def main():
import os
os.chdir("/media/fabio/Storage/UCT/Thesis/Coding/MSc_Thesis_Obj1/src")
# fetch data
meshes = loadMeshes("../meshes/femurs/", ply_Bool=False) # dims 50 36390
# create vertices dataset
rawData = meshToData(meshes)
mean = rawData.mean(axis=0)
# center the data
data = (rawData - mean)
# Get triangles
triangles = meshes[0].triangles
# Set colour
colour = [141, 182, 205] # light blue colour
#### TRAINING ####
# Full training scheme
# param_grid = {'dimension': [50],
# 'epochs': [10000],
# 'learning_rate': [1e-4],
# 'batch_size': [5,10],
# 'regularization': [1e-1,1e-2, 1e-3],
# 'activation': ['linear']}
# Best result
param_grid = {
'dimension': [50],
'epochs': [1000],
'learning_rate': [1e-4],
'batch_size': [10],
'regularization': [1e-5],
'activation': ['linear']
}
# training_function(data, param_grid,name='femur_linear_')
#### VISUALISING ####
# Set the directory and the wild cards to select all runs of choice
direc = '../results/'
paths = glob2.glob(direc + "*femur_linear_linear_AE_w2*")
trainingAEViz(rawData,
paths,
triangles,
"femur_linear_AE_",
colour,
cameraName="femur",
eigen_faust_Bool=False,
trim_type="femur")
print("fin")
def main():
# fetch data
meshes = loadMeshes("../meshes/faust/")
# create vertices dataset
rawData = meshToData(meshes)
mean = rawData.mean(axis=0)
# center the data
data = (rawData - mean)
# Get triangles
triangles = meshes[0].triangles
# Set colour
colour = [141, 182, 205] # light blue colour
#### TRAINING ####
# Full training scheme
# param_grid = {'dimension': [20,100],
# 'epochs': [100,1000,10000,20000],
# 'learning_rate': [1e-6, 1e-4, 1e-2],
# 'batch_size': [5, 25],
# 'regularization': [0, 1e-2, 1e-4],
# 'activation': ['linear']}
# Best result
param_grid = {
'dimension': [100],
'epochs': [1000],
'learning_rate': [1e-4],
'batch_size': [25],
'regularization': [1e-5],
'activation': ['linear']
}
# training_function(data, param_grid,name='faust_linear_')
#### VISUALISING ####
# Set the directory and the wild cards to select all runs of choice
direc = '../results/'
# paths = glob2.glob(direc + "*faust_linear_linear_AE_w2_*")
paths = glob2.glob(direc + "faust_linear_linear_AE_w2_*")
trainingAEViz(rawData,
paths,
triangles,
"faust_linear_AE_",
colour,
cameraName="faust",
eigen_faust_Bool=True,
trim_type="faust")
def main():
import os
# os.chdir("/media/fabio/Storage/UCT/Thesis/Coding/MSc_Thesis_Obj1/src")
# fetch data
meshes = loadMeshes("../meshes/faust/")
# print(np.array(meshes[0].triangles).shape, np.array(meshes[0].vertices).shape)
# create vertices dataset
rawData = meshToData(meshes)
mean = rawData.mean(axis=0)
# sd = np.std(data, axis=0)
data = (rawData - mean)
# Get triangles
triangles = meshes[0].triangles
# dimension to reduce to
dimension = 100
# Set the colour
colour = [180, 180, 180] # Grey
# PCA
# (components,eigenvalues) = AnalyticalPCA(data, dimension, "sklearn")
# print("sklearn eigenvalues", eigenvalues)
# print("sklearn components", components)
# (components,eigenvalues) = AnalyticalPCA(data, dimension, "eigen")
# print("eigen eigenvalues", eigenvalues)
# print("eigen components", components)
(components, eigenvalues) = AnalyticalPCA(data, dimension, "SVD")
print("SVD eigenvalues", eigenvalues)
print("SVD components", components)
eigenvalues = np.sqrt(eigenvalues * data.shape[1])**2 / data.shape[0]
np.savetxt('../results/faust_PCA_Eigen.csv', eigenvalues, delimiter=',')
