def nn_closer(img0, img1, labels0, labels1, DISPLACEMENT=30, MASSTHRES=0.25):
labels = -labels1.copy()
rps0 = regionprops(labels0, img0)
rps1 = regionprops(labels1, img1)
if not rps0 or not rps1:
return labels0, labels
dist = cdist([i.centroid for i in rps0], [i.centroid for i in rps1])
massdiff = calc_massdiff(rps0, rps1)
binary_cost = (dist < DISPLACEMENT) * (abs(massdiff) < MASSTHRES)
binary_cost = pick_closer_cost(binary_cost, dist)
binary_cost = pick_closer_cost(binary_cost.T, dist.T).T
idx1, idx0 = find_one_to_one_assign(binary_cost)
for i0, i1 in zip(idx0, idx1):
labels[labels1 == rps1[i1].label] = rps0[i0].label
labels0[labels0 == rps0[i0].label] = -rps0[i0].label
return labels0, labels
def nearest_neighbor(img0,
img1,
labels0,
labels1,
DISPLACEMENT=20,
MASSTHRES=0.2):
"""
labels0 and labels1: the positive values for non-tracked objects and the negative values for tracked objects.
"""
labels = -labels1.copy()
rps0 = regionprops(labels0, img0)
rps1 = regionprops(labels1, img1)
if not rps0 or not rps1:
return labels0, labels
dist = cdist([i.centroid for i in rps0], [i.centroid for i in rps1])
massdiff = calc_massdiff(rps0, rps1)
binary_cost = (dist < DISPLACEMENT) * (abs(massdiff) < MASSTHRES)
idx1, idx0 = find_one_to_one_assign(binary_cost)
for i0, i1 in zip(idx0, idx1):
labels[labels1 == rps1[i1].label] = rps0[i0].label
labels0[labels0 == rps0[i0].label] = -rps0[i0].label
return labels0, labels