

"""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 ConvNextImageProcessor







class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def __init__( self    :       Tuple     ,lowercase_    :       Dict     ,lowercase_    :       Dict=7     ,lowercase_    :       Optional[int]=3     ,lowercase_    :       str=1_8     ,lowercase_    :       Dict=3_0     ,lowercase_    :       Any=4_0_0     ,lowercase_    :       List[str]=True     ,lowercase_    :       Optional[int]=None     ,lowercase_    :       Optional[Any]=0.875     ,lowercase_    :       Any=True     ,lowercase_    :       List[str]=[0.5, 0.5, 0.5]     ,lowercase_    :       Union[str, Any]=[0.5, 0.5, 0.5]     ,):
						lowerCAmelCase__			:		int						       =			size if size is not None else {'''shortest_edge''': 2_0}
						lowerCAmelCase__			:		List[Any]						       =			parent
						lowerCAmelCase__			:		List[Any]						       =			batch_size
						lowerCAmelCase__			:		Tuple						       =			num_channels
						lowerCAmelCase__			:		Any						       =			image_size
						lowerCAmelCase__			:		str						       =			min_resolution
						lowerCAmelCase__			:		List[str]						       =			max_resolution
						lowerCAmelCase__			:		Optional[Any]						       =			do_resize
						lowerCAmelCase__			:		Tuple						       =			size
						lowerCAmelCase__			:		List[str]						       =			crop_pct
						lowerCAmelCase__			:		Tuple						       =			do_normalize
						lowerCAmelCase__			:		Dict						       =			image_mean
						lowerCAmelCase__			:		Any						       =			image_std







					def  __lowerCAmelCase				( self    :       List[Any]	):
						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_pct": self.crop_pct,
						}







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






					"""simple docstring"""



					lowercase__       				= ConvNextImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Optional[Any]	):
						lowerCAmelCase__			:		Any						       =			ConvNextImageProcessingTester(self	)







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







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Optional[int]						       =			self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowercase_     ,'''do_resize'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''size'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''crop_pct'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''do_normalize'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''image_mean'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''image_std'''	)	)







					def  __lowerCAmelCase				( self    :       Tuple	):
						lowerCAmelCase__			:		Union[str, Any]						       =			self.image_processing_class.from_dict(self.image_processor_dict	)
						self.assertEqual(image_processor.size     ,{'''shortest_edge''': 2_0}	)

						lowerCAmelCase__			:		List[Any]						       =			self.image_processing_class.from_dict(self.image_processor_dict     ,size=4_2	)
						self.assertEqual(image_processor.size     ,{'''shortest_edge''': 4_2}	)







					def  __lowerCAmelCase				( self    :       int	):
						pass







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

						# Test not batched input
						lowerCAmelCase__			:		int						       =			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.size['''shortest_edge'''],
						        self.image_processor_tester.size['''shortest_edge'''],
						    )     ,)

						# Test batched
						lowerCAmelCase__			:		Dict						       =			image_processing(lowercase_     ,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.size['''shortest_edge'''],
						        self.image_processor_tester.size['''shortest_edge'''],
						    )     ,)







					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						# Initialize image_processing
						lowerCAmelCase__			:		int						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		List[Any]						       =			prepare_image_inputs(self.image_processor_tester     ,equal_resolution=lowercase_     ,numpify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,np.ndarray	)

						# Test not batched input
						lowerCAmelCase__			:		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.size['''shortest_edge'''],
						        self.image_processor_tester.size['''shortest_edge'''],
						    )     ,)

						# Test batched
						lowerCAmelCase__			:		int						       =			image_processing(lowercase_     ,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.size['''shortest_edge'''],
						        self.image_processor_tester.size['''shortest_edge'''],
						    )     ,)







					def  __lowerCAmelCase				( self    :       List[str]	):
						# Initialize image_processing
						lowerCAmelCase__			:		Optional[Any]						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Any						       =			prepare_image_inputs(self.image_processor_tester     ,equal_resolution=lowercase_     ,torchify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,torch.Tensor	)

						# Test not batched input
						lowerCAmelCase__			:		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.size['''shortest_edge'''],
						        self.image_processor_tester.size['''shortest_edge'''],
						    )     ,)

						# Test batched
						lowerCAmelCase__			:		int						       =			image_processing(lowercase_     ,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.size['''shortest_edge'''],
						        self.image_processor_tester.size['''shortest_edge'''],
						    )     ,)







"""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)




