


'''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__  : Any				=	1_8_0







def _UpperCamelCase	(					__A				,      __A   )							->		str:


						'''simple docstring'''



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







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		str:


						'''simple docstring'''



						UpperCamelCase__  =    np.zeros(__A   )

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

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

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

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

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

						return destination







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A				,      __A				,      __A   )							->		str:


						'''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:
																								UpperCamelCase__  =    strong
																		elif destination[row, col] <= threshold_low:
																								UpperCamelCase__  =    0
																		else:
																								UpperCamelCase__  =    weak







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A   )							->		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],
																								):
																														UpperCamelCase__  =    strong
																								else:
																														UpperCamelCase__  =    0







def _UpperCamelCase	(					__A				,      __A=15				,      __A=30				,      __A=128				,      __A=255   )							->		Tuple:


						'''simple docstring'''



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

						UpperCamelCase__  =    suppress_non_maximum(image.shape				,      __A				,      __A   )

						detect_high_low_threshold(
						    image.shape				,      __A				,      __A				,      __A				,      __A				,      __A   )

						track_edge(image.shape				,      __A				,      __A				,      __A   )

						return destination


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









'''simple docstring'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__  : Any				=	{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	[
      'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TvltModel',
      'TvltForPreTraining',
      'TvltForAudioVisualClassification',
      'TvltPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : List[Any]				=	['TvltImageProcessor']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

  a__  : Any				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





