Metadata-Version: 2.4
Name: eseg
Version: 1.0.3
Summary: Models and utilities for event-based depth / segmentation (Surreal benchmark).
Author-email: Martin Barry <martin.barry@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Martin Barry
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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Project-URL: Homepage, https://github.com/youruser/surreal-events
Project-URL: Repository, https://github.com/youruser/surreal-events
Project-URL: Issues, https://github.com/youruser/surreal-events/issues
Keywords: event-camera,computer-vision,deep-learning,pytorch,depth,surreal
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 :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: ==3.12.*
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: opencv-python>=4.9.0
Requires-Dist: torch>=2.2.0
Requires-Dist: torchvision>=0.17.0
Requires-Dist: numpy<3.0,>=1.24
Requires-Dist: h5py>=3.10
Requires-Dist: tqdm>=4.64
Requires-Dist: lpips>=0.1.4
Requires-Dist: scikit-learn>=1.2
Requires-Dist: plotly>=5.18
Requires-Dist: pytorch-ignite>=0.5.2
Requires-Dist: matplotlib>=3.7
Requires-Dist: build>=1.3.0
Requires-Dist: twine>=6.1.0
Requires-Dist: bump>=1.3.2
Requires-Dist: dv-processing==2.0.2
Requires-Dist: imageio>=2.37.0
Provides-Extra: dev
Requires-Dist: black>=24.0; extra == "dev"
Requires-Dist: ruff>=0.4.0; extra == "dev"
Requires-Dist: mypy>=1.8.0; extra == "dev"
Requires-Dist: pytest>=8.0; extra == "dev"
Requires-Dist: pytest-cov>=4.1.0; extra == "dev"
Requires-Dist: types-PyYAML; extra == "dev"
Dynamic: license-file

# Human Instance Segmentation using Evn

![](pedestrians.gif)


## Features
- ConvLSTM-based depth estimation model for event streams
- MobileNetV2 feature encoder with UNet-like decoder
- Event voxelization and augmentation utilities
- Real-time camera viewers (Metavision / DAVIS) with overlay visualization
- Mixed perceptual + edge loss utilities (LPIPS + Sobel)

## Requirements
We implemented the dataviewers on Both [dvprocessing](https://dv-processing.inivation.com/master/index.html) and [metavisionSDK](https://docs.prophesee.ai/stable/get_started/get_started_python.html)


Please install [metavisionSDK](https://docs.prophesee.ai/stable/get_started/get_started_python.html) for Prophesee live camera. 

And / Or

[dvprocessing](https://dv-processing.inivation.com/master/index.html) for Davis Cameras

For hdf5 file we use [metavisionSDK](https://docs.prophesee.ai/stable/get_started/get_started_python.html)  aedat files can be processed using [dvprocessing](https://dv-processing.inivation.com/master/index.html)

## Installation

```bash
pip install eseg
```
(Once published to PyPI.)

For development:
```bash
git clone https://github.com/youruser/eseg.git
cd eseg
python -m venv .venv
source .venv/bin/activate  # Linux / macOS
pip install -e .[dev,viewer]

```
⚠️ Warning if you work on a virtualenvironment you will need to copy your global sdk library to your local environment
```
cp -r path/to/your/metavisionsdk/metavision_* <path/to/your/virtualenv/python<yourversion>/site-packages/
```
## Quick Start
```python
import torch
from eseg.models import ConvLSTM
# TODO: usage example after final API stabilizes
```

## Live Stream
```bash
python -m eseg.live_stream
```

## Testing
```bash
pytest
```

## License
MIT. See `LICENSE`.

## Disclaimer
Research code; APIs may change before 1.0.0.
