python - Can you "stream" images to ffmpeg to construct a video, instead of saving them to disk?

Question

My work recently involves programmatically making videos. In python, the typical workflow looks something like this:

import subprocess, Image, ImageDraw

for i in range(frames_per_second * video_duration_seconds):
    img = createFrame(i)
    img.save("%07d.png" % i)

subprocess.call(["ffmpeg","-y","-r",str(frames_per_second),"-i", "%07d.png","-vcodec","mpeg4", "-qscale","5", "-r", str(frames_per_second), "video.avi"])

This workflow creates an image for each frame in the video and saves it to disk. After all images have been saved, ffmpeg is called to construct a video from all of the images.

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. 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.

Answer 1

Ok I got it working. thanks to LordNeckbeard suggestion to use image2pipe. I had to use jpg encoding instead of png because image2pipe with png doesn't work on my verision of ffmpeg. 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 also added some code to time the execution.

serial execution

import subprocess, Image

fps, duration = 24, 100
for i in range(fps * duration):
    im = Image.new("RGB", (300, 300), (i, 1, 1))
    im.save("%07d.jpg" % i)
subprocess.call(["ffmpeg","-y","-r",str(fps),"-i", "%07d.jpg","-vcodec","mpeg4", "-qscale","5", "-r", str(fps), "video.avi"])

parallel execution (with no images saved to disk)

import Image
from subprocess import Popen, PIPE

fps, duration = 24, 100
p = Popen(['ffmpeg', '-y', '-f', 'image2pipe', '-vcodec', 'mjpeg', '-r', '24', '-i', '-', '-vcodec', 'mpeg4', '-qscale', '5', '-r', '24', 'video.avi'], stdin=PIPE)
for i in range(fps * duration):
    im = Image.new("RGB", (300, 300), (i, 1, 1))
    im.save(p.stdin, 'JPEG')
p.stdin.close()
p.wait()

the results are interesting, I ran each script 3 times to compare performance: serial:

12.9062321186
12.8965060711
12.9360799789

parallel:

8.67797684669
8.57139396667
8.38926696777

So it seems the parallel version is faster about 1.5 times faster.