Metadata-Version: 2.1
Name: tiatoolbox
Version: 1.2.0
Summary: Computational pathology toolbox developed by TIA Centre.
Home-page: https://github.com/TissueImageAnalytics/tiatoolbox
Author: TIA Centre
Author-email: tia@dcs.warwick.ac.uk
Keywords: tiatoolbox
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS.md

<p align="center">
  <img src="https://raw.githubusercontent.com/TissueImageAnalytics/tiatoolbox/develop/docs/tiatoolbox-logo.png">
</p>
<h1 align="center">TIA Toolbox</h1>
<p align="center">
  <a href="https://tia-toolbox.readthedocs.io/en/latest/?badge=latest">
    <img src="https://readthedocs.org/projects/tia-toolbox/badge/?version=latest" alt="Documentation Status" />
  </a>
  <a href="https://travis-ci.com/github/TissueImageAnalytics/tiatoolbox">
    <img src="https://app.travis-ci.com/TissueImageAnalytics/tiatoolbox.svg?branch=master" alt="Travis CI Status" />
  </a>
    <a href="https://codecov.io/gh/TissueImageAnalytics/tiatoolbox">
      <img src="https://codecov.io/gh/TissueImageAnalytics/tiatoolbox/branch/master/graph/badge.svg?token=7UZEMacQHm"/>
    </a>
    <a href="https://github.com/psf/black">
      <img src="https://img.shields.io/badge/code%20style-black-000000.svg"/>
    </a>
    <a href="https://github.com/TissueImageAnalytics/tiatoolbox/tree/master#license">
      <img src="https://img.shields.io/badge/license-BSD--3--clause-orange" />
    </a>
  <br>
    <br>
  <a href="https://badge.fury.io/py/tiatoolbox">
    <img src="https://badge.fury.io/py/tiatoolbox.svg" alt="PyPI Status" />
  </a>
    <a href="https://pepy.tech/project/tiatoolbox">
      <img src="https://static.pepy.tech/personalized-badge/tiatoolbox?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads"/>
    </a>
    <br>
    <a href="https://anaconda.org/conda-forge/tiatoolbox">
      <img src="https://img.shields.io/conda/vn/conda-forge/tiatoolbox" />
    </a>
    <a href="https://anaconda.org/conda-forge/tiatoolbox">
        <img src="https://anaconda.org/conda-forge/tiatoolbox/badges/downloads.svg" />
    </a>

  <br>
    <br>
  <a href="https://doi.org/10.1101/2021.12.23.474029"><img src="https://img.shields.io/badge/bioRxiv-10.1101%2F2021.12.23.474029-blue" alt="DOI"></a>
  <a href="https://zenodo.org/badge/latestdoi/267705904"><img src="https://zenodo.org/badge/267705904.svg" alt="DOI"></a>
</p>

Computational Pathology Toolbox developed at the TIA Centre

## Getting Started

### All Users

This package is for those interested in digital pathology: including graduate students, medical staff, members of the TIA Centre and of PathLAKE, and anyone, anywhere, who may find it useful. We will continue to improve this package, taking account of developments in pathology, microscopy, computing and related disciplines. Please send comments and criticisms to **[tia@dcs.warwick.ac.uk](mailto:tialab@dcs.warwick.ac.uk)**.

**`tiatoolbox`** is a multipurpose name that we use for 1) a certain computer program, 2) a Python package of related programs, created by us at the TIA Centre to help people get started in Digital Pathology, 3) this repository, 4) a certain virtual environment.


### Developers

Anyone wanting to contribute to this repository, please first look at our [Wiki](https://github.com/TissueImageAnalytics/tiatoolbox/wiki) and at our web page for [contributors](https://github.com/TissueImageAnalytics/tiatoolbox/blob/master/CONTRIBUTING.rst). See also the *Prepare for development* section of this document.

### Links, if needed
The [bash](https://www.gnu.org/software/bash) shell is available on all commonly encountered platforms. Commands in this README are in bash. Windows users can use the command prompt to install conda and python packages.

[conda](https://github.com/conda/conda) is a management system for software packages and [virtual environments](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html). To get `conda`, download [Anaconda](https://www.anaconda.com/), which includes hundreds of the most useful Python packages, using 2GB disk space. Alternatively, [miniconda](https://docs.conda.io/en/latest/miniconda.html) uses 400MB, and packages can be added as needed.

