Blocked Image Automation Algorithms - MATLAB & Simulink (original) (raw)
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The labeling apps enable you to create and import a custom automation algorithm to automatically label your data. Automation algorithms can be time-independent or time-dependent.
The Image Labeler app supports blocked image algorithms.
Create Blocked Image Automation Algorithm
To create a blocked image automation algorithm to use with a labeling app, on the app toolstrip, select > > . A class template opens, enabling you to define your algorithm. By default, the class inherits from the vision.labeler.AutomationAlgorithm and vision.labeler.mixin.BlockedImageAutomation classes, as shown by the class definition of the template:
classdef MyCustomAlgorithm < vision.labeler.AutomationAlgorithm && vision.labeler.mixin.BlockedImageAutomation
Blocked image algorithms must inherit from both of these classes. Inheriting from the blocked image mixin class enables you to access properties such as BatchSize, BorderSize and InclusionThreshold to design blocked image based algorithms. For more details on enabling blocked image properties, see the vision.labeler.mixin.BlockedImageAutomation class reference page. For more details on defining custom automation algorithms in general, see the vision.labeler.AutomationAlgorithm class reference page.
After creating your algorithm, follow the instructions in the class template on where to save the algorithm.
Run Blocked Image Automation Algorithm
To run your blocked image algorithm from the labeling app, first refresh the algorithm list. On the app toolstrip, select > . Then, reopen the list, select your algorithm, and run it on your data as you would any of the built-in automation algorithms.