The Image Labeler app provides an easy way to label rectangular regions of interest (ROIs) for object detection, pixels for semantic segmentation, and scenes for image classification.
ROI and Scene Label Definitions
ROI Label corresponds to either a rectangle or pixels. These labels include the name, such as "cars", and the ROI you create.
Scene Labels describe the nature of a scene, such as "sunny." You can associate this label with a frame.
Using this app, you can:
Interactively specify rectangular and pixel regions and scene labels.
Use pixel regions for areas such as backgrounds, roads, and buildings.
Use scene labels for conditions like lighting, weather conditions, or events like 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 app icon.
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 ROIs 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 plus sign, then specify a name to represent the label and then choose either Rectangle or Line for its 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.
After you set up the ROI label definitions, you can start labeling. You can do the labeling manually or use an automation algorithm to perform the labeling.
To draw ROI labels manually, select an ROI label definition from the left pane and use the mouse to draw the regions on the image frames.
To label individual pixels, see Label Pixels for Semantic Segmentation.
To mark scene labels manually, select a scene label defintion from the left pane and then click Add Label.
Use the Select Algorithm section to select an algorithm for automated labeling. You can use a built algorithm, import an algorithm, or you can create one.
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 Image Labeling.
Import an Algorithm: To import your own algorithm, selectAlgorithm > Add Algorithm > Import Algorithm.
Click Automate. Only ROI and scene label definitions that are valid for the selected algorithm are used. Valid label definitions are enabled in the left pane and algorithm instructions appear in the right pane.
Examine the results of running the algorithm. If they were not satisfactory, click Undo Run and change algorithm settings by clicking Settings.
When you are satisfied with the algorithm results, click Accept. Click Cancel to delete the labels generated during the automation session. The Cancel button cancels only the algorithm session, not the app session.
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. See Train an Object Detector from Ground Truth Data (Automated Driving System Toolbox).