From 09eb48ffbb4ad699e2eefd25678e10dc59d6a177 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Tue, 1 May 2018 09:44:07 +0100
Subject: new inpainters
---
Wrappers/Python/ccpi/filters/regularisers.py | 7 +++-
Wrappers/Python/setup-regularisers.py.in | 3 +-
Wrappers/Python/src/cpu_regularisers.pyx | 50 ++++++++++++++++++++++++++++
3 files changed, 58 insertions(+), 2 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index eec8c4d..e62c020 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
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 +110,8 @@ 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, iterationsNumb,
+ time_marching_parameter, penalty_type)
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..3625106 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -25,6 +25,9 @@ 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 dimX, int dimY, int dimZ);
+
#****************************************************************#
#********************** Total-variation ROF *********************#
#****************************************************************#
@@ -319,3 +322,50 @@ 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[0], dims[1], 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
--
cgit v1.2.3
From fa47bdc29ba4178254531174c02f790a9d10a187 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Tue, 1 May 2018 10:03:16 +0100
Subject: make updates
---
Wrappers/Python/CMakeLists.txt | 5 -----
1 file changed, 5 deletions(-)
(limited to 'Wrappers/Python')
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)
--
cgit v1.2.3
From 5ef87da22a31868fd88c7f0ab4c2201e816e92ed Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Tue, 1 May 2018 12:07:30 +0100
Subject: inpaint demo
---
Wrappers/Python/demos/demo_cpu_inpainters.py | 143 +++++++++++++++++++++++++++
1 file changed, 143 insertions(+)
create mode 100644 Wrappers/Python/demos/demo_cpu_inpainters.py
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
new file mode 100644
index 0000000..a022bc8
--- /dev/null
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -0,0 +1,143 @@
+#!/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
+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 == 'refdata':
+ 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_no_cut = sino.get('Sinogram')
+Mask = sino.get('Mask')
+[angles_dim,detectors_dim] = sino_no_cut.shape
+sinogram = sino_no_cut/np.max(sino_no_cut)
+#apply mask to sinogram
+sino_cut = sinogram*(1-Mask)
+
+plt.figure(1)
+plt.subplot(121)
+plt.imshow(sino_cut,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,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+ 'input' : sino_cut,\
+ 'maskData' : Mask,\
+ 'regularisation_parameter':6000,\
+ 'edge_parameter':0.0,\
+ 'number_of_iterations' :5000 ,\
+ 'time_marching_parameter':0.000075,\
+ 'penalty_type':1
+ }
+
+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(sinogram, 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,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+ 'input' : sino_cut,\
+ '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(sinogram, 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'))
+#%%
\ No newline at end of file
--
cgit v1.2.3
From 033c2030a05c7aa4c832e7a5e9fd13346d05e33d Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Tue, 1 May 2018 14:48:45 +0100
Subject: some correction
---
Wrappers/Python/ccpi/filters/regularisers.py | 3 +--
Wrappers/Python/demos/demo_cpu_inpainters.py | 40 ++++++++++++++++------------
2 files changed, 24 insertions(+), 19 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index e62c020..8120f72 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -113,5 +113,4 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
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, iterationsNumb,
- time_marching_parameter, penalty_type)
+ edge_parameter, iterations, time_marching_parameter, penalty_type)
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index a022bc8..b067b11 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -20,30 +20,36 @@ def printParametersToString(pars):
txt += "{0} = {1}".format(key, value.__name__)
elif key == 'input':
txt += "{0} = {1}".format(key, np.shape(value))
- elif key == 'refdata':
+ 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_no_cut = sino.get('Sinogram')
+sino_full = sino.get('Sinogram')
Mask = sino.get('Mask')
-[angles_dim,detectors_dim] = sino_no_cut.shape
-sinogram = sino_no_cut/np.max(sino_no_cut)
+[angles_dim,detectors_dim] = sino_full.shape
+sino_full = sino_full/np.max(sino_full)
#apply mask to sinogram
-sino_cut = sinogram*(1-Mask)
+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[:]
+mask = np.zeros((angles_dim,detectors_dim),'uint8')
+#mask =Mask.copy(order='c')
+mask[:] = Mask[:]
plt.figure(1)
plt.subplot(121)
-plt.imshow(sino_cut,vmin=0.0, vmax=1)
+plt.imshow(sino_cut_new,vmin=0.0, vmax=1)
plt.title('Missing Data sinogram')
plt.subplot(122)
-plt.imshow(Mask)
+plt.imshow(mask)
plt.title('Mask')
plt.show()
#%%
@@ -56,15 +62,15 @@ 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,cmap="gray")
+imgplot = plt.imshow(sino_cut_new,cmap="gray")
# set parameters
pars = {'algorithm' : NDF_INP, \
- 'input' : sino_cut,\
- 'maskData' : Mask,\
- 'regularisation_parameter':6000,\