[Github](https://github.com/about) is powered by the version control system [git](https://git-scm.com/), which has many users and uses. In Github, it is used to track versions of code and other documents.


### Examples Taster

1. [Click here](https://github.com/TissueImageAnalytics/tiatoolbox/tree/develop/examples) for [jupyter notebooks](https://jupyter.org/), hosted on the web, with demos of `tiatoolbox`. All necessary resources to run the notebooks are remotely provided, so you don't need to have Python installed on your computer.
2. Click on a filename with suffix `.ipynb` and the notebook will open in your browser.
3. Click on one of the two blue checkboxes in your browser window labelled either **Open in Colab** or **Open in Kaggle**: [colab](https://colab.research.google.com/notebooks/intro.ipynb#) and [kaggle](https://www.kaggle.com/) are websites providing free-of-charge platforms for running jupyter notebooks.
4. Operate the notebook in your browser, editing, inserting or deleting cells as desired.
5. Changes you make to the notebook will last no longer than your colab or kaggle session.

### Install Python package

If you wish to use our programs, perhaps without developing them further, run the command `pip install tiatoolbox` or `pip install --ignore-installed --upgrade tiatoolbox` to upgrade from an existing installation.
Detailed installation instructions can be found in the [documentation](https://tia-toolbox.readthedocs.io/en/latest/installation.html).

To understand better how the programs work, study the jupyter notebooks referred to under the heading **Examples Taster**.

### Command Line
tiatoolbox supports various features through command line. For more information, please try `tiatoolbox --help`

### Prepare for development

Prepare a computer as a convenient platform for further development of the Python package `tiatoolbox` and related programs as follows.
1. Install [pre-requisite software](https://tia-toolbox.readthedocs.io/en/latest/installation.html)
2. Open a terminal window<br/>

```sh
    $ cd <future-home-of-tiatoolbox-directory>
```

3. Download a complete copy of the `tiatoolbox`.

```sh
    $ git clone https://github.com/TissueImageAnalytics/tiatoolbox.git
```

4. Change directory to `tiatoolbox`

```sh
    $ cd tiatoolbox
```

5. Create virtual environment for TIAToolbox using

```sh
    $ conda env create -f requirements.dev.conda.yml # for linux/mac only.
    $ conda activate tiatoolbox-dev
```
or

```sh
    $ conda create -n tiatoolbox-dev python=3.8 # select version of your choice
    $ conda activate tiatoolbox-dev
    $ pip install -r requirements_dev.txt
```
6. To use the packages installed in the environment, run the command:

```sh
    $ conda activate tiatoolbox-dev
```


### License

The source code TIA Toolbox (tiatoolbox) as hosted on GitHub is released under the [The 3-Clause BSD License].

The full text of the licence is included in [LICENSE](https://raw.githubusercontent.com/TissueImageAnalytics/tiatoolbox/develop/LICENSE).

[The 3-Clause BSD License]: https://opensource.org/licenses/BSD-3-Clause

### Auxiliary Files

Auxiliary files, such as pre-trained model weights downloaded from the TIA Centre webpage (https://warwick.ac.uk/tia/), are provided under the [Creative Commons Attribution-NonCommercial-ShareAlike Version 4 (CC BY-NC-SA 4.0) license](https://creativecommons.org/licenses/by-nc-sa/4.0/).

### Dual License

If you would like to use any of the source code or auxiliary files (e.g. pre-trained model weights) under a different license agreement please contact the Tissue Image Analytics (TIA) Centre at the University of Warwick (tia@dcs.warwick.ac.uk).


History
=======

1.2.0 (2022-07-05)
------------------
### Major Updates and Feature Improvements
- Adds support for Python 3.10
- Adds short description for IDARS algorithm #383
- Adds support for NGFF v0.4 [OME-ZARR](https://ngff.openmicroscopy.org/latest/).
- Adds CLI for launching tile server.

### Changes to API
- Renames `stainnorm_target()` function to `stain_norm_target()`.
- Removes `get_wsireader`
- Replaces the custom PlattScaler in `tools/scale.py` with the regular Scikit-Learn [LogisticRegression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression).

