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Commit 1dc5b13a authored by Edward Andò's avatar Edward Andò
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generalisation of getImagettes for labels to avoid redundant code, some... 

generalisation of getImagettes for labels to avoid redundant code, some changes to input of spam-pixelSearchPropagate
parent 6ac0abe4
Pipeline #61447 passed with stages
in 13 minutes and 28 seconds
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scripts/spam-ddic
View file @ 1dc5b13a
......@@ -184,88 +184,53 @@ def correlateOneLabel(label):
# WARNING HACK BAD FIXME
labelDilateCurrent = args.LABEL_DILATE
initialDisplacement = numpy.round(PhiField[label][0:3, 3]).astype(int)
if args.DEBUG: print("\n\n\nWorking on label:", label, "\n")
if args.DEBUG: print("Position (ZYX):", centresOfMass[label])
imagetteReturns = spam.label.getImagettesLabelled(lab1, label,
PhiField[label],
im1, im2,
[0, 0, 0, 0, 0, 0], # Search range, don't worry about it
boundingBoxes, centresOfMass,
margin=args.LABEL_CORRELATE_MARGIN,
labelDilate=labelDilateCurrent,
maskOtherLabels=args.LABEL_CORRELATE_MASK_OTHERS,
applyF='no',
volumeThreshold=args.VOLUME_THRESHOLD)
badPhi = numpy.eye(4)
badPhi[0:3, 3] = numpy.nan
getLabel = spam.label.getLabel(lab1, label,
extractCube=False,
boundingBoxes=boundingBoxes,
centresOfMass=centresOfMass,
margin=labelDilateCurrent + args.LABEL_CORRELATE_MARGIN,
maskOtherLabels=args.MASK,
labelDilate=labelDilateCurrent,
labelDilateMaskOtherLabels=args.LABEL_CORRELATE_MASK_OTHERS)
# In case the label is missing or the Phi is duff
if getLabel is None or not numpy.all(numpy.isfinite(PhiField[label])):
if imagetteReturns['returnStatus'] != 1 or not numpy.all(numpy.isfinite(PhiField[label])):
return label, badPhi, -7, numpy.inf, 0, numpy.inf, labelDilateCurrent
else:
# Maskette 1 is either a boolean array if args.MASK
# otherwise it contains ints i.e., labels
if args.MASK:
maskette1 = getLabel['subvol']
maskette1vol = numpy.sum(maskette1)
else:
maskette1 = None
maskette1vol = numpy.inf # must pass "if" below
# Use new padded slicer, to remain aligned with getLabel['subvol']
# + add 1 on the "max" side for bounding box -> slice
imagette1 = spam.helpers.slicePadded(im1, getLabel['boundingBox'] + numpy.array([0,1,0,1,0,1]))
#imagette1 = im1[getLabel['slice']].copy()
if maskette1vol > args.VOLUME_THRESHOLD:
labelDisplacementInt = numpy.round(PhiField[label][0:3, -1]).astype(int)
# 2020-09-25 OS and EA: Prepare startStop array for imagette 2 to be extracted with new
# slicePadded, this should solved "Boss: failed imDiff" and RS=-5 forever
startStopIm2 = [int(int(boundingBoxes[label][0]) - args.LABEL_CORRELATE_MARGIN - max(labelDilateCurrent, 0) + labelDisplacementInt[0] ),
int(int(boundingBoxes[label][1]) + args.LABEL_CORRELATE_MARGIN + max(labelDilateCurrent, 0) + labelDisplacementInt[0] + 1),
int(int(boundingBoxes[label][2]) - args.LABEL_CORRELATE_MARGIN - max(labelDilateCurrent, 0) + labelDisplacementInt[1] ),
int(int(boundingBoxes[label][3]) + args.LABEL_CORRELATE_MARGIN + max(labelDilateCurrent, 0) + labelDisplacementInt[1] + 1),
