def nanFilter(af, kernel=3):
"""
3D phase contrast filter often creates 'nan'
this filter removes nan by averaging surrounding pixels
return af
"""
af = af.copy()
shape = N.array(af.shape)
radius = N.subtract(kernel, 1) // 2
box = kernel * af.ndim
nan = N.isnan(af)
nids = N.array(N.nonzero(nan)).T
for nidx in nids:
slc = [slice(idx, idx + 1) for idx in nidx]
slices = []
for dim in range(af.ndim):
slc2 = slice(slc[dim].start - radius, slc[dim].stop + radius)
while slc2.start < 0:
slc2 = slice(slc2.start + 1, slc2.stop)
while slc2.stop > shape[dim]:
slc2 = slice(slc2.start, slc2.stop - 1)
slices.append(slc2)
val = af[slices]
nanlocal = N.isnan(val)
ss = N.sum(N.where(nanlocal, 0, val)) / float((box - N.sum(nanlocal)))
af[slc] = ss
return af
def nanFilter(af, kernel=3):
"""
3D phase contrast filter often creates 'nan'
this filter removes nan by averaging surrounding pixels
return af
"""
af = af.copy()
shape = N.array(af.shape)
radius = N.subtract(kernel, 1) // 2
box = kernel * af.ndim
nan = N.isnan(af)
nids = N.array(N.nonzero(nan)).T
for nidx in nids:
slc = [slice(idx,idx+1) for idx in nidx]
slices = []
for dim in range(af.ndim):
slc2 = slice(slc[dim].start - radius, slc[dim].stop + radius)
while slc2.start < 0:
slc2 = slice(slc2.start + 1, slc2.stop)
while slc2.stop > shape[dim]:
slc2 = slice(slc2.start, slc2.stop -1)
slices.append(slc2)
val = af[slices]
nanlocal = N.isnan(val)
ss = N.sum(N.where(nanlocal, 0, val)) / float((box - N.sum(nanlocal)))
af[slc] = ss
return af
def centerOfMass(img, yx, window=5):
"""
find peak by center of mass in a 2D image
img: a 2D image array
yx: (y,x) in the image
window: a window where CM calculation is performed on
return yx
"""
# prepare small image
s = N.array([window,window])
c = s/2.
yx = N.round_(yx)
yx -= c
yi, xi = N.indices(s)
yi += yx[0]
xi += yx[1]
cc = img[yi,xi]
# calculate center of mass
yxi = N.indices(s)
yxi *= cc
yxi = yxi.T
vv = N.sum(yxi, axis=0)
vv = N.sum(vv, axis=0)
yxs = vv / float(N.sum(cc))
yxs += yx
return yxs
def centerOfMass(img, yx, window=5):
"""
find peak by center of mass in a 2D image
img: a 2D image array
yx: (y,x) in the image
window: a window where CM calculation is performed on
return yx
"""
# prepare small image
s = N.array([window, window])
c = s / 2.
yx = N.round_(yx)
yx -= c
yi, xi = N.indices(s)
yi += yx[0]
xi += yx[1]
cc = img[yi, xi]
# calculate center of mass
yxi = N.indices(s)
yxi *= cc
yxi = yxi.T
vv = N.sum(yxi, axis=0)
vv = N.sum(vv, axis=0)
yxs = vv / float(N.sum(cc))
yxs += yx
return yxs
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 img2polar2D(img,
center,
final_radius=None,
initial_radius=None,
phase_width=360,
return_idx=False):
"""
img: array
center: coordinate y, x
final_radius: ending radius
initial_radius: starting radius
phase_width: npixles / circle
return_idx: return transformation coordinates (y,x)
"""
if img.ndim > 2 or len(center) > 2:
raise ValueError(
'this function only support 2D, you entered %i-dim array and %i-dim center coordinate'
% (img.ndim, len(center)))
if initial_radius is None:
initial_radius = 0
if final_radius is None:
rad0 = N.ceil(N.array(img.shape) - center)
final_radius = min((int(min(rad0)), int(min(N.ceil(center)))))
if phase_width is None:
phase_width = N.sum(img.shape[-2:]) * 2
theta, R = np.meshgrid(np.linspace(0, 2 * np.pi, phase_width),
np.arange(initial_radius, final_radius))
Ycart, Xcart = polar2cart2D(R, theta, center)
Ycart = N.where(Ycart >= img.shape[0], img.shape[0] - 1, Ycart)
Xcart = N.where(Xcart >= img.shape[1], img.shape[1] - 1, Xcart)
Ycart = Ycart.astype(int)
Xcart = Xcart.astype(int)
polar_img = img[Ycart, Xcart]
polar_img = np.reshape(polar_img,
(final_radius - initial_radius, phase_width))
if return_idx:
return polar_img, Ycart, Xcart
else:
return polar_img
def img2polar2D(img, center, final_radius=None, initial_radius = None, phase_width = 360, return_idx=False):
"""
img: array
center: coordinate y, x
final_radius: ending radius
initial_radius: starting radius
phase_width: npixles / circle
return_idx: return transformation coordinates (y,x)
"""
if img.ndim > 2 or len(center) > 2:
raise ValueError('this function only support 2D, you entered %i-dim array and %i-dim center coordinate' % (img.ndim, len(center)))
if initial_radius is None:
initial_radius = 0
if final_radius is None:
rad0 = N.ceil(N.array(img.shape) - center)
final_radius = min((int(min(rad0)), int(min(N.ceil(center)))))
if phase_width is None:
phase_width = N.sum(img.shape[-2:]) * 2
theta , R = np.meshgrid(np.linspace(0, 2*np.pi, phase_width),
np.arange(initial_radius, final_radius))
Ycart, Xcart = polar2cart2D(R, theta, center)
Ycart = N.where(Ycart >= img.shape[0], img.shape[0]-1, Ycart)
Xcart = N.where(Xcart >= img.shape[1], img.shape[1]-1, Xcart)
Ycart = Ycart.astype(int)
Xcart = Xcart.astype(int)
polar_img = img[Ycart,Xcart]
polar_img = np.reshape(polar_img,(final_radius-initial_radius,phase_width))
if return_idx:
return polar_img, Ycart, Xcart
else:
return polar_img
