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 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 arr_edgeFilter(img, sigma=1.5):
"""
average-deviation with a gaussian prefilter
img must be in an even shape
"""
if sigma:
g = gaussianArrND(img.shape, sigma)
g = F.shift(g)
img = F.convolve(img.astype(N.float32), g)
gr = N.gradient(img.astype(N.float32))
ff = N.sum(N.power(gr, 2), 0)
return ff
def arr_edgeFilter(img, sigma=1.5):
"""
average-deviation with a gaussian prefilter
img must be in an even shape
"""
if sigma:
g = gaussianArrND(img.shape, sigma)
g = F.shift(g)
img = F.convolve(img.astype(N.float32), g)
gr = N.gradient(img.astype(N.float32))
ff = N.sum(N.power(gr, 2), 0)
return ff
def highPassF(af, highpassSigma=2.5, wiener=0.2, cutoffFreq=3):
"""
fourie space operations
af: array after rfft
half_nyx: half shape required for highpass filter
highpassSigma: highpass filter, if 0, highpass is not done
wiener: wiener coefficient for highpass filte
cutoffFreq: band-pass around origin
return: array BEFORE irfft
WARNING: af will be changed, so use copy() if necessary
"""
global _G, _G_SHAPE
if highpassSigma:
shape = N.array(af.shape)
shape[-1] = (shape[-1] - 1) * 2
szyx = shape / 2.
if _G is not None and N.alltrue(_G_SHAPE == shape):
g = _G
else:
g = imgFilters.gaussianArrND(shape,
highpassSigma,
peakVal=1,
orig=szyx)
g = F.shift(g)[..., :af.shape[-1]]
_G = g
_G_SHAPE = N.asarray(g.shape)
g += wiener
af /= g
# kill DC
af.flat[0] = 0
# kill lowest freq in YX
for d in range(af.ndim - 2, af.ndim):
upperdim = ':,' * d
exec('af[%s0:cutoffFreq] = 0' % upperdim)
return af
def highPassF(af, highpassSigma=2.5, wiener=0.2, cutoffFreq=3):
"""
fourie space operations
af: array after rfft
half_nyx: half shape required for highpass filter
highpassSigma: highpass filter, if 0, highpass is not done
wiener: wiener coefficient for highpass filte
cutoffFreq: band-pass around origin
return: array BEFORE irfft
WARNING: af will be changed, so use copy() if necessary
"""
global _G, _G_SHAPE
if highpassSigma:
shape = N.array(af.shape)
shape[-1] = (shape[-1] - 1) * 2
szyx = shape / 2.
if _G is not None and N.alltrue(_G_SHAPE == shape):
g = _G
else:
g = imgFilters.gaussianArrND(shape, highpassSigma, peakVal=1, orig=szyx)
g = F.shift(g)[...,:af.shape[-1]]
_G = g
_G_SHAPE = N.asarray(g.shape)
g += wiener
af /= g
# kill DC
af.flat[0] = 0
# kill lowest freq in YX
for d in range(af.ndim-2, af.ndim):
upperdim = ':,' * d
exec('af[%s0:cutoffFreq] = 0' % upperdim)
return af
def mexhatFilter(a, mexSize=1): #, trimRatio=0.9):
"""
returned array is trimmed to remove edge
"""
global mexhatC, mexhatC_size, mexhatCf
a = imgFilters.evenShapeArr(a)
try: # inside package
from ..Priithon.all import F as fftw
except ValueError: # Attempted relative import beyond toplevel package
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)
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,
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