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spam
Commits
0bcb2e05
Commit
0bcb2e05
authored
Apr 12, 2021
by
Olga Stamati
Browse files
LQC finds neighbours based on radius or number(useful when ddic field is passed)
parent
f7073231
Pipeline
#64568
passed with stages
in 12 minutes and 43 seconds
Changes
1
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
tools/DIC/kinematics.py
View file @
0bcb2e05
"""
Library of SPAM functions for
defining a regular grid in a reproducible way
.
Library of SPAM functions for
post processing a deformation field
.
Copyright (C) 2020 SPAM Contributors
This program is free software: you can redistribute it and/or modify it
...
...
@@ -22,14 +22,14 @@ import scipy.spatial
import
multiprocessing
import
progressbar
nProcessesDefault
=
multiprocessing
.
cpu_count
()
def
estimateLocalQuadraticCoherency
(
points
,
displacements
,
neighbourRadius
=
150
,
epsilon
=
0.1
,
nProcesses
=
nProcessesDefault
,
verbose
=
True
):
def
estimateLocalQuadraticCoherency
(
points
,
displacements
,
neighbourRadius
=
150
,
nNeighbours
=
None
,
epsilon
=
0.1
,
nProcesses
=
nProcessesDefault
,
verbose
=
True
):
'''
This function computes the local quadratic coherency (LQC) of a set of displacement vectors as per Masullo and Theunissen 2016.
LQC is the average residual between the point's displacement and a second-order (parabolic) surface Phi.
The quadratic surface Phi is fitted to the point's closest N neighbours and evaluated at the point's position.
Neighbours are selected based on: radius (default option) or number (activated if nNeighbours is not None).
A point with a LQC value smaller than a threshold (0.1 in Masullo and Theunissen 2016) is classified as coherent
Parameters
...
...
@@ -42,8 +42,14 @@ def estimateLocalQuadraticCoherency(points, displacements, neighbourRadius=150,
neighbourRadius: float, optional
Distance in pixels around the point to extract neighbours
If nNeighbours is given, this option is disactivated (neighbours will be selected based on number)
Default = 150
nNeighbours : int, optional
Number of the nearest neighbours to consider
If not None, then neighbourRadius is disactivated
Default = None
epsilon: float, optional
Background error as per (Westerweel and Scarano 2005)
Default = 0.1
...
...
@@ -72,18 +78,29 @@ def estimateLocalQuadraticCoherency(points, displacements, neighbourRadius=150,
# build KD-tree for quick neighbour identification
treeCoord
=
scipy
.
spatial
.
KDTree
(
points
)
# check if neighbours are selected based on radius or based on number, default is radius
ball
=
True
if
nNeighbours
is
not
None
:
ball
=
False
# calculate coherency for each point
global
coherencyOnePoint
def
coherencyOnePoint
(
point
):
radius
=
neighbourRadius
indices
=
numpy
.
array
(
treeCoord
.
query_ball_point
(
points
[
point
],
radius
))
while
len
(
indices
)
<=
27
:
#TODO this could vary (even a treeCoord.query could be used instead)
radius
*=
2
# select neighbours based on radius
if
ball
:
radius
=
neighbourRadius
indices
=
numpy
.
array
(
treeCoord
.
query_ball_point
(
points
[
point
],
radius
))
# make sure that at least 27 neighbours are selected
while
len
(
indices
)
<=
27
:
radius
*=
2
indices
=
numpy
.
array
(
treeCoord
.
query_ball_point
(
points
[
point
],
radius
))
N
=
len
(
indices
)
# select neighbours based on number
else
:
_
,
indices
=
treeCoord
.
query
(
points
[
point
],
k
=
nNeighbours
)
N
=
nNeighbours
N
=
len
(
indices
)
# fill in point+neighbours positions for the parabolic surface coefficients
X
=
numpy
.
zeros
((
N
,
10
),
dtype
=
float
)
X
=
numpy
.
zeros
((
N
,
10
),
dtype
=
float
)
for
i
,
neighbour
in
enumerate
(
indices
):
pos
=
points
[
neighbour
]
X
[
i
,
0
]
=
1
...
...
@@ -158,11 +175,12 @@ def estimateLocalQuadraticCoherency(points, displacements, neighbourRadius=150,
return
LQC
def
estimateDisplacementFromQuadraticFit
(
points
,
displacements
,
coherency
,
coherencyThreshold
=
0.1
,
neighbourRadius
=
150
,
epsilon
=
0.1
,
nProcesses
=
nProcessesDefault
,
verbose
=
True
):
def
estimateDisplacementFromQuadraticFit
(
points
,
displacements
,
coherency
,
coherencyThreshold
=
0.1
,
neighbourRadius
=
150
,
nNeighbours
=
None
,
epsilon
=
0.1
,
nProcesses
=
nProcessesDefault
,
verbose
=
True
):
'''
This function estimates the displacement of an incoherent point based on a local quadratic fit
of the displacements of N coherent neighbours, as per Masullo and Theunissen 2016.
A quadratic surface Phi is fitted to the point's closest coherent neighbours
A quadratic surface Phi is fitted to the point's closest coherent neighbours.
Neighbours are selected based on: radius (default option) or number (activated if nNeighbours is not None)
Parameters
----------
...
...
@@ -184,6 +202,11 @@ def estimateDisplacementFromQuadraticFit(points, displacements, coherency, coher
Distance around the point to extract neighbours
Default = 150
nNeighbours : int, optional
Number of the nearest neighbours to consider
If not None, then neighbourRadius is disactivated
Default = None
epsilon: float, optional
Background error as per (Westerweel and Scarano 2005)
Default = 0.1
...
...
@@ -218,18 +241,29 @@ def estimateDisplacementFromQuadraticFit(points, displacements, coherency, coher
# build KD-tree of coherent points for quick neighbour identification
treeCoord
=
scipy
.
spatial
.
KDTree
(
points
[
goodPoints
])
# check if neighbours are selected based on radius or based on number, default is radius
ball
=
True
if
nNeighbours
is
not
None
:
ball
=
False
# estimate disp for each incoherent point
global
dispOnePoint
def
dispOnePoint
(
badPoint
):
radius
=
neighbourRadius
indices
=
numpy
.
array
(
treeCoord
.
query_ball_point
(
points
[
badPoints
][
badPoint
],
radius
))
while
len
(
indices
)
<=
27
:
#TODO this could vary (even a treeCoord.query could be used instead)
radius
*=
2
# select neighbours based on radius
if
ball
:
radius
=
neighbourRadius
indices
=
numpy
.
array
(
treeCoord
.
query_ball_point
(
points
[
badPoints
][
badPoint
],
radius
))
# make sure that at least 27 neighbours are selected
while
len
(
indices
)
<=
27
:
radius
*=
2
indices
=
numpy
.
array
(
treeCoord
.
query_ball_point
(
points
[
badPoints
][
badPoint
],
radius
))
N
=
len
(
indices
)
# select neighbours based on number
else
:
_
,
indices
=
treeCoord
.
query
(
points
[
badPoints
][
badPoint
],
k
=
nNeighbours
)
N
=
nNeighbours
N
=
len
(
indices
)
# fill in neighbours positions for the parabolic surface coefficients
X
=
numpy
.
zeros
((
N
,
10
),
dtype
=
float
)
X
=
numpy
.
zeros
((
N
,
10
),
dtype
=
float
)
for
i
,
neighbour
in
enumerate
(
indices
):
pos
=
points
[
goodPoints
][
neighbour
]
X
[
i
,
0
]
=
1
...
...
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