def test_SplatWorldAdjoint(self, disp=False):
# Random input images
reg = common.RandImage(self.sz,
nSig=1.0,
gSig=0.0,
mType=ca.MEM_HOST,
sp=self.imSp)
# adjust hJ's grid so that voxels don't align perfectly
spfactor = 0.77382
small = common.RandImage(self.sz,
nSig=1.0,
gSig=0.0,
mType=ca.MEM_HOST,
sp=self.imSp * spfactor)
tmpsmall = ca.Image3D(small.grid(), ca.MEM_HOST)
ca.SetMem(tmpsmall, 0)
# compute < I(Phi(x)), J(x) >
ca.ResampleWorld(tmpsmall, reg)
smallIP = ca.Dot(tmpsmall, small)
# compute < I(y), |DPhi^{-1}(y)| J(Phi^{-1}(y)) >
tmpreg = ca.Image3D(reg.grid(), ca.MEM_HOST)
ca.SetMem(tmpreg, 0)
ca.SplatWorld(tmpreg, small)
regIP = ca.Dot(tmpreg, reg)
#print "a=%f b=%f" % (phiIdotJ, IdotphiJ)
self.assertLess(abs(smallIP - regIP), 2e-6)
def test_SplatAdjoint(self, disp=False):
hI = common.RandImage(self.sz,
nSig=1.0,
gSig=0.0,
mType=ca.MEM_HOST,
sp=self.imSp)
hJ = common.RandImage(self.sz,
nSig=1.0,
gSig=0.0,
mType=ca.MEM_HOST,
sp=self.imSp)
hPhi = common.RandField(self.sz,
nSig=5.0,
gSig=4.0,
mType=ca.MEM_HOST,
sp=self.imSp)
tmp = ca.Image3D(self.grid, ca.MEM_HOST)
# compute < I(Phi(x)), J(x) >
ca.ApplyH(tmp, hI, hPhi)
phiIdotJ = ca.Dot(tmp, hJ)
# compute < I(y), |DPhi^{-1}(y)| J(Phi^{-1}(y)) >
ca.Splat(tmp, hPhi, hJ)
IdotphiJ = ca.Dot(tmp, hI)
#print "a=%f b=%f" % (phiIdotJ, IdotphiJ)
self.assertLess(abs(phiIdotJ - IdotphiJ), 2e-6)
def test_AddMasked(self, disp=False):
SetMem(self.hIm, 0.0)
SetMem(self.dIm, 0.0)
hIm2 = common.RandImage(self.sz,
nSig=1.0,
gSig=0.0,
mType=MEM_HOST,
sp=self.imSp)
dIm2 = hIm2.copy()
dIm2.toType(MEM_DEVICE)
Add(self.hIm, self.hRandIm, hIm2, self.hRandMask)
Add(self.dIm, self.dRandIm, dIm2, self.dRandMask)
self.TestIm(self.hIm, self.dIm, name='AddMasked', disp=disp)
def RunTest():
# number of iterations
nIters = 2000
#nIters = 0
disp = True
dispEvery = 1000
if GetNumberOfCUDADevices() > 0:
mType = MEM_DEVICE
else:
print "No CUDA devices found, running on CPU"
mType = MEM_HOST
# data fidelity modifier
DataFidC = 20.0
# TV modifier
TVC = 1.0
TVPow = 1.0
UseMask = True
# regularization term to avoid zero denominator
Beta = 1e-5
stepI = 0.001
imagedir = './Images/'
#
# Run lena images
#
Data = common.LoadPNGImage(imagedir + 'lena_orig.png', mType)
imSz = Data.size()
sz = imSz.tolist()[0:2]
if True:
I0 = Data.copy()
else:
I0 = common.RandImage(nSig=1.0, gSig=5.0, mType=mType)
Mask = None
if UseMask:
bdr = 10
MaskArr = np.zeros(sz)
MaskArr[bdr:-bdr, bdr:-bdr] = 1.0
Mask = common.ImFromNPArr(MaskArr, mType)
(I, energy) = \
RunROFTV(Data=Data, \
I0 = I0, \
DataFidC=DataFidC, \
TVC=TVC, \
TVPow=TVPow, \
stepI=stepI, \
Beta=Beta, \
nIters=nIters, \
dispEvery=dispEvery, \
disp=disp, \
Mask=Mask)
print 'final energy: {ttl:n} = {im:n} + {tv:n}'\
.format(ttl=energy[2][-1], \
im=energy[0][-1], \
tv=energy[1][-1])
def randImSetUp(self):
self.hRandIm = \
common.RandImage(self.sz, nSig=1.0, gSig=0.0,
mType = MEM_HOST, sp = self.imSp)
self.dRandIm = self.hRandIm.copy()
self.dRandIm.toType(MEM_DEVICE)