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
"""
import numpy as np
from dipy.viz import window, actor, colormap
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import CenterOfMassFeature
from dipy.segment.metric import EuclideanMetric
# Enables/disables interactive visualization
interactive = False
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
feature = CenterOfMassFeature()
metric = EuclideanMetric(feature)
qb = QuickBundles(threshold=5., metric=metric)
clusters = qb.cluster(streamlines)
# Extract feature of every streamline.
centers = np.asarray(list(map(feature.extract, streamlines)))
# Color each center of mass according to the cluster they belong to.
colormap = colormap.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
# Visualization
scene = window.Scene()
#%% 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: