


"""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	,					lowerCamelCase__	,					lowerCamelCase__=7	,					lowerCamelCase__=3	,					lowerCamelCase__=18	,					lowerCamelCase__=30	,					lowerCamelCase__=400	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=0.8_75	,					lowerCamelCase__=True	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      size if size is not None else {"shortest_edge": 20}
								snake_case__  :						List[Any]		=      parent
								snake_case__  :						Dict		=      batch_size
								snake_case__  :						Any		=      num_channels
								snake_case__  :						Optional[int]		=      image_size
								snake_case__  :						int		=      min_resolution
								snake_case__  :						Tuple		=      max_resolution
								snake_case__  :						int		=      do_resize
								snake_case__  :						Union[str, Any]		=      size
								snake_case__  :						Tuple		=      crop_pct
								snake_case__  :						int		=      do_normalize
								snake_case__  :						str		=      image_mean
								snake_case__  :						Union[str, Any]		=      image_std







							def        UpperCAmelCase (						self)   ->	Dict:







								'''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_pct": self.crop_pct,
								}


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




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  ConvNextImageProcessor if is_vision_available() else None




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      ConvNextImageProcessingTester(self)




							@property
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_resize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"size"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"crop_pct"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_normalize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_mean"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_std"))




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 20})

								snake_case__  :						List[Any]		=      self.image_processing_class.from_dict(self.image_processor_dict	,					size=42)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 42})




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								snake_case__  :						Union[str, 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
								snake_case__  :						List[Any]		=      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
								snake_case__  :						Union[str, 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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								snake_case__  :						int		=      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
								snake_case__  :						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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								snake_case__  :						Any		=      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
								snake_case__  :						Union[str, Any]		=      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
								snake_case__  :						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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







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

_UpperCamelCase:					Tuple     =180



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  _lowerCAmelCase						=		k_size // 2
  _lowerCAmelCase	,       _lowerCAmelCase						=		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						=		(
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(__SCREAMING_SNAKE_CASE    ) + np.square(__SCREAMING_SNAKE_CASE    )) / (2 * np.square(__SCREAMING_SNAKE_CASE    ))    )
  )
  return g



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		np.zeros(__SCREAMING_SNAKE_CASE    )

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

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

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

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

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _lowerCAmelCase						=		sobel_grad[row, col]

  return destination



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[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:
        _lowerCAmelCase						=		strong
      elif destination[row, col] <= threshold_low:
        _lowerCAmelCase						=		0
      else:
        _lowerCAmelCase						=		weak



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Union[str, 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 255 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],
        ):
          _lowerCAmelCase						=		strong
        else:
          _lowerCAmelCase						=		0



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  int=15		,				__SCREAMING_SNAKE_CASE					:  int=30		,				__SCREAMING_SNAKE_CASE					:  Tuple=128		,				__SCREAMING_SNAKE_CASE					:  List[Any]=255    ):






  """simple docstring"""




  _lowerCAmelCase						=		img_convolve(__SCREAMING_SNAKE_CASE		,				gen_gaussian_kernel(9		,				sigma=1.4    )    )
  # get the gradient and degree by sobel_filter
  _lowerCAmelCase	,       _lowerCAmelCase						=		sobel_filter(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		PI + np.radadeg(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		suppress_non_maximum(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  detect_high_low_threshold(
      image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  track_edge(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  return destination


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