Update app.py

app.py

@@ -1,3 +1,4 @@
+import sqlite3
 import streamlit as st
 import cv2
 import numpy as np
@@ -13,131 +14,157 @@ st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recogni
 # Smaller subtitle
 st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)
 
-start = time.time()
+# Database setup
+DATABASE_NAME = "emotion_recognition.db"
+
+def init_db():
+    conn = sqlite3.connect(DATABASE_NAME)
+    cursor = conn.cursor()
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS registered_faces (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL,
+            image BLOB NOT NULL
+        )
+    ''')
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS attendance_log (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL,
+            emotion TEXT NOT NULL,
+            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+        )
+    ''')
+    conn.commit()
+    conn.close()
+
+init_db()
+
+def register_face(name, image):
+    conn = sqlite3.connect(DATABASE_NAME)
+    cursor = conn.cursor()
+    cursor.execute("INSERT INTO registered_faces (name, image) VALUES (?, ?)", (name, image))
+    conn.commit()
+    conn.close()
+
+def fetch_registered_faces():
+    conn = sqlite3.connect(DATABASE_NAME)
+    cursor = conn.cursor()
+    cursor.execute("SELECT id, name FROM registered_faces")
+    rows = cursor.fetchall()
+    conn.close()
+    return rows
+
+def log_attendance(name, emotion):
+    conn = sqlite3.connect(DATABASE_NAME)
+    cursor = conn.cursor()
+    cursor.execute("INSERT INTO attendance_log (name, emotion) VALUES (?, ?)", (name, emotion))
+    conn.commit()
+    conn.close()
+
+def fetch_recent_activity():
+    conn = sqlite3.connect(DATABASE_NAME)
+    cursor = conn.cursor()
+    cursor.execute("SELECT name, emotion, timestamp FROM attendance_log ORDER BY timestamp DESC LIMIT 10")
+    rows = cursor.fetchall()
+    conn.close()
+    return rows
 
 # Load the emotion model
 @st.cache_resource
 def load_emotion_model():
-    model = load_model('CNN_Model_acc_75.h5')  # Ensure this file is in your Space
+    model = load_model('CNN_Model_acc_75.h5')
     return model
 
 model = load_emotion_model()
-print("time taken to load model: ", time.time() - start)
 
