

import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__UpperCAmelCase      = (30, 487)
__UpperCAmelCase      = (36, 1_024)
__UpperCAmelCase      = (100, 100)
__UpperCAmelCase      = 100

__UpperCAmelCase      = datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

__UpperCAmelCase      = os.path.split(__file__)
__UpperCAmelCase      = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 	snake_case_      (__A     :						int					,   __A     :						str					,   __A     :						int     )			->	Dict:
   with ArrowWriter(features=__A					,   path=os.path.join(__A					,   """beta.arrow"""     )     ) as writer:
      for key, record in dummy_data:
         __lowerCAmelCase  :			Union[str, Any]					    =						my_features.encode_example(__A     )
         writer.write(__A     )
      __lowerCAmelCase    ,__lowerCAmelCase  :			Any					    =						writer.finalize()





@get_duration
def 	snake_case_      (__A     :						Tuple					,   __A     :						Optional[Any]     )			->	Optional[int]:
   __lowerCAmelCase  :			str					    =						datasets.Dataset.from_file(
       filename=os.path.join(__A					,   """beta.arrow"""     )					,   info=datasets.DatasetInfo(features=__A     )     )
   for _ in dataset:
      pass





@get_duration
def 	snake_case_      (__A     :						int					,   __A     :						List[str]     )			->	List[str]:
   __lowerCAmelCase  :			List[Any]					    =						datasets.Dataset.from_file(
       filename=os.path.join(__A					,   """beta.arrow"""     )					,   info=datasets.DatasetInfo(features=__A     )     )
   dataset.set_format("""numpy"""     )
   for _ in dataset:
      pass





@get_duration
def 	snake_case_      (__A     :						Tuple					,   __A     :						List[Any]     )			->	int:
   __lowerCAmelCase  :			Union[str, Any]					    =						1_0
   __lowerCAmelCase  :			Dict					    =						datasets.Dataset.from_file(
       filename=os.path.join(__A					,   """beta.arrow"""     )					,   info=datasets.DatasetInfo(features=__A     )     )
   for i in range(0					,   len(__A     )					,   __A     ):
      __lowerCAmelCase  :			int					    =						dataset[i : i + batch_size]





@get_duration
def 	snake_case_      (__A     :						Any					,   __A     :						Dict     )			->	List[str]:
   __lowerCAmelCase  :			int					    =						1_0
   __lowerCAmelCase  :			List[str]					    =						datasets.Dataset.from_file(
       filename=os.path.join(__A					,   """beta.arrow"""     )					,   info=datasets.DatasetInfo(features=__A     )     )
   dataset.set_format("""numpy"""     )
   for i in range(0					,   len(__A     )					,   __A     ):
      __lowerCAmelCase  :			Any					    =						dataset[i : i + batch_size]





@get_duration
def 	snake_case_      (__A     :						Tuple					,   __A     :						str     )			->	List[Any]:
   __lowerCAmelCase  :			List[Any]					    =						datasets.Dataset.from_file(
       filename=os.path.join(__A					,   """beta.arrow"""     )					,   info=datasets.DatasetInfo(features=__A     )     )
   for col in feats:
      __lowerCAmelCase  :			Optional[Any]					    =						dataset[col]





@get_duration
def 	snake_case_      (__A     :						str					,   __A     :						Optional[Any]     )			->	Any:
   __lowerCAmelCase  :			List[str]					    =						datasets.Dataset.from_file(
       filename=os.path.join(__A					,   """beta.arrow"""     )					,   info=datasets.DatasetInfo(features=__A     )     )
   dataset.set_format("""numpy"""     )
   for col in feats:
      __lowerCAmelCase  :			Dict					    =						dataset[col]





def 	snake_case_      ()			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						{}
   __lowerCAmelCase  :			Dict					    =						(
       read_unformated,
       read_formatted_as_numpy,
       read_batch_unformated,
       read_batch_formatted_as_numpy,
       read_col_unformated,
       read_col_formatted_as_numpy,
   )
   with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCAmelCase  :			str					    =						datasets.Features({"""image""": ArrayaD(__A					,   dtype="""float32"""     )}     )
      __lowerCAmelCase  :			str					    =						generate_examples(features=__A					,   num_examples=__A     )
      __lowerCAmelCase  :			List[Any]					    =						write(__A					,   __A					,   __A     )
      for read_func in read_functions:
         __lowerCAmelCase  :			Any					    =						read_func(__A					,   __A     )

   with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
      # )
      __lowerCAmelCase  :			List[Any]					    =						datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""     )     )     )}     )
      __lowerCAmelCase  :			int					    =						generate_examples(
          features=__A					,   num_examples=__A					,   seq_shapes={"""image""": SPEED_TEST_SHAPE}     )
      __lowerCAmelCase  :			Dict					    =						write(__A					,   __A					,   __A     )
      for read_func in read_functions:
         __lowerCAmelCase  :			Optional[Any]					    =						read_func(__A					,   __A     )

   with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
      # )
      __lowerCAmelCase  :			Union[str, Any]					    =						datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""     )     )}     )
      __lowerCAmelCase  :			Any					    =						generate_examples(
          features=__A					,   num_examples=__A					,   seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}					,   )
      __lowerCAmelCase  :			List[str]					    =						write(__A					,   __A					,   __A     )
      for read_func in read_functions:
         __lowerCAmelCase  :			List[str]					    =						read_func(__A					,   __A     )

   with open(__A					,   """wb"""     ) as f:
      f.write(json.dumps(__A     ).encode("""utf-8"""     )     )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_array_xd()










"""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

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







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






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

       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 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''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 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''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 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


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