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# How to resample NumPy array representing an image ?

• Last Updated : 05 Aug, 2021

In this article, we will be Resampling a NumPy array representing an image. For this, we are using scipy package. Scipy package comes with ndimage.zoom() method which exactly does this for us by zooming into a NumPy array using spline interpolation of a given order. Default is order 3 (aka cubic).

For input containing imaginary components, scipy. ndimage.zoom, zooms real and imaginary components independently.

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Syntax: scipy.ndimage.zoom(input, zoom, output=None, order=3, mode=’constant’, cval=0.0, prefilter=True, *, grid_mode=False)

Parameters:

• Input: It defines the ndarray
• zoom : It takes both a sequence or a single number , if a single number it means apply zoom with same value in on all axis , if a sequence is provided then apply in the given order to x,y,z…etc.
• Output: By default the output of same dtype of input will be created.
• Order: Spline interpolation value , it must range between [0,5] inclusive.
• Mode**: One of the most important parameters which decides how the interpolation  must happen beyond for boundary pixels it can take values from this list  [‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’].
• prefilter : Takes boolean value and determine if the input array should be prefiltered with spline filter before interpolation or not.

Returns: A ndarray zoomed input.

### Examples

For doing our tasks of zooming we will first create a ndarray as given below:

## Python3

 `import` `numpy as np``import` `scipy.ndimage` `ndarray ``=` `np.array([[``11``, ``12``, ``13``, ``14``],``                    ``[``21``, ``22``, ``23``, ``24``],``                    ``[``31``, ``32``, ``33``, ``34``],``                    ``[``41``, ``42``, ``43``, ``44``]])``print``(ndarray)`

Output:

```[[11 12 13 14]
[21 22 23 24]
[31 32 33 34]
[41 42 43 44]]```

Example 1: In this example, we will pass

• ndarray as the input array
• zoom: 2 (zoom with the value)
• order: 0 (spline interpolation)

as order  = 0 and zoom = 2 so, zooming is done at axis with the same value.

## Python3

 `print``(scipy.ndimage.zoom(``  ``ndarray, ``2``, order ``=` `0``))`

Output:

```[[11 11 12 12 13 13 14 14]
[11 11 12 12 13 13 14 14]
[21 21 22 22 23 23 24 24]
[21 21 22 22 23 23 24 24]
[31 31 32 32 33 33 34 34]
[31 31 32 32 33 33 34 34]
[41 41 42 42 43 43 44 44]
[41 41 42 42 43 43 44 44]]```

Example 2: In this example, we will pass

• ndarray as input array
• zoom : 2 (zoom with the value)
• order : 1 (spline interpolation)

as order  = 1 and zoom = 2 so, zooming is done at axis with the value+axis ie; value+4.

## Python3

 `print``(scipy.ndimage.zoom(``  ``ndarray, ``2``, order ``=` `1``))`

Output:

```[[11 11 12 12 13 13 14 14]
[15 16 16 17 17 17 18 18]
[20 20 20 21 21 22 22 23]
[24 24 25 25 26 26 26 27]
[28 29 29 29 30 30 31 31]
[32 33 33 34 34 35 35 35]
[37 37 38 38 38 39 39 40]
[41 41 42 42 43 43 44 44]]```

Example 3: In the case of multi-band images, we usually don’t want to interpolate along the z-axis to create add new bands into the images and therefore we should pass a sequence instead of a single number for the zoom factor parameter.

## Python3

 `import` `numpy as np``import` `scipy.ndimage` `ndarray ``=` `np.array([[[``11``, ``12``, ``13``, ``14``],``                     ``[``21``, ``22``, ``23``, ``24``]],``                    ``[[``31``, ``32``, ``33``, ``34``],``                     ``[``41``, ``42``, ``43``, ``44``]]])``print``(ndarray)` `print``(scipy.ndimage.zoom(ndarray, ``1``).shape)`

Output:

```[[[11 12 13 14]
[21 22 23 24]]

[[31 32 33 34]
[41 42 43 44]]]
(2, 2, 4)```

Example 4:

## Python3

 `import` `numpy as np``import` `scipy.ndimage` `ndarray ``=` `np.array([[[``11``, ``12``, ``13``, ``14``],``                     ``[``21``, ``22``, ``23``, ``24``]],``                    ``[[``31``, ``32``, ``33``, ``34``],``                     ``[``41``, ``42``, ``43``, ``44``]]])` `print``(scipy.ndimage.zoom(ndarray, (``2``, ``2``, ``4``)))`

Output:

```[[[11 11 11 11 12 12 12 12 13 13 13 13 14 14 14 14]
[14 14 14 14 14 15 15 15 15 15 16 16 16 16 17 17]
[18 18 19 19 19 19 20 20 20 20 20 21 21 21 21 21]
[21 21 21 21 22 22 22 22 23 23 23 23 24 24 24 24]]

[[16 16 16 17 17 17 17 18 18 18 18 18 19 19 19 19]
[19 19 19 19 20 20 20 20 20 21 21 21 21 22 22 22]
[24 24 24 24 24 25 25 25 25 25 26 26 26 26 27 27]
[26 26 26 27 27 27 27 28 28 28 28 28 29 29 29 29]]

[[26 26 26 26 27 27 27 27 27 28 28 28 28 29 29 29]
[28 28 29 29 29 29 30 30 30 30 30 31 31 31 31 31]
[33 33 33 34 34 34 34 35 35 35 35 35 36 36 36 36]
[36 36 36 36 37 37 37 37 37 38 38 38 38 39 39 39]]

[[31 31 31 31 32 32 32 32 33 33 33 33 34 34 34 34]
[34 34 34 34 34 35 35 35 35 35 36 36 36 36 37 37]
[38 38 39 39 39 39 40 40 40 40 40 41 41 41 41 41]
[41 41 41 41 42 42 42 42 43 43 43 43 44 44 44 44]]]```

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