update package details

* update setup
* update CI, tox
* reformat version

.shippable.yml

@@ -1,5 +1,3 @@
-# vim ft=yaml
-
 # After changing this file, check it on:
 #   http://yaml-online-parser.appspot.com/
 
@@ -32,7 +30,7 @@ before_install:
 
 install:
   - pip install -r requirements.txt
-  - pip install nose coverage codecov pytest codacy-coverage
+  - pip install -r requirements-dev.txt
   - pip --version ; pip freeze
 
 script:

.travis.yml

@@ -27,14 +27,20 @@ matrix:
     - python: 3.7
       env: TOXENV=py37
 
+# See http://docs.travis-ci.com/user/caching/#pip-cache
+cache: pip
+
 install:
   - sudo apt-get install python-opencv openslide-tools
   - pip install -r requirements.txt
+  - pip install -r requirements-dev.txt
   - pip install tox
-  - pip --version ; pip freeze
+  - pip --version ; pip list
 
 script:
-  - tox
+  # integration
+  - tox --sitepackages
+  # sample run
   - mkdir results && touch configs/sample_config.yaml
   - python bm_experiments/bm_comp_perform.py -o ./results -n 1
   - python birl/bm_template.py -c ./data_images/pairs-imgs-lnds_mix.csv -o ./results --visual --unique --path_sample_config configs/sample_config.yaml

MANIFEST.in

@@ -24,26 +24,22 @@ exclude bm_ANHIR
 recursive-exclude notebooks *.ipynb
 exclude notebooks
 
-# Exclude the data files
-recursive-exclude data_images *
-exclude data_images
+# Include the data files
+recursive-include data_images *
 
-# Exclude the method config files
-recursive-exclude configs *
-exclude configs
+# Include the method config files
+recursive-include configs *
 
 # Exclude the documentation files
 recursive-exclude docs *
 exclude docs
 
-# Exclude the Rscript files
-recursive-include scripts *.r
-
 # Exclude the ImageJ macro files
-recursive-include scripts *.ijm *.bsh *.py
+recursive-include scripts *.r *.ijm *.bsh *.py
 
 # Include the Requirements
 include requirements.txt
+exclude requirements-*.txt
 
 # Exclude build configs
 exclude *.yml

README.md

@@ -261,7 +261,7 @@ The project is using the standard [BSD license](http://opensource.org/licenses/B
 ## References
 
 For complete references see [bibtex](docs/references.bib).
-1. Borovec, J., Munoz-Barrutia, A., & Kybic, J. (2018). **[Benchmarking of image registration methods for differently stained histological slides](https://www.researchgate.net/publication/325019076_Benchmarking_of_image_registration_methods_for_differently_stained_histological_slides)**. In IEEE International Conference on Image Processing (ICIP) (pp. 3368–3372). Athens. [DOI: 10.1109/ICIP.2018.8451040](https://doi.org/10.1109/ICIP.2018.8451040)
+1. Borovec, J., Munoz-Barrutia, A., & Kybic, J. (2018). **[Benchmarking of image registration methods for differently stained histological slides](https://www.researchgate.net/publication/325019076_Benchmarking_of_image_registration_methods_for_differently_stained_histological_slides)**. In IEEE International Conference on Image Processing (ICIP) (pp. 3368–3372), Athens. [DOI: 10.1109/ICIP.2018.8451040](https://doi.org/10.1109/ICIP.2018.8451040)
 
 ## Appendix - Useful information
 

appveyor.yml

@@ -7,7 +7,6 @@ environment:
   CMD_IN_ENV: "cmd /E:ON /V:ON /C obvci_appveyor_python_build_env.cmd"
 
   matrix:
-
     # Pre-installed Python versions, which Appveyor may upgrade to
     # a later point release.
     # See: http://www.appveyor.com/docs/installed-software#python
@@ -44,25 +43,36 @@ environment:
 
 build: off
 
+# https://www.appveyor.com/docs/build-cache/
+cache:
+  - C:\ProgramData\chocolatey\bin -> appveyor.yml
+  - C:\ProgramData\chocolatey\lib -> appveyor.yml
+  - '%LOCALAPPDATA%\pip\Cache -> appveyor.yml'
+
+# scripts that run after cloning repository
 install:
   # If there is a newer build queued for the same PR, cancel this one.
   # The AppVeyor 'rollout builds' option is supposed to serve the same
   # purpose but it is problematic because it tends to cancel builds pushed
   # directly to master instead of just PR builds (or the converse).
-  # credits: JuliaLang developers.
   - choco upgrade chocolatey
   - choco install -y opencv
   - SET PATH=%PYTHON%;%PYTHON%\\Scripts;%path%
-  - python --version
   - pip install -U --user pip
-  - pip --version
   - pip install -r requirements.txt
-  - pip install tox coverage codecov
-  - pip freeze
+  - pip install -r requirements-dev.txt
+  - pip install tox
+
+# scripts to run before tests (working directory and environment changes are persisted from the previous steps such as "before_build")
+before_test:
+  - python --version
+  - pip --version
+  - pip list
   - dir
 
