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openvslam_bindings.cpp
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openvslam_bindings.cpp
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#include "openvslam/system.h"
#include "openvslam/config.h"
#include "openvslam/publish/frame_publisher.h"
#include "openvslam/publish/map_publisher.h"
#include "openvslam/type.h"
#include "pybind11/stl.h"
#include "pybind11/pybind11.h"
#include "pybind11/eigen.h"
#include "opencv2/core/core.hpp"
#include "yaml-cpp/yaml.h"
#include "nlohmann/json_fwd.hpp"
#include <Python.h>
namespace py = pybind11;
class NDArrayConverter {
public:
static bool init_numpy(); // must call this first, or the other routines don't work!
static bool toMat(PyObject* o, cv::Mat &m);
static PyObject* toNDArray(const cv::Mat& mat);
};
namespace pybind11{namespace detail{
template <> struct type_caster<cv::Mat>{
public:
PYBIND11_TYPE_CASTER(cv::Mat, _("numpy.ndarray"));
bool load(handle src, bool){
return NDArrayConverter::toMat(src.ptr(), value);
}
static handle cast(const cv::Mat &m, return_value_policy, handle defval){
return handle(NDArrayConverter::toNDArray(m));
}
};
}} // namespace pybind11::detail
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include <numpy/ndarrayobject.h>
#if PY_VERSION_HEX >= 0x03000000
#define PyInt_Check PyLong_Check
#define PyInt_AsLong PyLong_AsLong
#endif
struct Tmp {
const char * name;
Tmp(const char * name):name(name){}
} info("return value");
bool NDArrayConverter::init_numpy(){
// this has to be in this file, since PyArray_API is defined as static
import_array1(false);
return true;
}
/*
* The following conversion functions are taken/adapted from OpenCV's cv2.cpp file
* inside modules/python/src2 folder (OpenCV 3.1.0)
*/
static PyObject* opencv_error = 0;
static int failmsg(const char *fmt, ...){
char str[1000];
va_list ap;
va_start(ap, fmt);
vsnprintf(str, sizeof(str), fmt, ap);
va_end(ap);
PyErr_SetString(PyExc_TypeError, str);
return 0;
}
class PyAllowThreads{
public:
PyAllowThreads() : _state(PyEval_SaveThread()) {}
~PyAllowThreads(){
PyEval_RestoreThread(_state);
}
private:
PyThreadState* _state;
};
class PyEnsureGIL{
public:
PyEnsureGIL() : _state(PyGILState_Ensure()) {}
~PyEnsureGIL(){
PyGILState_Release(_state);
}
private:
PyGILState_STATE _state;
};
#define ERRWRAP2(expr) \
try {PyAllowThreads allowThreads; expr;} \
catch (const cv::Exception &e){PyErr_SetString(opencv_error, e.what()); return 0;}
using namespace cv;
class NumpyAllocator : public MatAllocator{
public:
NumpyAllocator(){stdAllocator = Mat::getStdAllocator();}
~NumpyAllocator(){}
UMatData* allocate(PyObject* o, int dims, const int* sizes, int type, size_t* step) const{
UMatData* u = new UMatData(this);
u->data = u->origdata = (uchar*)PyArray_DATA((PyArrayObject*) o);
npy_intp* _strides = PyArray_STRIDES((PyArrayObject*) o);
for( int i = 0; i < dims - 1; i++ )
step[i] = (size_t)_strides[i];
step[dims-1] = CV_ELEM_SIZE(type);
u->size = sizes[0]*step[0];
u->userdata = o;
return u;
}
#if CV_MAJOR_VERSION < 4
UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const
#else
UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, AccessFlag flags, UMatUsageFlags usageFlags) const
#endif
{
if( data != 0 ){
CV_Error(Error::StsAssert, "The data should normally be NULL!");
// probably this is safe to do in such extreme case
return stdAllocator->allocate(dims0, sizes, type, data, step, flags, usageFlags);
}
PyEnsureGIL gil;
int depth = CV_MAT_DEPTH(type);
int cn = CV_MAT_CN(type);
const int f = (int)(sizeof(size_t)/8);
int typenum = depth == CV_8U ? NPY_UBYTE : depth == CV_8S ? NPY_BYTE :
depth == CV_16U ? NPY_USHORT : depth == CV_16S ? NPY_SHORT :
depth == CV_32S ? NPY_INT : depth == CV_32F ? NPY_FLOAT :
