Update app.py

app.py

@@ -1,16 +1,13 @@
 import cv2
 import streamlit as st
 from fer import FER
-from PIL import Image
 import numpy as np
 
-# Initialize emotion detector
+# 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
+# Function to process each frame and detect emotions
+def process_frame(frame):
     emotions = emotion_detector.detect_emotions(frame)
     blurred_frame = cv2.GaussianBlur(frame, (51, 51), 0)
 
@@ -18,13 +15,16 @@ def process_image(image):
         (x, y, w, h) = face["box"]
         emotion, score = max(face["emotions"].items(), key=lambda item: item[1])
 
+        # Overlay rectangle on detected face
         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)
 
+        # Replace detected face region with unblurred version
         blurred_frame[y:y + h, x:x + w] = frame[y:y + h, x:x + w]
 
+        # Display Emotion Text
         font = cv2.FONT_HERSHEY_SIMPLEX
         font_scale = 0.7
         font_thickness = 2
@@ -39,22 +39,38 @@ def process_image(image):
         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
+    return blurred_frame
 
 # Streamlit UI
-st.title("Real-Time Emotion Recognition")
-st.write("Use the camera or upload an image to detect emotions.")
+st.title("Live Emotion Recognition")
+st.write("Allow camera access to start real-time emotion detection.")
 
-# Camera Input
-camera_image = st.camera_input("Take a picture")
+# Button to start webcam
+run = st.checkbox("Start Camera")
 
-# File Upload
-uploaded_file = st.file_uploader("Or upload an image...", type=["jpg", "png", "jpeg"])
+# Streamlit Image Containers
+col1, col2 = st.columns(2)
+with col1:
+    st.header("Live Camera Feed")
+    camera_placeholder = st.image([])
 
-# 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)
+with col2:
+    st.header("Emotion Detection Output")
+    output_placeholder = st.image([])
 
-    processed_image = process_image(image)
-    st.image(processed_image, caption="Processed Image with Emotions", use_column_width=True)
+# OpenCV Video Capture
+cap = cv2.VideoCapture(0)
+
+while run:
+    ret, frame = cap.read()
+    if not ret:
+        st.error("Error: Unable to access webcam.")
+        break
+
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    processed_frame = process_frame(frame)  # Detect emotions
+
+    camera_placeholder.image(frame, channels="RGB")
+    output_placeholder.image(processed_frame, channels="RGB")
+
+cap.release()