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getting RGB data from pixels on an image

So, to begin I'm starting a project with python that allows for it to grab each individual pixel from an image, and then print the color. However I am trying to begin grabbing each pixel by finding the y and then adding 1 onto x until we hit all the pixels on the y axis.
# Importing Image from PIL package
from PIL import Image
# creating a image object
filename = "pixels.png"
im = Image.open(filename)
#finding the size of the image
fs = im.size
fs_x = fs[0]
fs_y = fs[1]
print(fs_x, fs_y)
#converting the color to RGB
rgb_im = im.convert('RGB')
any = rgb_im.getpixel((25, 25))
print(any)
x_start = 0
if x_start != fs_x:
x_start = ++x_start
print(x_start)

You have to learn loops - for and while - because you can't do it with if.
from PIL import Image
filename = "pixels.png"
img = Image.open(filename)
width, height = img.size
print('width :', width)
print('height:', height)
rgb_img = img.convert('RGB')
for y in range(height):
for x in range(width):
pixel = rgb_img.getpixel((x, y))
#print(pixel)
r, g, b = pixel
print(f'| R: {r:3} | G: {g:3} | B: {b:3} |')

How to split a Numpy Array (image) into tiles to iterate over? [duplicate]

This question already has an answer here:
How do you divide an image/array into blocks?
(1 answer)
Closed 2 years ago.
I have a Numpy Array type image which I would like to split into 9 (3 x 3) even tiles which I can iterate over. How can I do this?
Here is my code so far to generate the numpy.ndarray but I haven't managed to split it:
The numpy image array to split is th1
import cv2
import numpy as np
# Only for the threshold display
from matplotlib import pyplot as plt
# The Image to be used
image = 'six.png'
# Finding the average greyscale value
image_bgr = cv2.imread(image, cv2.IMREAD_COLOR)
# Calculate the mean of each channel
channels = cv2.mean(image_bgr)
# Type Float
thresh = channels[0]/2
#print (thresh)
# Displaying the threshold value
img = cv2.imread(image,0)
img = cv2.medianBlur(img,5)
# If below then black else white
ret,th1 = cv2.threshold(img,thresh,255,cv2.THRESH_BINARY)
titles = ['Original Image', 'Global Thresholding']
images = [img, th1, ret]
for i in range(2):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
# Shows single image on its' own
plt.imshow(images[1], 'gray')
plt.xticks([]),plt.yticks([])
Here is the image (six.png):

What I would do is create a list or a 2-dimensional array in which I'd save the "tiles" of the full image. The tiles would be copied using the code below:
height, width, dim = img.shape
image_tile = img[0:height/3, 0:width/3]
It creates a new ndarray image_tile that contains the part of the full image array that's inside of the coordinates. In this case it is the top left tile.
The whole example with a for loop would look something like this:
image_tile = []
for i in range(0,3):
for j in range(0,3):
image_tile.append(img[i * height/3:(i+1) * height/3, j * width/3:(j+1) * width/3])
I hope this helps and good luck!

I have found a solution adapted from elsewhere and it works great!
img = th1
numrows, numcols = 3, 3
height = int(img.shape[0] / numrows)
width = int(img.shape[1] / numcols)
for row in range(numrows):
for col in range(numcols):
y0 = row * height
y1 = y0 + height
x0 = col * width
x1 = x0 + width
individual = (img[y0:y1, x0:x1])
plt.imshow(individual, 'gray')
plt.xticks([]),plt.yticks([])
plt.show()

Creating image tiles (m*n) of original image using Python and Numpy

I am using numpy to create tiles of (224*224) from my 16-bit tiff image (13777*16004). I was able to crop/slice into equal tiles of 224*224 along the rows and columns. I ran into problems while trying to create new tiles shifting by half of the tile size... For instance: A rough algorithm of what i am trying to achieve
(1:224, 1:224)
(1:224, 112:336)
( , 224:448)
The goal is to retain tile size (224*224) while shifting by half of tile size to obtain more image tiles...
Snippet of code written to perform task
row_x = img.shape[0]
column_y = img.shape[1]
tile_size_x = 224
tile_size_y = 224
range_x = mpz(ceil(row_x/tile_size_x))
range_y = mpz(ceil(column_y/tile_size_y))
for x in range(range_x, row_x):
for y in range(range_y, column_y):
x0 = x * tile_size_x
x1 = int(x0/2) + tile_size_x
y0 = y * tile_size_y
y1 = int(y0/2) + tile_size_y
z = img[x0:x1, y0:y1]
print (z.shape,z.dtype)
I keep getting wrong results, can anyone help ???

