def means(contours, prev):
X = []
means = []
clusters = {}
for contour in contours:
M = moments(contour)
try:
X.append((M['m10']/M['m00'],M['m01']/M['m00']))
except ZeroDivisionError:
print 'ZERO!'
if len(X) >= 2:
means, clusters = kcluster.cluster(np.asarray(X), ROBOTS_COUNT, np.asarray(prev))
return means, clusters
def means(contours, prev):
X = []
means = []
clusters = {}
for contour in contours:
M = moments(contour)
try:
centre = (M['m10']/M['m00'],M['m01']/M['m00'])
X.append(centre)
except ZeroDivisionError:
print 'ZERO!'
if len(X) >= 2:
means, clusters = kcluster.cluster(np.asarray(X), ROBOTS_COUNT, np.asarray(prev))
#km = KMeans(n_clusters = ROBOTS_COUNT,n_init=1, verbose=1, init=np.asarray(prev)).fit(np.asarray(X))
#means = km.cluster_centers_
#labels = km.labels_
#clusters = cluster_points(X,labels)
return means, clusters
