Combine several images horizontally with Python

I am trying to horizontally combine some JPEG images in Python.

Problem

I have 3 images - each is 148 x 95 - see attached. I just made 3 copies of the same image - that is why they are the same.

My attempt

I am trying to horizontally join them using the following code:

import sys
from PIL import Image

list_im = ['Test1.jpg','Test2.jpg','Test3.jpg']

# creates a new empty image, RGB mode, and size 444 by 95
new_im = Image.new('RGB', (444,95))

for elem in list_im:
    for i in xrange(0,444,95):
        im=Image.open(elem)
        new_im.paste(im, (i,0))
new_im.save('test.jpg')

However, this is producing the output attached as test.jpg.

Question

Is there a way to horizontally concatenate these images such that the sub-images in test.jpg do not have an extra partial image showing?

Additional Information

I am looking for a way to horizontally concatenate n images. I would like to use this code generally so I would prefer to:

• not to hard-code image dimensions, if possible
• specify dimensions in one line so that they can be easily changed

Solution 1:

You can do something like this:

import sys
from PIL import Image

images = [Image.open(x) for x in ['Test1.jpg', 'Test2.jpg', 'Test3.jpg']]
widths, heights = zip(*(i.size for i in images))

total_width = sum(widths)
max_height = max(heights)

new_im = Image.new('RGB', (total_width, max_height))

x_offset = 0
for im in images:
  new_im.paste(im, (x_offset,0))
  x_offset += im.size[0]

new_im.save('test.jpg')

Test1.jpg

Test2.jpg

Test3.jpg

test.jpg

The nested for for i in xrange(0,444,95): is pasting each image 5 times, staggered 95 pixels apart. Each outer loop iteration pasting over the previous.

for elem in list_im:
  for i in xrange(0,444,95):
    im=Image.open(elem)
    new_im.paste(im, (i,0))
  new_im.save('new_' + elem + '.jpg')

Solution 2:

I would try this:

import numpy as np
import PIL
from PIL import Image

list_im = ['Test1.jpg', 'Test2.jpg', 'Test3.jpg']
imgs    = [ PIL.Image.open(i) for i in list_im ]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted( [(np.sum(i.size), i.size ) for i in imgs])[0][1]
imgs_comb = np.hstack( (np.asarray( i.resize(min_shape) ) for i in imgs ) )

# save that beautiful picture
imgs_comb = PIL.Image.fromarray( imgs_comb)
imgs_comb.save( 'Trifecta.jpg' )    

# for a vertical stacking it is simple: use vstack
imgs_comb = np.vstack( (np.asarray( i.resize(min_shape) ) for i in imgs ) )
imgs_comb = PIL.Image.fromarray( imgs_comb)
imgs_comb.save( 'Trifecta_vertical.jpg' )

It should work as long as all images are of the same variety (all RGB, all RGBA, or all grayscale). It shouldn't be difficult to ensure this is the case with a few more lines of code. Here are my example images, and the result:

Test1.jpg

Test2.jpg

Test3.jpg

Trifecta.jpg:

Trifecta_vertical.jpg

Solution 3:

Edit: DTing's answer is more applicable to your question since it uses PIL, but I'll leave this up in case you want to know how to do it in numpy.

Here is a numpy/matplotlib solution that should work for N images (only color images) of any size/shape.

import numpy as np
import matplotlib.pyplot as plt

def concat_images(imga, imgb):
    """
    Combines two color image ndarrays side-by-side.
    """
    ha,wa = imga.shape[:2]
    hb,wb = imgb.shape[:2]
    max_height = np.max([ha, hb])
    total_width = wa+wb
    new_img = np.zeros(shape=(max_height, total_width, 3))
    new_img[:ha,:wa]=imga
    new_img[:hb,wa:wa+wb]=imgb
    return new_img

def concat_n_images(image_path_list):
    """
    Combines N color images from a list of image paths.
    """
    output = None
    for i, img_path in enumerate(image_path_list):
        img = plt.imread(img_path)[:,:,:3]
        if i==0:
            output = img
        else:
            output = concat_images(output, img)
    return output

Here is example use:

>>> images = ["ronda.jpeg", "rhod.jpeg", "ronda.jpeg", "rhod.jpeg"]
>>> output = concat_n_images(images)
>>> import matplotlib.pyplot as plt
>>> plt.imshow(output)
>>> plt.show()

Solution 4:

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):

Solution 5:

Based on DTing's answer I created a function that is easier to use:

from PIL import Image


def append_images(images, direction='horizontal',
                  bg_color=(255,255,255), aligment='center'):
    """
    Appends images in horizontal/vertical direction.

    Args:
        images: List of PIL images
        direction: direction of concatenation, 'horizontal' or 'vertical'
        bg_color: Background color (default: white)
        aligment: alignment mode if images need padding;
           'left', 'right', 'top', 'bottom', or 'center'

    Returns:
        Concatenated image as a new PIL image object.
    """
    widths, heights = zip(*(i.size for i in images))

    if direction=='horizontal':
        new_width = sum(widths)
        new_height = max(heights)
    else:
        new_width = max(widths)
        new_height = sum(heights)

    new_im = Image.new('RGB', (new_width, new_height), color=bg_color)


    offset = 0
    for im in images:
        if direction=='horizontal':
            y = 0
            if aligment == 'center':
                y = int((new_height - im.size[1])/2)
            elif aligment == 'bottom':
                y = new_height - im.size[1]
            new_im.paste(im, (offset, y))
            offset += im.size[0]
        else:
            x = 0
            if aligment == 'center':
                x = int((new_width - im.size[0])/2)
            elif aligment == 'right':
                x = new_width - im.size[0]
            new_im.paste(im, (x, offset))
            offset += im.size[1]

    return new_im

It allows choosing a background color and image alignment. It's also easy to do recursion:

images = map(Image.open, ['hummingbird.jpg', 'tiger.jpg', 'monarch.png'])

combo_1 = append_images(images, direction='horizontal')
combo_2 = append_images(images, direction='horizontal', aligment='top',
                        bg_color=(220, 140, 60))
combo_3 = append_images([combo_1, combo_2], direction='vertical')
combo_3.save('combo_3.png')