burkes Algorithm
In addition to the peerage, Burke's published books on royal family of Europe and Latin America, ruling family of Africa and the center East, distinguished family of the unite state and historical family of Ireland. Burke's Peerage limit is a British genealogical publisher established in 1826, when Irish genealogist John Burke begin release books dedicated to the ancestry and heraldry of the peerage, baronetage, knightage and landed gentry of the unite Kingdom.
His son Sir John Bernard Burke (1814–92) was Ulster King of Arms (1853–92) and his grandson, Sir Henry Farnham Burke (1859–1930), was Garter principal King of Arms (1919–30).Apart from the Burke family, editors have included Arthur Charles Fox-Davies, Alfred Trego Butler, Leslie Gilbert Pine, Peter Townend, and Hugh Montgomery-Massingberd. The firm was established in 1826 by John Burke (1786–1848), progenitor of a dynasty of genealogists and harbingers.
"""
Implementation Burke's algorithm (dithering)
"""
from cv2 import destroyAllWindows, imread, imshow, waitKey
import numpy as np
class Burkes:
"""
Burke's algorithm is using for converting grayscale image to black and white version
Source: Source: https://en.wikipedia.org/wiki/Dither
Note:
* Best results are given with threshold= ~1/2 * max greyscale value.
* This implementation get RGB image and converts it to greyscale in runtime.
"""
def __init__(self, input_img, threshold: int):
self.min_threshold = 0
# max greyscale value for #FFFFFF
self.max_threshold = int(self.get_greyscale(255, 255, 255))
if not self.min_threshold < threshold < self.max_threshold:
raise ValueError(f"Factor value should be from 0 to {self.max_threshold}")
self.input_img = input_img
self.threshold = threshold
self.width, self.height = self.input_img.shape[1], self.input_img.shape[0]
# error table size (+4 columns and +1 row) greater than input image because of
# lack of if statements
self.error_table = [
[0 for _ in range(self.height + 4)] for __ in range(self.width + 1)
]
self.output_img = np.ones((self.width, self.height, 3), np.uint8) * 255
@classmethod
def get_greyscale(cls, blue: int, green: int, red: int) -> float:
"""
>>> Burkes.get_greyscale(3, 4, 5)
3.753
"""
return 0.114 * blue + 0.587 * green + 0.2126 * red
def process(self) -> None:
for y in range(self.height):
for x in range(self.width):
greyscale = int(self.get_greyscale(*self.input_img[y][x]))
if self.threshold > greyscale + self.error_table[y][x]:
self.output_img[y][x] = (0, 0, 0)
current_error = greyscale + self.error_table[x][y]
else:
self.output_img[y][x] = (255, 255, 255)
current_error = greyscale + self.error_table[x][y] - 255
"""
Burkes error propagation (`*` is current pixel):
* 8/32 4/32
2/32 4/32 8/32 4/32 2/32
"""
self.error_table[y][x + 1] += int(8 / 32 * current_error)
self.error_table[y][x + 2] += int(4 / 32 * current_error)
self.error_table[y + 1][x] += int(8 / 32 * current_error)
self.error_table[y + 1][x + 1] += int(4 / 32 * current_error)
self.error_table[y + 1][x + 2] += int(2 / 32 * current_error)
self.error_table[y + 1][x - 1] += int(4 / 32 * current_error)
self.error_table[y + 1][x - 2] += int(2 / 32 * current_error)
if __name__ == "__main__":
# create Burke's instances with original images in greyscale
burkes_instances = [
Burkes(imread("image_data/lena.jpg", 1), threshold)
for threshold in (1, 126, 130, 140)
]
for burkes in burkes_instances:
burkes.process()
for burkes in burkes_instances:
imshow(
f"Original image with dithering threshold: {burkes.threshold}",
burkes.output_img,
)
waitKey(0)
destroyAllWindows()
