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}
"""
)

之前的答案已经失去了第一帧。把图片存储在文件夹里会很好。

# create a folder to store extracted images
import os
folder = 'test'  
os.mkdir(folder)
# use opencv to do the job
import cv2
print(cv2.__version__)  # my version is 3.1.0
vidcap = cv2.VideoCapture('test_video.mp4')
count = 0
while True:
    success,image = vidcap.read()
    if not success:
        break
    cv2.imwrite(os.path.join(folder,"frame{:d}.jpg".format(count)), image)     # save frame as JPEG file
    count += 1
print("{} images are extacted in {}.".format(count,folder))

顺便说一下，你可以通过VLC检查帧率。进入“windows ->媒体信息->编解码器详细信息”

我通过Anaconda的Spyder软件使用Python。使用@ gshock在这个线程的问题中列出的原始代码，代码不起作用(python不会读取mp4文件)。所以我下载了OpenCV 3.2，从bin文件夹中拷贝了“opencv_ffmpeg320.dll”和“opencv_ffmpeg320_64.dll”。我将这两个dll文件粘贴到Anaconda的“dll”文件夹。

蟒蛇也有一个“pckgs”文件夹…我复制并粘贴了整个“OpenCV 3.2”文件夹，我下载到Anaconda“pckgs”文件夹。

最后，Anaconda有一个“Library”文件夹，其中有一个“bin”子文件夹。我将“opencv_ffmpeg320.dll”和“opencv_ffmpeg320_64.dll”文件粘贴到该文件夹。

关闭并重新启动Spyder后，代码正常工作。我不确定这三种方法中哪一种有效，我也懒得回去研究。但它确实有效，干杯!

下面的脚本将提取帧每半秒的所有视频在文件夹。(适用于python 3.7)

import cv2
import os
listing = os.listdir(r'D:/Images/AllVideos')
count=1
for vid in listing:
    vid = r"D:/Images/AllVideos/"+vid
    vidcap = cv2.VideoCapture(vid)
    def getFrame(sec):
        vidcap.set(cv2.CAP_PROP_POS_MSEC,sec*1000)
        hasFrames,image = vidcap.read()
        if hasFrames:
            cv2.imwrite("D:/Images/Frames/image"+str(count)+".jpg", image) # Save frame as JPG file
        return hasFrames
    sec = 0
    frameRate = 0.5 # Change this number to 1 for each 1 second
    
    success = getFrame(sec)
    while success:
        count = count + 1
        sec = sec + frameRate
        sec = round(sec, 2)
        success = getFrame(sec)

这个函数以1帧/秒的速度从视频中提取图像，此外，它还识别最后一帧并停止读取:

import cv2
import numpy as np

def extract_image_one_fps(video_source_path):

    vidcap = cv2.VideoCapture(video_source_path)
    count = 0
    success = True
    while success:
      vidcap.set(cv2.CAP_PROP_POS_MSEC,(count*1000))      
      success,image = vidcap.read()

      ## Stop when last frame is identified
      image_last = cv2.imread("frame{}.png".format(count-1))
      if np.array_equal(image,image_last):
          break

      cv2.imwrite("frame%d.png" % count, image)     # save frame as PNG file
      print '{}.sec reading a new frame: {} '.format(count,success)
      count += 1