From 74ec16b72b077ea93c6e23330f8dfa4a7c3f7749 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Fri, 4 May 2018 11:57:39 +0100
Subject: TV energy ffunction cythonised
---
Wrappers/Python/src/cpu_regularisers.pyx | 44 ++++++++++++++++++++++++++++++++
1 file changed, 44 insertions(+)
(limited to 'Wrappers')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 549b046..bb55df5 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -28,6 +28,8 @@ 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 trigger, int dimX, int dimY, int dimZ);
+cdef extern float TV_energy2D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY);
+cdef extern float TV_energy3D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY, int dimZ);
#****************************************************************#
#********************** Total-variation ROF *********************#
#****************************************************************#
@@ -442,3 +444,45 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
SW_increment, iterationsNumb, 1, dims[1], dims[0], 1)
return (outputData, maskData_upd)
+
+
+#****************************************************************#
+#***************Calculation of TV-energy functional**************#
+#****************************************************************#
+def TV_ENERGY(inputData, regularisation_parameter, typeFunctional):
+ if inputData.ndim == 2:
+ return TV_ENERGY_2D(inputData, regularisation_parameter, typeFunctional)
+ elif inputData.ndim == 3:
+ return TV_ENERGY_3D(inputData, regularisation_parameter, typeFunctional)
+
+def TV_ENERGY_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ float regularisation_parameter,
+ int typeFunctional):
+ cdef long dims[2]
+ dims[0] = inputData.shape[0]
+ dims[1] = inputData.shape[1]
+
+ cdef np.ndarray[np.float32_t, ndim=1, mode="c"] outputData = \
+ np.zeros([1], dtype='float32')
+
+ # run function
+ TV_energy2D(&inputData[0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[1], dims[0])
+
+ return outputData
+
+def TV_ENERGY_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ float regularisation_parameter,
+ int typeFunctional):
+
+ 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=1, mode="c"] outputData = \
+ np.zeros([1], dtype='float32')
+
+ # Run function
+ TV_energy3D(&inputData[0,0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[2], dims[1], dims[0])
+
+ return outputData
--
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