Setup repo + demo

Dockerfile

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+# creates virtual ubuntu in docker image
+FROM ubuntu:22.04
+
+# set language, format and stuff
+ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
+
+# NOTE: using -y is conveniently to automatically answer yes to all the questions
+# installing python3 with a specific version
+RUN apt-get update -y
+RUN apt-get upgrade -y
+RUN apt install software-properties-common -y
+RUN add-apt-repository ppa:deadsnakes/ppa -y
+RUN apt update
+RUN apt install python3.7 -y
+RUN apt install python3.7-distutils -y
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1
+
+# installing other libraries
+RUN apt-get install python3-pip -y && \
+    apt-get -y install sudo
+RUN apt-get install curl -y
+RUN apt-get install nano -y
+RUN apt-get update && apt-get install -y git
+RUN apt-get install libblas-dev -y && apt-get install liblapack-dev -y
+RUN apt-get install gfortran -y
+RUN apt-get install libpng-dev -y
+RUN apt-get install python3-dev -y
+# RUN apt-get -y install cmake curl
+
+WORKDIR /code
+
+# install dependencies
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# resolve issue with tf==2.4 and gradio dependency collision issue
+RUN pip install --force-reinstall typing_extensions==4.7.1
+
+# Install wget
+RUN apt install wget -y && \
+    apt install unzip
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+# Download pretrained models
+RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-CT_Airways-ONNX-v12.zip" && \
+    unzip "Raidionics-CT_Airways-ONNX-v12.zip" && mkdir -p resources/models/ && mv CT_Airways/ resources/models/CT_Airways/
+RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-CT_Lungs-ONNX-v12.zip" && \
+    unzip "Raidionics-CT_Lungs-ONNX-v12.zip" && mv CT_Lungs/ resources/models/CT_Lungs/
+
+RUN rm -r *.zip
+
+# Download test sample
+RUN wget "https://github.com/andreped/neukit/releases/download/test-data/test_thorax_CT.nii.gz"
+
+# CMD ["/bin/bash"]
+CMD ["python3", "app.py"]

LICENSE.md

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+MIT License
+
+Copyright (c) 2023 André Pedersen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

demo/README.md

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+# Hugging Face demo - through docker SDK
+
+Deploying simple models in a gradio-based web interface in Hugging Face spaces is easy.
+For any other custom pipeline, with various dependencies and challenging behaviour, it
+might be necessary to use Docker containers instead.
+
+For every new push to the main branch, continuous deployment to the Hugging Face
+`AeroPath` space is performed through a GitHub Actions workflow.
+
+When the space is updated, the Docker image is rebuilt/updated (caching if possible).
+Then when finished, the end users can test the app as they please.
+
+Right now, the functionality of the app is extremely limited, only offering a widget
+for uploading a NIfTI file (`.nii` or `.nii.gz`) and visualizing the produced surface
+of the predicted lung tumor volume when finished processing.
+
+Analysis process can be monitored from the `Logs` tab next to the `Running` button
+in the Hugging Face `AeroPath` space.
+
+It is also possible to build the app as a docker image and deploy it. To do so follow these steps:
+
+```
+docker build -t AeroPath:latest ..
+docker run -it -p 7860:7860 AeroPath:latest
+```
+
+Then open `http://localhost:7860` in your favourite internet browser to view the demo.
+
+TODOs:
+- [X] Add gallery widget to enable scrolling through 2D slices
+- [X] Render segmentation for individual 2D slices as overlays

demo/app.py

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+from src.gui import WebUI
+
+
+def main():
+    print("Launching demo...")
+
+    # cwd = "/Users/andreped/workspace/AeroPath/"  # local testing -> macOS
+    cwd = "/home/user/app/"  # production -> docker
+
+    class_name = "tumor"
+
+    # initialize and run app
+    app = WebUI(class_name=class_name, cwd=cwd)
+    app.run()
+
+
+if __name__ == "__main__":
+    main()

demo/requirements.txt

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+lungtumormask @ git+https://github.com/vemundfredriksen/LungTumorMask.git
+gradio==3.44.4

