






"""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

_UpperCamelCase   :						Tuple     						= 1_80




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




def 				a (     UpperCamelCase_							:				List[Any]     ,	UpperCamelCase_							:				Optional[Any]     ,	UpperCamelCase_							:				Union[str, Any]		)  ->		Union[str, Any]:
 __A	:       Optional[Any]       =	np.zeros(UpperCamelCase_		)

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

   if (
       0 <= direction < PI / 8
       or 1_5 * PI / 8 <= direction <= 2 * PI
       or 7 * PI / 8 <= direction <= 9 * PI / 8
   ):
    __A	:       List[Any]       =	sobel_grad[row, col - 1]
    __A	:       Optional[int]       =	sobel_grad[row, col + 1]
    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
     __A	:       int       =	sobel_grad[row, col]

   elif (
       PI / 8 <= direction < 3 * PI / 8
       or 9 * PI / 8 <= direction < 1_1 * PI / 8
   ):
    __A	:       List[str]       =	sobel_grad[row + 1, col - 1]
    __A	:       Union[str, Any]       =	sobel_grad[row - 1, col + 1]
    if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
     __A	:       str       =	sobel_grad[row, col]

   elif (
       3 * PI / 8 <= direction < 5 * PI / 8
       or 1_1 * PI / 8 <= direction < 1_3 * PI / 8
   ):
    __A	:       Optional[Any]       =	sobel_grad[row - 1, col]
    __A	:       Optional[int]       =	sobel_grad[row + 1, col]
    if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
     __A	:       Optional[int]       =	sobel_grad[row, col]

   elif (
       5 * PI / 8 <= direction < 7 * PI / 8
       or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
   ):
    __A	:       List[str]       =	sobel_grad[row - 1, col - 1]
    __A	:       Optional[Any]       =	sobel_grad[row + 1, col + 1]
    if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
     __A	:       Tuple       =	sobel_grad[row, col]

 return destination




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




def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Dict     ,	UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				Union[str, Any]		)  ->		List[Any]:
 for row in range(1     ,	image_shape[0]		):
  for col in range(1     ,	image_shape[1]		):
   if destination[row, col] == weak:
    if 2_5_5 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],
    ):
     __A	:       Any       =	strong
    else:
     __A	:       int       =	0




def 				a (     UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Union[str, Any]=1_5     ,	UpperCamelCase_							:				Any=3_0     ,	UpperCamelCase_							:				str=1_2_8     ,	UpperCamelCase_							:				int=2_5_5		)  ->		Union[str, Any]:
 # gaussian_filter
 __A	:       Union[str, Any]       =	img_convolve(UpperCamelCase_     ,	gen_gaussian_kernel(9     ,	sigma=1.4		)		)
 # get the gradient and degree by sobel_filter
 __A	:       Optional[Any]       =	sobel_filter(UpperCamelCase_		)
 __A	:       int       =	PI + np.radadeg(UpperCamelCase_		)

 __A	:       List[Any]       =	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
   _UpperCamelCase   :						Tuple     						= cva.imread(R"""../image_data/lena.jpg""", 0)
   # canny edge detection
   _UpperCamelCase   :						str     						= canny(lena)
   cva.imshow("""canny""", canny_destination)
   cva.waitKey(0)













"""simple docstring"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_UpperCamelCase   :						List[Any]     						= """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_UpperCamelCase   :						Any     						= """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_UpperCamelCase   :						Dict     						= """"""

# TODO: Add the licence for the dataset here if you can find it
_UpperCamelCase   :						Tuple     						= """"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_UpperCamelCase   :						str     						= {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}






class __a   (   datasets.GeneratorBasedBuilder				):
  __lowercase    :    int						=					datasets.Version('1.1.0'				)

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  __lowercase    :    Optional[Any]						=					[
      datasets.BuilderConfig(name='first_domain' ,version=snake_case_ ,description='This part of my dataset covers a first domain'				),
      datasets.BuilderConfig(name='second_domain' ,version=snake_case_ ,description='This part of my dataset covers a second domain'				),
  ]

  __lowercase    :    Any						=					'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.



  def 						A_				(self  :		Optional[Any]   )		->							List[Any]:
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    __A	:       Dict       =	datasets.Features(
        {
            'sentence': datasets.Value('string'   ),
            'option1': datasets.Value('string'   ),
            'answer': datasets.Value('string'   )
            # These are the features of your dataset like images, labels ...
        }   )
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    __A	:       Optional[int]       =	datasets.Features(
        {
            'sentence': datasets.Value('string'   ),
            'option2': datasets.Value('string'   ),
            'second_domain_answer': datasets.Value('string'   )
            # These are the features of your dataset like images, labels ...
        }   )
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION				,			features=lowercase_				,			homepage=_HOMEPAGE				,			license=_LICENSE				,			citation=_CITATION				,			)



  def 						A_				(self  :		Dict				,			lowercase_  :		Union[str, Any]   )		->							Union[str, Any]:
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   __A	:       Optional[int]       =	_URLS[self.config.name]
   __A	:       Any       =	dl_manager.download_and_extract(lowercase_   )
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'train.jsonl'   ),
               'split': 'train',
           }				,			),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'dev.jsonl'   ),
               'split': 'dev',
           }				,			),
       datasets.SplitGenerator(
           name=datasets.Split.TEST				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'test.jsonl'   ),
               'split': 'test'
           }				,			),
   ]



  def 						A_				(self  :		Tuple				,			lowercase_  :		Dict				,			lowercase_  :		Optional[Any]   )		->							Tuple:
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(lowercase_				,			encoding='utf-8'   ) as f:
    for key, row in enumerate(lowercase_   ):
     __A	:       int       =	json.loads(lowercase_   )
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }





