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CNN_Model_acc_75.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f17414ec703aa77db97a22766b6f4454f766e367148285d16cbbc729e69725c
+size 94225136

app.py

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+import streamlit as st
+import cv2
+import face_recognition
+import os
+import sqlite3
+import pandas as pd
+import numpy as np  
+from datetime import datetime
+from PIL import Image
+from keras.models import load_model
+
+# --- Streamlit Configuration ---
+st.set_page_config(
+    page_title="Face and Emotion Recognition Attendance System",
+    page_icon=":camera:",
+    layout="wide"
+)
+
+# --- Initialize session_state attributes ---
+if "camera_active" not in st.session_state:
+    st.session_state.camera_active = False
+
+# --- Password Protection (Optional) ---
+password = st.text_input("Enter password", type="password")
+
+# Stop if the password is incorrect
+if password != "ravinder":
+    st.stop()
+
+# --- Database Setup ---
+conn = sqlite3.connect('attendance.db')
+cursor = conn.cursor()
+
+# Create Table with Emotion Column
+cursor.execute('''
+    CREATE TABLE IF NOT EXISTS attendance (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        name TEXT,
+        roll_no TEXT,
+        date TEXT,
+        time TEXT,
+        status TEXT,
+        emotion TEXT
+    )
+''')
+conn.commit()
+
+# --- Load Known Faces ---
+def load_known_faces():
+    images = []
+    classnames = []
+    directory = "Photos"
+
+    for cls in os.listdir(directory):
+        if os.path.splitext(cls)[1] in [".jpg", ".jpeg", ".png"]:
+            img_path = os.path.join(directory, cls)
+            curImg = cv2.imread(img_path)
+            images.append(curImg)
+            classnames.append(os.path.splitext(cls)[0])
+
+    return images, classnames
+
+# --- Encode Known Faces ---
+def find_encodings(images):
+    encode_list = []
+    for img in images:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        encode = face_recognition.face_encodings(img)[0]
+        encode_list.append(encode)
+    return encode_list
+
+Images, classnames = load_known_faces()
+encodeListKnown = find_encodings(Images)
+
+# --- Load Emotion Detection Model ---
+@st.cache_resource
+def load_emotion_model():
+    return load_model('CNN_Model_acc_75.h5')
+
+emotion_model = load_emotion_model()
+img_shape = 48
+emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# --- Preprocess Frame for Emotion Detection ---
+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))
+
+    emotions_detected = []
+
+    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))
+        face_roi = np.expand_dims(face_roi, axis=0)
+        face_roi = face_roi / float(img_shape)
+        predictions = emotion_model.predict(face_roi)
+        emotion = emotion_labels[np.argmax(predictions[0])]
+        emotions_detected.append(emotion)
+
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(frame, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+    return frame, emotions_detected
+
+# --- Camera Functionality ---
+camera_active = st.session_state.get("camera_active", False)
+
+if st.sidebar.button("Start Camera"):
+    st.session_state.camera_active = True
+
+if st.sidebar.button("Stop Camera"):
+    st.session_state.camera_active = False
+
+# --- Add New Face ---
+def add_new_face():
+    new_name = st.text_input("Enter your name:")
+    roll_no = st.text_input("Enter your roll number:")
+
+    if st.session_state.camera_active:
+        img_file_buffer = st.camera_input("Take a picture")
+        if img_file_buffer is not None and new_name and roll_no:
+            st.session_state.camera_active = False  # Stop camera after photo is taken
+
+            # Check if the user already exists in the database
+            cursor.execute("SELECT * FROM attendance WHERE name = ? AND roll_no = ?", (new_name, roll_no))
+            existing_user = cursor.fetchone()
+
+            if existing_user:
+                st.warning(f"{new_name} ({roll_no}) is already registered.")
+            else:
+                # Save the photo and update face encodings
+                image = np.array(Image.open(img_file_buffer))
+                img_path = os.path.join("Photos", f"{new_name}_{roll_no}.jpg")
+                cv2.imwrite(img_path, image)
+
+                global Images, classnames, encodeListKnown
+                Images, classnames = load_known_faces()
+                encodeListKnown = find_encodings(Images)
+
+                st.success(f"New face added for {new_name} ({roll_no}).")
+    else:
+        st.info("Camera is not active. Start the camera to take a picture.")
+
+# --- Recognize Face and Emotion ---
+def recognize_face():
+    if st.session_state.camera_active:
+        img_file_buffer = st.camera_input("Take a picture")
+        if img_file_buffer is not None:
+            st.session_state.camera_active = False  # Stop camera after photo is taken
+            with st.spinner("Processing..."):
+                image = np.array(Image.open(img_file_buffer))
+                imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
+                imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+
+                facesCurFrame = face_recognition.face_locations(imgS)
+                encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+
+                detected_emotions = []
+                if len(encodesCurFrame) > 0:
+                    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+                        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
+                        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
+                        matchIndex = np.argmin(faceDis)
+
+                        if matches[matchIndex]:
+                            name = classnames[matchIndex].split("_")[0]
+                            roll_no = classnames[matchIndex].split("_")[1]
+
+                            y1, x2, y2, x1 = faceLoc
+                            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
+                            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+
+                            frame, detected_emotions = process_frame(image)
+                            date = datetime.now().strftime('%Y-%m-%d')
+                            time = datetime.now().strftime('%H:%M:%S')
+                            emotion = detected_emotions[0] if detected_emotions else "Unknown"
+
+                            cursor.execute("INSERT INTO attendance (name, roll_no, date, time, status, emotion) VALUES (?, ?, ?, ?, 'Present', ?)", 
+                                           (name, roll_no, date, time, emotion))
+                            conn.commit()
+                            st.success(f"Attendance marked for {name} with emotion: {emotion}.")
+                        else:
+                            st.warning("Face not recognized.")
+                else:
+                    st.warning("No face detected.")
+                st.image(image, caption="Detected Face and Emotion", use_container_width=True)
+
+    else:
+        st.info("Camera is not active. Start the camera to take a picture.")
+
+# --- View Attendance Records ---
+def view_attendance_records():
+    st.subheader("Attendance Records")
+    cursor.execute("SELECT * FROM attendance ORDER BY date DESC, time DESC")
+    records = cursor.fetchall()
+
+    if records:
+        df = pd.DataFrame(records, columns=["ID", "Name", "Roll No", "Date", "Time", "Status", "Emotion"])
+        st.table(df)
+    else:
+        st.info("No attendance records available.")
+
+# --- Main Logic ---
+if __name__ == "__main__":
+    st.title("EMOTION-MARK-AI (FACIAL SENTIMENT ANALYSIZED ATTENDANCE TRACKER)")
+    # Larger title
+    st.markdown("<h2 style='text-align: center;'>Can Recognise Face and Detect:</h2>", unsafe_allow_html=True)
+    # Smaller subtitle
+    st.markdown("<h3 style='text-align: center;'>Emotions: angry, fear, happy, neutral, sad, surprise </h3>", unsafe_allow_html=True)
+
+    app_mode = st.sidebar.selectbox("Select Mode", ["Recognize Face & Emotion", "Add New Face", "View Records"])
+
+    if app_mode == "Recognize Face & Emotion":
+        recognize_face()
+    elif app_mode == "Add New Face":
+        add_new_face()
+    elif app_mode == "View Records":
+        view_attendance_records()
+
+    conn.close()

requirements.txt

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+streamlit
+opencv-python-headless
+face_recognition @ git+https://github.com/ravinder2023/dlib_face_recognition.git
+tensorflow
+keras
+pandas
+numpy