### Bug Fixes and Other Changes
- Fixes bugs in UNET architecture.
	- Number of channels in Batchnorm argument in the decoding path to match with the input channels.
	- Padding `0` creates feature maps in the decoder part with the same size as encoder.
- Fixes linter issues and typos
- Fixes incorrect output with overlap in `predictor.merge_predictions()` and `return_raw=True`
	- Thanks to @paulhacosta for raising #356, Fixed by #358.
- Fixes errors with JP2 read. Checks input path exists.
- Fixes errors with torch upgrade to 1.12.

### Development related changes
- Adds pre-commit hooks for consistency across the repo.
- Sets up GitHub Actions Workflow.
    - Travis CI will be removed in future release.


1.1.0 (2022-05-07)
------------------
### Major Updates and Feature Improvements
- Adds DICOM Support.
- Updates license to more permissive BSD 3-clause.
- Adds `micronet` model.
- Improves support for `tiff` files.
  - Adds a check for tiles in a TIFF file when opening.
  - Uses OpenSlide to read a TIFF if it has tiles instead of OpenCV (VirtualWSIReader).
  - Adds a fallback to tifffile if it is tiled but openslide cannot read it
  (e.g. jp2k or jpegxl tiles).
- Adds support for multi-channel images (HxWxC).
- Fixes performance issues in `semantic_segmentor.py`.
  - Performance gain measurement: 21.67s (new) vs 45.564 (old) using a 4k x 4k WSI.
  - External Contribution from @ByteHexler.
- Adds benchmark for Annotations Store.

### Changes to API
- None

### Bug Fixes and Other Changes
- Enhances the error messages to be more informative.
- Fixes Flake8 Errors, typos.
  - Fixes patch predictor models based after fixing a typo.
- Bug fixes in Graph functions.
- Adds documentation for docker support.
- General tidying up of docstrings.
- Adds metrics to readthedocs/docstrings for pretrained models.

### Development related changes
- Adds `pydicom` and `wsidicom` as dependency.
- Updates dependencies.
- Fixes Travis detection and makes improvements to run tests faster on Travis.
- Adds Dependabot to automatically update dependencies.
- Improves CLI definitions to make it easier to integrate new functions.
- Fixes compile options for test_annotation_stores.py


1.0.1 (2022-01-31)
------------------
### Major Updates and Feature Improvements
- Updates dependencies for conda recipe #262

### Changes to API
- None

### Bug Fixes and Other Changes
- Adds User Warning For Missing SQLite Functions
- Fixes Pixman version check errors
- Fixes empty query in instance segmentor

### Development related changes
- Fixes flake8 linting issues and typos
- Conditional pytest.skipif to skip GPU tests on travis while running them locally or elsewhere


1.0.0 (2021-12-23)
------------------
### Major Updates and Feature Improvements
- Adds nucleus instance segmentation base class
  - Adds  [HoVerNet](https://www.sciencedirect.com/science/article/abs/pii/S1361841519301045) architecture
- Adds multi-task segmentor [HoVerNet+](https://arxiv.org/abs/2108.13904) model
- Adds <a href="https://www.thelancet.com/journals/landig/article/PIIS2589-7500(2100180-1/fulltext">IDaRS</a> pipeline
- Adds [SlideGraph](https://arxiv.org/abs/2110.06042) pipeline
- Adds PCam patch classification models
- Adds support for stain augmentation feature
- Adds classes and functions under `tiatoolbox.tools.graph` to enable construction of graphs in a format which can be used with PyG (PyTorch Geometric).
- Add classes which act as a mutable mapping (dictionary like) structure and enables efficient management of annotations. (#135)
- Adds example notebook for adding advanced models
- Adds classes which can generate zoomify tiles from a WSIReader object.
- Adds WSI viewer using Zoomify/WSIReader API (#212)
- Adds README to example page for clarity
- Adds support to override or specify mpp and power

### Changes to API
- Replaces `models.controller` API with `models.engine`
- Replaces `CNNPatchPredictor` with `PatchPredictor`

### Bug Fixes and Other Changes
- Fixes  Fix `filter_coordinates` read wrong resolutions for patch extraction
- For `PatchPredictor`
  - `ioconfig` will supersede everything
  - if `ioconfig` is not provided
    - If `model` is pretrained (defined in `pretrained_model.yaml` )
      - Use the yaml ioconfig
      - Any other input patch reading arguments will overwrite the yaml ioconfig (at the same keyword).
    - If `model` is not defined, all input patch reading arguments must be provided else exception will be thrown.
- Improves performance of mask based patch extraction

