


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     :			Tuple      =1_8_0


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					str		      =		k_size // 2
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					str		      =		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						__SCREAMING_SNAKE_CASE						:					Any		      =		(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(lowercase__				) + np.square(lowercase__				)) / (2 * np.square(lowercase__				))				)
						)
						return g


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.zeros(lowercase__				)

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

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

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

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

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

						return destination


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__				):
						for row in range(1   ,	image_shape[0] - 1				):
												for col in range(1   ,	image_shape[1] - 1				):
																		if destination[row, col] >= threshold_high:
																								__SCREAMING_SNAKE_CASE						:					List[Any]		      =		strong
																		elif destination[row, col] <= threshold_low:
																								__SCREAMING_SNAKE_CASE						:					int		      =		0
																		else:
																								__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		weak


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__				):
						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],
																								):
																														__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		strong
																								else:
																														__SCREAMING_SNAKE_CASE						:					str		      =		0


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__=15   ,	lowercase__=30   ,	lowercase__=128   ,	lowercase__=255				):
						# gaussian_filter
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		img_convolve(lowercase__   ,	gen_gaussian_kernel(9   ,	sigma=1.4				)				)
						# get the gradient and degree by sobel_filter
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sobel_filter(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		PI + np.radadeg(lowercase__				)

						__SCREAMING_SNAKE_CASE						:					Dict		      =		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     :			Any      =cva.imread(r'../image_data/lena.jpg', 0)
						# canny edge detection
						__lowerCAmelCase     :			Dict      =canny(lena)
						cva.imshow('canny', canny_destination)
						cva.waitKey(0)




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
