snoofing_face.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 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()