【发布时间】：2021-08-12 02:21:56
【问题描述】：
我正在使用来自 github 的 Mask 检测代码（我几乎没有更改）并尝试使用计时器（或类似计时器的功能）关闭视频窗口。此代码检测掩码并显示文本“掩码”或“无掩码”。我想在标签显示“面具”5 秒时关闭窗口。 我尝试使用计时器代码
def startTimer():
global count
timer = threading.Timer(1, startTimer)
timer.start()
print(count)
count += 1
if count > 5:
timer.cancel()
但效果不好。它有3个问题。
- 计时器已运行，但在几秒钟内无法正常工作。
2.当计数变为5时计时器应该停止，但它会上升到9然后关闭窗口。 3. 当标签显示“面具”时，视频停止。 我想知道如何在这段代码中使用计时器关闭窗口，以及如何解决这三个问题。 非常感谢您的帮助
from imutils import video
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2
import os
import threading
from tensorflow.python.ops.math_ops import truediv
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
def detect_and_predict_mask(frame, faceNet, maskNet):
# grab the dimensions of the frame and then construct a blob
# from it
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
(104.0, 180.0, 124.0))
# pass the blob through the network and obtain the face detections
faceNet.setInput(blob)
detections = faceNet.forward()
# initialize our list of faces, their corresponding locations,
# and the list of predictions from our face mask network
faces = []
locs = []
preds = []
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is
# greater than the minimum confidence
if confidence > args["confidence"]:
# compute the (x, y)-coordinates of the bounding box for
# the object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# ensure the bounding boxes fall within the dimensions of
# the frame
(startX, startY) = (max(0, startX), max(0, startY))
(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
# extract the face ROI, convert it from BGR to RGB channel
# ordering, resize it to 224x224, and preprocess it
face = frame[startY:endY, startX:endX]
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (224, 224))
face = img_to_array(face)
face = preprocess_input(face)
# add the face and bounding boxes to their respective
# lists
faces.append(face)
locs.append((startX, startY, endX, endY))
# only make a predictions if at least one face was detected
if len(faces) > 0:
# for faster inference we'll make batch predictions on *all*
# faces at the same time rather than one-by-one predictions
# in the above `for` loop
faces = np.array(faces, dtype="float32")
preds = maskNet.predict(faces, batch_size=32)
# return a 2-tuple of the face locations and their corresponding
# locations
return (locs, preds)
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", type=str,
default="face_detector",
help="path to face detector model directory")
ap.add_argument("-m", "--model", type=str,
default="mask_detector.model",
help="path to trained face mask detector model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# load our serialized face detector model from disk
print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
"res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
# load the face mask detector model from disk
print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = cv2.VideoCapture(0 + cv2.CAP_DSHOW)
vs.set(cv2.CAP_PROP_FRAME_WIDTH,640)
vs.set(cv2.CAP_PROP_FRAME_HEIGHT,480)
time.sleep(1.0)
count = 0
Maskon = False
def startTimer():
global count
timer = threading.Timer(1, startTimer)
timer.start()
print(count)
count += 1
if count > 5:
timer.cancel()
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
check, frame = vs.read()
if isinstance(frame, np.ndarray):
pass
else:
continue
# detect faces in the frame and determine if they are wearing a
# face mask or not
(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
# loop over the detected face locations and their corresponding
# locations
for (box, pred) in zip(locs, preds):
# unpack the bounding box and predictions
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
# determine the class label and color we'll use to draw
# the bounding box and text
label = "Mask" if mask > withoutMask else "No Mask"
color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
if label == "Mask":
Maskon = True
else:
Maskon = False
# include the probability in the label
label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
# display the label and bounding box rectangle on the output
# frame
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
if Maskon == True:
startTimer()
# show the output frame
cv2.imshow('Frame', frame)
key = cv2.waitKey(1) & 0xFF
if count == 5:
break
# do a bit of cleanup
vs.release()
cv2.destroyAllWindows()
【问题讨论】：
标签： python python-3.x opencv timer