Python/digital_image_processing/resize/resize.py

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""" Multiple image resizing techniques """
import numpy as np
from cv2 import imread, imshow, waitKey, destroyAllWindows
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)
2
"""
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)
2
"""
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()