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, polygon 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 the imread function and additionally supports the Digital Imaging and Communication in Medicine (DICOM) format. To read additional file formats, you can create an imageDatastore and use the ReadFcn property.

When loading images, if an image has a dimension larger than 8000 pixels or is a multiresolution image, the Image Labeler app offers you the option to convert the image into a blocked image. A blocked image consists of a large image that has been divided into smaller blocks that can fit in memory. Once the Image Labeler converts the large image into a blocked, you can process it in the app as you would any other image. While using blocked images enables you to process images in the app that you might not otherwise be able to, there are some limitations. For more information, see Label Large Images in the Image Labeler.

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 ReadFcn property of the imageDatastore object specifies how to read the data.

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.

imageLabeler(gTruth)

imageLabeler(gTruth) opens the app and loads a groundTruth object . The ground truth object data source must be an image collection or an imageDatastore.

More About

ROI Labels, Sublabels, and Attributes

On the left side of the app, the ROI Labels pane contains the region of interest (ROI) label definitions that you can mark on the frames. You can create label definitions directly from this pane. Alternatively, you can create label definitions programmatically by using a labelDefinitionCreator object and then import these label definitions into an app session.

Selecting ROI Label and Drawing Tool for Your Application

You can use labeled data to validate or train algorithms such as image classifiers, object detectors, and semantic and instance segmentation networks. Consider your application when choosing a labeling drawing tool to create ROI labels. The figure below shows labeling techniques for four different applications.

Application	Label Types	Example
Image Classification	N/A	Boat, Plane
Object Detection	Line Rectangle Projected Rectangle
Semantic Segmentation	Pixel label Polygon
Instance Segmentation	Polygon

ROI Labels

An ROI label is a label that corresponds to a region of interest (ROI) in a signal frame. The table describes the supported label types.

ROI Label	Description	Scene
Rectangle	Draw rectangular ROI labels (bounding boxes) around objects.
Projected cuboid	Draw cuboidal ROI labels (3-D bounding boxes).
Line	Draw linear ROI labels to represent lines. To draw a polyline ROI, use two or more points.
Pixel label	Assign labels to pixels for semantic segmentation. You can label pixels manually using polygons, brushes, or flood fill. For more information about pixel labeling, see Label Pixels for Semantic Segmentation.
Polygon	Draw a pixel-filled polygon for instance segmentation. You can label distinct instances of the same class. For more information about instance segmentation, see Label Objects Using Polygons

ROI Sublabels

An ROI sublabel is an ROI label that belongs to a parent label. Use ROI sublabels to provide a greater level of detail about the ROIs in your labeled ground truth data. For example, a vehicle label might contain headlight, licensePlate, and wheel sublabels. You can create sublabels only for rectangular and polyline labels. For more details about sublabels, see Use Sublabels and Attributes to Label Ground Truth Data.

ROI Attributes

An ROI attribute specifies additional information about an ROI label or sublabel. For example, in an ocean scene, attributes might include the type or color of a boat. The table describes the supported attribute types.

Attribute Type	Sample Attribute Definition	Sample Default Values
Numeric Value
String
Logical
List

For more details about attributes, see Use Sublabels and Attributes to Label Ground Truth Data.

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 built-in algorithms by following these steps:

1. Import 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 (Automated Driving Toolbox) 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 (Automated Driving Toolbox) object into the MATLAB workspace. Specify the length and width ranges of the vehicle in world units, such as meters.