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| from shiny import App, ui, render | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image, ImageOps | |
| import numpy as np | |
| import torch | |
| from transformers import SamModel, SamProcessor | |
| # Load the processor and model | |
| processor = SamProcessor.from_pretrained("facebook/sam-vit-base") | |
| model = SamModel.from_pretrained("facebook/sam-vit-base") | |
| model_path = "SAM/mito_model_checkpoint.pth" | |
| 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 preprocess_image(image, target_size=(256, 256)): | |
| """ Resize the image to a standard dimension """ | |
| image = ImageOps.contain(image, target_size) | |
| return image | |
| def postprocess_mask(mask, threshold=0.95): | |
| """ Apply threshold to clean up mask """ | |
| return (mask > threshold).astype(np.uint8) * 255 | |
| def process_image(image_path): | |
| image = Image.open(image_path).convert("RGB") | |
| image = preprocess_image(image) # Resize image before processing | |
| image_np = np.array(image) | |
| inputs = processor(images=image_np, return_tensors="pt") | |
| inputs = {k: v.to(device) for k, v in inputs.items()} | |
| with torch.no_grad(): | |
| outputs = model(**inputs, multimask_output=False) | |
| pred_masks = torch.sigmoid(outputs.pred_masks).cpu().numpy() | |
| # Ensure we only use the first mask and squeeze out any singleton dimensions | |
| segmented_image = postprocess_mask(pred_masks.squeeze(), threshold=0.95) # Apply postprocessing | |
| pil_img = Image.fromarray(segmented_image, mode="L") | |
| buffered = BytesIO() | |
| pil_img.save(buffered, format="PNG") | |
| img_str = base64.b64encode(buffered.getvalue()).decode() | |
| return f"data:image/png;base64,{img_str}" | |
| 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_ui("segmented_image", "Segmented Image") # Use output_ui for HTML content | |
| ) | |
| ) | |
| ) | |
| def server(input, output, session): | |
| def uploaded_image(): | |
| file_info = input.image_upload() | |
| if file_info: | |
| file_path = file_info[0]['datapath'] if isinstance(file_info, list) else file_info['datapath'] | |
| return {'src': file_path} | |
| # Use render.ui for direct HTML output | |
| def segmented_image(): | |
| file_info = input.image_upload() | |
| if file_info: | |
| try: | |
| file_path = file_info[0]['datapath'] if isinstance(file_info, list) else file_info['datapath'] | |
| if file_path: | |
| base64_img = process_image(file_path) | |
| # Return an HTML image tag with the base64 data URI | |
| return ui.tags.img(src=base64_img, style="max-width: 100%; height: auto;") | |
| except Exception as e: | |
| print(f"Error processing image: {e}") | |
| return "No image processed." | |
| # Create and run the Shiny app | |
| app = App(app_ui, server) | |
| app.run() |