Python 获取视频中每一帧的时间戳
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/47743246/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Getting timestamp of each frame in a video
提问by machinery
I have recorded several videos from the front cam of my tablet with an Android 5.2 application I have written. I have stored the start timestamp in milliseconds (Unix time) for each video.
我已经使用我编写的 Android 5.2 应用程序从平板电脑的前置摄像头录制了几个视频。我以毫秒（Unix 时间）为单位存储了每个视频的开始时间戳。
Unfortunately each video has a different framerate (ranging from 20 to 30). With OpenCV I'm able to get the framerate for each video:
不幸的是，每个视频都有不同的帧率（从 20 到 30）。使用 OpenCV，我可以获得每个视频的帧率：
import cv2
video = cv2.VideoCapture(videoFile)
fps = video.get(cv2.CAP_PROP_FPS)
This works well and theoretically I could just add 1000/fps (due to milliseconds) for each frame in the video. But this assumes that the framerate remains stable throughout the whole recording. I don't know if this is the case.
这很有效，理论上我可以为视频中的每一帧添加 1000/fps（由于毫秒）。但这假设帧率在整个录制过程中保持稳定。我不知道是不是这种情况。
Is there a possibility in Python to get the timestamp (in milliseconds) of each frame in the video independent of the framerate?
Python 中是否有可能独立于帧率获取视频中每一帧的时间戳（以毫秒为单位）？
回答by alkasm
You want cv2.CAP_PROP_POS_MSEC. See all the different capture properties here.
你要cv2.CAP_PROP_POS_MSEC。在此处查看所有不同的捕获属性。
Edit: Actually, as Dan Ma?ekpointed out to me, when you grab that property, it looks like OpenCV is exactly doing that calculation(at least assuming you're using FFMPEG):
编辑：实际上，正如Dan Ma?ek向我指出的那样，当您获取该属性时，OpenCV 似乎正在执行该计算（至少假设您使用的是 FFMPEG）：
case CV_FFMPEG_CAP_PROP_POS_MSEC:
return 1000.0*(double)frame_number/get_fps();
So it seems you're always going to rely on a constant frame rate assumption. However, even assuming a constant frame rate, it's important that you multiply by the frame number and not just keep adding 1000/fps. Errors will build up when you're repeatedly adding floats which, over a long video, can make a big difference. For example:
所以看起来你总是会依赖一个恒定的帧率假设。但是，即使假设帧速率恒定，重要的是乘以帧数而不是继续添加1000/fps. 当您反复添加浮点数时，错误就会累积，这在长视频中会产生很大的不同。例如：
import cv2
cap = cv2.VideoCapture('vancouver2.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)
timestamps = [cap.get(cv2.CAP_PROP_POS_MSEC)]
calc_timestamps = [0.0]
while(cap.isOpened()):
frame_exists, curr_frame = cap.read()
if frame_exists:
timestamps.append(cap.get(cv2.CAP_PROP_POS_MSEC))
calc_timestamps.append(calc_timestamps[-1] + 1000/fps)
else:
break
cap.release()
for i, (ts, cts) in enumerate(zip(timestamps, calc_timestamps)):
print('Frame %d difference:'%i, abs(ts - cts))
Frame 0 difference: 0.0
Frame 1 difference: 0.0
Frame 2 difference: 0.0
Frame 3 difference: 1.4210854715202004e-14
Frame 4 difference: 0.011111111111091532
Frame 5 difference: 0.011111111111091532
Frame 6 difference: 0.011111111111091532
Frame 7 difference: 0.011111111111119953
Frame 8 difference: 0.022222222222183063
Frame 9 difference: 0.022222222222183063
...
Frame 294 difference: 0.8111111111411446
第0帧差：0.0
1点差：0.0
2帧差：0.0
3帧差：1.4210854715202004e-14
帧4的区别：0.011111111111091532
框架5的区别：0.011111111111091532
框架6的区别：0.011111111111091532
框架7的区别：0.011111111111119953
框架8差：0.022222222222183063
帧9差异：0.022222222222183063
...
294帧差异：0.8111111111411446
This is of course in milliseconds, so maybe it doesn't seem that big. But here I'm almost 1ms off in the calculation, and this is just for an 11-second video. And anyways, using this property is just easier.
这当然是以毫秒为单位的，所以也许它看起来没有那么大。但在这里我在计算中几乎关闭了 1 毫秒，这仅适用于 11 秒的视频。无论如何，使用这个属性更容易。
