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authorEdoardo Pasca <edo.paskino@gmail.com>2018-02-13 16:37:46 +0000
committerEdoardo Pasca <edo.paskino@gmail.com>2018-02-20 16:18:46 +0000
commitfe095e13aed4831c372a542ba83c409f6c71af87 (patch)
tree4cfb09ebc9c790a7302b5fc6850dc5b01908f9c9 /Wrappers/Python
parent09efb291c7edc57cff7300d16adc627fce13bcc7 (diff)
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added ROF_TV regularizer to library and wrapper
Diffstat (limited to 'Wrappers/Python')
-rw-r--r--Wrappers/Python/src/cpu_regularizers.pyx84
1 files changed, 84 insertions, 0 deletions
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index a8f8c8f..b22e603 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -17,3 +17,87 @@ Author: Edoardo Pasca
import cython
import numpy as np
cimport numpy as np
+
+cdef extern float TV_main(float *D1, float *D2, float *D3, float *B, float *A,
+ float lambda, float tau, int dimY, int dimX, int dimZ);
+cdef extern float D1_func(float *A, float *D1, int dimY, int dimX, int dimZ);
+cdef extern float D2_func(float *A, float *D2, int dimY, int dimX, int dimZ);
+cdef extern float D3_func(float *A, float *D3, int dimY, int dimX, int dimZ);
+cdef extern void copyIm (float *A, float *U, int dimX, int dimY, int dimZ);
+
+
+def ROF_TV(inputData, iterations, regularization_parameter, marching_step_parameter):
+ if inputData.ndim == 2:
+ return ROF_TV_2D(inputData, iterations, regularization_parameter,
+ marching_step_parameter)
+ elif inputData.ndim == 3:
+ return ROF_TV_3D(inputData, iterations, regularization_parameter,
+ marching_step_parameter)
+
+def ROF_TV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ int iterations,
+ float regularization_parameter
+ 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 = \
+ np.zeros([dims[0],dims[1]], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] D1 = \
+ np.zeros([dims[0],dims[1]], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] D2 = \
+ np.zeros([dims[0],dims[1]], dtype='float32')
+
+ copyIm(&inputData[0,0], &B[0,0], dims[0], dims[1], 1);
+ #/* start TV iterations */
+ cdef int i = 0;
+ for i in range(iterations):
+
+ #/* calculate differences */
+ D1_func(&B[0,0], &D1[0,0], dims[0], dims[1], 1);
+ D2_func(&B[0,0], &D2[0,0], dims[0], dims[1], 1);
+
+ #/* calculate divergence and image update*/
+ TV_main(&D1[0,0], &D2[0,0], &D2[0,0], &B[0,0], &A[0,0],
+ regularization_parameter, marching_step_parameter,
+ dims[0], dims[1], 1)
+ return B
+
+
+def ROF_TV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ int iterations,
+ float regularization_parameter
+ float marching_step_parameter
+ ):
+ cdef long dims[2]
+ 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 = \
+ np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] D1 = \
+ np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] D2 = \
+ np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] D3 = \
+ np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+
+ copyIm(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2]);
+ #/* start TV iterations */
+ cdef int i = 0;
+ for i in range(iterations):
+
+ #/* calculate differences */
+ D1_func(&B[0,0,0], &D1[0,0,0], dims[0], dims[1], dims[2]);
+ D2_func(&B[0,0,0], &D2[0,0,0], dims[0], dims[1], dims[2]);
+ D3_func(&B[0,0,0], &D3[0,0,0], dims[0], dims[1], dims[2]);
+
+ #/* calculate divergence and image update*/
+ TV_main(&D1[0,0,0], &D2[0,0,0], &D3[0,0,0], &B[0,0,0], &A[0,0,0],
+ regularization_parameter, marching_step_parameter,
+ dims[0], dims[1], dims[2])
+ return B
+ \ No newline at end of file