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-rw-r--r--Core/regularizers_CPU/FGP_TV_core.c8
-rw-r--r--Core/regularizers_CPU/FGP_TV_core.h2
-rw-r--r--Core/regularizers_CPU/ROF_TV_core.c4
-rw-r--r--Core/regularizers_CPU/ROF_TV_core.h6
-rw-r--r--Wrappers/Python/src/cpu_regularizers.pyx59
5 files changed, 37 insertions, 42 deletions
diff --git a/Core/regularizers_CPU/FGP_TV_core.c b/Core/regularizers_CPU/FGP_TV_core.c
index 5365631..9c0fcfc 100644
--- a/Core/regularizers_CPU/FGP_TV_core.c
+++ b/Core/regularizers_CPU/FGP_TV_core.c
@@ -37,7 +37,7 @@ limitations under the License.
* [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
*/
-float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ)
+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)
{
int ll, j, DimTotal;
float re, re1;
@@ -47,7 +47,7 @@ float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, fl
if (dimZ <= 1) {
/*2D case */
- float *Output_prev=NULL, *P1=NULL, *P2=NULL, *P1_prev=NULL, *P2_prev=NULL, *R1=NULL, *R2=NULL;
+ float *Output_prev=NULL, *P1=NULL, *P2=NULL, *P1_prev=NULL, *P2_prev=NULL, *R1=NULL, *R2=NULL;
DimTotal = dimX*dimY;
Output_prev = (float *) malloc( DimTotal * sizeof(float) );
@@ -59,7 +59,7 @@ float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, fl
R2 = (float *) malloc( DimTotal * sizeof(float) );
/* begin iterations */
- for(ll=0; ll<iter; ll++) {
+ for(ll=0; ll<iterationsNumb; ll++) {
/* computing the gradient of the objective function */
Obj_func2D(Input, Output, R1, R2, lambdaPar, dimX, dimY);
@@ -114,7 +114,7 @@ float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, fl
R3 = (float *) malloc( DimTotal * sizeof(float) );
/* begin iterations */
- for(ll=0; ll<iter; ll++) {
+ for(ll=0; ll<iterationsNumb; ll++) {
/* computing the gradient of the objective function */
Obj_func3D(Input, Output, R1, R2, R3, lambdaPar, dimX, dimY, dimZ);
diff --git a/Core/regularizers_CPU/FGP_TV_core.h b/Core/regularizers_CPU/FGP_TV_core.h
index 360251d..43a8519 100644
--- a/Core/regularizers_CPU/FGP_TV_core.h
+++ b/Core/regularizers_CPU/FGP_TV_core.h
@@ -47,7 +47,7 @@ limitations under the License.
#ifdef __cplusplus
extern "C" {
#endif
-CCPI_EXPORT float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+CCPI_EXPORT 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);
CCPI_EXPORT float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dimX, int dimY);
CCPI_EXPORT float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda, int dimX, int dimY);
diff --git a/Core/regularizers_CPU/ROF_TV_core.c b/Core/regularizers_CPU/ROF_TV_core.c
index a058f54..a59a3c6 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_main(float *Input, float *Output, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float lambda)
+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 *D1, *D2, *D3;
int i, DimTotal;
@@ -68,7 +68,7 @@ float TV_ROF_CPU_main(float *Input, float *Output, int dimX, int dimY, int dimZ,
D1_func(Output, D1, dimX, dimY, dimZ);
D2_func(Output, D2, dimX, dimY, dimZ);
if (dimZ > 1) D3_func(Output, D3, dimX, dimY, dimZ);
- TV_kernel(D1, D2, D3, Output, Input, lambda, tau, dimX, dimY, dimZ);
+ TV_kernel(D1, D2, D3, Output, Input, lambdaPar, tau, dimX, dimY, dimZ);
}
free(D1);free(D2); free(D3);
return *Output;
diff --git a/Core/regularizers_CPU/ROF_TV_core.h b/Core/regularizers_CPU/ROF_TV_core.h
index 63c0419..14daf58 100644
--- a/Core/regularizers_CPU/ROF_TV_core.h
+++ b/Core/regularizers_CPU/ROF_TV_core.h
@@ -31,8 +31,8 @@ limitations under the License.
