

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

__A     						=      1_80



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[int]:



  """simple docstring"""






  _snake_case		=				k_size // 2
  _snake_case,	_snake_case		=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _snake_case		=				(
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(_lowerCAmelCase	) + np.square(_lowerCAmelCase	)) / (2 * np.square(_lowerCAmelCase	))	)
  )
  return g



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Union[str, Any]:



  """simple docstring"""






  _snake_case		=				np.zeros(_lowerCAmelCase	)

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

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

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

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

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

  return destination



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      str:



  """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:
        _snake_case		=				strong
      elif destination[row, col] <= threshold_low:
        _snake_case		=				0
      else:
        _snake_case		=				weak



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Any:



  """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],
        ):
          _snake_case		=				strong
        else:
          _snake_case		=				0



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=15					,			_UpperCamelCase=30					,			_UpperCamelCase=128					,			_UpperCamelCase=255	)				->      Dict:



  """simple docstring"""






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

  _snake_case		=				suppress_non_maximum(image.shape					,			_lowerCAmelCase					,			_lowerCAmelCase	)

  detect_high_low_threshold(
      image.shape					,			_lowerCAmelCase					,			_lowerCAmelCase					,			_lowerCAmelCase					,			_lowerCAmelCase					,			_lowerCAmelCase	)

  track_edge(image.shape					,			_lowerCAmelCase					,			_lowerCAmelCase					,			_lowerCAmelCase	)

  return destination


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






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCAmelCase :				Any							=1000_0000_0000
UpperCAmelCase :				Any							=1_0000

UpperCAmelCase			,			UpperCAmelCase :				Tuple							=os.path.split(__file__)
UpperCAmelCase :				Tuple							=os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= pa.Table.from_pydict({"col": [0] * chunk_size})
					UpperCamelCase_			= pa.concat_tables([table] * (num_examples // chunk_size))
					return datasets.Dataset(_lowerCAmelCase ,						fingerprint="table_100B")


@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= np.random.default_rng(self.seed     )
									UpperCamelCase_			= rng.integers(low=self.low  ,			high=self.high  ,			size=self.size     ).tolist()

				def __iter__(			self     ):

									'''simple docstring'''


									return iter(self._sampled_values     )

				def __len__(			self     ):

									'''simple docstring'''


									return self.size




@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[0]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[-1]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[range(len(_lowerCAmelCase) // 2 ,						len(_lowerCAmelCase) // 2 + 10_24)]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[RandIter(0 ,						len(_lowerCAmelCase) ,						10_24 ,						seed=42)]



def  _lowerCAmelCase     ():
					UpperCamelCase_			= {"num examples": SPEED_TEST_N_EXAMPLES}
					UpperCamelCase_			= (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset")
					UpperCamelCase_			= generate_100B_dataset(num_examples=_lowerCAmelCase ,						chunk_size=_lowerCAmelCase)
					print("Functions")
					for func in functions:
										print(func.__name__)
										UpperCamelCase_			= func(_lowerCAmelCase)

					with open(_lowerCAmelCase ,						"wb") as f:
										f.write(json.dumps(_lowerCAmelCase).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
	benchmark_table_100B()
