




"""simple docstring"""




from typing import Dict, Optional, Tuple

import torch
def     A_	(						snake_case_   :				str			):







    '''simple docstring'''
    UpperCamelCase :      str      				=							boundaries[1] - boundaries[0]
    UpperCamelCase :      Any      				=							boundaries + step / 2
    UpperCamelCase :      Optional[int]      				=							torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1			)]  ,dim=0			)
    return bin_centers
def     A_	(						snake_case_   :				List[Any]  ,snake_case_   :				Any  ,):







    '''simple docstring'''
    UpperCamelCase :      Any      				=							_calculate_bin_centers(snake_case_			)
    return (
        torch.sum(aligned_distance_error_probs * bin_centers  ,dim=-1			),
        bin_centers[-1],
    )
def     A_	(						snake_case_   :				Optional[Any]  ,snake_case_   :				Union[str, Any] = 3_1  ,snake_case_   :				Tuple = 6_4  ,**snake_case_   :				Optional[int]  ,):







    '''simple docstring'''
    UpperCamelCase :      Optional[int]      				=							torch.linspace(0  ,snake_case_  ,steps=(no_bins - 1)  ,device=logits.device			)

    UpperCamelCase :      int      				=							torch.nn.functional.softmax(snake_case_  ,dim=-1			)
    UpperCamelCase				,							UpperCamelCase :      Dict      				=							_calculate_expected_aligned_error(
        alignment_confidence_breaks=snake_case_  ,aligned_distance_error_probs=snake_case_  ,)

    return {
        "aligned_confidence_probs": aligned_confidence_probs,
        "predicted_aligned_error": predicted_aligned_error,
        "max_predicted_aligned_error": max_predicted_aligned_error,
    }


def     A_	(						snake_case_   :				Dict  ,snake_case_   :				Any = None  ,snake_case_   :				List[str] = 3_1  ,snake_case_   :				Dict = 6_4  ,snake_case_   :				Optional[int] = 1e-8  ,**snake_case_   :				str  ,):







    '''simple docstring'''
    if residue_weights is None:
        UpperCamelCase :      Any      				=							logits.new_ones(logits.shape[-2]			)

    UpperCamelCase :      Tuple      				=							torch.linspace(0  ,snake_case_  ,steps=(no_bins - 1)  ,device=logits.device			)

    UpperCamelCase :      List[str]      				=							_calculate_bin_centers(snake_case_			)
    torch.sum(snake_case_			)
    UpperCamelCase :      Any      				=							logits.shape[-2]
    UpperCamelCase :      Optional[int]      				=							max(snake_case_  ,1_9			)

    UpperCamelCase :      Optional[int]      				=							1.24 * (clipped_n - 1_5) ** (1.0 / 3) - 1.8

    UpperCamelCase :      Union[str, Any]      				=							torch.nn.functional.softmax(snake_case_  ,dim=-1			)

    UpperCamelCase :      Tuple      				=							1.0 / (1 + (bin_centers**2) / (da**2))
    UpperCamelCase :      List[Any]      				=							torch.sum(probs * tm_per_bin  ,dim=-1			)

    UpperCamelCase :      Union[str, Any]      				=							residue_weights / (eps + residue_weights.sum())
    UpperCamelCase :      Tuple      				=							torch.sum(predicted_tm_term * normed_residue_mask  ,dim=-1			)

    UpperCamelCase :      Tuple      				=							per_alignment * residue_weights

    UpperCamelCase :      Optional[int]      				=							(weighted == torch.max(snake_case_			)).nonzero()[0]
    return per_alignment[tuple(snake_case_			)]












"""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)