# visualise and save the mean mesh
mean3DVis(rawData, triangles, "faust_PCA_", col=colour, cameraName="faust")
# Get and save shape parameters
b = shapeParameters(data, components)
np.savetxt('../results/faust_PCA_ShapeParamaters_b.csv', b, delimiter=',')
# Save modes of variation
modesOfVariationVis(mean,
components,
eigenvalues,
3,
triangles,
"faust_PCA_",
col=colour,
cameraName="faust")
# Plot modes of variation
PlotModesVaration(3, "faust_PCA_", trim_type="faust")
# Plot a basic scatterGram
PlotScatterGram(b, 3, "faust_PCA_")
# plot variation explained by PCA
variationExplainedPlot(eigenvalues, "faust_PCA_")
cutoff = 0.05
# fetch data
os.chdir("/media/fabio/Storage/UCT/Thesis/Coding/MSc_Thesis_Obj1/src")
# fetch data
faustmeshes = loadMeshes("../meshes/faust/")
femurmeshes = loadMeshes("../meshes/femurs/", ply_Bool=False)
faustShapeParameters = np.genfromtxt(
"../results/faust_PCA_ShapeParamaters_b.csv", delimiter=",")
femurShapeParameters = np.genfromtxt(
"../results/femur_PCA_ShapeParamaters_b.csv", delimiter=",")
# create vertices dataset
faustData = meshToData(faustmeshes)
femurData = meshToData(femurmeshes)
def normalityTestCoordiate(data, cutoff):
"""
This tests the univariate normality of the x , y , z of a mesh's vertices individually
Tests are Shapiro Wilks, Jarque Bera, D Agostino, Lilliefors
:param data:
:param cutoff:
:return: proportion that pass all tests
"""
shapiroList = []
jarque_beraList = []
def main():
import os
os.chdir("/media/fabio/Storage/UCT/Thesis/Coding/MSc_Thesis_Obj1/src")
# fetch data
meshes = loadMeshes("../meshes/femurs/", ply_Bool=False)
# create vertices dataset
rawData = meshToData(meshes)
mean = rawData.mean(axis=0)
# sd = np.std(rawData, axis=0)
# Centering
data = (rawData - mean)
# print(data.shape) # dims 50, 35982 # Faust is roughly 20000
# Get triangles
triangles = meshes[0].triangles
# dimension to reduce to
dimension = 50
# Set the colour
colour = [180, 180, 180] # Grey
# meshVis(meshes[0], colour)
# find the position you like and press "p" This will save the camera position
# save as femurCameraSettings.json in src
# PCA - The data is too large to do a full PCA, thus we do an SVD - This is built in in the PCA code
# PCA
# (components,eigenvalues) = AnalyticalPCA(data, dimension, "sklearn")
# print("sklearn eigenvalues", eigenvalues)
# print("sklearn components", components)
# (components,eigenvalues) = AnalyticalPCA(data, dimension, "eigen")
# print("eigen eigenvalues", eigenvalues)
# print("eigen components", components)
(components, eigenvalues) = AnalyticalPCA(data, dimension, "SVD")
print("SVD eigenvalues", eigenvalues)
print("SVD components", components)
eigenvalues = np.sqrt(eigenvalues * data.shape[1])**2 / data.shape[0]
np.savetxt('../results/femur_PCA_Eigen.csv', eigenvalues, delimiter=',')
# visualise and save the mean mesh
mean3DVis(rawData, triangles, "femur_PCA_", col=colour, cameraName="femur")
# Get and save shape parameters
b = shapeParameters(data, components)
np.savetxt('../results/femur_PCA_ShapeParamaters_b.csv', b, delimiter=',')
# Save modes of variation
modesOfVariationVis(mean,
components,
eigenvalues,
3,
triangles,
"femur_PCA_",
col=colour,
cameraName="femur")
# Plot modes of variation
PlotModesVaration(3, "femur_PCA_", trim_type="femur")
# Plot a basic scatterGram
PlotScatterGram(b, 3, "femur_PCA_")
# plot variation explained by PCA
variationExplainedPlot(eigenvalues, "femur_PCA_")
print("fin")