Numpy Resize Image

Question

I would like to take an image and change the scale of the image, while it is a numpy array. I would like to do it with native NumPy functions w/o PIL, cv2, SciPy etc

now I have this:

from copy import copy
import numpy as np
from scipy import misc


img = misc.face()  # racoon from SciPy(np.ndarray)
img2 = copy(img)  # copy of racoon, because misc.face() is Descriptor(?)
img2.shape()  # (768, 1024, 3)

Which I need shape = (3072, 4096, 3)

I can do it easy with Pillow

CONVERT_IMAGE = Image.fromarray(img.astype('uint8'), 'RGB')
CONVERT_IMAGE = CONVERT_IMAGE.resize((4096, 3072), Image.NEAREST)

IMAGE_AS_ARRAY = np.asarray(CONVERT_IMAGE)
IMAGE_AS_ARRAY.shape  # 3072 4096 3

but I realy need to do this only with NumPy functions w/o other libs

Can you help me ? I'm really weak in NumPy and 3D-arrays

numpy doesn't do that kind of resizing. It requires some sort of interpolation, which image libraries have developed. — hpaulj
– hpaulj, Commented Sep 25, 2020 at 11:34
@hpauji tnx for ur anwer, but i find downgrade of image size in numpy in some resources in Internet. But I cant find anywhere expand of image size — lolchto333
– lolchto333, Commented Sep 25, 2020 at 11:39
You can slice out a subset of the values, or as the answer shows replicate values. But image software can do more sophisticated resizing - averaging values, smoothing the image and so on. — hpaulj
– hpaulj, Commented Sep 25, 2020 at 18:32

HansHirse · Accepted Answer · 2020-09-25 11:40:27Z

2

Limited to whole integer upscaling with some scaling factor n and without actual interpolation, you could use np.repeat twice to get the described result:

import numpy as np

# Original image with shape (4, 3, 3)
img = np.random.randint(0, 255, (4, 3, 3), dtype=np.uint8)

# Scaling factor for whole integer upscaling
n = 4

# Actual upscaling (results to some image with shape (16, 12, 3)
img_up = np.repeat(np.repeat(img, n, axis=0), n, axis=1)

# Outputs
print(img[:, :, 1], '\n')
print(img_up[:, :, 1])

Here's some output:

[[148 242 171]
 [247  40 152]
 [151 131 198]
 [ 23 185 144]] 

[[148 148 148 148 242 242 242 242 171 171 171 171]
 [148 148 148 148 242 242 242 242 171 171 171 171]
 [148 148 148 148 242 242 242 242 171 171 171 171]
 [148 148 148 148 242 242 242 242 171 171 171 171]
 [247 247 247 247  40  40  40  40 152 152 152 152]
 [247 247 247 247  40  40  40  40 152 152 152 152]
 [247 247 247 247  40  40  40  40 152 152 152 152]
 [247 247 247 247  40  40  40  40 152 152 152 152]
 [151 151 151 151 131 131 131 131 198 198 198 198]
 [151 151 151 151 131 131 131 131 198 198 198 198]
 [151 151 151 151 131 131 131 131 198 198 198 198]
 [151 151 151 151 131 131 131 131 198 198 198 198]
 [ 23  23  23  23 185 185 185 185 144 144 144 144]
 [ 23  23  23  23 185 185 185 185 144 144 144 144]
 [ 23  23  23  23 185 185 185 185 144 144 144 144]
 [ 23  23  23  23 185 185 185 185 144 144 144 144]]

----------------------------------------
System information
----------------------------------------
Platform:     Windows-10-10.0.16299-SP0
Python:       3.8.5
NumPy:        1.19.2
----------------------------------------

answered Sep 25, 2020 at 11:40

HansHirse

19k10 gold badges48 silver badges78 bronze badges

Sign up to request clarification or add additional context in comments.

3 Comments

lolchto333 Over a year ago

No Hans, I can't do this. third massive is RGB color and length of third massive is must be three, don't 12. Thanks for ur answer

HansHirse Over a year ago

Third dimension is still 3 in my solution!? You upscale from (768, 1024, 3) to (3072, 4096, 3), which I also do, if I would start at (768, 1024, 3). Also, please pay attention, I just output one channel of the final result - for readibility.

Mattwmaster58 Over a year ago

this is nearest-neighbor right?

Collectives™ on Stack Overflow

Numpy Resize Image

1 Answer 1

3 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

3 Comments

Your Answer

Sign up or log in

Post as a guest

Related