You went a little off while calculating the range of your for loop. The number of slices to be made, must be calculated using the offset between two slices, which is x0/2 in your case, I have simplified your code and defined some meaningful variables which you can configure to get desired tiles from a given image:
import cv2
import math
img = cv2.imread("/path/to/lena.png") # 512x512
img_shape = img.shape
tile_size = (256, 256)
offset = (256, 256)
for i in xrange(int(math.ceil(img_shape[0]/(offset[1] * 1.0)))):
for j in xrange(int(math.ceil(img_shape[1]/(offset[0] * 1.0)))):
cropped_img = img[offset[1]*i:min(offset[1]*i+tile_size[1], img_shape[0]), offset[0]*j:min(offset[0]*j+tile_size[0], img_shape[1])]
# Debugging the tiles
cv2.imwrite("debug_" + str(i) + "_" + str(j) + ".png", cropped_img)
As current offset if exact multiple of image dimensions, which is 512x512, hence we will get 4 tiles of same size:
Changing the value of offset, would get you tiles of irregular size, if the offset if not exact multiple of the image dimensions, you may later filter those tiles if not required by changing the math.ceil to math.floor in the for loop.

You can use as_strided for this pretty efficiently I think.
def window_nd(a, window, steps = None):
ashp = np.array(a.shape)
wshp = np.array(window).reshape(-1)
if steps:
stp = np.array(steps).reshape(-1)
else:
stp = np.ones_like(ashp)
astr = np.array(a.strides)
assert np.all(np.r_[ashp.size == wshp.size, wshp.size == stp.size, wshp <= ashp])
shape = tuple((ashp - wshp) // stp + 1) + tuple(wshp)
strides = tuple(astr * stp) + tuple(astr)
as_strided = np.lib.stride_tricks.as_strided
aview = as_strided(a, shape = shape, strides = strides)
return aview
EDIT: Generalizing the striding method as much as I can.
For your specific question:
aview = window_nd(a, (288, 288), (144, 144))
z = aview.copy().reshape(-1, wx, wy) #to match expected output
print(z.shape, z.dtype) # z.shape should be (num_patches, 288, 288)

I think you can use this
def TileImage(image,rows,cols):
imagename = image
im = Image.open(imagename)
width, height = im.size
indexrow = 0
indexcolum = 0
left = 0
top = 0
right = width/col
buttom = 0
while(right<=width):
buttom = height/rows
top = 0
indexrow=0
while(top<height):
print(f"h : {height}, w : {width}, left : {left},top : {top},right : {right}, buttom : {buttom}")
cropimg= im.crop((left, top, right, buttom))
cropimg.save(imagename + str(indexrow) + str(indexcolum) +".jpg")
top = buttom
indexrow += 1
buttom += height/rows
indexcolum+=1
left = right
right += width/col

If you do not mind using ImageMagick, then it is trivial using the -crop command. See https://imagemagick.org/Usage/crop/#crop_tile
You can call imagemagick using Python subprocess call.
Input:
Lets say you want 4 256x256 tiles for simplicity.
convert lena512.png -crop 256x256 lena512_%d.png
or by percent
convert lena512.png -crop 50x50% lena512_%d.png

I prefer to calculate the number of tiles beforehand and then use a simple reshape. For example
tile = 512
img_height = img.shape[1]
img_width = img.shape[0]
number_of_vertical_tiles = img_height // tile
number_of_horizontal_tiles = img_width // tile
cropped_img = img[:tile*number_of_vertical_tiles, :tile*number_of_horizontal_tiles,:]
tiled_img = img.reshape(-1, tile, tile, 3)

