def test_watershed08(self):
"The border pixels + an edge are all the same value"
data = np.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 = np.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = np.sum(out == 1)
size2 = np.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed08(self):
"The border pixels + an edge are all the same value"
data = np.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 = np.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = np.sum(out == 1)
size2 = np.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed05(self):
"watershed 5"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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 = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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.assertTrue(error < eps)
def test_watershed03(self):
"watershed 3"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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.assertTrue(error < eps)
def test_watershed05(self):
"watershed 5"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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.assertTrue(error < eps)
def test_compact_watershed():
image = np.zeros((5, 6))
image[:, 3:] = 1
seeds = np.zeros((5, 6), dtype=int)
seeds[2, 0] = 1
seeds[2, 3] = 2
compact = watershed(image, seeds, compactness=0.01)
expected = np.array(
[[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]],
dtype=int)
np.testing.assert_equal(compact, expected)
normal = watershed(image, seeds)
expected = np.ones(image.shape, dtype=int)
expected[2, 3:] = 2
np.testing.assert_equal(normal, expected)
def test_watershed03(self):
"watershed 3"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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_watershed01(self):
"watershed 1"
data = np.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]],
np.uint8)
markers = np.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]],
np.int8)
out = watershed(data, markers, self.eight)
expected = np.array([[-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]])
error = diff(expected, out)
assert error < eps
out = _slow_watershed(data, markers, 8)
error = diff(expected, out)
assert error < eps
def test_watershed07(self):
"A regression test of a competitive case that failed"
data = np.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 = np.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = np.sum(out == 1)
size2 = np.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed07(self):
"A regression test of a competitive case that failed"
data = np.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 = np.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = np.sum(out == 1)
size2 = np.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed06(self):
"watershed 6"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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_compact_watershed():
image = np.zeros((5, 6))
image[:, 3:] = 1
seeds = np.zeros((5, 6), dtype=int)
seeds[2, 0] = 1
seeds[2, 3] = 2
compact = watershed(image, seeds, compactness=0.01)
expected = np.array([[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2]], dtype=int)
np.testing.assert_equal(compact, expected)
normal = watershed(image, seeds)
expected = np.ones(image.shape, dtype=int)
expected[2, 3:] = 2
np.testing.assert_equal(normal, expected)
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 = np.zeros((1000, 1000))
coords = np.random.uniform(0, 1000, (100, 2)).astype(int)
markers = np.zeros((1000, 1000), int)
idx = 1
for x, y in coords:
image[x, y] = 1
markers[x, y] = idx
idx += 1
image = ndi.gaussian_filter(image, 4)
watershed(image, markers, self.eight)
ndi.watershed_ift(image.astype(np.uint16), markers, self.eight)
def test_numeric_seed_watershed():
"""Test that passing just the number of seeds to watershed works."""
image = np.zeros((5, 6))
image[:, 3:] = 1
compact = watershed(image, 2, compactness=0.01)
expected = np.array(
[[1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2],
[1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2]],
dtype=np.int32)
np.testing.assert_equal(compact, expected)
def test_watershed10(self):
"watershed 10"
data = np.array(
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], np.uint8)
markers = np.array(
[[1, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0], [3, 0, 0, 4]], np.int8)
out = watershed(data, markers, self.eight)
error = diff([[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]],
out)
self.assertTrue(error < eps)
def test_numeric_seed_watershed():
"""Test that passing just the number of seeds to watershed works."""
