How to use Matlab's imresize in python
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I'm transferring Matlab's
imresize code into python. I found the scipy's
imresize, but I get a different results from Matlab.
How to get the same results as Matlab by python.
from scipy.misc import imresize import numpy as np dtest = np.array(([1,2,3],[4,5,6],[7,8,9])) scale = 1.4 dim = imresize(dtest,1/scale)
dtest = [1,2,3; 4,5,6; 7,8,9]; scale = 1.4; dim = imresize(dtest,1/scale);
These two pieces of code return different results.
scipy.misc.imresize function is a bit odd for me. For one thing, this is what happens when I specify the sample 2D image you provided to a
scipy.misc.imresize call on this image with a scale of 1.0. Ideally, it should give you the same image, but what we get is this (in IPython):
In : from scipy.misc import imresize In : import numpy as np In : dtest = np.array(([1,2,3],[4,5,6],[7,8,9])) In : out = imresize(dtest, 1.0) In : out Out: array([[ 0, 32, 64], [ 96, 127, 159], [191, 223, 255]], dtype=uint8)
Not only does it change the type of the output to
uint8, but it scales the values as well. For one thing, it looks like it makes the maximum value of the image equal to 255 and the minimum value equal to 0. MATLAB's
imresize does not do this and it resizes an image in the way we expect:
>> dtest = [1,2,3;4,5,6;7,8,9]; >> out = imresize(dtest, 1) out = 1 2 3 4 5 6 7 8 9
However, you need to be cognizant that MATLAB performs the resizing with anti-aliasing enabled by default. I'm not sure what
scipy.misc.resize does here but I'll bet that there is no anti-aliasing enabled.
Edit - November 23rd, 2016
As noted by Eric in his comments below, if you pre-cast the image to the desired type, you will get the expected results:
In : dtest = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype=np.uint8) In : out = imresize(dtest, 1.0) In : out Out: array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=uint8)
We can see that the image is not scaled to the
[0,255] range. To finally get where you need to go, we must obtain a floating-point representation of the image.
scipy.misc.imresize has an additional flag called
'mode' and you can specify this as
'F' to ensure the output is floating point.
In : scale = 1.4 In : out = imresize(dtest, 1/scale, mode='F') In : out Out: array([[ 2.5 , 3.75], [ 6.25, 7.5 ]], dtype=float32)
As you will see later, the results that you see with
scipy.misc.resize don't match with what you see in MATLAB.
For the best results, don't specify a scale - specify a target output size to reproduce results. As such,
1/scale in your case is close to a
2 x 2 size output, and so here's what you would do in MATLAB:
>> dtest = [1,2,3;4,5,6;7,8,9]; >> out = imresize(dtest, [2,2], 'bilinear', 'AntiAliasing', false) out = 2.0000 3.5000 6.5000 8.0000
You can see that some of the values in the matrix don't align with
scipy.misc.resize. To match what you see in MATLAB. The closest thing to what you want is either OpenCV's
resize function, or scikit-image's
resize function. Both of these have no anti-aliasing. If you want to make both Python and MATLAB match each other, use the bilinear interpolation method.
imresize in MATLAB uses bicubic interpolation by default and I know for a fact that MATLAB uses custom kernels to do so, and so it will be much more difficult to match their outputs if you use bicubic interpolation between the methods. See this post for some more informative results:
MATLAB vs C++ vs OpenCV - imresize
With Python OpenCV:
In : import numpy as np In : import cv2 In : dtest = np.array(([1,2,3],[4,5,6],[7,8,9]), dtype='float') In : out = cv2.resize(dtest, (2,2)) In : out Out: array([[ 2. , 3.5], [ 6.5, 8. ]])
In : from skimage.transform import resize In : dtest = np.array(([1,2,3],[4,5,6],[7,8,9]), dtype='uint8') In : out = resize(dtest, (2,2), order=1, preserve_range=True) In : out Out: array([[ 2. , 3.5], [ 6.5, 8. ]])
One last interesting thing to note is that MATLAB, OpenCV and scikit-image when specifying a floating point scale act differently with each other. I did some experiments and by specifying a floating point size, I was unable to get the results to match. Besides which, scikit-image does not support taking in a scale factor which is more reason to explicitly state an output size rather than a scale.
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To add one more option I found, while exploring the excellent answer by @rayryeng.
>>> from scipy import ndimage >>> dtest = np.array(([1,2,3],[4,5,6],[7,8,9]), dtype='float') >>> ndimage.zoom(dtest, 2/3) array([[ 1., 3.], [ 7., 9.]]) >>> ndimage.zoom(dtest, 2/3, prefilter=False) array([[ 2.33333333, 3.66666667], [ 6.33333333, 7.66666667]])
It does not give me the same result as matlab, but it comes close:
>> dtest = [1,2,3;4,5,6;7,8,9]; >> imresize(dtest, [2,2]) ans = 2.1296 3.5648 6.4352 7.8704
Depending on what you want to achieve, this could be useful. For me it has the advantage of not needing to include another package to the project, since scipy is already used.
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After a lot of digging, the only solution that I found which replicates matlab's imresize with anti-aliasing is the code by Alex (fatheral) at https://github.com/fatheral/matlab_imresize . Currently it only uses the bicubic kernel, but can be easily expanded to any other kernel provided in Matlab.
How to Write a MATLAB Program - Video - MATLAB, Chapter 5, “Programming with MATLAB” – describes how to use the. MATLAB language to create scripts and functions, and manipulate data structures, such as n Use the hold command to add lines/points to an existing plot. n hold on –retain existing axes, add new curves to current axes. Axes are rescaled when necessary. n hold off –release the current figure window for new plots 17 n Grids and Labels: Command Description grid on Adds dashed grids lines at the tick marks grid off removes grid lines (default)
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[PDF] Getting Started with MATLAB (PDF), In 2000, MATLAB was rewritten to use a newer set of libraries for matrix manipulation, LAPACK. MATLAB was first adopted by researchers and practitioners in Start learning MATLAB and Simulink with free tutorials. Expand your knowledge through interactive courses, explore documentation and code examples, or watch how-to videos on product capabilities.
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- can you show both outputs?
- Very interesting background info!
- I used the method that you said and I get the same output.Thanks a lot.
- No problem at all. Good luck!
- @excaza - Thanks :) It comes with hours of just fooling around and experimenting... and also StackOverflow reading. I couldn't have known this about MATLAB's
imresizeif it weren't for chappjc and Amro in the linked post above.
- @rayryeng - If you modify dtest's dtype in your first example (In ) to dtype='uint8', you will get the expected results from scipy's imresize. Using your example, dtest has a default type of int64. scipy specifies the default 'mode' as None, but doesn't explain what this means. Digging into pilutil.py shows None corresponds to a byte stream (uint8). pilutil.py doesn't appear to support 64 bit numeric types, but if you specify mode='F', you will get the expected results.