# Vectors to send to the estimator
features = []
labels = []
# Sample points
sampleIds = get_random_points(model, SAMPLE_RATE)
progressPoints = [int(len(sampleIds) / 10 * (k + 1)) for k in range(10)
] # just to get some progress meter action
# Go through each sample, create features and label for that sample
for j, sID in enumerate(sampleIds):
if j in progressPoints: # print out some progress
print("About {} percent of {} sample points processed.".format(
(progressPoints.index(j) + 1) * 10, len(sampleIds)))
if PATCH_METHOD == "order":
samplePatch = create_patch(model, modelGraph, idArray, invIdArray,
sID, ORDER)
elif PATCH_METHOD == "size":
samplePatch = create_patch_optimised(model, modelGraph, idArray,
invIdArray, sID, PATCH_SIZE)
sampleFeatures = get_features(model, samplePatch, FEATURES,
PCA_COMPONENTS, sID)
sampleLabel = mapScalars.GetValue(sID)
if PCA_COMPONENTS != None:
sampleFeatures = sampleFeatures.flatten()
features.append(sampleFeatures)
labels.append(sampleLabel)
if j in progressPoints:
print(os.path.join(mapLocation, mapFilename))
print("Got feature {}, labelled {} for this".format(
scalars = vtk.vtkDoubleArray()
scalars.SetNumberOfValues(model.GetNumberOfPoints())
# For example, set all scalars to 1
for i in range(model.GetNumberOfPoints()):
scalars.SetValue(i, 1)
# Make some patches at the landmarks, of a certain size
landmarkPatches = []
landmarkSize = 0
st = time.time()
# Iterative way
for i in landmarkIds:
landmarkPatches.append(
create_patch(model, modelGraph, idArray, invIdArray, i, landmarkSize))
t = time.time() - st
print("Generated {} landmark patches of size {} in {} seconds.".format(
len(landmarkIds), landmarkSize, t))
print("Average patch generation time: {} seconds.".format(t /
len(landmarkIds)))
print(model.GetNumberOfPoints())
# Paint the patches
for i, patch in enumerate(landmarkPatches):
actualLandmarkNo = LANDMARK_REGIONS[subBone][i]
for point in patch:
scalars.SetValue(point, 2)