The Image Labeler app labels rectangular regions of interest (ROIs) for object detection, pixels for semantic segmentation, and scenes for image classification.
ROI and Scene Label Definitions
An ROI label corresponds to either a rectangular or pixel region of interest. These labels contain two components: the label name, such as "cars," and the region you create.
A Scene label describes the nature of a scene, such as "sunny." You can associate this label with a frame.
Using the Image Labeler app, you can:
Interactively specify ROI labels and scene labels.
Use rectangular ROI labels for objects such as vehicles, pedestrians, and road signs.
Use pixel labels for areas such as backgrounds, roads, and buildings.
Use scene labels for conditions such as lighting and weather conditions, or for events such as lane changes.
Use built-in detection or tracking to automatically label the regions and scene labels.
Write, import, and use your own custom automation algorithm to automatically label a region and scene labels.
Export the ground truth labels for object detector training, semantic segmentation, or image classification.
MATLAB® Toolstrip: On the Apps tab, under Image Processing and Computer Vision, click the Image Labeler.
MATLAB command prompt: Enter
To load data into the Image Labeler, from the app toolstrip, click Load. You can load the following data:
Data Source: Add images from a folder or
by using the
Label Definitions: Load a previously saved set of label definitions from a file. Label definitions specify the names and types of items to label.
Session: Load a previously saved session.
To import ROI and scene labels into the app, click Import Labels.
You can import labels from the MATLAB workspace or from previously exported MAT-files. The imported labels must
Before you can label your images, you must define the name and type of each label category. To define an ROI label, click the Define new ROI label, specify a name to represent the label, and then choose either Rectangle or Pixel label for the label type. To define a scene label, specify a descriptive name and optionally enter a description.
In addition, you can enter descriptions for ROI and scene labels that can be used as instructions for labeling.
To create labels from the MATLAB command line, use the
After you set up the ROI label definitions, you can start labeling. You can create labels manually or use an automation algorithm.
Use the Select Algorithm section to select an algorithm for automated labeling. You can use a built-in algorithm, create a custom algorithm, or import an algorithm.
Built-In Algorithm: Track people using the aggregated channel features (ACF) people detector algorithm.
Add a Custom Algorithm: To define and use a custom automation algorithm with the Image Labeler app, see Create Automation Algorithm for Labeling.
Import an Algorithm: To import your own algorithm, select Algorithm > Add Algorithm > Import Algorithm.
To export the ground truth labels to the MATLAB workspace or to a MAT-file, click Export Labels. The
labels are exported as a
groundTruth object. Click
Save to save the session. The session and the exported labels
are saved as MAT-files. You can use the exported
groundTruth object to train an object detector or semantic segmentation
network. See Train Object Detector or Semantic Segmentation Network from Ground Truth Data.
Pixel label data and ground truth data are saved in separate files. The app saves both files in the same folder. Keep these tips in mind:
groundTruth object contains the file
paths corresponding to the data source and the pixel label data. If you move
the data source and pixel label data to a different folder, to update the
paths stored within the
groundTruth object, use the
If you used an image collection to create your ground truth, do not
delete images from the location you loaded them from. The path to those
images is saved in the
You can move the
groundTruth MAT-file to
a different folder.