### Development related changes
- Improve tests performance for Travis runs
- Adds feature detection mechanism to detect the platform and installed packages etc.
- On demand imports for some libraries for performance
- Improves performance of mask based patch extraction


0.8.0 (2021-10-27)
------------------
### Major Updates and Feature Improvements
- Adds `SemanticSegmentor` which is Predictor equivalent for semantic segmentation.
-  Add `TIFFWSIReader` class to support OMETiff reading.
- Adds `FeatureExtractor` API to controller.
- Adds WSI Serialization Dataset which support changing parallel workers on the fly. This would reduce the time spent to create new worker for every WSI/Tile (costly).
- Adds IOState data class to contain IO information for loading input to model and assembling model output back to WSI/Tile.
- Minor updates for `get_coordinates` to pave the way for getting patch IO for segmentation.
- Migrates old code to new variable names (patch extraction, patch wsi model).
- Change in API from `pretrained_weight` to `pretrained_weights`.
- Adds cli for semantic segmentation.
- Update python notebooks to add `read_rect` and `read_bounds` examples with `mpp` read.

### Changes to API
- Adds `WSIReader.open`. `get_wsireader` will deprecate in the next release. Please use `WSIReader.open` instead.
- CLI is now POSIX compatible
  - Replaces underscores in variable names with hyphens
- Models API updated to use `pretrained_weights` instead of `pretrained_weight`.
- Move string_to_tuple to tiatoolbox/utils/misc.py

### Bug Fixes and Other Changes
- Fixes README git clone instructions.
- Fixes stain normalisation due to changes in sklearn.
- Fixes a test in tests/test_slide_info
- Fixes readthedocs documentation issues

### Development related changes
- Adds dependencies for tiffile, imagecodecs, zarr.
- Adds more stringent pre-commit checks
- Moved local test files into `tiatoolbox/data`.
- Fixed `Manifest.ini` and added  `tiatoolbox/data`. This means that this directory will be downloaded with the package.
- Using `pkg_resources` to properly load bundled resources (e.g. `target_image.png`) in `tiatoolbox.data`.
- Removed duplicate code in `conftest.py` for downloading remote files. This is now in `tiatoolbox.data._fetch_remote_file`.
- Fixes errors raised by new flake8 rules.
  - Remove leading underscores from fixtures.
- Rename some remote sample files to make more sense.
- Moves all cli commands/options from cli.py to cli_commands to make it clean and easier to add new commands
- Removes redundant tests
- Updates to new GitHub organisation name in the repo
  - Fixes related links


0.7.0 (2021-09-16)
------------------
### Major and Feature Improvements
- Drops support for python 3.6
- Update minimum requirement to python 3.7
- Adds support for python 3.9
- Adds `models` base to the repository. Currently, PyTorch models are supported. New custom models can be added. The tiatoolbox also supports using custom weights to pre-existing built-in models.
  - Adds `classification` package and CNNPatchPredictor which takes predefined model architecture and pre-trained weights as input. The pre-trained weights for classification using kather100k data set is automatically downloaded if no weights are provided as input.
- Adds mask-based patch extraction functionality to extract patches based on the regions that are highlighted in the `input_mask`. If `'auto'` option is selected, a tissue mask is automatically generated for the `input_image` using tiatoolbox `TissueMasker` functionality.
- Adds visualisation module to overlay the results of an algorithm.

### Changes to API
- Command line interface for stain normalisation can be called using the keyword `stain-norm` instead of `stainnorm`
- Replaces `FixedWindowPatchExtractor` with `SlidingWindowPatchExtractor` .
- get_patchextractor takes the `slidingwindow` as an argument.
- Depreciates `VariableWindowPatchExtractor`

### Bug Fixes and Other Changes
- Significantly improved python notebook documentation for clarity, consistency and ease of use for non-experts.
- Adds detailed installation instructions for Windows, Linux and Mac

### Development related changes
- Moves flake8 above pytest in the `travis.yml` script stage.
- Adds `set -e` at the start of the script stage in `travis.yml` to cause it to exit on error and (hopefully) not run later parts of the stage.
- Readthedocs related changes
  - Uses `requirements.txt` in `.readthedocs.yml`
  - Uses apt-get installation for openjpeg and openslide
  - Removes conda build on readthedocs build
- Adds extra checks to pre-commit, e.g., import sorting, spellcheck etc. Detailed list can be found on this [commit](https://github.com/TissueImageAnalytics/tiatoolbox/commit/662a143e915fa55416badd992d8e7358211730a6).


