diff options
Diffstat (limited to 'Wrappers/Python')
| -rw-r--r-- | Wrappers/Python/CMakeLists.txt | 5 | ||||
| -rw-r--r-- | Wrappers/Python/ccpi/filters/regularisers.py | 9 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_inpainters.py | 192 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 43 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 18 | ||||
| -rw-r--r-- | Wrappers/Python/setup-regularisers.py.in | 3 | ||||
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 90 | ||||
| -rw-r--r-- | Wrappers/Python/src/gpu_regularisers.pyx | 10 |
8 files changed, 337 insertions, 33 deletions
diff --git a/Wrappers/Python/CMakeLists.txt b/Wrappers/Python/CMakeLists.txt index fb00706..7833b54 100644 --- a/Wrappers/Python/CMakeLists.txt +++ b/Wrappers/Python/CMakeLists.txt @@ -81,8 +81,3 @@ else() endif() configure_file("setup-regularisers.py.in" "setup-regularisers.py") - - -#add_executable(regulariser_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regulariser.cpp) - -#target_link_libraries (regulariser_test LINK_PUBLIC regularisers_lib) diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index eec8c4d..a07b39a 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -2,7 +2,7 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gpu') """ -from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU +from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_INPAINT_CPU, NVM_INPAINT_CPU from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU def ROF_TV(inputData, regularisation_parameter, iterations, @@ -110,3 +110,10 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations, else: raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) +def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations, + time_marching_parameter, penalty_type): + return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, + edge_parameter, iterations, time_marching_parameter, penalty_type) + +def NVM_INP(inputData, maskData, SW_increment, iterations): + return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations) diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py new file mode 100644 index 0000000..3b4191b --- /dev/null +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Demonstration of CPU inpainters +@authors: Daniil Kazantsev, Edoardo Pasca +""" + +import matplotlib.pyplot as plt +import numpy as np +import os +import timeit +from scipy import io +from ccpi.filters.regularisers import NDF_INP, NVM_INP +from qualitymetrics import rmse +############################################################################### +def printParametersToString(pars): + txt = r'' + for key, value in pars.items(): + if key== 'algorithm' : + txt += "{0} = {1}".format(key, value.__name__) + elif key == 'input': + txt += "{0} = {1}".format(key, np.shape(value)) + elif key == 'maskData': + txt += "{0} = {1}".format(key, np.shape(value)) + else: + txt += "{0} = {1}".format(key, value) + txt += '\n' + return txt +############################################################################### + +# read sinogram and the mask +filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat") +sino = io.loadmat(filename) +sino_full = sino.get('Sinogram') +Mask = sino.get('Mask') +[angles_dim,detectors_dim] = sino_full.shape +sino_full = sino_full/np.max(sino_full) +#apply mask to sinogram +sino_cut = sino_full*(1-Mask) +#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32') +#sino_cut_new = sino_cut.copy(order='c') +#sino_cut_new[:] = sino_cut[:] +sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32); +#mask = np.zeros((angles_dim,detectors_dim),'uint8') +#mask =Mask.copy(order='c') +#mask[:] = Mask[:] +mask = np.ascontiguousarray(Mask, dtype=np.uint8); + +plt.figure(1) +plt.subplot(121) +plt.imshow(sino_cut_new,vmin=0.0, vmax=1) +plt.title('Missing Data sinogram') +plt.subplot(122) +plt.imshow(mask) +plt.title('Mask') +plt.show() +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("___Inpainting using linear diffusion (2D)__") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(2) +plt.suptitle('Performance of linear inpainting using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Missing data sinogram') +imgplot = plt.imshow(sino_cut_new,cmap="gray") + +# set parameters +pars = {'algorithm' : NDF_INP, \ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ + 'regularisation_parameter':5000,\ + 'edge_parameter':0,\ + 'number_of_iterations' :5000 ,\ + 'time_marching_parameter':0.000075,\ + 'penalty_type':0 + } + +start_time = timeit.default_timer() +ndf_inp_linear = NDF_INP(pars['input'], + pars['maskData'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type']) + +rms = rmse(sino_full, ndf_inp_linear) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +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(ndf_inp_linear, cmap="gray") +plt.title('{}'.format('Linear diffusion inpainting