A Modern Computer Vision Library
I set to build ccv with a minimalism inspiration. That was back in 2010, out of the frustration with the computer vision library then I was using, ccv was meant to be a much easier to deploy, simpler organized code with a bit caution with dependency hygiene. The simplicity and minimalistic nature at then, made it much easier to integrate into any server-side deployment environments.
Fast forward to now, the world is quite different from then, but ccv adapts pretty well in this new, mobile-first environment. It now runs on Mac OSX, Linux, FreeBSD, Windows*, iPhone, iPad, Android, Raspberry Pi. In fact, anything that has a proper C compiler probably can run ccv. The majority (with notable exception of convolutional networks, which requires a BLAS library) of ccv will just work with no compilation flags or dependencies.
One core concept of ccv development is application driven. Thus, ccv ends up implementing a handful state-of-art algorithms. It includes a close to state-of-the-art image classifier, a state-of-the-art frontal face detector, reasonable collection of object detectors for pedestrians and cars, a useful text detection algorithm, a long-term general object tracking algorithm, and the long-standing feature point extraction algorithm.
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?
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.