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123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
import cv2
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import numpy as np
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from digital_image_processing.filters.convolve import img_convolve
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from digital_image_processing.filters.sobel_filter import sobel_filter
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PI = 180
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def gen_gaussian_kernel(k_size, sigma):
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center = k_size // 2
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x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
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g = (
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1
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/ (2 * np.pi * sigma)
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* np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma)))
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)
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return g
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def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
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image_row, image_col = image.shape[0], image.shape[1]
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# gaussian_filter
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gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4))
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# get the gradient and degree by sobel_filter
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sobel_grad, sobel_theta = sobel_filter(gaussian_out)
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gradient_direction = np.rad2deg(sobel_theta)
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gradient_direction += PI
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dst = np.zeros((image_row, image_col))
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"""
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Non-maximum suppression. If the edge strength of the current pixel is the largest
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compared to the other pixels in the mask with the same direction, the value will be
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preserved. Otherwise, the value will be suppressed.
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"""
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for row in range(1, image_row - 1):
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for col in range(1, image_col - 1):
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direction = gradient_direction[row, col]
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if (
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0 <= direction < 22.5
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or 15 * PI / 8 <= direction <= 2 * PI
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or 7 * PI / 8 <= direction <= 9 * PI / 8
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):
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W = sobel_grad[row, col - 1]
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E = sobel_grad[row, col + 1]
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if sobel_grad[row, col] >= W and sobel_grad[row, col] >= E:
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dst[row, col] = sobel_grad[row, col]
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elif (PI / 8 <= direction < 3 * PI / 8) or (
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9 * PI / 8 <= direction < 11 * PI / 8
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):
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SW = sobel_grad[row + 1, col - 1]
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NE = sobel_grad[row - 1, col + 1]
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if sobel_grad[row, col] >= SW and sobel_grad[row, col] >= NE:
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dst[row, col] = sobel_grad[row, col]
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elif (3 * PI / 8 <= direction < 5 * PI / 8) or (
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11 * PI / 8 <= direction < 13 * PI / 8
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):
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N = sobel_grad[row - 1, col]
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S = sobel_grad[row + 1, col]
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if sobel_grad[row, col] >= N and sobel_grad[row, col] >= S:
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dst[row, col] = sobel_grad[row, col]
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elif (5 * PI / 8 <= direction < 7 * PI / 8) or (
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13 * PI / 8 <= direction < 15 * PI / 8
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):
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NW = sobel_grad[row - 1, col - 1]
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SE = sobel_grad[row + 1, col + 1]
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if sobel_grad[row, col] >= NW and sobel_grad[row, col] >= SE:
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dst[row, col] = sobel_grad[row, col]
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"""
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High-Low threshold detection. If an edge pixel’s gradient value is higher
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than the high threshold value, it is marked as a strong edge pixel. If an
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edge pixel’s gradient value is smaller than the high threshold value and
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larger than the low threshold value, it is marked as a weak edge pixel. If
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an edge pixel's value is smaller than the low threshold value, it will be
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suppressed.
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"""
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if dst[row, col] >= threshold_high:
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dst[row, col] = strong
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elif dst[row, col] <= threshold_low:
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dst[row, col] = 0
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else:
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dst[row, col] = weak
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"""
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Edge tracking. Usually a weak edge pixel caused from true edges will be connected
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to a strong edge pixel while noise responses are unconnected. As long as there is
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one strong edge pixel that is involved in its 8-connected neighborhood, that weak
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edge point can be identified as one that should be preserved.
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"""
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for row in range(1, image_row):
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for col in range(1, image_col):
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if dst[row, col] == weak:
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if 255 in (
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dst[row, col + 1],
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dst[row, col - 1],
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dst[row - 1, col],
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dst[row + 1, col],
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dst[row - 1, col - 1],
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dst[row + 1, col - 1],
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dst[row - 1, col + 1],
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dst[row + 1, col + 1],
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):
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dst[row, col] = strong
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else:
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dst[row, col] = 0
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return dst
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if __name__ == "__main__":
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# read original image in gray mode
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lena = cv2.imread(r"../image_data/lena.jpg", 0)
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# canny edge detection
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canny_dst = canny(lena)
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cv2.imshow("canny", canny_dst)
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cv2.waitKey(0)
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