




import cva
import numpy as np





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     int:







				'''simple docstring'''


				try:
								return int(image[x_coordinate][y_coordinate] >= center   )
				except (IndexError, TypeError):
								return 0





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     int:







				'''simple docstring'''


				__lowerCAmelCase					=  image[x_coordinate][y_coordinate]
				__lowerCAmelCase					=  [1, 2, 4, 8, 16, 32, 64, 1_28]

				# skip get_neighbors_pixel if center is null
				if center is None:
								return 0

				# Starting from the top right, assigning value to pixels clockwise
				__lowerCAmelCase					=  [
				    get_neighbors_pixel(UpperCamelCase__						,			x_coordinate - 1						,			y_coordinate + 1						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			UpperCamelCase__						,			y_coordinate + 1						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			x_coordinate - 1						,			UpperCamelCase__						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			x_coordinate + 1						,			y_coordinate + 1						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			x_coordinate + 1						,			UpperCamelCase__						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			x_coordinate + 1						,			y_coordinate - 1						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			UpperCamelCase__						,			y_coordinate - 1						,			UpperCamelCase__   ),
				    get_neighbors_pixel(UpperCamelCase__						,			x_coordinate - 1						,			y_coordinate - 1						,			UpperCamelCase__   ),
				]

				# Converting the binary value to decimal.
				return sum(
				    binary_value * power for binary_value, power in zip(UpperCamelCase__						,			UpperCamelCase__   )   )


if __name__ == "__main__":
	# Reading the image and converting it to grayscale.
	__A						:		List[Any]          =						cva.imread(
	    "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
	)

	# Create a numpy array as the same height and width of read image
	__A						:		str          =						np.zeros((image.shape[0], image.shape[1]))

	# Iterating through the image and calculating the
	# local binary pattern value for each pixel.
	for i in range(0, image.shape[0]):
		for j in range(0, image.shape[1]):
			__A						:		Optional[int]          =						local_binary_value(image, i, j)

	cva.imshow("local binary pattern", lbp_image)
	cva.waitKey(0)
	cva.destroyAllWindows()







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 DeiTImageProcessor






class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=18  ,						SCREAMING_SNAKE_CASE_=30  ,						SCREAMING_SNAKE_CASE_=400  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						)-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      size if size is not None else {'''height''': 20, '''width''': 20}
													__UpperCamelCase  	=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      min_resolution
													__UpperCamelCase  	=      max_resolution
													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      do_center_crop
													__UpperCamelCase  	=      crop_size
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean
													__UpperCamelCase  	=      image_std



							def      A__		(	self      )-> int:





													'''simple docstring'''






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






@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	DeiTImageProcessor if is_vision_available() else None
							_snake_case  		=	True



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      DeiTImageProcessingTester(self      )



							@property
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													return self.image_processor_tester.prepare_image_processor_dict()



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_resize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''size'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_normalize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_mean'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_std'''      )      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class.from_dict(self.image_processor_dict      )
													self.assertEqual(image_processor.size  ,						{'''height''': 20, '''width''': 20}      )
													self.assertEqual(image_processor.crop_size  ,						{'''height''': 18, '''width''': 18}      )

													__UpperCamelCase  	=      self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42  ,						crop_size=84      )
													self.assertEqual(image_processor.size  ,						{'''height''': 42, '''width''': 42}      )
													self.assertEqual(image_processor.crop_size  ,						{'''height''': 84, '''width''': 84}      )



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





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						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      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													__UpperCamelCase  	=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_  ,						numpify=SCREAMING_SNAKE_CASE_      )
													for image in image_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						np.ndarray      )

													# Test not batched input
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						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      A__		(	self      )-> str:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						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'''],
													    )  ,						)




