Merge remote-tracking branch 'origin/v1-1-0' into 1.2

# Conflicts:
#	.gitignore
#	tests/__init__.py
#	tests/augmentations/__init__.py
#	tests/augmentations/test_transforms.py

.gitignore

@@ -2,4 +2,6 @@
 */__pycache__
 */*/__pycache__
 */*/*/__pycache__
-
+dist/
+dist-1/
+bio_volumentations.egg-info/
\ No newline at end of file

README.md

@@ -14,7 +14,7 @@ in terms of design and user interface. Therefore, it can easily be adopted by de
 
 # Installation
 
-Install the package from PyPi using:
+Install the package from pip using:
 ```python
 pip install bio-volumentations
 ```
@@ -60,6 +60,8 @@ The `Compose` class automatically checks and adjusts image format, datatype, sta
 individual transforms to a pipeline, and outputs the image as a contiguous array. 
 Optionally, it can also convert the transformed image to a desired format.
 
+More at the [documentation pages](https://www.google.com).
+
 Below, there are several examples of how to use this library.
 
 ### Example: Transforming a Single Image

__version__.py

-__version__ = "1.1.0"
+__version__ = "1.1.1"

pyproject.toml

+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
+
+[project]
+# Put the name of your project on PyPI.
+name = "bio-volumentations"
+
+# Put the version of your project. This field is required, although it is often marked as dynamic:
+version = "1.1.2"
+#dynamic = ["version"]
+# https://stackoverflow.com/questions/21064581/how-to-overwrite-pypi-package-when-doing-upload-from-command-line
+
+# If your project has dependencies, list them like this:
+dependencies = [
+  "numpy",
+  "scipy",
+  "scikit-image",
+  "matplotlib",
+  "SimpleITK",
+]
+# You may want to make some of your dependencies optional, if they are only needed for a specific feature of your package.
+# In that case, put them in optional-dependencies.
+# https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#dependencies-and-requirements
+
+# Declare the minimum version of Python that you support:
+requires-python = ">=3.1"
+
+# Authors (identified by a name and/or an email address)
+authors = [
+  {name = "Lucia Hradecká"},
+  {name = "Lucia Hradecká", email = "lucia.d.hradecka@gmail.com"},
+  {name = "Filip Lux"},
+  {name = "Filip Lux", email = "lux.filip@gmail.com"},
+  {name = "Samuel Šuľan"},
+]
+# Optionally, can also include the maintainers field
+
+# A one-line description of your project, to show as the “headline” of your project page on PyPI
+# and other places such as lists of search results:
+description = "Library for 3D-5D augmentations of volumetric multi-dimensional biomedical images"
+
+# A longer description - can use readme.md
+readme = "README.md"
+
+# Lincense - two possibilities
+#license = {file = "LICENSE"}
+license = {text = "MIT License"}
+
+# Keywords to help PyPI’s search box to suggest your project when people search for these keywords
+keywords = ["image", "augmentation", "3D", "volumetric", "biomedical", "bioimage",
+    "preprocessing", "transformation"]
+
+# Classifiers: this information is used for searching and browsing projects on PyPI
+# https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#classifiers
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+
+# A list of URLs associated with your project, displayed on the left sidebar of your PyPI project page.
+[project.urls]
+Homepage = "https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/v1-1-0?ref_type=heads"
+Documentation = "https://biovolumentations.readthedocs.io/v1-1-2/"
+Repository = "https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/v1-1-2?ref_type=tags"
+
+# MISSING FROM SETUP.PY
+# entry_points={
+#        'console_scripts': [
+#            'bioVolumentation=bio_volumentations:__init__'
+#        ]
+#    }
+# packages=setuptools.find_packages()
\ No newline at end of file