+# to run your custom scripts instead of automatic tests
 test_script:
-  - tox
+  - tox --sitepackages --parallel auto
   - mkdir results && touch configs/sample_config.yaml
   - python bm_experiments/bm_comp_perform.py -o ./results -n 1
   - python birl/bm_template.py -c ./data_images/pairs-imgs-lnds_mix.csv -o ./results --unique --visual --path_sample_config configs/sample_config.yaml

birl/__init__.py

@@ -1,3 +1,32 @@
 import birl.utilities
 
 birl.utilities
+
+__version__ = '0.2.2'
+__author__ = 'Jiri Borovec'
+__author_email__ = '[email protected]'
+__license__ = 'BSD 3-clause'
+__home__ = 'https://borda.github.io/BIRL',
+__copyright__ = 'Copyright (c) 2014-2019, Jiri Borovec.'
+__doc__ = """
+# BIRL: Benchmark on Image Registration methods with Landmark validation
+
+The project aims at automatic evaluation of state-of-the-art image registration
+ methods based on landmark annotation for given image dataset. In particular,
+ this project is the main evaluation framework for ANHIR challenge.
+
+## Main Features
+* **automatic** execution of image registration on a sequence of image pairs
+* integrated **evaluation** of registration performances
+* integrated **visualization** of performed registration
+* running several image registration experiment in **parallel**
+* **resuming** unfinished sequence of registration benchmark
+* handling around dataset and **creating own experiments**
+* rerun evaluation and visualisation for finished experiments
+
+## References
+Borovec, J., Munoz-Barrutia, A., & Kybic, J. (2018). Benchmarking of image
+ registration methods for differently stained histological slides.
+ In IEEE International Conference on Image Processing (ICIP) (pp. 3368-3372),
+ Athens. DOI: 10.1109/ICIP.2018.8451040
+"""

birl/utilities/dataset.py

@@ -687,7 +687,8 @@ def scale_large_images_landmarks(images, landmarks):
     :param [ndarray] landmarks: list of landmarks
     :return ([ndarray], [ndarray]): lists of images and landmarks
 
-    >>> scale_large_images_landmarks([np.zeros((8000, 500, 3), dtype=np.uint8)], [None, None])  # doctest: +ELLIPSIS
+    >>> scale_large_images_landmarks([np.zeros((8000, 500, 3), dtype=np.uint8)],
+    ...                              [None, None])  # doctest: +ELLIPSIS
     ([array(...)], [None, None])
     """
     if not images:

birl/utilities/evaluate.py

@@ -23,7 +23,8 @@ def compute_tre(points_1, points_2):
     :return ndarray:
 
     >>> np.random.seed(0)
-    >>> compute_tre(np.random.random((6, 2)), np.random.random((9, 2)))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    >>> compute_tre(np.random.random((6, 2)),
+    ...             np.random.random((9, 2)))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
     array([ 0.21...,  0.70...,  0.44...,  0.34...,  0.41...,  0.41...])
     """
     nb_common = min([len(pts) for pts in [points_1, points_2]
@@ -177,8 +178,10 @@ def compute_ranking(user_cases, field, reverse=False):
     ... }
     >>> user_cases = compute_ranking(user_cases, 'rTRE')
     >>> import pandas as pd
-    >>> pd.DataFrame({usr: {cs: user_cases[usr][cs]['rTRE_rank'] for cs in user_cases[usr]}
-    ...               for usr in user_cases})[sorted(user_cases.keys())]  # doctest: +NORMALIZE_WHITESPACE
+    >>> df = pd.DataFrame({usr: {cs: user_cases[usr][cs]['rTRE_rank']
+    ...                          for cs in user_cases[usr]}
+    ...                    for usr in user_cases})[sorted(user_cases.keys())]
+    >>> df  # doctest: +NORMALIZE_WHITESPACE
        franta  karel  pepa
     1       3      1     2
     2       1      2     3
@@ -278,7 +281,8 @@ def aggregate_user_score_timeline(df, col_aggreg, col_user, col_score,
     >>> df['day'] = np.random.randint(0, 5, 50)
     >>> df['user'] = np.array(list('abc'))[np.random.randint(0, 3, 50)]
     >>> df['score'] = np.random.random(50)
-    >>> aggregate_user_score_timeline(df, 'day', 'user', 'score').round(3)  # doctest: +NORMALIZE_WHITESPACE
+    >>> df_agg = aggregate_user_score_timeline(df, 'day', 'user', 'score')
+    >>> df_agg.round(3)  # doctest: +NORMALIZE_WHITESPACE
            b      c      a
     4  0.447  0.132  0.567
     0  0.223  0.005  0.094

birl/utilities/experiments.py

@@ -295,7 +295,8 @@ def wrap_execute_sequence(wrap_func, iterate_vals, nb_workers=NB_THREADS,
         if it is set None, bar is suppressed
     :param bool ordered: whether enforce ordering in the parallelism
 
