social-distance-detection | interpersonal social distance from videos | Machine Learning library

by Furkan-Gulsen Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | social-distance-detection Summary

social-distance-detection is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, OpenCV applications. social-distance-detection has no bugs, it has no vulnerabilities and it has low support. However social-distance-detection build file is not available. You can download it from GitHub.

It is a mid-level project that measures the social distance between people in the video through YOLOv3, OpenCV and algorithms, and shows this visually and numerically.

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social-distance-detection has a low active ecosystem.

It has 10 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

social-distance-detection has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of social-distance-detection is current.

Quality

social-distance-detection has no bugs reported.

Security

social-distance-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

social-distance-detection does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

social-distance-detection releases are not available. You will need to build from source code and install.

social-distance-detection has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed social-distance-detection and discovered the below as its top functions. This is intended to give you an instant insight into social-distance-detection implemented functionality, and help decide if they suit your requirements.

Performs a detection of a given video
Perform detection on the image
Calculate the distance between two boxes
Draw a distance image
Draws boxes
Distance between two points
Read a yolov tensor
Calculate the distance between two points
Performs a classification image
Loads a test image

Get all kandi verified functions for this library.

social-distance-detection Key Features

No Key Features are available at this moment for social-distance-detection.

social-distance-detection Examples and Code Snippets

No Code Snippets are available at this moment for social-distance-detection.

Community Discussions

Trending Discussions on social-distance-detection

Docker for GUI-based environments on windows

QUESTION

Docker for GUI-based environments on windows

Asked 2020-Sep-12 at 15:21

i am trying to make a dockerfile to opencv social distance detection project as a requirement to school project

the problem that i had is to make docker run on GUI-based environment

somehow i managed to make it work on linux by adding the fellow line to the run command

-e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix:ro

the full command

sudo docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix:ro social-distance-detection -y https://youtu.be/hTUyzF4v9KA

but it did not work on windows

which command should i add to docker run so it can run on windows ? and by any chance is there a way to make it even work without adding any extra line ?

there is the Dockerfile

...

ANSWER

Answered 2020-Sep-12 at 15:21

First, clean up your Dockerfile. I would suggest:

Source https://stackoverflow.com/questions/63859293

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install social-distance-detection

You can download it from GitHub.
You can use social-distance-detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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