编写 c => python 的接口文件
// vectory_py.c
extern "C" {
vector* new_vector(){
return new vector;
}
void delete_vector(vector* v){
cout << "destructor called in C++ for " << v << endl;
delete v;
}
int vector_size(vector* v){
return v->size();
}
point_t vector_get(vector* v, int i){
return v->at(i);
}
void vector_push_back(vector* v, point_t i){
v->push_back(i);
}
}
编译： gcc -fPIC -shared -lpython3.6m -o vector_py.so vectory_py.c
编写 ctypes 类型文件
from ctypes import *
class c_point_t(Structure):
_fields_ = [("x", c_int), ("y", c_int)]
class Vector(object):
lib = cdll.LoadLibrary('./vector_py_lib.so') # class level loading lib
lib.new_vector.restype = c_void_p
lib.new_vector.argtypes = []
lib.delete_vector.restype = None
lib.delete_vector.argtypes = [c_void_p]
lib.vector_size.restype = c_int
lib.vector_size.argtypes = [c_void_p]
lib.vector_get.restype = c_point_t
lib.vector_get.argtypes = [c_void_p, c_int]
lib.vector_push_back.restype = None
lib.vector_push_back.argtypes = [c_void_p, c_point_t]
lib.foo.restype = None
lib.foo.argtypes = []
def __init__(self):
self.vector = Vector.lib.new_vector() # pointer to new vector
def __del__(self): # when reference count hits 0 in Python,
Vector.lib.delete_vector(self.vector) # call C++ vector destructor
def __len__(self):
return Vector.lib.vector_size(self.vector)
def __getitem__(self, i): # access elements in vector at index
if 0 <= i < len(self):
return Vector.lib.vector_get(self.vector, c_int(i))
raise IndexError('Vector index out of range')
def __repr__(self):
return '[{}]'.format(', '.join(str(self[i]) for i in range(len(self))))
def push(self, i): # push calls vector's push_back
Vector.lib.vector_push_back(self.vector, i)
def foo(self): # foo in Python calls foo in C++
Vector.lib.foo(self.vector)
然后才是调用
from vector import *
a = Vector()
b = c_point_t(10, 20)
a.push(b)
a.foo()
for i in range(len(a)) :
print(a[i].x)
print(a[i].y)
为Python写扩展
完成上述的操作后，我头很大，很难想象当项目稍微修改后，我们要跟随变化的代码量有多大！于是换了一种思路，为Python写扩展。
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安装Python开发包
yum install -y python36-devel
修改数据交互文件
#include
PyObject* foo()
{
PyObject* result = PyList_New(0);
int i = 0, j = 0;
for (j = 0; j < 2; j++) {
PyObject* sub = PyList_New(0);
for (i = 0; i < 100; ++i)
{
PyList_Append(sub, Py_BuildValue("{s:i, s:i}", "x", i, "y", 100 - i));
}
PyList_Append(result, sub);
}
return result;
}
调用
from ctypes import *
lib = cdll.LoadLibrary('./extlist.so') # class level loading lib
lib.foo.restype = py_object
b = lib.foo()
for i in range(len(b)) :
for j in range(len(b[i])) :
d = b[i][j]
print(d['x'])
很显然，第二种方式中，我已经封装了很复杂的结构了，如果用 c++ 来表示的话，将是：
vector<vector >
遇到的问题
Python C 混编时 Segment
这个问题困扰了我有一段时间，开始一直在纠结是代码哪错了，后来恍然大悟，Python
和 C 的堆栈是完全不同的，而当我在交互大量数据的时候，Python GC 可能会把 C
的内存当作未使用，直接给释放了(尤其是上述第二种方案)，这就是问题所在。(Python GC 中使用的代龄后续专门开文章来说明，欢迎关注公众号
cn_isnap)
这里的解决方案其实有很多，内存能撑过Python前两代的检查就可了，或者是纯C管理。在这里我推荐一种粗暴的解决方案：
对于任何调用Python对象或Python C API的C代码，确保你首先已经正确地获取和释放了GIL。 这可以用 PyGILState_Ensure() 和 PyGILState_Release() 来做到，如下所示：
...
/* Make sure we own the GIL */
PyGILState_STATE state = PyGILState_Ensure();
/* Use functions in the interpreter */
...
/* Restore previous GIL state and return */
PyGILState_Release(state);
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