Object Detection and Counting in Images with Python and OpenCV

Manual inspection and counting of items in digital images is slow and prone to errors. Learning to automate this process with computer vision allows you to analyze hundreds of images in seconds. This text-based course guides you from the absolute basics of image processing to building a reliable object-counting system. You will learn how to load, clean, and manipulate digital images, apply thresholding, and detect contours using Python and OpenCV. What you will learn: - Understand the core concepts of digital image representation, pixels, and color spaces. - Set up a modern Python development environment using virtual environments and current packaging practices. - Apply image pre-processing techniques like grayscale conversion, blurring, and thresholding to reduce noise. - Detect edges and find contours using OpenCV to isolate individual objects. - Filter and count detected objects based on size, shape, and area properties. - Practice writing clean, type-hinted Python code to automate batch processing of multiple images. You will start with foundational image processing terminology and basic matrix operations. From there, you will progress through step-by-step written tutorials covering edge detection, contour analysis, and practical filtering techniques to build your counting pipeline. This course is designed for beginners who have a basic understanding of Python programming. No prior experience with computer vision or OpenCV is required. Start reading today to build your first computer vision program and automate image analysis.