As the results vary depending on the situation, I encourage you to check out the graphs in the repo for the one that best matches your usage case.įor this article, I present just the medium quality video (720x480). #Python ffmpeg images to video software#“ CPU limited” - this corresponds to a very typical use case in computer vision, image processing and machine learning pipelines, whereby your software is using the CPU between reading each frame- I emulated this I added a blocking call doing a fixed number of multiplications between each video read.Īnd for each usage case, I benchmarked the speed on a low-resolution (480x270) video, medium-resolution (720x480) video and a high-resolution (1920x1080) video.“ IO limited” - this corresponds to when you want your software to do something else but it’s not CPU heavy, and I emulated this by adding a blocking call doing a simple time.sleep().“ Unblocked” - this means that your software can dedicate all its resources to reading the video, and is not trying to do anything at the same time.I broke this down into three usage cases: This matters because an efficient video reading library will start preparing the next frame(s) in the background, and so what your software is doing can have a big effect on this background process. Whether your software can do useful work at the same time as reading the video - and then is that work CPU heavy or just takes time (I/O limited).How big your video is - the reason for this is that it affects how much CPU usage is needed to decode the compressed video, but also how much data needs to be read from the disk.It turns out the answer depends heavily on a lot of factors. #Python ffmpeg images to video full#imutils is a lightweight wrapper over the OpenCV video reader class that multi-threads it.īut which is the fastest? I did some experiments to find out, and you can find the source code and graphs in full here. OpenCV reads videos essentially as a wrapper of ffmpeg but if you want to have control over the underlying ffmpeg commands being used to read the video, then you can use libraries like PyAV or ffmpeg-python. For a full-featured video processing framework designed with efficiency in mind, you can check out VidGears (which has a library called CamGears for video reading). If you need frame-accurate seeking then a library like Decord will be super helpful. The good news is there is a large selection of open source libraries that will solve these problems. see this open bug ticket if you think the cap.set() method can be used safely for setting the frame number, be warned that it cannot). Source: admin of OpenCV Project Alexander AlekhinĪnd as a result of not being designed for video manipulation, it does not, and maybe will never have features such as the ability to accurately seek to a precise frame (i.e. Video I/O and Image I/O things added into OpenCV for demo purposes initially. main purpose of OpenCV library is not highly correlated with goals of Media I/O library. This one may surprise you (it certainly did me!) but the developers only added the ability to read videos like this for “demo purposes” initially
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