type the code in python here is the file name Grand$\_$Teton$-800-480.$jpg

A cool looking photographic effect is the miniature effect $$ diorama, or tilt$-$shift effect. As explained on

Wikipedia miniature faking $$is a process in which a photograph of a life$-$size location or object is

made to look like a photograph of a miniature scale model.$$

Depth of field is the $$distance between the nearest and the furthest objects that give an image judged to

be in focus in a camera.$$

The effect can be added with post processing to a normal photo with a wide depth of field by blurring

parts of the photograph to mimic a shallow depth of field typical for close$-$up photography. The result is

that the captured scene looks much smaller than in reality.

Here is an example. The original photograph with a long depth of field and no blurring is this:

Notice that all parts of the picture are in focus, i$.$e$.$ there is no blurring at all since the photo has a large

depth of field.

Compare it with this version of the same photo, where the top third and bottom third have been blurred

to some degree in a first attempt to produce the miniature effect:

Figure $1.$ The original photo. Notice the lack of any blurring, i$.$e$.$ long depth of

field.

The next version of this picture, shown below, is split in $12$ horizontal slices and each part has a

different degree of blurring, going from none $($at the crosswalk level$),$ then to $3$x$3$ and up to a $31$x$31$

blurring grid applied to the top $12$th of the photo:

Figure $2.$ Miniature photo with $3$ blurring zones.

Blurred

Blurred

Unblurred

Ideally, to accurately mimic the continuous optical phenomenon, the strength of the blurring must vary

depending on the camera angle and the actual depth of field of the subjects in the photo. We accept for

this problem the rough approximation obtained by blurring equal size horizontal slices of the

photograph with discrete levels of blurring, as seen above. We also must acknowledge that each picture

may require a different blurring map $($where and how much blurring$)$ and that each person may have

their own personal taste on how to make the effect look good.

$$ For this problem you have to write in file p$3\_$Lastname$\_$Firstname.py a function

img$\_$miniature$($img$,......)$ that takes as parameters an image ndarray object img obtained from

calling function image$\_$load$($filename$)$ and any other arguments you may consider necessary to

be used to customize the blurring $($location and level$).$ The img$\_$miniature function returns a

new image ndarray of the same shape as img with the miniature effect applied to your desire,

but with minimum three slices of different blurring levels.

$$ Write a function main$()$ that loads a picture from a file with image$\_$load$($filename$)$ into an object

img, applies function img$\_$miniature$($img$,......)$ to the loaded photo, and then saves the

resulting photograph with the miniature effect to a new file with function image$\_$save$($filename$,$

img$).$

$$ Include in the PDF file the Python code, followed by the original photo and the photo with the

miniature effect. Use an photograph $($with prior effects$)$ of your choice. No credit is given for

the screenshots if the miniature effect is NOT applied to the original photograph.

NOTICE: To get any credit for this problem you must use the image file$/$save and blurring functions

from file image$\_$blur.py$,$ posted on the Image Effects page on Canvas and discussed in class. Do not

use any blurring functions from any other library.