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Reduce the complexity of digital_image_processing/edge detection/canny.py (#8167)

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* Reduce the complexity of digital_image_processing/edge_detection/canny.py

* Fix

* updating DIRECTORY.md

* updating DIRECTORY.md

* updating DIRECTORY.md

* Fix review issues

* Rename dst to destination

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Maxim Smolskiy 2023-04-01 19:22:33 +03:00 committed by GitHub
parent 63710883c8
commit 59cae167e0
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1 changed files with 74 additions and 53 deletions
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111
digital_image_processing/edge_detection/canny.py
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@ -18,60 +18,61 @@ def gen_gaussian_kernel(k_size, sigma):
return g
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))
def suppress_non_maximum(image_shape, gradient_direction, sobel_grad):
"""
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):
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 < 22.5
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:
dst[row, col] = sobel_grad[row, col]
destination[row, col] = sobel_grad[row, col]
elif (PI / 8 <= direction < 3 * PI / 8) or (
9 * PI / 8 <= direction < 11 * PI / 8
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]
destination[row, col] = sobel_grad[row, col]
elif (3 * PI / 8 <= direction < 5 * PI / 8) or (
11 * PI / 8 <= direction < 13 * PI / 8
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]
destination[row, col] = sobel_grad[row, col]
elif (5 * PI / 8 <= direction < 7 * PI / 8) or (
13 * PI / 8 <= direction < 15 * PI / 8
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]
destination[row, col] = sobel_grad[row, col]
return destination
def detect_high_low_threshold(
image_shape, destination, threshold_low, threshold_high, weak, strong
):
"""
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
@ -80,43 +81,63 @@ def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
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
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:
dst[row, col] = weak
destination[row, col] = weak
def track_edge(image_shape, destination, weak, strong):
"""
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:
for row in range(1, image_shape[0]):
for col in range(1, image_shape[1]):
if destination[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],
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],
):
dst[row, col] = strong
destination[row, col] = strong
else:
dst[row, col] = 0
destination[row, col] = 0
return dst
def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
# 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 = 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__":
# read original image in gray mode
lena = cv2.imread(r"../image_data/lena.jpg", 0)
# canny edge detection
canny_dst = canny(lena)
cv2.imshow("canny", canny_dst)
canny_destination = canny(lena)
cv2.imshow("canny", canny_destination)
cv2.waitKey(0)