I'm trying to upsize OpenCV images in Python in such a manner that the individual pixels are spread out by an integral factor; I use this to visually examine deep detail and individual pixel values can be seen (using cv2.imshow in this instance).
For example, an array:
[[1,2],
[3,4]]
And a factor of 2 means I'd get:
[[1,1,2,2],
[1,1,2,2],
[3,3,4,4],
[3,3,4,4]]
I've done this by generating an array of size*factor using np.zeros, then iterating each point in the original array and copying it to the target array using (for example)
for y in range(src.shape[0]):
for x in range(src.shape[1]):
tgt[y*f:y*f+f, x*f:x*f+f, :] = src[y,x,:]
But as you can imagine, it's not the fastest approach, and I'm hoping I'm just not finding the right thing.
OpenCV (and PIL) do not have a resize capability that doesn't interpolate by one method or another, which seems weird all by itself.
I looked over & tried numpy broadcast*, numpy stride_trickks, opencv functions, PIL functions.
The semi-manual method works as long as I don't need interactivity, but I'm trying to adjust parameters to several opencv functions quickly so I can find the right combinations to solve my problem. (Which is proprietary, so I can't share imagery...) Waiting a significant time between results is counterproductive.
You can use opencv's cv.resize with nearest-neighbor as interpolation method (cv.INTER_NEAREST) to achieve what you need:
import cv2 as cv
import numpy as np
src = np.array([[1,2], [3,4]])
dst = cv.resize(src, (4,4), interpolation=cv.INTER_NEAREST)
print(dst)
Output:
[[1 1 2 2]
[1 1 2 2]
[3 3 4 4]
[3 3 4 4]]