0.6.0 (2021-05-11)
------------------
### Major and Feature Improvements
- Add `TissueMasker` class to allow tissue masking using `Otsu` and `Morphological` processing.
- Add helper/convenience method to WSIReader(s) to produce a mask. Add reader object to allow reading a mask conveniently as if it were a WSI i.e., use same location and resolution to read tissue area and mask area.
- Add `PointsPatchExtractor` returns patches that can be used by classification models. Takes `csv`, `json` or `pd.DataFrame` and returns patches corresponding to each pixel location.
- Add feature `FixedWindowPatchExtractor` to run sliding window deep learning algorithms.
- Add example notebooks for patch extraction and tissue masking.
- Update readme with improved instructions to use the toolbox. Make the README file somewhat more comprehensible to beginners, particularly those with not much background or experience.

### Changes to API
- `tiatoolbox.dataloader` replaced by `tiatoolbox.wsicore`

### Bug Fixes and Other Changes
- Minor bug fixes

### Development-related changes
- Improve unit test coverage.
- Move test data to tiatoolbox server.


------------------

0.5.2 (2021-03-12)
------------------
### Bug Fixes and Other Changes
- Fix URL for downloading test JP2 image.
- Update readme with new logo.

------------------

0.5.1 (2020-12-31)
------------------
### Bug Fixes and Other Changes
- Add `scikit-image` as dependency in `setup.py`
- Update notebooks to add instructions to install dependencies

------------------
0.5.0 (2020-12-30)
------------------
### Major and Feature Improvements

- Adds `get_wsireader()` to return appropriate WSIReader.
- Adds new functions to allow reading of regions using WSIReader at different resolutions given in units of:
  - microns per-pixel (mpp)
  - objective lens power (power)
  - pixels-per baseline (baseline)
  - resolution level (level)
- Adds functions for reading regions are `read_bounds` and `read_rect`.
  - `read_bounds` takes a tuple (left, top, right, bottom) of coordinates in baseline (level 0) reference frame and returns a region bounded by those.
  - `read_rect` takes one coordinate in baseline reference frame and an output size in pixels.
- Adds `VirtualWSIReader` as a subclass of WSIReader which can be used to read visual fields (tiles).
  - `VirtualWSIReader`  accepts ndarray or image path as input.
- Adds MPP fall back to standard TIFF resolution tags  with warning.
  - If OpenSlide cannot determine microns per pixel (`mpp`) from the metadata, checks the TIFF resolution units (TIFF tags: `ResolutionUnit`, `XResolution` and  `YResolution`) to calculate MPP. Additionally, add function to estimate missing objective power if MPP is known of derived from TIFF resolution tags.
- Estimates missing objective power from MPP with warning.
- Adds example notebooks for stain normalisation and WSI reader.
- Adds caching to slide info property. This is done by checking if a private `self._m_info` exists and returning it if so, otherwise `self._info` is called to create the info for the first time (or to force regenerating) and the result is assigned to `self._m_info`. This could in future be made much simpler with the `functools.cached_property` decorator in Python 3.8+.
- Adds pre processing step to stain normalisation where stain matrix encodes colour information from tissue region only.

### Changes to API
- `read_region` refactored to be backwards compatible with openslide arguments.
- `slide_info` changed to `info`
- Updates WSIReader which only takes one input
- `WSIReader` `input_path` variable changed to `input_img`
- Adds `tile_read_size`, `tile_objective_value` and `output_dir` to WSIReader.save_tiles()
- Adds `tile_read_size` as a tuple
- `transforms.imresize` takes additional arguments `output_size` and interpolation method 'optimise' which selects `cv2.INTER_AREA` for `scale_factor<1` and `cv2.INTER_CUBIC` for `scale_factor>1`

### Bug Fixes and Other Changes
- Refactors glymur code to use index slicing instead of deprecated read function.
- Refactors thumbnail code to use `read_bounds` and be a member of the WSIReader base class.
- Updates `README.md` to clarify installation instructions.
- Fixes slide_info.py for changes in WSIReader API.
- Fixes save_tiles.py for changes in WSIReader API.
- Updates `example_wsiread.ipynb` to reflect the changes in WSIReader.
- Adds Google Colab and Kaggle links to allow user to run notebooks directly on colab or kaggle.
- Fixes a bug in taking directory input for stainnorm operation for command line interface.
- Pins `numpy<=1.19.3` to avoid compatibility issues with opencv.
- Adds `scikit-image` or `jupyterlab` as a dependency.

