Setup demo app [no ci]

demo/app.py

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+from src.gui import WebUI
+
+
+def main():
+    print("Launching demo...")
+
+    # cwd = "/Users/andreped/workspace/LungTumorMask/"  # local testing -> macOS
+    cwd = "/home/user/app/"  # production -> docker
+
+    class_name = "tumor"
+
+    # initialize and run app
+    app = WebUI(model_name=model_name, 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==3.32.0

demo/src/compute.py

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+def run_model(input_path):
+    from lungtumormask import mask
+    mask.mask(input_path, "./output.nii.gz", lung_filter=True, threshold=0.5, radius=1, batch_size=1)

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 = 100
+
+        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-livermask.nii")
+        self.images = load_ct_to_numpy(path)
+        self.pred_images = load_pred_volume_to_numpy("./prediction-livermask.nii")
+        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 + "test-volume.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.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)

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 < -150] = -150
+    data[data > 250] = 250
+
+    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])]