def logpolar_cv(img, center=None, mag=1):
des = N.zeros_like(img)
if center is None:
center = N.divide(img.shape, 2)
# cv.fromarray: array can be 2D or 3D only
cimg = cv.fromarray(img)
cdes = cv.fromarray(des)
cv.LogPolar(cimg, cdes, tuple(center), mag)#, cv.CV_WARP_FILL_OUTLIERS)
return N.array(cdes)
def logpolar_cv(img, center=None, mag=1):
des = N.zeros_like(img)
if center is None:
center = N.divide(img.shape, 2)
# cv.fromarray: array can be 2D or 3D only
cimg = cv.fromarray(img)
cdes = cv.fromarray(des)
cv.LogPolar(cimg, cdes, tuple(center), mag) #, cv.CV_WARP_FILL_OUTLIERS)
return N.array(cdes)
def paddingValue(img, shape, value=0, shift=None, smooth=0, interpolate=True):
"""
shape: in the same dimension as img
value: value in padded region, can be scaler or array with the shape
shift: scaler or in the same dimension as img and shape (default 0)
smooth: scaler value to smoothen border (here value must be scaler)
interpolate: shift array by subpixel interpolation to adjust center
return: padded array with shape
"""
# create buffer
dtype = img.dtype.type
canvas = N.empty(shape, dtype)
canvas[:] = value
# calculate position
shape = N.array(shape)
shapeS = img.shape
center = N.divide(shape, 2)
if shift is None:
shift = 0#[0] * len(shapeS)
shapeL = shape#N.add(shapeS, center+shift)
#start, stop = (shapeL - shapeS)/2., (shapeL + shapeS)/2.
start = N.round_((shapeL - shapeS)/2.).astype(N.int)
stop = shapeS + start
slc = [slice(start[d], stop[d], None) for d in range(img.ndim)]
#slc = [slice(int(round(start[d])), int(round(stop[d])), None) for d in range(img.ndim)]
#print slc, shapeS, shapeL
# shift if necessary
if interpolate:
subpx_shift = start % 1 # should be 0.5 or 0
if N.sometrue(subpx_shift):
img = U.nd.shift(img, subpx_shift)
# padding
canvas[slc] = img
if smooth:
canvas = _smoothBorder(canvas, start, stop, smooth, value)
canvas = N.ascontiguousarray(canvas)
#print shapeS, shapeL, slc
return canvas
def paddingValue(img, shape, value=0, shift=None, smooth=0, interpolate=True):
"""
shape: in the same dimension as img
value: value in padded region, can be scaler or array with the shape
shift: scaler or in the same dimension as img and shape (default 0)
smooth: scaler value to smoothen border (here value must be scaler)
interpolate: shift array by subpixel interpolation to adjust center
return: padded array with shape
"""
# create buffer
dtype = img.dtype.type
canvas = N.empty(shape, dtype)
canvas[:] = value
# calculate position
shape = N.array(shape)
shapeS = img.shape
center = N.divide(shape, 2)
if shift is None:
shift = 0 #[0] * len(shapeS)
shapeL = shape #N.add(shapeS, center+shift)
#start, stop = (shapeL - shapeS)/2., (shapeL + shapeS)/2.
start = N.round_((shapeL - shapeS) / 2.).astype(N.int)
stop = shapeS + start
slc = [slice(start[d], stop[d], None) for d in range(img.ndim)]
#slc = [slice(int(round(start[d])), int(round(stop[d])), None) for d in range(img.ndim)]
#print slc, shapeS, shapeL
# shift if necessary
if interpolate:
subpx_shift = start % 1 # should be 0.5 or 0
if N.sometrue(subpx_shift):
img = U.nd.shift(img, subpx_shift)
# padding
canvas[slc] = img
if smooth:
canvas = _smoothBorder(canvas, start, stop, smooth, value)
canvas = N.ascontiguousarray(canvas)
#print shapeS, shapeL, slc
return canvas
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)
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,
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