def _doBilinear2D(a2d, tyx=(0,0), r=0, mag=1, anismag=1, dyx=(0,0), mr=0, b2d=None):
if b2d is None:
b2d = N.empty_like(a2d)
else:
b2d = N.ascontiguousarray(b2d)
a2d = a2d.copy() # otherwise, the following code will mess up the input
if N.any(dyx[-2:]) or mr:
temp = b2d
target = a2d
U.trans2d(target, temp, (dyx[-1], dyx[-2], mr, 1, 0, 1))
else:
temp = a2d
target = b2d
# rot mag first to make consistent with affine
magaxis = 1 # only this axis works
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
# then translate
tyx2 = N.array(tyx) # copy
tyx2[-2:] -= dyx[-2:]
if N.any(tyx2[-2:]) or mr:
U.trans2d(target, temp, (tyx2[-1], tyx2[-2], -mr, 1, 0, 1))
else:
temp[:] = target[:]
#a[z] = temp[:]
return temp
def _doBilinear2D(a2d, tyx=(0,0), r=0, mag=1, anismag=1, dyx=(0,0), mr=0, b2d=None):
if b2d is None:
b2d = N.empty_like(a2d)
else:
b2d = N.ascontiguousarray(b2d)
a2d = a2d.copy() # otherwise, the following code will mess up the input
if N.any(dyx[-2:]) or mr:
temp = b2d
target = a2d
U.trans2d(target, temp, (dyx[-1], dyx[-2], mr, 1, 0, 1))
else:
temp = a2d
target = b2d
# rot mag first to make consistent with affine
magaxis = 1 # only this axis works
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
# then translate
tyx2 = N.array(tyx) # copy
tyx2[-2:] -= dyx[-2:]
if N.any(tyx2[-2:]) or mr:
U.trans2d(target, temp, (tyx2[-1], tyx2[-2], -mr, 1, 0, 1))
else:
temp[:] = target[:]
#a[z] = temp[:]
return temp
def remap(img, mapy, mapx, interp=2):
"""
transform image using coordinate x,y
Interpolation method:
0 = CV_INTER_NN nearest-neigbor interpolation
1 = CV_INTER_LINEAR bilinear interpolation (used by default)
2 = CV_INTER_CUBIC bicubic interpolation
3 = CV_INTER_AREA resampling using pixel area relation. It is the preferred method for image decimation that gives moire-free results. In terms of zooming it is similar to the CV_INTER_NN method
return resulting array
"""
des = N.empty_like(img)
# cv.fromarray: array can be 2D or 3D only
if cv2.__version__.startswith('2'):
cimg = cv.fromarray(img)
cdes = cv.fromarray(des)
cmapx = cv.fromarray(mapx.astype(N.float32))
cmapy = cv.fromarray(mapy.astype(N.float32))
cv.Remap(cimg, cdes, cmapx, cmapy, flags=interp+cv.CV_WARP_FILL_OUTLIERS)
else:
cimg = img
cdes = des
cmapx = mapx.astype(N.float32)
cmapy = mapy.astype(N.float32)
cdes = cv2.remap(cimg, cmapx, cmapy, interp)
return N.asarray(cdes)
def logpolar(img, center=None, mag=1):
des = N.empty_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 shiftZ(af):
"""
af: 3D array in fourier space
return Z shifted array
"""
nz = af.shape[0]
cz = nz // 2
bf = N.empty_like(af)
bf[cz:] = af[:(nz-cz)]
bf[:(nz-cz)] = af[cz:]
return bf
def shiftZ(af):
"""
af: 3D array in fourier space
return Z shifted array
"""
nz = af.shape[0]
cz = nz // 2
bf = N.empty_like(af)
bf[cz:] = af[:(nz - cz)]
bf[:(nz - cz)] = af[cz:]
return bf
def logpolar(image, center=None, angles=None, radii=None):
"""Return log-polar transformed image and log base."""
