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 OnLabelWatershed(self, event):
import numpy as np
from PYME.contrib.cpmath import watershed
from PYME.IO.image import ImageStack
from PYME.DSView import ViewIm3D
nChans = self.image.data.shape[3]
filt_ims = [
np.atleast_3d(self.image.data[:, :, :, chanNum].squeeze())
for chanNum in range(nChans)
]
img = (-sum([im / im.max()
for im in filt_ims]) * (2**15) / nChans).astype('int16')
mask = (sum([
filt_ims[chanNum] > self.do.thresholds[chanNum]
for chanNum in range(nChans)
]) > .5).astype('int16')
#self.image.labelThresholds = [(self.dsviewer.do.Offs[chanNum] + 0.5/self.dsviewer.do.Gains[chanNum]) for chanNum in range(self.image.data.shape[3])]
#print sum(filt_ims).shape
labs = watershed.fast_watershed(img,
self.image.labels.astype('int16'),
mask=mask)
#store a copy in the image for measurements etc ...
self.image.labels = labs
im = ImageStack(labs, titleStub='Labelled Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
im.mdh['Labelling.WatershedThresholds'] = self.do.thresholds
#im.mdh['Labelling.MinSize'] = rSize
#im.mdh['Labelling.Thresholds'] = self.image.labelThresholds
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im,
mode=mode,
glCanvas=self.dsviewer.glCanvas,
parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
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_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)
