pgm3 = "/mnt/CLAWS1/tbeier/tmp/gm3.h5"
if True:
data = vigra.readHDF5(path, 'data')[0:250,0:255,0:255].astype(numpy.float32)
##########################
# compute non local mean #
##########################
if False:
print "non local mean"
policy = denoising.RatioPolicy(sigma=2.0, meanRatio=0.90, varRatio=0.80)
res = denoising.nonLocalMean(image=data, policy=policy, patchRadius=2, searchRadius=7, sigmaSpatial=2.0,
sigmaPresmoothing=1.0, stepSize=2, iterations=1, verbose=True)
vigra.impex.writeHDF5(res, pnlm, 'data')
if True:
print "non local mean"
policy = denoising.RatioPolicy(sigma=2.0, meanRatio=0.90, varRatio=0.80)
res = denoising.nonLocalMean(image=data, policy=policy, patchRadius=2, searchRadius=14, sigmaSpatial=1.5,
sigmaPresmoothing=1.0, stepSize=2, iterations=1, verbose=True)
vigra.impex.writeHDF5(res, pnlm2, 'data')
######################
# compute tv bregman #
######################
if False:
print "tvBregman isotropic"
res = denoising.tvBregman(data,weight=2.0, isotropic=True)
policy = denoising.RatioPolicy(sigma=1.0, meanRatio=0.95, varRatio=0.8)
nlmp = dict(image=data,
policy=policy,
patchRadius=2,
searchRadius=70,
sigmaSpatial=2.0,
sigmaPresmoothing=1.3,
stepSize=2,
iterations=1,
verbose=True,
nThreads=17)
if False:
print "non truncated"
smoothed = denoising.nonLocalMean(wTruncate=0.0, **nlmp)
vigra.impex.writeHDF5(smoothed, sPath, 'data')
if False:
print "truncated"
smoothedT = denoising.nonLocalMean(wTruncate=0.15, **nlmp)
vigra.impex.writeHDF5(smoothedT, stPath, 'data')
if False:
data = data.astype(numpy.float32)
ew = vigra.filters.hessianOfGaussianEigenvalues(data, 4.0)
ew = numpy.sort(ew, axis=3)[:, :, :, 2]
vigra.impex.writeHDF5(ew, ewPath, 'data')
if False:
lssPath = "/mnt/CLAWS1/tbeier/data/stack_with_holes/labelsSS.h5"
ewPath = "/mnt/CLAWS1/tbeier/data/stack_with_holes/ew.h5"
ewsPath = "/mnt/CLAWS1/tbeier/data/stack_with_holes/ews.h5"
ewssPath = "/mnt/CLAWS1/tbeier/data/stack_with_holes/ewss.h5"
data = vigra.readHDF5(path, 'data')[0:600,0:600,0:100].astype(numpy.float64)
policy = denoising.RatioPolicy(sigma=1.0, meanRatio=0.95, varRatio=0.8)
nlmp = dict(image=data, policy=policy, patchRadius=2, searchRadius=70, sigmaSpatial=2.0,
sigmaPresmoothing=1.3, stepSize=2, iterations=1, verbose=True, nThreads=17)
if False:
print "non truncated"
smoothed = denoising.nonLocalMean(wTruncate=0.0,**nlmp)
vigra.impex.writeHDF5(smoothed, sPath, 'data')
if False:
print "truncated"
smoothedT = denoising.nonLocalMean(wTruncate=0.15,**nlmp)
vigra.impex.writeHDF5(smoothedT, stPath, 'data')
if False:
data=data.astype(numpy.float32)
