face_encoding.py

import face_recognition
import cv2
import pickle

# Open webcam
cap = cv2.VideoCapture(0)

# Set a higher resolution for better face detection
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

print("\nLook at the camera to capture your face... HIT 'c' TO CAPTURE\n")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame. Exiting...")
        break

    # Display the frame to ensure the camera is working
    cv2.imshow("Frame", frame)

    # Press 'c' to take a snapshot and process the frame
    if cv2.waitKey(1) & 0xFF == ord("c"):
        print("Capturing frame...")
        break

# Destroy the preview window after capturing
cv2.destroyAllWindows()

# Directly calculate face encodings without manually passing face locations
try:
    print("\nGenerating face encoding...\n\n\t\tPRESS 'c' TO CAPTURE & EXIT\n\n")
    # Let face_recognition automatically detect faces and generate encodings
    face_encodings = face_recognition.face_encodings(frame)

    if face_encodings:
        print("Face encoding generated successfully!")

        # Save the face encoding to a file
        with open("your_face_encoding.pkl", "wb") as f:
            pickle.dump(face_encodings[0], f)
        print("\nFace encoding saved successfully!")
    else:
        print("\nNo face detected or unable to generate face encoding.")

except Exception as e:
    print(f"\nError during face encoding: {e}")

cap.release()