一种在 Python 中实现更快 OpenCV 视频流的多线程方法 |
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一种在 Python 中实现更快 OpenCV 视频流的多线程方法
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在本文中，我们将看到两个没有多线程的 Python 代码示例，用于从摄像头读取视频帧。我们将看到使用/不使用多线程获得的 FPS 的差异。 什么是多线程？线程是进程中的一个执行单元。多线程是指通过在线程之间快速切换对 CPU 的控制（称为上下文切换）来并发执行多个线程。在我们的示例中，我们将看到多线程通过提高 FPS（每秒帧数）实现更快的实时视频处理。 Python中的线程基础以下代码片段显示了如何使用python 中的threading模块创建线程： # importing the threading module import threading # importing the time module import time # Function to print "Hello", however, the function sleeps # for 2 seconds at the 11th iteration def print_hello(): for i in range(20): if i == 10: time.sleep(2) print("Hello") # Function to print numbers till a given number def print_numbers(num): for i in range(num+1): print(i) # Creating the threads. Target is set to the name of the # function that neeeds to be executed inside the thread and # args are the arguments to be supplied to the function that # needs to be executed. print("Greetings from the main thread.") thread1 = threading.Thread(target = print_hello, args = ()) thread2 = threading.Thread(target = print_numbers, args = (10,)) # Starting the two threads thread1.start() thread2.start() print("It's the main thread again!")让我们通过跟踪代码的执行来尝试理解输出： 主线程执行。打印“Greetings from the main thread”，创建thread1和thread2并启动线程。 发生上下文切换，开始执行thread1。 在前十次迭代之后，thread1进入睡眠状态，thread2开始执行，在下一次上下文切换之前完成。 现在，主线程获得了 CPU 的控制权并打印出“It’s the main thread again!” 另一个上下文切换发生，thread2恢复执行并完成。 由于主线程没有更多指令要执行，因此程序终止。 使用thread.join()如果需要阻塞主线程，直到thread1和thread2完成执行，该怎么办？ thread.join()会派上用场，因为它会阻塞调用线程，直到调用其 join() 方法的线程终止： # importing the threading module import threading # importing the time module import time # Function to print "Hello", however, the function sleeps # for 2 seconds at the 11th iteration def print_hello(): for i in range(20): if i == 10: time.sleep(2) print("Hello") # Function to print numbers till a given number def print_numbers(num): for i in range(num+1): print(i) # Creating the threads. Target is set to the name of the # function that neeeds to be executed inside the thread and # args are the arguments to be supplied to the function that # needs to be executed. print("Greetings from the main thread.") thread1 = threading.Thread(target = print_hello, args = ()) thread2 = threading.Thread(target = print_numbers, args = (10,)) # Starting the two threads thread1.start() thread2.start() thread1.join() thread2.join() print("It's the main thread again!") print("Threads 1 and 2 have finished executing.") 原因多线程有助于更快的处理视频处理代码分为两部分：从摄像头读取下一个可用帧并对帧进行视频处理，例如运行深度学习模型进行人脸识别等。 读取下一帧并在没有多线程的程序中按顺序进行处理。程序等待下一帧可用，然后再对其进行必要的处理。读取帧所需的时间主要与请求、等待和将下一个视频帧从相机传输到内存所需的时间有关。对视频帧进行计算所花费的时间，无论是在 CPU 还是 GPU 上，占据了视频处理所花费的大部分时间。 在具有多线程的程序中，读取下一帧并处理它不需要是顺序的。当一个线程执行读取下一帧的任务时，主线程可以使用 CPU 或 GPU 来处理最后读取的帧。这样，通过重叠两个任务，可以减少读取和处理帧的总时间。 OpenCV 代码——没有多线程 # importing required libraries import cv2 import time # opening video capture stream vcap = cv2.VideoCapture(0) if vcap.isOpened() is False : print("[Exiting]: Error accessing webcam stream.") exit(0) fps_input_stream = int(vcap.get(5)) print("FPS of webcam hardware/input stream: {}".format(fps_input_stream)) grabbed, frame = vcap.read() # reading single frame for initialization/ hardware warm-up # processing frames in input stream num_frames_processed = 0 start = time.time() while True : grabbed, frame = vcap.read() if grabbed is False : print('[Exiting] No more frames to read') break # adding a delay for simulating time taken for processing a frame delay = 0.03 # delay value in seconds. so, delay=1 is equivalent to 1 second time.sleep(delay) num_frames_processed += 1 cv2.imshow('frame' , frame) key = cv2.waitKey(1) if key == ord('q'): break end = time.time() # printing time elapsed and fps elapsed = end-start fps = num_frames_processed/elapsed print("FPS: {} , Elapsed Time: {} , Frames Processed: {}".format(fps, elapsed, num_frames_processed)) # releasing input stream , closing all windows vcap.release() cv2.destroyAllWindows() OpenCV 代码——多线程 # importing required libraries import cv2 import time from threading import Thread # library for implementing multi-threaded processing # defining a helper class for implementing multi-threaded processing class WebcamStream : def __init__(self, stream_id=0): self.stream_id = stream_id # default is 0 for primary camera # opening video capture stream self.vcap = cv2.VideoCapture(self.stream_id) if self.vcap.isOpened() is False : print("[Exiting]: Error accessing webcam stream.") exit(0) fps_input_stream = int(self.vcap.get(5)) print("FPS of webcam hardware/input stream: {}".format(fps_input_stream)) # reading a single frame from vcap stream for initializing self.grabbed , self.frame = self.vcap.read() if self.grabbed is False : print('[Exiting] No more frames to read') exit(0) # self.stopped is set to False when frames are being read from self.vcap stream self.stopped = True # reference to the thread for reading next available frame from input stream self.t = Thread(target=self.update, args=()) self.t.daemon = True # daemon threads keep running in the background while the program is executing # method for starting the thread for grabbing next available frame in input stream def start(self): self.stopped = False self.t.start() # method for reading next frame def update(self): while True : if self.stopped is True : break self.grabbed , self.frame = self.vcap.read() if self.grabbed is False : print('[Exiting] No more frames to read') self.stopped = True break self.vcap.release() # method for returning latest read frame def read(self): return self.frame # method called to stop reading frames def stop(self): self.stopped = True # initializing and starting multi-threaded webcam capture input stream webcam_stream = WebcamStream(stream_id=0) # stream_id = 0 is for primary camera webcam_stream.start() # processing frames in input stream num_frames_processed = 0 start = time.time() while True : if webcam_stream.stopped is True : break else : frame = webcam_stream.read() # adding a delay for simulating time taken for processing a frame delay = 0.03 # delay value in seconds. so, delay=1 is equivalent to 1 second time.sleep(delay) num_frames_processed += 1 cv2.imshow('frame' , frame) key = cv2.waitKey(1) if key == ord('q'): break end = time.time() webcam_stream.stop() # stop the webcam stream # printing time elapsed and fps elapsed = end-start fps = num_frames_processed/elapsed print("FPS: {} , Elapsed Time: {} , Frames Processed: {}".format(fps, elapsed, num_frames_processed)) # closing all windows cv2.destroyAllWindows() 下载所有代码https://github.com/SihabSahariar/Multi-threading-OpenCV- 资源https://stackoverflow.com/questions/55099413/python-opencv-streaming-from-camera-multithreading-timestamps https://stackoverflow.com/questions/55828451/video-streaming-from-ip-camera-in-python-using-opencv-cv2-videocapture https://stackoverflow.com/questions/58592291/how-to-capture-multiple-camera-streams-with-opencv https://stackoverflow.com/questions/58293187/opencv-real-time-streaming-video-capture-is-slow-how-to-drop-frames-or-get-sync https://stackoverflow.com/questions/55141315/storing-rtsp-stream-as-video-file-with-opencv-videowriter https://stackoverflow.com/questions/29317262/opencv-video-saving-in-python/71624807#71624807 https://stackoverflow.com/questions/72120491/python-opencv-multiprocessing-cv2-videocapture-mp4 参考https://github.com/PyImageSearch/imutils/tree/master/imutils/video https://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/ https://forum.opencv.org/t/videocapture-opens-video-sources-by-multi-thread/8045 ☆ END ☆ 如果看到这里，说明你喜欢这篇文章，请转发、点赞。微信搜索「uncle_pn」，欢迎添加小编微信「 woshicver」，每日朋友圈更新一篇高质量博文。 ↓扫描二维码添加小编↓
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