app.py

import torch
import streamlit as st
import numpy as np
from PIL import Image
from unet import UNet
from torchvision import transforms
from torchvision.transforms.functional import to_tensor, to_pil_image
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader

device = "cuda:0" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
# Load the trained model
model_path = 'cityscapes_dataUNet.pth'
num_classes = 10
model = UNet(num_classes=num_classes)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.to(device)
model.eval()

# Define the prediction function that takes an input image and returns the segmented image
def predict_segmentation(image):
    st.write(device)
    # Convert the input image to a PyTorch tensor and normalize it
    image = Image.fromarray(image, 'RGB')
    # image = transforms.functional.resize(image, (256, 256))
    image = to_tensor(image).unsqueeze(0)
    image = transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))(image)
    image = image.to(device)
    
    st.write("Input shape:", image.shape) # input shape torch.Size([1, 3, 256, 256])
    st.write("Input dtype:", image.dtype) # input dtype torch.float32

    # Make a prediction using the model
    with torch.no_grad():
        st.write(image.shape, image.dtype) # torch.Size([1, 3, 256, 256]) torch.float32

        output = model(image)
        predicted_class = torch.argmax(output, dim=1).squeeze(0)
        predicted_class = predicted_class.cpu().detach().numpy().astype(np.uint8)
        st.write("Predicted class dtype:", predicted_class.dtype)
        st.write("Predicted class shape:", predicted_class.shape)

        # Visualize the predicted segmentation mask
        plt.imshow(predicted_class)
        st.pyplot(plt)

        st.write("Predicted class:", predicted_class)

    # Return the predicted segmentation
    return predicted_class

# Define the Streamlit interface
st.title('UNet Image Segmentation IPPR')
st.write('Segment an image using a UNet model')

uploaded_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])

if uploaded_image is not None:
    # Read the uploaded image
    image = Image.open(uploaded_image)
    st.image(image, caption='Uploaded Image', use_column_width=True)

    # Process the image and get the segmented result
    segmented_image = predict_segmentation(np.array(image))

    # Display the segmented image
    # st.image(segmented_image, caption='Segmented Image', use_column_width=True)