def test_watershed01(self):
"watershed 1"
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
markers = numpy.array([[ -1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 1, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0]],
numpy.int8)
out = fast_watershed(data, markers,self.eight)
error = diff([[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]], out)
self.failUnless(error < eps)
def test_watershed09(self):
"""Test on an image of reasonable size
This is here both for timing (does it take forever?) and to
ensure that the memory constraints are reasonable
"""
image = numpy.zeros((1000, 1000))
coords = numpy.random.uniform(0, 1000, (100, 2)).astype(int)
markers = numpy.zeros((1000, 1000), int)
idx = 1
for x, y in coords:
image[x, y] = 1
markers[x, y] = idx
idx += 1
image = scipy.ndimage.gaussian_filter(image, 4)
before = time.clock()
out = fast_watershed(image, markers, self.eight)
elapsed = time.clock() - before
print "Fast watershed ran a megapixel image in %f seconds" % (elapsed)
before = time.clock()
out = scipy.ndimage.watershed_ift(image.astype(numpy.uint16), markers,
self.eight)
elapsed = time.clock() - before
print "Scipy watershed ran a megapixel image in %f seconds" % (elapsed)
def test_watershed08(self):
"The border pixels + an edge are all the same value"
data = numpy.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,255,255,204,153,141,141,153,204,255,255,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
mask = (data!=255)
markers = numpy.zeros(data.shape,int)
markers[6,7] = 1
markers[14,7] = 2
out = fast_watershed(data, markers,self.eight,mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = numpy.sum(out==1)
size2 = numpy.sum(out==2)
self.assertTrue(abs(size1-size2) <=6)
def test_watershed07(self):
"A regression test of a competitive case that failed"
data = numpy.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,255,255,204,153,103,103,153,204,255,255,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
mask = (data!=255)
markers = numpy.zeros(data.shape,int)
markers[6,7] = 1
markers[14,7] = 2
out = fast_watershed(data, markers,self.eight,mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = numpy.sum(out==1)
size2 = numpy.sum(out==2)
self.assertTrue(abs(size1-size2) <=6)
def test_watershed03(self):
"watershed 3"
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
markers = numpy.array([[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 2, 0, 3, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, -1]],
numpy.int8)
out = fast_watershed(data, markers)
error = diff([[-1, -1, -1, -1, -1, -1, -1],
[-1, 0, 2, 0, 3, 0, -1],
[-1, 2, 2, 0, 3, 3, -1],
[-1, 2, 2, 0, 3, 3, -1],
[-1, 2, 2, 0, 3, 3, -1],
[-1, 0, 2, 0, 3, 0, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]], out)
self.failUnless(error < eps)
def test_watershed02(self):
"watershed 2"
data = numpy.array(
[
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
],
numpy.uint8,
)
markers = numpy.array(
[
[-1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
],
numpy.int8,
)
out = fast_watershed(data, markers)
error = diff(
[
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, 1, 1, 1, -1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, -1, 1, 1, 1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
],
out,
)
self.failUnless(error < eps)
def test_watershed05(self):
"watershed 5"
data = numpy.array(
[
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
],
numpy.uint8,
)
markers = numpy.array(
[
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 3, 0, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, -1],
],
numpy.int8,
)
out = fast_watershed(data, markers, self.eight)
error = diff(
[
[-1, -1, -1, -1, -1, -1, -1],
[-1, 3, 3, 0, 2, 2, -1],
[-1, 3, 3, 0, 2, 2, -1],
[-1, 3, 3, 0, 2, 2, -1],
[-1, 3, 3, 0, 2, 2, -1],
[-1, 3, 3, 0, 2, 2, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
],
out,
)
self.failUnless(error < eps)
def test_watershed06(self):
"watershed 6"
data = numpy.array([[0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
markers = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[-1, 0, 0, 0, 0, 0, 0]], numpy.int8)
out = fast_watershed(data, markers, self.eight)
error = diff(
[[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1],
[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]], out)
self.failUnless(error < eps)
def test_watershed03(self):
"watershed 3"
data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]],
numpy.uint8)
markers = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]],
numpy.int8)
out = fast_watershed(data, markers)
error = diff(
[[-1, -1, -1, -1, -1, -1, -1], [-1, 0, 2, 0, 3, 0, -1],
[-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1],
[-1, 2, 2, 0, 3, 3, -1], [-1, 0, 2, 0, 3, 0, -1],
[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out)
self.failUnless(error < eps)
def test_watershed09(self):
"""Test on an image of reasonable size
This is here both for timing (does it take forever?) and to
ensure that the memory constraints are reasonable
"""
image = numpy.zeros((1000,1000))
coords = numpy.random.uniform(0,1000,(100,2)).astype(int)
