Caer -- a friendly API wrapper for OpenCV

Caer

https://github.com/jasmcaus/caer/

流形的现代计算机视觉库

Caer是一个轻量、高性能视觉库，为了高性能AI研究设计。

此框架简化了计算机视觉的使用方法，通过抽象掉非必要的模板代码，并给使用者以灵活性，快速建立深度学习原型，和研究的想法。

最终的结果是建立一个库，在设计上非常不同，容易理解，对于其它人易用，有很大的乐趣去使用。

使用目标人群，在深度学习和计算机视觉领域：

• 学生
• 研究者
• 业务爱好者
• 领域专家

Caer - Modern Computer Vision on the Fly

Caer is a lightweight, high-performance Vision library for high-performance AI research. We wrote this framework to simplify your approach towards Computer Vision by abstracting away unnecessary boilerplate code giving you the flexibility to quickly prototype deep learning models and research ideas. The end result is a library quite different in its design, that’s easy to understand, plays well with others, and is a lot of fun to use.

Our elegant, type-checked API and design philosophy makes Caer ideal for students, researchers, hobbyists and even experts in the fields of Deep Learning and Computer Vision.

Overview

此库支持GPU加速

包含以下组件：

• 颜色空间
• 样本数据
• 图像预处理
• 图像变换和增强
• 视频工具

通常适用于：

• OpenCV使用GPU特性的替代品
• 计算机视觉平台，提供最大化的特性和速度

 

Caer is a Python library that consists of the following components:

Component	Description
caer	A lightweight GPU-accelerated Computer Vision library for high-performance AI research
caer.color	Colorspace operations
caer.data	Standard high-quality test images and example data
caer.path	OS-specific path manipulations
caer.preprocessing	Image preprocessing utilities.
caer.transforms	Powerful image transformations and augmentations
caer.video	Video processing utilities

Usually, Caer is used either as:

• a replacement for OpenCV to use the power of GPUs.
• a Computer Vision research platform that provides maximum flexibility and speed.

Installation

See the Caer Installation guide for detailed installation instructions (including building from source).

Currently, caer supports releases of Python 3.6 onwards; Python 2 is not supported (nor recommended). To install the current release:

$ pip install --upgrade caer

Minimal Example

import caer

# Load a standard 640x427 test image that ships out-of-the-box with caer
sunrise = caer.data.sunrise(rgb=True)

# Resize the image to 400x400 while MAINTAINING aspect ratio
resized = caer.resize(sunrise, target_size=(400,400), preserve_aspect_ratio=True)

For more examples, see the Caer demos or Read the documentation

Sphinx Doc

https://caer.readthedocs.io/en/latest/

利用了 OpenCV 和 Pillow 库的能力， 加速你的计算机视觉工作流程，

完全兼容 其它框架 PyTorch 和 TensorFlow

Documentation

Caer is a lightweight Computer Vision library for high-performance AI research. It simplifies your approach towards Computer Vision by abstracting away unnecessary boilerplate code enabling maximum flexibility. By offering powerful image and video processing algorithms, Caer provides both casual and advanced users with an elegant interface for Machine vision operations.

It leverages the power of libraries like OpenCV and Pillow to speed up your Computer Vision workflow — making it fully compatible with other frameworks such as PyTorch and Tensorflow.

This design philosophy makes Caer ideal for students, researchers, hobbyists and even experts in the fields of Deep Learning and Computer Vision to quickly prototype deep learning models or research ideas.