def qb_metrics_features(streamlines,
threshold=10.0,
metric=None,
max_nb_clusters=np.iinfo('i4').max):
"""
Enhancing QuickBundles with different metrics and features
metric: 'IF', 'RF', 'CoMF', 'MF', 'AF', 'VBEF', None
"""
if metric == 'IF':
feature = IdentityFeature()
metric = AveragePointwiseEuclideanMetric(feature=feature)
elif metric == 'RF':
feature = ResampleFeature(nb_point=24)
metric = AveragePointwiseEuclideanMetric(feature=feature)
elif metric == 'CoMF':
feature = CenterOfMassFeature()
metric = EuclideanMetric(feature)
elif metric == 'MF':
feature = MidpointFeature()
metric = EuclideanMetric(feature)
elif metric == 'AF':
feature = ArcLengthFeature()
metric = EuclideanMetric(feature)
elif metric == 'VBEF':
feature = VectorOfEndpointsFeature()
metric = CosineMetric(feature)
else:
metric = "MDF_12points"
qb = QuickBundles(threshold=threshold,
metric=metric,
max_nb_clusters=max_nb_clusters)
clusters = qb.cluster(streamlines)
labels = np.array(len(streamlines) * [None])
N_list = []
for i in range(len(clusters)):
N_list.append(clusters[i]['N'])
data_clusters = []
for i in range(len(clusters)):
labels[clusters[i]['indices']] = i + 1
data_clusters.append(streamlines[clusters[i]['indices']])
return labels, data_clusters, N_list
in the clustering framework.*
"""
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import IdentityFeature
from dipy.segment.metric import AveragePointwiseEuclideanMetric
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
# Make sure our streamlines have the same number of points.
from dipy.tracking.streamline import set_number_of_points
streamlines = set_number_of_points(streamlines, nb_points=12)
# Create an instance of `IdentityFeature` and tell metric to use it.
feature = IdentityFeature()
metric = AveragePointwiseEuclideanMetric(feature=feature)
qb = QuickBundles(threshold=10., metric=metric)
clusters = qb.cluster(streamlines)
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", list(map(len, clusters)))
"""
::
Nb. clusters: 4
Cluster sizes: [64, 191, 47, 1]
#%% Closing
kernel = np.ones((12, 12), np.uint8)
closed_img = cv2.morphologyEx(crop_img, cv2.MORPH_CLOSE, kernel)
plt.figure(2)
plt.imshow(cv2.cvtColor(closed_img, cv2.COLOR_BGR2RGB))
#%% Filtering
#blurred_img = cv2.medianBlur(closed_img, 5)
#plt.imshow(cv2.cvtColor(blurred_frame, cv2.COLOR_BGR2RGB))
#%%
closed_img = np.float32(closed_img)
#%%
vector_feature = IdentityFeature()
metric = EuclideanMetric(feature=vector_feature)
qb = QuickBundles(threshold=120000, metric=metric)
clusters = qb.cluster(closed_img)
print("Nb. clusters:", len(clusters))
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#indices=clusters[1]
#A=closed_img[indices,:,:]
#%%
for j, group in enumerate(clusters):
if len(group) > 1:
for indx in group.indices: