Metadata-Version: 2.4
Name: cie-utils
Version: 0.2.0
Summary: Image processing based on CIELAB color space.
License: MIT
License-File: LICENSE
Keywords: image-processing,cielab,color-space,computer-vision
Author: Jesus Alberto Rodriguez Henao
Author-email: jarh1992@gmail.com
Requires-Python: >=3.11,<4.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Dist: matplotlib (>=3.10.0,<4.0.0)
Requires-Dist: numpy (>=2.2.0,<3.0.0)
Requires-Dist: opencv-python (>=4.12.0,<5.0.0)
Requires-Dist: pandas (>=2.3.0,<3.0.0)
Requires-Dist: pytz (>=2025.2,<2026.0)
Requires-Dist: pyyaml (>=6.0.0,<7.0.0)
Requires-Dist: scikit-image (>=0.25.0,<0.26.0)
Requires-Dist: scikit-learn (>=1.7.0,<2.0.0)
Requires-Dist: scipy (>=1.16.0,<2.0.0)
Project-URL: Documentation, https://jarh1992.github.io/cie-utils/
Project-URL: Homepage, https://github.com/jarh1992/cie-utils
Project-URL: Repository, https://github.com/jarh1992/cie-utils
Description-Content-Type: text/markdown

# 🌈 cie-utils

> Advanced image processing in Python using the **CIELAB** color space.

[![CI](https://github.com/jarh1992/cie-utils/actions/workflows/ci.yml/badge.svg)](https://github.com/jarh1992/cie-utils/actions/workflows/ci.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/Python-3.10+-blue?logo=python)](https://www.python.org/)
[![pre-commit](https://img.shields.io/badge/code%20style-pre--commit%20%7C%20black%2C%20isort%2C%20flake8-blueviolet?logo=pre-commit)](https://pre-commit.com/)

---

`cie-utils` is a Python library for **image preprocessing and color analysis** using the perceptually uniform CIELAB color space.

---

## ✨ Features

✅ RGB ➡️ LAB / Lch conversion \
✅ CLAHE, PCA, false color filters \
✅ Background removal via pixel std deviation \
✅ Cluster-based segmentation: KMeans, GaussianMixture, Agglomerative \
✅ Histogram, PDF, and scatter plot visualization \
✅ CSV export of processed image stats \
✅ Clean and modular structure for reuse

---

## 📦 Installation

**From PyPI**

```bash
pip install cie-utils
```

**From GitHub**

```bash
pip install git+https://github.com/jarh1992/cie-utils.git
```

**From local source (for development)**

```bash
# Using Poetry (recommended for development)
poetry install

# Or using pip
pip install .
```

---

## 🧪 Example Usage

```python
from cie-utils import normalize_img, rgb2lab

lab_img = rgb2lab(normalize_img(img, ref_img))
```

---

## 🧰 Development & Code Style

This project uses:

- 🖤 [`black`](https://github.com/psf/black) for code formatting
- 🔠 [`isort`](https://github.com/PyCQA/isort) for import sorting
- 🧼 [`flake8`](https://github.com/pycqa/flake8) for linting
- ✅ [`pytest`](https://docs.pytest.org/) for testing
- 🧪 `pre-commit` hooks enabled in `.pre-commit-config.yaml`

To install dev dependencies:

```bash
pip install -e .[dev]
pre-commit install
```

---

## 📚 Documentation

Documentation is automatically generated with **Sphinx** and hosted via **GitHub Pages** at:

👉 https://jarh1992.github.io/cie-utils/

---

## 📝 License

MIT License. See [LICENSE](LICENSE) for details.

