🚀 Computer Vision Dashboard 🚀

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Table of content

• 🚀 Computer Vision Dashboard 🚀
  • Table of content
  • GitHub URL
  • Features
  • Architecture
  • Demo
    • Video Applications
    • Image Applications
  • Launch the Dashboard
    • Run the dashboard from source
      • Prepare Python virtual environnement
      • Run the dashboard
    • Run the dashboard from docker
      • (Optional) Build the docker image
      • Run the dashboard

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GitHub URL

Link to this GitHub Repo.

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Features

• Apply Deep learning models for Computer Vision
• Support for images and videos
• Loading data from different source: local, web (URL), upload
• It is designer with a modular architecture, where each app is a class
• Export Analytics KPI to a CSV file
• Display Analytics
• Backend and Frontend separated and shipped in their respective docker image
• Librairies: OpenCV, Python, pandas, Tensorflow, Streamlit
  • Frontend developed with python and streamlit
  • Backend developed in python with FastAPI, OpenCV, Tensorflow...

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Architecture

Add and image architecture HERE

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Demo

Video Applications

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Image Applications

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Launch the Dashboard

Run the dashboard from source

Prepare Python virtual environnement

• Create a python virtual environnement using requirements.txt

  pipenv install -r requirements.txt

  Note the path for the created folder venv_folder

• Activate the environnement

  source venv_folder/bin/activate

  or

  pipenv shell

Run the dashboard

• First run this command from the terminal

  streamlit run main.py

  

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Run the dashboard from docker

(Optional) Build the docker image

• Build docker image

docker build -t aminehy/computervision-dashboard:latest .

• Push the docker image to Docker Hub

docker login
docker push aminehy/computervision-dashboard:latest

Run the dashboard

docker run -it --rm aminehy/computervision-dashboard:latest streamlit run main.py --server.port 8050