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tried the suggestion

edited Nov 18 at 9:48

import numpy as np from skimage import transform ysdelta = np.array([ [0.1639, 0.2759, 0.4142, 0.1323, 0.1234]1234, 0.2311, 0.3421], [0.5213, 0.5566, 0.6580, 0.2131, 0.1923]1923, 0.3422, 0.4312], [0.7943, 0.8750, 1.0000, 0.4231, 0.1233]1233, 0.2312, 0.5123], [0.1212, 0.0012, 0.1232, 0.1021, 0.1530]1530, 0.2313, 0.3412], ] [0.4321, dtype=np0.float645123, 0.6123, 0.3121, 0.2132, 0.3212, 0.4321], [0.2312, 0.3412, 0.4512, 0.2211, 0.1234, 0.2345, 0.3456], [0.6543, 0.7654, 0.8765, 0.4321, 0.3212, 0.4321, 0.5432] ]) ys_resized filter_kernel = transformnp.resizearray([ ys[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0.4050, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0] ]) # Step 1: Convolve input with the delta filter ys_filtered = convolve2d(2delta, 2filter_kernel, mode='same', boundary='symm') # Resize to 5x5 using bilinear interpolation delta_resized = resize( ys_filtered, (5, 5), order=1,  # bilinear mode='edge'mode='wrap', # mimic MATLAB edge replication anti_aliasing=Trueanti_aliasing=False, # no anti-aliasing to match MATLAB closely preserve_range=Truepreserve_range=False )   print(ys_resizeddelta_resized)

imgdelta = zeros(7,7); delta(4,4) = 1; [0% single pixel in the middle delta = [ 0.1639, 0.2759, 0.4142, 0.1323, 0.1234;1234, 0.2311, 0.3421; 0.5213, 0.5566, 0.6580, 0.2131, 0.1923;1923, 0.3422, 0.4312; 0.7943, 0.8750, 1.0000, 0.4231, 0.1233;1233, 0.2312, 0.5123; 0.1212, 0.0012, 0.1232, 0.1021, 0.1530];1530, 0.2313, 0.3412; res  0.4321, 0.5123, 0.6123, 0.3121, 0.2132, 0.3212, 0.4321; 0.2312, 0.3412, 0.4512, 0.2211, 0.1234, 0.2345, 0.3456; 0.6543, 0.7654, 0.8765, 0.4321, 0.3212, 0.4321, 0.5432 ]; resized = imresize(imgdelta, [2,2][5, "Method"5], 'bilinear'); % or your target size disp(resized)

Question: Is thereHere i have generated a way to exactly replicate MATLAB’s imresize behavior in Pythonoutput for arbitrary arrays? Are there any subtle differences7*7 matrix in how MATLABmatlab consisting of all 0 and Python handle bilinear interpolation,center pixel centers, or anti-aliasing1 and used that I should be aware of?output in python by first convolving it and then resizing it , but still for the same array in matlab and python output is different

import numpy as np from skimage import transform ys = np.array([ [0.1639, 0.2759, 0.4142, 0.1323, 0.1234], [0.5213, 0.5566, 0.6580, 0.2131, 0.1923], [0.7943, 0.8750, 1.0000, 0.4231, 0.1233], [0.1212, 0.0012, 0.1232, 0.1021, 0.1530] ], dtype=np.float64) ys_resized = transform.resize( ys, (2, 2), order=1, # bilinear mode='edge', anti_aliasing=True, preserve_range=True ) print(ys_resized)

img = [0.1639, 0.2759, 0.4142, 0.1323, 0.1234; 0.5213, 0.5566, 0.6580, 0.2131, 0.1923; 0.7943, 0.8750, 1.0000, 0.4231, 0.1233; 0.1212, 0.0012, 0.1232, 0.1021, 0.1530]; res = imresize(img, [2,2], "Method", 'bilinear');

