这是对python 3之前答案的一个调整。x来自@ gsurprised，我想把它贴在评论里，但是没有足够的声誉

import sys
import argparse

import cv2
print(cv2.__version__)

def extractImages(pathIn, pathOut):
    vidcap = cv2.VideoCapture(pathIn)
    success,image = vidcap.read()
    count = 0
    success = True
    while success:
      success,image = vidcap.read()
      print ('Read a new frame: ', success)
      cv2.imwrite( pathOut + "\\frame%d.jpg" % count, image)     # save frame as JPEG file
      count += 1

if __name__=="__main__":
    print("aba")
    a = argparse.ArgumentParser()
    a.add_argument("--pathIn", help="path to video")
    a.add_argument("--pathOut", help="path to images")
    args = a.parse_args()
    print(args)
    extractImages(args.pathIn, args.pathOut)

在2022年，你也可以选择使用ImageIO来做到这一点，恕我直言，这是更加轻松和易读的。

import imageio.v3 as iio

for idx, frame in enumerate(iio.imiter("imageio:cockatoo.mp4")):
    iio.imwrite(f"extracted_images/frame{idx:03d}.jpg", frame)

旁注1:"imageio:cockatoo.mp4"是imageio为测试和演示目的提供的标准图像。您可以简单地将其替换为“path/to/your/video.mp4”。

旁注2:你必须安装一个ImageIO的可选依赖项来读取视频数据，这可以通过pip install ImageIO -ffmpeg或pip install av来完成。

你可以对照OpenCV计算时间，你会发现，在这方面，OpenCV也没有太多好处:

Read-Only Timings
=================
OpenCV:         0.453
imageio_ffmpeg: 0.765
imageio_pyav:   0.272

Read + Write Timings
====================
OpenCV:         3.237
imageio_ffmpeg: 1.597
imageio_pyav:   1.506

默认情况下，OpenCV和ImageIO+av读取时的速度差不多。两者都在底层直接绑定到FFmpeg库，所以这并不奇怪。然而，ImageIO允许您调整FFmpeg的默认线程模型(thread_type="FRAME")，这在批量读取时要快得多。

更重要的是，ImageIO在编写JPEG方面比OpenCV快得多。这是因为pillow比OpenCV快，ImageIO利用了这一点。在这种情况下，写图像占据了运行时的主导地位，因此使用ImageIO而不是OpenCV时，整体性能提高了2倍。

下面是代码供参考:

import imageio.v3 as iio
import cv2
import timeit
from pathlib import Path

# create a common local file for benchmarking
video_file = "shared_video.mp4"

if not Path(video_file).exists():
    frames = iio.imread("imageio:cockatoo.mp4")
    meta = iio.immeta("imageio:cockatoo.mp4", exclude_applied=False)
    iio.imwrite(video_file, frames, fps=meta["fps"])

repeats = 10


def read_cv2():
    vidcap = cv2.VideoCapture(video_file)
    success, image = vidcap.read()
    idx = 0
    while success:
        cv2.imwrite(f"extracted_images/frame{idx:03d}.jpg", image)
        success, image = vidcap.read()
        idx += 1


def read_imageio_ffmpeg():
    for idx, frame in enumerate(iio.imiter(video_file, plugin="FFMPEG")):
        iio.imwrite(f"extracted_images/frame{idx:03d}.jpg", frame)


def read_imageio_pyav():
    for idx, frame in enumerate(
        iio.imiter(video_file, plugin="pyav", format="rgb24", thread_type="FRAME")
    ):
        iio.imwrite(f"extracted_images/frame{idx:03d}.jpg", frame)


time_cv2 = (
    timeit.timeit("read_cv2()", setup="from __main__ import read_cv2", number=repeats)
    / repeats
)
time_imageio_ffmpeg = (
    timeit.timeit(
        "read_imageio_ffmpeg()",
        setup="from __main__ import read_imageio_ffmpeg",
        number=repeats,
    )
    / repeats
)
time_imageio_pyav = (
    timeit.timeit(
        "read_imageio_pyav()",
        setup="from __main__ import read_imageio_pyav",
        number=repeats,
    )
    / repeats
)

print(
    f"""
Timings
=======
OpenCV:         {time_cv2:<3.3f}
imageio_ffmpeg: {time_imageio_ffmpeg:<3.3f}
imageio_pyav:   {time_imageio_pyav:<3.3f}
"""
)

从这里下载这个视频，这样我们就有了相同的测试视频文件。确保mp4文件与python代码位于同一目录。然后确保从相同的目录运行python解释器。

然后修改代码，抛弃浪费时间的waitKey，也没有窗口，它不能捕捉键盘事件。同时，我们打印success值以确保它成功读取帧。

import cv2
vidcap = cv2.VideoCapture('big_buck_bunny_720p_5mb.mp4')
success,image = vidcap.read()
count = 0
while success:
  cv2.imwrite("frame%d.jpg" % count, image)     # save frame as JPEG file      
  success,image = vidcap.read()
  print('Read a new frame: ', success)
  count += 1

这是怎么回事?

在对如何将帧转换为视频进行了大量研究后，我创建了这个函数，希望这能有所帮助。我们需要opencv来实现:

import cv2
import numpy as np
import os

def frames_to_video(inputpath,outputpath,fps):
   image_array = []
   files = [f for f in os.listdir(inputpath) if isfile(join(inputpath, f))]
   files.sort(key = lambda x: int(x[5:-4]))
   for i in range(len(files)):
       img = cv2.imread(inputpath + files[i])
       size =  (img.shape[1],img.shape[0])
       img = cv2.resize(img,size)
       image_array.append(img)
   fourcc = cv2.VideoWriter_fourcc('D', 'I', 'V', 'X')
   out = cv2.VideoWriter(outputpath,fourcc, fps, size)
   for i in range(len(image_array)):
       out.write(image_array[i])
   out.release()


inputpath = 'folder path'
outpath =  'video file path/video.mp4'
fps = 29
frames_to_video(inputpath,outpath,fps)

修改fps(帧/秒)的值，输入文件夹路径和输出文件夹路径根据您自己的本地位置

从视频演示中提取幻灯片/帧有几个原因，特别是在教育或会议相关视频的情况下。它可以让你在不观看整个视频的情况下访问学习笔记。 我曾多次遇到过这个问题，所以我决定使用python自己创建一个解决方案。我已经使代码开源，你可以很容易地设置这个工具，并在几个简单的步骤中运行它。 参考这个youtube视频教程。 关于如何使用此工具的步骤。

克隆这个项目video2pdfslides 通过运行"pip install -r requirements.txt"来设置您的环境 复制视频路径 执行"python video2pdfslide .py <video_path>" 繁荣!PDF幻灯片将在输出文件夹中提供 做笔记，享受吧!

这段代码从视频中提取帧并以.jpg格式保存帧

import cv2
import numpy as np
import os

# set video file path of input video with name and extension
vid = cv2.VideoCapture('VideoPath')


if not os.path.exists('images'):
    os.makedirs('images')

#for frame identity
index = 0
while(True):
    # Extract images
    ret, frame = vid.read()
    # end of frames
    if not ret: 
        break
    # Saves images
    name = './images/frame' + str(index) + '.jpg'
    print ('Creating...' + name)
    cv2.imwrite(name, frame)

    # next frame
    index += 1