
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

__magic_name__           =					180





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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			np.zeros(__lowerCAmelCase							)

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

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

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

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

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

    return destination





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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    for row in range(1      ,						image_shape[0]							):
        for col in range(1      ,						image_shape[1]							):
            if destination[row, col] == weak:
                if 255 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],
                ):
                    snake_case__  =			strong
                else:
                    snake_case__  =			0





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase=15      ,						__lowerCAmelCase=30      ,						__lowerCAmelCase=128      ,						__lowerCAmelCase=255							):
    # gaussian_filter
    snake_case__  =			img_convolve(__lowerCAmelCase      ,						gen_gaussian_kernel(9      ,						sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    snake_case__  ,   snake_case__  =			sobel_filter(__lowerCAmelCase							)
    snake_case__  =			PI + np.radadeg(__lowerCAmelCase							)

    snake_case__  =			suppress_non_maximum(image.shape      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)

    detect_high_low_threshold(
        image.shape      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)

    track_edge(image.shape      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)

    return destination


if __name__ == "__main__":
     # read original image in gray mode
     __magic_name__           =					cva.imread(r'''../image_data/lena.jpg''', 0)
     # canny edge detection
     __magic_name__           =					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
__magic_name__           =					[[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 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


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



