用多线程处理视频并用于目标检测能够加快处理的帧率，而Python的threading模块封装了相关的操作，通过编写功能类继承threading.Thread即可实现自己的逻辑：通过调用start()方法，线程实例开始在单独的线程上下文中运行自己的run()函数处理任务，直到线程退出。在此期间，主线程可以继续执行任务。当主线程任务执行结束时，主线程可通过设置全局状态变量告知子线程退出，同时调用join()方法等待子线程运行结束。

import numpy as np
import cv2
import threading
from copy import deepcopy

thread_lock = threading.Lock()
thread_exit = False

class myThread(threading.Thread):
    def __init__(self, camera_id, img_height, img_width):
        super(myThread, self).__init__()
        self.camera_id = camera_id
        self.img_height = img_height
        self.img_width = img_width
        self.frame = np.zeros((img_height, img_width, 3), dtype=np.uint8)

def get_frame(self):
        return deepcopy(self.frame)

def run(self):
        global thread_exit
        cap = cv2.VideoCapture(self.camera_id)
        while not thread_exit:
            ret, frame = cap.read()
            if ret:
                frame = cv2.resize(frame, (self.img_width, self.img_height))
                thread_lock.acquire()
                self.frame = frame
                thread_lock.release()
            else:
                thread_exit = True
        cap.release()

def main():
    global thread_exit
    camera_id = 0
    img_height = 480
    img_width = 640
    thread = myThread(camera_id, img_height, img_width)
    thread.start()

while not thread_exit:
        thread_lock.acquire()
        frame = thread.get_frame()
        thread_lock.release()

cv2.imshow('Video', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            thread_exit = True
    thread.join()

if __name__ == "__main__":
    main()

上述代码使用多线程并行处理任务，为确保资源访问不受冲突，使用threading.Lock进行保护；主线程使用thread_exit全局状态变量控制着两个线程的运行状态，使得两个线程都拥有终止运行流程的话语权。