Merge pull request #14 from andreped/dev

Added argparse support + linting CI + refactored

.github/workflows/linting.yml

@@ -0,0 +1,28 @@
+name: Linting
+
+on:
+  push:
+    branches:
+      - '*'
+  pull_request:
+    branches:
+      - '*'
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      
+      - name: Install lint dependencies
+        run: |
+          pip install wheel setuptools
+          pip install black==22.3.0 isort==5.10.1 flake8==4.0.1
+
+      - name: Lint the code
+        run: sh shell/lint.sh

README.md

@@ -10,7 +10,7 @@ license: mit
 app_file: app.py
 ---
 
-<div align="center">
+<div align="center">M
 <h1 align="center">neukit</h1>
 <h3 align="center">Automatic brain extraction and preoperative tumor segmentation from MRI</h3>
 
@@ -36,6 +36,8 @@ To access the live demo, click on the `Hugging Face` badge above. Below is a sna
 
 ## Development
 
+### Docker
+
 Alternatively, you can deploy the software locally. Note that this is only relevant for development purposes. Simply dockerize the app and run it:
 
 ```
@@ -45,6 +47,28 @@ docker run -it -p 7860:7860 neukit
 
 Then open `http://127.0.0.1:7860` in your favourite internet browser to view the demo.
 
+### Python
+
+It is also possible to run the app locally without Docker. Just setup a virtual environment and run the app.
+Note that the current working directory would need to be adjusted based on where `neukit` is located on disk.
+
+```
+git clone https://github.com/andreped/neukit.git
+cd neukit/
+
+virtualenv -ppython3 venv --clear
+source venv/bin/activate
+pip install -r requirements.txt
+
+python app.py --cwd ./
+```
+
+## Troubleshooting
+
+Note that due to `share=True` being enabled by default when launching the app,
+internet access is required for the app to be launched. This can disabled by setting
+the argument to `--share 0`.
+
 ## Citation
 
 If you found the tool useful in your research, please, cite the corresponding software paper:

app.py

@@ -1,14 +1,39 @@
+import os
+from argparse import ArgumentParser
+
 from neukit.gui import WebUI
 
 
 def main():
-    print("Launching demo...")
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--cwd",
+        type=str,
+        default="/home/user/app/",
+        help="Set current working directory (path to app.py).",
+    )
+    parser.add_argument(
+        "--share",
+        type=int,
+        default=1,
+        help="Whether to enable the app to be accessible online"
+        "-> setups a public link which requires internet access.",
+    )
+    args = parser.parse_args()
 
-    # cwd = "/Users/andreped/workspace/neukit/"  # local testing -> macOS
-    cwd = "/home/user/app/"  # production -> docker
+    print("Current working directory:", args.cwd)
+
+    if not os.path.exists(args.cwd):
+        raise ValueError("Chosen 'cwd' is not a valid path!")
+    if args.share not in [0, 1]:
+        raise ValueError(
+            "The 'share' argument can only be set to 0 or 1, but was:",
+            args.share,
+        )
 
     # initialize and run app
-    app = WebUI(cwd=cwd)
+    print("Launching demo...")
+    app = WebUI(cwd=args.cwd, share=args.share)
     app.run()
 
 

neukit/gui.py

@@ -1,10 +1,20 @@
+import os
+
 import gradio as gr
-from .utils import load_ct_to_numpy, load_pred_volume_to_numpy, nifti_to_glb
+
 from .inference import run_model
+from .utils import load_ct_to_numpy
+from .utils import load_pred_volume_to_numpy
+from .utils import nifti_to_glb
 
 
 class WebUI:
-    def __init__(self, model_name:str = None, cwd:str = "/home/user/app/"):
+    def __init__(
+        self,
+        model_name: str = None,
+        cwd: str = "/home/user/app/",
+        share: int = 1,
+    ):
         # global states
         self.images = []
         self.pred_images = []
@@ -14,8 +24,9 @@ class WebUI:
 
         self.model_name = model_name
         self.cwd = cwd
+        self.share = share
 
-        self.class_name = "meningioma"  # default - but can be updated based on which task is chosen from dropdown
+        self.class_name = "meningioma"  # default
         self.class_names = {
             "meningioma": "MRI_Meningioma",
             "low-grade": "MRI_LGGlioma",
@@ -33,41 +44,55 @@ class WebUI:
         }
 
         # 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.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 set_class_name(self, value):
         print("Changed task to:", value)
         self.class_name = value
 
     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):
+
+    def process(self, mesh_file_name):
         path = mesh_file_name.name
-        run_model(path, model_path=self.cwd + "resources/models/", task=self.class_names[self.class_name], name=self.result_names[self.class_name])
+        run_model(
+            path,
+            model_path=os.path.join(self.cwd, "resources/models/"),
+            task=self.class_names[self.class_name],
+            name=self.result_names[self.class_name],
+        )
         nifti_to_glb("prediction.nii.gz")
 
         self.images = load_ct_to_numpy(path)
         self.pred_images = load_pred_volume_to_numpy("./prediction.nii.gz")
         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)
+        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="""
+        css = """
         #model-3d {
         height: 512px;
         }
@@ -80,18 +105,15 @@ class WebUI:
         }
         """
         with gr.Blocks(css=css) as demo:
-
             with gr.Row():
-                
