diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5bce808 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,45 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.tif filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz 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 +sample_img/binary_mask.tif filter=lfs diff=lfs merge=lfs -text +sample_img/cytoplasm.tif filter=lfs diff=lfs merge=lfs -text +sample_img/sample_atp.jpg filter=lfs diff=lfs merge=lfs -text +sample_img/sample_he.jpg filter=lfs diff=lfs merge=lfs -text +sample_img/nuclei.tif filter=lfs diff=lfs merge=lfs -text +sample_img/binary_mask_fluo.tif filter=lfs diff=lfs merge=lfs -text +sample_img/sample_sdh.jpg filter=lfs diff=lfs merge=lfs -text +sample_img/binary_mask_sdh.tif filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 2b2d6b2..bbc457d 100644 --- a/.gitignore +++ b/.gitignore @@ -153,8 +153,6 @@ cython_debug/ # Custom Corentin *.html -MyoQuant -EHRoes *.h5 log pid @@ -174,3 +172,5 @@ data/* *.idea cellpose.sh nohup.out +*/.venv +*/DS_Store \ No newline at end of file diff --git a/.python-version b/.python-version deleted file mode 100644 index 09dcc78..0000000 --- a/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.10.11 diff --git a/.vscode/launch.json b/.vscode/launch.json deleted file mode 100644 index f11f1a9..0000000 --- a/.vscode/launch.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "SDH Analysis", - "type": "python", - "request": "launch", - "module": "myoquant", - "justMyCode": true, - "args": ["sdh-analysis", "/home/corentin/code-project/MyoQuant/sample_img/sample_sdh.jpg", "--cellpose-path", "sample_img/sample_sdh_cellpose_mask.tiff"], - }, - { - "name": "HE Analysis", - "type": "python", - "request": "launch", - "module": "myoquant", - "justMyCode": true, - "args": ["he-analysis", "sample_img/sample_he.jpg", "--stardist-path", "sample_img/sample_he_stardist_mask.tiff", "--cellpose-path", "sample_img/sample_he_cellpose_mask.tiff"], - }, - { - "name": "ATP Analysis", - "type": "python", - "request": "launch", - "module": "myoquant", - "justMyCode": true, - "args": ["atp-analysis", "sample_img/sample_atp.jpg", "--cellpose-path", "sample_img/sample_atp_cellpose_mask.tiff", "--intensity-method", "mean", "--n-classes", "2", "--erosion"], - } - ] -} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index d7338ad..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "python.testing.pytestArgs": [ - "tests" - ], - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true, -} \ No newline at end of file diff --git a/Makefile b/Makefile index 1f271d6..e69de29 100644 --- a/Makefile +++ b/Makefile @@ -1,41 +0,0 @@ -black: - black myoquant - black tests - -black-diff: - black myoquant --diff - black tests --diff - -build: - poetry build - -install: - poetry install - pre-commit install - -mkserve: - mkdocs serve - -mypy: - mypy myoquant/ --ignore-missing-imports - -pre-commit: black mypy ruff test - -publish: - poetry publish - -publish-release: pre-commit build publish - -ruff: - ruff app tests - -test: - pytest tests - -_update: - poetry update - -update-all: _update publish-release - -update-all-diff: - poetry update --dry-run | grep -i updat diff --git a/README.md b/README.md index 8ba31a9..0c4a492 100644 --- a/README.md +++ b/README.md @@ -54,7 +54,7 @@ _If you're running into an issue such as `myoquant: command not found` please ch ## Contact -Creator and Maintainer: [**Corentin Meyer**, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://cmeyer.fr) +Creator and Maintainer: [**Corentin Meyer**, PhD in Biomedical AI](https://cmeyer.fr) ## Citing MyoQuant🔬 @@ -81,7 +81,7 @@ For ATP Staining analysis, you can download this sample image: [HERE](https://ww ### Model path and manual download -For the SDH Analysis our custom model will be downloaded and placed inside the myoquant package directory. You can also download it manually here: [https://lbgi.fr/~meyer/SDH_models/model.h5](https://lbgi.fr/~meyer/SDH_models/model.h5) and then you can place it in the directory of your choice and provide the path to the model file using: +For the SDH Analysis our custom model will be downloaded and placed inside the myoquant package directory. You can also download it manually here: [https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/model.h5](https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/model.h5) and then you can place it in the directory of your choice and provide the path to the model file using: `myoquant sdh-analysis IMAGE_PATH --model_path /path/to/model.h5` ### HuggingFace🤗 repositories for Data and Model diff --git a/docs/api/myoquant.md b/docs/api/myoquant.md deleted file mode 100644 index 985e257..0000000 --- a/docs/api/myoquant.md +++ /dev/null @@ -1 +0,0 @@ -# MyoQuant diff --git a/docs/examples/example.md b/docs/examples/example.md deleted file mode 100644 index d83cc57..0000000 --- a/docs/examples/example.md +++ /dev/null @@ -1 +0,0 @@ -# PACKAGE_NAME diff --git a/docs/index.md b/docs/index.md deleted file mode 100644 index d83cc57..0000000 --- a/docs/index.md +++ /dev/null @@ -1 +0,0 @@ -# PACKAGE_NAME diff --git a/docs/quickstart/advanced.md b/docs/quickstart/advanced.md deleted file mode 100644 index d83cc57..0000000 --- a/docs/quickstart/advanced.md +++ /dev/null @@ -1 +0,0 @@ -# PACKAGE_NAME diff --git a/docs/quickstart/faq.md b/docs/quickstart/faq.md deleted file mode 100644 index d83cc57..0000000 --- a/docs/quickstart/faq.md +++ /dev/null @@ -1 +0,0 @@ -# PACKAGE_NAME diff --git a/docs/quickstart/index.md b/docs/quickstart/index.md deleted file mode 100644 index d83cc57..0000000 --- a/docs/quickstart/index.md +++ /dev/null @@ -1 +0,0 @@ -# PACKAGE_NAME diff --git a/mkdocs.yml b/mkdocs.yml deleted file mode 100644 index 73d7717..0000000 --- a/mkdocs.yml +++ /dev/null @@ -1,61 +0,0 @@ -site_name: myoquant -repo_url: https://github.com/lambda-science/myoquant.md -site_url: https://lambda-science.github.io/myoquant.md/ -site_description: myoquant - -site_author: Corentin Meyer - -nav: - - Home: index.md - - Getting Started: - - Quickstart: quickstart/index.md - - Advanced: quickstart/advanced.md - - FAQ: quickstart/faq.md - - Examples: - - Example: examples/example.md - - API: - - myoquant: api/myoquant.md -plugins: - - mkdocstrings - - search -copyright: Maintained by Corentin Meyer. -theme: - name: material - favicon: icon.png - logo: icon.png - font: - text: Roboto - code: Roboto Mono - palette: - # Palette toggle for light mode - - scheme: default - primary: white - toggle: - icon: material/lightbulb - name: Switch to dark mode - - # Palette toggle for dark mode - - scheme: slate - primary: black - toggle: - icon: material/lightbulb-outline - name: Switch to light mode - features: - - navigation.instant - - navigation.top - - search.suggest - - search.highlight -markdown_extensions: - - attr_list - - admonition - - tables - - md_in_html - - pymdownx.highlight: - anchor_linenums: true - - pymdownx.inlinehilite - - pymdownx.snippets - - pymdownx.superfences - - pymdownx.emoji: - emoji_index: !!python/name:materialx.emoji.twemoji - emoji_generator: !!python/name:materialx.emoji.to_svg - - pymdownx.arithmatex: - generic: true diff --git a/myoquant/.python-version b/myoquant/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/myoquant/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/myoquant/__init__.py b/myoquant/README.md similarity index 100% rename from myoquant/__init__.py rename to myoquant/README.md diff --git a/myoquant/pyproject.toml b/myoquant/pyproject.toml new file mode 100644 index 0000000..709e5bc --- /dev/null +++ b/myoquant/pyproject.toml @@ -0,0 +1,61 @@ +[project] +name = "myoquant" +version = "0.3.5" +description = "MyoQuant🔬: a tool to automatically quantify pathological features in muscle fiber histology images." +authors = [{name="Corentin Meyer", email="contact@cmeyer.fr"}] +maintainers = [{name="Corentin Meyer", email="contact@cmeyer.fr"}] +license = "AGPL-3.0-or-later" +readme = "README.md" +homepage = "https://lbgi.fr/MyoQuant/" +repository = "https://github.com/lambda-science/MyoQuant" +keywords = ["histology", "quantification", "biology","deep-learning"] +requires-python = "<3.13, >=3.10" +dependencies = [ + "tensorflow>=2.19.0", + "keras>=3.9.0", + "cellpose>=3.1.1.1", + "stardist>=0.9.1", + "numpy<=2.1.0", + "csbdeep>=0.8.1", + "imageio>=2.37.0", + "scikit-image>=0.25.2", + "pandas>=2.2.3", + "typer>=0.15.2", + "rich>=13.9.4", + "seaborn>=0.13.2", + "scikit-learn>=1.6.1", + "textual>=2.1.2", + "streamlit>=1.44.0", + "pyngrok>=5.2.3", + "matplotlib>=3.9.4", + "ipykernel>=6.29.5", + "pillow>=11.1.0", + "tifffile>=2025.3.13", +] + +[project.scripts] +myoquant = "myoquant.