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# Where’s Wally Problem using Mahotas

• Difficulty Level : Medium
• Last Updated : 22 Sep, 2021

In this article we will see how we can find the wally in the given image. Where’s Wally?, also called Where’s Waldo? in North America is a British puzzle books. The books consist of a series of detailed double-page spread illustrations showing dozens or more people doing a variety of amusing things at a given location. Readers are challenged to find a character named Wally hidden in the group.
Image used in the program –

Wally Description : Wally is identified by his red-and-white-striped shirt, bobble hat, and glasses, but many illustrations contain red herrings involving deceptive use of red-and-white striped objects.
In order to do this we will use mahotas library. Mahotas is a computer vision and image processing library for Python. It includes many algorithms implemented in C++ for speed while operating in numpy arrays and with a very clean Python interface.
Command to install mahotas –

`pip install mahotas`

Below is the implementation –

## Python3

 `# importing required libraries``from` `pylab ``import` `imshow, show``import` `mahotas``import` `mahotas.demos``import` `numpy as np` `# loading the image``wally ``=` `mahotas.demos.load(``'wally'``)` `# showing the original image``imshow(wally)``show()` `# getting float type value``# float values are better to use``wfloat ``=` `wally.astype(``float``)` `# splitting image into red, green and blue channel``r, g, b ``=` `wfloat.transpose((``2``, ``0``, ``1``))` `# white channel``w ``=` `wfloat.mean(``2``)` `# pattern of wally shirt``# pattern + 1, +1, -1, -1 on vertical axis``pattern ``=` `np.ones((``24``, ``16``), ``float``)``for` `i ``in` `range``(``2``):``    ``pattern[i::``4``] ``=` `-``1` `# convolve with the red minus white``# increase the response where shirt is``v ``=` `mahotas.convolve(r``-``w, pattern)` `# getting maximum value``mask ``=` `(v ``=``=` `v.``max``())` `# creating mask to tone down the image``# except the region where wally is``mask ``=` `mahotas.dilate(mask, np.ones((``48``, ``24``)))` `# subtraction mask from the wally``np.subtract(wally, .``8` `*` `wally ``*` `~mask[:, :, ``None``],``                   ``out ``=` `wally, casting ``=``'unsafe'``)` `# show the new image``imshow(wally)``show()`

Output :

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