История

Kebei Jiang 056f0637c2 Kejia/actions (#677 ) * Codeql Added * Adding lint and test yaml * Names added * Moved linters * One workflow * debug removed tasks * changed title * debug * minor * files included linting * check * minor * checkout code * codeql and lint * remove problematic .py from linting * ready for pr * ready for pr * test * revert to pr * update uncontrolled path * add werkzeug.utils.secure_filename * add werkzeug.utils.secure_filename Co-authored-by: saylibapat1997 <sayli.bapat@microsoft.com> Co-authored-by: kejia_microsoft <kebei.jiang@microsoft.com>		2022-10-27 19:07:57 -05:00
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action_recognition	Fixing various broken links (#674 )	2022-09-08 22:41:52 +03:00
crowd_counting	Kejia/actions (#677 )	2022-10-27 19:07:57 -05:00
document_cleanup	Adding ICDAR 2021 document binarization code	2021-09-15 10:09:38 -04:00
html_demo	Fixing various broken links (#674 )	2022-09-08 22:41:52 +03:00
vm_builder	…
vmss_builder	vmss setup (#512 )	2020-02-24 09:13:05 -05:00
README.md	Adding ICDAR 2021 document binarization code	2021-09-15 10:09:38 -04:00

README.md

Contributions

The contrib directory contains code which is not part of the main Computer Vision repository but deemed to be of interest and aligned with the goals of this repository. The code does not need to follow our strict coding guidelines, and in fact none of it is tested via our devops pipeline, and might be buggy, not maintained, etc.

Each project should live in its own subdirectory /contrib/<project> and contain a README.md file with detailed description what the project does and how to use it. In addition, when adding a new project, a brief description should be added to the table below.

Scenarios

Directory	Project description	Build status (optional)
Crowd counting	Counting the number of people in low-crowd-density (e.g. less than 10 people) and high-crowd-density (e.g. thousands of people) scenarios.
Action Recognition with I3D	Action recognition to identify video/webcam footage from what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times. Please note, that we also have a R(2+1)D implementation of action recognition that you can find under scenarios.
Document Image Cleanup	Given an input noisy document image, the aim of document image cleanup is to improve its readability and visibility by removing the noisy elements.

Tools

Directory	Project description	Build status (optional)
HTML Demo	These files provide an HTML web page that allows users to visualize the output of a deployed computer vision DNN model. Users can improve on and gain insights from their deployed model by uploading query/test images and examining the model results for correctness through the user interface. The interface includes sample query/test images for testing your own model and example output for 3 types of models: image classification, object detection, and image similarity.
vm_builder	This script helps users create a single Ubuntu Data Science Virtual Machine with a GPU with the computer vision recipes repo installed and ready to be used. If you find the script to be out-dated or not working, you can create the VM using the Azure portal or the Azure CLI tool with a few more steps.
vmss_builder	This script helps you setup a cluster of virtual machines with the computer vision recipes repo pre-installed using VMSS. This cluster is designed to be temporal, ie to be spun up and torn down. Users for this cluster will be prescribed a username/password/ip. This setup can be used for hands-on / lab sessions when you need to prepare multiple VM environments for a short period.