Calculate image similarity score
Language: Python Dev Modules Used: - opencv-python - scikit-image - click Test Modules Used: - tox - pytest - pylint - pytest-cov Build and Deployment: Travis-ci App Repository: PyPi
pip3 install imagescore
imagescore --infile 'input.csv' --outfile 'output.csv'
--infile | -i [str] -- [Input file path] --outfile | -o [str] -- [Output file path] --height | -h [int] -- [Optional: height to be resized, default = 4096] --width | -w [int] -- [Optional: width to be resized, default = 4096]
Expected Input: csv file with images and its absolute path
Expected Output: csv file with images and its absolute path, image score and elapsed time in secs.
The application is written in python3. Hence create a virtualenv with python3 and install the dependencies from
virtualenv --python=python3 venv pip install -e .
The unit test cases are located in tests directory. Install the dependencies
pip install -r requirements-dev.txt tox
The python package is build with the travis.yml script when there are changes on the master branch.
To push a new version:
Make the changes in the application, push it to any feature branch and merge with master.
To deploy a new version tag it. e.g
git tag -a v1.1.0 git push origin v1.1.0
When a tag is pushed, travis starts building and uploads the application to pypi
Pypi Release: https://pypi.org/project/imagescore/
Github Release: https://github.com/sandjaie/image_score/releases
Travis Builds: https://travis-ci.com/sandjaie/image_score/
We are using twine to upload the artifact to Pypi registry. Pypi registry needs an account to be created. Enter the credentials in the cli when prompted.
pip3 install twine python3 setup.py sdist bdist_wheel twine upload dist/*