mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-12 12:30:16 +08:00
ENH: Minor clean-ups to edge-detection patch.
This commit is contained in:
Stefan van der Walt
@@ -12,15 +12,15 @@ import numpy as np
|
||||
from scipy.ndimage import convolve, binary_erosion, generate_binary_structure
|
||||
|
||||
def sobel(image, mask=None):
|
||||
"""Calculate the absolute magnitude Sobel to find the edges.
|
||||
"""Calculate the absolute magnitude Sobel to find edges.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : array_like, dtype=float
|
||||
Image to process
|
||||
Image to process.
|
||||
mask : array_like, dtype=bool, optional
|
||||
An optional mask to limit the application to a certain area
|
||||
|
||||
An optional mask to limit the application to a certain area.
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
@@ -31,34 +31,36 @@ def sobel(image, mask=None):
|
||||
Take the square root of the sum of the squares of the horizontal and
|
||||
vertical Sobels to get a magnitude that's somewhat insensitive to
|
||||
direction.
|
||||
|
||||
Note that scipy's Sobel returns a directional Sobel which isn't
|
||||
useful for edge detection in its raw form.
|
||||
|
||||
Note that ``scipy.ndimage.sobel`` returns a directional Sobel which
|
||||
has to be further processed to perform edge detection.
|
||||
"""
|
||||
return np.sqrt(hsobel(image,mask)**2 + vsobel(image,mask)**2)
|
||||
return np.sqrt(hsobel(image, mask)**2 + vsobel(image, mask)**2)
|
||||
|
||||
def hsobel(image, mask=None):
|
||||
"""Find the horizontal edges of an image using the Sobel transform
|
||||
|
||||
"""Find the horizontal edges of an image using the Sobel transform.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : array_like, dtype=float
|
||||
Image to process
|
||||
Image to process.
|
||||
mask : array_like, dtype=bool, optional
|
||||
An optional mask to limit the application to a certain area.
|
||||
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
The Sobel edge map.
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
We use the following kernel and return the absolute value of the
|
||||
result at each point:
|
||||
1 2 1
|
||||
0 0 0
|
||||
-1 -2 -1
|
||||
result at each point::
|
||||
|
||||
1 2 1
|
||||
0 0 0
|
||||
-1 -2 -1
|
||||
|
||||
"""
|
||||
if mask is None:
|
||||
mask = np.ones(image.shape, bool)
|
||||
@@ -73,88 +75,92 @@ def hsobel(image, mask=None):
|
||||
|
||||
def vsobel(image, mask=None):
|
||||
"""Find the vertical edges of an image using the Sobel transform.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : array_like, dtype=float
|
||||
Image to process
|
||||
mask : array_like, dtype=bool, optional
|
||||
An optional mask to limit the application to a certain area
|
||||
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
The Sobel edge map.
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
We use the following kernel and return the absolute value of the
|
||||
result at each point:
|
||||
1 0 -1
|
||||
2 0 -2
|
||||
1 0 -1
|
||||
result at each point::
|
||||
|
||||
1 0 -1
|
||||
2 0 -2
|
||||
1 0 -1
|
||||
|
||||
"""
|
||||
if mask is None:
|
||||
mask = np.ones(image.shape, bool)
|
||||
big_mask = binary_erosion(mask,
|
||||
generate_binary_structure(2, 2),
|
||||
border_value = 0)
|
||||
result = np.abs(convolve(image, np.array([[ 1, 0,-1],
|
||||
[ 2, 0,-2],
|
||||
[ 1, 0,-1]]).astype(float) / 4.0))
|
||||
border_value=0)
|
||||
result = np.abs(convolve(image, np.array([[1, 0, -1],
|
||||
[2, 0, -2],
|
||||
[1, 0, -1]]).astype(float) / 4.0))
|
||||
result[big_mask == False] = 0
|
||||
return result
|
||||
|
||||
def prewitt(image, mask=None):
|
||||
"""Find the edge magnitude using the Prewitt transform.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : array_like, dtype=float
|
||||
Image to process
|
||||
Image to process.
|
||||
mask : array_like, dtype=bool, optional
|
||||
An optional mask to limit the application to a certain area
|
||||
|
||||
An optional mask to limit the application to a certain area.
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
The Prewitt edge map.
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
Return the square root of the sum of squares of the horizontal
|
||||
and vertical Prewitt transforms.
|
||||
"""
|
||||
return np.sqrt(hprewitt(image, mask) ** 2 + vprewitt(image, mask) ** 2)
|
||||
|
||||
|
||||
def hprewitt(image, mask=None):
|
||||
"""Find the horizontal edges of an image using the Prewitt transform.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : array_like, dtype=float
|
||||
Image to process
|
||||
Image to process.
|
||||
mask : array_like, dtype=bool, optional
|
||||
An optional mask to limit the application to a certain area
|
||||
|
||||
An optional mask to limit the application to a certain area.
