https://github.com/sandeep-kollipara/computer-vision-client

[Phase II: Consumer] This repository holds the code developed in partial fulfilment of online credit course "CS370 - OS" offered at Colorado State University Online for Spring 2024.
https://github.com/sandeep-kollipara/computer-vision-client

computer-vision consumer docker-container linux-desktop multi-processing producer-consumer

Last synced: about 2 months ago
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[Phase II: Consumer] This repository holds the code developed in partial fulfilment of online credit course "CS370 - OS" offered at Colorado State University Online for Spring 2024.

• Host: GitHub
• URL: https://github.com/sandeep-kollipara/computer-vision-client
• Owner: sandeep-kollipara
• License: gpl-3.0
• Created: 2024-03-05T18:23:36.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-05-02T02:34:19.000Z (8 months ago)
• Last Synced: 2024-10-19T11:55:40.748Z (3 months ago)
• Topics: computer-vision, consumer, docker-container, linux-desktop, multi-processing, producer-consumer
• Language: Python
• Homepage:
• Size: 1.27 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# computer-vision-client

[ Phase II: Consumer] This repository holds the code developed in partial fulfilment of online credit course "CS370 - OS" offered at Colorado State University Online for Spring 2024.

## Preface

This ReadMe file will highlight the objectives with precedence, setup instructions for first-time user and finally the step-by-step development progress for reference (also reflected in the commits).

## Objectives

The computer-vision-client is the consumer/slave/client of the Producer-Consumer Architectural Pattern on which this project is designed on. Following are it's objectives/features in order of import:

[1] Receive the RTSP/UDP stream over the network/internet and display to user via GUI (security concerns withstanding).

[2] The program should be docker-compatible (platform-agnostic) and any desktop with a monitor should be able to run and view it.

[3] Apply OpenCV algoritms on the input and detect objects as close to real-time as possible (by utilizing multiprocessing if available).

### Autorun:

```autorun 

./autorun.sh

```

### To setup the environment manually:

```setup 

./env-setup.sh

```