MuNG – tools for the MUSCIMA++ dataset¶
The mung
package implements tools for easier manipulation of the MUSCIMA++
dataset. Download the dataset here:
https://github.com/OMR-Research/muscima-pp/
A description of the dataset is on the project’s homepage:
https://ufal.mff.cuni.cz/muscima
And more thoroughly in an arXiv.org publication:
https://arxiv.org/pdf/1703.04824.pdf
This pacakge is licensed under the MIT license (see LICENSE.txt
file).
The package authors are Jan Hajič jr and Alexander Pacha. You can contact them at:
alexander.pacha@tuwien.ac.at
hajicj@ufal.mff.cuni.cz
Questions and comments are welcome! This package is also hosted on github, so if you find a bug, submit an issue (or a pull request!) there:
https://github.com/OMR-Research/mung
Requirements¶
Python 3.6, otherwise nothing beyond the requirements.txt
file: lxml
and numpy
.
If you want to apply pitch inference, you should also get music21
.
Installation¶
If you have pip
, just run:
pip install mung
If you don’t have pip
, then you should get it.
Or use the Anaconda distribution.
First steps¶
Let’s first download the dataset:
curl https://github.com/OMR-Research/muscima-pp/releases/download/v2.0/MUSCIMA-pp_v2.0.zip > MUSCIMA-pp_v2.0.zip
unzip MUSCIMA-pp_v2.0.zip
cd MUSCIMA-pp_v2.0
Take a look at the dataset’s README.md
file first. You can also read it online:
https://ufal.mff.cuni.cz/muscima
Please make sure you understand the license requirements – the data is licensed as CC-BY-NC-SA 4.0, and because it is built over a previous dataset, there are two attributions required.
Next, we fire up ipython
(or just the plain python
console, but definitely check out
ipython if you don’t use it!) and parse the data:
ipython
>>> import os
>>> from mung.io import read_nodes_from_file
>>> node_filenames = [os.path.join('data', 'nodes_with_staff_annotations', f) for f in os.listdir('data/nodes_with_staff_annotations')]
>>> docs = [read_nodes_from_file(f) for f in node_filenames]
>>> len(docs)
140
In docs
, we now have a list of Node lists for each of the 140 documents.
Now that the dataset has been parsed, we can try to do some experiments! We can do for example symbol classification. Go check out the MUSCIMA++ Tutorial!