Contour Detection Teaching Tool with Bounding Box Overlays

Contour detection is the workhorse behind shape analysis, object counting, and document scanning, yet learners often struggle to connect the algorithm's parameters to what actually appears on screen. An interactive environment where every setting change immediately redraws the result turns a confusing API into an understandable process.

The intern builds a Streamlit application where users upload an image and OpenCV finds and draws its contours in real time. The tool walks through the full pipeline the technique requires: grayscale conversion, thresholding or edge detection to produce a binary image, contour extraction, and finally rendering, with the intern drawing both the raw contour outlines and fitted bounding boxes around each detected shape. Controls let users adjust retrieval modes and approximation methods, filter contours by area to suppress noise, and read out per-contour measurements such as area and perimeter computed from the geometry.

The project teaches the intern how classical shape analysis actually works beneath convenience functions, builds solid skills in OpenCV image preprocessing and Python, and demonstrates an ability valued in any technical role: taking a concept that confuses newcomers and building the tool that makes it click.