Vidit123/Emotion-recognition
0
1import os2os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'3 4import streamlit as st5import cv26import numpy as np7from deepface import DeepFace8from PIL import Image, ImageDraw, ImageFont9import pandas as pd10import time11 12# Page config13st.set_page_config(14 page_title="🎭 Real-Time Emotion Detection", 15 page_icon="🎭",16 layout="wide"17)18 19st.title("🎭 Real-Time Emotion Detection")20st.markdown("Upload an image or use your camera to detect emotions in faces!")21 22# Load face cascade23@st.cache_resource24def load_face_cascade():25 return cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')26 27face_cascade = load_face_cascade()28 29# Emotion colors for visualization30emotion_colors = {31 'angry': (255, 0, 0),32 'disgust': (0, 128, 0), 33 'fear': (128, 0, 128),34 'happy': (0, 255, 0),35 'sad': (0, 0, 255),36 'surprise': (255, 255, 0),37 'neutral': (128, 128, 128)38}39 40def detect_emotions_in_image(image):41 """Detect emotions in uploaded image"""42 # Convert PIL to OpenCV format43 opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)44 gray = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2GRAY)45 46 # Detect faces47 faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))48 49 results = []50 annotated_image = opencv_image.copy()51 52 for (x, y, w, h) in faces:53 # Extract face ROI54 face_roi = opencv_image[y:y + h, x:x + w]55 rgb_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)56 57 try:58 # Analyze emotion59 result = DeepFace.analyze(rgb_roi, actions=['emotion'], enforce_detection=False)60 emotion = result[0]['dominant_emotion']61 confidence = result[0]['emotion'][emotion]62 63 # Store results64 results.append({65 'Face': len(results) + 1,66 'Emotion': emotion.title(),67 'Confidence': f"{confidence:.1f}%"68 })69 70 # Draw rectangle and text71 color = emotion_colors.get(emotion, (255, 0, 0))72 cv2.rectangle(annotated_image, (x, y), (x + w, y + h), color, 2)73 cv2.putText(annotated_image, f"{emotion} ({confidence:.1f}%)", 74 (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)75 76 except Exception as e:77 st.warning(f"Could not analyze face {len(results) + 1}: {str(e)}")78 79 # Convert back to RGB for display80 final_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)81 return Image.fromarray(final_image), results82 83def detect_emotions_realtime_frame(frame):84 """Detect emotions in a single frame for real-time processing"""85 # Mirror the frame horizontally for selfie view86 frame = cv2.flip(frame, 1)87 88 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)89 90 # Detect faces91 faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))92 93 results = []94 annotated_frame = frame.copy()95 96 for (x, y, w, h) in faces:97 # Extract face ROI98 face_roi = frame[y:y + h, x:x + w]99 rgb_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)100 101 try:102 # Analyze emotion103 result = DeepFace.analyze(rgb_roi, actions=['emotion'], enforce_detection=False)104 emotion = result[0]['dominant_emotion']105 confidence = result[0]['emotion'][emotion]106 107 # Store results108 results.append({109 'emotion': emotion,110 'confidence': confidence,111 'bbox': (x, y, w, h)112 })113 114 # Draw thick red rectangle around face115 cv2.rectangle(annotated_frame, (x, y), (x + w, y + h), (0, 0, 255), 3)116 117 # Draw emotion text above the rectangle118 font = cv2.FONT_HERSHEY_SIMPLEX119 font_scale = 1.2120 font_thickness = 2121 text_color = (0, 0, 255) # Red color122 123 # Get text size to position it properly124 text = emotion125 text_size = cv2.getTextSize(text, font, font_scale, font_thickness)[0]126 text_x = x127 text_y = y - 15128 129 # Draw text background for better visibility130 cv2.rectangle(annotated_frame, (text_x, text_y - text_size[1] - 10), 131 (text_x + text_size[0] + 10, text_y + 5), (255, 255, 255), -1)132 133 # Draw the emotion text134 cv2.putText(annotated_frame, text, (text_x + 5, text_y - 5), 135 font, font_scale, text_color, font_thickness)136 137 except Exception:138 # Skip failed detections in real-time to maintain performance139 pass140 141 return annotated_frame, results142 143# Main interface with tabs144tab1, tab2, tab3 = st.tabs(["📁 Upload Image", "📸 Camera", "🎥 Real-time Webcam"])145 146with tab1:147 st.subheader("Upload an Image")148 uploaded_file = st.file_uploader(149 "Choose an image file", 150 type=['jpg', 'jpeg', 'png'],151 help="Upload an image with faces to detect emotions"152 )153 154 if uploaded_file is not None:155 # Load and display original image156 image = Image.open(uploaded_file).convert('RGB')157 158 col1, col2 = st.columns(2)159 160 with col1:161 st.subheader("Original Image")162 st.image(image, use_column_width=True)163 164 with col2:165 st.subheader("Emotion Detection Results")166 with st.spinner("🔍 Analyzing emotions..."):167 result_image, emotions = detect_emotions_in_image(image)168 169 st.image(result_image, use_column_width=True)170 171 # Display results table172 if emotions:173 st.subheader("📊 Detection Summary")174 df = pd.DataFrame(emotions)175 st.dataframe(df, use_container_width=True)176 else:177 st.info("No faces detected in the image. Try uploading an image with clear faces.")178 179with tab2:180 st.subheader("Camera Capture")181 st.info("📸 Take