


'''simple docstring'''






from pathlib import Path

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






def 		_A			(lowerCAmelCase__					:np.ndarray			,  lowerCAmelCase__					:np.ndarray			,  lowerCAmelCase__					:np.ndarray			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:int						)				->					np.ndarray:




     '''simple docstring'''



     _a      =							cva.getAffineTransform(lowerCAmelCase__			,  lowerCAmelCase__						)
     return cva.warpAffine(lowerCAmelCase__			,  lowerCAmelCase__			,  (rows, cols)						)


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

 # set different points to rotate image
 a_   :      Union[str, Any]				    =      np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
 a_   :      Dict				    =      np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
 a_   :      int				    =      np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
 a_   :      Optional[Any]				    =      np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)

 # add all rotated images in a list
 a_   :      Optional[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
 a_   :      Dict				    =      plt.figure(1)
 a_   :      Tuple				    =      ["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()





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

