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

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.

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.

imageio supports this directly. It uses FFMPEG and the Video Acceleration API, making it very fast:

import imageio

writer = imageio.get_writer('video.avi', fps=fps)
for i in range(frames_per_second * video_duration_seconds):
    img = createFrame(i)
    writer.append_data(img)
writer.close()

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. Here's a basic python example:

# import libraries
from vidgear.gears import WriteGear
import cv2

output_params = {"-vcodec":"libx264", "-crf": 0, "-preset": "fast"} #define (Codec,CRF,preset) FFmpeg tweak parameters for writer

stream = cv2.VideoCapture(0) #Open live webcam video stream on first index(i.e. 0) device

writer = WriteGear(output_filename = 'Output.mp4', compression_mode = True, logging = True, **output_params) #Define writer with output filename 'Output.mp4' 

# infinite loop
while True:
	
	(grabbed, frame) = stream.read()
	# read frames

	# check if frame empty
	if not is grabbed:
		#if True break the infinite loop
		break
	

	# {do something with frame here}
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

	# write a modified frame to writer
    writer.write(gray) 
       
    # Show output window
	cv2.imshow("Output Frame", frame)

	key = cv2.waitKey(1) & 0xFF
	# check for 'q' key-press
	if key == ord("q"):
		#if 'q' key-pressed break out
		break

cv2.destroyAllWindows()
# close output window

stream.release()
# safely close video stream
writer.close()
# safely close writer

Source:https://abhitronix.github.io/vidgear/latest/gears/writegear/compression/usage/#using-compression-mode-with-opencv

You can check out VidGear Docs for more advanced applications and features.