Create automaticAttedance.py

automaticAttedance.py

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+import cv2
+import numpy as np
+
+def fill_attendance(update_message, video_path=None):
+    # Load the trained model and Haar Cascade
+    recognizer = cv2.face.LBPHFaceRecognizer_create()
+    recognizer.read("trainer.yml")
+    face_detector = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
+
+    # Initialize video capture
+    if video_path:
+        cap = cv2.VideoCapture(video_path)
+    else:
+        cap = cv2.VideoCapture(0)
+
+    print("Taking attendance...")
+    while True:
+        ret, frame = cap.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+
+        for (x, y, w, h) in faces:
+            face_image = frame[y:y + h, x:x + w]
+            label, confidence = recognizer.predict(face_image)
+
+            # Here, we can map label to the student's name using a CSV file or any database
+            print(f"Detected student ID: {label} with confidence: {confidence}")
+            
+            # For simplicity, let's just print the student ID
+            update_message(f"Student {label} marked present")
+
+            # Draw a rectangle around the face and display the student ID
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+
+        cv2.imshow("Attendance", frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to exit
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()