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。
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
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!
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。
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
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
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)
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!
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}
"""
)