+ 'input' : sino_cut_new,\
+ 'maskData' : mask,\
+ 'regularisation_parameter':1000,\
'edge_parameter':0.0,\
- 'number_of_iterations' :5000 ,\
+ 'number_of_iterations' :1000 ,\
'time_marching_parameter':0.000075,\
'penalty_type':1
}
@@ -78,7 +84,7 @@ ndf_inp_linear = NDF_INP(pars['input'],
pars['time_marching_parameter'],
pars['penalty_type'])
-rms = rmse(sinogram, ndf_inp_linear)
+rms = rmse(sino_full, ndf_inp_linear)
pars['rmse'] = rms
txtstr = printParametersToString(pars)
@@ -107,8 +113,8 @@ imgplot = plt.imshow(sino_cut,cmap="gray")
# set parameters
pars = {'algorithm' : NDF_INP, \
- 'input' : sino_cut,\
- 'maskData' : Mask,\
+ 'input' : sino_cut_new,\
+ 'maskData' : mask,\
'regularisation_parameter':80,\
'edge_parameter':0.00009,\
'number_of_iterations' :1500 ,\
@@ -125,7 +131,7 @@ ndf_inp_nonlinear = NDF_INP(pars['input'],
pars['time_marching_parameter'],
pars['penalty_type'])
-rms = rmse(sinogram, ndf_inp_nonlinear)
+rms = rmse(sino_full, ndf_inp_nonlinear)
pars['rmse'] = rms
txtstr = printParametersToString(pars)
--
cgit v1.2.3
From 42a10faa06bd56bff3f0f1804ddcdf1a3e1283cd Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Tue, 1 May 2018 15:16:49 +0100
Subject: inpaint NVM added
---
Wrappers/Python/ccpi/filters/regularisers.py | 5 +++-
Wrappers/Python/demos/demo_cpu_inpainters.py | 45 ++++++++++++++++++++++++++--
Wrappers/Python/src/cpu_regularisers.pyx | 31 ++++++++++++++++++-
3 files changed, 77 insertions(+), 4 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 8120f72..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 import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_INPAINT_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,
@@ -114,3 +114,6 @@ def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, itera
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
index b067b11..ab7ed2f 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -10,7 +10,7 @@ import numpy as np
import os
import timeit
from scipy import io
-from ccpi.filters.regularisers import NDF_INP
+from ccpi.filters.regularisers import NDF_INP, NVM_INP
from qualitymetrics import rmse
###############################################################################
def printParametersToString(pars):
@@ -146,4 +146,45 @@ 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'))
-#%%
\ No newline at end of file
+#%%
+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' :20
+ }
+
+start_time = timeit.default_timer()
+nvm_inp = 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/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 3625106..19dd707 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -26,7 +26,7 @@ cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxI
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 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 dimX, int dimY, int dimZ);
#****************************************************************#
#********************** Total-variation ROF *********************#
@@ -368,4 +368,33 @@ def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
# 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, iterations):
+ if inputData.ndim == 2:
+ return NVM_INP_2D(inputData, maskData, SW_increment, iterations)
+ 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,
+ dims[0], dims[1], 1)
+
return outputData
--
cgit v1.2.3
From c4f50db4f5b318aad785ae577908d37fe05f53d2 Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Tue, 1 May 2018 15:30:28 +0100
Subject: some updates in demo
---
Wrappers/Python/demos/demo_cpu_inpainters.py | 2 +-
Wrappers/Python/src/cpu_regularisers.pyx | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index ab7ed2f..9a677c4 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -167,7 +167,7 @@ pars = {'algorithm' : NVM_INP, \
}
start_time = timeit.default_timer()
-nvm_inp = NVM_INP(pars['input'],
+(nvm_inp, mask_upd) = NVM_INP(pars['input'],
pars['maskData'],
pars['SW_increment'],
pars['number_of_iterations'])
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 19dd707..52befd7 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -397,4 +397,4 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
SW_increment, iterationsNumb,
dims[0], dims[1], 1)
- return outputData
+ return (outputData, maskData_upd)
--
cgit v1.2.3
From 73965b6b80c49a2867d54e4a42f3069fe35d9cc6 Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Wed, 2 May 2018 09:47:58 +0100
Subject: corrections to dimens issues
---
Wrappers/Python/demos/demo_cpu_inpainters.py | 18 ++++++++++--------
Wrappers/Python/demos/demo_cpu_regularisers.py | 17 +++++++++++++++++
Wrappers/Python/demos/demo_gpu_regularisers.py | 16 ++++++++++++++++
Wrappers/Python/src/cpu_regularisers.pyx | 10 ++++++----
4 files changed, 49 insertions(+), 12 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index 9a677c4..348d235 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -37,12 +37,14 @@ Mask = sino.get('Mask')
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 = np.zeros((angles_dim,detectors_dim),'float32')
#sino_cut_new = sino_cut.copy(order='c')
-sino_cut_new[:] = sino_cut[:]
-mask = np.zeros((angles_dim,detectors_dim),'uint8')
+#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[:] = Mask[:]
+mask = np.ascontiguousarray(Mask, dtype=np.uint8);
plt.figure(1)
