
'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase=3	)->			List[Any]:







						"""simple docstring"""
						__A       	=			int(mask / 2	)
						# copy image size
						__A       	=			zeros_like(_A	)
						for i in range(_A      ,  gray_img.shape[0] - bd	):
												for j in range(_A      ,  gray_img.shape[1] - bd	):
																		# get mask according with mask
																		__A       	=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
																		# calculate mask median
																		__A       	=			sort(_A	)[inta(divide((multiply(_A      ,  _A	))      ,  2	) + 1	)]
																		__A       	=			median
						return median_img


if __name__ == "__main__":
	# read original image
	a__		:   Tuple            =       imread("../image_data/lena.jpg")
	# turn image in gray scale value
	a__		:   Dict            =       cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	a__		:   Optional[int]            =       median_filter(gray, 3)
	a__		:   int            =       median_filter(gray, 5)

	# show result images
	imshow("median filter with 3x3 mask", medianaxa)
	imshow("median filter with 5x5 mask", medianaxa)
	waitKey(0)








'''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_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VideoMAEImageProcessor


class  UpperCamelCase__  (    unittest.TestCase):






 def __init__(			self  :Dict	,				_A  :Optional[Any]	,				_A  :Any=7	,				_A  :Dict=3	,				_A  :Optional[Any]=10	,				_A  :List[Any]=18	,				_A  :int=30	,				_A  :List[Any]=400	,				_A  :Any=True	,				_A  :Any=None	,				_A  :Optional[int]=True	,				_A  :Tuple=[0.5, 0.5, 0.5]	,				_A  :int=[0.5, 0.5, 0.5]	,				_A  :List[Any]=None	,				)				->			int:







       '''simple docstring'''



       __A       	=			size if size is not None else {'shortest_edge': 18}
       __A       	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}

       __A       	=			parent
       __A       	=			batch_size
       __A       	=			num_channels
       __A       	=			num_frames
       __A       	=			image_size
       __A       	=			min_resolution
       __A       	=			max_resolution
       __A       	=			do_resize
       __A       	=			size
       __A       	=			do_normalize
       __A       	=			image_mean
       __A       	=			image_std
       __A       	=			crop_size






 def 	lowercase_						(			self  :Any			)				->			Union[str, Any]:







       '''simple docstring'''



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


@require_torch
@require_vision
class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    Tuple										=					VideoMAEImageProcessor if is_vision_available() else None






 def 	lowercase_						(			self  :Dict			)				->			Dict:







       '''simple docstring'''



       __A       	=			VideoMAEImageProcessingTester(self			)






 @property
 def 	lowercase_						(			self  :Dict			)				->			List[str]:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()






 def 	lowercase_						(			self  :Dict			)				->			Optional[int]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_A	,				'image_mean'			)			)
       self.assertTrue(hasattr(_A	,				'image_std'			)			)
       self.assertTrue(hasattr(_A	,				'do_normalize'			)			)
       self.assertTrue(hasattr(_A	,				'do_resize'			)			)
       self.assertTrue(hasattr(_A	,				'do_center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'size'			)			)






 def 	lowercase_						(			self  :Optional[int]			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			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       	=			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 	lowercase_						(			self  :Any			)				->			Dict:







       '''simple docstring'''



       pass






 def 	lowercase_						(			self  :Optional[int]			)				->			Tuple:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PIL videos
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				Image.Image			)

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

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :Dict			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A	,				numpify=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				np.ndarray			)

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

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :int			)				->			List[str]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A	,				torchify=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				torch.Tensor			)

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

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