-                file_output = gr.File(file_count="single", elem_id="upload")  # elem_id="upload"
+                file_output = gr.File(file_count="single", elem_id="upload")
                 file_output.upload(self.upload_file, file_output, file_output)
 
-                # with gr.Column():
-
                 model_selector = gr.Dropdown(
                     list(self.class_names.keys()),
                     label="Task",
-                    info="Which task to perform - one model for each brain tumor type and brain extraction",
+                    info="Which task to perform - one model for"
+                    "each brain tumor type and brain extraction",
                     multiselect=False,
                     size="sm",
                 )
@@ -101,39 +123,55 @@ class WebUI:
                     outputs=None,
                 )
 
-                run_btn = gr.Button("Run analysis").style(full_width=False, size="lg")
+                run_btn = gr.Button("Run analysis").style(
+                    full_width=False, size="lg"
+                )
                 run_btn.click(
-                    fn=lambda x: self.load_mesh(x),
+                    fn=lambda x: self.process(x),
                     inputs=file_output,
                     outputs=self.volume_renderer,
                 )
-            
+
             with gr.Row():
                 gr.Examples(
-                    examples=[self.cwd + "RegLib_C01_1.nii", self.cwd + "RegLib_C01_2.nii"],
+                    examples=[
+                        os.path.join(self.cwd, "RegLib_C01_1.nii"),
+                        os.path.join(self.cwd, "RegLib_C01_2.nii"),
+                    ],
                     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.Column():
+                        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.input(
+                            self.get_img_pred_pair, self.slider, image_boxes
+                        )
+
+                        self.slider.render()
+
                 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=True)
+        # 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=self.share
+        )

neukit/inference.py

@@ -1,23 +1,28 @@
-import os
-import shutil
 import configparser
 import logging
-import traceback
+import os
+import shutil
 
 
-def run_model(input_path: str, model_path: str, verbose: str = "info", task: str = "MRI_Meningioma", name: str = "Tumor"):
+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':
+    if verbose == "debug":
         logging.getLogger().setLevel(logging.DEBUG)
-    elif verbose == 'info':
+    elif verbose == "info":
         logging.getLogger().setLevel(logging.INFO)
-    elif verbose == 'error':
+    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/")
@@ -25,33 +30,42 @@ def run_model(input_path: str, model_path: str, verbose: str = "info", task: str
         shutil.rmtree("./result/")
 
     try:
-        # create sequence folder, rename patient, and add to temporary patient directory
+        # 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)
-        
+        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')
+        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)
@@ -59,11 +73,20 @@ def run_model(input_path: str, model_path: str, verbose: str = "info", task: str
         # finally, run inference
         from raidionicsrads.compute import run_rads
 
-        run_rads(config_filename='rads_config.ini')
-        
+        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")
-    
+        os.rename(
+            "./result/prediction-"
+            + splits[0]
+            + "/T0/"
+            + splits[0]
+            + "-t1gd_annotation-"
+            + name
+            + ".nii.gz",
+            "./prediction.nii.gz",
+        )
+
     except Exception as e:
         print(e)
 

neukit/utils.py

@@ -1,5 +1,5 @@
-import numpy as np
 import nibabel as nib
+import numpy as np
 from nibabel.processing import resample_to_output
 from skimage.measure import marching_cubes
 
@@ -52,12 +52,16 @@ def nifti_to_glb(path, output="prediction.obj"):
     verts, faces, normals, values = marching_cubes(data, 0)
     faces += 1
 
-    with open(output, 'w') as thefile:
+    with open(output, "w") as thefile:
         for item in verts:
-            thefile.write("v {0} {1} {2}\n".format(item[0],item[1],item[2]))
+            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]))
+            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]))
+            thefile.write(
+                "f {0}//{0} {1}//{1} {2}//{2}\n".format(
+                    item[0], item[1], item[2]
+                )
+            )

setup.cfg

@@ -0,0 +1,14 @@
+[metadata]
+description-file = README.md
+
+[isort]
+force_single_line=True
+known_first_party=neukit
+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

shell/format.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+isort --sl neukit app.py
+black --line-length 80 neukit app.py
+flake8 neukit app.py

shell/lint.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+isort --check --sl -c neukit app.py
+if ! [ $? -eq 0 ]
+then
+  echo "Please run \"sh shell/format.sh\" to format the code."
+  exit 1
+fi
+echo "no issues with isort"
+flake8 neukit app.py
+if ! [ $? -eq 0 ]
+then
+  echo "Please fix the code style issue."
+  exit 1
+fi
+echo "no issues with flake8"
+black --check --line-length 80 neukit app.py
+if ! [ $? -eq 0 ]
+then
+  echo "Please run \"sh shell/format.sh\" to format the code."
+    exit 1
+fi
+echo "no issues with black"
+echo "linting success!"