Numpy Resize Rescale Image

Question

I would like to take an image and change the scale of the image  while it is a numpy array   For example I have this image of a coca-cola bottle  bottle-1  Which translates to a numpy array of shape  528  203  3  and I want to resize that to say the size of this second image  bottle-2  Which has a shape of  140  54  3    How do I change the size of the image to a certain shape while still maintaining the original image  Other answers suggest stripping every other or third row out  but what I want to do is basically shrink the image how you would via an image editor but in python code  Are there any libraries to do this in numpy SciPy

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import cv2 import numpy as np  image read   cv2 imread  filename jpg  0   original image   np asarray image read  width   height   452 452 resize image   np zeros shape  width height    for W in range width       for H in range height           new width   int  W   original image shape 0    width           new height   int  H   original image shape 1    height           resize image W  H    original image new width  new height   print  Resized image size       resize image shape   cv2 imshow resize image  cv2 waitKey 0

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If anyone came here looking for a simple method to scale resize an image in Python  without using additional libraries  here s a very simple image resize function   simple image scaling to  nR x nC  size def scale im  nR  nC     nR0   len im        source number of rows    nC0   len im 0      source number of columns    return    im int nR0   r   nR   int nC0   c   nC                  for c in range nC   for r in range nR    Example usage  resizing a  30 x 30  image to  100 x 200   import matplotlib pyplot as plt  def sqr x     return x x  def f r  c  nR  nC     return 1 0 if sqr c - nC 2    sqr r - nR 2   lt  sqr nC 4  else 0 0    a red circle on a canvas of size  nR x nC  def circ nR  nC     return     f r  c  nR  nC   0  0                for c in range nC   for r in range nR    plt imshow scale circ 30  30   100  200    Output   This works to shrink scale images  and works fine with numpy arrays

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For people coming here from Google looking for a fast way to downsample images in numpy arrays for use in Machine Learning applications  here s a super fast method  adapted from here    This method only works when the input dimensions are a multiple of the output dimensions   The following examples downsample from 128x128 to 64x64  this can be easily changed    Channels last ordering    large image is shape  128  128  3    small image is shape  64  64  3  input size   128 output size   64 bin size   input size    output size small image   large image reshape  output size  bin size                                      output size  bin size  3   max 3  max 1    Channels first ordering    large image is shape  3  128  128    small image is shape  3  64  64  input size   128 output size   64 bin size   input size    output size small image   large image reshape  3  output size  bin size                                         output size  bin size   max 4  max 2    For grayscale images just change the 3 to a 1 like this   Channels first ordering    large image is shape  1  128  128    small image is shape  1  64  64  input size   128 output size   64 bin size   input size    output size small image   large image reshape  1  output size  bin size                                        output size  bin size   max 4  max 2    This method uses the equivalent of max pooling  It s the fastest way to do this that I ve found

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Are there any libraries to do this in numpy SciPy   Sure  You can do this without OpenCV  scikit-image or PIL    Image resizing is basically mapping the coordinates of each pixel from the original image to its resized position    Since the coordinates of an image must be integers  think of it as a matrix   if the mapped coordinate has decimal values  you should interpolate the pixel value to approximate it to the integer position  e g  getting the nearest pixel to that position is known as Nearest neighbor interpolation    All you need is a function that does this interpolation for you  SciPy has interpolate interp2d   You can use it to resize an image in numpy array  say arr  as follows   W  H   arr shape  2  new W  new H    600 300  xrange   lambda x  np linspace 0  1  x   f   interp2d xrange W   xrange H   arr  kind  linear   new arr   f xrange new W   xrange new H     Of course  if your image is RGB  you have to perform the interpolation for each channel   If you would like to understand more  I suggest watching Resizing Images - Computerphile

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One-line numpy solution for downsampling  by 2   smaller img   bigger img   2    2   And upsampling  by 2   bigger img   smaller img repeat 2  axis 0  repeat 2  axis 1    this asssumes HxWxC shaped image  h t to L  K  rkk  inen in the comments above  note this method only allows whole integer resizing  e g   2x but not 1 5x

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SciPy s imresize   method was another resize method  but it will be removed starting with SciPy v 1 3 0   SciPy refers to PIL image resize method  Image resize size  resample 0   size     The requested size in pixels  as a 2-tuple   width  height    resample     An optional resampling filter  This can be one of PIL Image NEAREST  use nearest neighbour   PIL Image BILINEAR  linear interpolation   PIL Image BICUBIC  cubic spline interpolation   or PIL Image LANCZOS  a high-quality downsampling filter   If omitted  or if the image has mode    1    or    P     it is set PIL Image NEAREST   Link here  https   pillow readthedocs io en 3 1 x reference Image html PIL Image Image resize

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While it might be possible to use numpy alone to do this  the operation is not built-in  That said  you can use scikit-image  which is built on numpy  to do this kind of image manipulation   Scikit-Image rescaling documentation is here   For example  you could do the following with your image   from skimage transform import resize bottle resized   resize bottle   140  54     This will take care of things like interpolation  anti-aliasing  etc  for you

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Yeah  you can install opencv  this is a library used for image processing  and computer vision   and use the cv2 resize function  And for instance use   import cv2 import numpy as np  img   cv2 imread  your image jpg   res   cv2 resize img  dsize  54  140   interpolation cv2 INTER CUBIC   Here img is thus a numpy array containing the original image  whereas res is a numpy array containing the resized image  An important aspect is the interpolation parameter  there are several ways how to resize an image  Especially since you scale down the image  and the size of the original image is not a multiple of the size of the resized image  Possible interpolation schemas are         INTER NEAREST - a nearest-neighbor interpolation   INTER LINEAR - a bilinear interpolation  used by default    INTER AREA - resampling using pixel area relation  It may be a preferred method for image decimation  as it gives moire   -free   results  But when the image is zoomed  it is similar to the   INTER NEAREST method    INTER CUBIC - a bicubic interpolation over 4x4 pixel neighborhood   INTER LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood      Like with most options  there is no  best  option in the sense that for every resize schema  there are scenarios where one strategy can be preferred over another

[python] Numpy Resize/Rescale Image

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