From e6ab844cd82080dab3a5f257fc15f4c1a20b498c Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Fri, 26 Jan 2018 16:28:38 +0000
Subject: cython wrapper builds without errors
TODO: test
---
Wrappers/Python/src/fista_module_gpu.pyx | 322 ++++++++++++++++---------------
1 file changed, 167 insertions(+), 155 deletions(-)
(limited to 'Wrappers/Python/src')
diff --git a/Wrappers/Python/src/fista_module_gpu.pyx b/Wrappers/Python/src/fista_module_gpu.pyx
index f18181b..7658e36 100644
--- a/Wrappers/Python/src/fista_module_gpu.pyx
+++ b/Wrappers/Python/src/fista_module_gpu.pyx
@@ -15,7 +15,6 @@ Author: Edoardo Pasca
"""
import cython
-
import numpy as np
cimport numpy as np
@@ -62,9 +61,12 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
#A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
#B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
- A_L = np.zeros((N,M), dtype=np.float)
- B_L = np.zeros((N,M), dtype=np.float)
- B = np.zeros((dims[0],dims[1]), dtype=np.float)
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] A_L = \
+ np.zeros([N,M], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B_L = \
+ np.zeros([N,M], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \
+ np.zeros([dims[0],dims[1]], dtype='float32')
#A = inputData
# copy A to the bigger A_L with boundaries
@@ -85,7 +87,7 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
edge_preserving_parameter,
iterations ,
tau,
- regularization_parameter)
+ regularization_parameter);
# copy the processed B_L to a smaller B
for i in range(N):
for j in range(M):
@@ -117,9 +119,12 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
#A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
#B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
- A_L = np.zeros((N,M,Z), dtype=np.float)
- B_L = np.zeros((N,M,Z), dtype=np.float)
- B = np.zeros((dims[0],dims[1],dims[2]), dtype=np.float)
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] A_L = \
+ np.zeros([N,M,Z], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] B_L = \
+ np.zeros([N,M,Z], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] B = \
+ np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
#A = inputData
# copy A to the bigger A_L with boundaries
@@ -142,7 +147,7 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
edge_preserving_parameter,
iterations ,
tau,
- regularization_parameter)
+ regularization_parameter);
# copy the processed B_L to a smaller B
for i in range(N):
for j in range(M):
@@ -155,149 +160,156 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
return B
-
-#def NML(inputData,
-# regularization_parameter,
-# iterations,
-# edge_preserving_parameter):
-# if inputData.ndim == 2:
-# return NML2D(inputData,
-# regularization_parameter,
-# iterations,
-# edge_preserving_parameter)
-# elif inputData.ndim == 3:
-# return NML3D(inputData,
-# regularization_parameter,
-# iterations,
-# edge_preserving_parameter)
-#
-# #SearchW_real = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
-# #SimilW = (int) mxGetScalar(prhs[2]); /* the similarity window ratio */
-# #h = (float) mxGetScalar(prhs[3]); /* parameter for the PB filtering function */
-# #lambda = (float) mxGetScalar(prhs[4]);
-#
-#def NML2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
-# SearchW_real,
-# SimilW,
-# h,
-# lambdaf):
-# N = inputData.shape[0]
-# M = inputData.shape[1]
-# Z = 0
-# numdims = inputData.ndim
-#
-# if h < 0:
-# raise ValueError('Parameter for the PB filtering function must be > 0')
-#
-# SearchW = SearchW_real + 2*SimilW;
-#
-# SearchW_full = 2*SearchW + 1; #/* the full searching window size */
-# SimilW_full = 2*SimilW + 1; #/* the full similarity window size */
-# h2 = h*h;
-#
-# padXY = SearchW + 2*SimilW; #/* padding sizes */
-# newsizeX = N + 2*(padXY); #/* the X size of the padded array */
-# newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
-# #newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
-#
-# #output
-# B = np.zeros((N,M), dtype=np.float )
