通过网页按钮终止PiCamera捕获

Question

你好，所以我有这个程序，它可以捕捉和检测一个人的脸，但我想能够关闭捕捉通过一个按钮在网页上，我已经有一个按钮，只是调用python脚本，但我没有运气能够创建一个关闭按钮。你知道吗

我使用的是树莓皮3，与金字塔Cookiecutter的网页 这是我的视图.py你知道吗

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"}

型号.py你知道吗

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"))

PIU检测_嗜睡.py你知道吗

# 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()