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| import streamlit as st | |
| import face_recognition | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| # Set the page config | |
| st.set_page_config(page_title="Emotion Recognition App", layout="centered") | |
| st.title("Emotion Recognition App") | |
| # Upload an image | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| # Define simple emotion mapping based on facial features (for demonstration purposes) | |
| def detect_emotion(face_landmarks): | |
| """ | |
| A simple mock-up function for detecting emotions based on landmarks. | |
| Replace with a more sophisticated model as needed. | |
| """ | |
| # Example: Arbitrarily assign "Happy" if eyes are close together | |
| if face_landmarks: | |
| return "Happy" | |
| return "Neutral" | |
| # Process the uploaded image | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| image_np = np.array(image) | |
| # Convert image to RGB | |
| rgb_image = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) | |
| # Detect faces in the image | |
| face_locations = face_recognition.face_locations(rgb_image) | |
| face_landmarks_list = face_recognition.face_landmarks(rgb_image) | |
| if face_locations: | |
| for face_location, face_landmarks in zip(face_locations, face_landmarks_list): | |
| # Draw a rectangle around the face | |
| top, right, bottom, left = face_location | |
| cv2.rectangle(image_np, (left, top), (right, bottom), (0, 255, 0), 2) | |
| # Detect emotion based on landmarks | |
| emotion = detect_emotion(face_landmarks) | |
| # Display emotion above the face | |
| cv2.putText( | |
| image_np, | |
| emotion, | |
| (left, top - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, | |
| 0.9, | |
| (255, 0, 0), | |
| 2, | |
| ) | |
| st.image(image_np, caption="Processed Image", use_column_width=True) | |
| else: | |
| st.warning("No faces detected in the image.") | |