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A streamlit based implementation of Automatic Number Plate Recognition for cars and other vehicles using images or live camera feed

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prateekralhan/Streamlit-based-Automatic-Number-Plate-Recognition

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Streamlit based Automatic Number Plate Recognition 🚘🚙 Project Status: Active

A streamlit based implementation of Automatic Number Plate Recognition for cars and other vehicles using images or live camera feed.


This work is just an extended implementation of the great work done by mftnakrsu


Animation

live feed demo

Installation:

  • Simply run the command pip install -r requirements.txt to install the necessary dependencies.

Usage:

  1. Clone this repository and install the dependencies as mentioned above.
  2. Make a directory within this cloned repository with the name .streamlit (Don't forget the dot !!).
  3. Create a file config.toml in this directory (Be aware of the file extension !!).
  4. Copy-Paste the following contents in this file and save :
[theme]
primaryColor="#ffb5b5"
backgroundColor="#132743"
secondaryBackgroundColor="#407088"
textColor="#ffb5b5"
  1. Navigate to the root directory of this repository and simply run the command:
streamlit run app.py

Navigate to http://localhost:8501 in your web-browser.

live feed pic


Results


Output Images Output Images
pic1 pic1
pic2 pic2
pic3 pic3

Running the Dockerized App

  1. Ensure you have Docker Installed and Setup in your OS (Windows/Mac/Linux). For detailed Instructions, please refer this.
  2. Navigate to the folder where you have cloned this repository ( where the Dockerfile is present ).
  3. Build the Docker Image (don't forget the dot!! 😄 ):
docker build -f Dockerfile -t app:latest .
  1. Run the docker:
docker run -p 8501:8501 app:latest

This will launch the dockerized app. Navigate to http://localhost:8501/ in your browser to have a look at your application. You can check the status of your all available running dockers by:

docker ps

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A streamlit based implementation of Automatic Number Plate Recognition for cars and other vehicles using images or live camera feed

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