dont_strain_your_eyes.py

# import the necessary packages
from scipy.spatial import distance as dist
from imutils.video import VideoStream
from imutils import face_utils
import argparse
import imutils
import time
import dlib
import cv2

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", required=True,
	help="path to facial landmark predictor")
args = vars(ap.parse_args())


# threshold value for eye aspect ratio
EYE_AR_THRESH = 0.23

# number of consecutive frames the eye must be below the threshold
EYE_AR_CONSEC_FRAMES = 1
 
# initialize the frame counters and the total number of blinks
COUNTER = 0
TOTAL = 0

# 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...")
#vs = FileVideoStream(args["video"]).start()
fileStream = True
vs = VideoStream(src=0).start()
#vs = VideoStream(usePiCamera=True).start()
fileStream = False
time.sleep(1.0)

# initialize a variable to store the number of blinks in a minute
BLINKS = 0
# initialize a varible to store the starting time to calculate time interval
start_time = time.time()
# 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, resizeit, 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)
    
    	# 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
        if ear < EYE_AR_THRESH:
            COUNTER += 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
                    BLINKS+=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)
        
        end_time = time.time()
        if(end_time - start_time > 60):             #time interval of 60s
            if(BLINKS < 10):
                print("Blink now !")
            else:
                print("All okay")
            #resetting the the starting time and the blinks count for next iteration
            start_time = time.time()
            BLINKS=0
                
	# 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
 
# end
cv2.destroyAllWindows()
vs.stop()