You can check out VidGear Docs for more advanced applications and features. Key = cv2.waitKey( 1) & 0xFF # check for 'q' key-press if key = ord( "q"):
Gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) Here's a basic python example: # import libraries from vidgear.gears import WriteGear I'm Kind of late, But VidGear Python Library's WriteGear API automates the process of pipelining OpenCV frames into FFmpeg on any platform in real-time with Hardware Encoders support and at the same time provides same opencv-python syntax. Writer = imageio.get_writer( 'video.avi', fps=fps) It uses FFMPEG and the Video Acceleration API, making it very fast: import imageio So it seems the parallel version is faster about 1.5 times faster. The results are interesting, I ran each script 3 times to compare performance: Parallel execution (with no images saved to disk) import Image
Ffmpeg python images to video serial#
Serial execution import subprocess, Imageįps, duration = 24, 100 for i in range(fps * duration):
Ffmpeg python images to video code#
I also added some code to time the execution. The first script is essentially the same as your question's code except I implemented a simple image creation that just creates images going from black to red. I had to use jpg encoding instead of png because image2pipe with png doesn't work on my verision of ffmpeg. thanks to LordNeckbeard suggestion to use image2pipe. Tags : python,image,stream,ffmpeg,python-imaging-library Is there some way to perform the same function, but without saving the images to disk? So, ffmpeg would be called and the images would be constructed and fed to ffmpeg immediately after being constructed. Saving the images to disk (not the creation of the images in memory) consumes the majority of the cycles here, and does not appear to be necessary. outputdictoutputparameters) iterate through the frames accumulation 0. Better alternatives: If we want more effects, sound with the video then it will be good to use the ffmpeg python library which. After all images have been saved, ffmpeg is called to construct a video from all of the images. skvideo.io is a module created for using a FFmpeg/LibAV backend to read and. video cv2.VideoWriter (videoname, 0, 1, (width, height)) for image in images: cv2.destroyAllWindows () video.release () generatevideo () Output: Get the link to the input images used and output video from here.
This workflow creates an image for each frame in the video and saves it to disk. In python, the typical workflow looks something like this: import subprocess, Image, ImageDrawįor i in range(frames_per_second * video_duration_seconds): My work recently involves programmatically making videos.
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