





'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor


class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=7						,	_UpperCAmelCase=3						,	_UpperCAmelCase=18						,	_UpperCAmelCase=30						,	_UpperCAmelCase=400						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	_UpperCAmelCase=True						,	_UpperCAmelCase=[0.5, 0.5, 0.5]						,	_UpperCAmelCase=[0.5, 0.5, 0.5]						,	):

          '''simple docstring'''






          __A						: List[str]      =       size if size is not None else {'shortest_edge': 18}
          __A						: Any      =       crop_size if crop_size is not None else {'height': 18, 'width': 18}
          __A						: Tuple      =       parent
          __A						: int      =       batch_size
          __A						: Dict      =       num_channels
          __A						: Dict      =       image_size
          __A						: Tuple      =       min_resolution
          __A						: Any      =       max_resolution
          __A						: List[str]      =       do_resize
          __A						: Union[str, Any]      =       size
          __A						: Optional[Any]      =       do_center_crop
          __A						: Optional[int]      =       crop_size
          __A						: Optional[int]      =       do_normalize
          __A						: str      =       image_mean
          __A						: Optional[int]      =       image_std


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "do_center_crop": self.do_center_crop,
              "size": self.size,
              "crop_size": self.crop_size,
          }




@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       LevitImageProcessor if is_vision_available() else None


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       LevitImageProcessingTester(self)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_UpperCAmelCase						,	'image_mean'))
          self.assertTrue(hasattr(_UpperCAmelCase						,	'image_std'))
          self.assertTrue(hasattr(_UpperCAmelCase						,	'do_normalize'))
          self.assertTrue(hasattr(_UpperCAmelCase						,	'do_resize'))
          self.assertTrue(hasattr(_UpperCAmelCase						,	'do_center_crop'))
          self.assertTrue(hasattr(_UpperCAmelCase						,	'size'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size						,	{'shortest_edge': 18})
          self.assertEqual(image_processor.crop_size						,	{'height': 18, 'width': 18})

          __A						: Union[str, Any]      =       self.image_processing_class.from_dict(self.image_processor_dict						,	size=42						,	crop_size=84)
          self.assertEqual(image_processor.size						,	{'shortest_edge': 42})
          self.assertEqual(image_processor.crop_size						,	{'height': 84, 'width': 84})


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          __A						: Dict      =       prepare_image_inputs(self.image_processor_tester						,	equal_resolution=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,	Image.Image)

          # Test not batched input
          __A						: Optional[Any]      =       image_processing(image_inputs[0]						,	return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape						,	(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )						,	)

          # Test batched
          __A						: Optional[Any]      =       image_processing(_UpperCAmelCase						,	return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape						,	(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          __A						: Union[str, Any]      =       prepare_image_inputs(self.image_processor_tester						,	equal_resolution=_UpperCAmelCase						,	numpify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,	np.ndarray)

          # Test not batched input
          __A						: Tuple      =       image_processing(image_inputs[0]						,	return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape						,	(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )						,	)

          # Test batched
          __A						: Optional[Any]      =       image_processing(_UpperCAmelCase						,	return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape						,	(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          __A						: str      =       prepare_image_inputs(self.image_processor_tester						,	equal_resolution=_UpperCAmelCase						,	torchify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,	torch.Tensor)

          # Test not batched input
          __A						: Dict      =       image_processing(image_inputs[0]						,	return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape						,	(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )						,	)

          # Test batched
          __A						: str      =       image_processing(_UpperCAmelCase						,	return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape						,	(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )						,	)






'''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()