demo/src/compute.py

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+import configparser
+import logging
+import os
+import shutil
+
+
+def run_model(
+    input_path: str,
+    model_path: str,
+    verbose: str = "info",
+    task: str = "MRI_Meningioma",
+    name: str = "Tumor",
+):
+    logging.basicConfig()
+    logging.getLogger().setLevel(logging.WARNING)
+
+    if verbose == "debug":
+        logging.getLogger().setLevel(logging.DEBUG)
+    elif verbose == "info":
+        logging.getLogger().setLevel(logging.INFO)
+    elif verbose == "error":
+        logging.getLogger().setLevel(logging.ERROR)
+    else:
+        raise ValueError("Unsupported verbose value provided:", verbose)
+
+    # delete patient/result folder if they exist
+    if os.path.exists("./patient/"):
+        shutil.rmtree("./patient/")
+    if os.path.exists("./result/"):
+        shutil.rmtree("./result/")
+
+    try:
+        # setup temporary patient directory
+        filename = input_path.split("/")[-1]
+        splits = filename.split(".")
+        extension = ".".join(splits[1:])
+        patient_directory = "./patient/"
+        os.makedirs(patient_directory + "T0/", exist_ok=True)
+        shutil.copy(
+            input_path,
+            patient_directory + "T0/" + splits[0] + "-t1gd." + extension,
+        )
+
+        # define output directory to save results
+        output_path = "./result/prediction-" + splits[0] + "/"
+        os.makedirs(output_path, exist_ok=True)
+
+        # Setting up the configuration file
+        rads_config = configparser.ConfigParser()
+        rads_config.add_section("Default")
+        rads_config.set("Default", "task", "neuro_diagnosis")
+        rads_config.set("Default", "caller", "")
+        rads_config.add_section("System")
+        rads_config.set("System", "gpu_id", "-1")
+        rads_config.set("System", "input_folder", patient_directory)
+        rads_config.set("System", "output_folder", output_path)
+        rads_config.set("System", "model_folder", model_path)
+        rads_config.set(
+            "System",
+            "pipeline_filename",
+            os.path.join(model_path, task, "pipeline.json"),
+        )
+        rads_config.add_section("Runtime")
+        rads_config.set(
+            "Runtime", "reconstruction_method", "thresholding"
+        )  # thresholding, probabilities
+        rads_config.set("Runtime", "reconstruction_order", "resample_first")
+        rads_config.set("Runtime", "use_preprocessed_data", "False")
+
+        with open("rads_config.ini", "w") as f:
+            rads_config.write(f)
+
+        # finally, run inference
+        from raidionicsrads.compute import run_rads
+
+        run_rads(config_filename="rads_config.ini")
+
+        # rename and move final result
+        os.rename(
+            "./result/prediction-"
+            + splits[0]
+            + "/T0/"
+            + splits[0]
+            + "-t1gd_annotation-"
+            + name
+            + ".nii.gz",
+            "./prediction.nii.gz",
+        )
+
+    except Exception as e:
+        print(e)
+
+    # Clean-up
+    if os.path.exists(patient_directory):
+        shutil.rmtree(patient_directory)
+    if os.path.exists(output_path):
+        shutil.rmtree(output_path)

demo/src/convert.py

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+import nibabel as nib
+from nibabel.processing import resample_to_output
+from skimage.measure import marching_cubes
+
+
+def nifti_to_glb(path, output="prediction.obj"):
+    # load NIFTI into numpy array
+    image = nib.load(path)
+    resampled = resample_to_output(image, [1, 1, 1], order=1)
+    data = resampled.get_fdata().astype("uint8")
+
+    # extract surface
+    verts, faces, normals, values = marching_cubes(data, 0)
+    faces += 1
+
+    with open(output, 'w') as thefile:
+        for item in verts:
+            thefile.write("v {0} {1} {2}\n".format(item[0],item[1],item[2]))
+
+        for item in normals:
+            thefile.write("vn {0} {1} {2}\n".format(item[0],item[1],item[2]))
+
+        for item in faces:
+            thefile.write("f {0}//{0} {1}//{1} {2}//{2}\n".format(item[0],item[1],item[2]))