markers = numpy.zeros((1000,1000),int)
idx = 1
for x,y in coords:
image[x,y] = 1
markers[x,y] = idx
idx += 1
image = scipy.ndimage.gaussian_filter(image, 4)
before = time.clock()
out = fast_watershed(image,markers,self.eight)
elapsed = time.clock()-before
print "Fast watershed ran a megapixel image in %f seconds"%(elapsed)
before = time.clock()
out = scipy.ndimage.watershed_ift(image.astype(numpy.uint16), markers, self.eight)
elapsed = time.clock()-before
print "Scipy watershed ran a megapixel image in %f seconds"%(elapsed)
def test_watershed10(self):
# https://github.com/scikit-image/scikit-image/issues/803
#
# Make sure that no point in a level image is farther away
# from its seed than any other
#
image = numpy.zeros((21, 21))
markers = numpy.zeros((21, 21), int)
markers[5, 5] = 1
markers[5, 10] = 2
markers[10, 5] = 3
markers[10, 10] = 4
structure = numpy.array([[False, True, False], [True, True, True], [False, True, False]])
out = fast_watershed(image, markers, structure)
i, j = numpy.mgrid[0:21, 0:21]
d = numpy.dstack(
[
numpy.sqrt((i.astype(float) - i0) ** 2, (j.astype(float) - j0) ** 2)
for i0, j0 in ((5, 5), (5, 10), (10, 5), (10, 10))
]
)
dmin = numpy.min(d, 2)
self.assertTrue(numpy.all(d[i, j, out[i, j] - 1] == dmin))
def test_watershed08(self):
"The border pixels + an edge are all the same value"
data = numpy.array([[
255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255
],
[
255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 204, 204, 204, 204,
204, 204, 255, 255, 255, 255, 255
],
[
255, 255, 255, 204, 204, 183, 153, 153, 153,
153, 183, 204, 204, 255, 255, 255
],
[
255, 255, 204, 183, 153, 141, 111, 103, 103,
111, 141, 153, 183, 204, 255, 255
],
[
255, 255, 204, 153, 111, 94, 72, 52, 52, 72,
94, 111, 153, 204, 255, 255
],
[
255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72,
111, 153, 204, 255, 255
],
[
255, 255, 204, 183, 141, 111, 72, 39, 39, 72,
111, 141, 183, 204, 255, 255
],
[
255, 255, 255, 204, 183, 141, 111, 72, 72, 111,
141, 183, 204, 255, 255, 255
],
[
255, 255, 255, 255, 204, 183, 141, 94, 94, 141,
183, 204, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 204, 153, 141, 141,
153, 204, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 204, 183, 141, 94, 94, 141,
183, 204, 255, 255, 255, 255
],
[
255, 255, 255, 204, 183, 141, 111, 72, 72, 111,
141, 183, 204, 255, 255, 255
],
[
255, 255, 204, 183, 141, 111, 72, 39, 39, 72,
111, 141, 183, 204, 255, 255
],
[
255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72,
111, 153, 204, 255, 255
],
[
255, 255, 204, 153, 111, 94, 72, 52, 52, 72,
94, 111, 153, 204, 255, 255
],
[
255, 255, 204, 183, 153, 141, 111, 103, 103,
111, 141, 153, 183, 204, 255, 255
],
[
255, 255, 255, 204, 204, 183, 153, 153, 153,
153, 183, 204, 204, 255, 255, 255
],
[
255, 255, 255, 255, 255, 204, 204, 204, 204,
204, 204, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255
]])
mask = (data != 255)
markers = numpy.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = fast_watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = numpy.sum(out == 1)
size2 = numpy.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed07(self):
"A regression test of a competitive case that failed"
data = numpy.array([[
255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255
],
[
255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 204, 204, 204, 204,
204, 204, 255, 255, 255, 255, 255
],
[
255, 255, 255, 204, 204, 183, 153, 153, 153,
153, 183, 204, 204, 255, 255, 255
],
[
255, 255, 204, 183, 153, 141, 111, 103, 103,
111, 141, 153, 183, 204, 255, 255
],
[
255, 255, 204, 153, 111, 94, 72, 52, 52, 72,
94, 111, 153, 204, 255, 255
],
[
255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72,
111, 153, 204, 255, 255
],
[
255, 255, 204, 183, 141, 111, 72, 39, 39, 72,
111, 141, 183, 204, 255, 255
],
[
255, 255, 255, 204, 183, 141, 111, 72, 72, 111,
141, 183, 204, 255, 255, 255
],
[
255, 255, 255, 255, 204, 183, 141, 94, 94, 141,
183, 204, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 204, 153, 103, 103,
153, 204, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 204, 183, 141, 94, 94, 141,
183, 204, 255, 255, 255, 255
],
[
255, 255, 255, 204, 183, 141, 111, 72, 72, 111,
141, 183, 204, 255, 255, 255
],
[
255, 255, 204, 183, 141, 111, 72, 39, 39, 72,
111, 141, 183, 204, 255, 255
],
[
255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72,
111, 153, 204, 255, 255
],
[
255, 255, 204, 153, 111, 94, 72, 52, 52, 72,
94, 111, 153, 204, 255, 255
],
[
255, 255, 204, 183, 153, 141, 111, 103, 103,
111, 141, 153, 183, 204, 255, 255
],
[
255, 255, 255, 204, 204, 183, 153, 153, 153,
153, 183, 204, 204, 255, 255, 255
],
[
255, 255, 255, 255, 255, 204, 204, 204, 204,
204, 204, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255
],
[
255, 255, 255, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255
]])
mask = (data != 255)
markers = numpy.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = fast_watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = numpy.sum(out == 1)
size2 = numpy.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
