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import streamlit as st |
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import tensorflow as tf |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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@st.cache_resource |
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def load_model(): |
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return tf.keras.models.load_model('synthetic_underwater_esrgan.h5') |
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model = load_model() |
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st.set_page_config(page_title="🌊 Underwater Image Enhancer", layout="wide") |
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st.title("🌊 Deep Sea Image Super Resolution") |
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st.write("Upload blurry underwater images to enhance their quality") |
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uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) |
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if uploaded_file is not None: |
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col1, col2 = st.columns(2) |
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with col1: |
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st.header("Original Image") |
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image = Image.open(uploaded_file) |
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st.image(image, use_column_width=True) |
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if st.button("Enhance Image", type="primary"): |
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with st.spinner("Enhancing image..."): |
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lr_img = np.array(image) / 255.0 |
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sr_img = model.predict(np.expand_dims(lr_img, axis=0))[0] |
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sr_img = np.clip(sr_img * 255, 0, 255).astype(np.uint8) |
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with col2: |
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st.header("Enhanced Image") |
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st.image(sr_img, use_column_width=True) |
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st.download_button( |
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label="⬇️ Download Enhanced Image", |
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data=cv2.imencode('.png', cv2.cvtColor(sr_img, cv2.COLOR_RGB2BGR))[1].tobytes(), |
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file_name="enhanced.png", |
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mime="image/png" |
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) |