I was not allowed to comment under the top answer from #ZdaR due to my lag of reputation. However, the code was so on point for my use case, I wanted to provide necessary changes for Python3 and Color-Channels from cv2. Thank you, #ZdaR.
This is his code adapted for Python3 with list(range()) instead of xrange() and cv2.COLOR_BGR2RGB when reading and writing the picture. Somehow cv2 uses the channels the other way around.
img = cv2.cvtColor(cv2.imread("path/to/lena.png"),cv2.COLOR_BGR2RGB)
img_shape = img.shape
tile_size = (640, 640)
offset = (640, 640)
for i in list(range(int(math.ceil(img_shape[0]/(offset[1] * 1.0))))):
for j in list(range(int(math.ceil(img_shape[1]/(offset[0] * 1.0))))):
cropped_img = img[offset[1]*i:min(offset[1]*i+tile_size[1], img_shape[0]), offset[0]*j:min(offset[0]*j+tile_size[0], img_shape[1])]
# Debugging the tiles
cv2.imwrite("debug_" + str(i) + "_" + str(j) + ".png", cv2.cvtColor(cropped_img,cv2.COLOR_BGR2RGB))

How do I create an image in PIL using a list of RGB tuples?

Suppose I have a list of pixels (represented as tuples with 3 RGB values) in a list that looks like list(im.getdata()), like this:
[(0,0,0),(255,255,255),(38,29,58)...]
How do I create a new image using RGB values (each tuple corresponds to a pixel) in this format?
Thanks for your help.

You can do it like this:
list_of_pixels = list(im.getdata())
# Do something to the pixels...
im2 = Image.new(im.mode, im.size)
im2.putdata(list_of_pixels)

You can also use scipy for that:
#!/usr/bin/env python
import scipy.misc
import numpy as np
# Image size
width = 640
height = 480
channels = 3
# Create an empty image
img = np.zeros((height, width, channels), dtype=np.uint8)
# Draw something (http://stackoverflow.com/a/10032271/562769)
xx, yy = np.mgrid[:height, :width]
circle = (xx - 100) ** 2 + (yy - 100) ** 2
# Set the RGB values
for y in range(img.shape[0]):
for x in range(img.shape[1]):
r, g, b = circle[y][x], circle[y][x], circle[y][x]
img[y][x][0] = r
img[y][x][1] = g
img[y][x][2] = b
# Display the image
scipy.misc.imshow(img)
# Save the image
scipy.misc.imsave("image.png", img)
gives

Here's a complete example since I didn't get the trick at first.
from PIL import Image
img = Image.new('RGB', [500,500], 255)
data = img.load()
for x in range(img.size[0]):
for y in range(img.size[1]):
data[x,y] = (
x % 255,
y % 255,
(x**2-y**2) % 255,
)
img.save('image.png')
And if you're looking for grayscale only, you can do Image.new('L', [500,500], 255) and then data[x,y] = <your value between 0 and 255>

How to Split Image Into Multiple Pieces in Python

I'm trying to split a photo into multiple pieces using PIL.
def crop(Path,input,height,width,i,k,x,y,page):
im = Image.open(input)
imgwidth = im.size[0]
imgheight = im.size[1]
for i in range(0,imgheight-height/2,height-2):
print i
for j in range(0,imgwidth-width/2,width-2):
print j
box = (j, i, j+width, i+height)
a = im.crop(box)
a.save(os.path.join(Path,"PNG","%s" % page,"IMG-%s.png" % k))
k +=1
but it doesn't seem to be working. It splits the photo but not in an exact way (you can try it).

