semvec/pypi-packages huggingface dataset:
SDDetector 0.1.0
lightweight video detection
Requires Python: >=3.6

# SocialDistance
Keep safe social distance is considered as an effective way of avoiding spreading of coronavirus. Our SocialDistance module is a lightweight package which provides an implementation of utlizing deep learning models for monitoring safe social distance.

# Demo
[Watch the demo video](https://www.youtube.com/watch?v=1s46BJJj6rw&t=5s)

# Dataset
We use the video clip collected from [OXFORD TOWN CENTRE](https://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) dataset and made the above demo video.

# Supported Models
We have tested our model using Faster-RCNN, YOLO-v3 and SSD, based on the performance of each model, we have chosen YOLO-v3 as our default model

All our models are pretrained models from [Gluno CV Tookit](https://github.com/dmlc/gluon-cv)

# Installation
You may be able to obtain the latest version our model from:
```
pip install -i https://test.pypi.org/simple/ SocialDistance==0.1
pip install gluoncv
pip install mxnet-cu101
```

# Usage
After Successfully installed SocialDistance, you can use it for detection by:
```
from SocialDistance.utils.Run import Detect
detect = Detect()
#you may want to give an image as input to check the validity of bird-eye view transformation
detect(image)
```
If no arguments is given, our model will run using the default data collected from 'OXFORD TOWN CENTRE' dataset, otherwise you may want to specify arguments expicitly:
```
from SocialDistance.utils.Run import Detect
detect = Detect(video_path, video_save_path, keypoints, keypoints_birds_eye_view, actual_length, actual_width, pretrained_models)
#you may want to give an image as input to check the validity of bird-eye view transformation
detect(image)
```
> Parameters
> ----------
- **video_path**: input path of video
- **video_save_path**: output path of labelled video
- **keypoints**: selected key points from first frame of the input video
- **keypoints_birds_eye_view**: mapping location of keypoints on the bird-eye view image
- **actual_length**: actual length in real-world
- **actual_width**: actual width in real-world
- **pretrained_models**: selected pretrained models
SocialDistanceDetect 0.1.3
lightweight video detection
Requires Python: >=3.6

# SocialDistance
Keep safe social distance is considered as an effective way of avoiding spreading of coronavirus. Our SocialDistance module is a lightweight package which provides an implementation of utlizing deep learning models for monitoring safe social distance.

# Demo
[Watch the demo video](https://www.youtube.com/watch?v=1s46BJJj6rw&t=5s)

# Dataset
We use the video clip collected from [OXFORD TOWN CENTRE](https://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) dataset and made the above demo video.

# Supported Models
We have tested our model using Faster-RCNN, YOLO-v3 and SSD, based on the performance of each model, we have chosen YOLO-v3 as our default model

All our models are pretrained models from [Gluno CV Tookit](https://github.com/dmlc/gluon-cv)

# Installation
You may be able to obtain the latest version our model from:
```
pip install -i https://test.pypi.org/simple/ SocialDistance==0.1
pip install gluoncv
pip install mxnet-cu101
```

# Usage
After Successfully installed SocialDistance, you can use it for detection by:
```
from SocialDistance.utils.Run import Detect
detect = Detect()
#you may want to give an image as input to check the validity of bird-eye view transformation
detect(image)
```
If no arguments is given, our model will run using the default data collected from 'OXFORD TOWN CENTRE' dataset, otherwise you may want to specify arguments expicitly:
```
from SocialDistance.utils.Run import Detect
detect = Detect(video_path, video_save_path, keypoints, keypoints_birds_eye_view, actual_length, actual_width, pretrained_models)
#you may want to give an image as input to check the validity of bird-eye view transformation
detect(image)
```
> Parameters
> ----------
- **video_path**: input path of video
- **video_save_path**: output path of labelled video
- **keypoints**: selected key points from first frame of the input video
- **keypoints_birds_eye_view**: mapping location of keypoints on the bird-eye view image
- **actual_length**: actual length in real-world
- **actual_width**: actual width in real-world
- **pretrained_models**: selected pretrained models
uc-daemon 0.0.1
A set of classes and functions for demonizing a process and related tools..
Requires Python: >=2.6, <4

# uc_daemon  
A set of classes and functions for demonizing a process and related tools.  

It is part of the Unicon project.

https://unicon.10k.me

## Usage:

##### Install
```sh
pip install --user uc-daemon
```
##### and use

```python
import uc_daemon
```

## Functions:

**daemonize()**

Become a daemon.
To base process returns daemonized process id on successful and less than zero on fails.
To daemonized process returns zero.

**redirectio(handle)**

Redirects standart descriptors to specified file handle.

## Global constants

**LOGGER**

You may override this constant to manage debugging messages.

## Example:

```python
import os
import sys
import uc_daemon

pid = uc_daemon.daemonize()

if (pid == 0):
    print("I'm daemon! My pid is %d" % os.getpid())
else:
    print("Daemon started with pid %d and my pid is %d" % (pid, os.getpid()))    
```