sleep_n_smash.py

# https://github.com/mans-men/eye-blink-detection-demo
# import the necessary packages
from scipy.spatial import distance as dist
from imutils.video import FileVideoStream
from imutils.video import VideoStream
from imutils import face_utils
import argparse
import imutils
import time
import dlib
import cv2
import serial

def eye_aspect_ratio(eye):
    # compute the euclidean distances between the two sets of
    # vertical eye landmarks (x, y)-coordinates
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])

    # compute the euclidean distance between the horizontal
    # eye landmark (x, y)-coordinates
    C = dist.euclidean(eye[0], eye[3])

    # compute the eye aspect ratio
    ear = (A + B) / (2.0 * C)

    # return the eye aspect ratio
    return ear
 
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor",default="shape_predictor_68_face_landmarks.dat",
    help="path to facial landmark predictor")
ap.add_argument("-v", "--video", type=str, default="camera",
    help="path to input video file")
ap.add_argument("-t", "--threshold", type = float, default=0.20,
    help="threshold to determine closed eyes")
ap.add_argument("-f", "--frames", type = int, default=2,
    help="the number of consecutive frames the eye must be below the threshold")
ap.add_argument("-s", "--serial", type=str, default="COM3",
    help="serial port path")
ap.add_argument("-e", "--speed", type=int, default=9600,
    help="serial connection speed")

def main() :
    args = vars(ap.parse_args())
    EYE_AR_THRESH = args['threshold']
    EYE_AR_CONSEC_FRAMES = args['frames']

    # initialize the frame counters and the total number of blinks
    COUNTER = 0
    TOTAL = 0
    SLEEP = 0
    SLEEP_TIME = 0

    # initialize serial port
    print("[INFO] connect to serial port...")
    ser = serial.Serial()
    ser.port = args['serial']
    ser.baudrate = args['speed']
    
    try:
        ser.open()
    except:
        print("[WARN] serial connection failed")
        exit()

    print("[INFO] verify serial is ready") 
    while not ser.is_open:
        pass

    # initialize dlib's face detector (HOG-based) and then create
    # the facial landmark predictor
    print("[INFO] loading facial landmark predictor...")
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(args["shape_predictor"])
 
    # grab the indexes of the facial landmarks for the left and
    # right eye, respectively
    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
    
    # start the video stream thread
    print("[INFO] starting video stream thread...")
    print("[INFO] print q to quit...")
    if args['video'] == "camera":
        vs = VideoStream(src=0).start()
        fileStream = False
    else:
        vs = FileVideoStream(args["video"]).start()
        fileStream = True
   
    time.sleep(1.0)
    # verify crane is okay
    ser.write(b'Hello, this is sleep & smash')
    print("[INFO] crane is ready!")
    
    # loop over frames from the video stream
    while True:
        # if this is a file video stream, then we need to check if
        # there any more frames left in the buffer to process
        if fileStream and not vs.more():
            break
    
        # grab the frame from the threaded video file stream, resize
        # it, and convert it to grayscale
        # channels)
        frame = vs.read()
        frame = imutils.resize(frame, width=450)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
        # detect faces in the grayscale frame
        rects = detector(gray, 0)

        # student is sleeping if camera cannot detect eyes
        if not rects:
            SLEEP = 1
    
        # loop over the face detections
        for rect in rects:
            # determine the facial landmarks for the face region, then
            # convert the facial landmark (x, y)-coordinates to a NumPy
            # array
            shape = predictor(gray, rect)
            shape = face_utils.shape_to_np(shape)
    
            # extract the left and right eye coordinates, then use the
            # coordinates to compute the eye aspect ratio for both eyes
            leftEye = shape[lStart:lEnd]
            rightEye = shape[rStart:rEnd]
            leftEAR = eye_aspect_ratio(leftEye)
            rightEAR = eye_aspect_ratio(rightEye)
    
            # average the eye aspect ratio together for both eyes
            ear = (leftEAR + rightEAR) / 2.0
    
            # compute the convex hull for the left and right eye, then
            # visualize each of the eyes
            leftEyeHull = cv2.convexHull(leftEye)
            rightEyeHull = cv2.convexHull(rightEye)
            cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
            cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
    
            # check to see if the eye aspect ratio is below the blink
            # threshold, and if so, increment the blink frame counter
            # student is sleeping if ear value under threshold
            if ear < EYE_AR_THRESH:
                COUNTER += 1
                SLEEP = 1
    
            # otherwise, the eye aspect ratio is not below the blink
            # threshold
            else:
                # if the eyes were closed for a sufficient number of
                # then increment the total number of blinks
                if COUNTER >= EYE_AR_CONSEC_FRAMES:
                    TOTAL += 1
    
                # reset the eye frame counter
                COUNTER = 0
            
            # draw the total number of blinks on the frame along with
            # the computed eye aspect ratio for the frame
            cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
            cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)


        # if student is not SLEEP, initialize timer
        if not SLEEP:
            SLEEP_TIME = time.time()
        else:
            SLEEP = 0

        # if sleep time is over 3s, detect as sleeping student
        if time.time() - SLEEP_TIME > 3.0:
            cv2.putText(frame, "DO NOT SLEEP!", (10, 90),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
            ser.write(b'W')
            SLEEP_TIME = time.time()
            
        cv2.putText(frame, "SLEEP: {0:.1f}s".format(time.time() - SLEEP_TIME), (10, 60),
            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
     
        # show the frame
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF
     
        # if the `q` key was pressed, break from the loop
        if key == ord("q"):
            break
    
    # do a bit of cleanup
    ser.close()
    cv2.destroyAllWindows()
    vs.stop()
    
if __name__ == '__main__' :
    main()