cinema_lib is a set of tools and library for interacting with a Cinema database (currently Spec A and Spec D) through Python and the command line tool, cinema.
Minimum requirements are:
- Python 3.6
Optional requirements are:
- numpy >=1.13
- image capabilities
- OpenCV capabilities
- scikit-image >=0.13.1 (newer versions may cause regression tests to fail
due to changing numerics and implementations of algorithms)
- image capabilities
- opencv-python >=3.4 (newer versions may cause regression tests to fail
due to changing numerics)
- OpenCV capabilities
To run the command line tool directly from the repository, after cloning:
$ git clone https://github.com/cinemascience/cinema_lib.git
$ cd cinema_lib
$ ./cinema
To install with pip:
$ git clone https://github.com/cinemascience/cinema_lib.git
$ cd cinema_lib
$ pip install .
$ cinema
$ cinema --help
- get all of the currently implemented commands
$ cinema -t -a cinema_lib/test/data/sphere.cdb
- validate a Spec A database
$ cinema -i -d cinema_lib/test/data/sphere.cdb
- return the header (parameters, columns) for a Spec D database
$ cinema -itvq -d cinema_lib/test/data/sphere.cdb
- quickly validate a Spec D database and report the header, verbosely
$ cinema -t --a2d -a cinema_lib/test/data/sphere.cdb
- validate a Spec A database and convert it to a Spec D database
$ cinema -d cinema_lib/test/data/sphere.cdb --image-grey 2
- convert RGB images to greyscale images
$ cinema -d cinema_lib/test/data/sphere.cdb --image-mean 2 --label average
- calculate the average color per component in images, naming the column "average"
$ cinema -d cinema_lib/test/data/sphere.cdb --cv-gaussian-blur 2
- convert apply a Gaussian blur to images
$ cinema -d cinema_lib/test/data/sphere.cdb --cv-fast-draw 2 --label FAST
- draw locations of FAST features in images, naming the column "FILE FAST"