^® Toolstrip: On the Apps tab, under Image Processing and Computer Vision, click the app icon.

• MATLAB command prompt: Enter imageLabeler.

Programmatic Use

imageLabeler

imageLabeler opens a new session of the app, enabling you to label ground truth data in images.

imageLabeler(imageFolder)

imageLabeler(imageFolder) opens the app and loads all the images from the folder named imageFolder.

The images in the folder can be unordered and can vary in size. To label a video, or a set of ordered images that resemble a video, use the Video Labeler app instead.

imageLabeler(imageDatastore)

imageLabeler(imageDatastore) opens the app and reads all of the images from an imageDatastore object. The imageDatastore files are read using imread. For example, to open the app with a collection of stop sign images:

   stopSignsFolder = fullfile(toolboxdir('vision'),'visiondata','stopSignImages');
   imds = imageDatastore(stopSignsFolder)
   imageLabeler(imds)

imageLabeler(sessionFile)

imageLabeler(sessionFile) opens the app and loads a saved Image Labeler session, sessionFile. The sessionFile input contains the path and file name. The MAT-file that sessionFile points to contains the saved session.

Algorithms

You can use label automation algorithms to speed up labeling within the app. To create your own label automation algorithm to use within the app, see Create Automation Algorithm for Labeling. You can also use one of the provided built-in algorithms. Follow these steps:

1. Load the data you want to label, and create at least one label definition.

2. On the app toolstrip, click Select Algorithm, and select one of the built-in automation algorithms.

3. Click Automate, and then follow the automation instructions in the right pane of the automation window.

ACF People Detector

Detect and label people using aggregate channel features (ACF). This algorithm is based on the peopleDetectorACF function. To use this algorithm, you must define at least one rectangle ROI label. You do not need to draw any ROI labels.

To help improve the algorithm results, first click Settings. You can change any of these settings.

• The pretrained people detector model that the algorithm uses — The 'inria-100x41' model was trained using the INRIA person data set. The 'caltech-50x21' model was trained using the Caltech Pedestrian data set.

• The overlap ratio threshold, from 0 to 1, for detecting people — When rectangle ROIs overlap by more than this threshold, the algorithm discards one of the ROIs.

• The classification score threshold for detecting people — Increase the score to increase the prediction confidence of the algorithm. Rectangles with scores below this threshold are discarded.

ACF Vehicle Detector (requires Automated Driving Toolbox)

Detect and label vehicles using aggregate channel features (ACF). This algorithm is based on the vehicleDetectorACF function. To use this algorithm, you must define at least one rectangle ROI label. You do not need to draw any ROI labels.

To help improve the algorithm results, first click Settings. You can change any of these settings.

• The pretrained vehicle detector model that the algorithm uses — The 'full-view' model was trained using unoccluded images of the front, rear, left, and right sides of vehicles. The 'front-rear-view' model was trained using images of only the front and rear sides of the vehicle.

• The overlap ratio threshold, from 0 to 1, for detecting vehicles — When rectangle ROIs overlap by more than this threshold, the algorithm discards one of the ROIs.

• The classification score threshold for detecting vehicles — Increase the score to increase the prediction confidence of the algorithm. Rectangles with scores below this threshold are discarded.

You can also configure the detector with a calibrated monocular camera by importing a monoCamera object into the MATLAB workspace. Specify the length and width ranges of the vehicle in world units, such as meters.