shape = image.shape
if center is None:
center = shape[0] / 2, shape[1] / 2
if angles is None:
angles = shape[0]
if radii is None:
radii = shape[1]
theta = N.zeros((angles, radii), dtype=N.float64)
theta.T[:] = -N.linspace(0, N.pi, angles, endpoint=False)
#d = radii
d = N.hypot(shape[0] - center[0], shape[1] - center[1])
log_base = 10.0**(N.log10(d) / (radii))
radius = N.empty_like(theta)
radius[:] = N.power(log_base, N.arange(radii, dtype=N.float64)) - 1.0
x = radius * N.sin(theta) + center[0]
y = radius * N.cos(theta) + center[1]
output = N.zeros_like(x)
ndii.map_coordinates(image, [x, y], output=output)
return output, log_base
def logpolar(image, center=None, angles=None, radii=None):
"""Return log-polar transformed image and log base."""
shape = image.shape
if center is None:
center = shape[0] / 2, shape[1] / 2
if angles is None:
angles = shape[0]
if radii is None:
radii = shape[1]
theta = N.zeros((angles, radii), dtype=N.float64)
theta.T[:] = -N.linspace(0, N.pi, angles, endpoint=False)
#d = radii
d = N.hypot(shape[0]-center[0], shape[1]-center[1])
log_base = 10.0 ** (N.log10(d) / (radii))
radius = N.empty_like(theta)
radius[:] = N.power(log_base, N.arange(radii,
dtype=N.float64)) - 1.0
x = radius * N.sin(theta) + center[0]
y = radius * N.cos(theta) + center[1]
output = N.zeros_like(x)
ndii.map_coordinates(image, [x, y], output=output)
return output, log_base
def rotateIndicesND(slicelist, dtype=N.float64, rot=0, mode=2):
"""
slicelist: even shape works much better than odd shape
rot: counter-clockwise, xy-plane
mode: testing different ways of doing, (1 or 2 and the same result)
return inds, LD
"""
global INDS_DIC
shape = []
LD = []
for sl in slicelist:
if isinstance(sl, slice):
shape.append(sl.stop - sl.start)
LD.append(sl.start)
shapeTuple = tuple(shape+[rot])
if shapeTuple in INDS_DIC:
inds = INDS_DIC[shapeTuple]
else:
shape = N.array(shape)
ndim = len(shape)
odd_even = shape % 2
s2 = N.ceil(shape * (2**0.5))
if mode == 1: # everything is even
s2 = N.where(s2 % 2, s2 + 1, s2)
elif mode == 2: # even & even or odd & odd
for d, s in enumerate(shape):
if (s % 2 and not s2[d] % 2) or (not s % 2 and s2[d] % 2):
s2[d] += 1
cent = s2 / 2.
dif = (s2 - shape) / 2.
dm = dif % 1
# print s2, cent, dif, dm
slc = [Ellipsis] + [slice(int(d), int(d)+shape[i]) for i, d in enumerate(dif)]
# This slice is float which shift array when cutting out!!
s2 = tuple([int(ss) for ss in s2]) # numpy array cannot use used for slice
inds = N.indices(s2, N.float32)
ind_shape = inds.shape
nz = N.product(ind_shape[:-2])
nsec = nz / float(ndim)
if ndim > 2:
inds = N.reshape(inds, (nz,)+ind_shape[-2:])
irs = N.empty_like(inds)
for d, ind in enumerate(inds):
idx = int(d//nsec)
c = cent[idx]
if rot and inds.ndim > 2:
U.trans2d(ind - c, irs[d], (0,0,rot,1,0,1))
irs[d] += c - dif[idx]
else:
irs[d] = ind - dif[idx]
if len(ind_shape) > 2:
irs = N.reshape(irs, ind_shape)
irs = irs[slc]
if mode == 1 and N.sometrue(dm):
