diff --git a/.github/workflows/mkdocs-build.yml b/.github/workflows/mkdocs-build.yml new file mode 100644 index 0000000..7cc105b --- /dev/null +++ b/.github/workflows/mkdocs-build.yml @@ -0,0 +1,11 @@ +name: MkDocs Build (RationAI Standard) +on: + push: + branches: ["master"] + pull_request: + branches: ["master"] + workflow_dispatch: + +jobs: + run: + uses: RationAI/.github/.github/workflows/mkdocs-build.yml@main diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml new file mode 100644 index 0000000..6bf101f --- /dev/null +++ b/.github/workflows/python-lint.yml @@ -0,0 +1,11 @@ +name: Python Lint (RationAI Standard) +on: + push: + branches: ["master"] + pull_request: + branches: ["master"] + workflow_dispatch: + +jobs: + run: + uses: RationAI/.github/.github/workflows/python-lint.yml@main diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index f6d4011..0000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,9 +0,0 @@ -include: - project: rationai/digital-pathology/templates/ci-templates - file: - - Python-Lint.gitlab-ci.yml - - MkDocs.gitlab-ci.yml - -stages: - - lint - - deploy diff --git a/.markdownlint.yaml b/.markdownlint.yaml new file mode 100644 index 0000000..495a1eb --- /dev/null +++ b/.markdownlint.yaml @@ -0,0 +1,6 @@ +MD013: false # Line length +MD023: false # Heading start left +MD024: false # Duplicate headings +MD033: false # Inline HTML +MD041: false # First line in file should be a top level heading +MD046: false # Fenced code blocks \ No newline at end of file diff --git a/.mypy.ini b/.mypy.ini index 8d15f3c..5d41c07 100644 --- a/.mypy.ini +++ b/.mypy.ini @@ -3,4 +3,8 @@ strict = True ignore_missing_imports = True disallow_untyped_calls = False disable_error_code = no-any-return -explicit_package_bases = True \ No newline at end of file +explicit_package_bases = True + +# Ignore type errors in the external piqe.py file +[mypy-rationai.qc.focus.piqe] +ignore_errors = True \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6a4813b..65cb974 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,16 +8,15 @@ repos: args: [--unsafe] - repo: https://github.com/commitizen-tools/commitizen - rev: v4.8.3 + rev: v4.10.0 hooks: - id: commitizen - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.10 + rev: v0.14.10 hooks: # Run the linter. - - id: ruff - entry: pdm lint --force-exclude + - id: ruff-check + args: [--fix] # Run the formatter. - id: ruff-format - entry: pdm format --force-exclude diff --git a/.ruff.toml b/.ruff.toml index fb5e992..661f7ef 100644 --- a/.ruff.toml +++ b/.ruff.toml @@ -2,6 +2,8 @@ fix = true line-length = 88 target-version = "py311" +extend-exclude = ["rationai/qc/blur/piqe.py"] + [format] # Enable reformatting of code snippets in docstrings. docstring-code-format = true @@ -20,10 +22,10 @@ extend-select = [ "PGH", # pygrep "RUF", # ruff checks "SIM", # flake8-simplify - "TCH", # flake8-type-checking + "TC", # flake8-type-checking "TID", # flake8-tidy-imports "UP", # pyupgrade - "D", # pydocstyle + "D", # pydocstyle ] extend-ignore = [ "ERA001", # commented out code @@ -36,7 +38,7 @@ extend-ignore = [ "D106", # missing docstring in public nested class "D107", # missing docstring in __init__ "N812", # lowercase imported as non lowercase - "TCH002", # move third-party into a type-checking block + "TC002", # move third-party into a type-checking block "F722", # jaxtyping ] diff --git a/README.md b/README.md index f4f3ef9..6893e5e 100644 --- a/README.md +++ b/README.md @@ -5,4 +5,21 @@ for QC methods** developed at RationAI. These functions are meant to be run on s regions and they **offer a straightforward and well-documented access** to the key parts of the complete QC methods to allow for easier debugging and experimenting. -TODO: Add link to mkdocs documentation \ No newline at end of file +## Documentation + +**[`RationAI Quality Control`](https://rationai.github.io/quality-control/)** + +## Setting up a Development Environment + +1. Create the development environment (including all optional and development dependencies): + + ```bash + uv sync --all-groups + ``` + +2. Install pre-commit hooks: + + ```bash + uv run pre-commit install + uv run pre-commit install --hook-type commit-msg + ``` diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 4489bfd..a05e98e 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -1,4 +1,4 @@ -1. ### Install **pyvips** for Efficient Image Processing +1. ### Install **pyvips** for Efficient Image Processing [Pyvips](https://pypi.org/project/pyvips/2.0.2/) is a powerful and fast image processing library, particularly suited for working with large images like