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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.
"""
if face_landmarks:
# Example: Assign "Happy" if eyes are close together
return "Happy"
return "Neutral"
# Resize image to reduce memory usage
def resize_image(image, max_size=(800, 800)):
"""
Resizes the image to the specified maximum size while maintaining aspect ratio.
"""
image.thumbnail(max_size, Image.ANTIALIAS)
return image
# Process the uploaded image
if uploaded_file is not None:
# Check file size to prevent loading large images
if uploaded_file.size > 10 * 1024 * 1024: # 10 MB limit
st.error("File too large. Please upload an image smaller than 10 MB.")
else:
# Open and resize the image
image = Image.open(uploaded_file)
image = resize_image(image)
# Convert image to numpy array
image_np = np.array(image)
# Convert image to RGB (ensure compatibility with face_recognition)
if len(image_np.shape) == 3 and image_np.shape[2] == 4: # RGBA to RGB
image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
elif len(image_np.shape) == 3 and image_np.shape[2] == 3: # BGR to RGB
image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
# Detect faces and landmarks
face_locations = face_recognition.face_locations(image_np)
face_landmarks_list = face_recognition.face_landmarks(image_np)
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,
)
# Display the processed image
st.image(image_np, caption="Processed Image", use_column_width=True)
else:
st.warning("No faces detected in the image.")
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