REL: Release 1.0.1 (TissueImageAnalytics#272)

### Major Updates and Feature Improvements
- Updates dependencies for conda recipe TissueImageAnalytics#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

REL: Release 1.0.0 (TissueImageAnalytics#243)

- Bump version: 0.8.0 → 1.0.0

### 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. (TissueImageAnalytics#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 (TissueImageAnalytics#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

Co-authored-by: @tialab

REL: Pre-release 0.8.0 (TissueImageAnalytics#178)

- Bump version: 0.7.0 → 0.8.0

### 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

Co-authored by: TIA Centre

REL: Pre-release 0.7.0 (TissueImageAnalytics#148)

- Bump version: 0.6.0 → 0.7.0
### 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](TissueImageAnalytics@662a143)

Co-authored by: @tialab TIA Centre

REL: Release 0.6.0 (TissueImageAnalytics#106)

### 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.

Co-authored by TIA Centre <tia>

REL: Pre-release 0.5.2 (TissueImageAnalytics#89)

### Bug Fixes and Other Changes

- Fix URL for downloading test JP2 image in test config (conftest.py) and notebooks.
- Update readme with new logo.

REL: Pre-release 0.5.1 (TissueImageAnalytics#77)

### Bug Fixes and Other Changes
- Add `scikit-image` as dependency in `setup.py`
- Update notebooks to install dependencies

Co-authored-by: Shan E Ahmed Raza <shaneahmed>

REL: Pre-release 0.5.0 (TissueImageAnalytics#73)

### 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.

Co-authored-by: Shan E Ahmed @shaneahmed, John Pocock @John-P, Simon Graham @simongraham, Dang Vu @vqdang, Mostafa Jahanifar @mostafajahanifar  Srijay Deshpande @Srijay-lab, Saad Bashir @rajasaad

REL: Pre-release 0.4.0 (TissueImageAnalytics#48)

### 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`
- Depericiates support for multiprocessing from within the toolbox. The toolbox is focussed on processing single whole slide and standard images. External libraries can be used to run using multi processing 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

Co-authored-by: Shan E Ahmed @shaneahmed, John Pocock @John-P, Simon Graham @simongraham, Dang Vu @vqdang, Srijay Deshpande @Srijay-lab, Saad Bashir @rajasaad

REL: Pre-release 0.3.0 (TissueImageAnalytics#20)

# 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

Co-authored-by: Shan E Ahmed <shaneahmed>
Co-authored-by: John Pocock <John-P>
Co-authored-by: Simon Graham <simongraham>
Co-authored-by: TIA Lab <[email protected]>
Co-authored-by: DeepSource Bot <[email protected]>