int(int(boundingBoxes[label][4]) - args.LABEL_CORRELATE_MARGIN - max(labelDilateCurrent, 0) + labelDisplacementInt[2] ),
int(int(boundingBoxes[label][5]) + args.LABEL_CORRELATE_MARGIN + max(labelDilateCurrent, 0) + labelDisplacementInt[2] + 1)]
imagette2 = spam.helpers.slicePadded(im2, startStopIm2)
#imagette2imagette1sizeDiff = numpy.array(imagette2.shape) - numpy.array(imagette1.shape)
# If all of imagette2 is nans it fell outside im2 (or in any case it's going to be difficult to correlate)
if not numpy.all(numpy.isnan(imagette2)):
# Remove int() part of displacement since it's already used to extract imagette2
PhiTemp = PhiField[label].copy()
PhiTemp[0:3, -1] -= labelDisplacementInt
if args.DEBUG: print("Starting lk iterations with Phi - int(disp):\n", PhiTemp)
if args.DEBUG: print("\nStarting lk iterations with int(disp):\n", labelDisplacementInt)
registerReturns = spam.DIC.correlate.registerMultiscale(imagette1,
imagette2,
args.LABEL_CORRELATE_MULTISCALE_BINNING,
im1mask=maskette1,
margin=1,
PhiInit=PhiTemp,
PhiRigid=args.LABEL_CORRELATE_RIGID,
updateGradient=args.LABEL_CORRELATE_UPDATE_GRADIENT,
maxIterations=args.LABEL_CORRELATE_MAX_ITERATIONS,
deltaPhiMin=args.LABEL_CORRELATE_MIN_PHI_CHANGE,
interpolationOrder=args.LABEL_CORRELATE_INTERPOLATION_ORDER,
verbose=args.DEBUG,
imShowProgress=args.DEBUG)
goodPhi = registerReturns['Phi']
goodPhi[0:3, -1] += labelDisplacementInt
return label, goodPhi, registerReturns['returnStatus'], registerReturns['error'], registerReturns['iterations'], registerReturns['deltaPhiNorm'], labelDilateCurrent
else: # failed imDiff condition, should now be impossible thanks to slicePadded() --> now it means if imagette2 is completely outside im2
print("\t\tBoss: Empty imagette 2 with initial displacement", labelDisplacementInt)
return label, badPhi, -4, numpy.inf, 0, numpy.inf, labelDilateCurrent
else: # failed the volume condition
return label, badPhi, -5, numpy.inf, 0, numpy.inf, labelDilateCurrent
# Remove int() part of displacement since it's already used to extract imagette2
PhiTemp = PhiField[label].copy()
PhiTemp[0:3, -1] -= initialDisplacement
if args.DEBUG: print("Starting lk iterations with Phi - int(disp):\n", PhiTemp)
if args.DEBUG: print("\nStarting lk iterations with int(disp):\n", initialDisplacement)
registerReturns = spam.DIC.correlate.registerMultiscale(imagetteReturns['imagette1'],
imagetteReturns['imagette2'],
args.LABEL_CORRELATE_MULTISCALE_BINNING,
im1mask=imagetteReturns['imagette1mask'],
margin=1,
PhiInit=PhiTemp,
PhiRigid=args.LABEL_CORRELATE_RIGID,
updateGradient=args.LABEL_CORRELATE_UPDATE_GRADIENT,
maxIterations=args.LABEL_CORRELATE_MAX_ITERATIONS,
deltaPhiMin=args.LABEL_CORRELATE_MIN_PHI_CHANGE,
interpolationOrder=args.LABEL_CORRELATE_INTERPOLATION_ORDER,
verbose=args.DEBUG,
imShowProgress=args.DEBUG)
goodPhi = registerReturns['Phi']
goodPhi[0:3, -1] += initialDisplacement
return label, goodPhi, registerReturns['returnStatus'], registerReturns['error'], registerReturns['iterations'], registerReturns['deltaPhiNorm'], labelDilateCurrent
# Add labels to a queue -- mostly useful for MPI
......
