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I need to split an RGBA image into an arbitrary number of boxes that are as equally sized as possible

I have attempted to use numpy.array_split, but am unsure of how to do so while preserving the RGBA channels

I have looked the following questions, none of them detail how to split an image into n boxes, they reference splitting the image into boxes of predetermined pixel size, or how to split the image into some shape.

While it seems that it would be some simple math to get number of boxes from box size and image size, I am unsure of how to do so.

How to Split Image Into Multiple Pieces in Python

Cutting one image into multiple images using the Python Image Library

Divide image into rectangles information in Python

While attempting to determine the number of boxes from pixel box size, I used the formula 

num_boxes = (img_size[0]*img_size[1])/ (box_size_x * box_size_y)

but that did not result in the image being split up properly

To clarify, I would like to be able to input an image that is a numpy array of size (a,b,4) and a number of boxes and output the images in some form (np array preferred, but whatever works)

I appreciate any help, even if you aren't able to provide the full method, I would appreciate some direction.

I have tried

def split_image(image, n_boxes): return numpy.array_split(image,n_boxes) #doesn't work with colors def split_image(image, n_boxes): box_size = factor_int(n_boxes) M = im.shape[0]//box_size[0] N = im.shape[1]//box_size[1] return [im[x:x+M,y:y+N] for x in range(0,im.shape[0],M) for y in range(0,im.shape[1],N)]

factor_int returns integer as close to a square as possible from Factor an integer to something as close to a square as possible

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    could you edit your question to provide any code you have tried? Commented Jul 5, 2019 at 9:49
  • 1x1 pixel boxes are equal size and guarantee to cover any rectangular image. Please state the real limitations you have for your boxes. Computing the box size is the hard part; cutting out sub-images by rectangle coordinates is trivial. Commented Jul 5, 2019 at 16:32
  • Just find whichever is larger of a and b and divide that by N. Commented Jul 5, 2019 at 16:46
  • @9000 I'm not sure what you mean by real limitations, I simply wish to cut an image into n boxes (n: 1→ num_pixels). Commented Jul 5, 2019 at 18:57
  • @MarkSetchell Are these 'a' and 'b' values the x and y sizes of the pixel box? Commented Jul 5, 2019 at 18:58

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I am still not sure if your inputs are actually the image and the dimensions of the boxes or the image and the number of boxes. Nor am I sure if your problem is deciding where to chop the image or knowing how to chop a 4-channel image, but maybe something in here will get you started.

I started with this RGBA image - the circles are transparent, not white:

enter image description here

#!/usr/bin/env python3 from PIL import Image import numpy as np import math # Open image and get dimensions im = Image.open('start.png').convert('RGBA') # Make Numpy array from image and get height and width ni = np.array(im) h ,w = ni.shape[:2] print(f'Height: {h}, width: {w}') BOXES = 4 for i in range(BOXES): this = ni[:, i*w//BOXES:(i+1)*w//BOXES, :] Image.fromarray(this).save(f'box-{i}.png')

You can change BOXES but leaving it at 4 gets you these 4 output images:

enter image description here [enter image description here] [enter image description here]4 enter image description here

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2 Comments

here is a version for two dimensions. columns = 3 rows = 4 for j in range(rows): for i in range(columns): elem = ni[ jw//columns:(j+1)*w//columns, ih//rows:(i+1)*h//rows,:] id=j*columns+i Image.fromarray(elem).save(f'img-{id}.png')
sorry for the post above I wanted to comment here

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