您好，所以我有这个程序，它可以捕获和检测人脸，但是我希望能够通过网页中的按钮关闭捕获，我已经有一个on按钮，它只能调用python脚本，但是我无法创建关闭按钮。
我正在使用Raspberry Pi 3，并将Pyramid Cookiecutter用作网页 这是我的views.py
from pyramid.view import view_config
import RPi.GPIO as GPIO
import os
from .models import *
from bson import ObjectId
from pyramid.httpexceptions import HTTPFound
import sys
from mongoengine import *
GPIO.setmode(GPIO.BOARD)
GPIO.setup(10, GPIO.OUT)
@view_config(route_name='home', renderer='templates/mytemplate.jinja2')
def my_view(request):
if 'switch' in request.params:
raise SystemExit
if 'blink' in request.params:
os.system("python3 /home/pi/Desktop/pi-drowsiness-detection/pi_detect_drowsiness.py -c haarcascade_frontalface_default.xml -p shape_predictor_68_face_landmarks.dat")
if 'register-now' in request.params:
print("REGISTER")
firstname = request.params['fname']
lastname = request.params['lname']
username = request.params['username']
password = request.params['password']
if AppUsers.objects(username=username).first():
return{"error": "USERNAME ALREADY EXISTS"}
x = AppUsers(firstname=firstname,lastname=lastname,username=username,password=password)
x.save()
return {'project': 'web-app-namin'}
def app_users(request):
finame=str(request.POST.get('firstname'))
laname=str(request.POST.get('lastname'))
uname=str(request.POST.get('username'))
if AppUsers.objects(username=uname).first():
return{"error": "USERNAME ALREADY EXISTS"}
x=AppUsers(firstname=finame,lastname=laname,username=uname)
x.save()
return{"response": "DATA ADDED"}
models.py
from mongoengine import *
from datetime import datetime
import hashlib
#connect to a mongodb database
connect('database_namin')
'''hashes a string using md5 hashing algorithm
returns a 32 character-length hashed string'''
def hash_mo_to(raw_string):
hasher=hashlib.md5()
hasher.update(raw_string.encode('ascii'))
return str(hasher.hexdigest())
#default admin credentials for the system
class Admin(DynamicDocument):
username=StringField(default='admin')
password=StringField(default=hash_mo_to('admin'))
#schema for mobile application users set by the admin...
class AppUsers(DynamicDocument):
firstname=StringField()
lastname=StringField()
username=StringField()
password=StringField(default=hash_mo_to("1234"))
pi_detect_drowsiness.py
# import the necessary packages
import RPi.GPIO as GPIO
from imutils.video import VideoStream
from imutils import face_utils
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2
from picamera.array import PiRGBArray
from picamera import PiCamera
from PIL import Image
led = 10
led2 = 13
GPIO.setmode(GPIO.BOARD)
GPIO.setup(led, GPIO.OUT)
GPIO.setup(led2, GPIO.OUT)
GPIO.output(led, False)
def euclidean_dist(ptA, ptB):
# compute and return the euclidean distance between the two
# points
return np.linalg.norm(ptA - ptB)
def eye_aspect_ratio(eye):
# compute the euclidean distances between the two sets of
# vertical eye landmarks (x, y)-coordinates
A = euclidean_dist(eye[1], eye[5])
B = euclidean_dist(eye[2], eye[4])
# compute the euclidean distance between the horizontal
# eye landmark (x, y)-coordinates
C = euclidean_dist(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("-c", "--cascade", required=False,
help = "/home/pi/Desktop/pi-drowsiness-detection/haarcascade_frontalface_default.xml")
ap.add_argument("-p", "--shape-predictor", required=False,
help="path to facial landmark predictor")
ap.add_argument("-a", "--alarm", type=int, default=0,
help="boolean used to indicate if TraffHat should be used")
args = vars(ap.parse_args())
# check to see if we are using GPIO/TrafficHat as an alarm
if args["alarm"] > 0:
from gpiozero import TrafficHat
th = TrafficHat()
print("[INFO] using TrafficHat alarm...")
# 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 for to set off the
# alarm
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 16
# initialize the frame counter as well as a boolean used to
# indicate if the alarm is going off
COUNTER = 0
ALARM_ON = False
# load OpenCV's Haar cascade for face detection (which is faster than
# dlib's built-in HOG detector, but less accurate), then create the
# facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = cv2.CascadeClassifier("/home/pi/Desktop/pi-drowsiness-detection/haarcascade_frontalface_default.xml")
predictor = dlib.shape_predictor("/home/pi/Desktop/pi-drowsiness-detection/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 = VideoStream(src=0).start()
vs = VideoStream(usePiCamera=True).start()
time.sleep(1.0)
# loop over frames from the video stream
while True:
# 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.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=5, minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
# loop over the face detections
for (x, y, w, h) in rects:
# construct a dlib rectangle object from the Haar cascade
# bounding box
rect = dlib.rectangle(int(x), int(y), int(x + w),
int(y + h))
# 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
# if the eyes were closed for a sufficient number of
# frames, then sound the alarm
if COUNTER >= EYE_AR_CONSEC_FRAMES:
# if the alarm is not on, turn it on
if not ALARM_ON:
ALARM_ON = True
GPIO.output(led2, True)
GPIO.output(led, True)
# check to see if the TrafficHat buzzer should
# be sounded
if args["alarm"] > 0:
th.buzzer.blink(0.1, 0.1, 10,
background=True)
# draw an alarm on the frame
print("DROWSY!!!")
cv2.putText(frame, "DROWSINESS ALERT!", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# otherwise, the eye aspect ratio is not below the blink
# threshold, so reset the counter and alarm
else:
COUNTER = 0
ALARM_ON = False
GPIO.output(led, False)
# draw the computed eye aspect ratio on the frame to help
# with debugging and setting the correct eye aspect ratio
# thresholds and frame counters
cv2.putText(frame, "EAR: {:.3f}".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()