inds = N.empty_like(irs)
# print 'translate', dm
for d, ind in enumerate(irs):
U.trans2d(ind, inds[d], (-dm[1], -dm[0], 0, 1, 0, 1))
else:
inds = irs
INDS_DIC[shapeTuple] = inds
r_inds = N.empty_like(inds)
for d, ld in enumerate(LD):
r_inds[d] = inds[d] + ld
return r_inds, LD
def trans3D_bilinear(a,
tzyx=(0, 0, 0),
r=0,
mag=1,
dzyx=(0, 0, 0),
rzy=0,
mr=0,
ncpu=1,
**kwds):
"""
magyx: scalar or [y,x] or [y,x, direction in degrees]
"""
a = a.copy()
ndim = a.ndim
if ndim == 2:
a = a.reshape((1, ) + a.shape)
#b2d = N.empty_like(a[0])
#dzyx = N.asarray(dzyx)
#tzyx = N.asarray(tzyx)
#tzyx[-2:] += dzyx[-2:]
# mag
magaxis = 1 # only this axis works
magz = 1
try:
if len(mag) == 3:
magz, magy, magx = mag
elif len(mag) == 2:
magy, magx = mag
else:
magy = magx = mag[0]
except:
magy = magx = mag
# vertical axis
if ndim == 3 and (magz != 1 or tzyx[-3]):
#print 'vertical'
at = a.T # zyx -> xyz
mag = 1
anismag = magz
canvas = N.empty_like(at)
old = """
for x, a2d in enumerate(at):
if dzyx[-3]:
U.trans2d(a2d, target, (0, -dyzx[-3], 0, 1, 1, 1))
else:
target = a2d
canvas[x] = U.trans2d(target, None, (tzyx[-3], 0, rzy, mag, magaxis, anismag))"""
if ndim == 3: # and ncpu > 1 and mp:
ret = ppro.pmap(_doBilinear2D, at, ncpu, (0, tzyx[-3]), rzy, mag,
anismag, (0, dzyx[-3]))
for z, a2d in enumerate(ret):
canvas[z] = a2d
else:
target = N.empty_like(a.T[0]) #N.ascontiguousarray(b2d.T)
for z, a2d in enumerate(at):
canvas[z] = _doBilinear2D(a2d, (0, tzyx[-3]),
rzy,
mag,
anismag, (0, dzyx[-3]),
b2d=target)
a = canvas.T
#return a
# Horizontal axis
mag = magy
anismag = magx / magy
oldcode = """
for z, a2d in enumerate(a):
if N.any(dzyx[-2:]) or mr:
temp = N.ascontiguousarray(b2d)
target = N.ascontiguousarray(a2d)
U.trans2d(target, temp, (-dzyx[-1], -dzyx[-2], -mr, 1, 0, 1))
else:
temp = N.ascontiguousarray(a2d)
target = N.ascontiguousarray(b2d)
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
if N.any(tzyx[-2:]) or mr:
U.trans2d(target, temp, (tzyx[-1], tzyx[-2], mr, 1, 0, 1))
else:
temp[:] = target[:]
a[z] = temp[:]"""
if ndim == 3 and ncpu > 1 and mp:
ret = ppro.pmap(_doBilinear2D, a, ncpu, tzyx[-2:], r, mag, anismag,
dzyx[-2:], mr)
for z, a2d in enumerate(ret):
a[z] = a2d
else:
b2d = N.empty_like(a[0])
for z, a2d in enumerate(a):
a[z] = _doBilinear2D(a2d, tzyx[-2:], r, mag, anismag, dzyx[-2:],
mr, b2d)
if ndim == 2:
a = a[0]
return a
def trans3D_bilinear(a,
tzyx=(0, 0, 0),
r=0,
mag=1,
dzyx=(0, 0, 0),
b=None,
rzy=0,
**kwds):
"""
magyx: scalar or [y,x] or [y,x, direction in degrees]
"""
a = a.copy()
ndim = a.ndim
if ndim == 2:
a = a.reshape((1, ) + a.shape)
try:
if len(magyx) == 3:
mr = magyx[-1]
magyx = magyx[:2]
else:
mr = 0
except:
mr = 0
if b is None:
b2d = N.empty_like(a[0])
dzyx = N.asarray(dzyx)
tzyx = N.asarray(tzyx)
tzyx[-2:] += dzyx[-2:]
magaxis = 1 # only this axis works
magz = 1
try:
if len(mag) == 3:
magz, magy, magx = mag
elif len(mag) == 2:
magy, magx = mag
else:
magy = magx = mag[0]
except:
magy = magx = mag
