def cplabel(self, threshold=0):
""" Label pixels using the connectedpixels assigment code
Fills in:
self.nlabels = number of peaks per frame
self.labels = peak labels (should be unique)
self.total_labels = total number of peaks
"""
self.nlabels = np.zeros(len(self.nnz), np.int32)
self.labels = np.zeros(len(self.row), "i")
nl = 0
# TODO: run this in parallel with threads?
for i, npx in enumerate(self.nnz):
s = self.ipt[i]
e = self.ipt[i + 1]
if npx > 0:
self.nlabels[i] = cImageD11.sparse_connectedpixels(
self.intensity[s:e], self.row[s:e], self.col[s:e],
threshold, self.labels[s:e])
# zero label is the background!
self.labels[s:e] = np.where(self.labels[s:e] > 0,
self.labels[s:e] + nl, 0)
else:
self.nlabels[i] = 0
nl += self.nlabels[i]
self.total_labels = nl
def testOK(self):
#import pdb; pdb.set_trace()
i = self.s.row.astype(np.uint16)
j = self.s.col.astype(np.uint16)
v = self.s.data
self.assertTrue((self.s.todense() == self.a).all())
dl = np.zeros(self.a.shape, 'i')
nd = cImageD11.connectedpixels(self.a, dl, self.threshold)
dstart = timer()
nd = cImageD11.connectedpixels(self.a, dl, self.threshold)
dend = timer()
sl = np.zeros(v.shape, 'i')
ns = cImageD11.sparse_connectedpixels(v, i, j, self.threshold, sl)
sbegin = timer()
ns = cImageD11.sparse_connectedpixels(v, i, j, self.threshold, sl)
send = timer()
sld = scipy.sparse.coo_matrix((sl, (i, j)), shape=self.a.shape)
testcase = (sld.todense() == dl).all()
densetime = dend - dstart
sparsetime = send - sbegin
print(
"sp_cp: %.3f ms vs %.3f ms, ratio %.1f nnz %d" %
(1e3 * sparsetime, 1e3 * densetime, densetime / sparsetime,
sld.nnz), " t=%.0f" % (self.threshold))
if ~testcase:
import pylab as pl
pl.ioff()
pl.figure()
pl.subplot(311)
pl.title("t=%f" % (self.threshold))
pl.imshow(dl, interpolation='nearest')
pl.colorbar()
pl.subplot(312)
pl.imshow(sld.todense(), interpolation='nearest')
pl.colorbar()
pl.subplot(313)
pl.imshow(sld.todense() - dl, interpolation='nearest')
pl.colorbar()
pl.show()
self.assertTrue(testcase)
def testsplat(self):
i = self.s.row.astype(np.uint16)
j = self.s.col.astype(np.uint16)
v = self.s.data
self.assertTrue((self.s.todense() == self.a).all())
ni, nj = self.s.shape
z = np.empty((ni + 2) * (nj + 2), np.int32)
sl = np.zeros(v.shape, 'i')
sls = np.zeros(v.shape, 'i')
ns = cImageD11.sparse_connectedpixels(v, i, j, self.threshold, sl)
s0 = timer()
ns = cImageD11.sparse_connectedpixels(v, i, j, self.threshold, sl)
s1 = timer()
ns = cImageD11.sparse_connectedpixels_splat(v, i, j, self.threshold,
sls, z, ni, nj)
s2 = timer()
ns = cImageD11.sparse_connectedpixels_splat(v, i, j, self.threshold,
sls, z, ni, nj)
s3 = timer()
sld = scipy.sparse.coo_matrix((sl, (i, j)), shape=self.a.shape)
testcase = (sls == sl).all()
print("\nsparse_connectedpixels %.3f ms" % (1e3 * (s1 - s0)),
"\nsparse_connectedpixels_splat %.3f ms" % (1e3 * (s3 - s2)))
if ~testcase:
import pylab as pl
pl.ioff()
pl.figure()
pl.subplot(311)
pl.title("t=%f" % (self.threshold))
pl.imshow(dl, interpolation='nearest')
pl.colorbar()
pl.subplot(312)
pl.imshow(sld.todense(), interpolation='nearest')
pl.colorbar()
pl.subplot(313)
pl.imshow(sld.todense() - dl, interpolation='nearest')
pl.colorbar()
pl.show()
self.assertTrue(testcase)
def __init__(self, frame, threshold, name='data'):
"""
guesses threshold as min val-1 or given threshold
adds an array named [cplabels_tx.xxx]
self.nclabel number of connected objects
"""
sparse_labelling.__init__(self, frame, name=name)
if threshold is None:
self.threshold = self.frame.pixels[name].min() - 1
else:
self.threshold = threshold
self.nlabel = cImageD11.sparse_connectedpixels(
self.frame.pixels[self.name], self.frame.row, self.frame.col,
self.threshold, self.labels)
def testsplat(self):
i = self.s.row.astype(np.uint16)
j = self.s.col.astype(np.uint16)
v = self.s.data
self.assertTrue((self.s.todense() == self.a).all())
ni, nj = self.s.shape
z = np.empty((ni + 2) * (nj + 2), np.int32)
sl = np.zeros(v.shape, 'i')
sls = np.zeros(v.shape, 'i')
ns = cImageD11.sparse_connectedpixels(v, i, j, self.threshold, sl)
s0 = timer()
ns = cImageD11.sparse_connectedpixels(v, i, j, self.threshold, sl)
s1 = timer()
ns = cImageD11.sparse_connectedpixels_splat(v, i, j, self.threshold,
sls, z, ni, nj)
s2 = timer()
ns = cImageD11.sparse_connectedpixels_splat(v, i, j, self.threshold,
sls, z, ni, nj)
s3 = timer()
sld = scipy.sparse.coo_matrix((sl, (i, j)), shape=self.a.shape)
testcase = (sls == sl).all()
print("\nsparse_connectedpixels %.3f ms" % (1e3 * (s1 - s0)),
"\nsparse_connectedpixels_splat %.3f ms" % (1e3 * (s3 - s2)))
self.assertTrue(testcase)
def sparse_connected_pixels( frame,
label_name="connectedpixels",
data_name="intensity",
threshold=None ):
"""
frame = a sparse frame
label_name = the array to save labels to in that frame
data_name = an array in that frame
threshold = float value or take data.threshold
"""
labels = np.zeros( frame.nnz, "i" )
if threshold is None:
threshold = frame.meta[data_name]["threshold"]
nlabel = cImageD11.sparse_connectedpixels(
frame.pixels[data_name], frame.row, frame.col,
threshold, labels )
frame.set_pixels( label_name, labels, { 'nlabel' : nlabel } )
def sparse_connected_pixels(frame,
label_name="connectedpixels",
data_name="intensity",
threshold=None):
"""
frame = a sparse frame
label_name = the array to save labels to in that frame
data_name = an array in that frame
threshold = float value or take data.threshold
"""
labels = np.zeros(frame.nnz, "i")
if threshold is None:
threshold = frame.pixels[data_name].header["threshold"]
nlabel = cImageD11.sparse_connectedpixels(frame.pixels[data_name].data,
frame.row, frame.col, threshold,
labels)
px = Container(
labels, **{
"data_name": data_name,
"threshold": threshold,
"nlabel": nlabel
})
frame.set_pixels(label_name, px)