def Xcorr(a, b, phaseContrast=PHASE, nyquist=NYQUIST, gFit=True, win=11, ret=None, searchRad=None, npad=4):
"""
sigma uses F.gaussianArr in the Fourier domain
if ret is None:
return zyx, xcf
elif ret is 2:
return s, v, zyx, xcf
elif ret is 3:
return zyx, xcf, a_phase_cotrast, b_phase_contrast
elif ret:
return v, zyx, xcf
"""
#print 'phase contrast: %s' % str(phaseContrast)
#global DATA
# correct odd shape particularly Z axis
a = N.squeeze(a)
b = N.squeeze(b)
a = imgFilters.evenShapeArr(a)
b = imgFilters.evenShapeArr(b)
shape = N.array(a.shape)
# padding strange shape
#nyx = max(shape[-2:])
#pshape = N.array(a.shape[:-2] + (nyx,nyx))
# apodize
a = paddAndApo(a, npad)#, pshape) #apodize(a)
b = paddAndApo(b, npad)#, pshape) #apodize(b)
# fourier transform
af = F.rfft(a.astype(N.float32))
bf = F.rfft(b.astype(N.float32))
del a, b
# phase contrast filter (removing any intensity information)
if phaseContrast:
afa = phaseContrastFilter(af, True, nyquist=nyquist)
bfa = phaseContrastFilter(bf, True, nyquist=nyquist)
else:
afa = af
bfa = bf
del af, bf
#targetShape = shape + (npad * 2)
targetShape = shape + (npad * 2)
# shift array
delta = targetShape / 2.
shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta)
bfa *= shiftarr
# cross correlation
bfa = bfa.conjugate()
#c = cc = F.irfft(afa * bfa)
c = F.irfft(afa * bfa)
# 20180214 the padded region was cutout before finding the peak.
c = cc = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False)
#cc = c
center = N.divide(c.shape, 2)
if searchRad:
slc = imgGeo.nearbyRegion(c.shape, center, searchRad)
cc = N.zeros_like(c)
cc[slc] = c[slc]
v, zyx, s = _findMaxXcor(cc, win, gFit=gFit)
#return cc
#print(zyx, center)
zyx -= center
#c = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False)
#c = imgFilters.cutOutCenter(c, shape, interpolate=False)
if ret == 3:
return zyx, c, F.irfft(afa), F.irfft(bfa)
elif ret == 2:
return s, v, zyx, c
elif ret:
return v, zyx, c
else:
return zyx, c
def Xcorr(a,
b,
phaseContrast=PHASE,
nyquist=NYQUIST,
removeEdge=0,
gFit=True,
win=11,
ret=None,
searchRad=None):
"""
sigma uses F.gaussianArr in the Fourier domain
if ret is None:
return zyx, xcf
elif ret is 2:
return s, v, zyx, xcf
elif ret:
return v, zyx, xcf
"""
#print 'phase contrast: %s' % str(phaseContrast)
#global DATA
# correct odd shape particularly Z axis
a = N.squeeze(a)
b = N.squeeze(b)
a = imgFilters.evenShapeArr(a)
b = imgFilters.evenShapeArr(b)
shape = N.array(a.shape)
# apodize
a = apodize(a)
b = apodize(b)
# fourier transform
af = F.rfft(a.astype(N.float32))
bf = F.rfft(b.astype(N.float32))
del a, b
# phase contrast filter (removing any intensity information)
if phaseContrast:
afa = phaseContrastFilter(af, True, nyquist=nyquist)
bfa = phaseContrastFilter(bf, True, nyquist=nyquist)
else:
afa = af
bfa = bf
del af, bf
# removing edge if gaussian is not sufficient
targetShape = shape - N.multiply(removeEdge, 2)
if removeEdge:
ap = imgFilters.cutOutCenter(F.irfft(afa), targetShape)
bp = imgFilters.cutOutCenter(F.irfft(bfa), targetShape)
afa = F.rfft(ap)
bfa = F.rfft(bp)
del ap, bp
# shift array
delta = targetShape / 2.
shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta)
bfa *= shiftarr
# cross correlation
bfa = bfa.conjugate()
c = cc = F.irfft(afa * bfa)
center = N.divide(c.shape, 2)
if searchRad:
slc = imgGeo.nearbyRegion(c.shape, center, searchRad)
cc = N.zeros_like(c)
cc[slc] = c[slc]
v, zyx, s = _findMaxXcor(cc, win, gFit=gFit)
zyx -= center
if ret == 2:
return s, v, zyx, c
elif ret:
return v, zyx, c
else:
return zyx, c
def Xcorr(a,
b,
phaseContrast=PHASE,
nyquist=NYQUIST,
gFit=True,
win=11,
ret=None,
searchRad=None,
npad=4):
"""
sigma uses F.gaussianArr in the Fourier domain
if ret is None:
return zyx, xcf
elif ret is 2:
return s, v, zyx, xcf
elif ret is 3:
return zyx, xcf, a_phase_cotrast, b_phase_contrast
elif ret:
return v, zyx, xcf
"""
#print 'phase contrast: %s' % str(phaseContrast)
#global DATA
# correct odd shape particularly Z axis
a = N.squeeze(a)
b = N.squeeze(b)
a = imgFilters.evenShapeArr(a)
b = imgFilters.evenShapeArr(b)
shape = N.array(a.shape)
# padding strange shape
#nyx = max(shape[-2:])
#pshape = N.array(a.shape[:-2] + (nyx,nyx))
# apodize
a = paddAndApo(a, npad) #, pshape) #apodize(a)
b = paddAndApo(b, npad) #, pshape) #apodize(b)
# fourier transform
af = F.rfft(a.astype(N.float32))
bf = F.rfft(b.astype(N.float32))
del a, b
# phase contrast filter (removing any intensity information)
if phaseContrast:
afa = phaseContrastFilter(af, True, nyquist=nyquist)
bfa = phaseContrastFilter(bf, True, nyquist=nyquist)
else:
afa = af
bfa = bf
del af, bf
#targetShape = shape + (npad * 2)
targetShape = shape + (npad * 2)
# shift array
delta = targetShape / 2.
shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta)
bfa *= shiftarr
# cross correlation
bfa = bfa.conjugate()
c = cc = F.irfft(afa * bfa)
center = N.divide(c.shape, 2)
if searchRad:
slc = imgGeo.nearbyRegion(c.shape, center, searchRad)
cc = N.zeros_like(c)
cc[slc] = c[slc]
v, zyx, s = _findMaxXcor(cc, win, gFit=gFit)
zyx -= center
c = imgFilters.cutOutCenter(c,
N.array(c.shape) - (npad * 2),
interpolate=False)
#c = imgFilters.cutOutCenter(c, shape, interpolate=False)
if ret == 3:
return zyx, c, F.irfft(afa), F.irfft(bfa)
elif ret == 2:
return s, v, zyx, c
elif ret:
return v, zyx, c
else:
return zyx, c