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| import pandas as pd | |
| import asyncio | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from transformers import SamModel, SamConfig, SamProcessor | |
| import torch | |
| from shiny import App, Inputs, Outputs, Session, reactive, render, ui | |
| from PIL import Image | |
| app_ui = ui.page_fluid( | |
| ui.input_file("file1", "Upload Tile image for sidewalk segmentation", accept=".tif", multiple=False), | |
| ui.output_plot("mask"), # Changed from ui.output_table to ui.output_plot based on the context of output | |
| ) | |
| def server(input: Inputs, output: Outputs, session: Session): | |
| def parsed_file(): | |
| file_info = input.file1() | |
| if file_info is None or len(file_info) == 0: | |
| return None | |
| return file_info[0]["datapath"] | |
| async def mask(): | |
| filepath = parsed_file() | |
| if filepath is None: | |
| return | |
| print(filepath) | |
| # Assuming the model and processor are correctly configured | |
| model_config = SamConfig.from_pretrained("facebook/sam-vit-base") | |
| processor = SamProcessor.from_pretrained("facebook/sam-vit-base") | |
| my_sidewalk_model = SamModel(model_config) | |
| my_sidewalk_model.load_state_dict(torch.load("./sidwalk_model_checkpoint.pth", map_location='cpu')) | |
| device = torch.device("cpu") | |
| my_sidewalk_model.to(device) | |
| # Load image | |
| image = Image.open(filepath) | |
| imarray = np.array(image) | |
| single_patch = Image.fromarray(imarray) | |
| inputs = processor(single_patch, return_tensors="pt") | |
| inputs = {k: v.to(device) for k, v in inputs.items()} | |
| my_sidewalk_model.eval() | |
| # Model inference | |
| with torch.no_grad(): | |
| outputs = my_sidewalk_model(**inputs, multimask_output=False) | |
| single_patch_prob = torch.sigmoid(outputs.pred_masks.squeeze(1)) | |
| # convert soft mask to hard mask | |
| single_patch_prob = single_patch_prob.cpu().numpy().squeeze() | |
| single_patch_prediction = (single_patch_prob > 0).astype(np.uint8) | |
| fig, axes = plt.subplots(1, 3, figsize=(15, 5)) | |
| # Plot the first image on the left | |
| axes[0].imshow(np.array(single_patch), cmap='gray') # Assuming the first image is grayscale | |
| axes[0].set_title("Image") | |
| # Plot the second image on the right | |
| axes[1].imshow(single_patch_prob) # Assuming the second image is grayscale | |
| axes[1].set_title("Probability Map") | |
| # Plot the second image on the right | |
| axes[2].imshow(single_patch_prediction, cmap='gray') # Assuming the second image is grayscale | |
| axes[2].set_title("Prediction") | |
| # Hide axis ticks and labels | |
| for ax in axes: | |
| ax.set_xticks([]) | |
| ax.set_yticks([]) | |
| ax.set_xticklabels([]) | |
| ax.set_yticklabels([]) | |
| # Display the images side by side | |
| return fig | |
| app = App(app_ui, server) | |