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| import time | |
| import streamlit as st | |
| import subprocess | |
| import numpy as np | |
| from PIL import Image | |
| from io import BytesIO | |
| from models.HAT.hat import * | |
| from models.RCAN.rcan import * | |
| from models.SRGAN.srgan import * | |
| from models.VDSR.vdsr import * | |
| from models.Interpolation.nearest_neighbor import NearestNeighbor_for_deployment | |
| from models.Interpolation.bilinear import Bilinear_for_deployment | |
| from models.Interpolation.bicubic import Bicubic_for_deployment | |
| subprocess.call('pip install natsort', shell=True) | |
| from models.SRFlow.srflow import * | |
| # Initialize session state for enhanced images | |
| if 'nearest_enhanced_image' not in st.session_state: | |
| st.session_state['nearest_enhanced_image'] = None | |
| if 'bilinear_enhanced_image' not in st.session_state: | |
| st.session_state['bilinear_enhanced_image'] = None | |
| if 'bicubic_enhanced_image' not in st.session_state: | |
| st.session_state['bicubic_enhanced_image'] = None | |
| if 'hat_enhanced_image' not in st.session_state: | |
| st.session_state['hat_enhanced_image'] = None | |
| if 'rcan_enhanced_image' not in st.session_state: | |
| st.session_state['rcan_enhanced_image'] = None | |
| if 'srgan_enhanced_image' not in st.session_state: | |
| st.session_state['srgan_enhanced_image'] = None | |
| if 'srflow_enhanced_image' not in st.session_state: | |
| st.session_state['srflow_enhanced_image'] = None | |
| if 'vdsr_enhanced_image' not in st.session_state: | |
| st.session_state['vdsr_enhanced_image'] = None | |
| # Initialize session state for button clicks | |
| if 'nearest_clicked' not in st.session_state: | |
| st.session_state['nearest_clicked'] = False | |
| if 'bilinear_clicked' not in st.session_state: | |
| st.session_state['bilinear_clicked'] = False | |
| if 'bicubic_clicked' not in st.session_state: | |
| st.session_state['bicubic_clicked'] = False | |
| if 'hat_clicked' not in st.session_state: | |
| st.session_state['hat_clicked'] = False | |
| if 'rcan_clicked' not in st.session_state: | |
| st.session_state['rcan_clicked'] = False | |
| if 'srgan_clicked' not in st.session_state: | |
| st.session_state['srgan_clicked'] = False | |
| if 'srflow_clicked' not in st.session_state: | |
| st.session_state['srflow_clicked'] = False | |
| if 'vdsr_clicked' not in st.session_state: | |
| st.session_state['vdsr_clicked'] = False | |
| st.markdown("<h1 style='text-align: center'>Image Super Resolution</h1>", unsafe_allow_html=True) | |
| # Sidebar for navigation | |
| st.sidebar.title("Options") | |
| app_mode = st.sidebar.selectbox("Choose the input source", ["Upload image", "Take a photo"]) | |
| # Depending on the choice, show the uploader widget or webcam capture | |
| if app_mode == "Upload image": | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"], on_change=lambda: reset_states()) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file).convert("RGB") | |
| elif app_mode == "Take a photo": | |
| camera_input = st.camera_input("Take a picture", on_change=lambda: reset_states()) | |
| if camera_input is not None: | |
| image = Image.open(camera_input).convert("RGB") | |
| def reset_states(): | |
| st.session_state['hat_enhanced_image'] = None | |
| st.session_state['rcan_enhanced_image'] = None | |
| st.session_state['srgan_enhanced_image'] = None | |
| st.session_state['srflow_enhanced_image'] = None | |
| st.session_state['bicubic_enhanced_image'] = None | |
| st.session_state['bilinear_enhanced_image'] = None | |
| st.session_state['nearest_enhanced_image'] = None | |
| st.session_state['vdsr_enhanced_image'] = None | |
| st.session_state['hat_clicked'] = False | |
| st.session_state['rcan_clicked'] = False | |
| st.session_state['srgan_clicked'] = False | |
| st.session_state['srflow_clicked'] = False | |
| st.session_state['bicubic_clicked'] = False | |
| st.session_state['bilinear_clicked'] = False | |
| st.session_state['nearest_clicked'] = False | |
| st.session_state['vdsr_clicked'] = False | |
| def get_image_download_link(img, filename): | |
| """Generates a link allowing the PIL image to be downloaded""" | |
| # Convert the PIL image to Bytes | |
| buffered = BytesIO() | |
| img.save(buffered, format="PNG") | |
| return st.download_button( | |
| label="Download Image", | |
| data=buffered.getvalue(), | |
| file_name=filename, | |
| mime="image/png" | |
| ) | |
| if 'image' in locals(): | |
| # st.image(image, caption='Uploaded Image', use_column_width=True) | |
| st.write("") | |
| # ------------------------ Nearest Neighbor ------------------------ # | |
| if st.button('Enhance with Nearest Neighbor'): | |
| with st.spinner('Processing using Nearest Neighbor...'): | |
| enhanced_image = NearestNeighbor_for_deployment(image) | |
| st.session_state['nearest_enhanced_image'] = enhanced_image | |