-# #/*allocating memory for the padded arrays */
-#
-# Ap = np.zeros((newsizeX, newsizeY), dtype=np.float)
-# Bp = np.zeros((newsizeX, newsizeY), dtype=np.float)
-# Eucl_Vec = np.zeros((SimilW_full*SimilW_full), dtype=np.float)
-#
-# #/*Gaussian kernel */
-# cdef int count, i_n, j_n;
-# cdef float val;
-# count = 0
-# for i_n in range (-SimilW, SimilW +1):
-# for j_n in range(-SimilW, SimilW +1):
-# val = (float)(i_n*i_n + j_n*j_n)/(2*SimilW*SimilW)
-# Eucl_Vec[count] = np.exp(-val)
-# count = count + 1
-#
-# #/*Perform padding of image A to the size of [newsizeX * newsizeY] */
-# switchpad_crop = 0; # /*padding*/
-# pad_crop(&inputData[0,0], &Ap[0,0], M, N, 0, newsizeY, newsizeX, 0, padXY,
-# switchpad_crop);
-#
-# #/* Do PB regularization with the padded array */
-# NLM_GPU_kernel(&Ap[0,0], &Bp[0,0], &Eucl_Vec[0,0], newsizeY, newsizeX, 0,
-# numdims, SearchW, SimilW, SearchW_real,
-# h2, lambdaf);
-#
-# switchpad_crop = 1; #/*cropping*/
-# pad_crop(&Bp[0,0], &B[0,0], M, N, 0, newsizeX, newsizeY, 0, padXY,
-# switchpad_crop)
-#
-# return B
-#
-#def NML3D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
-# SearchW_real,
-# SimilW,
-# h,
-# lambdaf):
-# N = inputData.shape[0]
-# M = inputData.shape[1]
-# Z = inputData.shape[2]
-# numdims = inputData.ndim
-#
-# if h < 0:
-# raise ValueError('Parameter for the PB filtering function must be > 0')
-#
-# SearchW = SearchW_real + 2*SimilW;
-#
-# SearchW_full = 2*SearchW + 1; #/* the full searching window size */
-# SimilW_full = 2*SimilW + 1; #/* the full similarity window size */
-# h2 = h*h;
-#
-# padXY = SearchW + 2*SimilW; #/* padding sizes */
-# newsizeX = N + 2*(padXY); #/* the X size of the padded array */
-# newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
-# newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
-#
-# #output
-# B = np.zeros((N,M,Z), dtype=np.float )
-# #/*allocating memory for the padded arrays */
-#
-# Ap = np.zeros((newsizeX, newsizeY, newsizeZ), dtype=np.float)
-# Bp = np.zeros((newsizeX, newsizeY, newsizeZ), dtype=np.float)
-# Eucl_Vec = np.zeros((SimilW_full*SimilW_full*SimilW_full), dtype=np.float)
-#
-# #/*Gaussian kernel */
-# cdef int count, i_n, j_n, k_n;
-# cdef float val;
-# count = 0
-# for i_n in range (-SimilW, SimilW +1):
-# for j_n in range(-SimilW, SimilW +1):
-# for k_n in range(-SimilW, SimilW+1):
-# val = (i_n*i_n + j_n*j_n + k_n*k_n)/(2*SimilW*SimilW*SimilW)
-# Eucl_Vec[count] = np.exp(-val)
-# count = count + 1
-#
-# #/*Perform padding of image A to the size of [newsizeX * newsizeY] */
-# switchpad_crop = 0; # /*padding*/
-# pad_crop(&inputData[0,0,0], &Ap[0,0,0],
-# M, N, Z,
-# newsizeY, newsizeX, newsizeZ,
-# padXY,
-# switchpad_crop);
-#
-# #/* Do PB regularization with the padded array */
-# NLM_GPU_kernel(&Ap[0,0,0], &Bp[0,0,0], &Eucl_Vec[0,0,0],
-# newsizeY, newsizeX, newsizeZ,
-# numdims, SearchW, SimilW, SearchW_real,
-# h2, lambdaf);
-#
-# switchpad_crop = 1; #/*cropping*/
-# pad_crop(&Bp[0,0,0], &B[0,0,0],
-# M, N, Z,
-# newsizeX, newsizeY, newsizeZ,
-# padXY,
-# switchpad_crop)
-#
-# return B
-#
-#
\ No newline at end of file
+def NML(inputData,
+ regularization_parameter,
+ iterations,
+ edge_preserving_parameter):
+ if inputData.ndim == 2:
+ return NML2D(inputData,
+ regularization_parameter,
+ iterations,
+ edge_preserving_parameter)
+ elif inputData.ndim == 3:
+ return NML3D(inputData,
+ regularization_parameter,
+ iterations,
+ edge_preserving_parameter)
+
+ #SearchW_real = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
+ #SimilW = (int) mxGetScalar(prhs[2]); /* the similarity window ratio */
+ #h = (float) mxGetScalar(prhs[3]); /* parameter for the PB filtering function */
+ #lambda = (float) mxGetScalar(prhs[4]);
+
+def NML2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ SearchW_real,
+ SimilW,
+ h,
+ lambdaf):
+ N = inputData.shape[0]
+ M = inputData.shape[1]
+ Z = 0
+ numdims = inputData.ndim
+
+ if h < 0:
