





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

__lowercase													=			180


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str     ,							__lowercase:		Any):



  '''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(__lowercase) + np.square(__lowercase)) / (2 * np.square(__lowercase)))
  )
  return g


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Tuple     ,							__lowercase:		Optional[int]     ,							__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase			=				np.zeros(__lowercase)

  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 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[str]     ,							__lowercase:		Union[str, Any]     ,							__lowercase:		Any     ,							__lowercase:		int     ,							__lowercase:		Tuple     ,							__lowercase:		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 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[str]     ,							__lowercase:		Dict     ,							__lowercase:		List[Any]     ,							__lowercase:		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],
        ):
          __lowerCAmelCase			=				strong
        else:
          __lowerCAmelCase			=				0


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Optional[Any]     ,							__lowercase:		str=15     ,							__lowercase:		Union[str, Any]=30     ,							__lowercase:		Tuple=1_28     ,							__lowercase:		Any=2_55):



  '''simple docstring'''






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

  __lowerCAmelCase			=				suppress_non_maximum(image.shape     ,							__lowercase     ,							__lowercase)

  detect_high_low_threshold(
      image.shape     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase)

  track_edge(image.shape     ,							__lowercase     ,							__lowercase     ,							__lowercase)

  return destination


if __name__ == "__main__":
      # read original image in gray mode
      __lowercase													=			cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      __lowercase													=			canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)







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 ViltImageProcessor

class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def __init__(   self						:		Tuple			,						snake_case						:		List[str]			,						snake_case						:		Dict=7			,						snake_case						:		str=3			,						snake_case						:		Union[str, Any]=18			,						snake_case						:		str=30			,						snake_case						:		str=400			,						snake_case						:		Optional[Any]=True			,						snake_case						:		List[str]=None			,						snake_case						:		Tuple=2			,						snake_case						:		Dict=True			,						snake_case						:		Dict=[0.5, 0.5, 0.5]			,						snake_case						:		Tuple=[0.5, 0.5, 0.5]			,						):

      """simple docstring"""





      __lowerCAmelCase			=				size if size is not None else {'''shortest_edge''': 30}
      __lowerCAmelCase			=				parent
      __lowerCAmelCase			=				batch_size
      __lowerCAmelCase			=				num_channels
      __lowerCAmelCase			=				image_size
      __lowerCAmelCase			=				min_resolution
      __lowerCAmelCase			=				max_resolution
      __lowerCAmelCase			=				do_resize
      __lowerCAmelCase			=				size
      __lowerCAmelCase			=				size_divisor
      __lowerCAmelCase			=				do_normalize
      __lowerCAmelCase			=				image_mean
      __lowerCAmelCase			=				image_std





    def 	A		(   self						:		int							):

      """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,
          "size_divisor": self.size_divisor,
      }





    def 	A		(   self						:		Dict			,						snake_case						:		List[Any]			,						snake_case						:		Any=False							):

      """simple docstring"""





      if not batched:
        __lowerCAmelCase			=				self.size['''shortest_edge''']
        __lowerCAmelCase			=				image_inputs[0]
        if isinstance(snake_case			,						Image.Image							):
          __lowerCAmelCase	,						__lowerCAmelCase			=				image.size
        else:
          __lowerCAmelCase	,						__lowerCAmelCase			=				image.shape[1], image.shape[2]
        __lowerCAmelCase			=				size / min(snake_case			,						snake_case							)
        if h < w:
          __lowerCAmelCase	,						__lowerCAmelCase			=				size, scale * w
        else:
          __lowerCAmelCase	,						__lowerCAmelCase			=				scale * h, size

        __lowerCAmelCase			=				int((1_333 / 800) * size							)
        if max(snake_case			,						snake_case							) > max_size:
          __lowerCAmelCase			=				max_size / max(snake_case			,						snake_case							)
          __lowerCAmelCase			=				newh * scale
          __lowerCAmelCase			=				neww * scale

        __lowerCAmelCase	,						__lowerCAmelCase			=				int(newh + 0.5							), int(neww + 0.5							)
        __lowerCAmelCase	,						__lowerCAmelCase			=				(
            newh // self.size_divisor * self.size_divisor,
            neww // self.size_divisor * self.size_divisor,
        )

      else:
        __lowerCAmelCase			=				[]
        for image in image_inputs:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.get_expected_values([image]							)
          expected_values.append((expected_height, expected_width)							)
        __lowerCAmelCase			=				max(snake_case			,						key=lambda snake_case							: item[0]							)[0]
        __lowerCAmelCase			=				max(snake_case			,						key=lambda snake_case							: item[1]							)[1]

      return expected_height, expected_width


@require_torch
@require_vision
class   __snake_case				(			_a       ,				unittest.TestCase   ):






    '''simple docstring'''
    _lowerCAmelCase     =	ViltImageProcessor if is_vision_available() else None





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				ViltImageProcessingTester(self							)





    @property
    def 	A		(   self						:		Any							):

      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(snake_case			,						'''image_mean'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''image_std'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''do_normalize'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''do_resize'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''size'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''size_divisor'''							)							)





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size			,						{'''shortest_edge''': 30}							)

      __lowerCAmelCase			=				self.image_processing_class.from_dict(self.image_processor_dict			,						size=42							)
      self.assertEqual(image_processor.size			,						{'''shortest_edge''': 42}							)





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      pass





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





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

      # Test not batched input
      __lowerCAmelCase			=				image_processing(image_inputs[0]			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case			,						batched=snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,						)





    def 	A		(   self						:		Tuple							):

      """simple docstring"""





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

      # Test not batched input
      __lowerCAmelCase			=				image_processing(image_inputs[0]			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case			,						batched=snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,						)





    def 	A		(   self						:		List[str]							):

      """simple docstring"""





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

      # Test not batched input
      __lowerCAmelCase			=				image_processing(image_inputs[0]			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case			,						batched=snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,						)