| st.session_state['nearest_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['nearest_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['nearest_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['nearest_enhanced_image'], 'nearest_enhanced.jpg') | |
| # ------------------------ Bilinear ------------------------ # | |
| if st.button('Enhance with Bilinear'): | |
| with st.spinner('Processing using Bilinear...'): | |
| enhanced_image = Bilinear_for_deployment(image) | |
| st.session_state['bilinear_enhanced_image'] = enhanced_image | |
| st.session_state['bilinear_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['bilinear_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['bilinear_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['bilinear_enhanced_image'], 'bilinear_enhanced.jpg') | |
| # ------------------------ Bicubic ------------------------ # | |
| if st.button('Enhance with Bicubic'): | |
| with st.spinner('Processing using Bicubic...'): | |
| enhanced_image = Bicubic_for_deployment(image) | |
| st.session_state['bicubic_enhanced_image'] = enhanced_image | |
| st.session_state['bicubic_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['bicubic_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['bicubic_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['bicubic_enhanced_image'], 'bicubic_enhanced.jpg') | |
| # ------------------------ HAT ------------------------ # | |
| if st.button('Enhance with HAT'): | |
| with st.spinner('Processing using HAT...'): | |
| with st.spinner('Wait for it... the model is processing the image'): | |
| enhanced_image = HAT_for_deployment(image) | |
| st.session_state['hat_enhanced_image'] = enhanced_image | |
| st.session_state['hat_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['hat_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['hat_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['hat_enhanced_image'], 'hat_enhanced.jpg') | |
| # ------------------------ RCAN ------------------------ # | |
| if st.button('Enhance with RCAN'): | |
| with st.spinner('Processing using RCAN...'): | |
| with st.spinner('Wait for it... the model is processing the image'): | |
| rcan_model = RCAN() | |
| device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda') | |
| rcan_model.load_state_dict(torch.load('models/RCAN/rcan_checkpoint.pth', map_location=device)) | |
| enhanced_image = rcan_model.inference(image) | |
| st.session_state['rcan_enhanced_image'] = enhanced_image | |
| st.session_state['rcan_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['rcan_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['rcan_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['rcan_enhanced_image'], 'rcan_enhanced.jpg') | |
| # --------------------------SRGAN-------------------------- # | |
| if st.button('Enhance with SRGAN'): | |
| with st.spinner('Processing using SRGAN...'): | |
| with st.spinner('Wait for it... the model is processing the image'): | |
| srgan_model = GeneratorResnet() | |
| device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda') | |
| srgan_model = torch.load('models/SRGAN/srgan_checkpoint.pth', map_location=device) | |
| enhanced_image = srgan_model.inference(image) | |
| st.session_state['srgan_enhanced_image'] = enhanced_image | |
| st.session_state['srgan_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['srgan_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['srgan_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['srgan_enhanced_image'], 'srgan_enhanced.jpg') | |
| # ------------------------ SRFlow ------------------------ # | |
| if st.button('Enhance with SRFlow'): | |
| with st.spinner('Processing using SRFlow...'): | |
| with st.spinner('Wait for it... the model is processing the image'): | |
| enhanced_image = return_SRFlow_result(image) | |
| st.session_state['srflow_enhanced_image'] = enhanced_image | |
| st.session_state['srflow_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['srflow_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['srflow_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['srflow_enhanced_image'], 'srflow_enhanced.jpg') | |
| # ------------------------ VDSR ------------------------ # | |
| if st.button('Enhance with VDSR'): | |
| with st.spinner('Processing using VDSR...'): | |
| # Load the VDSR model | |
| vdsr_model = torch.load('models/VDSR/vdsr_checkpoint.pth', map_location=torch.device('cpu')) | |
| enhanced_image = vdsr_model.inference(image) | |
| st.session_state['vdsr_enhanced_image'] = enhanced_image | |
| st.session_state['vdsr_clicked'] = True | |
| st.success('Done!') | |
| if st.session_state['vdsr_enhanced_image'] is not None: | |
| col1, col2 = st.columns(2) | |
| col1.header("Original") | |
| col1.image(image, use_column_width=True) | |
| col2.header("Enhanced") | |
| col2.image(st.session_state['vdsr_enhanced_image'], use_column_width=True) | |
| with col2: | |
| get_image_download_link(st.session_state['vdsr_enhanced_image'], 'vdsr_enhanced.jpg') |