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import cv2
import streamlit as st
from fer import FER
from PIL import Image
import numpy as np
# Initialize emotion detector
emotion_detector = FER()
# Function to process image and detect emotions
def process_image(image):
frame = np.array(image)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) # Convert from PIL to OpenCV format
emotions = emotion_detector.detect_emotions(frame)
blurred_frame = cv2.GaussianBlur(frame, (51, 51), 0)
for face in emotions:
(x, y, w, h) = face["box"]
emotion, score = max(face["emotions"].items(), key=lambda item: item[1])
overlay = frame.copy()
alpha = 0.4
cv2.rectangle(overlay, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
blurred_frame[y:y + h, x:x + w] = frame[y:y + h, x:x + w]
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.7
font_thickness = 2
text_color = (255, 255, 255)
bg_color = (0, 0, 0)
text = f"{emotion}: {score:.2f}"
(text_width, text_height), _ = cv2.getTextSize(text, font, font_scale, font_thickness)
text_x = x + 10
text_y = y - 10 if y - 10 > text_height else y + h + text_height
cv2.rectangle(blurred_frame, (text_x - 5, text_y - text_height - 5), (text_x + text_width + 5, text_y + 5), bg_color, -1)
cv2.putText(blurred_frame, text, (text_x, text_y), font, font_scale, text_color, font_thickness)
return cv2.cvtColor(blurred_frame, cv2.COLOR_BGR2RGB) # Convert back to RGB for Streamlit
# Streamlit UI
st.title("Real-Time Emotion Recognition")
st.write("Use the camera or upload an image to detect emotions.")
# Camera Input
camera_image = st.camera_input("Take a picture")
# File Upload
uploaded_file = st.file_uploader("Or upload an image...", type=["jpg", "png", "jpeg"])
# Process image if uploaded or captured via camera
if camera_image or uploaded_file:
image = Image.open(camera_image if camera_image else uploaded_file)
st.image(image, caption="Captured Image", use_column_width=True)
processed_image = process_image(image)
st.image(processed_image, caption="Processed Image with Emotions", use_column_width=True)
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