# Set the mesh filename
meshFilename = "bones/Asymknee22_boneSurface.vtk"
landmarksFilename = "bones/Asymknee22.csv"
# Select sub-bone of interest
subBones = ["Patella", "Tibia", "Femur", ""]
subBone = subBones[2]
# Check to see if it exists
subBoneFilename = meshFilename.split(
".")[0] + subBone + "." + meshFilename.split(".")[1]
open(subBoneFilename, 'r')
# Load the mesh model and generate graph
model = read_mesh(subBoneFilename)
modelGraph, idArray, invIdArray = generate_graph(model)
# Load all the landmarks Point IDs for this mesh
landmarks = load_landmarks(landmarksFilename)
allLandmarkIds = get_landmark_ids(model, landmarks)
landmarkIds = []
for i, ID in enumerate(allLandmarkIds):
if i in LANDMARK_REGIONS[subBone]:
landmarkIds.append(ID)
# These scalars may be set to many things, just initialising here
scalars = vtk.vtkDoubleArray()
scalars.SetNumberOfValues(model.GetNumberOfPoints())
# For example, set all scalars to 1
meshLocation = "bones"
landmarksFileLocation = meshLocation
mapLocation = "bones_markedLandmarksHeatmapTruthIndiv"
estimatorLocation = "estimators"
# Select sub-bone of interest
subBones = ["Patella", "Tibia", "Femur", ""]
subBone = subBones[1]
# Check to see if it exists
subBoneFilename = meshFilename.split(
".")[0] + subBone + "." + meshFilename.split(".")[1]
open(os.path.join(meshLocation, subBoneFilename), 'r')
# Load the mesh model and generate graph
model = read_mesh(os.path.join(meshLocation, subBoneFilename))
modelGraph, idArray, invIdArray = generate_graph(model)
# Load all the landmarks Point IDs for this mesh
landmarks = load_landmarks(
os.path.join(landmarksFileLocation, landmarksFilename))
allLandmarkIds = get_landmark_ids(model, landmarks)
landmarkIds = []
for i, ID in enumerate(allLandmarkIds):
if i in LANDMARK_REGIONS[subBone]:
landmarkIds.append(ID)
# For each landmark, create an estimator to locate that landmark
for i, ID in enumerate(landmarkIds):
actualLandmarkNo = LANDMARK_REGIONS[subBone][i]
print("Creating estimator for landmark {} on file {}".format(
subBoneFilename = meshFilename.split(
".")[0] + subBone + "." + meshFilename.split(".")[1]
mapFilename = subBoneFilename.split(".")[0] + "_heatmap_landmark{}".format(
actualLandmarkNo) + "." + subBoneFilename.split(".")[1]
open(os.path.join(mapLocation, mapFilename),
'r') # check to see if it exists
guessFilename = mapFilename.split(
"."
)[0] + "_guessedBy{}_Landmark{}_RFR_Order{}_SampleRate{}_Features-AvgNorms".format(
landmarksFilename.split(".")[0], actualLandmarkNo, ORDER,
int(SAMPLE_RATE * 100)) + "." + mapFilename.split(".")[1]
open(os.path.join(guessLocation, guessFilename),
'r') # check to see if it exists
mapModel = read_mesh(os.path.join(mapLocation, mapFilename))
guessModel = read_mesh(os.path.join(guessLocation, guessFilename))
mapScalars = mapModel.GetPointData().GetScalars()
guessScalars = guessModel.GetPointData().GetScalars()
pairwise_scalars = []
for i in range(mapModel.GetNumberOfPoints()):
pairwise_scalars.append(
(i, mapScalars.GetValue(i), guessScalars.GetValue(i)))
csvFilename = guessFilename.split(".")[0] + "_compared.csv"
with open(csvFilename, 'wb') as csvfile:
writer = csv.writer(csvfile)
from __future__ import print_function
from helper_loaders import read_mesh
from helper_loaders import generate_graph
from helper_loaders import load_landmarks
from helper_loaders import get_landmark_ids
# Set the mesh filename
meshFilename = "bones/Asymknee11_boneSurfaceRegions.vtk"
landmarksFilename = "bones/Asymknee11.csv"
# Check to see if it exists
open(meshFilename, 'r')
# Load the mesh model and generate graph
model = read_mesh(meshFilename)
modelGraph, idArray, invIdArray = generate_graph(model)
# Load all the landmarks Point IDs for this mesh
landmarks = load_landmarks(landmarksFilename)
landmarkIds = get_landmark_ids(model, landmarks)
# Find the landmarks and their associated scalar values
landmarkLocations = {}
scalars = model.GetPointData().GetScalars()
for i in landmarkIds:
location = scalars.GetValue(i)
if location not in landmarkLocations.keys():
landmarkLocations[location] = []
landmarkLocations[location].append(i)