def mexhatFilter(a, mexSize=1):#, trimRatio=0.9):
"""
returned array is trimmed to remove edge
"""
global mexhatC, mexhatC_size, mexhatCf
a = imgFilters.evenShapeArr(a)
from Priithon.all import F as fftw
try:
if mexhatC.shape != a.shape:
raise ValueError('go to except')
if mexhatC_size != mexSize:
raise ValueError('go to except')
except NameError as ValueError:
mexhatC_size = mexSize
shape = N.asarray(a.shape, N.int)#N.float32)
mexhatC = F.shift(F.mexhatArr(shape, scaleHalfMax=mexhatC_size, orig=None)) # orig 0.5 pixel does not work...
mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce( shape )
ar = fftw.irfft( fftw.rfft(a.astype(N.float32)) * mexhatCf )
#if trimRatio < 1:
# ar = trim3D(ar, trimRatio)
#ar = imgFilters.maskEdgeWithValue2D(ar) # 2 pixels at the edges
return ar
def phaseContrastFilter(a, inFourier=False, removeNan=True, nyquist=0.6):
global GAUSS
if inFourier:
af = a.copy()
else:
af = F.rfft(a)
# here is the phase contrast
amp = N.abs(af)
afa = af / amp
if removeNan:
afa = nanFilter(afa)
# lowpass gaussian filter of phase image
if nyquist: # since this takes long time, gaussian array is re-used if possible
if GAUSS is not None and GAUSS[0] == nyquist and GAUSS[
1].shape == afa.shape:
nq, gf = GAUSS
else:
sigma = af.shape[-1] * nyquist
gf = F.gaussianArr(afa.shape,
sigma,
peakVal=1,
orig=0,
wrap=(1, ) * (afa.ndim - 1) +
(0, )) #, dtype=afa.dtype.type)
GAUSS = (nyquist, gf)
afa *= gf
if inFourier:
ap = afa
else:
ap = F.irfft(afa)
return ap
def mexhatFilter(a, mexSize=1): #, trimRatio=0.9):
"""
returned array is trimmed to remove edge
"""
global mexhatC, mexhatC_size, mexhatCf
a = imgFilters.evenShapeArr(a)
from Priithon.all import F as fftw
try:
if mexhatC.shape != a.shape:
raise ValueError('go to except')
if mexhatC_size != mexSize:
raise ValueError('go to except')
except NameError as ValueError:
mexhatC_size = mexSize
shape = N.asarray(a.shape, N.int) #N.float32)
mexhatC = F.shift(
F.mexhatArr(shape, scaleHalfMax=mexhatC_size,
orig=None)) # orig 0.5 pixel does not work...
mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce(shape)
ar = fftw.irfft(fftw.rfft(a.astype(N.float32)) * mexhatCf)
#if trimRatio < 1:
# ar = trim3D(ar, trimRatio)
#ar = imgFilters.maskEdgeWithValue2D(ar) # 2 pixels at the edges
return ar
def phaseContrastFilter(a, inFourier=False, removeNan=True, nyquist=0.6):
global GAUSS
if inFourier:
af = a.copy()
else:
af = F.rfft(a)
# here is the phase contrast
amp = N.abs(af)
afa = af / amp
if removeNan:
afa = nanFilter(afa)
# lowpass gaussian filter of phase image
if nyquist: # since this takes long time, gaussian array is re-used if possible
if GAUSS is not None and GAUSS[0] == nyquist and GAUSS[1].shape == afa.shape:
nq, gf = GAUSS
else:
#sigma = af.shape[-1] * nyquist
#gf = F.gaussianArr(afa.shape, sigma, peakVal=1, orig=0, wrap=(1,)*(afa.ndim-1)+(0,))#, dtype=afa.dtype.type)
gshape = N.array(af.shape)
if inFourier:
gshape[-1] *= 2
sigma = gshape * nyquist
gf = imgFilters.gaussianArrND(gshape, sigma, peakVal=1)
gf = N.fft.fftshift(gf)[Ellipsis, :af.shape[-1]]
GAUSS = (nyquist, gf)
afa *= gf
if inFourier:
ap = afa
else:
ap = F.irfft(afa)
return ap
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
from Priithon.all import F as fftw
#from Priithon.all import F as fftw
try:
if mexhatC.shape != a.shape:
raise ValueError, 'go to except'
if mexhatC_size != mexSize:
raise ValueError, 'go to except'
except NameError, ValueError:
mexhatC_size = mexSize
shape = N.asarray(a.shape, N.float32)
mexhatC = F.shift(
F.mexhatArr(shape, scaleHalfMax=mexhatC_size,
orig=None)) # orig 0.5 pixel does not work...
mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce(shape)
ar = fftw.irfft(fftw.rfft(a.astype(N.float32)) * mexhatCf)
#if trimRatio < 1:
# ar = trim3D(ar, trimRatio)
#ar = imgFilters.maskEdgeWithValue2D(ar) # 2 pixels at the edges
return ar
GAUSS = None
def phaseContrastFilter(a, inFourier=False, removeNan=True, nyquist=0.6):
global GAUSS
if inFourier:
af = a.copy()
else:
def Xcorr(a, b, highpassSigma=2.5, wiener=0.2, cutoffFreq=3,
forceSecondPeak=None, acceptOrigin=True, maskSigmaFact=1., removeY=None, removeX=None, ret=None, normalize=True, gFit=True, lap=None, win=11):
"""
returns (y,x), image
if ret is True, returns [v, yx, image]
to get yx cordinate of the image,
yx += N.divide(picture.shape, 2)
a, b: 2D array
highpassSigma: sigma value used for highpass pre-filter
wiener: wiener value used for highpass pre-filter
cutoffFreq: kill lowest frequency component from 0 to this level
forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak
acceptOrigin: If None, result at origin is rejected, look for the next peak
maskSigmaFact: Modifier to remove previous peak to look for another peak
removeYX: Rremove given number of pixel high intensity lines of the Xcorr
Y: Vertical, X: Horizontal
normalize: intensity normalized
gFit: peak is fitted to 2D gaussian array, if None use center of mass
win: window for gFit
if b is a + (y,x) then, answer is (-y,-x)
"""
shapeA = N.asarray(a.shape)
shapeB = N.asarray(b.shape)
shapeM = N.max([shapeA, shapeB], axis=0)
shapeM = N.where(shapeM % 2, shapeM+1, shapeM)
center = shapeM / 2.
arrs = [a,b]
arrsS = ['a','b']
arrsF = []
for i, arr in enumerate(arrs):
if arr.dtype not in [N.float32, N.float64]:
arr = N.asarray(arr, N.float32)
# this convolution has to be done beforehand to remove 2 pixels at the edge
if lap == 'nothing':
pass
elif lap:
arr = arr_Laplace(arr, mask=2)
else:
arr = arr_sorbel(arr, mask=1)
if N.sometrue(shapeA < shapeM):
arr = paddingMed(arr, shapeM)
if normalize:
mi, ma, me, sd = U.mmms(arr)
arr = (arr - me) / sd
if i ==1:
arr = F.shift(arr)
af = F.rfft(arr)
af = highPassF(af, highpassSigma, wiener, cutoffFreq)
arrsF.append(af)
# start cross correlation
af, bf = arrsF
bf = bf.conjugate()
cf = af * bf
# go back to space domain
c = F.irfft(cf)
# c = _changeOrigin(cr)
# removing lines
if removeX:
yi, xi = N.indices((removeX, shapeM[-1]))#sx))
yi += center[-2] - removeX/2.#sy/2 - removeX/2
c[yi, xi] = 0
if removeY:
yi, xi = N.indices((shapeM[-2], removeY))#sy, removeY))
xi += center[-1] - removeY/2.#sx/2 - removeY/2
c[yi, xi] = 0
# find the first peak
if gFit:
v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx = N.add(yx, 0.5)
#yx += 0.5
else:
vzyx = U.findMax(c)
v = vzyx[0]
yx = vzyx[-2:]
s = 2.5
yx -= center
if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin:
forceSecondPeak = True
# forceSecondPeak:
if not forceSecondPeak:
forceSecondPeak = 0
for i in range(int(forceSecondPeak)):
print('%i peak was removed' % (i+1)) #, sigma: %.2f' % (i+1, s)
yx += center
g = gaussianArr2D(c.shape, sigma=s/maskSigmaFact, peakVal=v, orig=yx)
c = c - g
#c = mask_gaussian(c, yx[0], yx[1], v, s)
if gFit:
v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx -= (center - 0.5)
else:
vzyx = U.findMax(c)
v = vzyx[0]
if not gFit:
yx = centerOfMass(c, vzyx[-2:]) - center
if lap is not 'nothing':
c = paddingValue(c, shapeM+2)
if ret == 2:
return yx, af, bf.conjugate()
