Here is a function generalizing previous approaches, creating a grid of images in PIL:
from PIL import Image
import numpy as np
def pil_grid(images, max_horiz=np.iinfo(int).max):
n_images = len(images)
n_horiz = min(n_images, max_horiz)
h_sizes, v_sizes = [0] * n_horiz, [0] * (n_images // n_horiz)
for i, im in enumerate(images):
h, v = i % n_horiz, i // n_horiz
h_sizes[h] = max(h_sizes[h], im.size[0])
v_sizes[v] = max(v_sizes[v], im.size[1])
h_sizes, v_sizes = np.cumsum([0] + h_sizes), np.cumsum([0] + v_sizes)
im_grid = Image.new('RGB', (h_sizes[-1], v_sizes[-1]), color='white')
for i, im in enumerate(images):
im_grid.paste(im, (h_sizes[i % n_horiz], v_sizes[i // n_horiz]))
return im_grid
It will shrink each row and columns of the grid to the minimum. You can have only a row by using pil_grid(images), or only a column by using pil_grid(images, 1).
One benefit of using PIL over numpy-array based solutions is that you can deal with images structured differently (like grayscale or palette-based images).
Example outputs
def dummy(w, h):
"Produces a dummy PIL image of given dimensions"
from PIL import ImageDraw
im = Image.new('RGB', (w, h), color=tuple((np.random.rand(3) * 255).astype(np.uint8)))
draw = ImageDraw.Draw(im)
points = [(i, j) for i in (0, im.size[0]) for j in (0, im.size[1])]
for i in range(len(points) - 1):
for j in range(i+1, len(points)):
draw.line(points[i] + points[j], fill='black', width=2)
return im
dummy_images = [dummy(20 + np.random.randint(30), 20 + np.random.randint(30)) for _ in range(10)]
pil_grid(dummy_images):
pil_grid(dummy_images, 3):
pil_grid(dummy_images, 1):
