def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
image_row, image_col = image.shape[0], image.shape[1]
# gaussian_filter
gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4))
# get the gradient and degree by sobel_filter
sobel_grad, sobel_theta = sobel_filter(gaussian_out)
gradient_direction = np.rad2deg(sobel_theta)
gradient_direction += PI
dst = np.zeros((image_row, image_col))
"""
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.
"""
for row in range(1, image_row - 1):
for col in range(1, image_col - 1):
direction = gradient_direction[row, col]
if (
0 <= direction < 22.5
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:
dst[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:
dst[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:
dst[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:
dst[row, col] = sobel_grad[row, col]
"""
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.
"""
if dst[row, col] >= threshold_high:
dst[row, col] = strong
elif dst[row, col] <= threshold_low:
dst[row, col] = 0
else:
dst[row, col] = weak
"""
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.
"""
for row in range(1, image_row):
for col in range(1, image_col):
if dst[row, col] == weak:
if 255 in (
dst[row, col + 1],
dst[row, col - 1],
dst[row - 1, col],
dst[row + 1, col],
dst[row - 1, col - 1],
dst[row + 1, col - 1],
dst[row - 1, col + 1],
dst[row + 1, col + 1],
):
dst[row, col] = strong
else:
dst[row, col] = 0
return dst
def test_sobel_filter():
grad, theta = sob.sobel_filter(gray)
assert grad.any() and theta.any()
