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-rw-r--r--Core/CMakeLists.txt2
-rw-r--r--Core/regularizers_CPU/ROF_TV_core.c22
-rw-r--r--Core/regularizers_CPU/utils.c8
-rw-r--r--Wrappers/Python/demo/test_cpu_regularizers.py17
-rw-r--r--Wrappers/Python/src/cpu_regularizers.pyx14
5 files changed, 30 insertions, 33 deletions
diff --git a/Core/CMakeLists.txt b/Core/CMakeLists.txt
index 68b7111..4e85002 100644
--- a/Core/CMakeLists.txt
+++ b/Core/CMakeLists.txt
@@ -88,7 +88,7 @@ add_library(cilreg SHARED
${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/PatchBased_Regul_core.c
${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/SplitBregman_TV_core.c
${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/TGV_PD_core.c
- ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/ROF_TV_core.c
+ ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/ROF_TV_core.c
${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/utils.c
)
target_link_libraries(cilreg ${EXTRA_LIBRARIES} )
diff --git a/Core/regularizers_CPU/ROF_TV_core.c b/Core/regularizers_CPU/ROF_TV_core.c
index a59a3c6..a4f82a6 100644
--- a/Core/regularizers_CPU/ROF_TV_core.c
+++ b/Core/regularizers_CPU/ROF_TV_core.c
@@ -46,7 +46,7 @@ int sign(float x) {
*/
/* Running iterations of TV-ROF function */
-float TV_ROF_CPU_mainfloat TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
+float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
{
float *D1, *D2, *D3;
int i, DimTotal;
@@ -132,9 +132,9 @@ float D1_func(float *A, float *D1, int dimX, int dimY, int dimZ)
NOMy_0 = A[index] - A[(j)*dimX + i2]; /* y- */
denom1 = NOMx_1*NOMx_1;
- denom2 = 0.5*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+ denom2 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
denom2 = denom2*denom2;
- T1 = sqrt(denom1 + denom2 + EPS);
+ T1 = sqrtf(denom1 + denom2 + EPS);
D1[index] = NOMx_1/T1;
}}
}
@@ -170,11 +170,11 @@ float D2_func(float *A, float *D2, int dimX, int dimY, int dimZ)
denom1 = NOMy_1*NOMy_1;
- denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+ denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
denom2 = denom2*denom2;
- denom3 = 0.5*(sign(NOMz_1) + sign(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+ denom3 = 0.5f*(sign(NOMz_1) + sign(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
denom3 = denom3*denom3;
- T2 = sqrt(denom1 + denom2 + denom3 + EPS);
+ T2 = sqrtf(denom1 + denom2 + denom3 + EPS);
D2[index] = NOMy_1/T2;
}}}
}
@@ -196,9 +196,9 @@ float D2_func(float *A, float *D2, int dimX, int dimY, int dimZ)
/*NOMy_0 = A[(i)*dimY + j] - A[(i)*dimY + j2]; */ /* y- */
denom1 = NOMy_1*NOMy_1;
- denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+ denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
denom2 = denom2*denom2;
- T2 = sqrt(denom1 + denom2 + EPS);
+ T2 = sqrtf(denom1 + denom2 + EPS);
D2[index] = NOMy_1/T2;
}}
}
@@ -233,11 +233,11 @@ float D3_func(float *A, float *D3, int dimY, int dimX, int dimZ)
/*NOMz_0 = A[(dimX*dimY)*k + (i)*dimY + j] - A[(dimX*dimY)*k2 + (i)*dimY + j]; */ /* z- */
denom1 = NOMz_1*NOMz_1;
- denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+ denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
denom2 = denom2*denom2;
- denom3 = 0.5*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+ denom3 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
denom3 = denom3*denom3;
- T3 = sqrt(denom1 + denom2 + denom3 + EPS);
+ T3 = sqrtf(denom1 + denom2 + denom3 + EPS);
D3[index] = NOMz_1/T3;
}}}
return *D3;
diff --git a/Core/regularizers_CPU/utils.c b/Core/regularizers_CPU/utils.c
index 951fb91..cdf3d0e 100644
--- a/Core/regularizers_CPU/utils.c
+++ b/Core/regularizers_CPU/utils.c
@@ -45,10 +45,10 @@ float TV_energy2D(float *U, float *U0, float *E_val, float lambda, int dimX, int
j1 = j + 1; if (j == dimY-1) j1 = j;
/* Forward differences */
- NOMx_2 = pow(U[j1*dimX + i] - U[index],2); /* x+ */
- NOMy_2 = pow(U[j*dimX + i1] - U[index],2); /* y+ */
- E_Grad += sqrt(NOMx_2 + NOMy_2); /* gradient term energy */
- E_Data += 0.5f * lambda*(pow((U[index]-U0[index]),2)); /* fidelity term energy */
+ NOMx_2 = powf(U[j1*dimX + i] - U[index],2); /* x+ */
+ NOMy_2 = powf(U[j*dimX + i1] - U[index],2); /* y+ */
+ E_Grad += sqrtf(NOMx_2 + NOMy_2); /* gradient term energy */
+ E_Data += 0.5f * lambda*(powf((U[index]-U0[index]),2)); /* fidelity term energy */
}
}
E_val[0] = E_Grad + E_Data;
diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py
index 53b8538..f1eb3c3 100644
--- a/Wrappers/Python/demo/test_cpu_regularizers.py
+++ b/Wrappers/Python/demo/test_cpu_regularizers.py
@@ -131,9 +131,9 @@ imgplot = plt.imshow(splitbregman,\
start_time = timeit.default_timer()
pars = {'algorithm' : TV_FGP_CPU , \
'input' : u0,
- 'regularization_parameter':0.05, \
- 'number_of_iterations' :200 ,\
- 'tolerance_constant':1e-5,\
+ 'regularization_parameter':0.07, \
+ 'number_of_iterations' :300 ,\
+ 'tolerance_constant':0.00001,\
'methodTV': 0 ,\
'nonneg': 0 ,\
'printingOut': 0
@@ -156,7 +156,7 @@ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
-a=fig.add_subplot(2,4,3)
+a=fig.add_subplot(2,4,4)
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
@@ -168,8 +168,9 @@ imgplot = plt.imshow(fgp, \
a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
-###################### LLT_model #########################################
+###################### LLT_model #########################################
+"""
start_time = timeit.default_timer()
pars = {'algorithm': LLT_model , \
@@ -204,7 +205,7 @@ 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:
@@ -292,8 +293,8 @@ pars = {'algorithm': TV_ROF_CPU , \
'number_of_iterations': 300
}
rof = TV_ROF_CPU(pars['input'],
- pars['number_of_iterations'],
- pars['regularization_parameter'],
+ pars['regularization_parameter'],
+ pars['number_of_iterations'],
pars['marching_step']
)
#tgv = out
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index 2654831..d62ca59 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -21,14 +21,11 @@ cimport numpy as np
cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
-def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb
- marching_step_parameter):
+def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb, marching_step_parameter):
if inputData.ndim == 2:
- return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb
- marching_step_parameter)
+ return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
elif inputData.ndim == 3:
- return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb
- marching_step_parameter)
+ return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularization_parameter,
@@ -47,10 +44,9 @@ 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 iterations,
+ int iterationsNumb,
float regularization_parameter,
- float marching_step_parameter
- ):
+ float marching_step_parameter):
cdef long dims[3]
dims[0] = inputData.shape[0]
dims[1] = inputData.shape[1]
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-06 17:41:05 +0000