eseg

Name	eseg JSON
Version	1.0.0 JSON
	download
home_page	None
Summary	Models and utilities for event-based depth / segmentation (Surreal benchmark).
upload_time	2025-08-22 15:33:37
maintainer	None
docs_url	None
author	None
requires_python	==3.12.*
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 copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords	event-camera computer-vision deep-learning pytorch depth surreal
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # surreal-events

Event-based depth / segmentation utilities and models (experimental).  

## 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)

## 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]
```

## 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.

            

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