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
The Prewitt edge map.
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
We use the following kernel and return the absolute value of the
|
||||
result at each point:
|
||||
1 1 1
|
||||
0 0 0
|
||||
-1 -1 -1
|
||||
result at each point::
|
||||
|
||||
1 1 1
|
||||
0 0 0
|
||||
-1 -1 -1
|
||||
|
||||
"""
|
||||
if mask is None:
|
||||
mask = np.ones(image.shape, bool)
|
||||
big_mask = binary_erosion(mask,
|
||||
generate_binary_structure(2, 2),
|
||||
border_value = 0)
|
||||
border_value=0)
|
||||
result = np.abs(convolve(image, np.array([[ 1, 1, 1],
|
||||
[ 0, 0, 0],
|
||||
[-1,-1,-1]]).astype(float) / 3.0))
|
||||
@@ -163,34 +169,36 @@ def hprewitt(image, mask=None):
|
||||
|
||||
def vprewitt(image, mask=None):
|
||||
"""Find the vertical edges of an image using the Prewitt transform.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : array_like, dtype=float
|
||||
Image to process
|
||||
Image to process.
|
||||
mask : array_like, dtype=bool, optional
|
||||
An optional mask to limit the application to a certain area
|
||||
|
||||
An optional mask to limit the application to a certain area.
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
The Prewitt edge map.
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
We use the following kernel and return the absolute value of the
|
||||
result at each point:
|
||||
1 0 -1
|
||||
1 0 -1
|
||||
1 0 -1
|
||||
result at each point::
|
||||
|
||||
1 0 -1
|
||||
1 0 -1
|
||||
1 0 -1
|
||||
|
||||
"""
|
||||
if mask is None:
|
||||
mask = np.ones(image.shape, bool)
|
||||
big_mask = binary_erosion(mask,
|
||||
generate_binary_structure(2, 2),
|
||||
border_value=0)
|
||||
result = np.abs(convolve(image, np.array([[ 1, 0,-1],
|
||||
[ 1, 0,-1],
|
||||
[ 1, 0,-1]]).astype(float) / 3.0))
|
||||
result = np.abs(convolve(image, np.array([[1, 0, -1],
|
||||
[1, 0, -1],
|
||||
[1, 0, -1]]).astype(float) / 3.0))
|
||||
result[big_mask == False] = 0
|
||||
return result
|
||||
|
||||
@@ -8,11 +8,11 @@ class TestSobel():
|
||||
"""Sobel on an array of all zeros"""
|
||||
result = F.sobel(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
|
||||
def test_00_01_mask(self):
|
||||
"""Sobel on a masked array should be zero"""
|
||||
np.random.seed(0)
|
||||
result = F.sobel(np.random.uniform(size=(10, 10)),
|
||||
result = F.sobel(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
@@ -25,10 +25,10 @@ class TestSobel():
|
||||
i[np.abs(j) == 5] = 10000
|
||||
assert (np.all(result[i == 0] == 1))
|
||||
assert (np.all(result[np.abs(i) > 1] == 0))
|
||||
|
||||
|
||||
def test_01_02_vertical(self):
|
||||
"""Sobel on a vertical edge should be a vertical line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
result = F.sobel(image)
|
||||
j[np.abs(i) == 5] = 10000
|
||||
@@ -40,27 +40,27 @@ class TestHSobel():
|
||||
"""Horizontal sobel on an array of all zeros"""
|
||||
result = F.hsobel(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
|
||||
def test_00_01_mask(self):
|
||||
"""Horizontal Sobel on a masked array should be zero"""
|
||||
np.random.seed(0)
|
||||
result = F.hsobel(np.random.uniform(size=(10, 10)),
|
||||
result = F.hsobel(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
def test_01_01_horizontal(self):
|
||||
"""Horizontal Sobel on an edge should be a horizontal line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
result = F.hsobel(image)
|
||||
# Fudge the eroded points
|
||||
i[np.abs(j) == 5] = 10000
|
||||
assert (np.all(result[i == 0] == 1))
|
||||
assert (np.all(result[np.abs(i) > 1] == 0))
|
||||
|
||||
|
||||
def test_01_02_vertical(self):
|
||||
"""Horizontal Sobel on a vertical edge should be zero"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
result = F.hsobel(image)
|
||||
assert (np.all(result == 0))
|
||||
@@ -70,27 +70,27 @@ class TestVSobel():
|
||||
"""Vertical sobel on an array of all zeros"""
|
||||
result = F.vsobel(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
|
||||
def test_00_01_mask(self):
|
||||
"""Vertical Sobel on a masked array should be zero"""
|
||||
np.random.seed(0)
|
||||
result = F.vsobel(np.random.uniform(size=(10, 10)),
|
||||
