

'''simple docstring'''

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

lowerCAmelCase_        =	1_8_0

def 			__magic_name__				(       A    ,      A							)	->						Optional[Any]:
	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(lowercase_							) + np.square(lowercase_							)) / (2 * np.square(lowercase_							))							)
	)
	return g

def 			__magic_name__				(       A    ,      A    ,      A							)	->						List[Any]:
	snake_case							       = np.zeros(lowercase_							)

	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 1_5 * 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 < 1_1 * 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 1_1 * PI / 8 <= direction < 1_3 * 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 1_3 * PI / 8 <= direction < 1_5 * 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 			__magic_name__				(       A    ,      A    ,      A    ,      A    ,      A    ,      A							)	->						str:
	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 			__magic_name__				(       A    ,      A    ,      A    ,      A							)	->						int:
	for row in range(1    ,      image_shape[0]							):
		for col in range(1    ,      image_shape[1]							):
			if destination[row, col] == weak:
				if 2_5_5 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 			__magic_name__				(       A    ,      A=1_5    ,      A=3_0    ,      A=1_2_8    ,      A=2_5_5							)	->						Any:
	# gaussian_filter
	snake_case							       = img_convolve(lowercase_    ,      gen_gaussian_kernel(9    ,      sigma=1.4							)							)
	# get the gradient and degree by sobel_filter
	snake_case       ,       snake_case							       = sobel_filter(lowercase_							)
	snake_case							       = PI + np.radadeg(lowercase_							)

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

	detect_high_low_threshold(
	    image.shape    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_							)

	track_edge(image.shape    ,      lowercase_    ,      lowercase_    ,      lowercase_							)

	return destination


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










"""simple docstring"""
from .stack import Stack



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				bool:
				_lowerCamelCase					     = Stack()
				_lowerCamelCase					     = {'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
				for bracket in parentheses:
								if bracket in bracket_pairs:
												stack.push(lowercase_							)
								elif bracket in (")", "]", "}"):
												if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																return False
				return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					['''((()))''', '''((())''', '''(()))''']
			print('''Balanced parentheses demonstration:\n''')
			for example in examples:
						__SCREAMING_SNAKE_CASE     :  Any										=					'''''' if balanced_parentheses(example) else '''not '''
						print(F"""{example} is {not_str}balanced""")
