





from pathlib import Path

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		np.ndarray:
       UpperCamelCase_          =  cva.getAffineTransform(UpperCamelCase_					,		UpperCamelCase_  )
       return cva.warpAffine(UpperCamelCase_					,		UpperCamelCase_					,		(rows, cols)  )


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

     # set different points to rotate image
     _UpperCAmelCase        =				np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
     _UpperCAmelCase        =				np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
     _UpperCAmelCase        =				np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
     _UpperCAmelCase        =				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
     _UpperCAmelCase        =				[
         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        =				plt.figure(1)
     _UpperCAmelCase        =				['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()








import re
import string

import numpy as np

import datasets


_UpperCAmelCase        =				'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

_UpperCAmelCase        =				'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

_UpperCAmelCase        =				'\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,						_KWARGS_DESCRIPTION     )
class 							_UpperCamelCase					(	datasets.Metric     ):

       def lowercase (		self: Any		)       -> List[str]:




              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION					,   citation=_CITATION					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
                      {
                          "predictions": datasets.Value("string"					,   id="sequence"		),
                          "references": datasets.Value("string"					,   id="sequence"		),
                      }		)					,   reference_urls=[]					,   )



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: Tuple=False					,   _SCREAMING_SNAKE_CASE: Optional[int]=False					,   _SCREAMING_SNAKE_CASE: Any=False					,   )       -> Optional[Any]:




              """simple docstring"""


              if regexes_to_ignore is not None:
                     for s in regexes_to_ignore:
                            UpperCamelCase_          =  np.array([re.sub(_SCREAMING_SNAKE_CASE					,   ""					,   _SCREAMING_SNAKE_CASE		) for x in predictions]		)
                            UpperCamelCase_          =  np.array([re.sub(_SCREAMING_SNAKE_CASE					,   ""					,   _SCREAMING_SNAKE_CASE		) for x in references]		)
              else:
                     UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)

              if ignore_case:
                     UpperCamelCase_          =  np.char.lower(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.lower(_SCREAMING_SNAKE_CASE		)

              if ignore_punctuation:
                     UpperCamelCase_          =  string.punctuation.maketrans(""					,   ""					,   string.punctuation		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)

              if ignore_numbers:
                     UpperCamelCase_          =  string.digits.maketrans(""					,   ""					,   string.digits		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  predictions == references

              return {"exact_match": np.mean(_SCREAMING_SNAKE_CASE		) * 100}

