所以我遵循这个教程，但它似乎没有做任何事情。只是没有。它等待几秒钟并关闭程序。这段代码有什么问题?
import cv2
vidcap = cv2.VideoCapture('Compton.mp4')
success,image = vidcap.read()
count = 0
success = True
while success:
success,image = vidcap.read()
cv2.imwrite("frame%d.jpg" % count, image) # save frame as JPEG file
if cv2.waitKey(10) == 27: # exit if Escape is hit
break
count += 1
此外，在评论中它说这限制帧到1000?为什么?
编辑: 我试着先做success = True，但没有帮助。它只创建了一个0字节的图像。
当前回答
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
其他回答
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
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)
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
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}
"""
)
