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      :				Any     			=180







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    k_size // 2
    _lowerCamelCase    ,				_lowerCamelCase  :		Optional[Any]			    =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _lowerCamelCase  :		List[Any]			    =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__A						) + np.square(__A						)) / (2 * np.square(__A						))						)
    )
    return g







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    np.zeros(__A						)

    for row in range(1 ,			image_shape[0] - 1						):
        for col in range(1 ,			image_shape[1] - 1						):
            _lowerCamelCase  :		Optional[Any]			    =    gradient_direction[row, col]

            if (
                0 <= direction < PI / 8
                or 15 * PI / 8 <= direction <= 2 * PI
                or 7 * PI / 8 <= direction <= 9 * PI / 8
            ):
                _lowerCamelCase  :		Tuple			    =    sobel_grad[row, col - 1]
                _lowerCamelCase  :		Union[str, Any]			    =    sobel_grad[row, col + 1]
                if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                    _lowerCamelCase  :		Any			    =    sobel_grad[row, col]

            elif (
                PI / 8 <= direction < 3 * PI / 8
                or 9 * PI / 8 <= direction < 11 * PI / 8
            ):
                _lowerCamelCase  :		Optional[int]			    =    sobel_grad[row + 1, col - 1]
                _lowerCamelCase  :		str			    =    sobel_grad[row - 1, col + 1]
                if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                    _lowerCamelCase  :		Optional[Any]			    =    sobel_grad[row, col]

            elif (
                3 * PI / 8 <= direction < 5 * PI / 8
                or 11 * PI / 8 <= direction < 13 * PI / 8
            ):
                _lowerCamelCase  :		Optional[Any]			    =    sobel_grad[row - 1, col]
                _lowerCamelCase  :		Union[str, Any]			    =    sobel_grad[row + 1, col]
                if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                    _lowerCamelCase  :		List[Any]			    =    sobel_grad[row, col]

            elif (
                5 * PI / 8 <= direction < 7 * PI / 8
                or 13 * PI / 8 <= direction < 15 * PI / 8
            ):
                _lowerCamelCase  :		Dict			    =    sobel_grad[row - 1, col - 1]
                _lowerCamelCase  :		Tuple			    =    sobel_grad[row + 1, col + 1]
                if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                    _lowerCamelCase  :		Optional[int]			    =    sobel_grad[row, col]

    return destination







def     A__	(       __A ,			__A ,			__A ,			__A ,			__A ,			__A						):







    '''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:
                _lowerCamelCase  :		List[Any]			    =    strong
            elif destination[row, col] <= threshold_low:
                _lowerCamelCase  :		Optional[int]			    =    0
            else:
                _lowerCamelCase  :		Dict			    =    weak







def     A__	(       __A ,			__A ,			__A ,			__A						):







    '''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],
                ):
                    _lowerCamelCase  :		List[str]			    =    strong
                else:
                    _lowerCamelCase  :		List[Any]			    =    0







def     A__	(       __A ,			__A=15 ,			__A=30 ,			__A=128 ,			__A=255						):







    '''simple docstring'''
    # gaussian_filter
    _lowerCamelCase  :		str			    =    img_convolve(__A ,			gen_gaussian_kernel(9 ,			sigma=1.4						)						)
    # get the gradient and degree by sobel_filter
    _lowerCamelCase    ,				_lowerCamelCase  :		Tuple			    =    sobel_filter(__A						)
    _lowerCamelCase  :		Any			    =    PI + np.radadeg(__A						)

    _lowerCamelCase  :		Optional[int]			    =    suppress_non_maximum(image.shape ,			__A ,			__A						)

    detect_high_low_threshold(
        image.shape ,			__A ,			__A ,			__A ,			__A ,			__A						)

    track_edge(image.shape ,			__A ,			__A ,			__A						)

    return destination


if __name__ == "__main__":
    # read original image in gray mode
    lowerCAmelCase      :				Optional[int]     			=cva.imread(r"../image_data/lena.jpg", 0)
    # canny edge detection
    lowerCAmelCase      :				List[str]     			=canny(lena)
    cva.imshow("canny", canny_destination)
    cva.waitKey(0)





lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