setup.py

-# ============================================================================================= #
-#  Author:       Samuel Šuľan, Lucia Hradecká, Filip Lux                                        #
-#  Copyright:    Lucia Hradecká     : lucia.d.hradecka@gmail.com                                #
-#                Filip Lux          : lux.filip@gmail.com                                       #
-#                                                                                               #
-#  MIT License.                                                                                 #
-#                                                                                               #
-#  Permission is hereby granted, free of charge, to any person obtaining a copy                 #
-#  of this software and associated documentation files (the "Software"), to deal                #
-#  in the Software without restriction, including without limitation the rights                 #
-#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell                    #
-#  copies of the Software, and to permit persons to whom the Software is                        #
-#  furnished to do so, subject to the following conditions:                                     #
-#                                                                                               #
-#  The above copyright notice and this permission notice shall be included in all               #
-#  copies or substantial portions of the Software.                                              #
-#                                                                                               #
-#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR                   #
-#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,                     #
-#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE                  #
-#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER                       #
-#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,                #
-#  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE                #
-#  SOFTWARE.                                                                                    #
-# ============================================================================================= #
-
-try:
-    from setuptools import setup, find_packages
-except ImportError:
-    from distutils.core import setup
-
-setup(
-    name='bio_volumentations',
-    version='1.1.0',
-    author='Samuel Šuľan, Lucia Hradecká, Filip Lux',
-    packages=find_packages(),
-    url='https://gitlab.fi.muni.cz/cbia/bio-volumentations',
-    description='Library for 3D augmentations of multi-dimensional biomedical images',
-    keywords='image,augmentation,volumetric,bioimage,biomedical,preprocessing,transformation',
-    long_description_content_type='text/markdown',
-    long_description='''Bio-Volumentations is an image augmentation and preprocessing package for 3D, 4D, and 5D 
-        (volumetric, multi-channel, and time-lapse) biomedical images.
-
-        This library offers a wide range of image transformations implemented efficiently for large-volume image data. 
-        This includes both preprocessing transformations (such as intensity normalisation, padding, and type casting) and 
-        augmentation transformations (such as affine transform, noise addition and removal, and contrast manipulation).
-
-        The Bio-Volumentations library is a suitable tool for data manipulation in machine learning applications. 
-        It can be used with any major Python deep learning library, including PyTorch, PyTorch Lightning, TensorFlow, and Keras.
-
-        This library builds upon wide-spread libraries such as Albumentations (see the Contributions section below) 
-        in terms of design and user interface. Therefore, it can easily be adopted by developers.
-    
-        More details can be found at the project's GitLab (https://gitlab.fi.muni.cz/cbia/bio-volumentations)
-        or at the documentation pages.
-        
-        The Bio-Volumentations library was inspired by:
-        
-        - Albumentations:           https://github.com/albumentations-team/albumentations
-        - 3D Conversion:            https://github.com/ashawkey/volumentations
-        - Continued Development:    https://github.com/ZFTurbo/volumentations
-        - Enhancements:             https://github.com/qubvel/volumentations
-        - Further Enhancements:     https://github.com/muellerdo/volumentations
-        
-        The Bio-Volumentations library is distributed under the MIT License.
-        
-        Copyright (c) 2024 Samuel Šuľan, Lucia Hradecká, Filip Lux
-        ''',
-    install_requires=[
-        'numpy',
-        'scipy',
-        'scikit-image',
-        'matplotlib',
-        'SimpleITK',
-    ],
-    entry_points={
-        'console_scripts': [
-            'bioVolumentation=bio_volumentations:__init__'
-        ]
-    }
-)

bio_volumentations/tests/conversion/test_transforms.py → tests/conversion/test_transforms.py

 import unittest
 import numpy as np
-import torch
-from volumentations.conversion.transforms import *
+from bio_volumentations.conversion.transforms import *
 
 
 class TestNoConversion(unittest.TestCase):
@@ -34,67 +33,5 @@ class TestNoConversion(unittest.TestCase):
         self.assertListEqual(res_label.tolist(), lbl.tolist())
 
 
-class TestToTensor(unittest.TestCase):
-    def test_image3d(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh)
-        tr = ToTensor()
-        res_image = tr(force_apply=True, targets=[['image']], image=img)['image']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), sh)
-        self.assertListEqual(res_image.tolist(), img.tolist())
-
-    def test_image4d(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh + (4,))
-        tr = ToTensor()
-        res_image = tr(force_apply=True, targets=[['image']], image=img)['image']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (4,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-
-    def test_image_labels_transpose(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh + (2,), dtype=float)
-        lbl = np.empty(sh, dtype=float)
-        tr = ToTensor()
-        result = tr(force_apply=True, targets=[['image'], ['mask']], image=img, mask=lbl)
-        res_image = result['image']
-        res_label = result['mask']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (2,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-        self.assertIsInstance(res_label, torch.Tensor)
-        self.assertTupleEqual(tuple(res_label.shape), sh)
-        self.assertListEqual(res_label.tolist(), lbl.tolist())
-
-        lbl = np.empty(sh + (3,), dtype=float)
-        tr = ToTensor()
-        result = tr(force_apply=True, targets=[['image'], ['mask']], image=img, mask=lbl)
-        res_image = result['image']
-        res_label = result['mask']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (2,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-        self.assertIsInstance(res_label, torch.Tensor)
-        self.assertTupleEqual(tuple(res_label.shape), (3,) + sh)
-        self.assertListEqual(res_label.tolist(), lbl.transpose((3, 0, 1, 2)).tolist())
-
-    def test_image_labels_no_transpose(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh + (2,), dtype=float)
-        lbl = np.empty(sh + (3,), dtype=float)
-        tr = ToTensor(transpose_mask=False)
-        result = tr(force_apply=True, targets=[['image'], ['mask']], image=img, mask=lbl)
-        res_image = result['image']
-        res_label = result['mask']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (2,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-        self.assertIsInstance(res_label, torch.Tensor)
-        self.assertTupleEqual(tuple(res_label.shape), sh + (3,))
-        self.assertListEqual(res_label.tolist(), lbl.tolist())
-
-
 if __name__ == '__main__':
     unittest.main()