import numpy as np


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  _UpperCamelCase				=  np.floor(a__      )
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  _UpperCamelCase				=  int(n_draws - floored.sum()      )
  if need_to_add > 0:
    _UpperCamelCase				=  continuous - floored
    _UpperCamelCase				=  np.sort(np.unique(a__      )      )[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((_UpperCamelCase)	,		)				=  np.where(remainder == value      )
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      _UpperCamelCase				=  min(len(a__      )  ,			a__      )
      _UpperCamelCase				=  rng.choice(a__  ,			size=a__  ,			replace=a__      )
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa      )







def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__=10      )	->List[str]:




  '''simple docstring'''

  _UpperCamelCase	,		_UpperCamelCase				=  np.unique(a__  ,			return_inverse=a__      )
  _UpperCamelCase				=  classes.shape[0]
  _UpperCamelCase				=  np.bincount(a__      )
  if np.min(a__      ) < 2:
    raise ValueError("Minimum class count error"      )
  if n_train < n_classes:
    raise ValueError(
        "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)      )
  if n_test < n_classes:
    raise ValueError(
        "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)      )
  _UpperCamelCase				=  np.split(np.argsort(a__  ,			kind="mergesort"      )  ,			np.cumsum(a__      )[:-1]      )
  for _ in range(a__      ):
    _UpperCamelCase				=  approximate_mode(a__  ,			a__  ,			a__      )
    _UpperCamelCase				=  class_counts - n_i
    _UpperCamelCase				=  approximate_mode(a__  ,			a__  ,			a__      )

    _UpperCamelCase				=  []
    _UpperCamelCase				=  []

    for i in range(a__      ):
      _UpperCamelCase				=  rng.permutation(class_counts[i]      )
      _UpperCamelCase				=  class_indices[i].take(a__  ,			mode="clip"      )
      train.extend(perm_indices_class_i[: n_i[i]]      )
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
    _UpperCamelCase				=  rng.permutation(a__      )
    _UpperCamelCase				=  rng.permutation(a__      )

    yield train, test





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)




