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""" Multiple image resizing techniques """

import numpy as np

from cv2 import destroyAllWindows, imread, imshow, waitKey

class NearestNeighbour:

"""

Simplest and fastest version of image resizing.

Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation

"""

def __init__(self, img, dst_width: int, dst_height: int):

if dst_width < 0 or dst_height < 0:

raise ValueError("Destination width/height should be > 0")

self.img = img

self.src_w = img.shape[1]

self.src_h = img.shape[0]

self.dst_w = dst_width

self.dst_h = dst_height

self.ratio_x = self.src_w / self.dst_w

self.ratio_y = self.src_h / self.dst_h

self.output = self.output_img = (

np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255

)

def process(self):

for i in range(self.dst_h):

for j in range(self.dst_w):

self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]

def get_x(self, x: int) -> int:

"""

Get parent X coordinate for destination X

:param x: Destination X coordinate

:return: Parent X coordinate based on `x ratio`

>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",

... 1), 100, 100)

>>> nn.ratio_x = 0.5

>>> nn.get_x(4)

"""

return int(self.ratio_x * x)

def get_y(self, y: int) -> int:

"""

Get parent Y coordinate for destination Y

:param y: Destination X coordinate

:return: Parent X coordinate based on `y ratio`

>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",

... 1), 100, 100)

>>> nn.ratio_y = 0.5

>>> nn.get_y(4)

"""

return int(self.ratio_y * y)

if __name__ == "__main__":

dst_w, dst_h = 800, 600

im = imread("image_data/lena.jpg", 1)

n = NearestNeighbour(im, dst_w, dst_h)

n.process()

imshow(

f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output

)

waitKey(0)

destroyAllWindows()

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