__main__:app" + +[tool.uv] +dev-dependencies = [ + "ruff", + "mkdocs-material", + "ipykernel", + "pytest", + "pre-commit", + "mkdocstrings", + "mypy", +] + +[project.urls] +Homepage = "https://github.com/lambda-science/MyoQuant" +Documentation = "https://github.com/lambda-science/MyoQuant/blob/main/CLI_Documentation.md" +Repository = "https://github.com/lambda-science/MyoQuant" +Issues = "https://github.com/lambda-science/MyoQuant/issues" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/myoquant"] diff --git a/myoquant/src/__init__.py b/myoquant/src/__init__.py index e69de29..a3d0c04 100644 --- a/myoquant/src/__init__.py +++ b/myoquant/src/__init__.py @@ -0,0 +1 @@ +# This file is intentionally left empty to make the directory a Python package. \ No newline at end of file diff --git a/myoquant/src/ATP_analysis.py b/myoquant/src/myoquant/ATP_analysis.py similarity index 98% rename from myoquant/src/ATP_analysis.py rename to myoquant/src/myoquant/ATP_analysis.py index e11fbf7..11bdb0b 100644 --- a/myoquant/src/ATP_analysis.py +++ b/myoquant/src/myoquant/ATP_analysis.py @@ -1,7 +1,7 @@ import pandas as pd from scipy.stats import gaussian_kde from sklearn.mixture import GaussianMixture -from .common_func import extract_single_image, df_from_cellpose_mask +from myoquant.common_func import extract_single_image, df_from_cellpose_mask import numpy as np import matplotlib diff --git a/myoquant/src/HE_analysis.py b/myoquant/src/myoquant/HE_analysis.py similarity index 99% rename from myoquant/src/HE_analysis.py rename to myoquant/src/myoquant/HE_analysis.py index 4ba50b7..f8abe04 100644 --- a/myoquant/src/HE_analysis.py +++ b/myoquant/src/myoquant/HE_analysis.py @@ -1,12 +1,12 @@ import numpy as np import pandas as pd from skimage.draw import line -from .common_func import ( +from myoquant.common_func import ( extract_single_image, df_from_cellpose_mask, df_from_stardist_mask, ) -from .draw_line import ( +from myoquant.draw_line import ( line_equation, calculate_intersection, calculate_closest_point, diff --git a/myoquant/src/SDH_analysis.py b/myoquant/src/myoquant/SDH_analysis.py similarity index 96% rename from myoquant/src/SDH_analysis.py rename to myoquant/src/myoquant/SDH_analysis.py index a5e284e..94a6b03 100644 --- a/myoquant/src/SDH_analysis.py +++ b/myoquant/src/myoquant/SDH_analysis.py @@ -4,8 +4,8 @@ import pandas as pd import tensorflow as tf -from .common_func import extract_single_image, df_from_cellpose_mask -from .gradcam import make_gradcam_heatmap, save_and_display_gradcam +from myoquant.common_func import extract_single_image, df_from_cellpose_mask +from myoquant.gradcam import make_gradcam_heatmap, save_and_display_gradcam import numpy as np labels_predict = ["control", "sick"] diff --git a/myoquant/commands/__init__.py b/myoquant/src/myoquant/__init__.py similarity index 100% rename from myoquant/commands/__init__.py rename to myoquant/src/myoquant/__init__.py diff --git a/myoquant/__main__.py b/myoquant/src/myoquant/__main__.py similarity index 84% rename from myoquant/__main__.py rename to myoquant/src/myoquant/__main__.py index 26bc77f..a247c03 100644 --- a/myoquant/__main__.py +++ b/myoquant/src/myoquant/__main__.py @@ -8,8 +8,8 @@ __version_torch__ = pkg_resources.get_distribution("torch").version __version_tensorflow__ = pkg_resources.get_distribution("tensorflow").version -from .commands.docs import app as docs_app -from .commands import run_sdh, run_he, run_atp +from myoquant.commands.docs import app as docs_app +from myoquant.commands import run_sdh, run_he, run_atp console = Console() @@ -30,6 +30,11 @@ def version_callback(value: bool): ) app.add_typer(docs_app, name="docs", help="Generate documentation") +app.registered_commands += ( + run_sdh.app.registered_commands + + run_he.app.registered_commands + + run_atp.app.registered_commands +) @app.callback() def main( @@ -41,12 +46,5 @@ def main( MyoQuant Analysis Command Line Interface """ - -app.registered_commands += ( - run_sdh.app.registered_commands - + run_he.app.registered_commands - + run_atp.app.registered_commands -) - if __name__ == "__main__": app() diff --git a/myoquant/src/myoquant/commands/__init__.py b/myoquant/src/myoquant/commands/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/myoquant/commands/docs.py b/myoquant/src/myoquant/commands/docs.py similarity index 100% rename from myoquant/commands/docs.py rename to myoquant/src/myoquant/commands/docs.py diff --git a/myoquant/commands/run_atp.py b/myoquant/src/myoquant/commands/run_atp.py similarity index 98% rename from myoquant/commands/run_atp.py rename to myoquant/src/myoquant/commands/run_atp.py index 881e745..6afc861 100644 --- a/myoquant/commands/run_atp.py +++ b/myoquant/src/myoquant/commands/run_atp.py @@ -125,7 +125,7 @@ def atp_analysis( transient=False, ) as progress: progress.add_task(description="Importing the libraries...", total=None) - from ..src.common_func import ( + from myoquant.common_func import ( is_gpu_availiable, load_cellpose, run_cellpose, @@ -133,7 +133,7 @@ def atp_analysis( blend_image_with_label, HiddenPrints, ) - from ..src.ATP_analysis import run_atp_analysis + from myoquant.ATP_analysis import run_atp_analysis import numpy as np from PIL import Image from skimage.exposure import rescale_intensity @@ -271,9 +271,9 @@ def atp_analysis( table.add_column("Proportion (%)", justify="right", style="green") for index, row in result_df.iterrows(): table.add_row( - str(row[0]), - str(row[1]), - str(row[2]), + str(row.iloc[0]), + str(row.iloc[1]), + str(row.iloc[2]), ) console.print(table) csv_name = image_path.stem + "_results_summary.csv" diff --git a/myoquant/commands/run_he.py b/myoquant/src/myoquant/commands/run_he.py similarity index 98% rename from myoquant/commands/run_he.py rename to myoquant/src/myoquant/commands/run_he.py index 5257b58..d47dc2c 100644 --- a/myoquant/commands/run_he.py +++ b/myoquant/src/myoquant/commands/run_he.py @@ -135,7 +135,7 @@ def he_analysis( transient=False, ) as progress: progress.add_task(description="Importing the libraries...", total=None) - from ..src.common_func import ( + from myoquant.common_func import ( is_gpu_availiable, load_cellpose, load_stardist, @@ -145,7 +145,7 @@ def he_analysis( blend_image_with_label, HiddenPrints, ) - from ..src.HE_analysis import run_he_analysis + from myoquant.HE_analysis import run_he_analysis import numpy as np from PIL import Image @@ -310,9 +310,9 @@ def he_analysis( table.add_column("Proportion (%)", justify="right", style="green") for index, row in result_df.iterrows(): table.add_row( - str(row[0]), - str(row[1]), - str(row[2]), + str(row.iloc[0]), + str(row.iloc[1]), + str(row.iloc[2]), ) console.print(table) csv_name = image_path.stem + "_results_summary.csv" diff --git a/myoquant/commands/run_sdh.py b/myoquant/src/myoquant/commands/run_sdh.py similarity index 97% rename from myoquant/commands/run_sdh.py rename to myoquant/src/myoquant/commands/run_sdh.py index dd48d17..0957638 100644 --- a/myoquant/commands/run_sdh.py +++ b/myoquant/src/myoquant/commands/run_sdh.py @@ -112,7 +112,7 @@ def sdh_analysis( ) as progress: progress.add_task(description="Downloading SDH Model...", total=None) urllib.request.urlretrieve( - "https://lbgi.fr/~meyer/SDH_models/model.h5", + "https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/model.h5", model_path_abs, ) model_path = model_path_abs @@ -126,7 +126,7 @@ def sdh_analysis( transient=False, ) as progress: progress.add_task(description="Importing the libraries...", total=None) - from ..src.common_func import ( + from myoquant.common_func import ( is_gpu_availiable, load_cellpose, load_sdh_model, @@ -135,7 +135,7 @@ def sdh_analysis( blend_image_with_label, HiddenPrints, ) - from ..src.SDH_analysis import run_sdh_analysis + from myoquant.SDH_analysis import run_sdh_analysis import numpy as np from PIL import Image @@ -269,9 +269,9 @@ def sdh_analysis( table.add_column("Proportion (%)", justify="right", style="green") for index, row in result_df.iterrows(): table.add_row( - str(row[0]), - str(row[1]), - str(row[2]), + str(row.iloc[0]), + str(row.iloc[1]), + str(row.iloc[2]), ) console.print(table) csv_name = image_path.stem + "_results.csv" diff --git a/myoquant/src/common_func.py b/myoquant/src/myoquant/common_func.py similarity index 91% rename from myoquant/src/common_func.py rename to myoquant/src/myoquant/common_func.py index a980795..f9fc771 100644 --- a/myoquant/src/common_func.py +++ b/myoquant/src/myoquant/common_func.py @@ -13,15 +13,14 @@ from cellpose.models import Cellpose from csbdeep.utils import normalize from stardist.models import StarDist2D -from tensorflow import keras import numpy as np from PIL import Image from skimage.measure import regionprops_table from skimage.morphology import binary_erosion import pandas as pd +import keras +# from myoquant.gradcam import make_gradcam_heatmap, save_and_display_gradcam -# from .gradcam import make_gradcam_heatmap, save_and_display_gradcam -from .random_brightness import RandomBrightness import imageio @@ -92,9 +91,21 @@ def