+ raise ValueError('Parameter for the PB filtering function must be > 0')
+
+ SearchW = SearchW_real + 2*SimilW;
+
+ SearchW_full = 2*SearchW + 1; #/* the full searching window size */
+ SimilW_full = 2*SimilW + 1; #/* the full similarity window size */
+ h2 = h*h;
+
+ padXY = SearchW + 2*SimilW; #/* padding sizes */
+ newsizeX = N + 2*(padXY); #/* the X size of the padded array */
+ newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
+ #newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
+
+ #output
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \
+ np.zeros([N,M], dtype='float32')
+ #/*allocating memory for the padded arrays */
+
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] Ap = \
+ np.zeros([newsizeX, newsizeY], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=2, mode="c"] Bp = \
+ np.zeros([newsizeX, newsizeY], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=1, mode="c"] Eucl_Vec = \
+ np.zeros([SimilW_full*SimilW_full], dtype='float32')
+
+ #/*Gaussian kernel */
+ cdef int count, i_n, j_n;
+ cdef float val;
+ count = 0
+ for i_n in range (-SimilW, SimilW +1):
+ for j_n in range(-SimilW, SimilW +1):
+ val = (float)(i_n*i_n + j_n*j_n)/(2*SimilW*SimilW)
+ Eucl_Vec[count] = np.exp(-val)
+ count = count + 1
+
+ #/*Perform padding of image A to the size of [newsizeX * newsizeY] */
+ switchpad_crop = 0; # /*padding*/
+ pad_crop(&inputData[0,0], &Ap[0,0], M, N, 0, newsizeY, newsizeX, 0, padXY,
+ switchpad_crop);
+
+ #/* Do PB regularization with the padded array */
+ NLM_GPU_kernel(&Ap[0,0], &Bp[0,0], &Eucl_Vec[0], newsizeY, newsizeX, 0,
+ numdims, SearchW, SimilW, SearchW_real,
+ h2, lambdaf);
+
+ switchpad_crop = 1; #/*cropping*/
+ pad_crop(&Bp[0,0], &B[0,0], M, N, 0, newsizeX, newsizeY, 0, padXY,
+ switchpad_crop)
+
+ return B
+
+def NML3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ SearchW_real,
+ SimilW,
+ h,
+ lambdaf):
+ N = inputData.shape[0]
+ M = inputData.shape[1]
+ Z = inputData.shape[2]
+ numdims = inputData.ndim
+
+ if h < 0:
+ raise ValueError('Parameter for the PB filtering function must be > 0')
+
+ SearchW = SearchW_real + 2*SimilW;
+
+ SearchW_full = 2*SearchW + 1; #/* the full searching window size */
+ SimilW_full = 2*SimilW + 1; #/* the full similarity window size */
+ h2 = h*h;
+
+ padXY = SearchW + 2*SimilW; #/* padding sizes */
+ newsizeX = N + 2*(padXY); #/* the X size of the padded array */
+ newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
+ newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
+
+ #output
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] B = \
+ np.zeros([N,M,Z], dtype='float32')
+ #/*allocating memory for the padded arrays */
+
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Ap = \
+ np.zeros([newsizeX, newsizeY, newsizeZ], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Bp = \
+ np.zeros([newsizeX, newsizeY, newsizeZ], dtype='float32')
+ cdef np.ndarray[np.float32_t, ndim=1, mode="c"] Eucl_Vec = \
+ np.zeros([SimilW_full*SimilW_full*SimilW_full],
+ dtype='float32')
+
+
+ #/*Gaussian kernel */
+ cdef int count, i_n, j_n, k_n;
+ cdef float val;
+ count = 0
+ for i_n in range (-SimilW, SimilW +1):
+ for j_n in range(-SimilW, SimilW +1):
+ for k_n in range(-SimilW, SimilW+1):
+ val = (i_n*i_n + j_n*j_n + k_n*k_n)/(2*SimilW*SimilW*SimilW)
+ Eucl_Vec[count] = np.exp(-val)
+ count = count + 1
+
+ #/*Perform padding of image A to the size of [newsizeX * newsizeY] */
+ switchpad_crop = 0; # /*padding*/
+ pad_crop(&inputData[0,0,0], &Ap[0,0,0],
+ M, N, Z,
+ newsizeY, newsizeX, newsizeZ,
+ padXY,
+ switchpad_crop);
+
+ #/* Do PB regularization with the padded array */
+ NLM_GPU_kernel(&Ap[0,0,0], &Bp[0,0,0], &Eucl_Vec[0],
+ newsizeY, newsizeX, newsizeZ,
+ numdims, SearchW, SimilW, SearchW_real,
+ h2, lambdaf);
+
+ switchpad_crop = 1; #/*cropping*/
+ pad_crop(&Bp[0,0,0], &B[0,0,0],
+ M, N, Z,
+ newsizeX, newsizeY, newsizeZ,
+ padXY,
+ switchpad_crop)
+
+ return B
--
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