# end function
if __name__ == '__main__':
plt.close('all')
if GetNumberOfCUDADevices() > 0:
mType = MEM_DEVICE
else:
print "No CUDA devices found, running on CPU"
mType = MEM_HOST
imagedir='./Images/'
#
# Run lena images
#
I0 = common.LoadPNGImage(imagedir + 'lena_deformed.png', mType)
I1 = common.LoadPNGImage(imagedir + 'lena_orig.png', mType)
(Idef, h, energy) = \
GreedyReg(I0, \
I1, \
scales = [2,1], \
nIters = [1000,1000], \
ustep = [0.1, 0.1], \
fluidParams = [0.5, 0.5, 0.001], \
plotEvery = 500)
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])
if GetNumberOfCUDADevices() > 0:
mType = MEM_DEVICE
else:
print "No CUDA devices found, running on CPU"
mType = MEM_HOST
# data fidelity modifier
DataFidC = 1.0
TVC = 0.05
imagedir = './Images/'
#
# Run lena images
#
I0 = common.LoadPNGImage(imagedir + 'lena_orig.png', mType)
imSz = I0.size()
sz = imSz.tolist()[0:2]
(I,energy) = \
PrimalDualTV(I0, \
DataFidC, \
TVC = TVC, \
nIters = nIters, \
stepP = 1.0, \
stepI = 1.0/16.0, \
disp = disp, \
dispEvery = dispEvery)