resize Algorithm
These measures can, in the aggregate, let the generation of commercially useful distributions of merchandise that adapt expected body sizes, as with the creation of clothing sizes and shoe sizes, and with the standardization of door frame dimensions, ceiling heights, and bed sizes. The human experience of size can lead to a psychological tendency towards size bias, wherein the relative importance or perceived complexity of organisms and other objects is judged based on their size relative to humans, and particularly whether this size makes them easy to observe without aid.
""" 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()
