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author | algol <dkazanc@hotmail.com> | 2018-04-10 10:45:41 +0100 |
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committer | algol <dkazanc@hotmail.com> | 2018-04-10 10:45:41 +0100 |
commit | c409bd46a39357ca14b8ae48f6242700b1576396 (patch) | |
tree | 90505f9fce807852a050046acfa5b926ad2399c0 /Wrappers/Python | |
parent | f920d9e0373776493adc40e87b11b4f0939c2818 (diff) | |
download | regularization-c409bd46a39357ca14b8ae48f6242700b1576396.tar.gz regularization-c409bd46a39357ca14b8ae48f6242700b1576396.tar.bz2 regularization-c409bd46a39357ca14b8ae48f6242700b1576396.tar.xz regularization-c409bd46a39357ca14b8ae48f6242700b1576396.zip |
#43 all python/matlab 2d/3d DEMOS work OK
Diffstat (limited to 'Wrappers/Python')
-rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 23 | ||||
-rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 42 | ||||
-rw-r--r-- | Wrappers/Python/demos/qualitymetrics.py | 6 | ||||
-rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 6 | ||||
-rw-r--r-- | Wrappers/Python/src/gpu_regularisers.pyx | 4 |
5 files changed, 41 insertions, 40 deletions
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 84d86c0..487bac7 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -27,7 +27,7 @@ def printParametersToString(pars): txt += '\n' return txt ############################################################################### - +#%% filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") # read image @@ -135,22 +135,24 @@ imgplot = plt.imshow(fgp_cpu, cmap="gray") plt.title('{}'.format('CPU results')) # Uncomment to test 3D regularisation performance +#%% """ N = 512 slices = 20 +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") Im = plt.imread(filename) Im = np.asarray(Im, dtype='float32') Im = Im/255 perc = 0.05 -noisyVol = np.zeros((N,N,slices),dtype='float32') -idealVol = np.zeros((N,N,slices),dtype='float32') +noisyVol = np.zeros((slices,N,N),dtype='float32') +idealVol = np.zeros((slices,N,N),dtype='float32') for i in range (slices): - noisyVol[:,:,i] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) - idealVol[:,:,i] = Im + noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) + idealVol[i,:,:] = Im print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________ROF-TV (3D)_________________") @@ -161,7 +163,7 @@ fig = plt.figure(3) plt.suptitle('Performance of ROF-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy 15th slice of a volume') -imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm': ROF_TV, \ @@ -189,7 +191,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(rof_cpu3D[:,:,10], cmap="gray") +imgplot = plt.imshow(rof_cpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the CPU using ROF-TV')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -201,7 +203,7 @@ fig = plt.figure(4) plt.suptitle('Performance of FGP-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') -imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : FGP_TV, \ @@ -238,6 +240,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(fgp_cpu3D[:,:,10], cmap="gray") +imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the CPU using FGP-TV')) -"""
\ No newline at end of file +""" +#%%
\ No newline at end of file diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py index fd7b32c..5eb7876 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -27,7 +27,7 @@ def printParametersToString(pars): txt += '\n' return txt ############################################################################### - +#%% filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") # read image @@ -51,7 +51,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(1) plt.suptitle('Performance of the ROF-TV regulariser using the GPU') -a=fig.add_subplot(1,4,1) +a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") @@ -75,7 +75,7 @@ pars['algorithm'] = ROF_TV txtstr = printParametersToString(pars) txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) print (txtstr) -a=fig.add_subplot(1,4,3) +a=fig.add_subplot(1,2,2) # these are matplotlib.patch.Patch properties props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) @@ -93,7 +93,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(2) plt.suptitle('Performance of the FGP-TV regulariser using the GPU') -a=fig.add_subplot(1,4,1) +a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") @@ -124,7 +124,7 @@ pars['algorithm'] = FGP_TV txtstr = printParametersToString(pars) txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) print (txtstr) -a=fig.add_subplot(1,4,3) +a=fig.add_subplot(1,2,2) # these are matplotlib.patch.Patch properties props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) @@ -136,22 +136,24 @@ plt.title('{}'.format('GPU results')) # Uncomment to test 3D regularisation performance +#%% """ N = 512 slices = 20 +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") Im = plt.imread(filename) Im = np.asarray(Im, dtype='float32') Im = Im/255 perc = 0.05 -noisyVol = np.zeros((N,N,slices),dtype='float32') -idealVol = np.zeros((N,N,slices),dtype='float32') +noisyVol = np.zeros((slices,N,N),dtype='float32') +idealVol = np.zeros((slices,N,N),dtype='float32') for i in range (slices): - noisyVol[:,:,i] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) - idealVol[:,:,i] = Im + noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) + idealVol[i,:,:] = Im print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________ROF-TV (3D)_________________") @@ -162,7 +164,7 @@ fig = plt.figure(3) plt.suptitle('Performance of ROF-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy 15th slice of a volume') -imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm': ROF_TV, \ @@ -171,7 +173,7 @@ pars = {'algorithm': ROF_TV, \ 'number_of_iterations': 500,\ 'time_marching_parameter': 0.0025 } -print ("#############ROF TV CPU####################") +print ("#############ROF TV GPU####################") start_time = timeit.default_timer() rof_gpu3D = ROF_TV(pars['input'], pars['regularisation_parameter'], @@ -190,7 +192,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(rof_gpu3D[:,:,10], cmap="gray") +imgplot = plt.imshow(rof_gpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the GPU using ROF-TV')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -202,7 +204,7 @@ fig = plt.figure(4) plt.suptitle('Performance of FGP-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') -imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : FGP_TV, \ @@ -214,8 +216,8 @@ pars = {'algorithm' : FGP_TV, \ 'nonneg': 0 ,\ 'printingOut': 0 } - -print ("#############FGP TV CPU####################") + +print ("#############FGP TV GPU####################") start_time = timeit.default_timer() fgp_gpu3D = FGP_TV(pars['input'], pars['regularisation_parameter'], @@ -223,9 +225,8 @@ fgp_gpu3D = FGP_TV(pars['input'], pars['tolerance_constant'], pars['methodTV'], pars['nonneg'], - pars['printingOut'],'gpu') - - + pars['printingOut'],'gpu') + rms = rmse(idealVol, fgp_gpu3D) pars['rmse'] = rms @@ -239,9 +240,8 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(fgp_gpu3D[:,:,10], cmap="gray") +imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the GPU using FGP-TV')) - +#%% """ - diff --git a/Wrappers/Python/demos/qualitymetrics.py b/Wrappers/Python/demos/qualitymetrics.py index 32fa479..850829e 100644 --- a/Wrappers/Python/demos/qualitymetrics.py +++ b/Wrappers/Python/demos/qualitymetrics.py @@ -8,13 +8,11 @@ Created on Wed Feb 21 13:34:32 2018 import numpy as np def nrmse(im1, im2): - a, b = im1.shape - rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b)) + rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size)) max_val = max(np.max(im1), np.max(im2)) min_val = min(np.min(im1), np.min(im2)) return 1 - (rmse / (max_val - min_val)) def rmse(im1, im2): - a, b = im1.shape - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b)) + rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size)) return rmse diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 248bad1..0f08f7f 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -48,8 +48,8 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, return outputData def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, - int iterationsNumb, float regularisation_parameter, + int iterationsNumb, float marching_step_parameter): cdef long dims[3] dims[0] = inputData.shape[0] @@ -60,7 +60,7 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, np.zeros([dims[0],dims[1],dims[2]], dtype='float32') # Run ROF iterations for 3D data - TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], dims[2]) + TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[2], dims[1], dims[0]) return outputData @@ -122,5 +122,5 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], dims[2]) + dims[2], dims[1], dims[0]) return outputData diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index 7ebd011..ea746d3 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -106,7 +106,7 @@ def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, regularisation_parameter, iterations , time_marching_parameter, - dims[0], dims[1], dims[2]); + dims[2], dims[1], dims[0]); return outputData #****************************************************************# @@ -165,6 +165,6 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], dims[2]); + dims[2], dims[1], dims[0]); return outputData |