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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/CMU-1-Small-Region.svs filter=lfs diff=lfs merge=lfs -text
+images/sample_wsi_small.svs filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,22 @@
+from read_whole_sliding_images import *
+from stain_normalization import *
+import gradio as gr
+wsi2png = gr.Interface(fn=wsi2png, inputs=gr.inputs.File(), outputs=gr.outputs.Image(type='numpy'),
+                       examples=wsi2png_examples)
+bound_png = gr.Interface(fn=read_bounds_of_image, inputs=gr.inputs.Image(type='numpy'),
+                         outputs=gr.outputs.Image(type='numpy'), examples=read_bounds_examples)
+
+
+inp1 = gr.inputs.File()
+inp2 = gr.Slider(1, 10, 4, step=0.25, label="Resolution")
+mask_wsi = gr.Interface(fn=mask_image, inputs=[inp1, inp2], outputs=gr.outputs.Image(type='numpy'), examples=[mask_wsi_examples])
+
+normalize_wsi = gr.Interface(fn=normalize_stain, inputs=gr.inputs.File(), outputs=gr.outputs.Image(type='numpy'),
+                               examples=[stain_normalization_wsi_examples])
+
+
+demo = gr.TabbedInterface([wsi2png, bound_png, mask_wsi, normalize_wsi], ["WSI2PNG", "Bound Image", "Mask WSI",
+                                                                          "Normalize Stain"])
+
+if __name__ == "__main__":
+    demo.launch(show_api=True, auth=("patho", "vis"))

images/CMU-1-Small-Region.svs

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed92d5a9f2e86df67640d6f92ce3e231419ce127131697fbbce42ad5e002c8a7
+size 1938955

images/sample_wsi_small.svs

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed92d5a9f2e86df67640d6f92ce3e231419ce127131697fbbce42ad5e002c8a7
+size 1938955

read_whole_sliding_images.py

@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+from tiatoolbox.data import small_svs
+from tiatoolbox.wsicore.wsireader import WSIReader
+import gradio as gr
+from pprint import pprint
+from PIL import Image
+
+wsi2png_examples = [['images/sample_wsi_small.svs']]
+
+
+def wsi2png(wsi_file):
+    print(wsi_file.name)
+    reader = WSIReader.open(wsi_file.name)
+    reader.info.objective_power = 1.0
+    info_dict = reader.info.as_dict()
+    pprint(info_dict)
+    thumbnail = reader.slide_thumbnail(resolution=0.1, units="mpp")
+    return thumbnail
+
+
+read_bounds_examples = [['images/sample_wsi_small.png']]
+
+
+def read_bounds_of_image(image):
+    return image
+
+
+mask_wsi_examples = [['images/sample_wsi_small.svs']]
+
+
+def mask_image(wsi_file, resolution):
+    reader = WSIReader.open(wsi_file.name)
+    mask_reader = reader.tissue_mask(resolution=resolution, units="mpp")
+    mask_thumbnail = mask_reader.slide_thumbnail(resolution=resolution, units="power")
+    mask_thumbnail *= 255
+    return mask_thumbnail

