Metadata-Version: 2.4
Name: eseg
Version: 1.1.0.post1
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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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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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: 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

# eseg

Event-based depth/segmentation research package with ConvLSTM models, data utilities, and live camera streaming helpers.

![](pedestrians.gif)

## Features

- ConvLSTM-based models for event-stream inference
- Event voxelization and preprocessing helpers
- Utilities for HDF5/AEDAT4/RAW event data
- Live streaming pipeline for Prophesee and DAVIS cameras
- Training helpers (losses, plotting, and evaluation utilities)

## Python compatibility

This release currently targets **Python 3.12**.

## Installation

Install from PyPI:

```bash
pip install eseg
```

Install from source (development):

```bash
git clone <your-repository-url>
cd eseg
python -m venv .venv
source .venv/bin/activate  # Linux/macOS
pip install -e .[dev]
```

## Optional runtime dependencies for live cameras

For camera streaming, install one or both vendor SDKs:

- Prophesee: [Metavision SDK](https://docs.prophesee.ai/stable/get_started/get_started_python.html)
- iniVation DAVIS: [dv-processing](https://dv-processing.inivation.com/master/index.html)

If you use GPU inference/training, install a CUDA-enabled PyTorch build first:
[https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)

## Quick start

```python
import eseg
from eseg.models.ConvLSTM import EConvlstm

print(eseg.__version__)
model = EConvlstm(light=False)
```

## Run live stream

```bash
python -m eseg.stream --help
```

Example:

```bash
python -m eseg.stream -m full --slice-time-ms 100
```

## Development

Run tests:

```bash
pytest
```

## License

MIT License. See LICENSE.

## Notes

This is research-oriented software; interfaces may evolve between releases.