scripts/spam-pixelSearch
View file @ 1dc5b13a
......@@ -215,41 +215,26 @@ def pixelSearchOneNode(nodeNumber):
# All global variables, we will return a list with:
# nodeNumber,
if args.LAB1 is not None:
imagetteReturns = {}
gottenLabel = spam.label.getLabel(lab1, nodeNumber, boundingBoxes=boundingBoxes, centresOfMass=nodePositions, maskOtherLabels=True, labelDilate=args.LABEL_DILATE, margin=1+args.LABEL_DILATE)
if gottenLabel is not None:
# 2020-07-05 try applying F to im1 this is expected to help with pixel searching
PhiNoDisp = PhiField[nodeNumber].copy()
PhiNoDisp[0:3,-1] = 0.0
initialDisplacement = numpy.round(PhiField[nodeNumber, 0:3, 3]).astype(int)
imagette1 = spam.helpers.slicePadded(im1, gottenLabel['boundingBox'] + numpy.array([0,1,0,1,0,1]))
imagette1def = spam.DIC.applyPhi(imagette1, PhiNoDisp, PhiPoint=gottenLabel['centreOfMassREL'])
maskette1def = spam.DIC.applyPhi(gottenLabel['subvol'], PhiNoDisp, PhiPoint=gottenLabel['centreOfMassREL'], interpolationOrder=0)
imagette1def[maskette1def == 0] = numpy.nan
imagetteReturns['imagette1'] = imagette1def
imagetteReturns['imagette1mask'] = gottenLabel['subvol']
imagetteReturns['imagette2mask'] = None
# 2020-09-25 OS and EA: Prepare startStop array for imagette 2 to be extracted with new slicePadded
startStopIm2 = [int(gottenLabel['boundingBox'][0] - max(args.LABEL_DILATE, 0) + initialDisplacement[0] + searchRange[0] ),
int(gottenLabel['boundingBox'][1] + max(args.LABEL_DILATE, 0) + initialDisplacement[0] + searchRange[1] + 1),
int(gottenLabel['boundingBox'][2] - max(args.LABEL_DILATE, 0) + initialDisplacement[1] + searchRange[2] ),
int(gottenLabel['boundingBox'][3] + max(args.LABEL_DILATE, 0) + initialDisplacement[1] + searchRange[3] + 1),
int(gottenLabel['boundingBox'][4] - max(args.LABEL_DILATE, 0) + initialDisplacement[2] + searchRange[4] ),
int(gottenLabel['boundingBox'][5] + max(args.LABEL_DILATE, 0) + initialDisplacement[2] + searchRange[5] + 1)]
imagetteReturns['imagette2'] = spam.helpers.slicePadded(im2, startStopIm2)
imagetteReturns['pixelSearchOffset'] = searchRange[0::2] - numpy.array([max(args.LABEL_DILATE, 0)]*3)
imagetteReturns['returnStatus'] = 1
else:
imagetteReturns['returnStatus'] = 0
imagetteReturns = spam.label.getImagettesLabelled(lab1, nodeNumber,
PhiField[nodeNumber].copy(),
im1, im2,
searchRange.copy(),
boundingBoxes, nodePositions,
margin=args.LABEL_DILATE,
labelDilate=args.LABEL_DILATE,
applyF=args.APPLY_F,
volumeThreshold=3**3)
imagetteReturns['imagette2mask'] = None
else:
imagetteReturns = spam.DIC.getImagettes(im1, nodePositions[nodeNumber], args.HWS, PhiField[nodeNumber].copy(), im2, searchRange.copy(), im1mask=im1mask, im2mask=im2mask, minMaskCoverage=args.MASK_COVERAGE, greyThreshold=[args.GREY_LOW_THRESH, args.GREY_HIGH_THRESH], applyF=args.APPLY_F, twoD=twoD)
imagetteReturns = spam.DIC.getImagettes(im1,
nodePositions[nodeNumber],
args.HWS,
PhiField[nodeNumber].copy(),
im2, searchRange.copy(),
im1mask=im1mask, im2mask=im2mask, minMaskCoverage=args.MASK_COVERAGE,
greyThreshold=[args.GREY_LOW_THRESH, args.GREY_HIGH_THRESH],
applyF=args.APPLY_F,
twoD=twoD)
# If getImagettes was successful (size check and mask coverage check)
if imagetteReturns['returnStatus'] == 1:
......
