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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import pydicom | |
| import os | |
| from skimage import transform | |
| import torch | |
| from segment_anything import sam_model_registry | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| import io | |
| # Function to load bounding boxes from CSV | |
| def load_bounding_boxes(csv_file): | |
| # Assuming CSV file has columns: 'filename', 'x_min', 'y_min', 'x_max', 'y_max' | |
| df = pd.read_csv(csv_file) | |
| return df | |
| def load_image(file_path): | |
| if file_path.endswith(".dcm"): | |
| ds = pydicom.dcmread(file_path) | |
| img = ds.pixel_array | |
| else: | |
| img = np.array(Image.open(file_path).convert('L')) # Convert to grayscale | |
| H, W = img.shape | |
| return img, H, W | |
| # MedSAM inference function | |
| def medsam_inference(medsam_model, img, box, H, W, target_size): | |
| # Resize image and box to target size | |
| img_resized = transform.resize(img, (target_size, target_size), anti_aliasing=True) | |
| box_resized = np.array(box) * (target_size / np.array([W, H, W, H])) | |
| # Convert image to PyTorch tensor | |
| img_tensor = torch.from_numpy(img_resized).float().unsqueeze(0).unsqueeze(0).to(device) # Add channel and batch dimension | |
| # Model expects box in format (x0, y0, x1, y1) | |
| box_tensor = torch.tensor(box_resized, dtype=torch.float32).unsqueeze(0).to(device) # Add batch dimension | |
| # MedSAM inference | |
| img_embed = medsam_model.image_encoder(img_tensor) | |
| mask = medsam_model.predict(img_embed, box_tensor) | |
| # Post-process mask: resize back to original size | |
| mask_resized = transform.resize(mask[0].cpu().numpy(), (H, W)) | |
| return mask_resized | |
| # Function for visualizing images with masks | |
| def visualize(image, mask, box): | |
| fig, ax = plt.subplots(1, 2, figsize=(10, 5)) | |
| ax[0].imshow(image, cmap='gray') | |
| ax[0].add_patch(plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], edgecolor="red", facecolor="none")) | |
| ax[1].imshow(image, cmap='gray') | |
| ax[1].imshow(mask, alpha=0.5, cmap="jet") | |
| plt.tight_layout() | |
| buf = io.BytesIO() | |
| plt.savefig(buf, format='png') | |
| plt.close(fig) | |
| buf.seek(0) | |
| return buf | |
| # Main function for Gradio app | |
| def process_images(file, x_min, y_min, x_max, y_max): | |
| image, H, W = load_image(file) | |
| # Initialize MedSAM model | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| medsam_model = sam_model_registry['vit_b'](checkpoint="medsam_vit_b.pth") # Ensure the correct path | |
| medsam_model = medsam_model.to(device) | |
| medsam_model.eval() | |
| box = [x_min, y_min, x_max, y_max] | |
| mask = medsam_inference(medsam_model, image, box, H, W, H) # Assuming target size is the same as the image height | |
| visualization = visualize(image, mask, box) | |
| return visualization.getvalue() # Returning the byte stream | |
| # Set up Gradio interface | |
| iface = gr.Interface( | |
| fn=process_images, | |
| inputs=[ | |
| gr.File(label="MRI Slice (DICOM, PNG, etc.)"), | |
| gr.Number(label="X min"), | |
| gr.Number(label="Y min"), | |
| gr.Number(label="X max"), | |
| gr.Number(label="Y max") | |
| ], | |
| outputs="plot" | |
| ) | |
| iface.launch() | |