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High-performance Vision library in Python. Scale your research, not boilerplate.

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# 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

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

| Component                                                                                 | Description                                                                            |
| ----------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- |
| [**caer**](https://github.com/jasmcaus/caer/)                                             | A lightweight GPU-accelerated Computer Vision library for high-performance AI research |
| [**caer.color**](https://github.com/jasmcaus/caer/tree/master/caer/color)                 | Colorspace operations                                                                  |
| [**caer.data**](https://github.com/jasmcaus/caer/tree/master/caer/data)                   | Standard high-quality test images and example data                                     |
| [**caer.path**](https://github.com/jasmcaus/caer/tree/master/caer/path)                   | OS-specific path manipulations                                                         |
| [**caer.preprocessing**](https://github.com/jasmcaus/caer/tree/master/caer/preprocessing) | Image preprocessing utilities.                                                         |
| [**caer.transforms**](https://github.com/jasmcaus/caer/tree/master/caer/transforms)       | Powerful image transformations and augmentations                                       |
| [**caer.video**](https://github.com/jasmcaus/caer/tree/master/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][install]** 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:

```shell
$ pip install --upgrade caer
```

# Getting Started

## Minimal Example

```python
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](https://github.com/jasmcaus/caer/blob/master/examples/) or [Read the documentation](http://caer.rtfd.io)

# Resources

-   [**PyPi**](https://pypi.org/project/caer)
-   [**Documentation**](https://github.com/jasmcaus/caer/blob/master/docs/README.md)
-   [**Issue tracking**](https://github.com/jasmcaus/caer/issues)

# Contributing

We appreciate all contributions, feedback and issues. If you plan to contribute new features, utility functions, or extensions to the core, please go through our [Contribution Guidelines][contributing].

To contribute, start working through the `caer` codebase, read the [Documentation][docs], navigate to the [Issues][issues] tab and start looking through interesting issues.

Current contributors can be viewed either from the [Contributors][contributors] file or by using the `caer.__contributors__` command.

# Asking for help

If you have any questions, please:

1. [Read the docs](https://caer.rtfd.io/en/latest/).
2. [Look it up in our Github Discussions (or add a new question)](https://github.com/jasmcaus/caer/discussions).
3. [Search through the issues](https://github.com/jasmcaus/caer/issues).

# License

Caer is open-source and released under the [MIT License](LICENSE).

# BibTeX

If you want to cite the framework feel free to use this (but only if you loved it 😊):

```bibtex
@article{jasmcaus,
  title={Caer},
  author={Jason},
  journal={GitHub. Note: https://github.com/jasmcaus/caer},
  volume={2},
  year={2020-2025}
}
```

[contributing]: https://github.com/jasmcaus/caer/blob/master/.github/CONTRIBUTING.md
[docs]: https://caer.rtfd.io
[contributors]: https://github.com/jasmcaus/caer/blob/master/CONTRIBUTORS
[coc]: https://github.com/jasmcaus/caer/blob/master/CODE_OF_CONDUCT.md
[issues]: https://github.com/jasmcaus/caer/issues
[install]: https://github.com/jasmcaus/caer/blob/master/INSTALL.md
[demos]: https://github.com/jasmcaus/caer/blob/master/examples/
[twitter-badge]: https://twitter.com/jasmcaus
[downloads]: https://pepy.tech/project/caer
[py-versions]: https://pypi.org/project/caer/
[pypi-latest-version]: https://pypi.org/project/caer/
[license]: https://github.com/jasmcaus/caer/blob/master/LICENSE

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