



from pathlib import Path

import numpy as np
from PIL import Image



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray	)->  np.ndarray:
    A__ ,	A__ ,	A__		 =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray	)->  np.ndarray:
    return (gray > 1_2_7) & (gray <= 2_5_5)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  np.ndarray:
    A__		 =					np.zeros_like(UpperCamelCase_	)
    A__		 =					np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

    # Copy image to padded image
    A__		 =					image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]	):
        for y in range(image.shape[0]	):
            A__		 =					(
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            A__		 =					int(summation > 0	)
    return output


if __name__ == "__main__":
   # read original image
   _lowercase						  =		Path(__file__).resolve().parent / "image_data" / "lena.jpg"
   _lowercase						  =		np.array(Image.open(lena_path))
   # kernel to be applied
   _lowercase						  =		np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
   _lowercase						  =		dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
   # Save the output image
   _lowercase						  =		Image.fromarray(output).convert("RGB")
   pil_img.save("result_dilation.png")





import numpy as np



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return 1 / (1 + np.exp(-vector	))



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return vector * sigmoid(1.702 * vector	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