Splitting image to tiles of MxN pixels (assuming im is numpy.ndarray):
tiles = [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)]
In the case you want to split the image to four pieces:
M = im.shape[0]//2
N = im.shape[1]//2
tiles[0] holds the upper left tile

Edit: I believe this answer missed the intent to cut an image into rectangles in columns and rows. This answer cuts only into rows. It looks like other answers cut in columns and rows.
Simpler than all these is to use a wheel someone else invented :) It may be more involved to set up, but then it's a snap to use.
These instructions are for Windows 7; they may need to be adapted for other OSs.
Get and install pip from here.
Download the install archive, and extract it to your root Python installation directory. Open a console and type (if I recall correctly):
python get-pip.py install
Then get and install the image_slicer module via pip, by entering the following command at the console:
python -m pip install image_slicer
Copy the image you want to slice into the Python root directory, open a python shell (not the "command line"), and enter these commands:
import image_slicer
image_slicer.slice('huge_test_image.png', 14)
The beauty of this module is that it
Is installed in python
Can invoke an image split with two lines of code
Accepts any even number as an image slice parameter (e.g. 14 in this example)
Takes that parameter and automagically splits the given image into so many slices, and auto-saves the resultant numbered tiles in the same directory, and finally
Has a function to stitch the image tiles back together (which I haven't yet tested); files apparently must be named after the convention which you will see in the split files after testing the image_slicer.slice function.

from PIL import Image
def crop(path, input, height, width, k, page, area):
im = Image.open(input)
imgwidth, imgheight = im.size
for i in range(0,imgheight,height):
for j in range(0,imgwidth,width):
box = (j, i, j+width, i+height)
a = im.crop(box)
try:
o = a.crop(area)
o.save(os.path.join(path,"PNG","%s" % page,"IMG-%s.png" % k))
except:
pass
k +=1

As an alternative solution, we will construct the tiles by generating a grid of coordinates using itertools.product. We will ignore partial tiles on the edges, only iterating through the cartesian product between the two intervals, i.e. range(0, h-h%d, d) X range(0, w-w%d, d).
Given filename: the image file name, d: the tile size, dir_in: the path to the directory containing the image, and dir_out: the directory where tiles will be outputted:
from PIL import Image
from itertools import product
def tile(filename, dir_in, dir_out, d):
name, ext = os.path.splitext(filename)
img = Image.open(os.path.join(dir_in, filename))
w, h = img.size
grid = product(range(0, h-h%d, d), range(0, w-w%d, d))
for i, j in grid:
box = (j, i, j+d, i+d)
out = os.path.join(dir_out, f'{name}_{i}_{j}{ext}')
img.crop(box).save(out)

crop would be a more reusable
function if you separate the
cropping code from the
image saving
code. It would also make the call
signature simpler.
im.crop returns a
Image._ImageCrop instance. Such
instances do not have a save method.
Instead, you must paste the
Image._ImageCrop instance onto a
new Image.Image
Your ranges do not have the right
step sizes. (Why height-2 and not
height? for example. Why stop at
imgheight-(height/2)?).
So, you might try instead something like this:
import Image
import os
def crop(infile,height,width):
im = Image.open(infile)
imgwidth, imgheight = im.size
for i in range(imgheight//height):
for j in range(imgwidth//width):
box = (j*width, i*height, (j+1)*width, (i+1)*height)
yield im.crop(box)
if __name__=='__main__':
infile=...
height=...
width=...
start_num=...
for k,piece in enumerate(crop(infile,height,width),start_num):
img=Image.new('RGB', (height,width), 255)
img.paste(piece)
path=os.path.join('/tmp',"IMG-%s.png" % k)
img.save(path)

Here is a concise, pure-python solution that works in both python 3 and 2:
from PIL import Image
infile = '20190206-135938.1273.Easy8thRunnersHopefully.jpg'
chopsize = 300
img = Image.open(infile)
width, height = img.size
# Save Chops of original image
for x0 in range(0, width, chopsize):
for y0 in range(0, height, chopsize):
box = (x0, y0,
x0+chopsize if x0+chopsize < width else width - 1,
y0+chopsize if y0+chopsize < height else height - 1)
print('%s %s' % (infile, box))
img.crop(box).save('zchop.%s.x%03d.y%03d.jpg' % (infile.replace('.jpg',''), x0, y0))
Notes:
The crops that go over the right and bottom of the original image are adjusted to the original image limit and contain only the original pixels.
It's easy to choose a different chopsize for w and h by using two chopsize vars and replacing chopsize as appropriate in the code above.