plt.subplot(121)
@@ -68,11 +70,11 @@ imgplot = plt.imshow(sino_cut_new,cmap="gray")
pars = {'algorithm' : NDF_INP, \
'input' : sino_cut_new,\
'maskData' : mask,\
- 'regularisation_parameter':1000,\
- 'edge_parameter':0.0,\
+ 'regularisation_parameter':5000,\
+ 'edge_parameter':0,\
'number_of_iterations' :1000 ,\
'time_marching_parameter':0.000075,\
- 'penalty_type':1
+ 'penalty_type':0
}
start_time = timeit.default_timer()
@@ -163,7 +165,7 @@ pars = {'algorithm' : NVM_INP, \
'input' : sino_cut_new,\
'maskData' : mask,\
'SW_increment': 1,\
- 'number_of_iterations' :20
+ 'number_of_iterations' :0
}
start_time = timeit.default_timer()
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 3567f91..f803870 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -50,7 +50,24 @@ u_ref = Im + np.random.normal(loc = 0 ,
u0 = u0.astype('float32')
u_ref = u_ref.astype('float32')
+# change dims to check that modules work with non-squared images
+(N,M) = np.shape(u0)
+u_ref2 = np.zeros([N,M-100],dtype='float32')
+u_ref2[:,0:M-100] = u_ref[:,0:M-100]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M-100],dtype='float32')
+u02[:,0:M-100] = u0[:,0:M-100]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M-100],dtype='float32')
+Im2[:,0:M-100] = Im[:,0:M-100]
+Im = Im2
+del Im2
+#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("_______________ROF-TV (2D)_________________")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index b873700..dfdceee 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -49,6 +49,22 @@ u_ref = Im + np.random.normal(loc = 0 ,
u0 = u0.astype('float32')
u_ref = u_ref.astype('float32')
+(N,M) = np.shape(u0)
+u_ref2 = np.zeros([N,M-100],dtype='float32')
+u_ref2[:,0:M-100] = u_ref[:,0:M-100]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M-100],dtype='float32')
+u02[:,0:M-100] = u0[:,0:M-100]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M-100],dtype='float32')
+Im2[:,0:M-100] = Im[:,0:M-100]
+Im = Im2
+del Im2
+
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("____________ROF-TV regulariser_____________")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 52befd7..21a1a00 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -49,7 +49,7 @@ 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
@@ -333,18 +333,20 @@ def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_paramete
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,
+ 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')
+ 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[0], dims[1], 1)
--
cgit v1.2.3
From a64fe4d083173cc67dd7585c3160a94ea24bca80 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Wed, 2 May 2018 10:06:38 +0100
Subject: cyth corr
---
Wrappers/Python/src/cpu_regularisers.pyx | 13 ++++++-------
Wrappers/Python/src/gpu_regularisers.pyx | 10 +++++-----
2 files changed, 11 insertions(+), 12 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 21a1a00..7c06c28 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -102,7 +102,7 @@ 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
@@ -161,7 +161,7 @@ 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
@@ -222,7 +222,7 @@ 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
@@ -301,7 +301,7 @@ 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,
@@ -349,7 +349,7 @@ def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
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[0], dims[1], 1)
+ 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,
@@ -396,7 +396,6 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
# 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,
- dims[0], dims[1], 1)
+ SW_increment, iterationsNumb,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,
--
cgit v1.2.3
From 985fee04ac1abef2aaa69f282ae6c207e438b4af Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Wed, 2 May 2018 11:01:57 +0100
Subject: bugs in cython files
---
Wrappers/Python/demos/demo_cpu_inpainters.py | 2 +-
Wrappers/Python/demos/demo_cpu_regularisers.py | 40 +++++++++++---------------
Wrappers/Python/demos/demo_gpu_regularisers.py | 18 ++++++------
3 files changed, 28 insertions(+), 32 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index 348d235..7f452c1 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -72,7 +72,7 @@ pars = {'algorithm' : NDF_INP, \
'maskData' : mask,\
'regularisation_parameter':5000,\
'edge_parameter':0,\
- 'number_of_iterations' :1000 ,\
+ 'number_of_iterations' :5000 ,\
'time_marching_parameter':0.000075,\
'penalty_type':0
}
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index f803870..986e3e9 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -44,29 +44,30 @@ 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
-(N,M) = np.shape(u0)
-u_ref2 = np.zeros([N,M-100],dtype='float32')
-u_ref2[:,0:M-100] = u_ref[:,0:M-100]
+"""
+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-100],dtype='float32')
-u02[:,0:M-100] = u0[:,0:M-100]
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
u0 = u02
del u02
-Im2 = np.zeros([N,M-100],dtype='float32')
-Im2[:,0:M-100] = Im[:,0:M-100]
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
Im = Im2
del Im2
-
+"""
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("_______________ROF-TV (2D)_________________")
