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-rw-r--r--Wrappers/Python/ccpi/filters/regularisers.py27
-rw-r--r--Wrappers/Python/demos/demo_cpu_regularisers.py69
-rw-r--r--Wrappers/Python/src/cpu_regularisers.pyx90
3 files changed, 183 insertions, 3 deletions
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index c7ae808..bf7e23c 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, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
try:
from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU
gpu_enabled = True
@@ -144,6 +144,31 @@ def DIFF4th(inputData, regularisation_parameter, edge_parameter, iterations,
raise ValueError ('GPU is not available')
raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
.format(device))
+
+def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter, device='cpu'):
+ if device == 'cpu':
+ return PATCHSEL_CPU(inputData,
+ searchwindow,
+ patchwindow,
+ neighbours,
+ edge_parameter)
+ elif device == 'gpu' and gpu_enabled:
+ return 1
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+
+def NLTV(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
+ return NLTV_CPU(inputData,
+ H_i,
+ H_j,
+ H_k,
+ Weights,
+ regularisation_parameter,
+ iterations)
+
def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
LipshitzConst, device='cpu'):
if device == 'cpu':
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index e99b271..31e4cad 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -13,6 +13,7 @@ import numpy as np
import os
import timeit
from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, DIFF4th
+from ccpi.filters.regularisers import PatchSelect, NLTV
from qualitymetrics import rmse
###############################################################################
def printParametersToString(pars):
@@ -350,7 +351,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
imgplot = plt.imshow(ndf_cpu, cmap="gray")
plt.title('{}'.format('CPU results'))
-
+#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("___Anisotropic Diffusion 4th Order (2D)____")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -395,7 +396,71 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
imgplot = plt.imshow(diff4_cpu, cmap="gray")
plt.title('{}'.format('CPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal patches pre-calculation____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# set parameters
+pars = {'algorithm' : PatchSelect, \
+ 'input' : u0,\
+ 'searchwindow': 7, \
+ 'patchwindow': 2,\
+ 'neighbours' : 15 ,\
+ 'edge_parameter':0.23}
+
+H_i, H_j, Weights = PatchSelect(pars['input'],
+ pars['searchwindow'],
+ pars['patchwindow'],
+ pars['neighbours'],
+ pars['edge_parameter'],'cpu')
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal Total Variation penalty____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+## plot
+fig = plt.figure()
+plt.suptitle('Performance of NLTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars2 = {'algorithm' : NLTV, \
+ 'input' : u0,\
+ 'H_i': H_i, \
+ 'H_j': H_j,\
+ 'H_k' : 0,\
+ 'Weights' : Weights,\
+ 'regularisation_parameter': 0.085,\
+ 'iterations': 2
+ }
+#%%
+start_time = timeit.default_timer()
+nltv_cpu = NLTV(pars2['input'],
+ pars2['H_i'],
+ pars2['H_j'],
+ pars2['H_k'],
+ pars2['Weights'],
+ pars2['regularisation_parameter'],
+ pars2['iterations'])
+
+rms = rmse(Im, nltv_cpu)
+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(nltv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("_____________FGP-dTV (2D)__________________")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -447,7 +512,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
plt.title('{}'.format('CPU results'))
-
+#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("__________Total nuclear Variation__________")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index bf9c861..e51e6d8 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -27,6 +27,8 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa
cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
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 PatchSelect_CPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h, int switchM);
+cdef extern float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambdaReg, int IterNumb);
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);
@@ -446,6 +448,94 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
Diffus4th_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])
return outputData
+
+#****************************************************************#
+#***************Patch-based weights calculation******************#
+#****************************************************************#
+def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter):
+ if inputData.ndim == 2:
+ return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+ elif inputData.ndim == 3:
+ return PatchSel_3D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ int searchwindow,
+ int patchwindow,
+ int neighbours,
+ float edge_parameter):
+ cdef long dims[3]
+ dims[0] = neighbours
+ dims[1] = inputData.shape[0]
+ dims[2] = inputData.shape[1]
+
+
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \
+ np.zeros([dims[0], dims[1],dims[2]], dtype='float32')
+
+ cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \
+ np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+ cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \
+ np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+ # Run patch-based weight selection function
+ PatchSelect_CPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], 0, searchwindow, patchwindow, neighbours, edge_parameter, 1)
+ return H_i, H_j, Weights
+
+def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ int searchwindow,
+ int patchwindow,
+ int neighbours,
+ float edge_parameter):
+ cdef long dims[4]
+ dims[0] = inputData.shape[0]
+ dims[1] = inputData.shape[1]
+ dims[2] = inputData.shape[2]
+ dims[3] = neighbours
+
+ cdef np.ndarray[np.float32_t, ndim=4, mode="c"] Weights = \
+ np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='float32')
+
+ cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_i = \
+ np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+
+ cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_j = \
+ np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+
+ cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_k = \
+ np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+
+ # Run patch-based weight selection function
+ PatchSelect_CPU_main(&inputData[0,0,0], &H_i[0,0,0,0], &H_j[0,0,0,0], &H_k[0,0,0,0], &Weights[0,0,0,0], dims[2], dims[1], dims[0], searchwindow, patchwindow, neighbours, edge_parameter, 1)
+ return H_i, H_j, H_k, Weights
+
+
+#****************************************************************#
+#***************Non-local Total Variation******************#
+#****************************************************************#
+def NLTV_CPU(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
+ if inputData.ndim == 2:
+ return NLTV_2D(inputData, H_i, H_j, Weights, regularisation_parameter, iterations)
+ elif inputData.ndim == 3:
+ return 1
+def NLTV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i,
+ np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j,
+ np.ndarray[np.float32_t, ndim=3, mode="c"] Weights,
+ float regularisation_parameter,
+ int iterations):
+
+ cdef long dims[2]
+ dims[0] = inputData.shape[0]
+ dims[1] = inputData.shape[1]
+ neighbours = H_i.shape[0]
+
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+ np.zeros([dims[0],dims[1]], dtype='float32')
+
+ # Run nonlocal TV regularisation
+ Nonlocal_TV_CPU_main(&inputData[0,0], &outputData[0,0], &H_i[0,0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[1], dims[0], 0, neighbours, regularisation_parameter, iterations)
+ return outputData
+
#*********************Inpainting WITH****************************#
#***************Nonlinear (Isotropic) Diffusion******************#
#****************************************************************#
generated by cgit v1.2.3 (git 2.25.1) at 2025-01-24 17:20:15 +0000