From c9ee9ecc84881595b04f19280c93bcd587171270 Mon Sep 17 00:00:00 2001
From: dkazanc <dkazanc@hotmail.com>
Date: Tue, 4 Dec 2018 16:13:38 +0000
Subject: GPU version, this completes implementation of nltv #68
---
Wrappers/Python/src/cpu_regularisers.pyx | 6 +++---
Wrappers/Python/src/gpu_regularisers.pyx | 32 ++++++++++++++++++++++++++++++++
2 files changed, 35 insertions(+), 3 deletions(-)
(limited to 'Wrappers/Python/src')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index e51e6d8..4aa3251 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -456,7 +456,7 @@ def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_paramete
if inputData.ndim == 2:
return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
elif inputData.ndim == 3:
- return PatchSel_3D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+ return 1
def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
int searchwindow,
int patchwindow,
@@ -480,7 +480,7 @@ def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
# Run patch-based weight selection function
PatchSelect_CPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], 0, searchwindow, patchwindow, neighbours, edge_parameter, 1)
return H_i, H_j, Weights
-
+"""
def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
int searchwindow,
int patchwindow,
@@ -507,7 +507,7 @@ def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
# Run patch-based weight selection function
PatchSelect_CPU_main(&inputData[0,0,0], &H_i[0,0,0,0], &H_j[0,0,0,0], &H_k[0,0,0,0], &Weights[0,0,0,0], dims[2], dims[1], dims[0], searchwindow, patchwindow, neighbours, edge_parameter, 1)
return H_i, H_j, H_k, Weights
-
+"""
#****************************************************************#
#***************Non-local Total Variation******************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 82d3e01..302727e 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -26,6 +26,7 @@ cdef extern void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF,
cdef extern void NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
+cdef extern void PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h);
# Total-variation Rudin-Osher-Fatemi (ROF)
def TV_ROF_GPU(inputData,
@@ -542,3 +543,34 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
Diffus4th_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])
return outputData
+#****************************************************************#
+#************Patch-based weights pre-selection******************#
+#****************************************************************#
+def PATCHSEL_GPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter):
+ if inputData.ndim == 2:
+ return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+ elif inputData.ndim == 3:
+ return 1
+def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ int searchwindow,
+ int patchwindow,
+ int neighbours,
+ float edge_parameter):
+ cdef long dims[3]
+ dims[0] = neighbours
+ dims[1] = inputData.shape[0]
+ dims[2] = inputData.shape[1]
+
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \
+ np.zeros([dims[0], dims[1],dims[2]], dtype='float32')
+
+ cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \
+ np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+ cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \
+ np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+ # Run patch-based weight selection function
+ PatchSelect_GPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], searchwindow, patchwindow, neighbours, edge_parameter)
+
+ return H_i, H_j, Weights
--
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