How to Draw Segmentation Masks with Python
Blog post from Roboflow
Image segmentation models, such as SAM 2 and YOLOv8 Segmentation, produce masks that can be manipulated according to project needs, and visualizing these model predictions is crucial for understanding model behavior. The Roboflow supervision Python package facilitates this by allowing users to draw segmentation masks from various computer vision models onto images, using a suite of annotators. This guide demonstrates the process by installing the supervision package, loading data into a Detections object, and using the MaskAnnotator to visualize model predictions on images. Specifically, it outlines steps to load model predictions, choose an annotator, and draw predictions, using a model trained on Roboflow and deployed with Roboflow Inference. The guide emphasizes the utility of supervision for plotting segmentation masks and suggests further exploration of its functionalities, such as detection filtering and metrics, through the supervision documentation.
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