Not sure if this is the most efficient answer, but it works for me:
import os
import glob
from PIL import Image
Image.MAX_IMAGE_PIXELS = None # to avoid image size warning
imgdir = "/path/to/image/folder"
# if you want file of a specific extension (.png):
filelist = [f for f in glob.glob(imgdir + "**/*.png", recursive=True)]
savedir = "/path/to/image/folder/output"
start_pos = start_x, start_y = (0, 0)
cropped_image_size = w, h = (500, 500)
for file in filelist:
img = Image.open(file)
width, height = img.size
frame_num = 1
for col_i in range(0, width, w):
for row_i in range(0, height, h):
crop = img.crop((col_i, row_i, col_i + w, row_i + h))
name = os.path.basename(file)
name = os.path.splitext(name)[0]
save_to= os.path.join(savedir, name+"_{:03}.png")
crop.save(save_to.format(frame_num))
frame_num += 1
This is mostly based on DataScienceGuy answer here

Here is a late answer that works with Python 3
from PIL import Image
import os
def imgcrop(input, xPieces, yPieces):
filename, file_extension = os.path.splitext(input)
im = Image.open(input)
imgwidth, imgheight = im.size
height = imgheight // yPieces
width = imgwidth // xPieces
for i in range(0, yPieces):
for j in range(0, xPieces):
box = (j * width, i * height, (j + 1) * width, (i + 1) * height)
a = im.crop(box)
try:
a.save("images/" + filename + "-" + str(i) + "-" + str(j) + file_extension)
except:
pass
Usage:
imgcrop("images/testing.jpg", 5, 5)
Then the images will be cropped into pieces according to the specified X and Y pieces, in my case 5 x 5 = 25 pieces

Here is another solution, just using NumPy built-in np.array_split :
def divide_img_blocks(img, n_blocks=(5, 5)):
horizontal = np.array_split(img, n_blocks[0])
splitted_img = [np.array_split(block, n_blocks[1], axis=1) for block in horizontal]
return np.asarray(splitted_img, dtype=np.ndarray).reshape(n_blocks)
It returns a NumPy array with the dimension passed as n_blocks.
Each element of the array is a block, so to access each block and save it as an image you should write something like the following:
result = divide_img_blocks(my_image)
for i in range(result.shape[0]):
for j in range(result.shape[1]):
cv2.imwrite(f"my_block_{i}_{j}.jpg", result[i,j])
This answer is very fast, faster than #Nir answer, which among the posted ones was the cleanest. Additionally is almost three orders of magnitude faster than the suggested package (i.e. image_slicer).
Time taken by divide_img_blocks: 0.0009832382202148438
Time taken by Nir answer: 0.002960681915283203
Time taken by image_slicer.slice: 0.4419238567352295
Hope it can still be useful.

I find it easier to skimage.util.view_as_windows or `skimage.util.view_as_blocks which also allows you to configure the step
http://scikit-image.org/docs/dev/api/skimage.util.html?highlight=view_as_windows#skimage.util.view_as_windows

import os
import sys
from PIL import Image
savedir = r"E:\new_mission _data\test"
filename = r"E:\new_mission _data\test\testing1.png"
img = Image.open(filename)
width, height = img.size
start_pos = start_x, start_y = (0, 0)
cropped_image_size = w, h = (1024,1024)
frame_num = 1
for col_i in range(0, width, w):
for row_i in range(0, height, h):
crop = img.crop((col_i, row_i, col_i + w, row_i + h))
save_to= os.path.join(savedir, "testing_{:02}.png")
crop.save(save_to.format(frame_num))
frame_num += 1