scripts/spam-pixelSearchPropagate
View file @ 1dc5b13a
......@@ -49,10 +49,18 @@ for key in sorted(argsDict):
print("\t{}: {}".format(key, argsDict[key]))
# Fill in search range
searchRange = numpy.array([args.SEARCH_RANGE[0], args.SEARCH_RANGE[1], args.SEARCH_RANGE[2], args.SEARCH_RANGE[3], args.SEARCH_RANGE[4], args.SEARCH_RANGE[5]])
searchRange = numpy.array([args.SEARCH_RANGE[0],
args.SEARCH_RANGE[1],
args.SEARCH_RANGE[2],
args.SEARCH_RANGE[3],
args.SEARCH_RANGE[4],
args.SEARCH_RANGE[5]])
pixelSearchCCmin = args.CC_MIN
weightingDistance = args.DIST
startPoint = numpy.array(args.START_POINT_DISP[0:3])
startPointDisplacement = numpy.array(args.START_POINT_DISP[3:6])
# Load reference image
im1 = tifffile.imread(args.im1.name)
# Detect unpadded 2D image first:
......@@ -78,18 +86,35 @@ if args.MASK2:
else:
im2mask = None
### There are 3 modes:
# - points-to-correlate defined by input "guiding points", which should be points with good texture
# - points-to-correlate defined by labelled image
# - points-to-correlate defined by regular grid
# Detect guiding points mode
if args.GUIDING_POINTS_FILE is not None:
gp = numpy.genfromtxt(args.gp.name)
# ...or label mode
elif args.LAB1 is not None:
lab1 = tifffile.imread(args.LAB1.name).astype(spam.label.labelType)
boundingBoxes = spam.label.boundingBoxes(lab1)
centresOfMass = spam.label.centresOfMass(lab1, boundingBoxes=boundingBoxes)
im1mask = None
im2mask = None
gp = numpy.vstack([startPoint, centresOfMass])
else:
gp, nodesDim = spam.DIC.makeGrid(im1.shape, args.NS)
gp = numpy.vstack([startPoint, gp])
print("\n\tRanking points")
gp = numpy.genfromtxt(args.gp.name)
#gp = numpy.abs(gp[:, -1:1:-1]) #swap x-z axes and give abs coordinates
guidingPoints = spam.mesh.rankPoints(gp, neighbourRadius=args.RADIUS)
numberOfPoints = guidingPoints.shape[0]
# Possible to do it on a structured grid, too!
# (provided that the fist point is well chosen...)
#gp, nodesDim = spam.DIC.makeGrid(im1.shape, args.NS)
#gp[0] = [26, 25, 245] #overwrite the first point (this can easily be an input actually)
#guidingPoints = spam.mesh.rankPoints(gp, neighbourRadius=args.RADIUS)
#numberOfPoints = guidingPoints.shape[0]
print(guidingPoints[0:10])
# Initialise arrays
PhiField = numpy.zeros((numberOfPoints, 4, 4))
......@@ -100,7 +125,7 @@ pixelSearchCC = numpy.zeros((numberOfPoints), dtype=float)
# Returns compatible with register()
error = numpy.zeros((numberOfPoints), dtype=float)
returnStatus = numpy.zeros((numberOfPoints), dtype=int)
deltaPhiNorm = numpy.ones((numberOfPoints), dtype=int)
deltaPhiNorm = numpy.ones((numberOfPoints), dtype=int)
iterations = numpy.zeros((numberOfPoints), dtype=int)
print("\n\tStarting sequential Pixel Search")
......@@ -109,7 +134,20 @@ pbar = progressbar.ProgressBar(widgets=widgets, maxval=numberOfPoints)
pbar.start()
# Step 1: simple PS for first point
imagetteReturnsTop = spam.DIC.getImagettes(im1, guidingPoints[0], args.HWS, PhiField[0].copy(), im2, searchRange.copy(), applyF='no')
if args.LAB1 is not None:
imagetteReturnsTop = spam.label.getImagettesLabelled(lab1, nodeNumber,
PhiField[0].copy(),
im1, im2,
searchRange.copy(),
boundingBoxes, nodePositions,
margin=args.LABEL_DILATE,
labelDilate=args.LABEL_DILATE,
applyF=args.APPLY_F,
volumeThreshold=3**3)
imagetteReturnsTop['imagette2mask'] = None
else:
imagetteReturnsTop = spam.DIC.getImagettes(im1, guidingPoints[0], args.HWS, PhiField[0].copy(), im2, searchRange.copy(), applyF='no')
# If getImagettes was successful (size check and mask coverage check)
if imagetteReturnsTop['returnStatus'] == 1:
......@@ -123,7 +161,11 @@ if imagetteReturnsTop['returnStatus'] == 1:
error[0] = PSreturnsTop[2]
# Failed to extract imagettes or something
else:
print("That's a bit of a problem I'm afraid!")
