


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return 0.0




def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1]       )]       )
       UpperCamelCase    :	str   	=		max([2_0, np.max(fft_results[1 : samplerate // 2 - 1]       )]       )
       return lowest, highest


def      a	(							SCREAMING_SNAKE_CASE_      :		FilterType							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	List[Any]   	=		5_1_2
       UpperCamelCase    :	str   	=		[1] + [0] * (size - 1)
       UpperCamelCase    :	Dict   	=		[filter_type.process(SCREAMING_SNAKE_CASE_       ) for item in inputs]

       UpperCamelCase    :	int   	=		[0] * (samplerate - size)  # zero-padding
       outputs += filler
       UpperCamelCase    :	int   	=		np.abs(np.fft.fft(SCREAMING_SNAKE_CASE_       )       )
       UpperCamelCase    :	str   	=		2_0 * np.logaa(SCREAMING_SNAKE_CASE_       )

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(2_4							,			samplerate / 2 - 1       )
       plt.xlabel('''Frequency (Hz)'''       )
       plt.xscale('''log'''       )

       # Display within reasonable bounds
       UpperCamelCase    :	str   	=		get_bounds(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       plt.ylim(max([-8_0, bounds[0]]       )							,			min([8_0, bounds[1]]       )       )
       plt.ylabel('''Gain (dB)'''       )

       plt.plot(SCREAMING_SNAKE_CASE_       )
       plt.show()


def      a	(							SCREAMING_SNAKE_CASE_      :		FilterType							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		5_1_2
       UpperCamelCase    :	Union[str, Any]   	=		[1] + [0] * (size - 1)
       UpperCamelCase    :	str   	=		[filter_type.process(SCREAMING_SNAKE_CASE_       ) for item in inputs]

       UpperCamelCase    :	List[str]   	=		[0] * (samplerate - size)  # zero-padding
       outputs += filler
       UpperCamelCase    :	List[str]   	=		np.angle(np.fft.fft(SCREAMING_SNAKE_CASE_       )       )

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(2_4							,			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(SCREAMING_SNAKE_CASE_							,			-2 * pi       )       )
       plt.show()





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Tuple 			=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __UpperCAmelCase : int 			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __UpperCAmelCase			,    __UpperCAmelCase : Tuple 			=				gray_img.shape

  # set different points to rotate image
  __UpperCAmelCase : Optional[int] 			=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __UpperCAmelCase : Optional[int] 			=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __UpperCAmelCase : Any 			=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __UpperCAmelCase : int 			=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
  plt.show()

