如果检测到随机生成的 class，我该如何停止该程序？

Question

我的问题是：

当程序检测到使用 'CLASSES_RANDOM' 从 'CLASSES' 中随机选择的 class 时如何停止程序（通过使用 randint 生成随机索引）
如何创建一个检测程序是运行的函数？

代码：

#hoangAI
#DIKY Whosebug JSEM SE NAUCIL OOP V PYTHONU DIKY <3
#TENTO SCRIPT JE PRO DETEKCI OBJEKTU SPECIFIKOVANYCH V CLASSES[]
#omlouvam se za anglicke nazvy promennych, prislo mi blby to jmenovat  cesky  
# importujeme vsechny potrebne knihovny(dekuji moc Whosebug :3)
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
import random

#prikazove argumenty
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
    help="pridej k argumentu -p path k prototxt souboru")
ap.add_argument("-m", "--model", required=True,
    help="pridej path k trenovacimu modelu")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
    help="minimalni sance (na odstraneni tzv slabych detekci")
args = vars(ap.parse_args())
#"nacteni" hoangAI :D
print("Loading hoangAI(TM) V.1.0.0")
time.sleep(2.0)
# nacteme si list stitku veci, ktere byly pouzity pri detekcnim treningu hoangAI, a pak nahodne vybereme barvu pro objektove ramecky
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
CLASSES_RANDOM = CLASSES[random.randint(0,len(CLASSES) - 1)]
print("vygoogli si" + CLASSES_RANDOM)
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# nacteme si serializovany model caffe
print("[INFO] nacitani modelu...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# zacatek video snimani
# a take zacneme pocitat fps
print("[INFO] video snimani zacina...TED")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()

# cyklus/smycka pres ramecek vystupu videa
while True:
    # vezmeme ramecek z threadovaneho video vystupu a omezime sirku ramecku na 400 pixelu(aby to nebylo moc narocne na vykon)
    frame = vs.read()
    frame = imutils.resize(frame, width=400)

    # vezmem rozmery ramecku a prevedeme do blobu(objektu)
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
        0.007843, (300, 300), 127.5)

    # objekt(blob) projde siti aby ziskal detekce a predpovedi
    net.setInput(blob)
    detections = net.forward()

    # cyklus/smycka pres detekce
    for i in np.arange(0, detections.shape[2]):
        # vytahnout confidence (tedy jak si je svym uhodnutim pocitac jisty) spojeny s predpovedi
        confidence = detections[0, 0, i, 2]

        # odstranime "slabe" detekce s tim ze `confidence` (tedy jak si je svym uhodnutim pocitac jisty) je vetsi nez minimalni confidence uvedeny v argumentaci pri spusteni
        if confidence > args["confidence"]:
            #vytahneme index ze stitku tridy
            # `detections`, a pak vypocitame souradnice (x, y)
            # pro ohranicujici ramecek objektu
            idx = int(detections[0, 0, i, 1])
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # vykresleni "uhodnuti" pocitace
            label = "{}: {:.2f}%".format(CLASSES[idx],
                confidence * 100)
            cv2.rectangle(frame, (startX, startY), (endX, endY),
                COLORS[idx], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, label, (startX, y),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

    # vystup z kamery a z detekcniho programu
    cv2.imshow("detekce veci :DDDDDDDDDDDDDDD", frame)
    key = cv2.waitKey(1) & 0xFF


    # pokud je stiskle tlacitko "q" (funkce ord, aby jsme nepotrebovali input())
    if key == ord("q"):
        break

    #funkce ktera aktualizuje fps
    fps.update()

# ukonceni pocitani fps a ukonceni casovace (pouze pro test jestli to funguje)
fps.stop()
print("[INFO] cas od startu: {:.2f}".format(fps.elapsed()))
print("[INFO] prum. FPS: {:.2f}".format(fps.fps()))

