README.md

metadata

title: Deep Barcode Reader
emoji: 🔳
colorFrom: gray
colorTo: red
sdk: streamlit
sdk_version: 1.42.2
app_file: app.py
pinned: false

Deep-Barcode-Reader

This repository is used for reading different types of barcodes and QR codes from images. The code is written in Python and uses OpenCV, Pyzbar and other libraries for reading barcodes and QR codes.

How to Use

The library offers several functions for reading barcodes and QR codes from images. General options can be selected before applying the reader method. These options are:

• --data_path is the path to the image file.
• --result_path is the path to the output file.
• --verbose is the verbosity level of the code.
• --method is the method for reading the barcode or QR code.
• --model_size is the size of the deep learning model.

The available methods are as:

• Opencv Barcode Reader
• Zbar Barcode Reader
• QR Reader

Opencv Barcode Reader

This method can only detect barcodes but NOT QR codes. It can detect and decode EAN-8, EAN-13, UPC-A and UPC-E barcode types. You can use the method by running the following command:

deep_barcode_reader -vv -d tests/test_data/sample.jpg -m opencv

This method can only detect specific types of barcodes. However, it is faster than the other methods.

Zbar Barcode Reader

This method can detect and decode both barcodes and QR codes. It is very powerful to detect and decode different types of barcodes and QR codes. You can use the method by running the following command:

deep_barcode_reader -vv -d tests/test_data/sample.jpg -m zbar

QR Reader

This method can only detect and decode QR codes. Depending on the model size as n as nano, s as small, m as medium, or l as large, the detection and decoding process time and accuracy can be changed.

deep_barcode_reader -vv -d tests/test_data/sample.jpg -m zbar --model_size l

How to Develop

Do the following only once after creating your project:

• Init the git repo with git init.
• Add files with git add ..
• Then git commit -m 'initialize the project'.
• Add remote url with git remote add origin REPO_URL.
• Then git branch -M master.
• git push origin main. Then create a branch with git checkout -b BRANCH_NAME for further developments.
• Install poetry if you do not have it in your system from here.
• Create a virtual env preferably with virtualenv wrapper and mkvirtualenv -p $(which python3.10) ENVNAME.
• Then git add poetry.lock.
• Then pre-commit install.
• For applying changes use pre-commit run --all-files.

Docker Container

To run the docker with ssh, do the following first and then based on your need select ,test, development, or production containers:

export DOCKER_BUILDKIT=1
export DOCKER_SSHAGENT="-v $SSH_AUTH_SOCK:$SSH_AUTH_SOCK -e SSH_AUTH_SOCK"

Test Container

This container is used for testing purposes while it runs the test

docker build --progress plain --ssh default --target test -t barcode_docker:test .
docker run -it --rm -v "$(pwd):/app" $(echo $DOCKER_SSHAGENT) barcode_docker:test

Development Container

This container can be used for development purposes:

docker build --progress plain --ssh default --target development -t barcode_docker:development .
docker run -it --rm -v "$(pwd):/app" -v /tmp:/tmp $(echo $DOCKER_SSHAGENT) barcode_docker:development

Production Container

This container can be used for production purposes:

docker build --progress plain --ssh default --target production -t barcode_docker:production .
docker run -it --rm -v "$(pwd):/app" -v /tmp:/tmp $(echo $DOCKER_SSHAGENT) barcode_docker:production deep_barcode_reader -vv -d tests/test_data/sample.jpg -m zbar --model_size l

Hugging Face Deployment

The repository is also deployed in hugging face in which one can upload images, select the appropriate method and its parameters and detect and decode the barcodes or QR codes. It is good to mention that you can also run the application locally by running the following command:

streamlit run app.py

and then open the browser and go to the address http://localhost:8501