* Input Parameters:
* 1. Noisy image/volume [REQUIRED]
* 2. lambda - regularization parameter [REQUIRED]
- * 3. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
- * 4. Number of iterations, for explicit scheme >= 150 is recommended [REQUIRED]
+ * 3. Number of iterations, for explicit scheme >= 150 is recommended [REQUIRED]
+ * 4. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
*
* Output:
* [1] Regularized image/volume
@@ -47,7 +47,7 @@ limitations under the License.
extern "C" {
#endif
CCPI_EXPORT float TV_kernel(float *D1, float *D2, float *D3, float *B, float *A, float lambda, float tau, int dimY, int dimX, int dimZ);
-CCPI_EXPORT float TV_ROF_CPU_main(float *Input, float *Output, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float lambda);
+CCPI_EXPORT float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
CCPI_EXPORT float D1_func(float *A, float *D1, int dimY, int dimX, int dimZ);
CCPI_EXPORT float D2_func(float *A, float *D2, int dimY, int dimX, int dimZ);
CCPI_EXPORT float D3_func(float *A, float *D3, int dimY, int dimX, int dimZ);
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index 448da31..2654831 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -11,73 +11,68 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-Author: Edoardo Pasca
+Author: Edoardo Pasca, Daniil Kazantsev
"""
import cython
import numpy as np
cimport numpy as np
-cdef extern float TV_ROF_CPU_main(float *Input, float *Output, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float flambda);
-cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+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);
-# Can we use the same name here in "def" as the C function?
-def TV_ROF_CPU(inputData, iterations, regularization_parameter,
+def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb
marching_step_parameter):
if inputData.ndim == 2:
- return TV_ROF_2D(inputData, iterations, regularization_parameter,
+ return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb
marching_step_parameter)
elif inputData.ndim == 3:
- return TV_ROF_3D(inputData, iterations, regularization_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,
- int iterations,
float regularization_parameter,
- float marching_step_parameter
- ):
-
+ int iterationsNumb,
+ float marching_step_parameter):
cdef long dims[2]
dims[0] = inputData.shape[0]
dims[1] = inputData.shape[1]
- cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
np.zeros([dims[0],dims[1]], dtype='float32')
- #/* Run ROF iterations for 2D data */
- TV_ROF_CPU_main(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations,
- marching_step_parameter, regularization_parameter)
+ # Run ROF iterations for 2D data
+ TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1)
- return B
+ return outputData
def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
int iterations,
float regularization_parameter,
float marching_step_parameter
):
- cdef long dims[2]
+ 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"] B = \
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
- #/* Run ROF iterations for 3D data */
- TV_ROF_CPU_main(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2], iterations,
- marching_step_parameter, regularization_parameter)
+ # Run ROF iterations for 3D data
+ TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularization_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], dims[2])
- return B
+ return outputData
-def TV_FGP_CPU(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM):
+def TV_FGP_CPU(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM):
if inputData.ndim == 2:
- return TV_FGP_2D(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM)
+ return TV_FGP_2D(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
elif inputData.ndim == 3:
- return TV_FGP_3D(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM)
+ return TV_FGP_3D(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularization_parameter,
- int iterations,
+ int iterationsNumb,
float tolerance_param,
int methodTV,
int nonneg,
@@ -92,7 +87,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
#/* Run ROF iterations for 2D data */
TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter,
- iterations,
+ iterationsNumb,
tolerance_param,
methodTV,
nonneg,
@@ -103,22 +98,22 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularization_parameter,
- int iterations,
+ int iterationsNumb,
float tolerance_param,
int methodTV,
int nonneg,
int printM):
- cdef long dims[2]
+ 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')
+ np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
#/* Run ROF iterations for 3D data */
- TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0, 0], regularization_parameter,
- iterations,
+ TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularization_parameter,
+ iterationsNumb,
tolerance_param,
methodTV,
nonneg,
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-06 21:29:17 +0000