demo/src/gui.py

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+import gradio as gr
+from .utils import load_ct_to_numpy, load_pred_volume_to_numpy
+from .compute import run_model
+from .convert import nifti_to_glb
+
+
+class WebUI:
+    def __init__(self, class_name:str = None, cwd:str = None):
+        # global states
+        self.images = []
+        self.pred_images = []
+
+        # @TODO: This should be dynamically set based on chosen volume size
+        self.nb_slider_items = 300
+
+        self.class_name = class_name
+        self.cwd = cwd
+
+        # define widgets not to be rendered immediantly, but later on
+        self.slider = gr.Slider(1, self.nb_slider_items, value=1, step=1, label="Which 2D slice to show")
+        self.volume_renderer = gr.Model3D(
+            clear_color=[0.0, 0.0, 0.0, 0.0],
+            label="3D Model",
+            visible=True,
+            elem_id="model-3d",
+        ).style(height=512)
+
+    def combine_ct_and_seg(self, img, pred):
+        return (img, [(pred, self.class_name)])
+    
+    def upload_file(self, file):
+        return file.name
+    
+    def load_mesh(self, mesh_file_name):
+        path = mesh_file_name.name
+        run_model(path)
+        nifti_to_glb("./prediction.nii.gz")
+        self.images = load_ct_to_numpy(path)
+        self.pred_images = load_pred_volume_to_numpy("./prediction.nii.gz")
+        self.slider = self.slider.update(value=2)
+        return "./prediction.obj"
+    
+    def get_img_pred_pair(self, k):
+        k = int(k) - 1
+        out = [gr.AnnotatedImage.update(visible=False)] * self.nb_slider_items
+        out[k] = gr.AnnotatedImage.update(self.combine_ct_and_seg(self.images[k], self.pred_images[k]), visible=True)
+        return out
+
+    def run(self):
+        css="""
+        #model-3d {
+        height: 512px;
+        }
+        #model-2d {
+        height: 512px;
+        margin: auto;
+        }
+        """
+        with gr.Blocks(css=css) as demo:
+
+            with gr.Row():
+                file_output = gr.File(
+                    file_types=[".nii", ".nii.nz"],
+                    file_count="single"
+                ).style(full_width=False, size="sm")
+                file_output.upload(self.upload_file, file_output, file_output)
+
+                run_btn = gr.Button("Run analysis").style(full_width=False, size="sm")
+                run_btn.click(
+                    fn=lambda x: self.load_mesh(x),
+                    inputs=file_output,
+                    outputs=self.volume_renderer
+                )
+            
+            with gr.Row():
+                gr.Examples(
+                    examples=[self.cwd + "lung_001.nii.gz"],
+                    inputs=file_output,
+                    outputs=file_output,
+                    fn=self.upload_file,
+                    cache_examples=True,
+                )
+            
+            with gr.Row():
+                with gr.Box():
+                    image_boxes = []
+                    for i in range(self.nb_slider_items):
+                        visibility = True if i == 1 else False
+                        t = gr.AnnotatedImage(visible=visibility, elem_id="model-2d")\
+                            .style(color_map={self.class_name: "#ffae00"}, height=512, width=512)
+                        image_boxes.append(t)
+
+                    self.slider.change(self.get_img_pred_pair, self.slider, image_boxes)
+                
+                with gr.Box():
+                    self.volume_renderer.render()
+            
+            with gr.Row():
+                self.slider.render()
+
+        # sharing app publicly -> share=True: https://gradio.app/sharing-your-app/
+        # inference times > 60 seconds -> need queue(): https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
+        demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)

demo/src/utils.py

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+import nibabel as nib
+import numpy as np
+
+
+def load_ct_to_numpy(data_path):
+    if type(data_path) != str:
+        data_path = data_path.name
+
+    image = nib.load(data_path)
+    data = image.get_fdata()
+
+    data = np.rot90(data, k=1, axes=(0, 1))
+
+    data[data < -1024] = 1024
+    data[data > 1024] = 1024
+
+    data = data - np.amin(data)
+    data = data / np.amax(data) * 255
+    data = data.astype("uint8")
+
+    print(data.shape)
+    return [data[..., i] for i in range(data.shape[-1])]
+
+
+def load_pred_volume_to_numpy(data_path):
+    if type(data_path) != str:
+        data_path = data_path.name
+
+    image = nib.load(data_path)
+    data = image.get_fdata()
+
+    data = np.rot90(data, k=1, axes=(0, 1))
+
+    data[data > 0] = 1
+    data = data.astype("uint8")
+
+    print(data.shape)
+    return [data[..., i] for i in range(data.shape[-1])]

setup.cfg

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+[metadata]
+description-file = README.md
+
+[isort]
+force_single_line=True
+known_first_party=aeropath
+line_length=80
+profile=black
+
+[flake8]
+# imported but unused in __init__.py, that's ok.
+per-file-ignores=*__init__.py:F401
+ignore=E203,W503,W605,F632,E266,E731,E712,E741
+max-line-length=80