# konec programu
cv2.destroyAllWindows()
vs.stop()

Answer 1

这是另一个线程中带有蜂鸣器的示例：

from threading import Event, Thread

from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
import random


program_ending_event = Event()
buzz = False

def buzzer_script():
    program_ending_event.wait()
    if buzz:
        pass
        #Here you put your logic for the buzzer or whathave you
        #Rember to return from this or the program will not exit

buzz_thread = Thread(target=buzzer_script)
buzz_thread.start()


ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
                help="pridej k argumentu -p path k prototxt souboru")
ap.add_argument("-m", "--model", required=True,
                help="pridej path k trenovacimu modelu")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
                help="minimalni sance (na odstraneni tzv slabych detekci")
args = vars(ap.parse_args())
# "nacteni" hoangAI :D
print("Loading hoangAI(TM) V.1.0.0")
time.sleep(2.0)
# nacteme si list stitku veci, ktere byly pouzity pri detekcnim treningu hoangAI, a pak nahodne vybereme barvu pro objektove ramecky
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
           "sofa", "train", "tvmonitor"]
CLASSES_RANDOM = CLASSES[random.randint(0, len(CLASSES) - 1)]
print("vygoogli si" + CLASSES_RANDOM)
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# nacteme si serializovany model caffe
print("[INFO] nacitani modelu...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# zacatek video snimani
# a take zacneme pocitat fps
print("[INFO] video snimani zacina...TED")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()

# cyklus/smycka pres ramecek vystupu videa


while True:
    # vezmeme ramecek z threadovaneho video vystupu a omezime sirku ramecku na 400 pixelu(aby to nebylo moc narocne na vykon)
    frame = vs.read()
    frame = imutils.resize(frame, width=400)

    # vezmem rozmery ramecku a prevedeme do blobu(objektu)
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
                                 0.007843, (300, 300), 127.5)

    # objekt(blob) projde siti aby ziskal detekce a predpovedi
    net.setInput(blob)
    detections = net.forward()

    # cyklus/smycka pres detekce
    detected_class = (0, 0)
    for i in np.arange(0, detections.shape[2]):
        # vytahnout confidence (tedy jak si je svym uhodnutim pocitac jisty) spojeny s predpovedi
        confidence = detections[0, 0, i, 2]

        # odstranime "slabe" detekce s tim ze `confidence` (tedy jak si je svym uhodnutim pocitac jisty) je vetsi nez minimalni confidence uvedeny v argumentaci pri spusteni
        if confidence > args["confidence"]:
            # vytahneme index ze stitku tridy
            # `detections`, a pak vypocitame souradnice (x, y)
            # pro ohranicujici ramecek objektu
            idx = int(detections[0, 0, i, 1])

            # Check if new class has the highest probability  <--------------- ADDED
            if confidence > detected_class[0]:
                detected_class = (confidence, CLASSES[idx])

            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # vykresleni "uhodnuti" pocitace
            label = "{}: {:.2f}%".format(CLASSES[idx],
                                         confidence * 100)
            cv2.rectangle(frame, (startX, startY), (endX, endY),
                          COLORS[idx], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, label, (startX, y),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

    if detected_class[1] == CLASSES_RANDOM:
        buzz = True
        break

    # vystup z kamery a z detekcniho programu
    cv2.imshow("detekce veci :DDDDDDDDDDDDDDD", frame)
    key = cv2.waitKey(1) & 0xFF

    # pokud je stiskle tlacitko "q" (funkce ord, aby jsme nepotrebovali input())
    if key == ord("q"):
        break

    # funkce ktera aktualizuje fps
    fps.update()


program_ending_event.set() #<----------- SIGNAL PROGRAM END
# ukonceni pocitani fps a ukonceni casovace (pouze pro test jestli to funguje)
fps.stop()

如果检测到随机生成的 class，我该如何停止该程序？

How do i make this program stop if it detects a randomly generated class?

python

opencv

caffe