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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):
@reactive.calc
def parsed_file():
file_info = input.file1()
if file_info is None or len(file_info) == 0:
return None
return file_info[0]["datapath"]
@output
@render.plot
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)
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