


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   	=  180



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Tuple				      =     k_size // 2
     a_      ,      a_     :  Optional[Any]				      =     np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     a_     :  Dict				      =     (
         1
         / (2 * np.pi * sigma)
         * np.exp(-(np.square(__UpperCamelCase			) + np.square(__UpperCamelCase			)) / (2 * np.square(__UpperCamelCase			))			)
     )
     return g



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     np.zeros(__UpperCamelCase			)

     for row in range(1	,	image_shape[0] - 1			):
          for col in range(1	,	image_shape[1] - 1			):
               a_     :  List[Any]				      =     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
               ):
                    a_     :  Optional[int]				      =     sobel_grad[row, col - 1]
                    a_     :  str				      =     sobel_grad[row, col + 1]
                    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                         a_     :  Optional[Any]				      =     sobel_grad[row, col]

               elif (
                   PI / 8 <= direction < 3 * PI / 8
                   or 9 * PI / 8 <= direction < 1_1 * PI / 8
               ):
                    a_     :  Dict				      =     sobel_grad[row + 1, col - 1]
                    a_     :  str				      =     sobel_grad[row - 1, col + 1]
                    if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                         a_     :  Any				      =     sobel_grad[row, col]

               elif (
                   3 * PI / 8 <= direction < 5 * PI / 8
                   or 1_1 * PI / 8 <= direction < 1_3 * PI / 8
               ):
                    a_     :  List[str]				      =     sobel_grad[row - 1, col]
                    a_     :  List[str]				      =     sobel_grad[row + 1, col]
                    if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                         a_     :  Any				      =     sobel_grad[row, col]

               elif (
                   5 * PI / 8 <= direction < 7 * PI / 8
                   or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
               ):
                    a_     :  Dict				      =     sobel_grad[row - 1, col - 1]
                    a_     :  int				      =     sobel_grad[row + 1, col + 1]
                    if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                         a_     :  Optional[Any]				      =     sobel_grad[row, col]

     return destination



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     for row in range(1	,	image_shape[0] - 1			):
          for col in range(1	,	image_shape[1] - 1			):
               if destination[row, col] >= threshold_high:
                    a_     :  Tuple				      =     strong
               elif destination[row, col] <= threshold_low:
                    a_     :  Dict				      =     0
               else:
                    a_     :  Union[str, Any]				      =     weak



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     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],
                    ):
                         a_     :  Dict				      =     strong
                    else:
                         a_     :  Optional[Any]				      =     0



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=1_5	,	__UpperCamelCase=3_0	,	__UpperCamelCase=1_2_8	,	__UpperCamelCase=2_5_5			):
     # gaussian_filter
     a_     :  int				      =     img_convolve(__UpperCamelCase	,	gen_gaussian_kernel(9	,	sigma=1.4			)			)
     # get the gradient and degree by sobel_filter
     a_      ,      a_     :  int				      =     sobel_filter(__UpperCamelCase			)
     a_     :  Union[str, Any]				      =     PI + np.radadeg(__UpperCamelCase			)

     a_     :  Any				      =     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
       __lowerCamelCase   	=  cva.imread(R'''../image_data/lena.jpg''', 0)
       # canny edge detection
       __lowerCamelCase   	=  canny(lena)
       cva.imshow('''canny''', canny_destination)
       cva.waitKey(0)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase   	=  {'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''VisualBertForMultipleChoice''',
           '''VisualBertForPreTraining''',
           '''VisualBertForQuestionAnswering''',
           '''VisualBertForRegionToPhraseAlignment''',
           '''VisualBertForVisualReasoning''',
           '''VisualBertLayer''',
           '''VisualBertModel''',
           '''VisualBertPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       __lowerCamelCase   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



