Smooth timelapses by averaging frames. Full notes below.

avg.py

#Averages a number of frames from your timelapse to create a smoother output.
#The number of frames you might want to average depends on the issues you're running into, but
#if you average too many it will make the world look like it's melting, and its slow.
#My timelapses tend to be multiday, and have long shooting intervals(5-20 minutes), so 6 frames 
#averaged covers the day/night and night/day transition pretty well. 6-24 might be a reasonable range,
#but I would test ~10 seconds of frames at different averages, maybe try 6, 12, & 24.



#Place in a folder of JPGs that will sort correctly with a natsort,
#set the variable "average_number" and this runs multithreaded, so
#change "max_threads" to the number of threads you want. 

#(I have some stats from my computer at the bottom, for me 4 threads
#was almost the fastest, and only consumes 4 threads. I have a reasonably 
#nice computer as of 2023, but YMMV


import os
from natsort import natsorted
from PIL import Image
import numpy as np
import concurrent.futures


average_number=6

max_threads=4

# Get a list of all JPG files in the folder
jpg_files = [f for f in os.listdir('.') if f.endswith('.jpg')]

# Natsort the list
jpg_files = natsorted(jpg_files)

#print(jpg_files)

def combine_images(i):
    jpgs_to_combine = jpg_files[i:i+average_number]

    images = [Image.open(jpg) for jpg in jpgs_to_combine]

    # Convert images to numpy arrays
    np_images = [np.array(img) for img in images]

    # Calculate the average
    average_image = np.mean(np_images, axis=0)

    # Convert back to PIL Image and save
    Image.fromarray(np.uint8(average_image)).save(f'OUT_{average_number}_{i:05}.jpg')
    
    print(f'Saving: OUT_{average_number}_{i:05}.jpg')


with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor:
    executor.map(combine_images, range(len(jpg_files) - average_number - 1))

"""
Was going to try all the way from 1 - 16 (cpu has 8 cores / 16 threads), but pretty clear 4 is the sweet spot for my machine.

Time: 170.86919450759888s       Workers: 1
Time: 107.62791514396667s       Workers: 2
Time: 92.45315217971802s        Workers: 3
Time: 85.73750138282776s        Workers: 4
Time: 82.29942393302917s        Workers: 5
Time: 88.5809121131897s         Workers: 6
Time: 86.28025555610657s        Workers: 7
Time: 83.46813654899597s        Workers: 8
Time: 87.20684361457825s        Workers: 9
"""