Interactive Image Augmentation Preview Tool

Added Jun 2025 3 design docs

Data augmentation is one of the most effective techniques for improving computer vision models, yet learners usually apply it blindly through library defaults without ever seeing what the transformed images look like. Badly chosen augmentation silently corrupts training data, for example flipping images where orientation carries meaning, so building visual intuition matters. The intern creates a Streamlit application where users upload sample images and interactively apply the core augmentation techniques: rotation, horizontal and vertical flipping, zooming, and brightness adjustment, each implemented with OpenCV and exposed through sliders and toggles. The interface previews results instantly and displays a grid of randomized augmented variants of the same source image, exactly as a training pipeline would generate them, so users can judge whether the transformations preserve the meaning of the image. Parameter ranges can be tuned and compared, making the tool useful for classroom demonstrations and for sanity-checking augmentation settings before a real training run. The project grounds the intern in practical image manipulation with OpenCV and Python, teaches why augmentation choices are dataset-dependent, and demonstrates the ability to build a focused, genuinely useful utility with a clean interactive interface.

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