pi_detect_drowsiness.py

# USAGE
# python pi_detect_drowsiness.py --cascade haarcascade_frontalface_default.xml --shape-predictor shape_predictor_68_face_landmarks.dat
# python pi_detect_drowsiness.py --cascade haarcascade_frontalface_default.xml --shape-predictor shape_predictor_68_face_landmarks.dat --alarm 1

# import the necessary packages
import RPi.GPIO as GPIO
from picamera.array import PiRGBArray
from picamera import PiCamera
import numpy as np
import argparse
import imutils
from imutils.video import VideoStream
from imutils import face_utils
import time
import dlib
import cv2

def euclidean_dist(ptA, ptB):
	# compute and return the euclidean distance between the two
	# points
	return np.linalg.norm(ptA - ptB)

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

	# compute the euclidean distance between the horizontal
	# eye landmark (x, y)-coordinates
	C = euclidean_dist(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("-c", "--cascade", required=True,
	help = "path to where the face cascade resides")
ap.add_argument("-p", "--shape-predictor", required=True,
	help="path to facial landmark predictor")
ap.add_argument("-a", "--alarm", type=int, default=0,
	help="boolean used to indicate if TraffHat should be used")
args = vars(ap.parse_args())

# check to see if we are using GPIO/TrafficHat as an alarm
if args["alarm"] > 0:
	from gpiozero import Buzzer
	from time import sleep
	bz=Buzzer(15)
	print("[INFO] using TrafficHat alarm...")
 
# 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 for to set off the
# alarm
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 8

# initialize the frame counter as well as a boolean used to
# indicate if the alarm is going off
COUNTER = 0
ALARM_ON = False

# load OpenCV's Haar cascade for face detection (which is faster than
# dlib's built-in HOG detector, but less accurate), then create the
# facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = cv2.CascadeClassifier(args["cascade"])
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 = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(1.0)

# loop over frames from the video stream
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
	# 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)
	image = frame.array
	
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

	# detect faces in the grayscale frame
	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
		minNeighbors=5, minSize=(30, 30),
		flags=cv2.CASCADE_SCALE_IMAGE)

	# loop over the face detections
	for (x, y, w, h) in rects:
		# construct a dlib rectangle object from the Haar cascade
		# bounding box
		rect = dlib.rectangle(int(x), int(y), int(x + w),
			int(y + h))

		# 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(image, [leftEyeHull], -1, (0, 255, 0), 1)
		cv2.drawContours(image, [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

			# if the eyes were closed for a sufficient number of
			# frames, then sound the alarm
			if COUNTER >= EYE_AR_CONSEC_FRAMES:
				# if the alarm is not on, turn it on
				if not ALARM_ON:
					ALARM_ON = True

					# check to see if the TrafficHat buzzer should
					# be sounded
					if args["alarm"] > 0:
						bz.on()
						sleep(1)
						bz.off()

				# draw an alarm on the frame
				cv2.putText(image, "DROWSINESS ALERT!", (10, 30),
					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

		# otherwise, the eye aspect ratio is not below the blink
		# threshold, so reset the counter and alarm
		else:
			COUNTER = 0
			ALARM_ON = False

		# draw the computed eye aspect ratio on the frame to help
		# with debugging and setting the correct eye aspect ratio
		# thresholds and frame counters
		cv2.putText(image, "EAR: {:.3f}".format(ear), (300, 30),
			cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
 
	# show the frame
	cv2.imshow("Frame", image)
	key = cv2.waitKey(1) & 0xFF
	rawCapture.truncate(0)
 
	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()