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2cfef0913a
* Fix greyscale computation and inverted coords * Fix test * Add test cases * Add reference to the greyscaling formula --------- Co-authored-by: Colin Leroy-Mira <colin.leroy-mira@sigfox.com>
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
"""
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Implementation Burke's algorithm (dithering)
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"""
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import numpy as np
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from cv2 import destroyAllWindows, imread, imshow, waitKey
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class Burkes:
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"""
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Burke's algorithm is using for converting grayscale image to black and white version
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Source: Source: https://en.wikipedia.org/wiki/Dither
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Note:
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* Best results are given with threshold= ~1/2 * max greyscale value.
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* This implementation get RGB image and converts it to greyscale in runtime.
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"""
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def __init__(self, input_img, threshold: int):
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self.min_threshold = 0
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# max greyscale value for #FFFFFF
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self.max_threshold = int(self.get_greyscale(255, 255, 255))
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if not self.min_threshold < threshold < self.max_threshold:
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msg = f"Factor value should be from 0 to {self.max_threshold}"
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raise ValueError(msg)
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self.input_img = input_img
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self.threshold = threshold
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self.width, self.height = self.input_img.shape[1], self.input_img.shape[0]
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# error table size (+4 columns and +1 row) greater than input image because of
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# lack of if statements
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self.error_table = [
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[0 for _ in range(self.height + 4)] for __ in range(self.width + 1)
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]
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self.output_img = np.ones((self.width, self.height, 3), np.uint8) * 255
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@classmethod
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def get_greyscale(cls, blue: int, green: int, red: int) -> float:
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"""
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>>> Burkes.get_greyscale(3, 4, 5)
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4.185
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>>> Burkes.get_greyscale(0, 0, 0)
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0.0
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>>> Burkes.get_greyscale(255, 255, 255)
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255.0
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"""
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"""
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Formula from https://en.wikipedia.org/wiki/HSL_and_HSV
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cf Lightness section, and Fig 13c.
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We use the first of four possible.
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"""
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return 0.114 * blue + 0.587 * green + 0.299 * red
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def process(self) -> None:
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for y in range(self.height):
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for x in range(self.width):
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greyscale = int(self.get_greyscale(*self.input_img[y][x]))
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if self.threshold > greyscale + self.error_table[y][x]:
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self.output_img[y][x] = (0, 0, 0)
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current_error = greyscale + self.error_table[y][x]
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else:
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self.output_img[y][x] = (255, 255, 255)
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current_error = greyscale + self.error_table[y][x] - 255
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"""
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Burkes error propagation (`*` is current pixel):
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* 8/32 4/32
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2/32 4/32 8/32 4/32 2/32
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"""
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self.error_table[y][x + 1] += int(8 / 32 * current_error)
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self.error_table[y][x + 2] += int(4 / 32 * current_error)
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self.error_table[y + 1][x] += int(8 / 32 * current_error)
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self.error_table[y + 1][x + 1] += int(4 / 32 * current_error)
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self.error_table[y + 1][x + 2] += int(2 / 32 * current_error)
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self.error_table[y + 1][x - 1] += int(4 / 32 * current_error)
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self.error_table[y + 1][x - 2] += int(2 / 32 * current_error)
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if __name__ == "__main__":
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# create Burke's instances with original images in greyscale
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burkes_instances = [
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Burkes(imread("image_data/lena.jpg", 1), threshold)
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for threshold in (1, 126, 130, 140)
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]
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for burkes in burkes_instances:
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burkes.process()
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for burkes in burkes_instances:
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imshow(
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f"Original image with dithering threshold: {burkes.threshold}",
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burkes.output_img,
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)
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waitKey(0)
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destroyAllWindows()
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