Blink Detection using dlib and opencv - python-3.x

Question

I have gotten this code from pyimagesearch and tried to run it but when i run the file i get these errors. Can anyone please tell me what is wrong here? I have installed all the needed packages and libraries. All conda packages are up to date. Please look in to the error codes section and have a look if you can figure out what is wrong here.
# USAGE
# python detect_blinks.py --shape-predictor
shape_predictor_68_face_landmarks.dat --video blink_detection_demo.mp4
# python detect_blinks.py --shape-predictor
shape_predictor_68_face_landmarks.dat
# 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
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", required=True,
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 = 3
# 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"])
# 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
time.sleep(1.0)
# 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():
break
# 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)
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
# 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.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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)
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()
the errors are
usage: detect_blinks.py [-h] -p SHAPE_PREDICTOR [-v VIDEO]
detect_blinks.py: error: the following arguments are required: -p/--shape-
predictor
An exception has occurred, use %tb to see the full traceback.
SystemExit: 2
%tb
Traceback (most recent call last):
File "<ipython-input-6-55db51806586>", line 1, in <module>
runfile('C:/Users/Rayhan/Downloads/Compressed/blink-detection/blink-detection/detect_blinks.py', wdir='C:/Users/Rayhan/Downloads/Compressed/blink-detection/blink-detection')
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile
execfile(filename, namespace)
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Rayhan/Downloads/Compressed/blink-detection/blink-detection/detect_blinks.py", line 39, in <module>
args = vars(ap.parse_args())
File "C:\ProgramData\Anaconda3\lib\argparse.py", line 1734, in parse_args
args, argv = self.parse_known_args(args, namespace)
File "C:\ProgramData\Anaconda3\lib\argparse.py", line 1766, in parse_known_args
namespace, args = self._parse_known_args(args, namespace)
File "C:\ProgramData\Anaconda3\lib\argparse.py", line 2001, in _parse_known_args
', '.join(required_actions))
File "C:\ProgramData\Anaconda3\lib\argparse.py", line 2393, in error
self.exit(2, _('%(prog)s: error: %(message)s\n') % args)
File "C:\ProgramData\Anaconda3\lib\argparse.py", line 2380, in exit
_sys.exit(status)
SystemExit: 2

Answer 1

This is my code which works :
import numpy as np
import cv2
import dlib
from scipy.spatial import distance as dist
PREDICTOR_PATH = "/home/erp-next/Downloads/shape_predictor_68_face_landmarks.dat"
# FULL_POINTS = list(range(0, 68))
# FACE_POINTS = list(range(17, 68))
# JAWLINE_POINTS = list(range(0, 17))
# RIGHT_EYEBROW_POINTS = list(range(17, 22))
# LEFT_EYEBROW_POINTS = list(range(22, 27))
# NOSE_POINTS = list(range(27, 36))
RIGHT_EYE_POINTS = list(range(36, 42))
LEFT_EYE_POINTS = list(range(42, 48))
# MOUTH_OUTLINE_POINTS = list(range(48, 61))
# MOUTH_INNER_POINTS = list(range(61, 68))
EYE_AR_THRESH = 0.2
EYE_AR_CONSEC_FRAMES = 2
frame_c=0
COUNTER_LEFT = 0
TOTAL_LEFT = 0
COUNTER_RIGHT = 0
TOTAL_RIGHT = 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
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
# Start capturing the WebCam
video_capture = cv2.VideoCapture(0)
while True:
global frame_c
print(frame_c)
frame_c +=1
ret, frame = video_capture.read()
if ret:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 0)
for rect in rects:
x = rect.left()
y = rect.top()
# x1 = rect.right()
# y1 = rect.bottom()
landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, rect).parts()])
left_eye = landmarks[LEFT_EYE_POINTS]
right_eye = landmarks[RIGHT_EYE_POINTS]
left_eye_hull = cv2.convexHull(left_eye)
right_eye_hull = cv2.convexHull(right_eye)
cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1)
ear_left = eye_aspect_ratio(left_eye)
ear_right = eye_aspect_ratio(right_eye)
cv2.putText(frame, "E.A.R. Left : {:.2f}".format(ear_left), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
cv2.putText(frame, "E.A.R. Right: {:.2f}".format(ear_right), (300, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
if ear_left < EYE_AR_THRESH:
COUNTER_LEFT += 1
else:
if COUNTER_LEFT >= EYE_AR_CONSEC_FRAMES:
TOTAL_LEFT += 1
print("Left eye winked")
COUNTER_LEFT = 0
if ear_right < EYE_AR_THRESH:
COUNTER_RIGHT += 1
else:
if COUNTER_RIGHT >= EYE_AR_CONSEC_FRAMES:
TOTAL_RIGHT += 1
print("Right eye winked")
COUNTER_RIGHT = 0
cv2.putText(frame, "Wink Left : {}".format(TOTAL_LEFT), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
cv2.putText(frame, "Wink Right: {}".format(TOTAL_RIGHT), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
cv2.imshow("Faces found", frame)
ch = 0xFF & cv2.waitKey(1)
if ch == ord('q'):
break
cv2.destroyAllWindows()
Above code uses "shape_predictor_68_face_landmarks.dat" library which plotes 68 predefine points on face.
using those points it will track eye and using euclidean distance algorithm it check if eye is blinked or not.
try this.