YawnDetection.py

# coding: utf-8

# In[ ]:

import cv2
import dlib
import numpy as np


PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
predictor = dlib.shape_predictor(PREDICTOR_PATH)
# cascade_path='haarcascade_frontalface_default.xml'
#cascade = cv2.CascadeClassifier(cascade_path)
detector = dlib.get_frontal_face_detector()


def get_landmarks(im):
    # The 1 in the second argument indicates that we should upsample the image
    # 1 time.  This will make everything bigger and allow us to detect more
    # faces.
    rects = detector(im, 1)

    if len(rects) > 1:
        return "error"
    if len(rects) == 0:
        return "error"
    return np.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])


def annotate_landmarks(im, landmarks):
    im = im.copy()
    for idx, point in enumerate(landmarks):
        pos = (point[0, 0], point[0, 1])
        cv2.putText(im, str(idx), pos,
                    fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
                    fontScale=0.4,
                    color=(0, 0, 255))
        cv2.circle(im, pos, 3, color=(0, 255, 255))
    return im


def top_lip(landmarks):
    top_lip_pts = []
    for i in range(50, 53):
        top_lip_pts.append(landmarks[i])
    for i in range(61, 64):
        top_lip_pts.append(landmarks[i])
    top_lip_all_pts = np.squeeze(np.asarray(top_lip_pts))
    top_lip_mean = np.mean(top_lip_pts, axis=0)
    return int(top_lip_mean[:, 1])


def bottom_lip(landmarks):
    bottom_lip_pts = []
    for i in range(65, 68):
        bottom_lip_pts.append(landmarks[i])
    for i in range(56, 59):
        bottom_lip_pts.append(landmarks[i])
    bottom_lip_all_pts = np.squeeze(np.asarray(bottom_lip_pts))
    bottom_lip_mean = np.mean(bottom_lip_pts, axis=0)
    return int(bottom_lip_mean[:, 1])


def mouth_open(image):
    landmarks = get_landmarks(image)

    if landmarks == "error":
        return image, 0

    image_with_landmarks = annotate_landmarks(image, landmarks)
    top_lip_center = top_lip(landmarks)
    bottom_lip_center = bottom_lip(landmarks)
    lip_distance = abs(top_lip_center - bottom_lip_center)
    print(lip_distance)
    return image_with_landmarks, lip_distance

    #cv2.imshow('Result', image_with_landmarks)
    # cv2.imwrite('image_with_landmarks.jpg',image_with_landmarks)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()


cap = cv2.VideoCapture(0)
yawns = 0
yawn_status = False

while True:
    ret, frame = cap.read()
    image_landmarks, lip_distance = mouth_open(frame)

    prev_yawn_status = yawn_status

    if lip_distance > 20:
        yawn_status = True

        cv2.putText(frame, "Subject is Yawning", (50, 450),
                    cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)

        output_text = " Yawn Count: " + str(yawns + 1)

        cv2.putText(frame, output_text, (50, 50),
                    cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 127), 2)

    else:
        yawn_status = False

    if prev_yawn_status == True and yawn_status == False:
        yawns += 1

    #cv2.imshow('Live Landmarks', image_landmarks)
    cv2.imshow('Yawn Detection', frame)

    if cv2.waitKey(1) == 13:  # 13 is the Enter Key
        break

cap.release()
cv2.destroyAllWindows()