






from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def 							A     (       a_							,a_					)     ->						Union[str, Any]:
					__UpperCamelCase      :					List[Any]       		=k_size // 2
					__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					__UpperCamelCase      :					List[str]       		=1 / (2 * pi * sigma) * exp(-(square(a_					) + square(a_					)) / (2 * square(a_					))					)
					return g






def 							A     (       a_							,a_							,a_					)     ->						Any:
					__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=image.shape[0], image.shape[1]
					# dst image height and width
					__UpperCamelCase      :					List[str]       		=height - k_size + 1
					__UpperCamelCase      :					Dict       		=width - k_size + 1

					# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
					__UpperCamelCase      :					Tuple       		=zeros((dst_height * dst_width, k_size * k_size)					)
					__UpperCamelCase      :					Optional[Any]       		=0
					for i, j in product(range(a_					)							,range(a_					)					):
										__UpperCamelCase      :					Union[str, Any]       		=ravel(image[i : i + k_size, j : j + k_size]					)
										__UpperCamelCase      :					List[Any]       		=window
										row += 1

					#  turn the kernel into shape(k*k, 1)
					__UpperCamelCase      :					List[str]       		=gen_gaussian_kernel(a_							,a_					)
					__UpperCamelCase      :					Any       		=ravel(a_					)

					# reshape and get the dst image
					__UpperCamelCase      :					int       		=dot(a_							,a_					).reshape(a_							,a_					).astype(a_					)

					return dst


if __name__ == "__main__":
							# read original image
							A_       :Any       =						imread(R'''../image_data/lena.jpg''')
							# turn image in gray scale value
							A_       :Any       =						cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							A_       :Any       =						gaussian_filter(gray, 3, sigma=1)
							A_       :Any       =						gaussian_filter(gray, 5, sigma=0.8)

							# show result images
							imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
							imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
							waitKey()





'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for data in source_data:
    for i, el in enumerate(A__					):
      if len(A__					) < i + 1:
        data_lists.append([]					)
      data_lists[i].append(float(A__					)					)
  return data_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      str			=   max(A__					)

    lowerCAmelCase_			:      list[float]			=   []
    # for weight 0 score is 1 - actual score
    if weight == 0:
      for item in dlist:
        try:
          score.append(1 - ((item - mind) / (maxd - mind))					)
        except ZeroDivisionError:
          score.append(1					)

    elif weight == 1:
      for item in dlist:
        try:
          score.append((item - mind) / (maxd - mind)					)
        except ZeroDivisionError:
          score.append(0					)

        # weight not 0 or 1
    else:
      lowerCAmelCase_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[float]			=   [0 for i in range(len(score_lists[0]					)					)]

  for slist in score_lists:
    for j, ele in enumerate(A__					):
      lowerCAmelCase_			:      List[Any]			=   final_scores[j] + ele

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

  # append scores to source data
  for i, ele in enumerate(A__					):
    source_data[i].append(A__					)

  return source_data