load_sdh_model(model_path: str): Returns: Keras model instance: tensorflow keras model """ + # Load the model without compiling to avoid issues with 'reduction=auto' in Keras 3 model_sdh = keras.models.load_model( - model_path, custom_objects={"RandomBrightness": RandomBrightness} + model_path, + custom_objects={"RandomBrightness": keras.layers.RandomBrightness(factor=0.2)}, + compile=False ) + + # Recompile the model with valid parameters for Keras 3 + # You may need to adjust the optimizer, loss, and metrics based on your original model + model_sdh.compile( + optimizer='adam', + loss='categorical_crossentropy', + metrics=['accuracy'] + ) + return model_sdh diff --git a/myoquant/src/draw_line.py b/myoquant/src/myoquant/draw_line.py similarity index 100% rename from myoquant/src/draw_line.py rename to myoquant/src/myoquant/draw_line.py diff --git a/myoquant/src/gradcam.py b/myoquant/src/myoquant/gradcam.py similarity index 100% rename from myoquant/src/gradcam.py rename to myoquant/src/myoquant/gradcam.py diff --git a/myoquant/src/random_brightness.py b/myoquant/src/random_brightness.py deleted file mode 100644 index d4df8c2..0000000 --- a/myoquant/src/random_brightness.py +++ /dev/null @@ -1,345 +0,0 @@ -# @title Random Brightness Layer -import tensorflow as tf -from keras import backend -from keras.engine import base_layer -from keras.engine import base_preprocessing_layer -from keras.layers.preprocessing import preprocessing_utils as utils -from keras.utils import tf_utils - -from tensorflow.python.ops import stateless_random_ops -from tensorflow.python.util.tf_export import keras_export -from tensorflow.tools.docs import doc_controls - - -@keras_export("keras.__internal__.layers.BaseImageAugmentationLayer") -class BaseImageAugmentationLayer(base_layer.BaseRandomLayer): - """Abstract base layer for image augmentaion. - This layer contains base functionalities for preprocessing layers which - augment image related data, eg. image and in future, label and bounding boxes. - The subclasses could avoid making certain mistakes and reduce code - duplications. - This layer requires you to implement one method: `augment_image()`, which - augments one single image during the training. There are a few additional - methods that you can implement for added functionality on the layer: - `augment_label()`, which handles label augmentation if the layer supports - that. - `augment_bounding_box()`, which handles the bounding box augmentation, if the - layer supports that. - `get_random_transformation()`, which should produce a random transformation - setting. The tranformation object, which could be any type, will be passed to - `augment_image`, `augment_label` and `augment_bounding_box`, to coodinate - the randomness behavior, eg, in the RandomFlip layer, the image and - bounding_box should be changed in the same way. - The `call()` method support two formats of inputs: - 1. Single image tensor with 3D (HWC) or 4D (NHWC) format. - 2. A dict of tensors with stable keys. The supported keys are: - `"images"`, `"labels"` and `"bounding_boxes"` at the moment. We might add - more keys in future when we support more types of augmentation. - The output of the `call()` will be in two formats, which will be the same - structure as the inputs. - The `call()` will handle the logic detecting the training/inference - mode, unpack the inputs, forward to the correct function, and pack the output - back to the same structure as the inputs. - By default the `call()` method leverages the `tf.vectorized_map()` function. - Auto-vectorization can be disabled by setting `self.auto_vectorize = False` - in your `__init__()` method. When disabled, `call()` instead relies - on `tf.map_fn()`. For example: - ```python - class SubclassLayer(BaseImageAugmentationLayer): - def __init__(self): - super().__init__() - self.auto_vectorize = False - ``` - Example: - ```python - class RandomContrast(BaseImageAugmentationLayer): - def __init__(self, factor=(0.5, 1.5), **kwargs): - super().__init__(**kwargs) - self._factor = factor - def augment_image(self, image, transformation=None): - random_factor = tf.random.uniform([], self._factor[0], self._factor[1]) - mean = tf.math.reduced_mean(inputs, axis=-1, keep_dim=True) - return (inputs - mean) * random_factor + mean - ``` - Note that since the randomness is also a common functionnality, this layer - also includes a tf.keras.backend.RandomGenerator, which can be used to produce - the random numbers. The random number generator is stored in the - `self._random_generator` attribute. - """ - - def __init__(self, rate=1.0, seed=None, **kwargs): - super().__init__(seed=seed, **kwargs) - self.rate = rate - - @property - def auto_vectorize(self): - """Control whether automatic vectorization occurs. - By default the `call()` method leverages the `tf.vectorized_map()` function. - Auto-vectorization can be disabled by setting `self.auto_vectorize = False` - in your `__init__()` method. When disabled, `call()` instead relies - on `tf.map_fn()`. For example: - ```python - class SubclassLayer(BaseImageAugmentationLayer): - def __init__(self): - super().__init__() - self.auto_vectorize = False - ``` - """ - return getattr(self, "_auto_vectorize", True) - - @auto_vectorize.setter - def auto_vectorize(self, auto_vectorize): - self._auto_vectorize = auto_vectorize - - @property - def _map_fn(self): - if self.auto_vectorize: - return tf.vectorized_map - else: - return tf.map_fn - - @doc_controls.for_subclass_implementers - def augment_image(self, image, transformation=None): - """Augment a single image during training. - Args: - image: 3D image input tensor to the layer. Forwarded from `layer.call()`. - transformation: The transformation object produced by - `get_random_transformation`. Used to coordinate the randomness between - image, label and bounding box. - Returns: - output 3D tensor, which will be forward to `layer.call()`. - """ - raise NotImplementedError() - - @doc_controls.for_subclass_implementers - def augment_label(self, label, transformation=None): - """Augment a single label during training. - Args: - label: 1D label to the layer. Forwarded from `layer.call()`. - transformation: The transformation object produced by - `get_random_transformation`. Used to coordinate the randomness between - image, label and bounding box. - Returns: - output 1D tensor, which will be forward to `layer.call()`. - """ - raise NotImplementedError() - - @doc_controls.for_subclass_implementers - def augment_bounding_box(self, bounding_box, transformation=None): - """Augment bounding boxes for one image during training. - Args: - bounding_box: 2D bounding boxes to the layer. Forwarded from `call()`. - transformation: The transformation object produced by - `get_random_transformation`. Used to coordinate the randomness between - image, label and bounding box. - Returns: - output 2D tensor, which will be forward to `layer.call()`. - """ - raise NotImplementedError() - - @doc_controls.for_subclass_implementers - def get_random_transformation(self, image=None, label=None, bounding_box=None): - """Produce random transformation config for one single input. - This is used to produce same randomness between image/label/bounding_box. - Args: - image: 3D image tensor from inputs. - label: optional 1D label tensor from inputs. - bounding_box: optional 2D bounding boxes tensor from inputs. - Returns: - Any type of object, which will be forwarded to `augment_image`, - `augment_label` and `augment_bounding_box` as the `transformation` - parameter. - """ - return None - - def call(self, inputs, training=True): - inputs = self._ensure_inputs_are_compute_dtype(inputs) - if training: - inputs, is_dict = self._format_inputs(inputs) - images = inputs["images"] - if images.shape.rank == 3: - return self._format_output(self._augment(inputs), is_dict) - elif images.shape.rank == 4: - return self._format_output(self._batch_augment(inputs), is_dict) - else: - raise ValueError( - "Image augmentation layers are expecting inputs to be " - "rank 3 (HWC) or 4D (NHWC) tensors. Got shape: " - f"{images.shape}" - ) - else: - return inputs - - def _augment(self, inputs): - image = inputs.get("images", None) - label = inputs.get("labels", None) - bounding_box = inputs.get("bounding_boxes", None) - transformation = self.get_random_transformation( - image=image, label=label, bounding_box=bounding_box - ) # pylint: disable=assignment-from-none - image = self.augment_image(image, transformation=transformation) - result = {"images": image} - if label is not None: - label = self.augment_label(label, transformation=transformation) - result["labels"] = label - if bounding_box is not None: - bounding_box = self.augment_bounding_box( - bounding_box, transformation=transformation - ) - result["bounding_boxes"] = bounding_box - return result - - def _batch_augment(self, inputs): - return