@@ -305,7 +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 ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -318,9 +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))
@@ -361,25 +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 ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -420,6 +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 dfdceee..f3ed50c 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -44,26 +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')
-
-(N,M) = np.shape(u0)
-u_ref2 = np.zeros([N,M-100],dtype='float32')
-u_ref2[:,0:M-100] = u_ref[:,0:M-100]
+"""
+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-100],dtype='float32')
-u02[:,0:M-100] = u0[:,0:M-100]
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
u0 = u02
del u02
-Im2 = np.zeros([N,M-100],dtype='float32')
-Im2[:,0:M-100] = Im[:,0:M-100]
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
Im = Im2
del Im2
+"""
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("____________ROF-TV regulariser_____________")
--
cgit v1.2.3
From 14edd18d07c871c0a355d70e68350a899014dbc7 Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Wed, 2 May 2018 13:11:50 +0100
Subject: bugs in NVM fixed
---
Wrappers/Python/demos/demo_cpu_inpainters.py | 6 ++++--
Wrappers/Python/src/cpu_regularisers.pyx | 12 ++++++------
2 files changed, 10 insertions(+), 8 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index 7f452c1..9197e91 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -55,6 +55,7 @@ plt.imshow(mask)
plt.title('Mask')
plt.show()
#%%
+"""
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("___Inpainting using linear diffusion (2D)__")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -111,7 +112,7 @@ 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,cmap="gray")
+imgplot = plt.imshow(sino_cut_new,cmap="gray")
# set parameters
pars = {'algorithm' : NDF_INP, \
@@ -148,6 +149,7 @@ 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")
@@ -165,7 +167,7 @@ pars = {'algorithm' : NVM_INP, \
'input' : sino_cut_new,\
'maskData' : mask,\
'SW_increment': 1,\
- 'number_of_iterations' :0
+ 'number_of_iterations' : 150
}
start_time = timeit.default_timer()
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 7c06c28..732b4cb 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -27,7 +27,6 @@ cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output,
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 dimX, int dimY, int dimZ);
-
#****************************************************************#
#********************** Total-variation ROF *********************#
#****************************************************************#
@@ -374,14 +373,14 @@ def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
#*********************Inpainting WITH****************************#
#***************Nonlocal Vertical Marching method****************#
#****************************************************************#
-def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations):
+def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterationsNumb):
if inputData.ndim == 2:
- return NVM_INP_2D(inputData, maskData, SW_increment, iterations)
+ 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,
+ np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
int SW_increment,
int iterationsNumb):
cdef long dims[2]
@@ -395,7 +394,8 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
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,dims[1], dims[0], 1)
+ NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0],
+ &maskData_upd[0,0],
+ SW_increment, iterationsNumb, dims[1], dims[0], 1)
return (outputData, maskData_upd)
--
cgit v1.2.3
From 37ae2bdb0a15298f312e9f6545a465d4d20c57f1 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Wed, 2 May 2018 15:47:19 +0100
Subject: bugs of NVM are fixed
---
Wrappers/Python/src/cpu_regularisers.pyx | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 732b4cb..c934f1d 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -26,7 +26,7 @@ cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxI
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 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 *********************#
#****************************************************************#
@@ -396,6 +396,6 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
# 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, dims[1], dims[0], 1)
+ SW_increment, iterationsNumb, 1, dims[1], dims[0], 1)
return (outputData, maskData_upd)
--
cgit v1.2.3
From 6e285c109938a43b5f8a84b7a48afaeb6b058c90 Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Wed, 2 May 2018 15:43:55 +0100
Subject: demo inpainters fixed for python
---
Wrappers/Python/demos/demo_cpu_inpainters.py | 2 --
1 file changed, 2 deletions(-)
(limited to 'Wrappers/Python')
diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
index 9197e91..3b4191b 100644
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
@@ -55,7 +55,6 @@ plt.imshow(mask)
plt.title('Mask')
plt.show()
#%%
-"""
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("___Inpainting using linear diffusion (2D)__")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -149,7 +148,6 @@ 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")
--
cgit v1.2.3