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import setuptools
DESCRIPTION = r"""
<h1 align="center"><img src="https://raw.githubusercontent.com/mantasu/glasses-detector/main/docs/_static/img/logo-light.png" width=27px height=27px> Glasses Detector</h1>
<div align="center">
[](https://colab.research.google.com/github/mantasu/glasses-detector/blob/main/notebooks/demo.ipynb)
[](https://mantasu.github.io/glasses-detector/)
[](https://pypi.org/project/glasses-detector/)
[](https://docs.python.org/3/)
[](https://developer.nvidia.com/cuda-toolkit)
[](https://zenodo.org/badge/latestdoi/610509640)
[](https://opensource.org/licenses/MIT)
</div>
## About
Package for processing images with different types of glasses and their parts. It provides a quick way to use the pre-trained models for **3** kinds of tasks, each divided into multiple categories, for instance, *classification of sunglasses* or *segmentation of glasses frames*.
<br>
<div align="center">
<table align="center"><tbody>
<tr><td><strong>Classification</string></td> <td> 👓 <em>transparent</em> 🕶️ <em>opaque</em> 🥽 <em>any</em> ➿<em>shadows</em></td></tr>
<tr><td><strong>Detection</string></td> <td> 🤓 <em>worn</em> 👓 <em>standalone</em> 👀 <em>eye-area</em></td></tr>
<tr><td><strong>Segmentation</string></td> <td> 😎 <em>full</em> 🖼️ <em>frames</em> 🦿 <em>legs</em> 🔍 <em>lenses</em> 👥 <em>shadows</em></td></tr>
</tbody></table>
$\color{gray}{\textit{Note: }\text{refer to}}$ [Glasses Detector Features](https://mantasu.github.io/glasses-detector/docs/features.html) $\color{gray}{\text{for visual examples.}}$
</div>
## Installation
> [!IMPORTANT]
> Minimum version of [Python 3.12](https://www.python.org/downloads/release/python-3120/) is **REQUIRED**. Also, you may want to install [Pytorch](https://pytorch.org/get-started/locally/) in advance to select specific configuration for your device and environment.
### Pip Package
If you only need the library with pre-trained models, just install the [pip package](https://pypi.org/project/glasses-detector/) and see **Quick Start** for usage (also check [Glasses Detector Installation](https://mantasu.github.io/glasses-detector/docs/features.html) for more details):
```bash
pip install glasses-detector
```
You can also install it from the source:
```bash
git clone https://github.com/mantasu/glasses-detector
cd glasses-detector && pip install .
```
### Local Project
If you want to train your own models on the given datasets (or on some other datasets), just clone the project and install training requirements, then see **[Running](https://github.com/mantasu/glasses-detector?tab=readme-ov-file#running)** section to see how to run training and testing.
```bash
git clone https://github.com/mantasu/glasses-detector
cd glasses-detector && pip install -r requirements.txt
```
You can create a virtual environment for your packages via [venv](https://docs.python.org/3/library/venv.html), however, if you have conda, then you can simply use it to create a new environment, for example:
```bash
conda create -n glasses-detector python=3.12
conda activate glasses-detector
```
> To set-up the datasets, refer to **[Data](https://github.com/mantasu/glasses-detector?tab=readme-ov-file#data)** section.
## Quick Start
### Command Line
You can run predictions via the command line. For example, classification of a single image and segmentation of images inside a directory can be performed by running:
```bash
glasses-detector -i path/to/img.jpg -t classification -d cuda -f int # Prints 1 or 0
glasses-detector -i path/to/img_dir -t segmentation -f mask -e .jpg # Generates masks
```
> [!TIP]
> You can also specify things like `--output-path`, `--size`, `--batch-size` etc. Check the [Glasses Detector CLI](https://mantasu.github.io/glasses-detector/docs/cli.html) and [Command Line Examples](https://mantasu.github.io/glasses-detector/docs/examples.html#command-line) for more details.
### Python Script
You can import the package and its models via the python script for more flexibility. Here is an example of how to classify people wearing sunglasses:
```python
from glasses_detector import GlassesClassifier
# Generates a CSV with each line "<img_name.jpg>,<True|False>"
classifier = GlassesClassifier(size="small", kind="sunglasses")
classifier.process_dir("path/to/dir", "path/to/preds.csv", format="bool")
```
And here is a more efficient way to process a dir for detection task (only single bbox per image is currently supported):
```python
from glasses_detector import GlassesDetector
# Generates dir_preds with bboxes as .txt for each img
detector = GlassesDetector(kind="eyes", device="cuda")
detector.process_dir("path/to/dir", ext=".txt", batch_size=64)
```
> [!TIP]
> Again, there are a lot more things that can be specified, for instance, `output_size` and `pbar`. It is also possible to directly output the results or save them in a variable. See [Glasses Detector API](https://mantasu.github.io/glasses-detector/docs/api.html) and [Python Script Examples](https://mantasu.github.io/glasses-detector/docs/examples.html#python-script) for more details.
### Demo
Feel free to play around with some [demo image files](https://github.com/mantasu/glasses-detector/demo/). For example, after installing through [pip](https://pypi.org/project/glasses-detector/), you can run:
```bash
git clone https://github.com/mantasu/glasses-detector && cd glasses-detector/data
glasses-detector -i demo -o demo_labels.csv --task classification:eyeglasses
```
You can also check out the [demo notebook](https://github.com/mantasu/glasses-detector/notebooks/demo.ipynb) which can be also accessed via [Google Colab](https://colab.research.google.com/github/mantasu/glasses-detector/blob/master/notebooks/demo.ipynb).
## Credits
For references and citation, please see [Glasses Detector Credits](https://mantasu.github.io/glasses-detector/docs/credits.html).
"""
setuptools.setup(
name="glasses-detector",
version="1.0.4",
author="Mantas Birškus",
author_email="mantix7@gmail.com",
license="MIT",
description="Glasses classification, detection, and segmentation.",
long_description=DESCRIPTION,
long_description_content_type="text/markdown",
url="https://github.com/mantasu/glasses-detector",
project_urls={
"Documentation": "https://mantasu.github.io/glasses-detector",
"Bug Tracker": "https://github.com/mantasu/glasses-detector/issues",
},
keywords=[
"python",
"pytorch",
"torchvision",
"computer",
"vision",
"image",
"face",
"eyes",
"transparent",
"opaque",
"glasses",
"googles",
"spectacles",
"eyeglasses",
"sunglasses",
"frames",
"lenses",
"legs",
"shadows",
"binary",
"identification",
"identifier",
"classification",
"classifier",
"segmentation",
"segmenter",
"detection",
"detector",
"cuda",
"mps",
"gpu",
],
install_requires=[
"tqdm",
"torch",
"torchvision",
],
classifiers=[
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Image Processing",
"Topic :: Scientific/Engineering :: Image Recognition",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Software Development :: Libraries :: Application Frameworks",
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Intended Audience :: Education",
"Intended Audience :: Developers",
"Intended Audience :: Information Technology",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Natural Language :: English",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Environment :: GPU :: NVIDIA CUDA :: 12",
"Environment :: Console",
"Environment :: MacOS X",
"Environment :: Win32 (MS Windows)",
"Environment :: Other Environment",
],
package_dir={"": "src"},
packages=setuptools.find_packages(where="src"),
entry_points={
"console_scripts": [
"glasses-detector=glasses_detector.__main__:main",
]
},
python_requires=">=3.12",
)