print("Failed to extact correlation window for starting point, exiting")
exit()
if pixelSearchCC[0] < args.CC_MIN:
print("CC obtained for starting point is less than threshold, not continuing")
exit()
# Step 2: Loop sequentially over the guiding points list
# 2.1: create the tree of the coordinates to find easily neighbours
......
tools/helpers/optionsParser.py
View file @ 1dc5b13a
......@@ -420,12 +420,6 @@ def ddicParser(parser):
dest='NEIGHBOURS',
help="Number of neighbours for field interpolation. Default = 12")
parser.add_argument('-nomask',
'--no-mask',
action="store_false",
dest='MASK',
help='Don\'t mask each label\'s image')
parser.add_argument('-lcm',
'--label-correlate-margin',
type=numpy.uint,
......@@ -2206,11 +2200,35 @@ def pixelSearchPropagate(parser):
type=argparse.FileType('r'),
help="Greyscale image of deformed state for correlation")
parser.add_argument('gp',
metavar='gp',
parser.add_argument('-sp',
'--starting-point-and-displacement',
nargs=6,
type=int,
default=[-1, -1, -1, 0, 0, 0],
dest='START_POINT_DISP',
help='Z Y X of first point for the propagation, Z Y X displacement of that point, required')
parser.add_argument('-gp',
'--guiding-points-file',
dest='GUIDING_POINTS_FILE',
default=None,
type=argparse.FileType('r'),
help='Path to file containing the guiding points')
parser.add_argument('-lab1',
'--labelledFile1',
dest='LAB1',
default=None,
type=argparse.FileType('r'),
help="Path to tiff file containing a labelled image 1 that defines zones to correlate. Disactivates -hws and -ns options")
#parser.add_argument('-ld',
#'--label-dilate',
#type=int,
#default=1,
#dest='LABEL_DILATE',
#help="Only if -lab1 is defined: Number of times to dilate labels. Default = 1")
parser.add_argument('-mf1',
'--maskFile1',
dest='MASK1',
......@@ -2245,26 +2263,35 @@ def pixelSearchPropagate(parser):
'--half-window-size',
nargs=1,
type=int,
default=10,
default=[5],
dest='HWS',
help="Half correlation window size (in pixels), measured each side of the node pixel (assumed equal in all 3 directions -- see -hws3 for different setting). Default = 10 px")
help="Half correlation window size (in pixels), measured each side of the node pixel (assumed equal in all 3 directions -- see -hws3 for different setting). Default = 5 px")
# Possible: window size different in all three directions
parser.add_argument('-hws3',
'--half-window-size-3',
nargs=3,
type=int,
default=[10, 10, 10],
default=None,
dest='HWS',
help="Half correlation window size (in pixels), measured each side of the node pixel (different in 3 directions). Default = 10, 10, 10px")
#parser.add_argument('-ns3',
#'--node-spacing-3',
#nargs=3,
#type=int,
#default=None,
#dest='NS',
#help="Node spacing in pixels (different in 3 directions). Default = 10, 10, 10px")
# Default: node spacing equal in all three directions
parser.add_argument('-ns',
'--node-spacing',
nargs=1,
type=int,
default=None,
dest='NS',
help="Node spacing in pixels (assumed equal in all 3 directions -- see -ns3 for different setting). Default = 10px")
parser.add_argument('-ns3',
'--node-spacing-3',
nargs=3,
type=int,
default=None,
dest='NS',
help="Node spacing in pixels (different in 3 directions). Default = 10, 10, 10px")
parser.add_argument('-nr',
'--neighbourhood-radius',
......@@ -2337,6 +2364,11 @@ def pixelSearchPropagate(parser):
# twoD = False
#tiff.close()
