def propagate(image, labels, mask, weight):
"""Propagate the labels to the nearest pixels
image - gives the Z height when computing distance
labels - the labeled image pixels
mask - only label pixels within the mask
weight - the weighting of x/y distance vs z distance
high numbers favor x/y, low favor z
returns a label matrix and the computed distances
"""
if image.shape != labels.shape:
raise ValueError("Image shape %s != label shape %s"%(repr(image.shape),repr(labels.shape)))
if image.shape != mask.shape:
raise ValueError("Image shape %s != mask shape %s"%(repr(image.shape),repr(mask.shape)))
labels_out = np.zeros(labels.shape, np.int32)
distances = -np.ones(labels.shape,np.float64)
distances[labels > 0] = 0
labels_and_mask = np.logical_and(labels != 0, mask)
coords = np.argwhere(labels_and_mask)
i1,i2 = _propagate.convert_to_ints(0.0)
ncoords = coords.shape[0]
pq = np.column_stack((np.ones((ncoords,),int) * i1,
np.ones((ncoords,),int) * i2,
labels[labels_and_mask],
coords))
_propagate.propagate(np.ascontiguousarray(image,np.float64),
np.ascontiguousarray(pq,np.int32),
np.ascontiguousarray(mask,np.int8),
labels_out, distances, float(weight))
labels_out[labels > 0] = labels[labels > 0]
return labels_out,distances
def propagate(image, labels, mask, weight):
"""Propagate the labels to the nearest pixels
image - gives the Z height when computing distance
labels - the labeled image pixels
mask - only label pixels within the mask
weight - the weighting of x/y distance vs z distance
high numbers favor x/y, low favor z
returns a label matrix and the computed distances
"""
if image.shape != labels.shape:
raise ValueError("Image shape %s != label shape %s" %
(repr(image.shape), repr(labels.shape)))
if image.shape != mask.shape:
raise ValueError("Image shape %s != mask shape %s" %
(repr(image.shape), repr(mask.shape)))
labels_out = np.zeros(labels.shape, np.int32)
distances = -np.ones(labels.shape, np.float64)
distances[labels > 0] = 0
labels_and_mask = np.logical_and(labels != 0, mask)
coords = np.argwhere(labels_and_mask)
i1, i2 = _propagate.convert_to_ints(0.0)
ncoords = coords.shape[0]
pq = np.column_stack((np.ones(
(ncoords, ), int) * i1, np.ones(
(ncoords, ), int) * i2, labels[labels_and_mask], coords))
_propagate.propagate(np.ascontiguousarray(image, np.float64),
np.ascontiguousarray(pq, np.int32),
np.ascontiguousarray(mask, np.int8), labels_out,
distances, float(weight))
labels_out[labels > 0] = labels[labels > 0]
return labels_out, distances