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Updated on 2024-11-14

Yawn detection based on Python implementation details

Yawn detection based on Python implementation details

Updated April 12, 2022 08:34:39 by Kawakawa Rookie
This article mainly introduces how to use Python + OpenCV to achieve human yawn detection, the sample code in the text explains the details of our study of Python has a certain degree of help, interested in learning about it!

rendering (visual representation of how things will turn out)

basic idea

  • Initialize a video rendering object in OpenCV using the VideoCapture method
  • Creating grayscale images
  • Importing pre-trained models to recognize faces and face signs
  • Calculate the distance between the upper and lower lip (other similar)
  • Create an If condition for the lip distance that is satisfied by yawning and not satisfied by simply opening the mouth
  • Display frames/images

Partial source code

  suc, frame = ()
    # Exit without reading
    if not suc:
        break

    # ---------FPS------------#
    ctime = ()
    fps = int(1 / (ctime - ptime))
    ptime = ctime
    (frame, f'FPS:{fps}', ([1] - 120, [0] - 20), cv2.FONT_HERSHEY_PLAIN, 2,
                (0, 200, 0), 3)

    # ------ detecting faces ------#
    # To grayscale
    img_gray = (frame, cv2.COLOR_BGR2GRAY)
    faces = face_model(img_gray)
    for face in faces:
        # Detect the face, frame it up-#
        x1 = ()
        y1 = ()
        x2 = ()
        y2 = ()
        # print(())
        (frame, (x1, y1), (x2, y2), (200, 0, 00), 2)

        # ---------- detecting face annotation -----------#
        shapes = landmark_model(img_gray, face)
        shape = face_utils.shape_to_np(shapes)

        # ------- detecting upper and lower lips --------#
        lip = shape[48:60]
        (frame, [lip], -1, (0, 165, 255), thickness=3)

        # ------- Calculate the distance between the upper and lower lip -----#
        lip_dist = cal_yawn(shape)
        # Print distance
        # print(lip_dist)
        # Greater than the set value, it is recognized as yawning
        if lip_dist > yawn_thresh:
            (frame, f'User Yawning!', ([1] // 2 - 170, [0] // 2),
                        cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 200), 2)

    # Press the letter q to exit
    ('Webcam', frame)
    if (1) & 0xFF == ord('q'):
        break

This article on the realization of yawn detection based on Python detailed article is introduced to this, more related Python yawn detection content, please search for my previous posts or continue to browse the following related articles I hope that you will support me in the future more!

  • Python
  • yawn
  • sensing

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