# You really need a start point...
if args.START_POINT_DISP[0:3] == [-1, -1, -1]:
print("You need to input a starting point from which to propagate!\n(even if displacement is zero)")
exit()
# If we have no out dir specified, deliver on our default promise -- this can't be done inline before since parser.parse_args() has not been run at that stage.
if args.OUT_DIR is None:
args.OUT_DIR = os.path.dirname(args.im1.name)
......@@ -2360,9 +2392,6 @@ def pixelSearchPropagate(parser):
else:
args.PREFIX += "-pixelSearchPropagate"
# Catch 3D options
if len(args.HWS) == 1:
args.HWS = [args.HWS[0]]*3
# Fill radius and dist if not given
if args.RADIUS is None:
......@@ -2371,11 +2400,57 @@ def pixelSearchPropagate(parser):
if args.DIST is None:
args.DIST = numpy.sum(args.HWS) + numpy.sum(args.SEARCH_RANGE[1]+args.SEARCH_RANGE[3]+args.SEARCH_RANGE[5])
## Catch and overwrite 2D options
#if twoD:
#args.HWS[0] = 0
#args.SEARCH_RANGE[0] = 0
#args.SEARCH_RANGE[1] = 0
### There are 3 modes:
# - points-to-correlate defined by input "guiding points", which should be points with good texture
# - points-to-correlate defined by labelled image
# - points-to-correlate defined by regular grid
if args.GUIDING_POINTS_FILE is not None:
print("I have been passed a guiding points file, so I am disactivating:")
print("\t- node spacing")
print("\t- label file")
args.NS = None
args.LAB1 = None
# Catch 3D options
if len(args.HWS) == 1:
args.HWS = [args.HWS[0]]*3
elif args.LAB1 is not None:
# We have a labelled image and so no nodeSpacing or halfWindowSize
print("I have been passed a labelled image and so I am disactivating:")
print("\t- node spacing")
print("\t- half-window size")
args.HWS = None
args.NS = None
args.MASK1 = None
args.MASK_COVERAGE = 0
else:
print("Regular grid mode")
# We are in grid, with a nodeSpacing and halfWindowSize
# Catch interdependent node spacing and correlation window sizes
if args.NS is None:
print("\nUsing default node spacing: "),
if args.HWS is None:
print("2x default half window size"),
args.HWS = [10]
print("({}) which is".format(args.HWS[0])),
args.NS = [args.HWS[0] * 2]
else:
print("2x user-set half window size"),
if len(args.HWS) == 1:
print("({}) which is".format(args.HWS[0])),
args.NS = [int(args.HWS[0] * 2)]
elif len(args.HWS) == 3:
print("({} -- selecting smallest) which is".format(args.HWS)),
args.NS = [int(min(args.HWS) * 2)]
print(args.NS)
# Catch 3D options
if len(args.NS) == 1:
args.NS = [args.NS[0], args.NS[0], args.NS[0]]
if len(args.HWS) == 1:
args.HWS = [args.HWS[0], args.HWS[0], args.HWS[0]]
return args
tools/label/label.py
View file @ 1dc5b13a
......@@ -602,6 +602,149 @@ def getLabel(labelledVolume, label, boundingBoxes=None, centresOfMass=None, marg
return output
def getImagettesLabelled(lab1, label, Phi, im1, im2, searchRange, boundingBoxes, centresOfMass, margin=0, labelDilate=0, maskOtherLabels=True, applyF='all', volumeThreshold=100):
"""
This function is responsible for extracting correlation windows ("imagettes") from two larger images (im1 and im2) with the help of a labelled im1.
