nonmaximum suppression if the edge strength of the current pixel is the largest compared to the other pixels in the mask with the same direction the value will be preserved otherwise the value will be suppressed highlow threshold detection if an edge pixels gradient value is higher than the high threshold value it is marked as a strong edge pixel if an edge pixels gradient value is smaller than the high threshold value and larger than the low threshold value it is marked as a weak edge pixel if an edge pixel s value is smaller than the low threshold value it will be suppressed edge tracking usually a weak edge pixel caused from true edges will be connected to a strong edge pixel while noise responses are unconnected as long as there is one strong edge pixel that is involved in its 8connected neighborhood that weak edge point can be identified as one that should be preserved gaussianfilter get the gradient and degree by sobelfilter read original image in gray mode canny edge detection non maximum suppression if the edge strength of the current pixel is the largest compared to the other pixels in the mask with the same direction the value will be preserved otherwise the value will be suppressed high low threshold detection if an edge pixel s gradient value is higher than the high threshold value it is marked as a strong edge pixel if an edge pixel s gradient value is smaller than the high threshold value and larger than the low threshold value it is marked as a weak edge pixel if an edge pixel s value is smaller than the low threshold value it will be suppressed edge tracking usually a weak edge pixel caused from true edges will be connected to a strong edge pixel while noise responses are unconnected as long as there is one strong edge pixel that is involved in its 8 connected neighborhood that weak edge point can be identified as one that should be preserved gaussian_filter get the gradient and degree by sobel_filter read original image in gray mode canny edge detection
import cv2
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

PI = 180


def gen_gaussian_kernel(k_size, sigma):
    center = k_size // 2
    x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    g = (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma)))
    )
    return g


def suppress_non_maximum(image_shape, gradient_direction, sobel_grad):
    
    destination = np.zeros(image_shape)

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

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

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

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

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

    return destination


def detect_high_low_threshold(
    image_shape, destination, threshold_low, threshold_high, weak, strong
):
    
    for row in range(1, image_shape[0] - 1):
        for col in range(1, image_shape[1] - 1):
            if destination[row, col] >= threshold_high:
                destination[row, col] = strong
            elif destination[row, col] <= threshold_low:
                destination[row, col] = 0
            else:
                destination[row, col] = weak


def track_edge(image_shape, destination, weak, strong):
    
    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],
                ):
                    destination[row, col] = strong
                else:
                    destination[row, col] = 0


def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
    
    gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4))
    
    sobel_grad, sobel_theta = sobel_filter(gaussian_out)
    gradient_direction = PI + np.rad2deg(sobel_theta)

    destination = suppress_non_maximum(image.shape, gradient_direction, sobel_grad)

    detect_high_low_threshold(
        image.shape, destination, threshold_low, threshold_high, weak, strong
    )

    track_edge(image.shape, destination, weak, strong)

    return destination


if __name__ == "__main__":
    
    lena = cv2.imread(r"../image_data/lena.jpg", 0)
    
    canny_destination = canny(lena)
    cv2.imshow("canny", canny_destination)
    cv2.waitKey(0)