mag = magy
anismag = magx / magy
for z, a2d in enumerate(a):
if N.any(dzyx[-2:]) or mr:
temp = N.ascontiguousarray(b2d)
target = N.ascontiguousarray(a2d)
#U.trans2d(target, temp, (-dyx[1], -dyx[0], -mr, 1, 0, 1))
U.trans2d(target, temp, (-dzyx[-1], -dzyx[-2], -mr, 1, 0, 1))
else:
temp = N.ascontiguousarray(a2d)
target = N.ascontiguousarray(b2d)
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
#if rzx: # is this correct?? havn't tried yet
# target = U.nd.rotate(target, rzx, axes=(0,2), order=1, prefilter=False)
if N.any(tzyx[-2:]) or mr: #N.any(dyx) or mr:
#U.trans2d(target, temp, (dyx[1], dyx[0], mr, 1, 0, 1))
U.trans2d(target, temp, (tzyx[-1], tzyx[-2], mr, 1, 0, 1))
else:
temp[:] = target[:]
a[z] = temp[:]
if ndim == 2:
a = a[0]
elif ndim == 3 and (magz != 1 or tzyx[-3]):
at = a.T # zyx -> xyz
mag = 1 #magz
anismag = magz #magy / magz
canvas = N.empty_like(at)
target = b2d.T
for x, a2d in enumerate(at):
if dzyx[-3]:
U.trans2d(a2d, target, (0, -dyzx[-3], 0, 1, 1, 1))
else:
target = a2d
canvas[x] = U.trans2d(target, None,
(tzyx[-3], 0, rzy, mag, magaxis, anismag))
#canvas = canvas.T
a = canvas.T
return a
def rotateIndicesND(slicelist, dtype=N.float64, rot=0, mode=2):
"""
slicelist: even shape works much better than odd shape
rot: counter-clockwise, xy-plane
mode: testing different ways of doing, (1 or 2 and the same result)
return inds, LD
"""
global INDS_DIC
shape = []
LD = []
for sl in slicelist:
if isinstance(sl, slice):
shape.append(sl.stop - sl.start)
LD.append(sl.start)
shapeTuple = tuple(shape + [rot])
if shapeTuple in INDS_DIC:
inds = INDS_DIC[shapeTuple]
else:
shape = N.array(shape)
ndim = len(shape)
odd_even = shape % 2
s2 = N.ceil(shape * (2**0.5))
if mode == 1: # everything is even
s2 = N.where(s2 % 2, s2 + 1, s2)
elif mode == 2: # even & even or odd & odd
for d, s in enumerate(shape):
if (s % 2 and not s2[d] % 2) or (not s % 2 and s2[d] % 2):
s2[d] += 1
cent = s2 / 2.
dif = (s2 - shape) / 2.
dm = dif % 1
# print s2, cent, dif, dm
slc = [Ellipsis] + [
slice(int(d),
int(d) + shape[i]) for i, d in enumerate(dif)
]
# This slice is float which shift array when cutting out!!
s2 = tuple([int(ss)
for ss in s2]) # numpy array cannot use used for slice
inds = N.indices(s2, N.float32)
ind_shape = inds.shape
nz = N.product(ind_shape[:-2])
nsec = nz / float(ndim)
if ndim > 2:
inds = N.reshape(inds, (nz, ) + ind_shape[-2:])
irs = N.empty_like(inds)
for d, ind in enumerate(inds):
idx = int(d // nsec)
c = cent[idx]
if rot and inds.ndim > 2:
U.trans2d(ind - c, irs[d], (0, 0, rot, 1, 0, 1))
irs[d] += c - dif[idx]
else:
irs[d] = ind - dif[idx]
if len(ind_shape) > 2:
irs = N.reshape(irs, ind_shape)
irs = irs[slc]
if mode == 1 and N.sometrue(dm):
inds = N.empty_like(irs)
# print 'translate', dm
for d, ind in enumerate(irs):
U.trans2d(ind, inds[d], (-dm[1], -dm[0], 0, 1, 0, 1))
else:
inds = irs
INDS_DIC[shapeTuple] = inds
r_inds = N.empty_like(inds)
for d, ld in enumerate(LD):