WSIs. To install `pyvips`, ensure that the `libvips` system dependencies are installed first: @@ -18,41 +18,20 @@ Follow the instructions on the official [libvips installation page](https://www.libvips.org/install.html). - -2. ### Install the Core Quality Control Library +2. ### Install the Core Quality Control Library You can install the core library using one of the following methods, depending on your package manager: - === "pdm" - - ```bash - pdm add git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git - ``` - - === "pip" - - ```bash - pip install git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git - ``` - - #### Installing a Specific Version - - To install a specific version of the **Quality Control** library, use the following command, replacing `v1.0.0` with your desired version. - - === "pdm" + === "uv" ```bash - pdm add git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git@v1.0.0 + uv add git+https://github.com/RationAI/quality-control.git ``` === "pip" ```bash - pip install git+https://gitlab.ics.muni.cz/rationai/digital-pathology/quality-control/quality-control.git@v1.0.0 + pip install git+https://github.com/RationAI/quality-control.git ``` - !!! info - - Replace `v1.0.0` with the specific version you want to install. - Once **pyvips**, and the core library are installed, you're ready to start using QC functions. diff --git a/docs/getting-started/quality-control.md b/docs/getting-started/quality-control.md index 78a7b3f..815ee87 100644 --- a/docs/getting-started/quality-control.md +++ b/docs/getting-started/quality-control.md @@ -1,22 +1,27 @@ # Using the Provided Quality Control Functions + This document presents several examples of using the provided QC functions. These examples should provide you with good overview of the offered functionality and potential limitations. ## Suggested Workflow + This library is not suited for running the provided QC methods on complete WSIs. Instead, the functions are expected to be run on smaller WSI regions and regular PNG/JPG images. This should provide reasonable options for debugging potential problems, while avoiding the technical overhead needed for processing the complete WSIs. ## Examples + The following examples should provide you with the neccessary information for running the provided QC functions. ### Detecting Residual Artifacts + In this example, we will work with the following image: ![The input image](data/residual_input.png) -#### Sample code +#### Sample Code + First, we need to import the necessary functionality and load the image into numpy array: ```python linenums="1" @@ -24,17 +29,18 @@ import numpy as np from PIL import Image from rationai.qc import residual_artifacts_and_coverage -from rationai.staining import ColorConversion +from rationai.staining import StandardConversions img = np.asarray(Image.open("input.png").convert("RGB")) ``` -Next, we will need to prepare the arguments for the [residual_artifacts_and_coverage](../api/residual_artifacts/residual-artifacts-and-coverage.md) function and call it on the input image: +Next, we will need to prepare the arguments for the [residual_artifacts_and_coverage](../api/residual_artifacts/residual-artifacts-and-coverage.md) +function and call it on the input image: ```python linenums="10" -conversion = ColorConversion.RGB2HER +conversion = StandardConversions.RGB2HER nucleus_area = 150 res_index = 2 threshold = 0.005 @@ -46,39 +52,42 @@ artifacts = residual_artifacts_and_coverage( Since the tissue is stained with the **H&E protocol**, we used the `RGB2HER` color conversion with the corresponding index of the residual channel. The threshold value is based on the currently recommended value in the function's documentation. Finally, the nucleus area is manually approximated from the input image. -After obtaining the results, we can save the binary artifact mask and print the computed `coverage` number: +After obtaining the results, we can save the binary artifact mask and print the number of examined and flagged pixels: ```python linenums="19" mask = Image.fromarray(255 * artifacts["coverage_mask"].astype(np.uint8)) mask.save("residual_mask.png") -print("Coverage Number:", artifacts["coverage"]) +print(f"Number of flagged pixels: {artifacts["number_of_flagged_pixels"]}") +print(f"Number of examined pixels: {artifacts["number_of_examined_pixels"]}") ``` -The computed mask is returned as a binary image. Therefore, the computed values need to be scaled into the [0, 255] range before visualization. +The computed mask is returned as a binary image. Therefore, the computed values need to be scaled into the `[0, 