

from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     float):
									return 0.0







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       min([-20, np.min(fft_results[1 : samplerate // 2 - 1]    )]    )
						SCREAMING_SNAKE_CASE_: str  		=       max([20, np.max(fft_results[1 : samplerate // 2 - 1]    )]    )
						return lowest, highest







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       5_12
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [1] + [0] * (size - 1)
						SCREAMING_SNAKE_CASE_: str  		=       [filter_type.process(_UpperCAmelCase    ) for item in inputs]

						SCREAMING_SNAKE_CASE_: str  		=       [0] * (samplerate - size)  # zero-padding
						outputs += filler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       np.abs(np.fft.fft(_UpperCAmelCase    )    )
						SCREAMING_SNAKE_CASE_: int  		=       20 * np.logaa(_UpperCAmelCase    )

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24				,	samplerate / 2 - 1    )
						plt.xlabel("Frequency (Hz)"    )
						plt.xscale("log"    )

						# Display within reasonable bounds
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_bounds(_UpperCAmelCase				,	_UpperCAmelCase    )
						plt.ylim(max([-80, bounds[0]]    )				,	min([80, bounds[1]]    )    )
						plt.ylabel("Gain (dB)"    )

						plt.plot(_UpperCAmelCase    )
						plt.show()







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       5_12
						SCREAMING_SNAKE_CASE_: Tuple  		=       [1] + [0] * (size - 1)
						SCREAMING_SNAKE_CASE_: str  		=       [filter_type.process(_UpperCAmelCase    ) for item in inputs]

						SCREAMING_SNAKE_CASE_: Any  		=       [0] * (samplerate - size)  # zero-padding
						outputs += filler
						SCREAMING_SNAKE_CASE_: Tuple  		=       np.angle(np.fft.fft(_UpperCAmelCase    )    )

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24				,	samplerate / 2 - 1    )
						plt.xlabel("Frequency (Hz)"    )
						plt.xscale("log"    )

						plt.ylim(-2 * pi				,	2 * pi    )
						plt.ylabel("Phase shift (Radians)"    )
						plt.plot(np.unwrap(_UpperCAmelCase				,	-2 * pi    )    )
						plt.show()













'''simple docstring'''




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        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

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

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

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    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()