def processProjectionSet(Is, Ir):
sigma = 0.9
alpha = 0
subImage = Is - Ir
subImage = np.median(subImage, axis=0)
dI = (subImage * (np.mean(Is) / np.mean(Ir)))
dx, dy = derivativesByOpticalflow(np.mean(Ir, axis=0),
dI,
alpha=alpha,
sig_scale=sigma)
phi = fc.frankotchellappa(dx, dy, False)
phi3 = kottler(dx, dy)
phi2 = LarkinAnissonSheppard(dx, dy)
mintoAdd = min(np.amin(dx), np.amin(dy))
dxPlus = dx + mintoAdd
dyPlus = dy + mintoAdd
gradientNorm = np.sqrt(dxPlus**2 + dyPlus**2)
return {
'dx': dx,
'dy': dy,
'phi': phi,
'phi2': phi2,
'phi3': phi3,
'gradientNorm': gradientNorm
}
def processOneProjection(Is, Ir):
sigma = 0.9
alpha = 0
dI = (Is - Ir * (np.mean(gaussian_filter(Is, sigma=2.)) /
np.mean(gaussian_filter(Ir, sigma=2.))))
alpha = np.finfo(np.float32).eps
dx, dy = derivativesByOpticalflow(Is, dI, alpha=alpha, sig_scale=sigma)
phi = fc.frankotchellappa(dx, dy, False)
phi3 = kottler(dx, dy)
phi2 = LarkinAnissonSheppard(dx, dy)
mintoAdd = min(np.amin(dx), np.amin(dy))
dxPlus = dx + mintoAdd
dyPlus = dy + mintoAdd
gradientNorm = np.sqrt(dxPlus**2 + dyPlus**2)
return {
'dx': dx,
'dy': dy,
'phi': phi,
'phi2': phi2,
'phi3': phi3,
'gradientNorm': gradientNorm
}
def processOneProjection(Is, Ir):
sigma = 0.9
alpha = 0
dI = (Is - Ir * (np.mean(Is) / np.mean(Ir)))
dx, dy = derivativesByOpticalflow(Ir, dI, alpha=alpha, sig_scale=sigma)
phi = fc.frankotchellappa(dx, dy, False)
phi3 = kottler(dx, dy)
phi2 = LarkinAnissonSheppard(dx, dy)
return {'dx': dx, 'dy': dy, 'phi': phi, 'phi2': phi2, 'phi3': phi3}
def processProjectionSet(Is, Ir):
sigma = 1
alpha = 0
subImage = Is - Ir
subImage = np.mean(subImage, axis=0)
dI = (subImage * (np.mean(Is) / np.mean(Ir)))
dx, dy = derivativesByOpticalflow(np.mean(Is, axis=0),
dI,
alpha=alpha,
sig_scale=sigma)
phi = fc.frankotchellappa(dx, dy, False)
phi3 = kottler(dx, dy)
phi2 = LarkinAnissonSheppard(dx, dy)
return {'dx': dx, 'dy': dy, 'phi': phi, 'phi2': phi2, 'phi3': phi3}
def processProjectionSetWithDarkFields(Is, Ir, dark):
sigma = 1
alpha = 0
#Is=corrections.normalizationMultipleDarkField(Is,dark)
#Ir=corrections.normalizationMultipleDarkField(Ir,dark)
subImage = Is - Ir
subImage = np.median(subImage, axis=0)
dI = (subImage * (np.mean(Is) / np.mean(Ir)))
dx, dy = derivativesByOpticalflow(np.mean(Ir, axis=0),
dI,
alpha=alpha,
sig_scale=sigma)
phi = fc.frankotchellappa(dx, dy, False)
phi3 = kottler(dx, dy)
phi2 = LarkinAnissonSheppard(dx, dy)
return {'dx': dx, 'dy': dy, 'phi': phi, 'phi2': phi2, 'phi3': phi3}