Classical Computer Vision in Python: Denoising, Thresholding, FFT and K-means with OpenCV

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A developer has launched a programming series called From Lab to Code, aimed at turning engineering concepts into hands-on implementations. The first project is an interactive image processing toolkit built with Python, OpenCV, NumPy, and Streamlit. It covers four classical techniques: denoising with Gaussian and median filters, object counting via thresholding and connected components, periodic noise removal using the Fast Fourier Transform, and color clustering with K-means. The article explains the difference between Gaussian and salt-and-pepper noise and why each filter suits different noise types. The toolkit requires no pretrained models, external APIs, or datasets, since all demonstration images are generated programmatically. The project's code is publicly available on GitHub.

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