From deac578f1919cba530d4dd0a92ea4975eb8ed43b Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Fri, 16 Feb 2018 14:09:52 +0000
Subject: ROF_TV modified and GPU code added, all cython related files updated,
but not testes
---
Wrappers/Python/src/cpu_regularizers.pyx | 57 ++++++-----------------------
Wrappers/Python/src/gpu_regularizers.pyx | 62 ++++++++++++++++++++++++++++++++
2 files changed, 73 insertions(+), 46 deletions(-)
(limited to 'Wrappers/Python/src')
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index b22e603..e306ab3 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -18,12 +18,7 @@ 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);
+cdef extern float TV_ROF(float *A, float *B, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float lambda);
def ROF_TV(inputData, iterations, regularization_parameter, marching_step_parameter):
@@ -39,30 +34,17 @@ def ROF_TV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
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)
+
+ #/* Run ROF iterations for 2D data */
+ TV_ROF(&A[0,0], &B[0,0], dims[0], dims[1], 1, iterations, marching_step_parameter, regularization_parameter)
+
return B
@@ -78,26 +60,9 @@ def ROF_TV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
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])
+
+ #/* Run ROF iterations for 3D data */
+ TV_ROF(&A[0,0, 0], &B[0,0, 0], dims[0], dims[1], dims[2], iterations, marching_step_parameter, regularization_parameter)
+
return B
-
\ No newline at end of file
+
diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularizers.pyx
index 84fc30a..649015e 100644
--- a/Wrappers/Python/src/gpu_regularizers.pyx
+++ b/Wrappers/Python/src/gpu_regularizers.pyx
@@ -25,6 +25,7 @@ cdef extern void NLM_GPU_kernel(float *A, float* B, float *Eucl_Vec,
int N, int M, int Z, int dimension,
int SearchW, int SimilW,
int SearchW_real, float denh2, float lambdaf);
+cdef extern void TV_ROF_GPU(float* A, float* B, int N, int M, int Z, int iter, float tau, float lambdaf);
cdef extern float pad_crop(float *A, float *Ap,
int OldSizeX, int OldSizeY, int OldSizeZ,
int NewSizeX, int NewSizeY, int NewSizeZ,
@@ -65,6 +66,22 @@ def NML(inputData,
SimilW,
h,
lambdaf)
+
+def ROF_TV(inputData,
+ iterations,
+ time_marching_parameter,
+ regularization_parameter):
+ if inputData.ndim == 2:
+ return ROFTV2D(inputData,
+ iterations,
+ time_marching_parameter,
+ regularization_parameter)
+ elif inputData.ndim == 3:
+ return ROFTV3D(inputData,
+ iterations,
+ time_marching_parameter,
+ regularization_parameter)
+
def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float edge_preserv_parameter,
@@ -311,4 +328,49 @@ def NML3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
padXY,
switchpad_crop)
+ return B
+
+def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ int iterations,
+ float time_marching_parameter,
+ float regularization_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')
+
+ # Running CUDA code here
+ TV_ROF_GPU(
+ &inputData[0,0], &B[0,0],
+ dims[0], dims[1], 0,
+ iterations ,
+ time_marching_parameter,
+ regularization_parameter);
+
+ return B
+
+def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ int iterations,
+ float time_marching_parameter,
+ float regularization_parameter):
+
+ 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 = \
+ np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+
+ # Running CUDA code here
+ TV_ROF_GPU(
+ &inputData[0,0,0], &B[0,0,0],
+ dims[0], dims[1], dims[2],
+ iterations ,
+ time_marching_parameter,
+ regularization_parameter);
+
return B
--
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