requirements.txt

@@ -0,0 +1,196 @@
+aiofiles==23.2.1
+alabaster==0.7.16
+albumentations==1.3.1
+altair==5.3.0
+annotated-types==0.7.0
+anyio==4.4.0
+appnope==0.1.4
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asciitree==0.3.3
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==23.2.0
+Babel==2.15.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+blinker==1.8.2
+bokeh==3.3.2
+certifi==2024.7.4
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+comm==0.2.2
+contourpy==1.2.1
+cycler==0.12.1
+debugpy==1.8.2
+decorator==5.1.1
+defusedxml==0.7.1
+dicomweb-client==0.59.1
+dnspython==2.6.1
+docutils==0.20.1
+email_validator==2.2.0
+exceptiongroup==1.2.2
+executing==2.0.1
+fastapi==0.111.1
+fastapi-cli==0.0.4
+fasteners==0.19
+fastjsonschema==2.20.0
+ffmpy==0.3.2
+filelock==3.13.1
+Flask==3.0.0
+Flask-Cors==4.0.0
+fonttools==4.53.1
+fqdn==1.5.1
+fsspec==2024.6.1
+Glymur==0.12.8
+gradio==3.50.0
+gradio_client==0.6.1
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.24.2
+idna==3.7
+imagecodecs==2023.9.18
+imageio==2.34.2
+imagesize==1.4.1
+importlib_metadata==8.2.0
+importlib_resources==6.4.0
+ipykernel==6.29.5
+ipython==8.18.1
+isoduration==20.11.0
+itsdangerous==2.2.0
+jedi==0.19.1
+Jinja2==3.1.4
+joblib==1.3.2
+json5==0.9.25
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.2
+jupyter_core==5.7.2
+jupyter_server==2.14.2
+jupyter_server_terminals==0.5.3
+jupyterlab==4.0.9
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+kiwisolver==1.4.5
+lazy_loader==0.4
+llvmlite==0.41.1
+lxml==5.2.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.2
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mistune==3.0.2
+mpmath==1.3.0
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.2.1
+notebook_shim==0.2.4
+numba==0.58.1
+numcodecs==0.12.1
+numpy==1.26.2
+opencv-python==4.8.0.76
+opencv-python-headless==4.10.0.84
+openslide-python==1.3.1
+orjson==3.10.6
+overrides==7.7.0
+packaging==24.1
+pandas==2.1.4
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+Pillow==10.1.0
+platformdirs==4.2.2
+prometheus_client==0.20.0
+prompt_toolkit==3.0.47
+psutil==6.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pycparser==2.22
+pydantic==2.8.2
+pydantic_core==2.20.1
+pydicom==2.4.4
+pydub==0.25.1
+Pygments==2.18.0
+pynndescent==0.5.13
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-json-logger==2.0.7
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+pyzmq==26.0.3
+qudida==0.0.4
+referencing==0.35.1
+requests==2.31.0
+retrying==1.3.4
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rich==13.7.1
+rpds-py==0.19.1
+ruff==0.5.5
+scikit-image==0.24.0
+scikit-learn==1.3.2
+scipy==1.11.4
+semantic-version==2.10.0
+Send2Trash==1.8.3
+shapely==2.0.2
+shellingham==1.5.4
+SimpleITK==2.3.1
+six==1.16.0
+sniffio==1.3.1
+snowballstemmer==2.2.0
+soupsieve==2.5
+Sphinx==7.2.6
+sphinxcontrib-applehelp==1.0.8
+sphinxcontrib-devhelp==1.0.6
+sphinxcontrib-htmlhelp==2.0.6
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.8
+sphinxcontrib-serializinghtml==1.1.10
+stack-data==0.6.3
+starlette==0.37.2
+sympy==1.13.1
+terminado==0.18.1
+threadpoolctl==3.5.0
+tiatoolbox==1.5.1
+tifffile==2023.12.9
+tinycss2==1.3.0
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.1.2
+torchvision==0.16.2
+tornado==6.4.1
+tqdm==4.66.1
+traitlets==5.14.3
+typer==0.12.3
+types-python-dateutil==2.9.0.20240316
+typing_extensions==4.12.2
+tzdata==2024.1
+umap-learn==0.5.5
+uri-template==1.3.0
+urllib3==2.2.2
+uvicorn==0.30.3
+uvloop==0.19.0
+watchfiles==0.22.0
+wcwidth==0.2.13
+webcolors==24.6.0
+webencodings==0.5.1
+websocket-client==1.8.0
+websockets==11.0.3
+Werkzeug==3.0.3
+wsidicom==0.17.0
+xyzservices==2024.6.0
+zarr==2.16.1
+zipp==3.19.2

stain_normalization.py

@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+# Clear logger to use tiatoolbox.logger
+import logging
+
+if logging.getLogger().hasHandlers():
+    logging.getLogger().handlers.clear()
+
+from pathlib import Path
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import requests
+import skimage.color
+
+from tiatoolbox import data, logger
+from tiatoolbox.tools import stainnorm
+from tiatoolbox.wsicore import wsireader
+from PIL import Image
+import numpy as np
+
+stain_normalization_wsi_examples = [['images/sample_wsi_small.svs']]
+
+def normalize_stain(source_wsi_file, output):
+    # create a file handler
+    wsi_reader = wsireader.WSIReader.open(input_img=source_wsi_file.name)
+    wsi_info = wsi_reader.info.as_dict()
+    # we will print out each info line by line
+    print(*list(wsi_info.items()), sep="\n")  # noqa: T201
+    wsi_thumb = wsi_reader.slide_thumbnail(resolution=1.25, units="power")
+    sample = wsi_reader.read_region(
+        location=[800, 1600],
+        level=0,
+        size=[800, 800],  # in X, Y
+    )
+    target_image = data.stain_norm_target()
+    method_name_list = ["Reinhard", "Ruifrok", "Macenko", "Vahadane"]
+    plt.subplot(2, 3, 1)
+    plt.imshow(sample)
+    plt.title("Source Image")
+    plt.axis("off")
+    plt.subplot(2, 3, 4)
+    plt.imshow(target_image)
+    plt.title("Target Image")
+    plt.axis("off")
+
+    pos = [2, 3, 5, 6]
+    for idx, method_name in enumerate(method_name_list):
+        stain_normalizer = stainnorm.get_normalizer(method_name)
+        stain_normalizer.fit(target_image)
+
+        normed_sample = stain_normalizer.transform(sample.copy())
+        plt.subplot(2, 3, pos[idx])
+        plt.imshow(normed_sample)
+        plt.title(method_name.capitalize())
+        plt.axis("off")
+    plt.tight_layout()
+    # plt.show()
+    img = plt.savefig('tmp/test.png')
+    image = Image.open('tmp/test.png')
+    numpy_array = np.array(image)
+    return numpy_array