self._map_fn(self._augment, inputs) - - def _format_inputs(self, inputs): - if tf.is_tensor(inputs): - # single image input tensor - return {"images": inputs}, False - elif isinstance(inputs, dict): - # TODO(scottzhu): Check if it only contains the valid keys - return inputs, True - else: - raise ValueError( - f"Expect the inputs to be image tensor or dict. Got {inputs}" - ) - - def _format_output(self, output, is_dict): - if not is_dict: - return output["images"] - else: - return output - - def _ensure_inputs_are_compute_dtype(self, inputs): - if isinstance(inputs, dict): - inputs["images"] = utils.ensure_tensor(inputs["images"], self.compute_dtype) - else: - inputs = utils.ensure_tensor(inputs, self.compute_dtype) - return inputs - - -@keras_export("keras.layers.RandomBrightness", v1=[]) -class RandomBrightness(BaseImageAugmentationLayer): - """A preprocessing layer which randomly adjusts brightness during training. - This layer will randomly increase/reduce the brightness for the input RGB - images. At inference time, the output will be identical to the input. - Call the layer with `training=True` to adjust the brightness of the input. - Note that different brightness adjustment factors - will be apply to each the images in the batch. - For an overview and full list of preprocessing layers, see the preprocessing - [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). - Args: - factor: Float or a list/tuple of 2 floats between -1.0 and 1.0. The - factor is used to determine the lower bound and upper bound of the - brightness adjustment. A float value will be chosen randomly between - the limits. When -1.0 is chosen, the output image will be black, and - when 1.0 is chosen, the image will be fully white. When only one float - is provided, eg, 0.2, then -0.2 will be used for lower bound and 0.2 - will be used for upper bound. - value_range: Optional list/tuple of 2 floats for the lower and upper limit - of the values of the input data. Defaults to [0.0, 255.0]. Can be changed - to e.g. [0.0, 1.0] if the image input has been scaled before this layer. - The brightness adjustment will be scaled to this range, and the - output values will be clipped to this range. - seed: optional integer, for fixed RNG behavior. - Inputs: 3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel - values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) - Output: 3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the - `factor`. By default, the layer will output floats. The output value will - be clipped to the range `[0, 255]`, the valid range of RGB colors, and - rescaled based on the `value_range` if needed. - Sample usage: - ```python - random_bright = tf.keras.layers.RandomBrightness(factor=0.2) - # An image with shape [2, 2, 3] - image = [[[1, 2, 3], [4 ,5 ,6]], [[7, 8, 9], [10, 11, 12]]] - # Assume we randomly select the factor to be 0.1, then it will apply - # 0.1 * 255 to all the channel - output = random_bright(image, training=True) - # output will be int64 with 25.5 added to each channel and round down. - tf.Tensor([[[26.5, 27.5, 28.5] - [29.5, 30.5, 31.5]] - [[32.5, 33.5, 34.5] - [35.5, 36.5, 37.5]]], - shape=(2, 2, 3), dtype=int64) - ``` - """ - - _FACTOR_VALIDATION_ERROR = ( - "The `factor` argument should be a number (or a list of two numbers) " - "in the range [-1.0, 1.0]. " - ) - _VALUE_RANGE_VALIDATION_ERROR = ( - "The `value_range` argument should be a list of two numbers. " - ) - - def __init__(self, factor, value_range=(0, 255), seed=None, **kwargs): - base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomBrightness").set(True) - super().__init__(seed=seed, force_generator=True, **kwargs) - self._set_factor(factor) - self._set_value_range(value_range) - self._seed = seed - - def augment_image(self, image, transformation=None): - return self._brightness_adjust(image, transformation["rgb_delta"]) - - def augment_label(self, label, transformation=None): - return label - - def get_random_transformation(self, image=None, label=None, bounding_box=None): - rgb_delta_shape = (1, 1, 1) - random_rgb_delta = self._random_generator.random_uniform( - shape=rgb_delta_shape, - minval=self._factor[0], - maxval=self._factor[1], - ) - random_rgb_delta = random_rgb_delta * ( - self._value_range[1] - self._value_range[0] - ) - return {"rgb_delta": random_rgb_delta} - - def _set_value_range(self, value_range): - if not isinstance(value_range, (tuple, list)): - raise ValueError(self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}") - if len(value_range) != 2: - raise ValueError(self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}") - self._value_range = sorted(value_range) - - def _set_factor(self, factor): - if isinstance(factor, (tuple, list)): - if len(factor) != 2: - raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {factor}") - self._check_factor_range(factor[0]) - self._check_factor_range(factor[1]) - self._factor = sorted(factor) - elif isinstance(factor, (int, float)): - self._check_factor_range(factor) - factor = abs(factor) - self._factor = [-factor, factor] - else: - raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {factor}") - - def _check_factor_range(self, input_number): - if input_number > 1.0 or input_number < -1.0: - raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {input_number}") - - def _brightness_adjust(self, image, rgb_delta): - image = utils.ensure_tensor(image, self.compute_dtype) - rank = image.shape.rank - if rank != 3: - raise ValueError( - "Expected the input image to be rank 3. Got " - f"inputs.shape = {image.shape}" - ) - rgb_delta = tf.cast(rgb_delta, image.dtype) - image += rgb_delta - return tf.clip_by_value(image, self._value_range[0], self._value_range[1]) - - def get_config(self): - config = { - "factor": self._factor, - "value_range": self._value_range, - "seed": self._seed, - } - base_config = super().get_config() - return dict(list(base_config.items()) + list(config.items())) diff --git a/myoquant/streamlit/Dockerfile b/myoquant/streamlit/Dockerfile new file mode 100644 index 0000000..9ab76d5 --- /dev/null +++ b/myoquant/streamlit/Dockerfile @@ -0,0 +1,26 @@ +FROM python:3.12-slim-bullseye +COPY --from=ghcr.io/astral-sh/uv:0.5.30 /uv /bin/uv + +ENV PYTHONUNBUFFERED=1 +ENV PYTHONIOENCODING=UTF-8 +ENV UV_COMPILE_BYTECODE=1 +ENV UV_NO_CACHE=1 +EXPOSE 8501 + +WORKDIR /app + +RUN apt-get update && apt-get install -y \ + build-essential \ + software-properties-common \ + git \ + wget \ + && rm -rf /var/lib/apt/lists/* + +#RUN git clone https://github.com/lambda-science/MyoQuant.git . +COPY myoquant /app/myoquant +WORKDIR /app/myoquant +RUN wget https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/model.h5 +RUN uv sync + +WORKDIR /app/myoquant/streamlit +ENTRYPOINT ["uv", "run", "streamlit", "run", "run.py", "--server.port=8501", "--server.address=0.0.0.0"] diff --git a/myoquant/streamlit/README.md b/myoquant/streamlit/README.md new file mode 100644 index 0000000..bc2b00f --- /dev/null +++ b/myoquant/streamlit/README.md @@ -0,0 +1,75 @@ +--- +title: MyoQuant Streamlit +emoji: 🔬 +colorFrom: yellow +colorTo: purple +sdk: streamlit +sdk_version: 1.19.0 +app_file: run.py +license: agpl-3.0 +python: 3.10.9 +--- + +![Twitter Follow](https://img.shields.io/twitter/follow/corentinm_py?style=social) ![Demo Version](https://img.shields.io/badge/Demo-https%3A%2F%2Flbgi.fr%2FMyoQuant%2F-9cf) ![PyPi](https://img.shields.io/badge/PyPi-https%3A%2F%2Fpypi.org%2Fproject%2Fmyoquant%2F-blueviolet) ![Pypi verison](https://img.shields.io/pypi/v/myoquant) + +# MyoQuant-Streamlit🔬: a demo web interface for the MyoQuant tool. + +## Please refer to the [MyoQuant GitHub Repository](https://github.com/lambda-science/MyoQuant) or the [MyoQuant PyPi repository](https://pypi.org/project/myoquant/) for full documentation. + +MyoQuant-Streamlit🔬 is a demo web interface to showcase the usage of MyoQuant. + +

+ MyoQuant Banner +

+ +

+ MyoQuant Illustration +