This is generally to do image correlation, this function will be used for spam-ddic and pixelSearch modes.
Parameters
----------
lab1 : 3D numpy array of ints
Labelled image containing nLabels
label : int
Label of interest
Phi : 4x4 numpy array of floats
Phi matrix representing the movement of imagette1,
if not equal to `I`, imagette1 is deformed by the non-translation parts of Phi (F)
and the displacement is added to the search range (see below)
im1 : 3D numpy array
This is the large input reference image of greyvalues
im2 : 3D numpy array
This is the large input deformed image of greyvalues
searchRange : 6-component numpy array of ints
This defines where imagette2 should be extracted with respect to imagette1's position in im1.
The 6 components correspond to [ Zbot Ztop Ybot Ytop Xbot Xtop ].
If Z, Y and X values are the same, then imagette2 will be displaced and the same size as imagette1.
If 'bot' is lower than 'top', imagette2 will be larger in that dimension
boundingBoxes : nLabels*2 array of ints
Bounding boxes as returned by ``boundingBoxes``
centresOfMass : nLabels*3 array of floats
Centres of mass as returned by ``centresOfMass``
margin : int, optional
Margin around the grain to extract in pixels
Default = 0
labelDilate : int, optional
How much to dilate the label before computing the mask?
Default = 0
maskOtherLabels : bool, optional
In the returned subvolume, should other labels be masked?
If true, the mask is directly returned.
Default = True
applyF : string, optional
If a non-identity Phi is passed, should the F be applied to the returned imagette1?
Options are: 'all', 'rigid', 'no'
Default = 'all'
Note: as of January 2021, it seems to make more sense to have this as 'all' for pixelSearch, and 'no' for local DIC
volumeThreshold : int, optional
Pixel volume of labels that are discarded
Default = 100
Returns
-------
Dictionary :
'imagette1' : 3D numpy array,
'imagette1mask': 3D numpy array of same size as imagette1 or None,
'imagette2': 3D numpy array, bigger or equal size to imagette1
'returnStatus': int,
Describes success in extracting imagette1 and imagette2.
If == 1 success, otherwise negative means failure.
'pixelSearchOffset': 3-component list of ints
Coordinates of the top of the pixelSearch range in im1, i.e., the displacement that needs to be
added to the raw pixelSearch output to make it a im1 -> im2 displacement
"""
returnStatus = 1
imagette1mask = None
imagette2mask = None
initialDisplacement = numpy.round(Phi[0:3, 3]).astype(int)
PhiNoDisp = Phi.copy()
PhiNoDisp[0:3,-1] -= initialDisplacement
if applyF == 'rigid':
PhiNoDisp = spam.deformation.computeRigidPhi(PhiNoDisp)
gottenLabel = getLabel(lab1, label,
extractCube=False,
boundingBoxes=boundingBoxes,
centresOfMass=centresOfMass,
margin=labelDilate + margin,
maskOtherLabels=True,
labelDilate=labelDilate,
labelDilateMaskOtherLabels=maskOtherLabels)
# In case the label is missing or the Phi is duff
if gottenLabel is None or not numpy.all(numpy.isfinite(Phi)):
returnStatus = -7
else:
# Maskette 1 is either a boolean array if args.MASK
# otherwise it contains ints i.e., labels
# Use new padded slicer, to remain aligned with getLabel['subvol']
# + add 1 on the "max" side for bounding box -> slice
imagette1 = spam.helpers.slicePadded(im1, gottenLabel['boundingBox'] + numpy.array([0,1,0,1,0,1]))
if applyF == "all" or applyF == "rigid":
imagette1 = spam.DIC.applyPhi(imagette1, PhiNoDisp, PhiPoint=gottenLabel['centreOfMassREL'])
maskette1 = spam.DIC.applyPhi(gottenLabel['subvol'], PhiNoDisp, PhiPoint=gottenLabel['centreOfMassREL'], interpolationOrder=0)
elif applyF == "no":
maskette1 = gottenLabel['subvol']
else: