semvec/pypi-packages huggingface dataset:
SDDect 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 __SDD__ 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
# Reference
1. Landing AI 16 April 2020, Landing AI Creates an AI Tool to Help Customers Monitor Social Distancing in the Workplace, accessed 19 April 2020, <https://landing.ai/landing-ai-creates-an-ai-tool-to-help-customers-monitor-social-distancing-in-the-workplace/>
r2c-cli 0.0.24b3
A CLI for R2C
Requires Python: >=3.6,<4.0

# r2c-cli

This is the CLI for interacting with the R2C platform.

## Installation

### Prerequisites

- [Install Docker](https://docs.docker.com/install/) for your platform
- [Python 3.7 and up](https://www.python.org/about/gettingstarted/) for your platform

### Setup

- Install r2c-cli, either via `pip` or via [`pipx`](https://pypi.org/project/pipx/) to provide better package isolation.

  ```
  pip3 install r2c-cli
  ```

- Run `r2c` to check that the CLI was installed properly. If installed properly, you should see our help text.

## Documentation

See [docs.r2c.dev](https://docs.r2c.dev) for details on how write analyzer using `r2c-cli`.

## Usage

```bash
r2c <command> [options]
```

You can also run `r2c --help` or just `r2c` to see usage information.

For help with a command in particular, you can run `r2c <command> --help` and see help specifically for that command.

For the commands `run` `test` `push` and `unittest` they will require that you run them in an analyzer directory (i.e. a directory containing an `analyzer.json` and associated files).

## Unit Testing

Instructions to run unittests are defined `src/unittest.sh`. Make sure to add `mocha test` or `npm test` to enable
unittesting for your analyzer.

## Integration Testing

Integration tests should be defined in the `src/examples` directory.
Integration test on a github REPO@COMMIT could be defined as

```json
{
  "target": "{REPO}",
  "target_hash": "{COMMIT}",
  "expected": []
}
```

## Uploading new analyzer

Once you are done developing and testing your analyzer locally, you must update `version` in your
`analyzer.json` and run

```bash
r2c push
```

to upload your analyzer to your repository.

## Troubleshooting

- If you run into issues running `r2c` commands, you can run with `--verbose` flag and reach out to `support@ret2.co` with the error log.