当瘫痪患者连续闪烁 2 次或 3 次时，我正在执行执行命令的项目（现在只是打印“用户单击：是”）。对于眨眼检测，我使用了来自“Adrian Rosebrock”的代码，该代码可以在 pyimagesearch 网站上找到。我已经对其进行了很多修改，但我被困在检测 2 次连续眨眼或 3 次连续眨眼的逻辑上。我似乎找不到从网络摄像头的视频或 LiveStream 中检测它们的方法。这个概念背后的想法在图像中呈现： Blinking Idea Project的图像. 作为输入，我拍摄了一个人单次眨眼的视频，然后他连续眨眼两次，然后连续眨眼三次。我的目的是检测所有这些眨眼。将双闪烁同时分类为 2 次闪烁。并且还将三个闪烁同时归类为 3 次闪烁。之后，我将分配一些命令。单次闪烁将执行不同的命令。两次闪烁将有不同的命令，三次闪烁将有不同的命令执行。整个代码如下：
# 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 timeit
import time
import dlib
import cv2
import tkinter as tk
from tkinter import filedialog
import sys
root = tk.Tk()
root.withdraw()
click = 'YES'
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())
shape_predictor = filedialog.askopenfilename()
video = filedialog.askopenfilename()
USER_INPUT = input("Please select a mode 1 = Single Blink , 2 = Double Blink, 3 = Triple Blink:\n ")
# 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
# if USER_INPUT == "1":
# EYE_AR_CONSEC_FRAMES = 6
# elif USER_INPUT == "2":
# EYE_AR_CONSEC_FRAMES = 3
# elif USER_INPUT == "3":
# EYE_AR_CONSEC_FRAMES = 0
# initialize the frame counters and the total number of blinks
COUNTER = 0
TOTAL = 0
D_TOTAL = 0
D_COUNT = 0
T_TOTAL = 0
SPEED_BLINK_COUNTER = 0
S_blink = []
S_blink_show = 0
click = ''
D_blink = []
D_time = []
# 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)
# 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()
vs = FileVideoStream(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
# Jb tk Ankh bnd hy to ye Counter increase hta jay ga kuink for loop lga hy
if ear < EYE_AR_THRESH:
COUNTER += 1
# otherwise, the eye aspect ratio is not below the blink
# threshold
# Jese e Ankh khulay gi aur counter 3 frames ya is sy zyada hjay ga to Total mai increment ajaye ga
else:
# if the eyes were closed for a sufficient number of
# then increment the total number of blinks
if USER_INPUT == "1" and COUNTER >= 2:
S_blink.append(TOTAL + 1)
S_blink_show = sum(S_blink)
#TOTAL += 1
#S_blink = 1
if USER_INPUT == "2" and COUNTER >= 2:
D_blink = S_blink.append(TOTAL + 1)
if D_blink is not None:
for i in D_blink:
D_time.append(timeit.timeit(str(D_blink[i]), number=1000))
if D_time is not None:
for i in D_time:
if D_time[i+1] < D_time[i]:
#if timeit.timeit(str(D_blink[i+1]), number=1000) < timeit.timeit(str(D_blink[i]), number=1000):
D_COUNT += 1
if USER_INPUT == "3" and COUNTER == 0.5 :
T_TOTAL += 1
# reset the eye frame counter
COUNTER = 0
#S_blink = 0
# draw the total number of blinks on the frame along with
# the computed eye aspect ratio for the frame
MODE_text = "Blink Mode"
MODE1_text = "Single Blink Mode"
MODE2_text = "Double Blink Mode"
MODE3_text = "Triple Blink Mode"
if USER_INPUT == "1":
SPEED_BLINK_COUNTER = S_blink_show
MODE_text = MODE1_text
#if S_blink == 1 :
#click = "YES"
#else:
#click =""
elif USER_INPUT == "2":
SPEED_BLINK_COUNTER = D_COUNT
MODE_text = MODE2_text
# if TOTAL == 0 and D_TOTAL >= 1 and COUNTER == 0:
# click = "YES"
# else:
# click =""
elif USER_INPUT == "3":
SPEED_BLINK_COUNTER = T_TOTAL
MODE_text = MODE3_text
# if COUNTER == 1:
# click = "YES"
# else:
# click =""
cv2.putText(frame, "Blinks: {}".format(SPEED_BLINK_COUNTER), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.rectangle(frame, (0, 250), (450, 350), (104, 104, 104), thickness=-1, lineType=8, shift=0)
cv2.putText(frame, "User Clicked: {}".format(click), (220, 270),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, MODE_text, (10, 270),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# show the frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(2) & 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()
在 Furas 爵士发表评论后，我更改了代码。这就是我想出的。我现在一直在比较眨眼时间列表。索引值之间会重复相同的减法，但它们都应该根据它们的位置顺利通过。
if COUNTER >= 2:
S_blink.append(TOTAL + 1)
S_blink_show = sum(S_blink)
end = time.time()
blink_time.append(end - start)
# print("Blink time: " + str(blink_time))
print("This is [::]: " + str(blink_time[::]))
print("This is [1:]: " + str(blink_time[1:]))
for i, j in zip(blink_time[0:], blink_time[1:]):
#print("I'm i: " + str(i), "I'm j: " + str(j))
time_mid.append(j - i)
print("Time Medium: " + str(time_mid))
我现在没有主意了。任何形式的帮助将不胜感激。谢谢你