For developers starting with OpenCV, three books stand out as foundational resources. These books balance practical examples with clear explanations of core concepts, making them ideal for building a strong understanding of computer vision and OpenCV’s tools. They focus on hands-on learning while avoiding unnecessary jargon, which is critical for beginners.
First, “Learning OpenCV 4 Computer Vision with Python 3” by Joseph Howse and Joe Minichino is an excellent starting point. It introduces OpenCV’s Python bindings, which are widely used due to their simplicity and accessibility. The book walks through installation, basic image operations (like filtering and edge detection), and progresses to advanced topics like object detection and facial recognition. For example, it includes projects such as building a face detector using Haar cascades and working with depth cameras. The Python-centric approach ensures developers can quickly experiment with code without getting bogged down by complex setup. This book is particularly useful for those who want to apply OpenCV in real-time applications or integrate it with libraries like NumPy.
Next, “OpenCV 4 with Python Blueprints” by Michael Beyeler focuses on practical projects to reinforce learning. It guides readers through building applications like a motion-tracking system, a document scanner, and an augmented reality overlay. Each project introduces new concepts, such as feature matching, optical flow, or camera calibration, while emphasizing problem-solving. For instance, the motion-tracking example teaches background subtraction and contour analysis, which are essential for video surveillance or robotics. This book is ideal for developers who learn best by creating tangible solutions rather than memorizing theory. It also covers integrating OpenCV with frameworks like TensorFlow for basic machine learning tasks.
Finally, “Learning OpenCV” (3rd Edition) by Gary Bradski and Adrian Kaehler provides a comprehensive, C+±oriented perspective. While newer editions exist, this classic remains relevant for explaining core algorithms (e.g., image transformations, camera models) in detail. It’s particularly valuable for understanding the underlying math, such as homography matrices or the pinhole camera model, which are critical for advanced applications like 3D reconstruction. Though some code examples may require updates for OpenCV 4, the conceptual clarity makes it a reference for developers aiming to work on performance-critical systems or contribute to OpenCV itself. Pairing this with online documentation helps bridge gaps between theory and modern implementations.