For anyone looking for a simple approach to this, here is a simple working function for splitting an image into NxN sections.
def slice_image(filename, N):
i = Image.open(filename)
width = i.width
height = i.height
for x in range(N):
for y in range(N):
index = (x * pieces) + 1 + y
img = i.crop((x * width/N, y * height/N,
x * width/N+ width/N, y * height/N+ height/N))
img.save(f"{filename}_sliced_{index}.jpeg")

Thanks #Ivan for teaching me something about itertools and grids. Came here to split up tomographic 3D image data (tif-files) into smaller regions for evaluation. I adapted the script to 3D-TIF files (using the tiffile library) and added a "centered" approach. So the tiles don't start in the upper-left corner but are centered and crop too small tiles at the borders at each direction. Maybe this also help other people.
from itertools import product
import tifffile as tif
import numpy as np
path = 'PATH'
filename= 'FILENAME.tif'
img = tif.imread(path+filename)
depth, height, width = img.shape
tilesize = 100
grid = product(range(int((depth%tilesize)/2), int(depth-(depth%tilesize)/2), tilesize),
range(int((width%tilesize)/2), int(width-((width%tilesize)/2)), tilesize),
range(int((height%tilesize)/2), int(height-(height%tilesize)/2), tilesize))
for z,y,x in grid:
crop = img[z:z+tilesize, y:y+tilesize, x:x+tilesize]
tif.imwrite(path+filename+f'{z:04d}z_{y:04d}y_{x:04d}x.tif', crop, dtype = np.uint8)

This is my script tools, it is very sample to splite css-sprit image into icons:
Usage: split_icons.py img dst_path width height
Example: python split_icons.py icon-48.png gtliu 48 48
Save code into split_icons.py :
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import os
import sys
import glob
from PIL import Image
def Usage():
print '%s img dst_path width height' % (sys.argv[0])
sys.exit(1)
if len(sys.argv) != 5:
Usage()
src_img = sys.argv[1]
dst_path = sys.argv[2]
if not os.path.exists(sys.argv[2]) or not os.path.isfile(sys.argv[1]):
print 'Not exists', sys.argv[2], sys.argv[1]
sys.exit(1)
w, h = int(sys.argv[3]), int(sys.argv[4])
im = Image.open(src_img)
im_w, im_h = im.size
print 'Image width:%d height:%d will split into (%d %d) ' % (im_w, im_h, w, h)
w_num, h_num = int(im_w/w), int(im_h/h)
for wi in range(0, w_num):
for hi in range(0, h_num):
box = (wi*w, hi*h, (wi+1)*w, (hi+1)*h)
piece = im.crop(box)
tmp_img = Image.new('L', (w, h), 255)
tmp_img.paste(piece)
img_path = os.path.join(dst_path, "%d_%d.png" % (wi, hi))
tmp_img.save(img_path)

I tried the solutions above, but sometimes you just gotta do it yourself.
Might be off by a pixel in some cases but works fine in general.
import matplotlib.pyplot as plt
import numpy as np
def image_to_tiles(im, number_of_tiles = 4, plot=False):
"""
Function that splits SINGLE channel images into tiles
:param im: image: single channel image (NxN matrix)
:param number_of_tiles: squared number
:param plot:
:return tiles:
"""
n_slices = np.sqrt(number_of_tiles)
assert int(n_slices + 0.5) ** 2 == number_of_tiles, "Number of tiles is not a perfect square"
n_slices = n_slices.astype(np.int)
[w, h] = cropped_npy.shape
r = np.linspace(0, w, n_slices+1)
r_tuples = [(np.int(r[i]), np.int(r[i+1])) for i in range(0, len(r)-1)]
q = np.linspace(0, h, n_slices+1)
q_tuples = [(np.int(q[i]), np.int(q[i+1])) for i in range(0, len(q)-1)]
tiles = []
for row in range(n_slices):
for column in range(n_slices):
[x1, y1, x2, y2] = *r_tuples[row], *q_tuples[column]
tiles.append(im[x1:y1, x2:y2])
if plot:
fig, axes = plt.subplots(n_slices, n_slices, figsize=(10,10))
c = 0
for row in range(n_slices):
for column in range(n_slices):
axes[row,column].imshow(tiles[c])
axes[row,column].axis('off')
c+=1
return tiles
Hope it helps.