+ +MyoQuant🔬 is a command-line tool to automatically quantify pathological features in muscle fiber histology images. +It is built using CellPose, Stardist, custom neural-network models and image analysis techniques to automatically analyze myopathy histology images. Currently MyoQuant is capable of quantifying centralization of nuclei in muscle fiber with HE staining, anomaly in the mitochondria distribution in muscle fibers with SDH staining and the number of type 1 muscle fiber vs type 2 muscle fiber with ATP staining. +This web application is intended for demonstration purposes only. + +## How to install or deploy the interface + +The demo version is deployed at https://lbgi.fr/MyoQuant/. You can deploy your own demo version using Docker, your own python environment or google Colab for GPU support. + +### Docker + +You can build the docker image by running `docker build -t streamlit .` and launch the container using `docker run -p 8501:8501 streamlit`. + +### Non-Docker + +If you do not want to use Docker you can install the poetry package in a miniconda (python 3.9, 3.10) base env, run `poetry install` to install the python env, activate the env with `poetry shell` and launch the app by running `streamlit run run.py`. + +### Deploy on Google Colab for GPU + +As this application uses various deep-learning model, you could benefit from using a deployment solution that provides a GPU. +To do so, you can leverage Google Colab free GPU to boost this Streamlit application. +To run this app on Google Colab, simply clone the notebook called `google_colab_deploy.ipynb` into Colab and run the four cells. It will automatically download the latest code version, install dependencies and run the app. A link will appear in the output of the lat cell with a structure like `https://word1-word2-try-01-234-567-890.loca.lt`. Click it and the click continue and you’re ready to use the app! + +## How to Use + +Once on the demo, click on the corresponding staining analysis on the sidebar, and upload your histology image. Results will be displayed in the main area automatically. +For all analysis you can press the "Load Default File" to load a sample image to try the tool. + +## Troubleshooting + +If you have an error like `libcublas.so[0-9] cannot be found` +It probably means that there is a weird CUDA on CPU-only hardware installation error. Try `pip remove torch` and `pip install torch --index-url https://download.pytorch.org/whl/cpu`, in your python virtual env. It should do the trick. + +## Contact + +Creator and Maintainer: [**Corentin Meyer**, PhD in Biomedical AI](https://cmeyer.fr/) . The source code for MyoQuant is available [HERE](https://github.com/lambda-science/MyoQuant). + +## Citing MyoQuant🔬 + +[placeholder] + +## Partners + +

+ Partner Banner +

+ +MyoQuant is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte diff --git a/myoquant/streamlit/google_colab_deploy.ipynb b/myoquant/streamlit/google_colab_deploy.ipynb new file mode 100644 index 0000000..880603b --- /dev/null +++ b/myoquant/streamlit/google_colab_deploy.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q streamlit\n", + "!pip install -q pyngrok\n", + "!pip install -q cellpose\n", + "!pip install -q stardist\n", + "!pip install -q imageio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyngrok import ngrok \n", + "public_url = ngrok.connect(port='8501')\n", + "# public_url" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!git clone https://github.com/lambda-science/MyoQuant.git app\n", + "!wget https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/model.h5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "!streamlit run /content/myoquant/streamlit/run.py & npx localtunnel --port 8501" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/myoquant/streamlit/pages/ATP_staining.py b/myoquant/streamlit/pages/ATP_staining.py new file mode 100644 index 0000000..156591b --- /dev/null +++ b/myoquant/streamlit/pages/ATP_staining.py @@ -0,0 +1,195 @@ +import streamlit as st +from streamlit.components.v1 import html +import matplotlib +import requests +from io import BytesIO + + +try: + from imageio.v2 import imread +except: + from imageio import imread +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np +from myoquant.common_func import ( + load_cellpose, + run_cellpose, + is_gpu_availiable, + df_from_cellpose_mask, +) +from myoquant.ATP_analysis import ( + get_all_intensity, + estimate_threshold, + plot_density, + predict_all_cells, + paint_full_image, +) + +labels_predict = {1: "fiber type 1", 2: "fiber type 2"} + +use_GPU = is_gpu_availiable() +np.random.seed(42) + +@st.cache_resource +def st_load_cellpose(): + return load_cellpose() + + +@st.cache_data +def st_run_cellpose(image_atp, _model): + return run_cellpose(image_atp, _model) + + +@st.cache_data +def st_df_from_cellpose_mask(mask): + return df_from_cellpose_mask(mask) + + +@st.cache_data +def st_get_all_intensity(image_atp, df_cellpose): + return get_all_intensity(image_atp, df_cellpose) + + +@st.cache_data +def st_estimate_threshold(intensity_list): + return estimate_threshold(intensity_list) + + +@st.cache_data +def st_plot_density(all_cell_median_intensity, intensity_threshold): + return plot_density(all_cell_median_intensity, intensity_threshold) + + +@st.cache_data +def st_predict_all_cells(image_atp, cellpose_df, intensity_threshold): + return predict_all_cells(image_atp, cellpose_df, intensity_threshold) + + +@st.cache_data +def st_paint_full_image(image_atp, df_cellpose, class_predicted_all): + return paint_full_image(image_atp, df_cellpose, class_predicted_all) + + +model_cellpose = st_load_cellpose() + +with st.sidebar: + st.write("Threshold Parameters") + intensity_threshold = st.slider("Intensity Threshold (0=auto)", 0, 255, 0, 5) + +st.title("ATP Staining Analysis") +st.write( + "This demo will automatically quantify the number of type 1 muscle fibers vs the number of type 2 muscle fiber on ATP stained images." +) +default_file_url_5 = "https://raw.githubusercontent.com/lambda-science/MyoQuant/refs/heads/main/sample_img/sample_atp.jpg" + + +st.write("Upload your ATP Staining image OR click the Load Default File button !") +col1, col2 = st.columns(2) +with col1: + uploaded_file_atp = st.file_uploader("Choose a file") + if uploaded_file_atp is not None: + st.session_state["uploaded_file5"] = uploaded_file_atp + +with col2: + if st.button("Load Default File", type="primary"): + # Download the default file + response = requests.get(default_file_url_5) + # Convert the downloaded content into a file-like object + uploaded_file_atp = BytesIO(response.content) + st.session_state["uploaded_file5"] = uploaded_file_atp + +if "uploaded_file5" in st.session_state: + uploaded_file_atp = st.session_state["uploaded_file5"] + # Now you can use the uploaded_file as needed + +if uploaded_file_atp is not None: + image_ndarray_atp = imread(uploaded_file_atp) + + st.write("Raw Image") + image = st.image(image_ndarray_atp) + + mask_cellpose = st_run_cellpose(image_ndarray_atp, model_cellpose) + + st.header("Segmentation Results") + st.subheader("CellPose results") + fig, ax = plt.subplots(1, 1) + ax.imshow(mask_cellpose, cmap="viridis") + ax.axis("off") + st.pyplot(fig) + + df_cellpose = st_df_from_cellpose_mask(mask_cellpose) + + st.header("Cell Intensity Plot") + all_cell_median_intensity = st_get_all_intensity(image_ndarray_atp, df_cellpose) + figure_intensity = st_plot_density(all_cell_median_intensity, intensity_threshold) + st.pyplot(figure_intensity) + + st.header("ATP Cell Classification Results") + if intensity_threshold == 0: + ( + muscle_fiber_type_all, + all_cell_median_intensity, + intensity_threshold, + ) = st_predict_all_cells( + image_ndarray_atp, df_cellpose, intensity_threshold=None + ) + else: + ( + muscle_fiber_type_all, + all_cell_median_intensity, + intensity_threshold, + ) = st_predict_all_cells( + image_ndarray_atp, df_cellpose, intensity_threshold=intensity_threshold + ) + df_cellpose["muscle_fiber_type"] = muscle_fiber_type_all + df_cellpose["median_intensity"] = all_cell_median_intensity + count_per_label = np.unique(muscle_fiber_type_all, return_counts=True) + + st.dataframe( + df_cellpose.drop( + [ + "centroid-0", + "centroid-1", + "bbox-0", + "bbox-1", + "bbox-2", + "bbox-3", + "image", + ], + axis=1, + ) + ) + st.write("Total number of cells detected: ", len(muscle_fiber_type_all)) + for index, elem in enumerate(count_per_label[0]): + st.write( + "Number of cells classified as ", + labels_predict[int(elem)], + ": ", + count_per_label[1][int(index)], + " ", + 100 * count_per_label[1][int(index)] / len(muscle_fiber_type_all), + "%", + ) + + st.header("Painted predicted image") + st.write( + "Green color indicates cells classified as control, red color indicates cells classified as sick" + ) + paint_img = st_paint_full_image( + image_ndarray_atp, df_cellpose, muscle_fiber_type_all + ) + fig3, ax3 = plt.subplots(1, 1) + cmap = matplotlib.colors.LinearSegmentedColormap.from_list( + "", ["white", "green", "red"] + ) + ax3.imshow(image_ndarray_atp) + ax3.imshow(paint_img, cmap=cmap, alpha=0.5) + ax3.axis("off") + st.pyplot(fig3) + +html( + f""" + + """ +) diff --git a/myoquant/streamlit/pages/Fluo_staining.py b/myoquant/streamlit/pages/Fluo_staining.py new file mode 100644 index 0000000..098f7f0 --- /dev/null +++ b/myoquant/streamlit/pages/Fluo_staining.py @@ -0,0 +1,340 @@ +import streamlit as st +from streamlit.components.v1 import html +import matplotlib +import requests +from io import BytesIO + + +try: + from imageio.v2 import imread +except: + from imageio import imread +import matplotlib.pyplot as plt +from stardist import random_label_cmap +import numpy as np + +from myoquant.common_func import ( + load_cellpose, + load_stardist, + run_cellpose, + run_stardist, + is_gpu_availiable, + df_from_cellpose_mask, + df_from_stardist_mask, +) +from