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# 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 pickle
import os
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", default="",
help="path to facial landmark predictor")
#ap.add_argument("-v", "--video", type=str, default="",
#help="path to input video file")
args = vars(ap.parse_args())
# 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
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 4
# 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"])
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"]
# 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
#writer = None
time.sleep(2.0)
#load haarcascade FaceRecognition
faceCascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
rectanglecolor=(23,6,227)
# 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():
ak
# 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=900)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = detector(gray, 0)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.5,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), rectanglecolor, 2)
# 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, rectanglecolor, 1)
cv2.drawContours(frame, [rightEyeHull], -1, rectanglecolor, 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
# 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.circle(frame,(670,75), 43, rectanglecolor, -1)
#cv2.putText(frame, "Cerrado", (520, 83), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (23,6,227), 2)
cv2.putText(frame,"Parpadea 3 veces para acceder: {}/3".format(TOTAL), (60, 83),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, rectanglecolor, 2)
#cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),
# cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
if TOTAL > 2:
rectanglecolor=(7,240,49)
cv2.circle(frame,(670,75), 43, rectanglecolor, -1)
#cv2.putText(frame, "Abierto", (520, 83), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (7,240,49), 2)
# show the frame
cv2.imshow("Face Recognition Verification", 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()
vs.stop()
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