在稍微不同的情况下扩展这个问题(@user2700065的回答)，如果有人不想提取每一帧，但想每一秒提取一帧。所以1分钟的视频会有60帧(图像)。

import sys
import argparse

import cv2
print(cv2.__version__)

def extractImages(pathIn, pathOut):
    count = 0
    vidcap = cv2.VideoCapture(pathIn)
    success,image = vidcap.read()
    success = True
    while success:
        vidcap.set(cv2.CAP_PROP_POS_MSEC,(count*1000))    # added this line 
        success,image = vidcap.read()
        print ('Read a new frame: ', success)
        cv2.imwrite( pathOut + "\\frame%d.jpg" % count, image)     # save frame as JPEG file
        count = count + 1

if __name__=="__main__":
    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)

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

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”

这是函数，它将大多数视频格式转换为视频中的帧数。它可以在Python3和OpenCV 3+上工作

import cv2
import time
import os

def video_to_frames(input_loc, output_loc):
    """Function to extract frames from input video file
    and save them as separate frames in an output directory.
    Args:
        input_loc: Input video file.
        output_loc: Output directory to save the frames.
    Returns:
        None
    """
    try:
        os.mkdir(output_loc)
    except OSError:
        pass
    # Log the time
    time_start = time.time()
    # Start capturing the feed
    cap = cv2.VideoCapture(input_loc)
    # Find the number of frames
    video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
    print ("Number of frames: ", video_length)
    count = 0
    print ("Converting video..\n")
    # Start converting the video
    while cap.isOpened():
        # Extract the frame
        ret, frame = cap.read()
        if not ret:
            continue
        # Write the results back to output location.
        cv2.imwrite(output_loc + "/%#05d.jpg" % (count+1), frame)
        count = count + 1
        # If there are no more frames left
        if (count > (video_length-1)):
            # Log the time again
            time_end = time.time()
            # Release the feed
            cap.release()
            # Print stats
            print ("Done extracting frames.\n%d frames extracted" % count)
            print ("It took %d seconds forconversion." % (time_end-time_start))
            break

if __name__=="__main__":

    input_loc = '/path/to/video/00009.MTS'
    output_loc = '/path/to/output/frames/'
    video_to_frames(input_loc, output_loc)

它支持。mts和普通文件，如。mp4和。avi。在.mts文件上尝试和测试。工作就像一个咒语。

我可能在这里迟到了，但你可以使用这个PIP包从视频中快速生成图像。你也可以使用特定的fps来获取图像。

pip安装videoToImages

然后在终端中输入以下命令

视频到图像 --视频文件夹 [路径到视频文件夹]

示例:videoToimages——videoofolder "c:/videos"

对于特定的输出FPS，将——FPS 10设置为任何所需的值。-fps 1表示视频中每秒钟一个图像。

完整的命令:

videoToimages—视频文件夹“c:/videos” videoToimages——videoofolder "c:/videos"——fps 10——img_size (512, 512)

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}
"""
)