CODEWITHSUNDEEP
pythonnumpy
Q2Learn
Q2Learn

Reputation: 323

Downsample numpy image array in Python by averaging

How do I downsample an image of any resolution to a quarter of the size by averaging the pixels in numpy?

What I came up through research only works for images that are square (i.e 512 X 512 down to 128 X 128) but will not work for images that are different dimensions (i.e 2400 X 1800 down to 600 X 450). In those cases I get a IndexError: index 450 is out of bounds for axis 1 with size 450.

I am trying to perform this task with numpy array manipulation and without installing other packages and libraries.

I researched a function

numpy.mean()

but I don't know how to use it in reference to this problem.

import cv2
import numpy as np

def quarter_res_avg(im):

    original_width = im.shape[1]
    original_height = im.shape[0]

    width = original_width / 4
    height = original_height / 4

    resized_image = np.zeros(shape=(width, height, 3), dtype=np.uint8)

    scale = 4

    for i in range(width):
        for j in range(height):
            temp = np.array([0, 0, 0])
            for x in range(scale):
                for y in range(scale):
                    temp += im[i*scale+x, j*scale+y]
            resized_image[i, j] = temp/(scale*scale)

    return resized_image

im = cv2.imread('Lenna_test_image.png', 1)
cv2.imwrite('Lenna_test_image_avg.png', quarter_res_avg(im))

Any ideas are much appreciated.

Thanks.

Upvotes: 0

Views: 4160

Answers (3)

Q2Learn
Q2Learn

Reputation: 323

The answer that worked for me with the help from @MarkSetchell in the comments of the question.

Without using np.mean()

def quarter_res_avg(im):

    original_width = im.shape[1]
    original_height = im.shape[0]
    width = original_width / 4
    height = original_height / 4

    resized_image = np.zeros(shape=(height, width, 3), dtype=np.uint8)
    scale = 4

    for i in range(height):
        for j in range(width):
            temp = np.array([0, 0, 0])
            for x in range(scale):
                for y in range(scale):
                    temp += im[i*scale + x, j*scale + y]
            resized_image[i, j] = temp/(scale*scale)

return resized_image

im = cv2.imread('Lenna_test_image.png', 1)
cv2.imwrite('Lenna_test_image_resized.png', quarter_res_avg(im))

By using np.mean() replace the for loops with:

for i in range(0, original_height, scale):
    for j in range(0, original_width, scale):
        resized_image[i/scale, j/scale] = np.mean(im[i:i + scale, j:j+scale], axis=(0,1))

Upvotes: 0

Maxpxt
Maxpxt

Reputation: 189

First reshape your M x N image into a (M//4) x 4 x (N//4) x 4 array, then use np.mean in the second and last dimensions.

from typing import Tuple
import numpy as np

def downsample_by_averaging(img: np.ndarray, window_shape: Tuple[int, int]) -> np.ndarray:
    return np.mean(
        img.reshape((
            *img.shape[:-2],
            img.shape[-2] // window_shape[-2], window_shape[-2],
            img.shape[-1] // window_shape[-1], window_shape[-1],
        )),
        axis=(-1, -3),
    )

downsample_by_averaging(img, (4, 4))

Upvotes: 0

Nicolas Gervais
Nicolas Gervais

Reputation: 36584

import numpy as np
import skimage.measure

your_array = np.random.rand(2400, 800)

new_array = skimage.measure.block_reduce(your_array, (4,4), np.mean)
print(new_array.shape)

Out[18]: (600, 450)

Upvotes: 3

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