
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available



@dataclass
class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
							from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
							from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





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

UpperCAmelCase__				:  str       					=				180



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			UpperCamelCase__    :		Dict     =				k_size // 2
			UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCamelCase__    :		int     =				(
			    1
			    / (2 * np.pi * sigma)
			    * np.exp(-(np.square(__SCREAMING_SNAKE_CASE ) + np.square(__SCREAMING_SNAKE_CASE )) / (2 * np.square(__SCREAMING_SNAKE_CASE )) )
			)
			return g



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			UpperCamelCase__    :		Dict     =				np.zeros(__SCREAMING_SNAKE_CASE )

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

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

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

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

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

			return destination



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



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			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],
												):
															UpperCamelCase__    :		List[str]     =				strong
												else:
															UpperCamelCase__    :		Union[str, Any]     =				0



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

			UpperCamelCase__    :		Any     =				suppress_non_maximum(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			detect_high_low_threshold(
			    image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			track_edge(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			return destination


if __name__ == "__main__":
							# read original image in gray mode
							UpperCAmelCase__				:  List[str]       					=				cva.imread(r'''../image_data/lena.jpg''', 0)
							# canny edge detection
							UpperCAmelCase__				:  Tuple       					=				canny(lena)
							cva.imshow('''canny''', canny_destination)
							cva.waitKey(0)



