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-rw-r--r--Wrappers/Python/src/cpu_regularizers.pyx14
1 files changed, 5 insertions, 9 deletions
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]