Classical Image Segmentation Sandbox with Watershed and GrabCut

Added Jun 2025 3 design docs

Separating an object from its background is a foundational operation in image editing, media production, and computer vision, but the classical algorithms that solve it, and their failure modes, remain abstract until you watch them work on your own pictures. Students and content creators alike benefit from a hands-on environment where segmentation stops being a black box. The intern builds a Streamlit application implementing two classical segmentation techniques with OpenCV: the Watershed algorithm, which grows regions from markers to find object boundaries, and GrabCut, which iteratively refines a foreground extraction from a user-drawn rectangle. Users upload their own images, adjust each algorithm's inputs, and view the resulting masks and cutouts side by side. The intern implements the marker preparation and preprocessing steps each method requires and presents intermediate stages visually so the mechanics of each algorithm are visible rather than hidden. The project builds real fluency with OpenCV's segmentation toolkit and the image preprocessing that supports it, teaches when classical methods are sufficient without any neural network, and demonstrates the intern can turn textbook algorithms into an interactive tool that teaches others by letting them experiment.

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