Image Thresholding Techniques Playground

Added Jun 2025 3 design docs

Thresholding, converting a grayscale image into a clean black-and-white segmentation, is the first step of countless vision pipelines from document scanning to defect inspection, and choosing the wrong method quietly ruins everything downstream. Learners need to see for themselves why a global threshold fails under uneven lighting while adaptive methods succeed. The intern builds a Streamlit tool for experimenting with the three principal approaches using OpenCV: simple binary thresholding with a user-controlled cutoff, adaptive thresholding that computes local thresholds across image regions, and Otsu's method, which derives an optimal global threshold automatically from the image histogram. Users upload their own images, adjust parameters such as threshold value, block size, and constant offset with interactive sliders, and watch results update in real time, with all three methods viewable side by side on the same input for direct comparison. By building and using the playground, the intern learns to reason about when each technique applies, gains practical experience with OpenCV's thresholding APIs and Python image handling, and demonstrates the ability to construct a focused interactive learning tool, the kind of small, polished utility that showcases both technical understanding and product sense.

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