255]` range before visualization. #### Results -In our example, the `coverage` number came out to be `0.0766`, meaning that little more than 7% of the image's foreground area is covered by the artifact. + +In our example, 17900 out of the 233744 examined pixels were labeled as artifacts, +meaning that **little more than 7% of the image's foreground area** is covered by artifacts. Finally, the computed artifact mask looks like this: ![The generated mask](data/residual_mask.png) -### Detecting blurry areas +### Detecting Blurry Areas In this example, we will work with the following images: -| ![](data/blur/blur_A.png) | ![](data/blur/blur_B.png) | ![](data/blur/blur_C.png) | -|:-------------------------:|:------------------------:|:------------------------:| -| Focused image | Partially blurred image | Blurred image | +| ![Focused Image](data/blur/blur_A.png) | ![Partially Blurred Image](data/blur/blur_B.png) | ![Blurred Image](data/blur/blur_C.png) | +|:--------------------------------------:|:------------------------------------------------:|:--------------------------------------:| +| Focused Image | Partially Blurred Image | Blurred Image | -#### Sample code First, we need to import the necessary functionality and load the images into numpy arrays: ```python linenums="1" import numpy as np from PIL import Image -# optional, tissue mask will be calculated inside the blur_score function if not provided as an argument +# optional, tissue mask will be calculated inside the blur_score function +# if not provided as an argument import pyvips from rationai.masks import tissue_mask @@ -90,7 +99,7 @@ img_c = np.asarray(Image.open("blur_C.png").convert("RGB")) # Blurred image Next, we will need to prepare the arguments for the [blur_score_piqe](../api/blur/blur-score-piqe.md) function and call it on the input images: -```python linenums="9" +```python linenums="14" pixel_size = 0.44 # pixel size of input images in micrometers # blur score without tissue mask @@ -100,22 +109,22 @@ blur_score_b = blur_score_piqe(img_b, pixel_size) # blur score with tissue mask tissue_mask = tissue_mask( pyvips.Image.new_from_array(img_c), mpp=pixel_size - ).numpy() +).numpy() tissue_mask = (tissue_mask > 0).astype(int) # binarize mask blur_score_c = blur_score_piqe(img_c, pixel_size, tissue_mask ) ``` -The `pixel_size` parameter is used to calculate the kernel size for median filter that is used during the computation. Function gives most accurate results on images with pixel size around 0.44 micrometers. The `tissue_mask` param is optional and will be calculated inside the function if not present, but can be provided by the user to avoid unnecessary computation. +The `pixel_size` parameter is used to calculate the kernel size for median filter that is used during the computation. Function gives most accurate results on images with pixel size around 0.44 micrometers. The `tissue_mask` param is optional and will be calculated inside the function if not present, but can be provided by the user to avoid unnecessary computation. -blur function outputs 2 masks - blur_score_coverage and blur_score_per_pixel. -blur_score_per_pixel is a binary mask marking the areas of the image that are considered blurred, while blur_score_coverage provides a blur coverage score for the whole image indicating the degree of blur. +The blur function outputs 2 masks - `blur_score_coverage` and `blur_score_per_pixel`. +`blur_score_per_pixel` is a binary mask marking the areas of the image that are considered blurred, while `blur_score_coverage` provides a blur coverage score for the whole image indicating the degree of blur. After obtaining the results, we can save the computated blur score masks. -Since blur_score_coverage is homogeneous across the image, we can just print out any value from the score mask for quick inspection: +Since `blur_score_coverage` is homogeneous across the image, we can just print out any value from the score mask for quick inspection: -```python linenums="18" +```python linenums="29" print(blur_score_a["blur_score_coverage"][0][0]) @@ -144,33 +153,32 @@ mask.save("blur_score_c_pixel.png") ``` #### Results + Coverage scores range from 0 to 1, where 0 represents the best focus and 1 represents complete blur. -| Score | Focus Level | -|--------------|---------------------------| +| Score | Focus Level | +|--------------|------------------------------| | 0 - 0.1 | Perfect/almost perfect focus | | 0.1 - 0.3 | Slightly/partially blurred | | 0.3 - 0.7 | Visibly blurred | | 0.7 - 1 | Severely blurred | - In our example, the focus scores and masks look like this: -| ![](data/blur/blur_score_a.png) | ![](data/blur/blur_score_b.png) | ![](data/blur/blur_score_c.png) | -|:-------------------------:|:------------------------:|:------------------------:| -| Focused image, Score ~0.005 | Partially blurred image, Score ~0.488 | Blurred image, Score ~0.985 | - +| ![Focused Image](data/blur/blur_score_a.png) | ![Partially Blurred Image](data/blur/blur_score_b.png) | ![Blurred Image](data/blur/blur_score_c.png) | +|:--------------------------------------------:|:------------------------------------------------------:|:--------------------------------------------:| +| Focused Image, Score ~0.005 | Partially Blurred Image, Score ~0.488 | Blurred Image, Score ~0.985 | Per-pixel mask is binary mask composed of 16x16 px blocks marking blurred areas. Background pixels and focused blocks are marked as 0, while blurred blocks are marked as 1. -| ![](data/blur/blur_score_a_pixel.png) | ![](data/blur/blur_score_b_pixel.png) | ![](data/blur/blur_score_c_pixel.png) | -|:-------------------------:|:------------------------:|:------------------------:| -| Focused image | Partially blurred image | Blurred image | +| ![Focused Image](data/blur/blur_score_a_pixel.png) | ![Partially Blurred Image](data/blur/blur_score_b_pixel.png) | ![Blurred Image](data/blur/blur_score_c_pixel.png) | +|:--------------------------------------------------:|:------------------------------------------------------------:|:--------------------------------------------------:| +| Focused Image | Partially Blurred Image | Blurred Image | Please note that blocks of the per-pixel mask may be 1 not only for globally blurred regions, but also for locally defocused or sparse areas (e.g. tissue gaps or empty regions). This means a value of 1 does not always imply a complete blur. -### Detecting folded areas +### Detecting Folded Areas In this example, we will work with the following images: @@ -182,10 +190,12 @@ The local area of the tile. ![Local area around tile](data/fold_area.png) -The local area is an optional image. It increases the detection rate of particularly large folds. A suggested size is (3 * width, 3 * height) of investigated image +The local area is an optional image. It increases the detection rate of particularly large folds. A suggested size is `(3 * width, 3 * height)` of investigated image + +#### Sample Code -#### Sample code First, we need to import the necessary functionality and load the images into numpy arrays: + ```python linenums="1" import numpy as np from PIL import Image @@ -194,12 +204,13 @@ import pyvips from rationai.masks import tissue_mask -img = np.asarray(Image.open("fold.png").conver("RGB")) # Investigated image +img = np.asarray(Image.open("fold.png").convert("RGB")) # Investigated image local_area_image =img_area = np.asarray(Image.open("fold_area.png").convert("RGB")) # Local area of investigated image ``` Now we can calculate the tissue masks: -```python linenums="1" + +```python linenums="11" img_mask = tissue_mask( pyvips.Image.new_from_array(img), mpp=pixel_size).numpy() > 0 @@ -208,7 +219,8 @@ img_area_mask = tissue_mask( ``` Now we have all the arguments prepared. The folding function can be called: -```python linenums="1" + +```python linenums="17" artifacts = folding(img=img, mpp=1.76, hematoxylin_eosin_stained=True, @@ -216,12 +228,16 @@ artifacts = folding(img=img, local_tiles=local_area_img, local_mask=img_area_mask) ``` -The level_downsample argument is the downsample rate between the highest resolution level and the level from which the image was taken. nuclues_diameter_at_base_level specifies the nucleus diameter at the highest resolution level (it can be easily measured when browsing the WSI). + +The `level_downsample argument` is the downsample rate between the highest resolution level and the level from which the image was taken. +`nuclues_diameter_at_base_level` specifies the nucleus diameter at the highest resolution level (it can be easily measured when browsing the WSI). #### Results -To recover the results, one need to access the dictionary `artifacts`. -```python linenums="1" +To recover the results, one needs to access the `artifacts` dictionary. + +```python linenums="24" mask = Image.fromarray(255 * artifacts["folding"].astype(np.uint8)) ``` + ![The resulting mask](data/fold_detection.png) diff --git a/docs/index.md b/docs/index.md index 463c077..a7d39a2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -3,4 +3,4 @@ This repository contains a selection of image-processing routines, **forming a foundation for QC methods** developed at RationAI. 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