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| from shiny import App, ui, render, reactive | |
| import os | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from transformers import SamModel, SamProcessor | |
| # Load the processor and the finetuned model | |
| processor = SamProcessor.from_pretrained("facebook/sam-vit-base") | |
| model_path = "mito_model_checkpoint.pth" | |
| model = SamModel.from_pretrained("facebook/sam-vit-base") | |
| model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| model.eval() | |
| def process_image(image_path): | |
| # Open and prepare the image | |
| image = Image.open(image_path).convert("RGB") # Ensure RGB format for consistency | |
| image_np = np.array(image) | |
| # Prepare the image for the model using the processor | |
| inputs = processor(images=image_np, return_tensors="pt") | |
| inputs = {k: v.to(device) for k, v in inputs.items()} | |
| # Perform inference | |
| with torch.no_grad(): | |
| outputs = model(**inputs, multimask_output=False) | |
| # Process the prediction to create a binary mask | |
| pred_masks = torch.sigmoid(outputs.pred_masks).cpu().numpy() | |
| segmented_image = (pred_masks[0] > .99).astype(np.uint8) * 255 | |
| print(segmented_image) | |
| # Save the segmented image | |
| root, ext = os.path.splitext(image_path) | |
| output_path = f"{root}_segmented.png" | |
| segmented_image_pil = Image.fromarray(segmented_image.squeeze(), mode="L") | |
| segmented_image_pil.save(output_path) | |
| return output_path | |
| # Define the Shiny app UI layout | |
| app_ui = ui.page_fluid( | |
| ui.layout_sidebar( | |
| ui.panel_sidebar( | |
| ui.input_file("image_upload", "Upload Satellite Image", accept=".jpg,.jpeg,.png,.tif") | |
| ), | |
| ui.panel_main( | |
| ui.output_image("uploaded_image", "Uploaded Image"), | |
| ui.output_image("segmented_image", "Segmented Image") | |
| ) | |
| ) | |
| ) | |
| def server(input, output, session): | |
| def uploaded_image(): | |
| file_info = input.image_upload() | |
| if file_info: | |
| if isinstance(file_info, list): | |
| file_path = file_info[0].get('datapath') | |
| if file_path: | |
| return {'src': file_path} | |
| else: | |
| file_path = file_info.get('datapath') | |
| if file_path: | |
| return {'src': file_path} | |
| return None | |
| def segmented_image(): | |
| file_info = input.image_upload() | |
| if file_info: | |
| try: | |
| file_path = file_info[0].get('datapath') if isinstance(file_info, list) else file_info.get('datapath') | |
| if file_path: | |
| segmented_path = process_image(file_path) | |
| return {'src': segmented_path} | |
| except Exception as e: | |
| print(f"Error processing image: {e}") | |
| return None | |
| # Create and run the Shiny app | |
| app = App(app_ui, server) | |
| app.run(port=7860) |