Metadata-Version: 2.1
Name: streamgrid
Version: 1.0.0
Summary: Ultra-fast multi-stream video display
Author: Muhammad Rizwan Munawar
Project-URL: Homepage, https://github.com/RizwanMunawar/streamgrid
Project-URL: Repository, https://github.com/RizwanMunawar/streamgrid
Project-URL: Issues, https://github.com/RizwanMunawar/streamgrid/issues
Project-URL: Documentation, https://github.com/RizwanMunawar/streamgrid#readme
Keywords: video,streaming,display,multi-stream,opencv,real-time,python,ultralytics
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Multimedia :: Video :: Display
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: opencv-python>=4.5.0
Requires-Dist: numpy>=1.21.0

# StreamGrid ⚡

**Ultra-fast multi-stream video display** - Display multiple video sources with object detection simultaneously using the CPU 
or GPU device.

## Installation

```bash
pip install streamgrid
```

## Quick Start

### Python

```python
from ultralytics import YOLO
from streamgrid import StreamGrid

# Video paths
paths = ["Video1.mp4", "Video2.mp4", "Video3.mp4", "Video4.mp4"]

model = YOLO("yolo11n.pt")
StreamGrid(paths, model).run()

```

## Performance

StreamGrid automatically optimizes performance:

- **1-2 streams**: 640x360 cells, up to 15 FPS each
- **3-4 streams**: 480x270 cells, up to 7 FPS each  
- **5-9 streams**: 320x180 cells, up to 5 FPS each
- **10+ streams**: 240x135 cells, up to 3 FPS each

## Contributing

Contributions welcome! Please feel free to submit a Pull Request.