r_inds[d] = inds[d] + ld
return r_inds, LD
def arr_invert(arr):
canvas = N.empty_like(arr)
canvas[:] = U.max(arr)
return canvas - arr
def trans3D_bilinear(a, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0,0), rzy=0, mr=0, ncpu=1, **kwds):
"""
magyx: scalar or [y,x] or [y,x, direction in degrees]
"""
a = a.copy()
ndim = a.ndim
if ndim == 2:
a = a.reshape((1,)+a.shape)
#b2d = N.empty_like(a[0])
#dzyx = N.asarray(dzyx)
#tzyx = N.asarray(tzyx)
#tzyx[-2:] += dzyx[-2:]
# mag
magaxis = 1 # only this axis works
magz = 1
try:
if len(mag) == 3:
magz, magy, magx = mag
elif len(mag) == 2:
magy, magx = mag
else:
magy = magx = mag[0]
except:
magy = magx = mag
# vertical axis
if ndim == 3 and (magz != 1 or tzyx[-3]):
#print 'vertical'
at = a.T # zyx -> xyz
mag = 1
anismag = magz
canvas = N.empty_like(at)
old="""
for x, a2d in enumerate(at):
if dzyx[-3]:
U.trans2d(a2d, target, (0, -dyzx[-3], 0, 1, 1, 1))
else:
target = a2d
canvas[x] = U.trans2d(target, None, (tzyx[-3], 0, rzy, mag, magaxis, anismag))"""
if ndim == 3:# and ncpu > 1 and mp:
ret = ppro.pmap(_doBilinear2D, at, ncpu, (0,tzyx[-3]), rzy, mag, anismag, (0,dzyx[-3]))
for z, a2d in enumerate(ret):
canvas[z] = a2d
else:
target = N.empty_like(a.T[0])#N.ascontiguousarray(b2d.T)
for z, a2d in enumerate(at):
canvas[z] = _doBilinear2D(a2d, (0,tzyx[-3]), rzy, mag, anismag, (0,dzyx[-3]), b2d=target)
a = canvas.T
#return a
# Horizontal axis
mag = magy
anismag = magx / magy
oldcode="""
for z, a2d in enumerate(a):
if N.any(dzyx[-2:]) or mr:
temp = N.ascontiguousarray(b2d)
target = N.ascontiguousarray(a2d)
U.trans2d(target, temp, (-dzyx[-1], -dzyx[-2], -mr, 1, 0, 1))
else:
temp = N.ascontiguousarray(a2d)
target = N.ascontiguousarray(b2d)
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
if N.any(tzyx[-2:]) or mr:
U.trans2d(target, temp, (tzyx[-1], tzyx[-2], mr, 1, 0, 1))
else:
temp[:] = target[:]
a[z] = temp[:]"""
if ndim == 3 and ncpu > 1 and mp:
ret = ppro.pmap(_doBilinear2D, a, ncpu, tzyx[-2:], r, mag, anismag, dzyx[-2:], mr)
for z, a2d in enumerate(ret):
a[z] = a2d
else:
b2d = N.empty_like(a[0])
for z, a2d in enumerate(a):
a[z] = _doBilinear2D(a2d, tzyx[-2:], r, mag, anismag, dzyx[-2:], mr, b2d)
if ndim == 2:
a = a[0]
return a
def trans3D_affine(arr, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0,0), rzy=0, ncpu=NCPU, order=ORDER):#**kwds):
"""
return array
"""
dtype = arr.dtype.type
arr = arr.astype(N.float32)
ndim = arr.ndim
if ndim == 2:
arr = arr.reshape((1,)+arr.shape)
elif ndim == 3:
if len(tzyx) < ndim:
tzyx = (0,)*(ndim-len(tzyx)) + tuple(tzyx)
if len(dzyx) < ndim:
dzyx = (0,)*(ndim-len(dzyx)) + tuple(dzyx)
dzyx = N.asarray(dzyx)
magz = 1
try:
if len(mag) == 3:
magz = mag[0]
mag = mag[1:]
except TypeError:
pass
if ndim == 3 and (magz != 1 or tzyx[-3]):
#print magz, arr.shape
# because, mergins introduced after 2D transformation may interfere the result of this vertical transform, vertical axis was processed first, since rzy is 0 usually.
