





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										=					1_8_0




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       UpperCamelCase_ 							=	k_size // 2
       UpperCamelCase_							,			UpperCamelCase_ 							=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase_ 							=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(UpperCamelCase_	) + np.square(UpperCamelCase_	)) / (2 * np.square(UpperCamelCase_	))	)
       )
       return g




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	np.zeros(UpperCamelCase_	)

       for row in range(1       ,		image_shape[0] - 1	):
              for col in range(1       ,		image_shape[1] - 1	):
                     UpperCamelCase_ 							=	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_ 							=	sobel_grad[row, col - 1]
                            UpperCamelCase_ 							=	sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   UpperCamelCase_ 							=	sobel_grad[row, col]

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

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

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

       return destination




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		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_ 							=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCamelCase_ 							=	0
                     else:
                            UpperCamelCase_ 							=	weak




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       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_ 							=	strong
                            else:
                                   UpperCamelCase_ 							=	0




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

       UpperCamelCase_ 							=	suppress_non_maximum(image.shape       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       detect_high_low_threshold(
           image.shape       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       track_edge(image.shape       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       return destination


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








import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              warnings.warn(
                  "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                  " Please use ConvNextImageProcessor instead."       , _SCREAMING_SNAKE_CASE       , )
              super().__init__(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

