detect_videofile_mouth.py

# USAGE
# python detect_drowsiness.py --shape-predictor shape_predictor_68_face_landmarks.dat
# python detect_drowsiness.py --shape-predictor shape_predictor_68_face_landmarks.dat --alarm alarm.wav

# import the necessary packages
from scipy.spatial import distance as dist
from imutils.video import FileVideoStream
from imutils import face_utils
from threading import Thread
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2

def mouth_aspect_ratio(mouth):
	# compute the euclidean distances between the two sets of
	# vertical mouth landmarks (x, y)-coordinates
	A = dist.euclidean(mouth[2], mouth[9]) # 51, 59
	B = dist.euclidean(mouth[4], mouth[7]) # 53, 57

	# compute the euclidean distance between the horizontal
	# mouth landmark (x, y)-coordinates
	C = dist.euclidean(mouth[0], mouth[6]) # 49, 55

	# compute the mouth aspect ratio
	mar = (A + B) / (2.0 * C)

	# return the mouth aspect ratio
	return mar

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=False, default='shape_predictor_68_face_landmarks.dat',
	help="path to facial landmark predictor")
ap.add_argument("-v", "--video", default="trump.mp4",
	help="video path input")
args = vars(ap.parse_args())

# define one constants, for mouth aspect ratio to indicate open mouth
MOUTH_AR_THRESH = 0.6

# 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 mouth
(mStart, mEnd) = (49, 68)

# start the video stream thread
print("[INFO] starting video stream thread...")
fvs = FileVideoStream(path=args["video"]).start()
time.sleep(1.0)

frame_width = 640
frame_height = 360

# Define the codec and create VideoWriter object.The output is stored in 'outpy.avi' file.
out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30, (frame_width,frame_height))
time.sleep(1.0)

# loop over frames from the video stream
while True:
	# grab the frame from the threaded video file stream, resize
	# it, and convert it to grayscale
	# channels)
	frame = fvs.read()
	frame = imutils.resize(frame, width=640)
	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 mouth coordinates, then use the
		# coordinates to compute the mouth aspect ratio
		mouth = shape[mStart:mEnd]
		mar = mouth_aspect_ratio(mouth)

		# compute the convex hull for the mouth, then
		# visualize the mouth
		mouthHull = cv2.convexHull(mouth)

		cv2.drawContours(frame, [mouthHull], -1, (0, 255, 0), 1)
		cv2.putText(frame, "MAR: {:.2f}".format(mar), (30, 30),
			cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

        # Draw text if mouth is open
		if mar > MOUTH_AR_THRESH:
			cv2.putText(frame, "Mouth is Open!", (30,60),
			cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255),2)
	# Write the frame into the file 'output.avi'
	out.write(frame)
	# 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
cv2.destroyAllWindows()
fvs.stop()