def psf__wPSF(parm, t):
"""
parm: s0, c, d, A, B
example (72, 125, 246, 1, 1)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt(sq)
def psf__wPSF_fromSigma(t, parm):
"""
t: now this is sigma that you got
parm: s0, c, d, A, B (use parm form psf__wPSF fit)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt(1 + sq)
def psf__wPSF_fromSigma(t, parm):
"""
t: now this is sigma that you got
parm: s0, c, d, A, B (use parm form psf__wPSF fit)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt( 1 + sq)
def psf__wPSF(parm, t):
"""
parm: s0, c, d, A, B
example (72, 125, 246, 1, 1)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt(sq)
def psf__wPSF_yPolyInv_bare(t, *parm):
"""
before abs
"""
if len(parm) == 1:
parm = parm[0]
r = 0.0
for i in range(len(parm)):
r = r + parm[i] * N.power(t, i)
return r
def psf__wPSF_yPolyInv_bare(t, *parm):
"""
before abs
"""
if len(parm) == 1:
parm = parm[0]
r = 0.0
for i in range(len(parm)):
r = r + parm[i]*N.power(t, i)
return r
def yPoly(parms=(1, 1, 1, 1, 0, 0), t=0):
'''
t can be a scalar or a vector
returns y value(s) of a polygon model
parms:
baseline, first-order coeff, 2nd, ...
'''
r = 0.0
for i in range(0, len(parms), 2):
c = parms[i] * t + parms[i + 1]
r = r + c * N.power(t, i)
return r
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 yPoly(parms=(1,1,1,1,0,0), t=0):
'''
t can be a scalar or a vector
returns y value(s) of a polygon model
parms:
baseline, first-order coeff, 2nd, ...
'''
r = 0.0
for i in range(0, len(parms), 2):
c = parms[i] * t + parms[i+1]
r = r + c*N.power(t, i)
return r
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
