





"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Union[str, Any]       ):
 lowerCAmelCase  							=      np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]       )
 lowerCAmelCase  							=      np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]       )

 lowerCAmelCase  							=      np.abs(img_convolve(_UpperCAmelCase   ,			_UpperCAmelCase       )       )
 lowerCAmelCase  							=      np.abs(img_convolve(_UpperCAmelCase   ,			_UpperCAmelCase       )       )
 # modify the pix within [0, 255]
 lowerCAmelCase  							=      dst_x * 255 / np.max(_UpperCAmelCase       )
 lowerCAmelCase  							=      dst_y * 255 / np.max(_UpperCAmelCase       )

 lowerCAmelCase  							=      np.sqrt((np.square(_UpperCAmelCase       )) + (np.square(_UpperCAmelCase       ))       )
 lowerCAmelCase  							=      dst_xy * 255 / np.max(_UpperCAmelCase       )
 lowerCAmelCase  							=      dst_xy.astype(np.uinta       )

 lowerCAmelCase  							=      np.arctana(_UpperCAmelCase   ,			_UpperCAmelCase       )
 return dst, theta


if __name__ == "__main__":
       # read original image
       __UpperCamelCase			:		Dict		    =   imread('''../image_data/lena.jpg''')
       # turn image in gray scale value
       __UpperCamelCase			:		str		    =   cvtColor(img, COLOR_BGR2GRAY)

       __UpperCamelCase    ,__UpperCamelCase			:		Union[str, Any]		    =   sobel_filter(gray)

       # show result images
       imshow('''sobel filter''', sobel_grad)
       imshow('''sobel theta''', sobel_theta)
       waitKey(0)







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

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



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

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


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

