



import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A__			   = 1_80


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					k_size // 2
					_lowerCAmelCase			,			_lowerCAmelCase      			=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					_lowerCAmelCase      			=					(
					    1
					    / (2 * np.pi * sigma)
					    * np.exp(-(np.square(snake_case    ) + np.square(snake_case    )) / (2 * np.square(snake_case    ))    )
					)
					return g


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					np.zeros(snake_case    )

					for row in range(1			,		image_shape[0] - 1    ):
										for col in range(1			,		image_shape[1] - 1    ):
															_lowerCAmelCase      			=					gradient_direction[row, col]

															if (
															    0 <= direction < PI / 8
															    or 15 * PI / 8 <= direction <= 2 * PI
															    or 7 * PI / 8 <= direction <= 9 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row, col - 1]
																				_lowerCAmelCase      			=					sobel_grad[row, col + 1]
																				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

															elif (
															    PI / 8 <= direction < 3 * PI / 8
															    or 9 * PI / 8 <= direction < 11 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row + 1, col - 1]
																				_lowerCAmelCase      			=					sobel_grad[row - 1, col + 1]
																				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

															elif (
															    3 * PI / 8 <= direction < 5 * PI / 8
															    or 11 * PI / 8 <= direction < 13 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row - 1, col]
																				_lowerCAmelCase      			=					sobel_grad[row + 1, col]
																				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

															elif (
															    5 * PI / 8 <= direction < 7 * PI / 8
															    or 13 * PI / 8 <= direction < 15 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row - 1, col - 1]
																				_lowerCAmelCase      			=					sobel_grad[row + 1, col + 1]
																				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

					return destination


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					for row in range(1			,		image_shape[0] - 1    ):
										for col in range(1			,		image_shape[1] - 1    ):
															if destination[row, col] >= threshold_high:
																				_lowerCAmelCase      			=					strong
															elif destination[row, col] <= threshold_low:
																				_lowerCAmelCase      			=					0
															else:
																				_lowerCAmelCase      			=					weak


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					for row in range(1			,		image_shape[0]    ):
										for col in range(1			,		image_shape[1]    ):
															if destination[row, col] == weak:
																				if 2_55 in (
																				    destination[row, col + 1],
																				    destination[row, col - 1],
																				    destination[row - 1, col],
																				    destination[row + 1, col],
																				    destination[row - 1, col - 1],
																				    destination[row + 1, col - 1],
																				    destination[row - 1, col + 1],
																				    destination[row + 1, col + 1],
																				):
																									_lowerCAmelCase      			=					strong
																				else:
																									_lowerCAmelCase      			=					0


def 	_UpperCAmelCase       (   snake_case			,		snake_case=15			,		snake_case=30			,		snake_case=1_28			,		snake_case=2_55    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					img_convolve(snake_case			,		gen_gaussian_kernel(9			,		sigma=1.4    )    )
					# get the gradient and degree by sobel_filter
					_lowerCAmelCase			,			_lowerCAmelCase      			=					sobel_filter(snake_case    )
					_lowerCAmelCase      			=					PI + np.radadeg(snake_case    )

					_lowerCAmelCase      			=					suppress_non_maximum(image.shape			,		snake_case			,		snake_case    )

					detect_high_low_threshold(
					    image.shape			,		snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case    )

					track_edge(image.shape			,		snake_case			,		snake_case			,		snake_case    )

					return destination


if __name__ == "__main__":
	# read original image in gray mode
	A__			   = cva.imread(R"""../image_data/lena.jpg""", 0)
	# canny edge detection
	A__			   = canny(lena)
	cva.imshow("""canny""", canny_destination)
	cva.waitKey(0)








from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__			   = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					return [int(snake_case    ) for c in F'{ruleset:08}'[:8]]


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					len(cells[0]    )  # 31
					_lowerCAmelCase      			=					[]
					for i in range(snake_case    ):
										# Get the neighbors of each cell
										# Handle neighbours outside bounds by using 0 as their value
										_lowerCAmelCase      			=					0 if i == 0 else cells[time][i - 1]
										_lowerCAmelCase      			=					0 if i == population - 1 else cells[time][i + 1]
										# Define a new cell and add it to the new generation
										_lowerCAmelCase      			=					7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,		2    )
										next_generation.append(rule[situation]    )
					return next_generation


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					Image.new("""RGB"""			,		(len(cells[0]    ), len(snake_case    ))    )
					_lowerCAmelCase      			=					img.load()
					# Generates image
					for w in range(img.width    ):
										for h in range(img.height    ):
															_lowerCAmelCase      			=					2_55 - int(2_55 * cells[h][w]    )
															_lowerCAmelCase      			=					(color, color, color)
					return img


if __name__ == "__main__":
	A__			   = bin(int(input("""Rule:\n""").strip()))[2:]
	A__			   = format_ruleset(int(rule_num))
	for time in range(16):
		CELLS.append(new_generation(CELLS, rule, time))
	A__			   = generate_image(CELLS)
	# Uncomment to save the image
	# img.save(f"rule_{rule_num}.png")
	img.show()



