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
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) 
fp in my source code it seems, I have changed it to the correct argument naming.
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
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 
k a parameter of this function? Shouldn't it always be 0 when calling the function? Also what is area? Why do you crop the image twice?
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.Imageheight-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) 
img.paste(piece)
img=Image.new('RGB', (height,width), 255), width and height should have been switched. I suggested an edit but it was rejected ¯_(ツ)_/¯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 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:
I find it easier to skimage.util.view_as_windows or `skimage.util.view_as_blocks which also allows you to configure the step
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 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.
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") 
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() 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) It's awkward to post another one solution, but have not found an answer for:
def ungrid_image(img: PIL.Image, nx=1, ny=1, flatten=False): """ Without flatten=True returns row major [tile_j][tile_i]: PIL.Image """ w, h = img.size assert w >= nx > 0 and h >= ny > 0 cw, ch = w // nx, h // ny if w % nx != 0 or h % ny != 0: warn('Unexpected image size for pixel-perfect subdivision. Tiles will match cropped source image.') def box(i, j): # left, top, right, bottom for (i, j) tile return i * cw, j * ch, (i + 1) * cw, (j + 1) * ch res = [[img.crop(box(i, j)) for i in range(nx)] for j in range(ny)] return list(itertools.chain.from_iterable(res)) if flatten else res 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 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 
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/') 
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)]- See my answer below