Computer Vision Foundations: Shape Detection with OpenCV and Python

Extracting meaningful information from digital images is at the heart of modern computer vision. If you want to teach computers to recognize and analyze geometric shapes, mastering image preprocessing and contour detection is your essential first step. In this text-based course, you will transition from a beginner to a confident practitioner capable of writing clean, modern Python code to detect, isolate, and draw shapes in images. You will learn how to prepare image data, apply edge detection algorithms, and retrieve contours to extract structural properties. What you'll learn: 1. Understand foundational image processing concepts including grayscale conversion and thresholding. 2. Apply Canny edge detection to find boundaries within digital images. 3. Retrieve and analyze contours to classify and isolate geometric shapes. 4. Write clean, modern Python code utilizing type hints for better readability and maintenance. 5. Draw detected shapes and overlays back onto images using OpenCV functions. 6. Practice processing real-world shapes through step-by-step written exercises. The course begins with core image processing theory and setting up your environment, then moves systematically through preprocessing, edge detection, and advanced contour manipulation. This course is designed for beginners with basic Python knowledge who want to explore computer vision without any complex prerequisites. Start reading today and build your first shape detection program.