arrT = arr.T # zyx -> xyz
magzz = (1,magz)
canvas = N.empty_like(arrT)
tzy = (0,tzyx[-3])
dzy = (dzyx[-2], dzyx[-3])
if ncpu > 1 and mp:
ret = ppro.pmap(_dothat, arrT, ncpu, tzy, rzy, magzz, dzy, order)
for x, a in enumerate(ret):
canvas[x] = a
else:
for x, a in enumerate(arrT):
canvas[x] = _dothat(a, tzy, rzy, magzz, dzy, order)
arr = canvas.T
#del arrT
if N.any(tzyx[-2:]) or r or N.any(mag):
#print ndim, arr.shape
canvas = N.empty_like(arr)
if ndim == 3 and ncpu > 1 and mp:
# dividing XY into pieces did not work for rotation and magnification
# here parallel processing is done section-wise since affine works only for 2D
ret = ppro.pmap(_dothat, arr, ncpu, tzyx[-2:], r, mag, dzyx[-2:], order)
for z, a in enumerate(ret):
canvas[z] = a
else:
for z, a in enumerate(arr):
canvas[z] = _dothat(a, tzyx[-2:], r, mag, dzyx[-2:], order)
if ndim == 2:
canvas = canvas[0]
arr = canvas
if dtype in (N.int, N.uint8, N.uint16, N.uint32):
arr = N.where(arr < 0, 0, arr)
return arr.astype(dtype)
def trans3D_bilinear(a, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0,0), b=None, rzy=0, **kwds):
"""
magyx: scalar or [y,x] or [y,x, direction in degrees]
"""
a = a.copy()
ndim = a.ndim
if ndim == 2:
a = a.reshape((1,)+a.shape)
try:
if len(magyx) == 3:
mr = magyx[-1]
magyx = magyx[:2]
else:
mr = 0
except:
mr = 0
if b is None:
b2d = N.empty_like(a[0])
dzyx = N.asarray(dzyx)
tzyx = N.asarray(tzyx)
tzyx[-2:] += dzyx[-2:]
magaxis = 1 # only this axis works
magz = 1
try:
if len(mag) == 3:
magz, magy, magx = mag
elif len(mag) == 2:
magy, magx = mag
else:
magy = magx = mag[0]
except:
magy = magx = mag
mag = magy
anismag = magx / magy
for z, a2d in enumerate(a):
if N.any(dzyx[-2:]) or mr:
temp = N.ascontiguousarray(b2d)
target = N.ascontiguousarray(a2d)
#U.trans2d(target, temp, (-dyx[1], -dyx[0], -mr, 1, 0, 1))
U.trans2d(target, temp, (-dzyx[-1], -dzyx[-2], -mr, 1, 0, 1))
else:
temp = N.ascontiguousarray(a2d)
target = N.ascontiguousarray(b2d)
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
#if rzx: # is this correct?? havn't tried yet
# target = U.nd.rotate(target, rzx, axes=(0,2), order=1, prefilter=False)
if N.any(tzyx[-2:]) or mr:#N.any(dyx) or mr:
#U.trans2d(target, temp, (dyx[1], dyx[0], mr, 1, 0, 1))
U.trans2d(target, temp, (tzyx[-1], tzyx[-2], mr, 1, 0, 1))
else:
temp[:] = target[:]
a[z] = temp[:]
if ndim == 2:
a = a[0]
elif ndim == 3 and (magz != 1 or tzyx[-3]):
at = a.T # zyx -> xyz
mag = 1#magz
anismag = magz #magy / magz
canvas = N.empty_like(at)
target = b2d.T
for x, a2d in enumerate(at):
if dzyx[-3]:
U.trans2d(a2d, target, (0, -dyzx[-3], 0, 1, 1, 1))
else:
target = a2d
canvas[x] = U.trans2d(target, None, (tzyx[-3], 0, rzy, mag, magaxis, anismag))
#canvas = canvas.T
a = canvas.T
return a
def arr_invert(arr):
canvas = N.empty_like(arr)
canvas[:] = U.max(arr)
return canvas - arr