elif ret:
return v, yx, c
else:
return yx, 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)
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,
highpassSigma=2.5,
wiener=0.2,
cutoffFreq=3,
forceSecondPeak=None,
acceptOrigin=True,
maskSigmaFact=1.,
removeY=None,
removeX=None,
ret=None,
normalize=True,
gFit=True,
lap=None,
win=11):
"""
returns (y,x), image
if ret is True, returns [v, yx, image]
to get yx cordinate of the image,
yx += N.divide(picture.shape, 2)
a, b: 2D array
highpassSigma: sigma value used for highpass pre-filter
wiener: wiener value used for highpass pre-filter
cutoffFreq: kill lowest frequency component from 0 to this level
forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak
acceptOrigin: If None, result at origin is rejected, look for the next peak
maskSigmaFact: Modifier to remove previous peak to look for another peak
removeYX: Rremove given number of pixel high intensity lines of the Xcorr
Y: Vertical, X: Horizontal
normalize: intensity normalized
gFit: peak is fitted to 2D gaussian array, if None use center of mass
win: window for gFit
if b is a + (y,x) then, answer is (-y,-x)
"""
shapeA = N.asarray(a.shape)
shapeB = N.asarray(b.shape)
shapeM = N.max([shapeA, shapeB], axis=0)
shapeM = N.where(shapeM % 2, shapeM + 1, shapeM)
center = shapeM / 2.
arrs = [a, b]
arrsS = ['a', 'b']
arrsF = []
for i, arr in enumerate(arrs):
if arr.dtype not in [N.float32, N.float64]:
arr = N.asarray(arr, N.float32)
# this convolution has to be done beforehand to remove 2 pixels at the edge
if lap == 'nothing':
pass
elif lap:
arr = arr_Laplace(arr, mask=2)
else:
arr = arr_sorbel(arr, mask=1)
if N.sometrue(shapeA < shapeM):
arr = paddingMed(arr, shapeM)
if normalize:
mi, ma, me, sd = U.mmms(arr)
arr = (arr - me) / sd
if i == 1:
arr = F.shift(arr)
af = F.rfft(arr)
af = highPassF(af, highpassSigma, wiener, cutoffFreq)
arrsF.append(af)
# start cross correlation
af, bf = arrsF
bf = bf.conjugate()
cf = af * bf
# go back to space domain
c = F.irfft(cf)
# c = _changeOrigin(cr)
# removing lines
if removeX:
yi, xi = N.indices((removeX, shapeM[-1])) #sx))
yi += center[-2] - removeX / 2. #sy/2 - removeX/2
c[yi, xi] = 0
if removeY:
yi, xi = N.indices((shapeM[-2], removeY)) #sy, removeY))
xi += center[-1] - removeY / 2. #sx/2 - removeY/2
c[yi, xi] = 0
# find the first peak
if gFit:
v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win +
2) #, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx = N.add(yx, 0.5)
#yx += 0.5
else:
vzyx = U.findMax(c)
v = vzyx[0]
yx = vzyx[-2:]
s = 2.5
yx -= center
if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin:
forceSecondPeak = True
# forceSecondPeak:
if not forceSecondPeak:
forceSecondPeak = 0
for i in range(int(forceSecondPeak)):
print('%i peak was removed' % (i + 1)) #, sigma: %.2f' % (i+1, s)
yx += center
g = gaussianArr2D(c.shape, sigma=s / maskSigmaFact, peakVal=v, orig=yx)
c = c - g
#c = mask_gaussian(c, yx[0], yx[1], v, s)
if gFit:
v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win +
2) #, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx -= (center - 0.5)
else:
vzyx = U.findMax(c)
v = vzyx[0]
if not gFit:
yx = centerOfMass(c, vzyx[-2:]) - center
if lap is not 'nothing':
c = paddingValue(c, shapeM + 2)
if ret == 2:
return yx, af, bf.conjugate()
elif ret:
return v, yx, c
else:
return yx, 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