Metadata-Version: 2.1
Name: SDD
Version: 0.2.1
Summary: lightweight video detection
Home-page: https://github.com/thomascong121/SocialDistance
Author: SocialDistance model contributors
Author-email: thomascong@outlook.com
License: UNKNOWN
Description: # 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 SDD
        ```
        
        # 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/>
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
