Metadata-Version: 2.1
Name: asone
Version: 0.1.2.dev3
Summary: UNKNOWN
Home-page: https://github.com/axcelerateai/asone
Author: AxcelerateAI
Author-email: 
License: BSD 2-clause
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
Requires-Dist: Cython
Requires-Dist: easydict
Requires-Dist: gdown
Requires-Dist: lap
Requires-Dist: loguru
Requires-Dist: norfair
Requires-Dist: numpy
Requires-Dist: onnxruntime-gpu (==1.12.1)
Requires-Dist: opencv-python
Requires-Dist: pandas
Requires-Dist: pyyaml
Requires-Dist: scipy
Requires-Dist: tabulate
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: typing-extensions (==3.10.0.2)
Requires-Dist: wheel

# ASOne

![croped](https://user-images.githubusercontent.com/107035454/195083948-4873d60a-3ac7-4279-8770-535488f4a097.png)


# Introduction

Asone is a python wrapper for multiple detection and tracking algorithms all at one place. Different trackers such as `ByteTrack`, `DeepSort` or `NorFair` can be integrated with different versions of `YOLO` with minimum lines of code.
This python wrapper provides yolo models in both `ONNX` and `PyTorch` versions.

Usage:

```
pip install asone


# for windows
pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox
# for linux
pip install cython-bbox

# for gpu
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113


```

Sample code:

```
import asone
from asone import ASOne

dt_obj = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOX_DARKNET_PYTORCH, use_cuda=True)
dt_obj.start_tracking('sample_videos/test.mp4')
```
Results on provided sample video

https://user-images.githubusercontent.com/107035454/195079926-aee47eac-0430-4ada-8cc7-cc9d1d13c889.mp4