-    >>> list(wrap_execute_sequence(np.sqrt, range(5), nb_workers=1, ordered=True))  # doctest: +ELLIPSIS
+    >>> list(wrap_execute_sequence(np.sqrt, range(5), nb_workers=1,
+    ...                            ordered=True))  # doctest: +ELLIPSIS
     [0.0, 1.0, 1.41..., 1.73..., 2.0]
     >>> list(wrap_execute_sequence(sum, [[0, 1]] * 5, nb_workers=2, desc=None))
     [1, 1, 1, 1, 1]
@@ -373,7 +374,8 @@ def dict_deep_update(dict_base, dict_update):
     >>> d = {'level1': {'level2': {'levelA': 0, 'levelB': 1}}}
     >>> u = {'level1': {'level2': {'levelB': 10}}}
     >>> import json
-    >>> print(json.dumps(dict_deep_update(d, u), sort_keys=True, indent=2))  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
+    >>> d = json.dumps(dict_deep_update(d, u), sort_keys=True, indent=2)
+    >>> print(d)  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
     {
       "level1": {
         "level2": {

birl/utilities/registration.py

@@ -76,7 +76,8 @@ def get_affine_components(matrix):
 
     >>> mtx = np.array([[ -0.95,   0.1,  65.], [  0.1,   0.95, -60.], [  0.,   0.,   1.]])
     >>> import  pandas as pd
-    >>> pd.Series(get_affine_components(mtx)).sort_index()  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
+    >>> aff = pd.Series(get_affine_components(mtx)).sort_index()
+    >>> aff  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
     rotation                           173.9...
     scale                    (0.95..., 0.95...)
     shear                              -3.14...

circle.yml

@@ -15,10 +15,10 @@ references:
       name: Install PyPI dependences
       command: |
         sudo pip install -U backports.functools_lru_cache  # required for matplotlib @py2
-        sudo pip install coverage pytest pytest-cov codecov flake8
         pip install -r requirements.txt --user
+        sudo pip install -r requirements-dev.txt
         python --version ; pwd ; ls -l
-        pip --version ; pip freeze
+        pip --version ; pip list
 
   test_coverage: &test_coverage
     run:

requirements-dev.txt

@@ -0,0 +1,8 @@
+nose>=1.3.7
+coverage
+codecov
+pytest>=3.0.5
+pytest-cov
+flake8
+check-manifest
+codacy-coverage

setup.py

@@ -16,33 +16,33 @@
 # Python 3 only projects can skip this import
 from io import open
 
-here = path.abspath(path.dirname(__file__))
+import birl
 
-with open(path.join(here, 'requirements.txt'), encoding='utf-8') as fp:
-    requirements = [r.rstrip() for r in fp.readlines() if not r.startswith('#')]
+PATH_HERE = path.abspath(path.dirname(__file__))
+
+with open(path.join(PATH_HERE, 'requirements.txt'), encoding='utf-8') as fp:
+    requirements = [rq.rstrip() for rq in fp.readlines() if not rq.startswith('#')]
 
 # Get the long description from the README file
-with open(path.join(here, 'README.md'), encoding='utf-8') as fp:
-    long_description = fp.read()
+# with open(path.join(PATH_HERE, 'README.md'), encoding='utf-8') as fp:
+#     long_description = fp.read()
 
 # Arguments marked as "Required" below must be included for upload to PyPI.
 # Fields marked as "Optional" may be commented out.
-
 setup(
     name='BIRL',
-    version='0.2.2',
-    url='https://borda.github.io/BIRL',
+    version=birl.__version__,
+    url=birl.__home__,
 
-    author='Jiri Borovec',
-    author_email='[email protected]',
-    license='BSD 3-clause',
+    author=birl.__author__,
+    author_email=birl.__author_email__,
+    license=birl.__license__,
     description='Benchmark on Image Registration methods with Landmark validation',
 
-    long_description=long_description,
+    long_description=birl.__doc__,
     long_description_content_type='text/markdown',
 
-    packages=find_packages(
-        exclude=['docs', 'notebooks', 'scripts*', 'bm_*']),
+    packages=find_packages(exclude=['docs', 'notebooks', 'scripts*', 'bm_*']),
 
     keywords='benchmark image registration landmarks',
     install_requires=requirements,

tox.ini

@@ -30,13 +30,8 @@ basepython =
     py36: python3.6
     py37: python3.7
 deps =
-    check-manifest
-    # If your project uses README.rst, uncomment the following: 
-    # readme_renderer
-    flake8
-    pytest
-    coverage
-    codecov
+    -r requirements.txt
+    -r requirements-dev.txt
 commands =
     check-manifest --ignore tox.ini
     python setup.py check -m -s