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import streamlit as st
from PIL import Image
import torch
from RealESRGAN import RealESRGAN
from io import BytesIO
# Define the target size for the image
TARGET_SIZE = (240, 240)
# Function to load the model based on scale and anime toggle
def load_model(scale, anime=False):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RealESRGAN(device, scale=scale, anime=anime)
model_path = {
(2, False): 'model/RealESRGAN_x2.pth',
(4, False): 'model/RealESRGAN_x4plus.pth',
(8, False): 'model/RealESRGAN_x8.pth',
(4, True): 'model/RealESRGAN_x4plus_anime_6B.pth'
}[(scale, anime)]
model.load_weights(model_path)
return model
def enhance_image(image, scale, anime):
model = load_model(scale, anime=anime)
# Convert image to RGB if it has an alpha channel
if image.mode != 'RGB':
image = image.convert('RGB')
# Resize image to target dimensions
image = image.resize(TARGET_SIZE)
sr_image = model.predict(image)
buffer = BytesIO()
sr_image.save(buffer, format="PNG")
buffer.seek(0)
return sr_image, buffer
def main():
st.title("Generative AI Image Restoration")
# Image upload
uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"])
if uploaded_image is not None:
image = Image.open(uploaded_image)
# Anime toggle
anime = st.checkbox("Anime Image", value=False)
# Conditional scale options
if anime:
scale = "4x" # Set to 4x automatically when anime is selected
else:
scale = st.radio("Upscaling Factor", ["2x", "4x", "8x"], index=0)
scale_value = int(scale.replace('x', ''))
# Enhance button
if st.button("Restore Image"):
enhanced_image, buffer = enhance_image(image, scale_value, anime)
# Show images side by side
col1, col2 = st.columns(2)
with col1:
st.image(image, caption="Original Image", use_column_width=True)
with col2:
st.image(enhanced_image, caption="Enhanced Image", use_column_width=True)
# Download button
st.download_button(
label="Download Enhanced Image",
data=buffer,
file_name="enhanced_image.png",
mime="image/png"
)
if __name__ == "__main__":
main() |