Nine ways to see the world like it’s 2012

What it does This is the companion source code for Packt Publishing’s 2012 book Mastering OpenCV with Practical Computer Vision Projects. It bundles nine self-contained C++ projects—one per chapter—covering desktop, mobile, and depth-sensor applications built atop OpenCV 2.4.x. Each chapter lives in its own directory with its own README, treating the collection more like a sampler plate than a unified framework.

The interesting bit The repo is a pre-deep-learning curriculum: face recognition via Eigenfaces and Fisherfaces, head-pose estimation with AAM and POSIT, and number-plate recognition using SVM and neural networks. Seeing these techniques wired to early iOS AR, Android cartoonifiers, and Microsoft Kinect hacks makes the collection feel like a museum of classical computer vision ambition.

Key highlights

• Nine distinct chapter projects, including marker-less AR, structure-from-motion, and non-rigid face tracking.
• Cross-platform scope spanning Android, iOS, and desktop targets from a single repo.
• Explicitly targets OpenCV 2.4.2 through 2.4.11; OpenCV 3.0 is explicitly unsupported.
• Chapters rely on era-specific hardware and libraries: a Microsoft Kinect depth sensor, PCL, SSBA, and at least a 1-megapixel webcam.
• Assumes solid C/C++, CMake familiarity, and linear-algebra fundamentals; not an introductory tutorial.

Caveats

• Locked to a legacy OpenCV 2.4.x release line, so expect friction with modern toolchains and package managers.
• Several chapters require hard-to-source 2012-era dependencies or hardware (Kinect, PCL/SSBA, Apple Developer Certificates for iOS builds).
• No unified build system at the repo root; each chapter is an island with its own dependencies and training data requirements.

Verdict Worth browsing if you are maintaining legacy CV code, studying the pre-neural evolution of the field, or following the 2012 Packt book page-for-page. Skip it if you need modern, out-of-the-box deep-learning models or an actively maintained framework.