### Development related changes
- Moved `test_wsireader_jp2_save_tiles` to test_wsireader.py.
- Change recipe in Makefile for coverage to use pytest-cov instead of coverage.
- Runs travis only on PR.
- Adds [pre-commit](https://pre-commit.com/#install) for easy setup of client-side git [hooks](https://git-scm.com/book/en/v2/Customizing-Git-Git-Hooks) for [black code formatting](https://github.com/psf/black#version-control-integration) and flake8 linting.
- Adds [flake8-bugbear](https://github.com/PyCQA/flake8-bugbear) to pre-commit for catching potential deepsource errors.
- Adds constants for test regions in `test_wsireader.py`.
- Rearranges `usage.rst` for better readability.
- Adds `pre-commit`, `flake8`, `flake8-bugbear`, `black`, `pytest-cov` and `recommonmark` as dependency.


------------------
0.4.0 (2020-10-25)
------------------

### Major and Feature Improvements

- Adds `OpenSlideWSIReader` to read Openslide image formats
- Adds support to read Omnyx jp2 images using `OmnyxJP2WSIReader`.
- New feature added to perform stain normalisation using `Ruifork`, `Reinhard`, `Vahadane`, `Macenko` methods and using custom stain matrices.
- Adds example notebook to read whole slide images via the toolbox.
- Adds `WSIMeta` class to save meta data for whole slide images. `WSIMeta` casts properties to python types. Properties from OpenSlide are returned as string. raw values can always be accessed via `slide.raw`. Adds data validation e.g., checking that level_count matches up with the length of the `level_dimensions` and `level_downsamples`. Adds type hints to `WSIMeta`.
- Adds exceptions `FileNotSupported` and `MethodNotSupported`


### Changes to API

-  Restructures `WSIReader` as parent class to allow support to read whole slide images in other formats.
- Adds `slide_info` as a property of `WSIReader`
- Updates `slide_info` type to `WSIMeta` from `dict`
- Depreciates support for multiprocessing from within the toolbox. The toolbox is focused on processing single whole slide and standard images. External libraries can be used to run using multiprocessing on multiple files.

### Bug Fixes and Other Changes

- Adds `scikit-learn`, `glymur` as a dependency
- Adds licence information
- Removes `pathos` as a dependency
- Updates `openslide-python` requirement to 1.1.2

------------------
0.3.0 (2020-07-19)
------------------

### Major and Feature Improvements

- Adds feature `read_region` to read a small region from whole slide images
- Adds feature `save_tiles` to save image tiles from whole slide images
- Adds feature `imresize` to resize images
- Adds feature `transforms.background_composite` to avoid creation of black tiles from whole slide images.

### Changes to API

- None

### Bug Fixes and Other Changes

- Adds `pandas` as dependency

------------------
0.2.2 (2020-07-12)
------------------

### Major and Feature Improvements

-   None

### Changes to API

-   None

### Bug Fixes and Other Changes

-   Fix command line interface for `slide-info` feature and travis pypi deployment

------------------
0.2.1 (2020-07-10)
------------------

### Major and Feature Improvements

-   None

### Changes to API

-   None

### Bug Fixes and Other Changes

-   Minor changes to configuration files.

------------------
0.2.0 (2020-07-10)
------------------

### Major and Feature Improvements

-   Adds feature slide\_info to read whole slide images and display meta
    data information
-   Adds multiprocessing decorator TIAMultiProcess to allow running
    toolbox functions using multiprocessing.

### Changes to API

-   None

### Bug Fixes and Other Changes

-   Adds Sphinx Readthedocs support
    <https://readthedocs.org/projects/tia-toolbox/> for stable and
    develop branches
-   Adds code coverage tools to test the pytest coverage of the package
-   Adds deepsource integration to highlight and fix bug risks,
    performance issues etc.
-   Adds README to allow users to setup the environment.
-   Adds conda and pip requirements instructions

------------------
0.1.0 (2020-05-28)
------------------

-   First release on PyPI.
