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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from transformers import pipeline | |
| # Set up the CLIP classifier | |
| model_name = "openai/clip-vit-large-patch14-336" | |
| classifier = pipeline("zero-shot-image-classification", model=model_name) | |
| labels_for_classification = ["genuine face", "morphed face"] | |
| # Define the restoration function | |
| def restore(image): | |
| # Convert the image to float32 | |
| image = np.float32(image) | |
| # Apply a median blur to the image | |
| image = cv2.medianBlur(image, 5) | |
| return image | |
| # Define the enhancement function | |
| def enhance(image): | |
| # Convert the image to grayscale | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Apply histogram equalization to the image | |
| equalized = cv2.equalizeHist(gray) | |
| return equalized | |
| # Define the Streamlit app | |
| def app(): | |
| # Create a file uploader | |
| uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Read the image file | |
| image = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| image = cv2.imdecode(image, cv2.IMREAD_COLOR) | |
| # Classify the image using CLIP | |
| scores = classifier(image, candidate_labels=labels_for_classification) | |
| if scores[0]['label'] == "genuine face": | |
| st.write("The image contains a genuine face") | |
| else: | |
| st.write("The image contains a morphed face") | |
| # Restore the image | |
| restored = restore(image) | |
| st.image(restored, caption="Restored Image") | |
| # Enhance the image | |
| enhanced = enhance(restored) | |
| st.image(enhanced, caption="Enhanced Image") | |
| # Run the app | |
| if __name__ == "__main__": | |
| app() | |