Most conversations about computer vision today revolve around deep learning, neural networks, and increasingly sophisticated AI models.

But not every image processing problem requires a trained model.

Sometimes, a Gaussian filter is enough to reduce noise. A threshold can separate objects from their background. A Fourier transform can reveal interference that is difficult to understand in the spatial domain.

These techniques are relatively simple, mathematically interpretable, and still useful in practical applications.

Recently, while exploring image and signal processing concepts in my computer engineering studies, I decided to start a small programming series called From Lab to Code .

The idea is simple: take engineering concepts I encounter, build independent implementations, visualize the results, and share what I learn.

For the first project, I built a small interactive image processing toolkit using Python, OpenCV, NumPy, and Streamlit.

GitHub repository:…