def best_cost(matrix):
hun = linear_assignment(matrix)
cost = 0.0
#print hun
for pair in hun:
cost += matrix[pair[0], pair[1]]
return cost
def _disjoint_max_assignment(similarities):
global sparse
# form n*n adjacency matrix
where_true, where_pred = similarities.nonzero()
where_pred = where_pred + similarities.shape[0]
n = sum(similarities.shape)
A = sparse.coo_matrix((np.ones(len(where_true)), (where_true, where_pred)),
shape=(n, n))
try:
n_components, components = csgraph.connected_components(A,
directed=False)
except (AttributeError, TypeError):
warnings.warn(
'Could not use scipy.sparse.csgraph.connected_components.'
'Please update your scipy installation. '
'Calculating max-score assignment the slow way.')
# HACK!
sparse = None
return _disjoint_max_assignment(similarities)
if hasattr(similarities, 'toarray'):
# faster to work in dense
similarities = similarities.toarray()
total = 0
for i in range(n_components):
mask = components == i
component_true = np.flatnonzero(mask[:similarities.shape[0]])
component_pred = np.flatnonzero(mask[similarities.shape[0]:])
component_sim = similarities[component_true, :][:, component_pred]
if 0 in component_sim.shape:
pass
if component_sim.shape == (1, 1):
total += component_sim[0, 0]
else:
indices = linear_assignment(-component_sim)
total += component_sim[indices[:, 0], indices[:, 1]].sum()
return total
im_with_keypoints = cv2.drawKeypoints(fgmask, keypoints, np.array([]), (0,0,255))
if len(bees) == 0: # no bees yet, no matching to do, just add them
for kp in keypoints:
bees.append(Bee(kp.pt))
hive.append(kp.pt)
else :
# MUNKRES assignment, slightly better
freeBees = [True for i in xrange(len(bees))]
freeKP = [True for i in xrange(len(keypoints))]
# build cost matrix
cost = np.zeros((len(keypoints), len(bees)))
for i,kp in enumerate(keypoints):
for j,b in enumerate(bees):
cost[i,j] = b.dist(kp.pt)
# proper assignment
assignment = linear_assignment(cost)
for ass in assignment :
if cost[ass[0], ass[1]] < THRESHBEE:
bees[ass[1]].move(keypoints[ass[0]].pt)
freeBees[ass[1]] = False
freeKP[ass[0]] = False
for i in xrange(len(freeBees)): # lost bees
if freeBees[i]:
bees[i].lastSeen += 1
for i in xrange(len(freeKP)): # new keypoints
if freeKP[i]:
bees.append(Bee(keypoints[i].pt))
hive.append(keypoints[i].pt)
# remove lost bees (not seen for at least 15 frames)
tmp = []
for b in bees:
im_with_keypoints = cv2.drawKeypoints(fgmask, keypoints, np.array([]), (0,0,255))
if len(bees) == 0: # no bees yet, no matching to do, just add them
for kp in keypoints:
bees.append(Bee(kp.pt))
hive.append(kp.pt)
else :
# MUNKRES assignment, slightly better
freeBees = [True for i in xrange(len(bees))]
freeKP = [True for i in xrange(len(keypoints))]
# build cost matrix
cost = np.zeros((len(keypoints), len(bees)))
for i,kp in enumerate(keypoints):
for j,b in enumerate(bees):
cost[i,j] = b.dist(kp.pt)
# proper assignment
assignment = linear_assignment(cost)
for ass in assignment :
if cost[ass[0], ass[1]] < THRESHBEE:
bees[ass[1]].move(keypoints[ass[0]].pt)
freeBees[ass[1]] = False
freeKP[ass[0]] = False
for i in xrange(len(freeBees)): # lost bees
if freeBees[i]:
bees[i].lastSeen += 1
for i in xrange(len(freeKP)): # new keypoints
if freeKP[i]:
bees.append(Bee(keypoints[i].pt))
hive.append(keypoints[i].pt)
# remove lost bees (not seen for at least 15 frames)
tmp = []
for b in bees:
def best_pairs(matrix):
return linear_assignment(matrix)
