



"""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 OwlViTImageProcessor

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Optional[Any],    lowerCamelCase				:    Dict=7,    lowerCamelCase				:    Dict=3,    lowerCamelCase				:    Optional[int]=18,    lowerCamelCase				:    int=30,    lowerCamelCase				:    Any=400,    lowerCamelCase				:    str=True,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=None,    lowerCamelCase				:    List[str]=True,    lowerCamelCase				:    Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073],    lowerCamelCase				:    int=[0.26_862_954, 0.26_130_258, 0.27_577_711],    lowerCamelCase				:    Dict=True,    )->   Tuple:
								lowerCamelCase__						:						str						      =parent
								lowerCamelCase__						:						Dict						      =batch_size
								lowerCamelCase__						:						Optional[Any]						      =num_channels
								lowerCamelCase__						:						List[str]						      =image_size
								lowerCamelCase__						:						Tuple						      =min_resolution
								lowerCamelCase__						:						Dict						      =max_resolution
								lowerCamelCase__						:						Tuple						      =do_resize
								lowerCamelCase__						:						List[str]						      =size if size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						Optional[Any]						      =do_center_crop
								lowerCamelCase__						:						int						      =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						Dict						      =do_normalize
								lowerCamelCase__						:						List[Any]						      =image_mean
								lowerCamelCase__						:						Tuple						      =image_std
								lowerCamelCase__						:						Any						      =do_convert_rgb




					def 						snake_case			(			self				:    Optional[int]	)->   Optional[int]:
								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,
								    "do_convert_rgb": self.do_convert_rgb,
								}





@require_torch
@require_vision
class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				OwlViTImageProcessor if is_vision_available() else None



					def 						snake_case			(			self				:    Optional[Any]	)->   Tuple:
								lowerCamelCase__						:						List[Any]						      =OwlViTImageProcessingTester(self	)



					@property
					def 						snake_case			(			self				:    int	)->   List[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						snake_case			(			self				:    Tuple	)->   int:
								lowerCamelCase__						:						str						      =self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_convert_rgb'''	)	)



					def 						snake_case			(			self				:    List[Any]	)->   str:
								lowerCamelCase__						:						Any						      =self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size,    {'''height''': 18, '''width''': 18}	)
								self.assertEqual(image_processor.crop_size,    {'''height''': 18, '''width''': 18}	)

								lowerCamelCase__						:						Any						      =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 						snake_case			(			self				:    Optional[Any]	)->   Union[str, Any]:
								# Initialize image_processing
								lowerCamelCase__						:						Dict						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								lowerCamelCase__						:						Optional[Any]						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase	)

								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    Image.Image	)

								# Test not batched input
								lowerCamelCase__						:						str						      =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
								lowerCamelCase__						:						Dict						      =image_processing(lowerCamelCase,    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 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								# Initialize image_processing
								lowerCamelCase__						:						int						      =self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								lowerCamelCase__						:						Dict						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    numpify=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    np.ndarray	)

								# Test not batched input
								lowerCamelCase__						:						Optional[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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						List[Any]						      =image_processing(lowerCamelCase,    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 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								# Initialize image_processing
								lowerCamelCase__						:						Optional[Any]						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								lowerCamelCase__						:						int						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    torchify=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						List[str]						      =image_processing(lowerCamelCase,    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"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_a	:	int=					180
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							Tuple    )				->     Union[str, Any]:

	'''simple docstring'''





	__snake_case     :				Optional[Any] 						=					k_size // 2
	__snake_case						,							__snake_case     :				Union[str, Any] 						=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	__snake_case     :				List[Any] 						=					(
	    1
	    / (2 * np.pi * sigma)
	    * np.exp(-(np.square(UpperCAmelCase_    ) + np.square(UpperCAmelCase_    )) / (2 * np.square(UpperCAmelCase_    ))    )
	)
	return g
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							Optional[int]    )				->     List[str]:

	'''simple docstring'''