result = F.vsobel(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
def test_01_01_vertical(self):
|
||||
"""Vertical Sobel on an edge should be a vertical line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
result = F.vsobel(image)
|
||||
# Fudge the eroded points
|
||||
j[np.abs(i) == 5] = 10000
|
||||
assert (np.all(result[j == 0] == 1))
|
||||
assert (np.all(result[np.abs(j) > 1] == 0))
|
||||
|
||||
|
||||
def test_01_02_horizontal(self):
|
||||
"""vertical Sobel on a horizontal edge should be zero"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
result = F.vsobel(image)
|
||||
eps = .000001
|
||||
@@ -101,18 +101,18 @@ class TestPrewitt():
|
||||
"""Prewitt on an array of all zeros"""
|
||||
result = F.prewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
|
||||
def test_00_01_mask(self):
|
||||
"""Prewitt on a masked array should be zero"""
|
||||
np.random.seed(0)
|
||||
result = F.prewitt(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
result = F.prewitt(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
eps = .000001
|
||||
assert (np.all(np.abs(result) < eps))
|
||||
|
||||
def test_01_01_horizontal(self):
|
||||
"""Prewitt on an edge should be a horizontal line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
result = F.prewitt(image)
|
||||
# Fudge the eroded points
|
||||
@@ -120,10 +120,10 @@ class TestPrewitt():
|
||||
eps = .000001
|
||||
assert (np.all(result[i == 0] == 1))
|
||||
assert (np.all(np.abs(result[np.abs(i) > 1]) < eps))
|
||||
|
||||
|
||||
def test_01_02_vertical(self):
|
||||
"""Prewitt on a vertical edge should be a vertical line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
result = F.prewitt(image)
|
||||
eps = .000001
|
||||
@@ -136,18 +136,18 @@ class TestHPrewitt():
|
||||
"""Horizontal sobel on an array of all zeros"""
|
||||
result = F.hprewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
|
||||
def test_00_01_mask(self):
|
||||
"""Horizontal prewitt on a masked array should be zero"""
|
||||
np.random.seed(0)
|
||||
result = F.hprewitt(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
result = F.hprewitt(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
eps = .000001
|
||||
assert (np.all(np.abs(result) < eps))
|
||||
|
||||
def test_01_01_horizontal(self):
|
||||
"""Horizontal prewitt on an edge should be a horizontal line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
result = F.hprewitt(image)
|
||||
# Fudge the eroded points
|
||||
@@ -155,10 +155,10 @@ class TestHPrewitt():
|
||||
eps = .000001
|
||||
assert (np.all(result[i == 0] == 1))
|
||||
assert (np.all(np.abs(result[np.abs(i) > 1]) < eps))
|
||||
|
||||
|
||||
def test_01_02_vertical(self):
|
||||
"""Horizontal prewitt on a vertical edge should be zero"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
result = F.hprewitt(image)
|
||||
eps = .000001
|
||||
@@ -169,17 +169,17 @@ class TestVPrewitt():
|
||||
"""Vertical prewitt on an array of all zeros"""
|
||||
result = F.vprewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
|
||||
def test_00_01_mask(self):
|
||||
"""Vertical prewitt on a masked array should be zero"""
|
||||
np.random.seed(0)
|
||||
result = F.vprewitt(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
result = F.vprewitt(np.random.uniform(size=(10, 10)),
|
||||
np.zeros((10, 10), bool))
|
||||
assert (np.all(result == 0))
|
||||
|
||||
def test_01_01_vertical(self):
|
||||
"""Vertical prewitt on an edge should be a vertical line"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
result = F.vprewitt(image)
|
||||
# Fudge the eroded points
|
||||
@@ -187,15 +187,15 @@ class TestVPrewitt():
|
||||
assert (np.all(result[j == 0] == 1))
|
||||
eps = .000001
|
||||
assert (np.all(np.abs(result[np.abs(j) > 1]) < eps))
|
||||
|
||||
|
||||
def test_01_02_horizontal(self):
|
||||
"""Vertical prewitt on a horizontal edge should be zero"""
|
||||
i,j = np.mgrid[-5:6, -5:6]
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
result = F.vprewitt(image)
|
||||
eps = .000001
|
||||
assert (np.all(np.abs(result) < eps))
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_module_suite()
|
||||
|
||||
Reference in New Issue
Block a user