Dev: gradio app

app.py

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+import gradio as gr
+from fastMONAI.vision_all import *
+from huggingface_hub import snapshot_download
+from pathlib import Path
+import torch
+import cv2
+
+def initialize_system():
+    """Initial setup of model paths and other constants."""
+    models_path = Path(snapshot_download(repo_id="skaliy/endometrial_cancer_segmentation", cache_dir='models', revision='main'))
+    save_dir =  Path.cwd() / 'ec_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 load_system_resources(models_path):
+    """Load necessary resources like learner and variables."""
+    
+    learner = load_learner(models_path / 'vibe-learner.pkl', cpu=True)  # TODO: add an option to run on GPU
+    vars_fn = models_path / 'vars.pkl'
+    _, reorder, resample = load_variables(pkl_fn=vars_fn)
+
+    return learner, reorder, resample
+
+def get_mid_slice(img, mask_data): 
+    """Extract the middle slice of the mask in a 3D array."""
+    
+    sums = mask_data.sum(axis=(0,1))
+    mid_idx = np.argmax(sums)
+    img, mask_data = img[:, :, mid_idx], mask_data[:, :, mid_idx]
+    
+    return np.fliplr(np.rot90(img, -1)), np.fliplr(np.rot90(mask_data, -1))
+
+
+def get_fused_image(img, pred_mask, alpha=0.8):
+    """Overlay the mask on the image."""
+    gray_img_colored = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    mask_color = np.array([0, 0, 255])
+    colored_mask = (pred_mask[..., None] * mask_color).astype(np.uint8)
+    
+    return cv2.addWeighted(gray_img_colored, alpha, colored_mask, 1 - alpha, 0)
+
+def compute_tumor_volume(mask_data):
+    """Compute the volume of the tumor in milliliters (ml)."""
+    
+    dx, dy, dz = mask_data.spacing
+    voxel_volume_ml = dx * dy * dz / 1000  
+    return np.sum(mask_data) * voxel_volume_ml
+
+def predict(fileobj, learner, 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(learner, 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 #TEMP
+    
+    org_img.set_data(mask_data)
+    org_img.save(save_path)
+
+    img, pred_mask = get_mid_slice(img[0], mask_data[0])
+    img = ((img - img.min()) / (img.max() - img.min()) * 255).astype(np.uint8) #normalize
+    volume = compute_tumor_volume(org_img)
+   
+    return get_fused_image(img, pred_mask), round(volume, 2)
+
+
+models_path, save_dir = initialize_system()
+learner, reorder, resample = load_system_resources(models_path)
+output_text = gr.Textbox(label="Volume of the predicted tumor:")
+
+demo = gr.Interface(
+    fn=lambda fileobj: predict(fileobj, learner, reorder, resample, save_dir),
+    inputs=["file"],
+    outputs=["image", output_text],
+    examples=[[save_dir/"vibe.nii.gz"]]
+)
+
+demo.launch()

requirements.txt

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+fastMONAI 
+cv2