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70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
""" Multiple image resizing techniques """
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import numpy as np
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from cv2 import imread, imshow, waitKey, destroyAllWindows
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class NearestNeighbour:
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"""
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Simplest and fastest version of image resizing.
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Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
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"""
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def __init__(self, img, dst_width: int, dst_height: int):
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if dst_width < 0 or dst_height < 0:
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raise ValueError(f"Destination width/height should be > 0")
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self.img = img
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self.src_w = img.shape[1]
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self.src_h = img.shape[0]
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self.dst_w = dst_width
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self.dst_h = dst_height
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self.ratio_x = self.src_w / self.dst_w
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self.ratio_y = self.src_h / self.dst_h
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self.output = self.output_img = (
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np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
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)
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def process(self):
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for i in range(self.dst_h):
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for j in range(self.dst_w):
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self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]
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def get_x(self, x: int) -> int:
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"""
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Get parent X coordinate for destination X
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:param x: Destination X coordinate
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:return: Parent X coordinate based on `x ratio`
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>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg", 1), 100, 100)
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>>> nn.ratio_x = 0.5
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>>> nn.get_x(4)
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2
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"""
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return int(self.ratio_x * x)
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def get_y(self, y: int) -> int:
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"""
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Get parent Y coordinate for destination Y
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:param y: Destination X coordinate
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:return: Parent X coordinate based on `y ratio`
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>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg", 1), 100, 100)
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>>> nn.ratio_y = 0.5
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>>> nn.get_y(4)
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2
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"""
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return int(self.ratio_y * y)
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if __name__ == "__main__":
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dst_w, dst_h = 800, 600
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im = imread("image_data/lena.jpg", 1)
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n = NearestNeighbour(im, dst_w, dst_h)
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n.process()
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imshow(
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f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
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)
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waitKey(0)
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destroyAllWindows()
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