Around 2010, when Lian and I was working on our gesture recognition demo, out of the frustration to abstract redundant image preprocessing operations into a set of clean and concise functions, I started to consider moving away from the stack. Why? Well, after two years, ccv is the very answer.
Many computer vision tasks nowadays consist of quite a few preprocessing layers: image pyramid generation, color space conversion etc. These potentially redundant operations cannot be easily eliminated within a mature API. ccv provides a built-in cache mechanism that, while maintains a clean function interface, effectively does transparent cache for you. - How?
While it depends on quite a few libraries for the best performance and complete feature, ccv's majority functionalities will still work without these libraries. You can even drop the ccv source code into your project, and it will work!
One core concept of ccv development is "application driven". As a result, ccv end up implementing a handful state-of-art algorithms. It includes a very fast detection algorithm for rigid object (face etc.), a strong rigid object detection algorithm (pedestrian etc.), an accurate object detection algorithm for somewhat difficult object (car, cat etc.), a deep-learning based near state-of-the-art image classifier, a state-of-the-art text detection algorithm, a long term object tracking algorithm, and the long-standing feature point detection algorithm.
For computer vision community, there is no shortage of good algorithms, good implementation is what it lacks of. After years, we stuck in between either the high-performance, battle-tested but old algorithm implementations, or the new, shining but Matlab algorithms. ccv is my take on this problem, hope you enjoy it.
ccv source code is distributed under BSD 3-clause License.
ccv's data models and documentations are distributed under Creative Commons Attribution 4.0 International License.