import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter




class  _UpperCAmelCase       (			TensorFormatter[Mapping, np.ndarray, Mapping]   ):



 '''simple docstring'''




 def __init__(						self							:					str				,      lowercase_							:					int=None				,      **lowercase_							:					List[Any])		->     List[Any]:


   """simple docstring"""







   super().__init__(features=lowercase_)
   _UpperCamelCase				=  np_array_kwargs







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					List[str])		->     int:


   """simple docstring"""







   if isinstance(lowercase_				,      lowercase_):
     if column and all(
         isinstance(lowercase_				,      np.ndarray) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
       return np.stack(lowercase_)
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       _UpperCamelCase				=  np.empty(len(lowercase_)				,      dtype=lowercase_)
       _UpperCamelCase				=  column
       return out
   return column







 def 						__UpperCAmelCase							(						self							:					Optional[int]				,      lowercase_							:					str)		->     str:


   """simple docstring"""







   if isinstance(lowercase_				,      (str, bytes, type(lowercase_))):
     return value
   elif isinstance(lowercase_				,      (np.character, np.ndarray)) and np.issubdtype(value.dtype				,      np.character):
     return value
   elif isinstance(lowercase_				,      np.number):
     return value

   _UpperCamelCase				=  {}

   if isinstance(lowercase_				,      np.ndarray) and np.issubdtype(value.dtype				,      np.integer):
     _UpperCamelCase				=  {"dtype": np.intaa}
   elif isinstance(lowercase_				,      np.ndarray) and np.issubdtype(value.dtype				,      np.floating):
     _UpperCamelCase				=  {"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowercase_				,      PIL.Image.Image):
       return np.asarray(lowercase_				,      **self.np_array_kwargs)

   return np.asarray(lowercase_				,      **{**default_dtype, **self.np_array_kwargs})







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					str)		->     Any:


   """simple docstring"""







   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowercase_				,      torch.Tensor):
       return self._tensorize(data_struct.detach().cpu().numpy()[()])
   if hasattr(lowercase_				,      "__array__") and not isinstance(lowercase_				,      (np.ndarray, np.character, np.number)):
     _UpperCamelCase				=  data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowercase_				,      np.ndarray):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowercase_) for substruct in data_struct])
   if isinstance(lowercase_				,      (list, tuple)):
     return self._consolidate([self.recursive_tensorize(lowercase_) for substruct in data_struct])
   return self._tensorize(lowercase_)







 def 						__UpperCAmelCase							(						self							:					Optional[int]				,      lowercase_							:					dict)		->     List[str]:


   """simple docstring"""







   return map_nested(self._recursive_tensorize				,      lowercase_				,      map_list=lowercase_)







 def 						__UpperCAmelCase							(						self							:					List[str]				,      lowercase_							:					pa.Table)		->     Mapping:


   """simple docstring"""







   _UpperCamelCase				=  self.numpy_arrow_extractor().extract_row(lowercase_)
   _UpperCamelCase				=  self.python_features_decoder.decode_row(lowercase_)
   return self.recursive_tensorize(lowercase_)







 def 						__UpperCAmelCase							(						self							:					Any				,      lowercase_							:					pa.Table)		->     np.ndarray:


   """simple docstring"""







   _UpperCamelCase				=  self.numpy_arrow_extractor().extract_column(lowercase_)
   _UpperCamelCase				=  self.python_features_decoder.decode_column(lowercase_				,      pa_table.column_names[0])
   _UpperCamelCase				=  self.recursive_tensorize(lowercase_)
   _UpperCamelCase				=  self._consolidate(lowercase_)
   return column







 def 						__UpperCAmelCase							(						self							:					str				,      lowercase_							:					pa.Table)		->     Mapping:


   """simple docstring"""







   _UpperCamelCase				=  self.numpy_arrow_extractor().extract_batch(lowercase_)
   _UpperCamelCase				=  self.python_features_decoder.decode_batch(lowercase_)
   _UpperCamelCase				=  self.recursive_tensorize(lowercase_)
   for column_name in batch:
     _UpperCamelCase				=  self._consolidate(batch[column_name])
   return batch





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 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''simple docstring'''

  _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(a__      ) + np.square(a__      )) / (2 * np.square(a__      ))      )
  )
  return g


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  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__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''simple docstring'''

  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__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''simple docstring'''

  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__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  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)