 # Emotion labels
 emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 
-# Load known faces (from images in a folder)
-known_faces = []
-known_names = []
-face_recognizer = cv2.face.LBPHFaceRecognizer_create()
-
-def load_known_faces():
-    folder_path = "known_faces"  # Place your folder with known faces here
-    for image_name in os.listdir(folder_path):
-        if image_name.endswith(('.jpg', '.jpeg', '.png')):
-            image_path = os.path.join(folder_path, image_name)
-            image = cv2.imread(image_path)
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            # Detect face in the image
-            faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-            for (x, y, w, h) in faces:
-                roi_gray = gray[y:y+h, x:x+w]
-                # We only need the face, so we crop it and store it for training
-                known_faces.append(roi_gray)
-                known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
-
-    # Train the recognizer with the known faces
-    face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))
-
-load_known_faces()
-
-# Face detection using OpenCV
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-img_shape = 48
-
-def process_frame(frame):
-    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-    result_text = ""  # Initialize the result text for display
-
-    for (x, y, w, h) in faces:
-        roi_gray = gray_frame[y:y+h, x:x+w]
-        roi_color = frame[y:y+h, x:x+w]
-        face_roi = cv2.resize(roi_color, (img_shape, img_shape))  # Resize to 48x48
-        face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)  # Convert to RGB (3 channels)
-        face_roi = np.expand_dims(face_roi, axis=0)  # Add batch dimension
-        face_roi = face_roi / 255.0  # Normalize the image
-
-        # Emotion detection
-        predictions = model.predict(face_roi)
-        emotion = emotion_labels[np.argmax(predictions[0])]
-
-        # Face recognition using LBPH
-        label, confidence = face_recognizer.predict(roi_gray)
-        name = "Unknown"
-        if confidence < 100:
-            name = known_names[label]
-
-        # Format the result text as "Name is feeling Emotion"
-        result_text = f"{name} is feeling {emotion}"
-
-        # Draw bounding box and label on the frame
-        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-        cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
-    return frame, result_text
-
-# Video feed
-def video_feed(video_source):
-    frame_placeholder = st.empty()  # This placeholder will be used to replace frames in-place
-    text_placeholder = st.empty()  # This placeholder will display the result text
-
-    while True:
-        ret, frame = video_source.read()
-        if not ret:
-            break
-
-        frame, result_text = process_frame(frame)
-
-        # Display the frame in the placeholder
-        frame_placeholder.image(frame, channels="BGR", use_column_width=True)
-
-        # Display the result text in the text placeholder
-        text_placeholder.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
-
-# Sidebar for video or image upload
-upload_choice = st.sidebar.radio("Choose input source", ["Upload Image", "Upload Video", "Camera"])
-
-if upload_choice == "Camera":
-    # Use Streamlit's built-in camera input widget for capturing images from the webcam
-    image = st.camera_input("Take a picture")
-
-    if image is not None:
-        # Convert the image to a numpy array
-        frame = np.array(Image.open(image))
-        frame, result_text = process_frame(frame)
-        st.image(frame, caption='Processed Image', use_column_width=True)
-        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
-
-elif upload_choice == "Upload Image":
-    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])
-    if uploaded_image:
-        image = Image.open(uploaded_image)
-        frame = np.array(image)
-        frame, result_text = process_frame(frame)
-        st.image(frame, caption='Processed Image', use_column_width=True)
-        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
-
-elif upload_choice == "Upload Video":
-    uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi", "mkv", "webm"])
-    if uploaded_video:
-        # Temporarily save the video to disk
-        with tempfile.NamedTemporaryFile(delete=False) as tfile:
-            tfile.write(uploaded_video.read())
-            video_source = cv2.VideoCapture(tfile.name)
-            video_feed(video_source)
-
-st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")
+# Sidebar options
+sidebar_choice = st.sidebar.selectbox("Choose an option", ["Emotion Detection", "Register New Face", "View Registered Faces", "Recent Activity"])
+
+if sidebar_choice == "Register New Face":
+    st.header("Register New Face")
+    name = st.text_input("Enter Name")
+    uploaded_image = st.file_uploader("Upload Face Image", type=["png", "jpg", "jpeg"])
+    if name and uploaded_image:
+        image = np.array(Image.open(uploaded_image))
+        _, buffer = cv2.imencode('.jpg', image)
+        register_face(name, buffer.tobytes())
+        st.success(f"Successfully registered {name}!")
+
+elif sidebar_choice == "View Registered Faces":
+    st.header("Registered Faces")
+    faces = fetch_registered_faces()
+    if faces:
+        for face_id, name in faces:
+            st.write(f"ID: {face_id}, Name: {name}")
+    else:
+        st.write("No faces registered yet.")
+
+elif sidebar_choice == "Recent Activity":
+    st.header("Recent Activity (Attendance Log)")
+    logs = fetch_recent_activity()
+    if logs:
+        for name, emotion, timestamp in logs:
+            st.write(f"Name: {name}, Emotion: {emotion}, Timestamp: {timestamp}")
+    else:
+        st.write("No recent activity found.")
+
+else:  # Emotion Detection
+    st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")
+
+    upload_choice = st.radio("Choose input source", ["Upload Image", "Upload Video", "Camera"])
+
+    def process_frame(frame):
+        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+        result_text = ""
+        for (x, y, w, h) in faces:
+            roi_gray = gray_frame[y:y+h, x:x+w]
+            roi_color = frame[y:y+h, x:x+w]
+            face_roi = cv2.resize(roi_color, (48, 48))
+            face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB) / 255.0
+            face_roi = np.expand_dims(face_roi, axis=0)
+
+            predictions = model.predict(face_roi)
+            emotion = emotion_labels[np.argmax(predictions[0])]
+
+            label = "Unknown"  # Placeholder for face recognition (add later)
+            log_attendance(label, emotion)
+
+            result_text = f"{label} is feeling {emotion}"
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+            cv2.putText(frame, result_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        return frame, result_text
+
+    if upload_choice == "Upload Image":
+        uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"])
+        if uploaded_image:
+            image = np.array(Image.open(uploaded_image))
+            frame, result_text = process_frame(image)
+            st.image(frame, caption='Processed Image', use_column_width=True)
+            st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
+
+    elif upload_choice == "Upload Video":
+        uploaded_video = st.file_uploader("Upload Video", type=["mp4", "avi", "mkv"])
+        if uploaded_video:
+            with tempfile.NamedTemporaryFile(delete=False) as tfile:
+                tfile.write(uploaded_video.read())
+                video_source = cv2.VideoCapture(tfile.name)
+                while True:
+                    ret, frame = video_source.read()
+                    if not ret:
+                        break
+                    frame, result_text = process_frame(frame)
+                    st.image(frame, channels="BGR", use_column_width=True)
+
+    elif upload_choice == "Camera":
+        image = st.camera_input("Take a picture")
+        if image:
+            frame = np.array(Image.open(image))
+            frame, result_text = process_frame(frame)
+            st.image(frame, caption='Processed Image', use_column_width=True)
+            st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)