Repository files navigation

• GitHub-Hosted:
• Linux x64:
• Mac ARM64:
• Raspberry Pi 4:
• Clang Analyzer:
• Test Coverage:

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 isapplication 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.

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.

https://libnnc.org

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.