

"""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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size







class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       List[str]     ,lowercase_    :       List[str]=7     ,lowercase_    :       List[Any]=3     ,lowercase_    :       str=1_8     ,lowercase_    :       Any=3_0     ,lowercase_    :       int=4_0_0     ,lowercase_    :       Tuple=True     ,lowercase_    :       Optional[int]=1 / 2_5_5     ,lowercase_    :       int=True     ,lowercase_    :       Union[str, Any]=8     ,):
						lowerCAmelCase__			:		Tuple						       =			parent
						lowerCAmelCase__			:		Optional[Any]						       =			batch_size
						lowerCAmelCase__			:		int						       =			num_channels
						lowerCAmelCase__			:		Union[str, Any]						       =			image_size
						lowerCAmelCase__			:		Optional[Any]						       =			min_resolution
						lowerCAmelCase__			:		Union[str, Any]						       =			max_resolution
						lowerCAmelCase__			:		int						       =			do_rescale
						lowerCAmelCase__			:		Tuple						       =			rescale_factor
						lowerCAmelCase__			:		Dict						       =			do_pad
						lowerCAmelCase__			:		str						       =			pad_size







					def  __lowerCAmelCase				( self    :       int	):
						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       List[str]=False     ,lowercase_    :       Any=False     ,lowercase_    :       Dict=False	):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							lowerCAmelCase__			:		int						       =			[]
							for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        2_5_5     ,size=(self.num_channels, self.max_resolution, self.max_resolution)     ,dtype=np.uinta	)	)
						else:
							lowerCAmelCase__			:		str						       =			[]
							for i in range(self.batch_size	):
								lowerCAmelCase__			:		Any						       =			np.random.choice(np.arange(self.min_resolution     ,self.max_resolution	)     ,2	)
								image_inputs.append(np.random.randint(2_5_5     ,size=(self.num_channels, width, height)     ,dtype=np.uinta	)	)

						if not numpify and not torchify:
							# PIL expects the channel dimension as last dimension
							lowerCAmelCase__			:		Optional[int]						       =			[Image.fromarray(np.moveaxis(lowercase_     ,0     ,-1	)	) for x in image_inputs]

						if torchify:
							lowerCAmelCase__			:		int						       =			[torch.from_numpy(lowercase_	) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE      (	a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= SwinaSRImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			SwinaSRImageProcessingTester(self	)







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return self.image_processor_tester.prepare_image_processor_dict()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowercase_     ,'''do_rescale'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''rescale_factor'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''do_pad'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''pad_size'''	)	)







					def  __lowerCAmelCase				( self    :       Dict	):
						pass







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Optional[Any]	):
						lowerCAmelCase__			:		Tuple						       =			get_image_size(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			self.image_processor_tester.pad_size

						lowerCAmelCase__			:		List[Any]						       =			(old_height // size + 1) * size - old_height
						lowerCAmelCase__			:		Union[str, Any]						       =			(old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width







					def  __lowerCAmelCase				( self    :       List[Any]	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						lowerCAmelCase__			:		int						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,Image.Image	)

						# Test not batched input
						lowerCAmelCase__			:		List[str]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(np.array(image_inputs[0]	)	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       int	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		Tuple						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,numpify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,np.ndarray	)

						# Test not batched input
						lowerCAmelCase__			:		Optional[Any]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       Dict	):
						# Initialize image_processor
						lowerCAmelCase__			:		Tuple						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Union[str, Any]						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,torchify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,torch.Tensor	)

						# Test not batched input
						lowerCAmelCase__			:		Tuple						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__			:		int						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







"""simple docstring"""





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

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			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
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


if __name__ == "__main__":
							# read original image
							__UpperCamelCase						:   Dict							   =	imread('''../image_data/lena.jpg''')
							# turn image in gray scale value
							__UpperCamelCase						:   Optional[Any]							   =	cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

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




