Background isolation from complementing images-CodePudding

I'm trying to isolate a background from multiple images that have something different between each other, that is overlapping the background. the images I have are individually listed here:

I wanted to do it in a sequence of images, as of I'm reading some video feed, and by getting the last frames I'm processing them to isolate the background, like this:

import os
import cv2

first = True
bwand = None
for filename in os.listdir('images'):
    curImage = cv2.imread('images/%s' % filename)
    if(first):
        first = False
        bwand = curImage
        continue
    bwand = cv2.bitwise_and(bwand,curImage)
cv2.imwrite("and.png",bwand)

From this code, I'm always incrementing my buffer with bitwise operations, but the results I get is not what I'm looking for: Bitwise and:

the way of concurrent adding to a buffer its the best approach for me in terms of video filtering and performance, but if I treat it like a list, I can look for the median value like so:

import os
import cv2
import numpy as np

sequence = []
for filename in os.listdir('images'):
    curImage = cv2.imread('images/%s' % filename)
    sequence.append(curImage)
imgs = np.asarray(sequence)
median = np.median(imgs, axis=0)
cv2.imwrite("res.png",median)

it results me:

Which is still not perfect, because I'm looking for the median value, if I would look for the mode value the performance would decrease significantly.

Is there an approach for obtaining the result that works as a buffer like the first alternative but outputs me the best result with good performance?

--Edit As suggested by @Christoph Rackwitz I used OpenCV background subtractor, it works as one of the requested features which is a buffer, but the result is not the most pleasant:

code:

import os
import cv2

mog = cv2.createBackgroundSubtractorMOG2()
for filename in os.listdir('images'):
    curImage = cv2.imread('images/%s' % filename)
    mog.apply(curImage)
x = mog.getBackgroundImage()
cv2.imwrite("res.png",x)

CodePudding user response：

Since scipy.stats.mode takes ages to do its thing, I did the same manually:

calculate histogram (for every channel of every pixel of every row of every image)
argmax gets mode
reshape and cast

Still not video speed but oh well. numba can probably speed this up.

filenames = ...
assert len(filenames) < 256, "need larger dtype for histogram"
stack = np.array([cv.imread(fname) for fname in filenames])
sheet = stack[0]

hist = np.zeros((sheet.size, 256), dtype=np.uint8)
index = np.arange(sheet.size)

for sheet in stack:
    hist[index, sheet.flat]  = 1

result = np.argmax(hist, axis=1).astype(np.uint8).reshape(sheet.shape)
del hist # because it's huge

cv.imshow("result", result); cv.waitKey()

And if I didn't use histograms and extensive amounts of memory, but a fixed number of sheets and data access that's cache-friendly, it could likely be even faster.