detector.py

# 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 numpy as np
import argparse
import imutils
import time
import dlib
import cv2
import matplotlib.pyplot as plt
import winsound

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

# define two constants, one for the eye aspect ratio to indicate
# blink and then a second constant for the number of consecutive
# frames the eye must be below the threshold
EAR_THRESHOLD = 0.18
BLINK_CONSEC_FRAMES = 2 # 2 frames below the threshold must occur for a blink to happen
SLEEPY_CONSEC_FRAMES= 10

# initialize the frame counters and the total number of blinks
blink_frame_counter = 0
total_blinks = 0

# initialize the frame counters for sleepiness and the state of sleepiness
sleepy_frame_counter = 0
sleepy=False

# 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("shape_predictor_68_face_landmarks.dat")

# 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"]

#Generate graph

plt.ylabel("EAR")
plt.xlabel("Frame")
y_points=[]

# start the video stream thread
print("[INFO] starting video stream thread...")
vs = VideoStream(src=0).start()
fileStream = False
time.sleep(1.0)

# 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)
    
    # 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
        
        #plot ear
		y_points.append(ear)
        # 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, meaning a person's eyes are closed(does not indicate a blink, just 1 frame)
		if ear < EAR_THRESHOLD:
			blink_frame_counter += 1
			sleepy_frame_counter += 1
			if(sleepy_frame_counter>= SLEEPY_CONSEC_FRAMES):
				sleepy=True
 			
		# ratio is above threshold, meaning eyes are open
		else:
			# if the eyes were closed for a sufficient number of frames
			# then increment the total number of blinks
			# person cannot be sleepy else it will be equal to several blinks
			if blink_frame_counter >= BLINK_CONSEC_FRAMES and not(sleepy):
				total_blinks += 1
 			
			# reset the eye frame counter
			blink_frame_counter = 0
			sleepy_frame_counter = 0
			
			# user has opened eyes and can operate again
			sleepy=False
			
        # 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_blinks), (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 sleepy:
			winsound.Beep(440,1000)
			cv2.putText(frame, "GO TO SLEEP!", (120,330),
					cv2.FONT_HERSHEY_SIMPLEX, 1, (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
        
vs.stop()
cv2.destroyAllWindows()
plt.plot(list(range(1,len(y_points)+1)),y_points,'.b-')
plt.show()