I would suggest to use multiprocessing instead of a regular for loop as follows:
from PIL import Image
import os
def crop(infile,height,width):
im = Image.open(infile)
imgwidth, imgheight = im.size
for i in range(imgheight//height):
for j in range(imgwidth//width):
box = (j*width, i*height, (j+1)*width, (i+1)*height)
yield im.crop(box)
def til_image(infile):
infile=...
height=...
width=...
start_num=...
for k,piece in enumerate(crop(infile,height,width),start_num):
img=Image.new('RGB', (height,width), 255)
img.paste(piece)
path=os.path.join('/tmp',"IMG-%s.png" % k)
img.save(path)
from multiprocessing import Pool, cpu_count
try:
pool = Pool(cpu_count())
pool.imap_unordered(tile_image, os.listdir(root), chunksize=4)
finally:
pool.close()

the easiest way:
import image_slicer
image_slicer.slice('/Address of image for exp/A1.png',16)
this command splits the image into 16 slices and saves them in the directory that the input image is there.
you should first install image_slicer:
pip install image_slicer

Splitting an image into squares of a specific size
I adapted a solution so that it accepts a specific tile size instead of an amount of tiles because I needed to cut the image up into a grid of 32px squares.
The parameters are the image_path and the size of the tiles in pixels.
I tried to make the code as readable as possible.
# Imports
from PIL import Image
import os
import random
# Function
def image_to_tiles(im, tile_size = 32):
"""
Function that splits an image into tiles
:param im: image: image path
:param tile_size: width in pixels of a tile
:return tiles:
"""
image = Image.open(im)
w = image.width
h = image.height
row_count = np.int64((h-h%tile_size)/tile_size)
col_count = np.int64((w-w%tile_size)/tile_size)
n_slices = np.int64(row_count*col_count)
# Image info
print(f'Image: {im}')
print(f'Dimensions: w:{w} h:{h}')
print(f'Tile count: {n_slices}')
r = np.linspace(0, w, row_count+1)
r_tuples = [(np.int64(r[i]), np.int64(r[i])+tile_size) for i in range(0, len(r)-1)]
q = np.linspace(0, h, col_count+1)
q_tuples = [(np.int64(q[i]), np.int64(q[i])+tile_size) for i in range(0, len(q)-1)]
#print(f'r_tuples:{r_tuples}\n\nq_tuples:{q_tuples}\n')
tiles = []
for row in range(row_count):
for column in range(col_count):
[y1, y2, x1, x2] = *r_tuples[row], *q_tuples[column]
x2 = x1+tile_size
y2 = y1+tile_size
tile_image = image.crop((x1,y1,x2,y2))
tile_coords = {'x1':x1,'y1':y1,'x2':x2,'y2':y2}
tiles.append({'image':tile_image,'coords':tile_coords})
return tiles
# Testing:
img_path ='/home/user/path/to/image.jpg'
tiles = image_to_tiles(img_path)
for i in range(20):
tile = random.choice(tiles)
tile['image'].show()

you can use numpy stride tricks to achive this, but be careful, as this function has to be used with extreme care (doc)
import numpy as np
from numpy.lib.stride_tricks import as_strided
def img_pieces(img, piece_size):
height, width, chanels = img.shape
n_bytes = img.strides[-1]
return np.reshape(
as_strided(
img,
(
height // piece_size,
width // piece_size,
piece_size,
piece_size,
chanels
),
(
n_bytes * chanels * width * piece_size,
n_bytes * chanels * piece_size,
n_bytes * chanels * width,
n_bytes * chanels,
n_bytes
)
),
(
-1,
piece_size,
piece_size,
chanels
)
)

