I want to resize images. My images contain specific values [0, 1, 2, 7, 9]. After resizing, new values are introduced like 5 and whatnot. I want to prevent that.
I'm currently using scikit image resize function. I've tried all interpolation flags but to no avail.
EDIT: a simple code to show the problem
import numpy as np
from skimage.transform import resize
vals = [0, 1, 4, 6]
N, M = 100, 100
image = np.random.choice(vals, N * M).reshape(N, M).astype('uint8')
resized_image = resize(image, (50, 50), preserve_range=True).astype('uint8')
print('vals before resizing ', np.unique(image))
print('vals after resizing ', np.unique(resized_image))
Set anti_aliasing to False:
resized_image = resize(image, (50, 50), order=0, preserve_range=True, anti_aliasing=False).astype('uint8')
anti_aliasingbool, optional
Whether to apply a Gaussian filter to smooth the image prior to down-scaling. It is crucial to filter when down-sampling the image to avoid aliasing artifacts.
The aliasing filter applies Gaussian filter, that produces new values.
Result:
vals before resizing [0 1 4 6]
vals after resizing [0 1 4 6]