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detector.py
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detector.py
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# Todo
# Make GUI
# Create Goal file
# Run in background
# USAGE
# python detectector.py --shape-predictor shape_predictor_68_face_landmarks.dat
# python detector.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 VideoStream
from imutils import face_utils
from threading import Thread
import ctypes
import numpy as np
import playsound
import argparse
import imutils
import time
import dlib
import cv2
import os.path
GOAL = ""
def sound_alarm(path):
# play an alarm sound
playsound.playsound(path)
ctypes.windll.user32.MessageBoxA(0, GOAL, "Sleep detected!", 0)
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
def run_detector():
# 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 = 48
# initialize the frame counter as well as a boolean used to
# indicate if the alarm is going off
COUNTER = 0
ALARM_ON = 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
# 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=args["webcam"]).start()
# # time.sleep(1.0)
cam = cv2.VideoCapture(1 + cv2.CAP_DSHOW)
# Current time in millis
start_time = int(round(time.time() * 1000))
#time 30 seconds later in millis
stop_time = int(round(time.time() * 1000)) + 5000
# 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)
ret, frame = cam.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
# if the eyes were closed for a sufficient number of
# 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 an alarm file was supplied,
# and if so, start a thread to have the alarm
# sound played in the background
t = Thread(target=sound_alarm,
args=("alarm.wav",))
t.deamon = True
t.start()
# draw an alarm on the frame
cv2.putText(frame, "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(frame, "EAR: {:.2f}".format(ear), (300, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# show the frame
cv2.imshow("Frame", frame)
cv2.waitKey(1) & 0xFF
# Time to run detector for
# Currently set to 1 seconds for testing
start_time = int(round(time.time() * 1000))
if start_time > stop_time:
break
# do a bit of cleanup
cv2.destroyAllWindows()
cam.release()
def check_file():
if os.path.isfile("goal.txt"):
f = open("goal.txt", "r")
global GOAL
GOAL = f.read()
else:
# Todo
print "ask for goal and print"
# 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")
# ap.add_argument("-a", "--alarm", type=str, default="",
# help="path alarm .WAV file")
ap.add_argument("-w", "--webcam", type=int, default=0,
help="index of webcam on system")
args = vars(ap.parse_args())
check_file()
while True:
run_detector()
time.sleep(3)