	__snake_case     :				List[Any] 						=					np.zeros(UpperCAmelCase_    )

	for row in range(1     ,				image_shape[0] - 1    ):
		for col in range(1     ,				image_shape[1] - 1    ):
			__snake_case     :				Any 						=					gradient_direction[row, col]

			if (
			    0 <= direction < PI / 8
			    or 15 * PI / 8 <= direction <= 2 * PI
			    or 7 * PI / 8 <= direction <= 9 * PI / 8
			):
				__snake_case     :				str 						=					sobel_grad[row, col - 1]
				__snake_case     :				str 						=					sobel_grad[row, col + 1]
				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
					__snake_case     :				Union[str, Any] 						=					sobel_grad[row, col]

			elif (
			    PI / 8 <= direction < 3 * PI / 8
			    or 9 * PI / 8 <= direction < 11 * PI / 8
			):
				__snake_case     :				Optional[int] 						=					sobel_grad[row + 1, col - 1]
				__snake_case     :				Optional[Any] 						=					sobel_grad[row - 1, col + 1]
				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
					__snake_case     :				Union[str, Any] 						=					sobel_grad[row, col]

			elif (
			    3 * PI / 8 <= direction < 5 * PI / 8
			    or 11 * PI / 8 <= direction < 13 * PI / 8
			):
				__snake_case     :				Union[str, Any] 						=					sobel_grad[row - 1, col]
				__snake_case     :				Optional[int] 						=					sobel_grad[row + 1, col]
				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
					__snake_case     :				int 						=					sobel_grad[row, col]

			elif (
			    5 * PI / 8 <= direction < 7 * PI / 8
			    or 13 * PI / 8 <= direction < 15 * PI / 8
			):
				__snake_case     :				Optional[int] 						=					sobel_grad[row - 1, col - 1]
				__snake_case     :				Union[str, Any] 						=					sobel_grad[row + 1, col + 1]
				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
					__snake_case     :				List[str] 						=					sobel_grad[row, col]

	return destination
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							List[Any]    )				->     Any:

	'''simple docstring'''





	for row in range(1     ,				image_shape[0] - 1    ):
		for col in range(1     ,				image_shape[1] - 1    ):
			if destination[row, col] >= threshold_high:
				__snake_case     :				str 						=					strong
			elif destination[row, col] <= threshold_low:
				__snake_case     :				int 						=					0
			else:
				__snake_case     :				Tuple 						=					weak
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	for row in range(1     ,				image_shape[0]    ):
		for col in range(1     ,				image_shape[1]    ):
			if destination[row, col] == weak:
				if 2_55 in (
				    destination[row, col + 1],
				    destination[row, col - 1],
				    destination[row - 1, col],
				    destination[row + 1, col],
				    destination[row - 1, col - 1],
				    destination[row + 1, col - 1],
				    destination[row - 1, col + 1],
				    destination[row + 1, col + 1],
				):
					__snake_case     :				Tuple 						=					strong
				else:
					__snake_case     :				Tuple 						=					0
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Any=15     ,				UpperCAmelCase_       :							int=30     ,				UpperCAmelCase_       :							Optional[Any]=1_28     ,				UpperCAmelCase_       :							Dict=2_55    )				->     Optional[Any]:

	'''simple docstring'''





	__snake_case     :				Tuple 						=					img_convolve(UpperCAmelCase_     ,				gen_gaussian_kernel(9     ,				sigma=1.4    )    )
	# get the gradient and degree by sobel_filter
	__snake_case						,							__snake_case     :				str 						=					sobel_filter(UpperCAmelCase_    )
	__snake_case     :				str 						=					PI + np.radadeg(UpperCAmelCase_    )

	__snake_case     :				int 						=					suppress_non_maximum(image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	detect_high_low_threshold(
	    image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	track_edge(image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	return destination


if __name__ == "__main__":
	# read original image in gray mode
	_a	:	List[Any]=					cva.imread(R"../image_data/lena.jpg", 0)
	# canny edge detection
	_a	:	Optional[int]=					canny(lena)
	cva.imshow("canny", canny_destination)
	cva.waitKey(0)