a photo using your device camera")182 183 camera_image = st.camera_input("Take a picture")184 185 if camera_image is not None:186 image = Image.open(camera_image).convert('RGB')187 188 col1, col2 = st.columns(2)189 190 with col1:191 st.subheader("Captured Image")192 st.image(image, use_column_width=True)193 194 with col2:195 st.subheader("Emotion Detection Results")196 with st.spinner("🔍 Analyzing emotions..."):197 result_image, emotions = detect_emotions_in_image(image)198 199 st.image(result_image, use_column_width=True)200 201 # Display results202 if emotions:203 st.subheader("📊 Detection Summary")204 df = pd.DataFrame(emotions)205 st.dataframe(df, use_container_width=True)206 207with tab3:208 st.subheader("🎥 Real-time Emotion Detection")209 210 # Use Streamlit's camera input with continuous refresh for real-time feel211 st.markdown("📱 **Live Camera Feed** - Take photos to detect emotions accurately")212 213 # Camera input with unique key that changes to force refresh214 if 'photo_count' not in st.session_state:215 st.session_state.photo_count = 0216 217 # Auto-refresh button218 col1, col2 = st.columns([1, 3])219 with col1:220 if st.button("🔄 Refresh Feed", type="primary"):221 st.session_state.photo_count += 1222 st.rerun()223 224 with col2:225 auto_refresh = st.checkbox("Auto-refresh every 3 seconds", value=False)226 227 # Camera input228 camera_image = st.camera_input(229 "Live Emotion Detection", 230 key=f"realtime_cam_{st.session_state.photo_count}",231 help="Take a photo to detect emotions with high accuracy"232 )233 234 if camera_image is not None:235 # Convert to PIL Image236 image = Image.open(camera_image).convert('RGB')237 238 # Convert to OpenCV format for processing239 opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)240 241 # Process with actual emotion detection242 processed_frame, emotion_results = detect_emotions_realtime_frame(opencv_image)243 244 # Convert back to RGB for display245 result_image = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)246 247 # Display the processed image with emotions248 st.image(result_image, use_column_width=True, caption="🎯 Accurate Emotion Detection Results")249 250 # Show detailed emotion analysis251 if emotion_results:252 st.success(f"✅ Detected {len(emotion_results)} face(s)")253 254 # Create columns for each detected face255 cols = st.columns(min(len(emotion_results), 3))256 for i, emotion_data in enumerate(emotion_results):257 with cols[i % 3]:258 emotion = emotion_data['emotion']259 confidence = emotion_data['confidence']260 261 # Emotion emoji mapping262 emotion_emojis = {263 'happy': '😊',264 'sad': '😢', 265 'angry': '😠',266 'fear': '😨',267 'surprise': '😲',268 'disgust': '🤢',269 'neutral': '😐'270 }271 272 emoji = emotion_emojis.get(emotion, '😐')273 274 # Display emotion with confidence275 st.metric(276 label=f"{emoji} Face {i+1}",277 value=emotion.upper(),278 delta=f"{confidence:.1f}% confident"279 )280 281 # Color code based on confidence282 if confidence > 80:283 st.success("High confidence")284 elif confidence > 60:285 st.warning("Medium confidence") 286 else:287 st.info("Low confidence")288 else:289 st.warning("👤 No faces detected. Please ensure your face is clearly visible and well-lit.")290 st.info("**Tips for better detection:**\n- Face the camera directly\n- Ensure good lighting\n- Remove glasses if possible\n- Make sure face is not too close or far")291 292 # Auto-refresh logic293 if auto_refresh:294 time.sleep(3)295 st.session_state.photo_count += 1296 st.rerun()297 298 # Instructions for accurate detection299 with st.expander("📋 Tips for Accurate Emotion Detection"):300 st.markdown("""301 **For best accuracy:**302 303 🎯 **Camera Setup:**304 - Use good lighting (natural light works best)305 - Position camera at eye level306 - Keep face centered in frame307 - Maintain 1-2 feet distance from camera308 309 📸 **Taking Photos:**310 - Look directly at camera311 - Remove sunglasses if wearing312 - Try different expressions to test accuracy313 - Use 'Refresh Feed' button for new analysis314 315 🤖 **AI Analysis:**316 - Uses DeepFace AI for high accuracy317 - Analyzes 7 different emotions318 - Shows confidence scores for reliability319 - Works best with clear, front-facing photos320 """)321 322 # Performance info323 st.info("🚀 **Real-time Mode:** Click 'Refresh Feed' frequently or enable auto-refresh for continuous emotion monitoring!")324 325# Sidebar info326with st.sidebar:327 st.markdown("### 🎭 About This App")328 st.info("""329 This app detects emotions in human faces using AI:330 331 **Features:**332 - Upload images or use camera333 - Real-time webcam emotion detection334 - Detects 7 emotions: Happy, Sad, Angry, Fear, Surprise, Disgust, Neutral335 - Shows confidence scores336 - Works with multiple faces337 338 **Powered by:**339 - DeepFace AI library340 - OpenCV face detection341 - Streamlit web framework342 """)343 344 st.markdown("### 🚀 How to Use")345 st.markdown("""346 1. Choose **Upload Image**, **Camera**, or **Real-time Webcam** tab347 2. For real-time: webcam starts automatically348 3. For others: upload a photo or take a picture349 4. View detected emotions with confidence scores350 """)351 352 st.markdown("---")353 st.markdown("Made with ❤️ using Streamlit & DeepFace")