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app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+from pathlib import Path
+from huggingface_hub import snapshot_download
+from fastMONAI.vision_all import *
+#import pathlib
+#temp = pathlib.PosixPath
+#pathlib.PosixPath = pathlib.WindowsPath
+#pathlib.PosixPath = temp
+
+def initialize_system():
+    """Initial setup of model paths and other constants."""
+    models_path = Path.cwd()
+    save_dir = Path.cwd() / 'hs_pred'
+    save_dir.mkdir(parents=True, exist_ok=True)
+    download_example_endometrial_cancer_data(path=save_dir, multi_channel=False)
+    
+    return models_path, save_dir
+
+def extract_slices_from_mask(img, mask_data):
+    """Extract all slices from the 3D [W, H, D] image and mask data."""
+    slices = []
+    for idx in range(img.shape[-1]):
+        slice_img, slice_mask = img[:, :, idx], mask_data[:, :, idx]
+        slice_img = np.fliplr(np.rot90(slice_img, -1))
+        slice_mask = np.fliplr(np.rot90(slice_mask, -1))
+        slices.append((slice_img, slice_mask))
+    return slices
+
+def get_fused_image(img, pred_mask, alpha=0.8):
+    """Fuse a grayscale image with a mask overlay and flip both horizontally and vertically."""
+    gray_img_colored = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    mask_color = np.array([255, 0, 0])
+    colored_mask = (pred_mask[..., None] * mask_color).astype(np.uint8)
+    
+    fused = cv2.addWeighted(gray_img_colored, alpha, colored_mask, 1 - alpha, 0)
+    
+    # Flip the fused image vertically and horizontally
+    fused_flipped = cv2.flip(fused, -1)  # Flip both vertically and horizontally
+    
+    return fused_flipped
+
+def gradio_image_segmentation(fileobj, learn, reorder, resample, save_dir):
+    """Predict function using the learner and other resources."""
+    img_path = Path(fileobj.name)
+
+    save_fn = 'pred_' + img_path.stem
+    save_path = save_dir / save_fn
+    org_img, input_img, org_size = med_img_reader(img_path, 
+                                                  reorder=reorder,
+                                                  resample=resample,
+                                                  only_tensor=False)
+    
+    mask_data = inference(learn, reorder=reorder, resample=resample,
+                          org_img=org_img, input_img=input_img,
+                          org_size=org_size).data
+    
+    if "".join(org_img.orientation) == "LSA":        
+        mask_data = mask_data.permute(0,1,3,2)
+        mask_data = torch.flip(mask_data[0], dims=[1])
+        mask_data = torch.Tensor(mask_data)[None]
+
+    img = org_img.data
+    org_img.set_data(mask_data)
+    org_img.save(save_path)
+
+    slices = extract_slices_from_mask(img[0], mask_data[0])
+    fused_images = [(get_fused_image(
+                        ((slice_img - slice_img.min()) / (slice_img.max() - slice_img.min()) * 255).astype(np.uint8), 
+                        slice_mask))
+                    for slice_img, slice_mask in slices]
+    
+    volume = compute_binary_tumor_volume(org_img)
+
+    return fused_images, round(volume, 2)
+
+# Initialize the system
+models_path, save_dir = initialize_system()
+
+# Load the model and other required resources
+learn, reorder, resample = load_system_resources(models_path=Path.cwd(),
+                                                 learner_fn='heart_model.pkl',
+                                                 variables_fn='vars.pkl')
+
+# Gradio interface setup
+output_text = gr.Textbox(label="Volume of the Left Atrium (mL):")
+
+demo = gr.Interface(
+    fn=lambda fileobj: gradio_image_segmentation(fileobj, learn, reorder, resample, save_dir),
+    inputs=["file"],
+    outputs=[gr.Gallery(label="Click an Image, and use Arrow Keys to scroll slices", columns=3, height=450), output_text],
+    examples=[[str(Path.cwd() /"sample.nii.gz")]],
+    allow_flagging='never')
+
+# Launch the Gradio interface
+demo.launch()

heart_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8afefff7a465f013ca5978f03f6d0a0c4aa1dd2650dc0308962f1ad66cee4ae6
+size 19363377

requirements.txt

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+fastMONAI

sample.nii.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:05477be1b339567c8b304dafa737ad22be268024140197eeae9f14172a76e0c4
+size 16059519

vars.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd8458577a45f5ee60fc50a8ec5f6a499c6733b0f241a33cf76fa22bb9e715d3
+size 173