Question: Is there a way to exactly replicate MATLAB’s imresize behavior in Python for arbitrary arrays? Are there any subtle differences in how MATLAB and Python handle bilinear interpolation, pixel centers, or anti-aliasing that I should be aware of?

delta = np.array([ [0.1639, 0.2759, 0.4142, 0.1323, 0.1234, 0.2311, 0.3421], [0.5213, 0.5566, 0.6580, 0.2131, 0.1923, 0.3422, 0.4312], [0.7943, 0.8750, 1.0000, 0.4231, 0.1233, 0.2312, 0.5123], [0.1212, 0.0012, 0.1232, 0.1021, 0.1530, 0.2313, 0.3412],  [0.4321, 0.5123, 0.6123, 0.3121, 0.2132, 0.3212, 0.4321], [0.2312, 0.3412, 0.4512, 0.2211, 0.1234, 0.2345, 0.3456], [0.6543, 0.7654, 0.8765, 0.4321, 0.3212, 0.4321, 0.5432] ])  filter_kernel = np.array([ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0.4050, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0] ]) # Step 1: Convolve input with the delta filter ys_filtered = convolve2d(delta, filter_kernel, mode='same', boundary='symm') # Resize to 5x5 using bilinear interpolation delta_resized = resize( ys_filtered, (5, 5), order=1,  # bilinear mode='wrap', # mimic MATLAB edge replication anti_aliasing=False, # no anti-aliasing to match MATLAB closely preserve_range=False )   print(delta_resized)

delta = zeros(7,7); delta(4,4) = 1; % single pixel in the middle delta = [ 0.1639, 0.2759, 0.4142, 0.1323, 0.1234, 0.2311, 0.3421; 0.5213, 0.5566, 0.6580, 0.2131, 0.1923, 0.3422, 0.4312; 0.7943, 0.8750, 1.0000, 0.4231, 0.1233, 0.2312, 0.5123; 0.1212, 0.0012, 0.1232, 0.1021, 0.1530, 0.2313, 0.3412;   0.4321, 0.5123, 0.6123, 0.3121, 0.2132, 0.3212, 0.4321; 0.2312, 0.3412, 0.4512, 0.2211, 0.1234, 0.2345, 0.3456; 0.6543, 0.7654, 0.8765, 0.4321, 0.3212, 0.4321, 0.5432 ]; resized = imresize(delta, [5,5], 'bilinear'); % or your target size disp(resized)

Question: Here i have generated a output for 7*7 matrix in matlab consisting of all 0 and center pixel 1 and used that output in python by first convolving it and then resizing it , but still for the same array in matlab and python output is different

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asked Nov 17 at 6:59

Researcher

Why does Python skimage.transform.resize give different results from MATLAB imresize for the same array?

I'm trying to replicate MATLAB's imresize behavior in Python using skimage.transform.resize. Here's my test case:

Python

import numpy as np from skimage import transform ys = np.array([ [0.1639, 0.2759, 0.4142, 0.1323, 0.1234], [0.5213, 0.5566, 0.6580, 0.2131, 0.1923], [0.7943, 0.8750, 1.0000, 0.4231, 0.1233], [0.1212, 0.0012, 0.1232, 0.1021, 0.1530] ], dtype=np.float64) ys_resized = transform.resize( ys, (2, 2), order=1, # bilinear mode='edge', anti_aliasing=True, preserve_range=True ) print(ys_resized)

Matlab

img = [0.1639, 0.2759, 0.4142, 0.1323, 0.1234; 0.5213, 0.5566, 0.6580, 0.2131, 0.1923; 0.7943, 0.8750, 1.0000, 0.4231, 0.1233; 0.1212, 0.0012, 0.1232, 0.1021, 0.1530]; res = imresize(img, [2,2], "Method", 'bilinear');

Problem: Even though I’m using bilinear interpolation and trying different modes in Python (mode='edge', mode='reflect', etc.), the output from skimage.transform.resize does not match MATLAB's imresize.

I’ve also tried cv2.resize and scipy.ndimage.zoom, but the results are still different.

matlab python