






"""simple docstring"""



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												A__						:  Union[str, Any]		  =  ksize + 1
						A__						:  Optional[int]		  =  np.zeros((ksize, ksize),						dtype=np.floataa      )

						# each value
						for y in range(UpperCAmelCase__      ):
												for x in range(UpperCAmelCase__      ):
																		# distance from center
																		A__						:  Any		  =  x - ksize // 2
																		A__						:  Optional[int]		  =  y - ksize // 2

																		# degree to radiant
																		A__						:  Any		  =  theta / 1_8_0 * np.pi
																		A__						:  List[Any]		  =  np.cos(_theta      )
																		A__						:  Dict		  =  np.sin(_theta      )

																		# get kernel x
																		A__						:  List[Any]		  =  cos_theta * px + sin_theta * py

																		# get kernel y
																		A__						:  Optional[Any]		  =  -sin_theta * px + cos_theta * py

																		# fill kernel
																		A__						:  Optional[int]		  =  np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)      ) * np.cos(2 * np.pi * _x / lambd + psi      )

						return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						A_    =							imread('''../image_data/lena.jpg''')
						# turn image in gray scale value
						A_    =							cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						A_    =							np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												A_    =							gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						A_    =							out / out.max() * 255
						A_    =							out.astype(np.uinta)

						imshow('''Original''', gray)
						imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

						waitKey(0)














"""simple docstring"""



from timeit import timeit




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Optional[int]		  =  0
						while number:
												number &= number - 1
												result += 1
						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Tuple		  =  0
						while number:
												if number % 2 == 1:
																		result += 1
												number >>= 1
						return result




def      _lowerCAmelCase		(							)		->None:

						def do_benchmark(UpperCAmelCase__       :					int      ) -> None:
												A__						:  Optional[int]		  =  """import __main__ as z"""
												print(f'Benchmark when {number = }:'      )
												print(f'{get_set_bits_count_using_modulo_operator(UpperCAmelCase__      ) = }'      )
												A__						:  Any		  =  timeit("""z.get_set_bits_count_using_modulo_operator(25)""",						setup=UpperCAmelCase__      )
												print(f'timeit() runs in {timing} seconds'      )
												print(f'{get_set_bits_count_using_brian_kernighans_algorithm(UpperCAmelCase__      ) = }'      )
												A__						:  List[str]		  =  timeit(
												    """z.get_set_bits_count_using_brian_kernighans_algorithm(25)""",						setup=UpperCAmelCase__,						)
												print(f'timeit() runs in {timing} seconds'      )

						for number in (2_5, 3_7, 5_8, 0):
												do_benchmark(UpperCAmelCase__      )
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()






