Applied Computer Vision: Image and Video Processing with OpenCV

Visual data is everywhere, yet transforming raw pixels into actionable insights remains one of the most exciting challenges in software development. This comprehensive text-based course guides you through the core concepts of computer vision, helping you write clean, efficient Python code to analyze images and videos. You will transition from a beginner to a confident practitioner capable of building robust computer vision pipelines. By reading through clear conceptual explanations and analyzing practical, modern code snippets, you will master the foundational mathematics and algorithms that power modern visual software. What you'll learn: - Understand foundational computer vision concepts, coordinate systems, and image representation in code. - Perform essential image manipulations including cropping, resizing, color space conversions, and geometric transformations. - Apply advanced image processing techniques such as thresholding, edge detection, filtering, and morphological operations. - Process live video feeds and file inputs to analyze motion and temporal data in real time. - Implement object detection and image alignment using modern OpenCV features and deep learning-based DNN modules. - Configure clean Python development environments using virtual environments and write type-hinted computer vision pipelines. The curriculum starts with essential terminology and basic concepts before moving systematically through image transformations, video handling, and practical object detection patterns. You will progress through structured written lessons designed to build your skills step-by-step. This course is designed for beginners, developers, and tech enthusiasts who want to enter the field of computer vision. No prior experience with image processing is required, though a basic familiarity with Python is helpful. Start your journey into the world of computer vision and learn how to programmatically analyze visual data today.