Here's my attempt on a grayscale image with only numpy based on the solution from here, with some minor tweaks (adding channels) it might suit your needs:
import numpy as np
# Seperate grayscale images to w * h tiles, add padding with zeros if image not scaled
def to_tiles(arr: np.ndarray, tilesize: tuple[int, int]) -> np.ndarray:
def f(x: tuple[int, int]) -> tuple[int, int]:
tmp = list(x)
if tmp[1] > 0:
tmp[0] = tmp[0] + 1
return tuple(tmp)
# # Stride Implementation
# bytelength = np.int8(np.divide(arr.nbytes, arr.size))
assert arr.ndim == 2, "array must be 2d (grayscale) image"
a_h, a_w = arr.shape
h, w = tilesize
assert a_h > h, "tile height is larger than arr height"
assert a_w > w, "tile width is larger than arr width"
row, row_r = f(np.divmod(a_h, h))
col, col_r = f(np.divmod(a_w, w))
arr = np.pad(
arr,
[
(
np.int8(np.ceil(np.divide(h-row_r, 2))) if row_r != 0 else 0,
np.int8(np.floor(np.divide(h-row_r, 2))) if row_r != 0 else 0,
),
(
np.int8(np.ceil(np.divide(w-col_r, 2))) if col_r != 0 else 0,
np.int8(np.floor(np.divide(w-col_r, 2))) if col_r != 0 else 0,
),
],
"constant",
constant_values=(0),
)
# # Stride Implementation
# arr = np.lib.stride_tricks.as_strided(
# arr, shape=(row, col, h, w), strides=(h*a_w*bytelength, w*bytelength, a_w*bytelength, bytelength)
# )
arr = arr.reshape(row, h, col, w).swapaxes(1, 2)
arr = arr.reshape(-1, h, w)
return arr
Here's an example of the result. Image from FUNSD dataset.

def split(img,nbxsplit,nbysplit):
xdemi=int(img.shape[0]/nbxsplit)
ydemi=int(img.shape[1]/nbxsplit)
arr=[]
for i in range(0,img.shape[0]-xdemi+1,xdemi):
for j in range(0,img.shape[1]-ydemi+1,ydemi):
arr.append(img[i:i+xdemi][j:j+ydemi])
a=np.reshape(a,(img.shape[0]-xdemi,img.shape[1]-xdemi))
return a

Not sure if it's still relevant, but my attempt is following:
(I am assuming the image is a numpy array. I am not using Pil or anything, since i didn't want to have any dependencies other than numpy.)
def cut_image_grid(image:np.ndarray, grid_size:int=4):
height, width = image.shape[0], image.shape[1]
piece_height, piece_width = height//grid_size, width//grid_size
pieces = []
for i in range(grid_size):
for j in range(grid_size):
y = i * piece_height
x = j * piece_width
h = (i+1) * piece_height if i < grid_size else None
w = (j+1) * piece_width if j < grid_size else None
piece = image[y:h, x:w]
pieces.append(piece)
return np.array(pieces)
As input the function is receiving a numpy image and an integer (which you could also turn into tuples, but i wanted to have evenly spaced grid cells always with same amount of cells row and column wise).
At first, the code calculates the width and height of the cells based on the given grid_size. After that the code iterates over all rows and columns and generates x, y Coordinates, as well as x0 and y0 (y+height, x+width) for defining the cells.
Every cell is saved as a list into pieces, which is then transformed into a numpy array and returned.

import cv2
def crop_image(image_path, output_path):
im = cv2.imread(os.listdir()[2])
imgheight=im.shape[0]
imgwidth=im.shape[1]
y1 = 0
M = 2000
N = 2000
for y in range(0,imgheight,M):
for x in range(0, imgwidth, N):
y1 = y + M
x1 = x + N
tiles = im[y:y+M,x:x+N]
if tiles.shape[0] < 100 or tiles.shape[1]<100:
continue
cv2.rectangle(im, (x, y), (x1, y1), (0, 255, 0))
cv2.imwrite(output_path + str(x) + '_' + str(y)+"{}.png".format(image_path),tiles)
crop_image(os.listdir()[2], './cutted/')