myoquant.HE_analysis import ( + predict_all_cells, + extract_ROIs, + single_cell_analysis, + paint_histo_img, +) + + +use_GPU = is_gpu_availiable() + + +@st.cache_resource +def st_load_cellpose(): + return load_cellpose() + + +@st.cache_resource +def st_load_stardist(fluo=False): + return load_stardist(fluo) + + +@st.cache_data +def st_run_cellpose(image_ndarray, _model): + return run_cellpose(image_ndarray, _model) + + +@st.cache_data +def st_run_stardist(image_ndarray, _model, nms_thresh, prob_thresh): + return run_stardist(image_ndarray, _model, nms_thresh, prob_thresh) + + +@st.cache_data +def st_df_from_cellpose_mask(mask): + return df_from_cellpose_mask(mask) + + +@st.cache_data +def st_df_from_stardist_mask(mask): + return df_from_stardist_mask(mask) + + +@st.cache_data +def st_predict_all_cells( + image_ndarray, df_cellpose, mask_stardist, internalised_threshold +): + return predict_all_cells( + image_ndarray, df_cellpose, mask_stardist, internalised_threshold + ) + + +@st.cache_data +def st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist): + return extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist) + + +@st.cache_data +def st_single_cell_analysis( + single_cell_img, + single_cell_mask, + df_nuc_single, + x_fiber, + y_fiber, + selected_fiber, + internalised_threshold, + draw_and_return=True, +): + return single_cell_analysis( + single_cell_img, + single_cell_mask, + df_nuc_single, + x_fiber, + y_fiber, + selected_fiber + 1, + internalised_threshold, + draw_and_return=True, + ) + + +@st.cache_data +def st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat): + return paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat) + + +with st.sidebar: + st.write("Nuclei detection Parameters (Stardist)") + nms_thresh = st.slider("Stardist NMS Tresh", 0.0, 1.0, 0.4, 0.1) + prob_thresh = st.slider("Stardist Prob Tresh", 0.5, 1.0, 0.5, 0.05) + st.write("Nuclei Classification Parameter") + eccentricity_thresh = st.slider("Eccentricity Score Tresh", 0.0, 1.0, 0.75, 0.05) + +model_cellpose = st_load_cellpose() +model_stardist = st_load_stardist(fluo=True) + +st.title("Breast Muscle Fiber Analysis") +st.write( + "This demo will automatically detect cells and nucleus in the image and try to quantify a certain number of features." +) + + +default_file_url_3 = "https://raw.githubusercontent.com/lambda-science/MyoQuant/refs/heads/main/sample_img/cytoplasmes.tif" +default_file_url_4 = "https://raw.githubusercontent.com/lambda-science/MyoQuant/refs/heads/main/sample_img/noyaux.tif" + +st.write( + "Upload your singles channels images (cytoplasme images and nuclei image) OR click the Load Default File button !" +) +col1, col2 = st.columns(2) +with col1: + uploaded_file_cyto = st.file_uploader("Choose a file (cyto)") + uploaded_file_nuc = st.file_uploader("Choose a file (nuc)") + if uploaded_file_cyto is not None: + st.session_state["uploaded_file3"] = uploaded_file_cyto + if uploaded_file_nuc is not None: + st.session_state["uploaded_file4"] = uploaded_file_nuc + +with col2: + if st.button("Load Default Files", type="primary"): + # Download the default file + response3 = requests.get(default_file_url_3) + response4 = requests.get(default_file_url_4) + # Convert the downloaded content into a file-like object + uploaded_file_cyto = BytesIO(response3.content) + uploaded_file_nuc = BytesIO(response4.content) + + st.session_state["uploaded_file3"] = uploaded_file_cyto + st.session_state["uploaded_file4"] = uploaded_file_nuc + +if "uploaded_file3" in st.session_state and "uploaded_file4" in st.session_state: + uploaded_file_cyto = st.session_state["uploaded_file3"] + uploaded_file_nuc = st.session_state["uploaded_file4"] + uploaded_file_cyto.seek(0) + uploaded_file_nuc.seek(0) + # Now you can use the uploaded_file as needed + +if uploaded_file_cyto is not None and uploaded_file_nuc is not None: + image_ndarray_cyto = imread(uploaded_file_cyto) + image_ndarray_nuc = imread(uploaded_file_nuc) + mergedarrays = np.maximum(image_ndarray_cyto, image_ndarray_nuc) + st.write("Raw Image (Cyto)") + image_cyto = st.image(image_ndarray_cyto) + st.write("Raw Image Nuc") + image_nuc = st.image(image_ndarray_nuc) + + mask_cellpose = st_run_cellpose(image_ndarray_cyto, model_cellpose) + mask_stardist = st_run_stardist( + image_ndarray_nuc, model_stardist, nms_thresh, prob_thresh + ) + mask_stardist_copy = mask_stardist.copy() + + st.header("Segmentation Results") + st.subheader("CellPose and Stardist overlayed results") + fig, ax = plt.subplots(1, 1) + ax.imshow(mask_cellpose, cmap="viridis") + lbl_cmap = random_label_cmap() + ax.imshow(mask_stardist, cmap=lbl_cmap, alpha=0.5) + ax.axis("off") + st.pyplot(fig) + + st.header("Full Nucleus Analysis Results") + df_cellpose = st_df_from_cellpose_mask(mask_cellpose) + cellpose_df_stat, all_nuc_df_stats = st_predict_all_cells( + image_ndarray_cyto, + df_cellpose, + mask_stardist, + internalised_threshold=eccentricity_thresh, + ) + st.dataframe( + cellpose_df_stat.drop( + [ + "centroid-0", + "centroid-1", + "bbox-0", + "bbox-1", + "bbox-2", + "bbox-3", + "image", + ], + axis=1, + ) + ) + st.write("Total number of nucleus : ", cellpose_df_stat["N° Nuc"].sum()) + st.write( + "Total number of internalized nucleus : ", + cellpose_df_stat["N° Nuc Intern"].sum(), + " (", + round( + 100 + * cellpose_df_stat["N° Nuc Intern"].sum() + / cellpose_df_stat["N° Nuc"].sum(), + 2, + ), + "%)", + ) + st.write( + "Total number of peripherical nucleus : ", + cellpose_df_stat["N° Nuc Periph"].sum(), + " (", + round( + 100 + * cellpose_df_stat["N° Nuc Periph"].sum() + / cellpose_df_stat["N° Nuc"].sum(), + 2, + ), + "%)", + ) + st.write( + "Number of cell with at least one internalized nucleus : ", + cellpose_df_stat["N° Nuc Intern"].astype(bool).sum(axis=0), + " (", + round( + 100 + * cellpose_df_stat["N° Nuc Intern"].astype(bool).sum(axis=0) + / len(cellpose_df_stat), + 2, + ), + "%)", + ) + st.header("Single Nucleus Analysis Details") + selected_fiber = st.selectbox("Select a cell", list(range(len(df_cellpose)))) + selected_fiber = int(selected_fiber) + single_cell_img = mergedarrays[ + df_cellpose.iloc[selected_fiber, 5] : df_cellpose.iloc[selected_fiber, 7], + df_cellpose.iloc[selected_fiber, 6] : df_cellpose.iloc[selected_fiber, 8], + ] + nucleus_single_cell_img = mask_stardist_copy[ + df_cellpose.iloc[selected_fiber, 5] : df_cellpose.iloc[selected_fiber, 7], + df_cellpose.iloc[selected_fiber, 6] : df_cellpose.iloc[selected_fiber, 8], + ] + single_cell_mask = df_cellpose.iloc[selected_fiber, 9] + single_cell_img[~single_cell_mask] = 0 + nucleus_single_cell_img[~single_cell_mask] = 0 + + ( + single_cell_img, + nucleus_single_cell_img, + single_cell_mask, + df_nuc_single, + ) = st_extract_ROIs(mergedarrays, selected_fiber, df_cellpose, mask_stardist) + + st.markdown( + """ + * White point represent cell centroid. + * Green point represent nucleus centroid. Green dashed line represent the fiber centrer - nucleus distance. + * Red point represent the cell border from a straight line between the cell centroid and the nucleus centroid. The red dashed line represent distance between the nucelus and the cell border. + * The periphery ratio is calculated by the division of the distance centroid - nucleus and the distance centroid - cell border.""" + ) + fig2, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(single_cell_img, cmap="gray") + ax2.imshow(nucleus_single_cell_img, cmap="viridis") + # Plot Fiber centroid + x_fiber = df_cellpose.iloc[selected_fiber, 3] - df_cellpose.iloc[selected_fiber, 6] + y_fiber = df_cellpose.iloc[selected_fiber, 2] - df_cellpose.iloc[selected_fiber, 5] + + ( + n_nuc, + n_nuc_intern, + n_nuc_periph, + df_nuc_single_stats, + ax_nuc, + ) = st_single_cell_analysis( + single_cell_img, + single_cell_mask, + df_nuc_single, + x_fiber, + y_fiber, + selected_fiber + 1, + internalised_threshold=eccentricity_thresh, + draw_and_return=True, + ) + + for index, value in df_nuc_single_stats.iterrows(): + st.write("Nucleus #{} has a periphery ratio of: {}".format(index, value[12])) + ax1.axis("off") + ax2.axis("off") + # st.pyplot(fig2) + ax_nuc.imshow(single_cell_img) + ax_nuc.imshow(nucleus_single_cell_img, cmap="viridis", alpha=0.5) + f = ax_nuc.figure + + st.pyplot(fig2) + st.pyplot(f) + st.subheader("All nucleus inside selected cell") + + st.dataframe( + df_nuc_single_stats.drop( + [ + "centroid-0", + "centroid-1", + "bbox-0", + "bbox-1", + "bbox-2", + "bbox-3", + "image", + ], + axis=1, + ) + ) + + st.header("Painted predicted image") + st.write( + "Green color indicates cells with only peripherical nuclei, red color indicates cells with at least one internal nucleus (regeneration)." + ) + painted_img = st_paint_histo_img(image_ndarray_cyto, df_cellpose, cellpose_df_stat) + fig3, ax3 = plt.subplots(1, 1) + cmap = matplotlib.colors.LinearSegmentedColormap.from_list( + "", ["white", "green", "red"] + ) + ax3.imshow(mergedarrays) + ax3.imshow(painted_img, cmap=cmap, alpha=0.5) + ax3.axis("off") + st.pyplot(fig3) + +html( + f""" + + """ +) diff --git a/myoquant/streamlit/pages/HE_staining.py b/myoquant/streamlit/pages/HE_staining.py new file mode 100644 index 0000000..08c6c65 --- /dev/null +++ b/myoquant/streamlit/pages/HE_staining.py @@ -0,0 +1,312 @@ +import streamlit as st +from streamlit.components.v1 import html +import matplotlib +import requests +from io import BytesIO + +try: + from imageio.v2 import imread +except: + from imageio import imread +import matplotlib.pyplot as plt +from stardist import random_label_cmap +from myoquant.common_func import ( + load_cellpose, + load_stardist, + run_cellpose, + run_stardist, + is_gpu_availiable, + df_from_cellpose_mask, + df_from_stardist_mask, +) +from myoquant.HE_analysis import ( + predict_all_cells, + extract_ROIs, + single_cell_analysis, + paint_histo_img, +) + + +use_GPU = is_gpu_availiable() + + +@st.cache_resource +def st_load_cellpose(): + return load_cellpose() + + +@st.cache_resource +def st_load_stardist(): + return load_stardist() + + +@st.cache_data +def st_run_cellpose(image_ndarray, _model): + return run_cellpose(image_ndarray, _model) + + +@st.cache_data +def st_run_stardist(image_ndarray, _model, nms_thresh, prob_thresh): + return run_stardist(image_ndarray, _model, nms_thresh, prob_thresh) + + +@st.cache_data +def st_df_from_cellpose_mask(mask): + return df_from_cellpose_mask(mask) + + +@st.cache_data +def st_df_from_stardist_mask(mask): + return df_from_stardist_mask(mask) + + +@st.cache_data +def st_predict_all_cells( + image_ndarray, df_cellpose, mask_stardist, internalised_threshold +): + return predict_all_cells( + image_ndarray, df_cellpose, mask_stardist, internalised_threshold + ) + + +@st.cache_data +def st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist): + return extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist) + + +@st.cache_data +def st_single_cell_analysis( + single_cell_img, + single_cell_mask, + df_nuc_single, + x_fiber, + y_fiber, + selected_fiber, + internalised_threshold, + draw_and_return=True, +): + return single_cell_analysis( + single_cell_img, + single_cell_mask, + df_nuc_single, + x_fiber, + y_fiber, + selected_fiber + 1, + internalised_threshold, + draw_and_return=True, + ) + + +@st.cache_data +def st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat): + return paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat) + + +with st.sidebar: + st.write("Nuclei detection Parameters (Stardist)") + nms_thresh = st.slider("Stardist NMS Tresh", 0.0, 1.0, 0.4, 0.1) + prob_thresh = st.slider("Stardist Prob Tresh", 0.5, 1.0, 0.5, 0.05) + st.write("Nuclei Classification Parameter") + eccentricity_thresh = st.slider("Eccentricity Score Tresh", 0.0, 1.0, 0.75, 0.05) + +model_cellpose = st_load_cellpose() +model_stardist = st_load_stardist() + +st.title("HE Staining Analysis") +st.write( + "This demo will automatically detect cells and nucleus in the image and try to quantify a certain number of features." +) + +default_file_url = "https://raw.githubusercontent.com/lambda-science/MyoQuant/refs/heads/main/sample_img/sample_he.jpg" + + +st.write("Upload your HE Staining image OR click the Load Default File button !") +col1, col2 = st.columns(2) +with col1: + uploaded_file = st.file_uploader("Choose a file") + if uploaded_file is not None: + st.session_state["uploaded_file"] = uploaded_file + +with col2: + if st.button("Load Default File", type="primary"): + # Download the default file + response = requests.get(default_file_url) + # Convert the downloaded content into a file-like object + uploaded_file = BytesIO(response.content) + st.session_state["uploaded_file"] = uploaded_file + +if "uploaded_file" in st.session_state: + uploaded_file = st.session_state["uploaded_file"] + uploaded_file.seek(0) + +if uploaded_file is not None: + image_ndarray = imread(uploaded_file) + + st.write("Raw Image") + image = st.image(image_ndarray) + + mask_cellpose = st_run_cellpose(image_ndarray, model_cellpose) + mask_stardist = st_run_stardist( + image_ndarray, model_stardist, nms_thresh, prob_thresh + ) + mask_stardist_copy = mask_stardist.copy() + st.header("Segmentation Results") + st.subheader("CellPose and Stardist overlayed results") + fig, ax = plt.subplots(1, 1) + ax.imshow(mask_cellpose, cmap="viridis") + lbl_cmap = random_label_cmap() + ax.imshow(mask_stardist, cmap=lbl_cmap, alpha=0.5) + ax.axis("off") + st.pyplot(fig) + + st.subheader("All cells detected by CellPose") + df_cellpose = st_df_from_cellpose_mask(mask_cellpose) + + st.header("Full Nucleus Analysis Results") + cellpose_df_stat, all_nuc_df_stats = st_predict_all_cells( + image_ndarray, + df_cellpose, + mask_stardist, + internalised_threshold=eccentricity_thresh, + ) + st.dataframe( + cellpose_df_stat.drop( + [ + "centroid-0", + "centroid-1", + "bbox-0", + "bbox-1", + "bbox-2", + "bbox-3", + "image", + ], + axis=1, + ) + ) + st.write("Total number of nucleus : ", cellpose_df_stat["N° Nuc"].sum()) + st.write( + "Total number of internalized nucleus : ", + cellpose_df_stat["N° Nuc Intern"].sum(), + " (", + round( + 100 + * cellpose_df_stat["N° Nuc Intern"].sum() + / cellpose_df_stat["N° Nuc"].sum(), + 2, + ), + "%)", + ) + st.write( + "Total number of peripherical nucleus : ", + cellpose_df_stat["N° Nuc Periph"].sum(), + " (", + round( + 100 + * cellpose_df_stat["N° Nuc Periph"].sum() + / cellpose_df_stat["N° Nuc"].sum(), + 2, + ), + "%)", + ) + st.write( + "Number of cell with at least one internalized nucleus : ", + cellpose_df_stat["N° Nuc Intern"].astype(bool).sum(axis=0), + " (", + round( + 100 + * cellpose_df_stat["N° Nuc Intern"].astype(bool).sum(axis=0) + / len(cellpose_df_stat), + 2, + ), + "%)", + ) + + st.header("Single Nucleus Analysis Details") + selected_fiber = st.selectbox("Select a cell", list(range(len(df_cellpose)))) + selected_fiber = int(selected_fiber) + ( + single_cell_img, + nucleus_single_cell_img, + single_cell_mask, + df_nuc_single, + ) = st_extract_ROIs(image_ndarray, selected_fiber, df_cellpose, mask_stardist) + + # df_nuc_single = df_from_stardist_mask(mask_stardist) + st.markdown( + """ + * White point represent cell centroid. + * Green point represent nucleus centroid. Green dashed line represent the fiber centrer - nucleus distance. + * Red point represent the cell border from a straight line between the cell centroid and the nucleus centroid. The red dashed line represent distance between the nucelus and the cell border. + * The periphery ratio is calculated by the division of the distance centroid - nucleus and the distance centroid - cell border.""" + ) + + fig2, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(single_cell_img) + ax2.imshow(nucleus_single_cell_img, cmap="viridis") + # Plot Fiber centroid + x_fiber = df_cellpose.iloc[selected_fiber, 3] - df_cellpose.iloc[selected_fiber, 6] + y_fiber = df_cellpose.iloc[selected_fiber, 2] - df_cellpose.iloc[selected_fiber, 5] + + ( + n_nuc, + n_nuc_intern, + n_nuc_periph, + df_nuc_single_stats, + ax_nuc, + ) = st_single_cell_analysis( + single_cell_img, + single_cell_mask, + df_nuc_single, + x_fiber, + y_fiber, + selected_fiber + 1, + internalised_threshold=eccentricity_thresh, + draw_and_return=True, + ) + for index, value in df_nuc_single_stats.iterrows(): + st.write("Nucleus #{} has a periphery ratio of: {}".format(index, value[12])) + ax1.axis("off") + ax2.axis("off") + # st.pyplot(fig2) + ax_nuc.imshow(single_cell_img) + ax_nuc.imshow(nucleus_single_cell_img, cmap="viridis", alpha=0.5) + f = ax_nuc.figure + + st.pyplot(fig2) + st.pyplot(f) + st.subheader("All nucleus inside selected cell") + + st.dataframe( + df_nuc_single_stats.drop( + [ + "centroid-0", + "centroid-1", + "bbox-0", + "bbox-1", + "bbox-2", + "bbox-3", + "image", + ], + axis=1, + ) + ) + + st.header("Painted predicted image") + st.write( + "Green color indicates cells with only peripherical nuclei, red color indicates cells with at least one internal nucleus." + ) + painted_img = st_paint_histo_img(image_ndarray, df_cellpose, cellpose_df_stat) + fig4, ax4 = plt.subplots(1, 1) + cmap = matplotlib.colors.LinearSegmentedColormap.from_list( + "", ["white", "green", "red"] + ) + ax4.imshow(image_ndarray) + ax4.imshow(painted_img, cmap=cmap, alpha=0.5) + ax4.axis("off") + st.pyplot(fig4) + +html( + f""" + + """ +) diff --git a/myoquant/streamlit/pages/SDH_staining.py b/myoquant/streamlit/pages/SDH_staining.py new file mode 100644 index 0000000..a83fa1d --- /dev/null +++ b/myoquant/streamlit/pages/SDH_staining.py @@ -0,0 +1,223 @@ +import streamlit as st +from streamlit.components.v1 import html +import requests +from io import BytesIO + +try: + from imageio.v2 import imread +except: + from imageio import imread +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt +import tensorflow as tf +import numpy as np +from os import path +import urllib.request +from myoquant.SDH_analysis import ( + predict_all_cells, + predict_single_cell, + paint_full_image, +) +from myoquant.common_func import ( + load_cellpose, + run_cellpose, + is_gpu_availiable, + df_from_cellpose_mask, + load_sdh_model, + extract_single_image, +) + + +use_GPU = is_gpu_availiable() + + +@st.cache_resource +def st_load_sdh_model(model_path): + return load_sdh_model(model_path) + + +@st.cache_resource +def st_load_cellpose(): + return load_cellpose() + + +@st.cache_data +def st_run_cellpose(image_ndarray, _model): + return run_cellpose(image_ndarray, _model) + + +@st.cache_data +def st_df_from_cellpose_mask(mask): + return df_from_cellpose_mask(mask) + + +@st.cache_data +def st_predict_all_cells(image_ndarray, cellpose_df, _model_SDH): + return predict_all_cells(image_ndarray, cellpose_df, _model_SDH) + + +@st.cache_data +def st_extract_single_image(image_ndarray, cellpose_df, index): + return extract_single_image(image_ndarray, cellpose_df, index) + + +@st.cache_data +def st_predict_single_cell(image_ndarray, _model_SDH): + return predict_single_cell(image_ndarray, _model_SDH) + + +@st.cache_data +def st_paint_full_image(image_sdh, df_cellpose, class_predicted_all): + return paint_full_image(image_sdh, df_cellpose, class_predicted_all) + + +labels_predict = ["control", "sick"] + +tf.random.set_seed(42) +np.random.seed(42) + + +if path.exists("model.h5"): + st.success("SDH Model ready to use !") + pass +else: + with st.spinner("Please wait we are downloading the SDH Model."): + urllib.request.urlretrieve( + "https://lbgi.fr/~meyer/SDH_models/model.h5", "model.h5" + ) + st.success("SDH Model have been downloaded !") + +model_cellpose = st_load_cellpose() + +model_SDH = st_load_sdh_model("model.h5") + +st.title("SDH Staining Analysis") +st.write( + "This demo will automatically detect cells classify the SDH stained cell as sick or healthy using our deep-learning model." +) + + +default_file_url_2 = "https://raw.githubusercontent.com/lambda-science/MyoQuant/refs/heads/main/sample_img/sample_sdh.jpg" + + +st.write("Upload your SDH Staining image OR click the Load Default File button !") +col1, col2 = st.columns(2) +with col1: + uploaded_file_sdh = st.file_uploader("Choose a file") + if uploaded_file_sdh is not None: + st.session_state["uploaded_file2"] = uploaded_file_sdh + +with col2: + if st.button("Load Default File", type="primary"): + # Download the default file + response = requests.get(default_file_url_2) + # Convert the downloaded content into a file-like object + uploaded_file_sdh = BytesIO(response.content) + st.session_state["uploaded_file2"] = uploaded_file_sdh + +if "uploaded_file2" in st.session_state: + uploaded_file_sdh = st.session_state["uploaded_file2"] + # Now you can use the uploaded_file as needed + uploaded_file_sdh.seek(0) # reset the file pointer to the start + + +if uploaded_file_sdh is not None: + image_ndarray_sdh = imread(uploaded_file_sdh) + + st.write("Raw Image") + image = st.image(image_ndarray_sdh) + + mask_cellpose = st_run_cellpose(image_ndarray_sdh, model_cellpose) + + st.header("Segmentation Results") + st.subheader("CellPose results") + fig, ax = plt.subplots(1, 1) + ax.imshow(mask_cellpose, cmap="viridis") + ax.axis("off") + st.pyplot(fig) + + st.header("SDH Cell Classification Results") + df_cellpose = st_df_from_cellpose_mask(mask_cellpose) + df_cellpose_results = st_predict_all_cells( + image_ndarray_sdh, df_cellpose, model_SDH + ) + class_predicted_all = df_cellpose_results["class_predicted"].values + proba_predicted_all = df_cellpose_results["proba_predicted"].values + count_per_label = np.unique(class_predicted_all, return_counts=True) + class_and_proba_df = pd.DataFrame( + list(zip(class_predicted_all, proba_predicted_all)), + columns=["class", "proba"], + ) + st.dataframe( + df_cellpose_results.drop( + [ + "centroid-0", + "centroid-1", + "bbox-0", + "bbox-1", + "bbox-2", + "bbox-3", + "image", + ], + axis=1, + ) + ) + st.write("Total number of cells detected: ", len(class_predicted_all)) + for elem in count_per_label[0]: + st.write( + "Number of cells classified as ", + labels_predict[int(elem)], + ": ", + count_per_label[1][int(elem)], + " ", + 100 * count_per_label[1][int(elem)] / len(class_predicted_all), + "%", + ) + + st.header("Single Cell Grad-CAM") + selected_fiber = st.selectbox("Select a cell", list(range(len(df_cellpose)))) + selected_fiber = int(selected_fiber) + single_cell_img = st_extract_single_image( + image_ndarray_sdh, df_cellpose, selected_fiber + ) + + grad_img, class_predicted, proba_predicted = st_predict_single_cell( + single_cell_img, model_SDH + ) + + fig2, (ax1, ax2) = plt.subplots(1, 2) + resized_single_cell_img = tf.image.resize(single_cell_img, (256, 256)) + ax1.imshow(single_cell_img) + ax2.imshow(grad_img) + ax1.axis("off") + # ax2.axis("off") + + xlabel = ( + labels_predict[int(class_predicted)] + + " (" + + str(round(proba_predicted, 2)) + + ")" + ) + ax2.set_xlabel(xlabel) + st.pyplot(fig2) + + st.header("Painted predicted image") + st.write( + "Green color indicates cells classified as control, red color indicates cells classified as sick" + ) + paint_img = st_paint_full_image(image_ndarray_sdh, df_cellpose, class_predicted_all) + fig3, ax3 = plt.subplots(1, 1) + cmap = matplotlib.colors.LinearSegmentedColormap.from_list( + "", ["white", "green", "red"] + ) + ax3.imshow(image_ndarray_sdh) + ax3.imshow(paint_img, cmap=cmap, alpha=0.5) + ax3.axis("off") + st.pyplot(fig3) + +html( + f""" + + """ +) diff --git a/myoquant/streamlit/pages/home.py b/myoquant/streamlit/pages/home.py new file mode 100644 index 0000000..4b0965b --- /dev/null +++ b/myoquant/streamlit/pages/home.py @@ -0,0 +1,33 @@ +import streamlit as st +from streamlit.components.v1 import html + +st.write("# MyoQuant-Streamlit🔬") + +st.sidebar.success("Select the corresponding staining analysis above.") + +st.markdown( + """ +## MyoQuant-Streamlit🔬 is a demo web interface to showcase the usage of [MyoQuant](https://github.com/lambda-science/MyoQuant). + +![MyoQuant Banner](https://i.imgur.com/mzALgZL.png) +![MyoQuant Illustration](https://i.imgur.com/FxpFUT3.png) + +[MyoQuant🔬 is a command-line tool to automatically quantify pathological features in muscle fiber histology images.](https://github.com/lambda-science/MyoQuant) +It is built using CellPose, Stardist, custom neural-network models and image analysis techniques to automatically analyze myopathy histology images. Currently MyoQuant is capable of quantifying centralization of nuclei in muscle fiber with HE staining and anomaly in the mitochondria distribution in muscle fibers with SDH staining. +This web application is intended for demonstration purposes only. + +## How to Use + +Once on the demo, click on the corresponding staining analysis on the sidebar, and upload your histology image. Results will be displayed in the main area automatically. +For all analysis you can press the "Load Default File" to load a sample image to try the tool. + +## Contact +Creator and Maintainer: [**Corentin Meyer**, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://lambda-science.github.io/) +The source code for MyoQuant is available [HERE](https://github.com/lambda-science/MyoQuant). + +## Partners + +![Partners Banner](https://i.imgur.com/Xk9wBFQ.png) +MyoQuant is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte. +""" +) \ No newline at end of file diff --git a/myoquant/streamlit/run.py b/myoquant/streamlit/run.py new file mode 100644 index 0000000..6aa4f5f --- /dev/null +++ b/myoquant/streamlit/run.py @@ -0,0 +1,49 @@ +import streamlit as st +from streamlit.components.v1 import html + +st.set_page_config( + page_title="MyoQuant", + page_icon="🔬", +) + +navigation_dict = { + "MyoQuant": [ + st.Page( + page="pages/home.py", + title="Home", + icon="👨‍👨‍👦‍👦", + url_path="home", + default=True, + ), + st.Page( + page="pages/ATP_staining.py", + title="ATP Staining", + icon="👨‍👨‍👦‍👦", + url_path="atp_staining", + default=False, + ), + st.Page( + page="pages/HE_staining.py", + title="HE Staining", + icon="👨‍👨‍👦‍👦", + url_path="he_staining", + default=False, + ), + st.Page( + page="pages/SDH_staining.py", + title="SDH Staining", + icon="👨‍👨‍👦‍👦", + url_path="sdh_staining", + default=False, + ), + st.Page( + page="pages/Fluo_staining.py", + title="Fluo Staining", + icon="👨‍👨‍👦‍👦", + url_path="fluo_staining", + default=False, + ), + ], +} +selected_page = st.navigation(navigation_dict, position="sidebar") +selected_page.run() diff --git a/tests/test_cli.py b/myoquant/tests/test_cli.py similarity index 100% rename from tests/test_cli.py rename to myoquant/tests/test_cli.py diff --git a/myoquant/uv.lock b/myoquant/uv.lock new file mode 100644 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