[python] Numpy Resize/Rescale Image

I would like to take an image and change the scale of the image, while it is a numpy array.

For example I have this image of a coca-cola bottle: bottle-1

Which translates to a numpy array of shape (528, 203, 3) and I want to resize that to say the size of this second image: bottle-2

Which has a shape of (140, 54, 3).

How do I change the size of the image to a certain shape while still maintaining the original image? Other answers suggest stripping every other or third row out, but what I want to do is basically shrink the image how you would via an image editor but in python code. Are there any libraries to do this in numpy/SciPy?

One-line numpy solution for downsampling (by 2):

smaller_img = bigger_img[::2, ::2]

And upsampling (by 2):

bigger_img = smaller_img.repeat(2, axis=0).repeat(2, axis=1)

(this asssumes HxWxC shaped image. h/t to L. Kärkkäinen in the comments above. note this method only allows whole integer resizing (e.g., 2x but not 1.5x))

While it might be possible to use numpy alone to do this, the operation is not built-in. That said, you can use scikit-image (which is built on numpy) to do this kind of image manipulation.

Scikit-Image rescaling documentation is here.

For example, you could do the following with your image:

from skimage.transform import resize
bottle_resized = resize(bottle, (140, 54))

This will take care of things like interpolation, anti-aliasing, etc. for you.

SciPy's imresize() method was another resize method, but it will be removed starting with SciPy v 1.3.0 . SciPy refers to PIL image resize method: Image.resize(size, resample=0)

size – The requested size in pixels, as a 2-tuple: (width, height).
resample – An optional resampling filter. This can be one of PIL.Image.NEAREST (use nearest neighbour), PIL.Image.BILINEAR (linear interpolation), PIL.Image.BICUBIC (cubic spline interpolation), or PIL.Image.LANCZOS (a high-quality downsampling filter). If omitted, or if the image has mode “1” or “P”, it is set PIL.Image.NEAREST.

Link here: https://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.resize

import cv2
import numpy as np

image_read = cv2.imread('filename.jpg',0) 
original_image = np.asarray(image_read)
width , height = 452,452
resize_image = np.zeros(shape=(width,height))

for W in range(width):
    for H in range(height):
        new_width = int( W * original_image.shape[0] / width )
        new_height = int( H * original_image.shape[1] / height )
        resize_image[W][H] = original_image[new_width][new_height]

print("Resized image size : " , resize_image.shape)

cv2.imshow(resize_image)
cv2.waitKey(0)

For people coming here from Google looking for a fast way to downsample images in numpy arrays for use in Machine Learning applications, here's a super fast method (adapted from here ). This method only works when the input dimensions are a multiple of the output dimensions.

The following examples downsample from 128x128 to 64x64 (this can be easily changed).

Channels last ordering

# large image is shape (128, 128, 3)
# small image is shape (64, 64, 3)
input_size = 128
output_size = 64
bin_size = input_size // output_size
small_image = large_image.reshape((output_size, bin_size, 
                                   output_size, bin_size, 3)).max(3).max(1)

Channels first ordering

# large image is shape (3, 128, 128)
# small image is shape (3, 64, 64)
input_size = 128
output_size = 64
bin_size = input_size // output_size
small_image = large_image.reshape((3, output_size, bin_size, 
                                      output_size, bin_size)).max(4).max(2)

For grayscale images just change the 3 to a 1 like this:

Channels first ordering

# large image is shape (1, 128, 128)
# small image is shape (1, 64, 64)
input_size = 128
output_size = 64
bin_size = input_size // output_size
small_image = large_image.reshape((1, output_size, bin_size,
                                      output_size, bin_size)).max(4).max(2)

This method uses the equivalent of max pooling. It's the fastest way to do this that I've found.

If anyone came here looking for a simple method to scale/resize an image in Python, without using additional libraries, here's a very simple image resize function:

#simple image scaling to (nR x nC) size
def scale(im, nR, nC):
  nR0 = len(im)     # source number of rows 
  nC0 = len(im[0])  # source number of columns 
  return [[ im[int(nR0 * r / nR)][int(nC0 * c / nC)]  
             for c in range(nC)] for r in range(nR)]

Example usage: resizing a (30 x 30) image to (100 x 200):

import matplotlib.pyplot as plt

def sqr(x):
  return x*x

def f(r, c, nR, nC):
  return 1.0 if sqr(c - nC/2) + sqr(r - nR/2) < sqr(nC/4) else 0.0

# a red circle on a canvas of size (nR x nC)
def circ(nR, nC):
  return [[ [f(r, c, nR, nC), 0, 0] 
             for c in range(nC)] for r in range(nR)]

plt.imshow(scale(circ(30, 30), 100, 200))

Output:

This works to shrink/scale images, and works fine with numpy arrays.

Are there any libraries to do this in numpy/SciPy

Sure. You can do this without OpenCV, scikit-image or PIL.

Image resizing is basically mapping the coordinates of each pixel from the original image to its resized position.

Since the coordinates of an image must be integers (think of it as a matrix), if the mapped coordinate has decimal values, you should interpolate the pixel value to approximate it to the integer position (e.g. getting the nearest pixel to that position is known as Nearest neighbor interpolation).

All you need is a function that does this interpolation for you. SciPy has interpolate.interp2d.

You can use it to resize an image in numpy array, say arr, as follows:

W, H = arr.shape[:2]
new_W, new_H = (600,300)
xrange = lambda x: np.linspace(0, 1, x)

f = interp2d(xrange(W), xrange(H), arr, kind="linear")
new_arr = f(xrange(new_W), xrange(new_H))

Of course, if your image is RGB, you have to perform the interpolation for each channel.

If you would like to understand more, I suggest watching Resizing Images - Computerphile.