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| author | algol <dkazanc@hotmail.com> | 2018-03-06 12:48:21 +0000 |
|---|---|---|
| committer | algol <dkazanc@hotmail.com> | 2018-03-06 12:48:21 +0000 |
| commit | 69ecdd57434d591eb3fa4afefb72174d3e025fb9 (patch) | |
| tree | abf99229b45f959d1e09c687ff25bd9c3403d7e5 /Wrappers | |
| parent | 309d84445b5ec2980db7c79832958a6481343f17 (diff) | |
| download | regularization-69ecdd57434d591eb3fa4afefb72174d3e025fb9.tar.gz regularization-69ecdd57434d591eb3fa4afefb72174d3e025fb9.tar.bz2 regularization-69ecdd57434d591eb3fa4afefb72174d3e025fb9.tar.xz regularization-69ecdd57434d591eb3fa4afefb72174d3e025fb9.zip | |
FGP_CPU (Cythonized) now works in demo
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/demo/test_cpu_regularizers.py | 10 |
1 files changed, 3 insertions, 7 deletions
diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py index 7f08605..1d97857 100644 --- a/Wrappers/Python/demo/test_cpu_regularizers.py +++ b/Wrappers/Python/demo/test_cpu_regularizers.py @@ -137,7 +137,7 @@ pars = {'algorithm' : TV_FGP_CPU , \ 'printingOut': 0 } -out = TV_FGP_CPU (pars['input'], +fgp = TV_FGP_CPU(pars['input'], pars['regularization_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], @@ -145,7 +145,6 @@ out = TV_FGP_CPU (pars['input'], pars['nonneg'], pars['printingOut']) -fgp = out[0] rms = rmse(Im, fgp) pars['rmse'] = rms @@ -154,7 +153,7 @@ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) print (txtstr) -a=fig.add_subplot(2,4,4) +a=fig.add_subplot(2,4,3) # these are matplotlib.patch.Patch properties props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) @@ -168,7 +167,6 @@ a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, ###################### LLT_model ######################################### -""" start_time = timeit.default_timer() pars = {'algorithm': LLT_model , \ @@ -203,8 +201,6 @@ a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(llt,\ cmap="gray" ) -""" - # ###################### PatchBased_Regul ######################################### # # Quick 2D denoising example in Matlab: # # Im = double(imread('lena_gray_256.tif'))/255; % loading image @@ -286,7 +282,7 @@ start_time = timeit.default_timer() pars = {'algorithm': TV_ROF_CPU , \ 'input' : u0,\ - 'regularization_parameter':0.04,\ + 'regularization_parameter':0.07,\ 'marching_step': 0.0025,\ 'number_of_iterations': 300 } |