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detect_blinks_new_video.py
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detect_blinks_new_video.py
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# 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 pandas as pd
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 = 0.30
EYE_AR_CONSEC_FRAMES = 2
# 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)
FRAME=0
ear_list=list()
array_blink_threshold=list()
# 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=900)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = detector(gray, 0)
# no face detected
if(len(rects)==0):
ear_list.append(np.nan)
array_blink_threshold.append(np.nan)
# 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
ear_list.append(ear)
array_blink_threshold.append(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)
# 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
array_blink_threshold[FRAME]=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, "Frame: {}".format(FRAME), (10, 300),
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)
for (x, y) in shape:
cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)
# show the frame
# cv2.imshow("Frame", frame)
print(FRAME)
key = cv2.waitKey(1) & 0xFF
FRAME += 1
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
#moving avareage function
def moving_av(mylist, N):
cumsum, moving_aves = [0], []
for i, x in enumerate(mylist, 1):
cumsum.append(cumsum[i-1] + x)
if i>=N:
moving_ave = (cumsum[i] - cumsum[i-N])/N
#can do stuff with moving_ave here
moving_aves.append(moving_ave)
return moving_aves
# try:
# users_final = pd.read_csv("tag/{}.tag".format(args["video"][6:-4]), sep='\t', header=None,
# names=['frame', 'tag'], index_col="frame")
# except FileNotFoundError:
# users_final = pd.read_csv("tag/{}.tag".format(args["video"][7:-4]), sep='\t', header=None,
# names=['frame', 'tag'], index_col="frame")
mov_ear_3=moving_av(ear_list,3)
mov_ear_5=moving_av(ear_list,5)
mov_ear_7=moving_av(ear_list,7)
ear_list = pd.Series(ear_list, index=range(0, len(ear_list)))
array_blink_threshold=pd.Series(array_blink_threshold,index=range(0, len(array_blink_threshold)))
mov_ear_3=pd.Series(mov_ear_3, index=range(2, len(mov_ear_3)+2))
mov_ear_5=pd.Series(mov_ear_5, index=range(3, len(mov_ear_5)+3))
mov_ear_7=pd.Series(mov_ear_7, index=range(4, len(mov_ear_7)+4))
ear_list = pd.DataFrame(ear_list)
ear_list["threshold"] = array_blink_threshold
ear_list["mov_ear_3"] = mov_ear_3
ear_list["mov_ear_5"] = mov_ear_5
ear_list["mov_ear_7"] = mov_ear_7
ear_list.columns = ["ear", "threshold", "mov_ear_3","mov_ear_5","mov_ear_7"]
#ear_list = ear_list.fillna(0)
#mask = ear_list.tag == 0
#ear_list.tag = ear_list.tag.where(mask, 1)
ear_list.index.name="frame"
try:
ear_list.to_csv("non_training_data_raw_data/{}/{}.csv".format(
args["video"][6:-4]), index=True, header=True)
except FileNotFoundError:
ear_list.to_csv("non_training_data_raw_data/{}.csv".format(
args["video"][7:-4]), index=True, header=True)
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()