image = np.zeros((5, 6))
image[:, 3:] = 1
compact = watershed(image, 2, compactness=0.01)
expected = np.array([[1, 1, 1, 1, 2, 2],
[1, 1, 1, 1, 2, 2],
[1, 1, 1, 1, 2, 2],
[1, 1, 1, 1, 2, 2],
[1, 1, 1, 1, 2, 2]], dtype=np.int32)
np.testing.assert_equal(compact, expected)
def test_watershed02(self):
"watershed 2"
data = np.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],
],
np.uint8,
)
markers = np.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],
],
np.int8,
)
out = 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_watershed01(self):
"watershed 1"
data = np.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],
],
np.uint8,
)
markers = np.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],
],
np.int8,
)
out = watershed(data, markers, self.eight)
expected = np.array(
[
[-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],
]
)
error = diff(expected, out)
assert error < eps
out = _slow_watershed(data, markers, 8)
error = diff(expected, out)
assert error < eps
def test_watershed05(self):
"watershed 5"
data = np.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],
],
np.uint8,
)
markers = np.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],
],
np.int8,
)
out = 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_watershed07(self):
"A regression test of a competitive case that failed"
data = blob
mask = (data != 255)
markers = np.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = np.sum(out == 1)
size2 = np.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed10(self):
"watershed 10"
data = np.array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]], np.uint8)
markers = np.array([[1, 0, 0, 2],
[0, 0, 0, 0],
[0, 0, 0, 0],
[3, 0, 0, 4]], np.int8)
out = watershed(data, markers, self.eight)
error = diff([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]], out)
self.assertTrue(error < eps)
def test_watershed08(self):
"The border pixels + an edge are all the same value"
data = blob.copy()
data[10, 7:9] = 141
mask = (data != 255)
markers = np.zeros(data.shape, int)
markers[6, 7] = 1
markers[14, 7] = 2
out = watershed(data, markers, self.eight, mask=mask)
#
# The two objects should be the same size, except possibly for the
# border region
#
size1 = np.sum(out == 1)
size2 = np.sum(out == 2)
self.assertTrue(abs(size1 - size2) <= 6)
def test_watershed02(self):
"watershed 2"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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_watershed12(self):
"The watershed line"
data = np.array([[203, 255, 203, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153],
[203, 255, 203, 153, 153, 153, 102, 102, 102, 102, 102, 102, 153, 153, 153, 153],
[203, 255, 203, 203, 153, 153, 102, 102, 77, 0, 102, 102, 153, 153, 203, 203],
[203, 255, 255, 203, 153, 153, 153, 102, 102, 102, 102, 153, 153, 203, 203, 255],
[203, 203, 255, 203, 203, 203, 153, 153, 153, 153, 153, 153, 203, 203, 255, 255],
[153, 203, 255, 255, 255, 203, 203, 203, 203, 203, 203, 203, 203, 255, 255, 203],
[153, 203, 203, 203, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 203, 203],
[153, 153, 153, 203, 203, 203, 203, 203, 255, 203, 203, 203, 203, 203, 203, 153],
[102, 102, 153, 153, 153, 153, 203, 203, 255, 203, 203, 255, 203, 153, 153, 153],
[102, 102, 102, 102, 102, 153, 203, 255, 255, 203, 203, 203, 203, 153, 102, 153],
[102, 51, 51, 102, 102, 153, 203, 255, 203, 203, 153, 153, 153, 153, 102, 153],
[ 77, 51, 51, 102, 153, 153, 203, 255, 203, 203, 203, 153, 102, 102, 102, 153],
[ 77, 0, 51, 102, 153, 203, 203, 255, 203, 255, 203, 153, 102, 51, 102, 153],
[ 77, 0, 51, 102, 153, 203, 255, 255, 203, 203, 203, 153, 102, 0, 102, 153],
[102, 0, 51, 102, 153, 203, 255, 203, 203, 153, 153, 153, 102, 102, 102, 153],
[102, 102, 102, 102, 153, 203, 255, 203, 153, 153, 153, 153, 153, 153, 153, 153]])
markerbin = (data==0)
marker = label(markerbin)
ws = watershed(data, marker, connectivity=2, watershed_line=True)
for lab, area in zip(range(4), [34,74,74,74]):
self.assertTrue(np.sum(ws == lab) == area)
def test_watershed02(self):
"watershed 2"
data = np.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]], np.uint8)
markers = np.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]], np.int8)
out = 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.assertTrue(error < eps)
def test_watershed11(self):
'''Make sure that all points on this plateau are assigned to closest seed'''
# 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 = np.zeros((21, 21))
markers = np.zeros((21, 21), int)
markers[5, 5] = 1
markers[5, 10] = 2
markers[10, 5] = 3
markers[10, 10] = 4
structure = np.array([[False, True, False],
[True, True, True],
[False, True, False]])
out = watershed(image, markers, structure)
i, j = np.mgrid[0:21, 0:21]
d = np.dstack(
[np.sqrt((i.astype(float)-i0)**2, (j.astype(float)-j0)**2)
for i0, j0 in ((5, 5), (5, 10), (10, 5), (10, 10))])
dmin = np.min(d, 2)
self.assertTrue(np.all(d[i, j, out[i, j]-1] == dmin))
def test_no_markers():
data = blob
mask = (data != 255)
out = watershed(data, mask=mask)
assert np.max(out) == 2
def test_markers_in_mask():
data = blob
mask = (data != 255)
out = watershed(data, 25, connectivity=2, mask=mask)
# There should be no markers where the mask is false
assert np.all(out[~mask] == 0)
def test_incorrect_mask_shape():
with pytest.raises(ValueError):
image = np.ones((5, 6))
mask = np.ones((5, 7))
output = watershed(image, markers=4, mask=mask)
