将numpy 3D数组连接到C函数，用三重指针作为参数

Question

目前，我尝试过：

import ctypes as ct
import numpy as np

arr = np.array([
    [
        [1, 2, 3, 4],
        [5, 6, 7, 8],
        [9, 10, 11, 12]
    ],
    [
        [13, 14, 15, 16],
        [17, 18, 19, 20],
        [21, 22, 23, 24]
    ]
])

arr_c = np.ascontiguousarray(arr)

lib = ct.CDLL("path/to/share_object.so")
_effect_array = lib.effect_array
_effect_array.argtypes = [ct.POINTER(ct.POINTER(ct.POINTER(ct.c_int))), ct.c_int, ct.c_int, ct.c_int]
_effect_array.restype = None

rows, cols, depth = arr.shape
t_ptr = (ct.POINTER(ct.POINTER(ct.c_int)) * rows)()
for i in range(rows):
    t_ptr[i] = (ct.POINTER(ct.c_int) * cols)()
    for j in range(cols):
        t_ptr[i][j] = arr_c[i][j].ctypes.data_as(ct.POINTER(ct.c_int))
        
print("Original array =")
print(arr_c)
print()
_effect_array(t_ptr, rows, cols, depth)
print("Array after pass to function =")
print(arr_c)

这导致了一个输出：

Original array =
[[[ 1  2  3  4]
  [ 5  6  7  8]
  [ 9 10 11 12]]

 [[13 14 15 16]
  [17 18 19 20]
  [21 22 23 24]]]

Array after pass to function =
[[[ 2  4  3  4]
  [10 12  7  8]
  [18 20 11 12]]

 [[26 28 15 16]
  [34 36 19 20]
  [42 44 23 24]]]

我想发生的事情是：

Original array =
[[[ 1  2  3  4]
  [ 5  6  7  8]
  [ 9 10 11 12]]

 [[13 14 15 16]
  [17 18 19 20]
  [21 22 23 24]]]

Array after pass to function =
[[[ 2  4  6  8]
  [10 12  14  16]
  [18 20 22 24]]

 [[26 28 30 32]
  [34 36 38 40]
  [42 44 46 48]]]

我不确定为什么C函数无法连续访问前两个以外的元素。我的理解是，我当前的尝试可以访问数组的开始，并且应该能够像在此C示例中一样通过数组的其余部分迭代：

#include <stdio.h>
#include <stdlib.h>

void effect_array(int ***ptr, int rows, int cols, int depth)
{
    for(int i = 0; i < rows; i++)
    {
        for(int j = 0; j < cols; j++)
        {
            for(int k = 0; k < depth; k++)
            {
                ptr[i][j][k] *= 2;
            }
        }
    }
}

int main()
{
    int arr[2][2][2] = {
        {
            {1,2},
            {3,4}
        },
        {
            {5,6},
            {7,8}
        }
    };
    
    int arr2[5];
    int *p = arr2;
    
    int ***ptr = (int ***)malloc(2 * sizeof(int **));
    for(int i = 0; i < 2; i++)
    {
        ptr[i] = (int **)malloc(2 * sizeof(int *));
        for(int j = 0; j < 2; j++)
        {
            ptr[i][j] = &arr[i][j][0];
        }
    }
    
    printf("Print array before:\n");
    for(int i = 0; i < 2; i++)
    {
        for(int j = 0; j < 2; j++)
        {
            for(int k = 0; k < 2; k++)
            {
                printf("%d ", arr[i][j][k]);
            }
            printf("\n");
        }
        printf("\n");
    }
    
    effect_array(ptr, 2, 2, 2);
    
    printf("Print array after:\n");
    for(int i = 0; i < 2; i++)
    {
        for(int j = 0; j < 2; j++)
        {
            for(int k = 0; k < 2; k++)
            {
                printf("%d ", arr[i][j][k]);
            }
            printf("\n");
        }
        printf("\n");
    }
    
    for(int i = 0; i < 2; i++) free(ptr[i]);
    free(ptr);

    return 0;
}

这也基于关于审查问题的讨论。

任何人都非常感谢。

在您的原始Python代码中，

dtype是arr

。 将

np.int64

添加到您的NP.Array`声明，并且代码工作。

Answer 1

dtype=np.int32

和指针数学直接传递多维Numpy数组。

示例：

 -test.c

int*

 -test.py

#include <stdio.h>

__declspec(dllexport)
void affect_array(int *ptr, int rows, int cols, int depth) {
    for(int i = 0; i < rows; i++) {
        for(int j = 0; j < cols; j++) {
            for(int k = 0; k < depth; k++) {
                printf("%d ", ptr[i*rows*cols + j*cols + k]);
            }
            printf("\n");
        }
    }
    for(int i = 0; i < rows; i++) {
        for(int j = 0; j < cols; j++) {
            for(int k = 0; k < depth; k++) {
                ptr[i*cols*depth + j*depth + k] *= 2;
            }
        }
    }
}

输出：

import ctypes as ct
import numpy as np

arr = np.array([[[1, 2, 3, 4],
                 [5, 6, 7, 8],
                 [9, 10, 11, 12]],
                [[13, 14, 15, 16],
                 [17, 18, 19, 20],
                 [21, 22, 23, 24]]], dtype=np.int32)

lib = ct.CDLL('./test')
affect_array = lib.affect_array
affect_array.argtypes = ct.POINTER(ct.c_int), ct.c_int, ct.c_int, ct.c_int
affect_array.restype = None

rows, cols, depth = arr.shape
print('Original array =')
print(arr)
print()
affect_array(arr.ctypes.data_as(ct.POINTER(ct.c_int)), rows, cols, depth)
print('Array after pass to function =')
print(arr)