results')) +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_Inpainting using nonlinear diffusion (2D)_") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Missing data sinogram') +imgplot = plt.imshow(sino_cut_new,cmap="gray") + +# set parameters +pars = {'algorithm' : NDF_INP, \ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ + 'regularisation_parameter':80,\ + 'edge_parameter':0.00009,\ + 'number_of_iterations' :1500 ,\ + 'time_marching_parameter':0.000008,\ + 'penalty_type':1 + } + +start_time = timeit.default_timer() +ndf_inp_nonlinear = NDF_INP(pars['input'], + pars['maskData'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type']) + +rms = rmse(sino_full, ndf_inp_nonlinear) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +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(ndf_inp_nonlinear, cmap="gray") +plt.title('{}'.format('Nonlinear diffusion inpainting results')) +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("Inpainting using nonlocal vertical marching") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(4) +plt.suptitle('Performance of NVM inpainting using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Missing data sinogram') +imgplot = plt.imshow(sino_cut,cmap="gray") + +# set parameters +pars = {'algorithm' : NVM_INP, \ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ + 'SW_increment': 1,\ + 'number_of_iterations' : 150 + } + +start_time = timeit.default_timer() +(nvm_inp, mask_upd) = NVM_INP(pars['input'], + pars['maskData'], + pars['SW_increment'], + pars['number_of_iterations']) + +rms = rmse(sino_full, nvm_inp) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +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(nvm_inp, cmap="gray") +plt.title('{}'.format('Nonlocal Vertical Marching inpainting results')) +#%% diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 3567f91..986e3e9 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -44,13 +44,31 @@ u0 = Im + np.random.normal(loc = 0 , u_ref = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) - +(N,M) = np.shape(u0) # map the u0 u0->u0>0 # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) u0 = u0.astype('float32') u_ref = u_ref.astype('float32') - +# change dims to check that modules work with non-squared images +""" +M = M-100 +u_ref2 = np.zeros([N,M],dtype='float32') +u_ref2[:,0:M] = u_ref[:,0:M] +u_ref = u_ref2 +del u_ref2 + +u02 = np.zeros([N,M],dtype='float32') +u02[:,0:M] = u0[:,0:M] +u0 = u02 +del u02 + +Im2 = np.zeros([N,M],dtype='float32') +Im2[:,0:M] = Im[:,0:M] +Im = Im2 +del Im2 +""" +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________ROF-TV (2D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -288,7 +306,6 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray") plt.title('{}'.format('CPU results')) - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("__________Total nuclear Variation__________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -301,9 +318,8 @@ a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") channelsNo = 5 -N = 512 -noisyVol = np.zeros((channelsNo,N,N),dtype='float32') -idealVol = np.zeros((channelsNo,N,N),dtype='float32') +noisyVol = np.zeros((channelsNo,N,M),dtype='float32') +idealVol = np.zeros((channelsNo,N,M),dtype='float32') for i in range (channelsNo): noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) @@ -344,25 +360,19 @@ 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((slices,N,N),dtype='float32') -noisyRef = np.zeros((slices,N,N),dtype='float32') -idealVol = np.zeros((slices,N,N),dtype='float32') +noisyVol = np.zeros((slices,N,M),dtype='float32') +noisyRef = np.zeros((slices,N,M),dtype='float32') +idealVol = np.zeros((slices,N,M),dtype='float32') for i in range (slices): noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) idealVol[i,:,:] = Im + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -403,6 +413,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(rof_cpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the CPU using ROF-TV')) + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py index b873700..f3ed50c 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -44,10 +44,28 @@ u0 = Im + np.random.normal(loc = 0 , u_ref = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) +(N,M) = np.shape(u0) # map the u0 u0->u0>0 # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) u0 = u0.astype('float32') u_ref = u_ref.astype('float32') +""" +M = M-100 +u_ref2 = np.zeros([N,M],dtype='float32') +u_ref2[:,0:M] = u_ref[:,0:M] +u_ref = u_ref2 +del u_ref2 + +u02 = np.zeros([N,M],dtype='float32') +u02[:,0:M] = u0[:,0:M] +u0 = u02 +del u02 + +Im2 = np.zeros([N,M],dtype='float32') +Im2[:,0:M] = Im[:,0:M] +Im = Im2 +del Im2 +""" print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV regulariser_____________") diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in index b900efe..f55c6fe 100644 --- a/Wrappers/Python/setup-regularisers.py.in +++ b/Wrappers/Python/setup-regularisers.py.in @@ -34,6 +34,7 @@ extra_libraries = ['cilreg'] extra_include_dirs += [os.path.join(".." , ".." , "Core"), os.path.join(".." , ".." , "Core", "regularisers_CPU"), + os.path.join(".." , ".." , "Core", "inpainters_CPU"), os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_FGP" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_ROF" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_SB" ) , @@ -52,7 +53,7 @@ setup( description='CCPi Core Imaging Library - Image regularisers', version=cil_version, cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("ccpi.filters.cpu_regularisers_cython", + ext_modules = [Extension("ccpi.filters.cpu_regularisers", sources=[os.path.join("." , "src", "cpu_regularisers.pyx" ) ], include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 7ed8fa1..c934f1d 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -25,6 +25,8 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ); cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); +cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); +cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ); #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -46,7 +48,7 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') # Run ROF iterations for 2D data - TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1) + TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1) return outputData @@ -99,7 +101,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1) + dims[1],dims[0],1) return outputData @@ -158,7 +160,7 @@ def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, tolerance_param, methodTV, printM, - dims[0], dims[1], 1) + dims[1],dims[0],1) return outputData @@ -219,7 +221,7 @@ def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1) + dims[1], dims[0], 1) return outputData @@ -298,7 +300,7 @@ def NDF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') # Run Nonlinear Diffusion iterations for 2D data - Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1) return outputData def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, @@ -319,3 +321,81 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) return outputData + +#*********************Inpainting WITH****************************# +#***************Nonlinear (Isotropic) Diffusion******************# +#****************************************************************# +def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type): + if inputData.ndim == 2: + return NDF_INP_2D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + elif inputData.ndim == 3: + return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + +def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + # Run Inpaiting by Diffusion iterations for 2D data + Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1) + return outputData + +def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=3, mode="c"] maskData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + # Run Inpaiting by Diffusion iterations for 3D data + Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) + + return outputData +#*********************Inpainting WITH****************************# +#***************Nonlocal Vertical Marching method****************# +#****************************************************************# +def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterationsNumb): + if inputData.ndim == 2: + return NVM_INP_2D(inputData, maskData, SW_increment, iterationsNumb) + elif inputData.ndim == 3: + return + +def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + int SW_increment, + int iterationsNumb): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData_upd = \ + np.zeros([dims[0],dims[1]], dtype='uint8') + + # Run Inpaiting by Nonlocal vertical marching method for 2D data + NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], + &maskData_upd[0,0], + SW_increment, iterationsNumb, 1, dims[1], dims[0], 1) + + return (outputData, maskData_upd) diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index b0775054..7eab5d5 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -157,7 +157,7 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, regularisation_parameter, iterations , time_marching_parameter, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -210,7 +210,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -266,7 +266,7 @@ def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, tolerance_param, methodTV, printM, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -325,7 +325,7 @@ def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -381,7 +381,7 @@ def NDF_GPU_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Run Nonlinear Diffusion iterations for 2D data # Running CUDA code here - NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1) return outputData def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, |