depth == CV_64F ? NPY_DOUBLE : f*NPY_ULONGLONG + (f^1)*NPY_UINT;
int i, dims = dims0;
cv::AutoBuffer<npy_intp> _sizes(dims + 1);
for( i = 0; i < dims; i++ )
_sizes[i] = sizes[i];
if( cn > 1 )
_sizes[dims++] = cn;
PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);
if(!o)
CV_Error_(Error::StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));
return allocate(o, dims0, sizes, type, step);
}
#if CV_MAJOR_VERSION < 4
bool allocate(UMatData* u, int accessFlags, UMatUsageFlags usageFlags) const
#else
bool allocate(UMatData* u, AccessFlag accessFlags, UMatUsageFlags usageFlags) const
#endif
{
return stdAllocator->allocate(u, accessFlags, usageFlags);
}
void deallocate(UMatData* u) const{
if(!u)
return;
PyEnsureGIL gil;
CV_Assert(u->urefcount >= 0);
CV_Assert(u->refcount >= 0);
if(u->refcount == 0){
PyObject* o = (PyObject*)u->userdata;
Py_XDECREF(o);
delete u;
}
}
const MatAllocator* stdAllocator;
};
NumpyAllocator g_numpyAllocator;
bool NDArrayConverter::toMat(PyObject *o, Mat &m){
bool allowND = true;
if(!o || o == Py_None){
if(!m.data)
m.allocator = &g_numpyAllocator;
return true;
}
if(PyInt_Check(o)){
double v[] = {static_cast<double>(PyInt_AsLong((PyObject*)o)), 0., 0., 0.};
m = Mat(4, 1, CV_64F, v).clone();
return true;
}
if(PyFloat_Check(o)){
double v[] = {PyFloat_AsDouble((PyObject*)o), 0., 0., 0.};
m = Mat(4, 1, CV_64F, v).clone();
return true;
}
if(PyTuple_Check(o)){
int i, sz = (int)PyTuple_Size((PyObject*)o);
m = Mat(sz, 1, CV_64F);
for(i = 0; i < sz; i++){
PyObject* oi = PyTuple_GET_ITEM(o, i);
if( PyInt_Check(oi) )
m.at<double>(i) = (double)PyInt_AsLong(oi);
else if( PyFloat_Check(oi) )
m.at<double>(i) = (double)PyFloat_AsDouble(oi);
else{
failmsg("%s is not a numerical tuple", info.name);
m.release();
return false;
}
}
return true;
}
if(!PyArray_Check(o)){
failmsg("%s is not a numpy array, neither a scalar", info.name);
return false;
}
PyArrayObject* oarr = (PyArrayObject*) o;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U :
typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if(type < 0){
if( typenum == NPY_INT64 || typenum == NPY_UINT64 || typenum == NPY_LONG ){
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
} else {
failmsg("%s data type = %d is not supported", info.name, typenum);
return false;
}
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
if(ndims >= CV_MAX_DIM){
failmsg("%s dimensionality (=%d) is too high", info.name, ndims);
return false;
}
int size[CV_MAX_DIM+1];
size_t step[CV_MAX_DIM+1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for(int i = ndims-1; i >= 0 && !needcopy; i--)
if(
(i == ndims-1 && _sizes[i] > 1 && (size_t)_strides[i] != elemsize) ||
(i < ndims-1 && _sizes[i] > 1 && _strides[i] < _strides[i+1])
)
needcopy = true;
if(ismultichannel && _strides[1] != (npy_intp)elemsize*_sizes[2])
needcopy = true;
if(needcopy){
if(needcast){
o = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) o;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
o = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
// Normalize strides in case NPY_RELAXED_STRIDES is set
size_t default_step = elemsize;
for(int i = ndims - 1; i >= 0; --i){
size[i] = (int)_sizes[i];
if ( size[i] > 1 ){
step[i] = (size_t)_strides[i];
default_step = step[i] * size[i];
} else {
step[i] = default_step;
default_step *= size[i];
}
}
// handle degenerate case
if(ndims == 0){
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if(ismultichannel){
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if(ndims > 2 && !allowND){
failmsg("%s has more than 2 dimensions", info.name);
return false;
}
m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
m.addref();
if(!needcopy){
Py_INCREF(o);
}
m.allocator = &g_numpyAllocator;
return true;
}
PyObject* NDArrayConverter::toNDArray(const cv::Mat& m){
if( !m.data ){
Py_RETURN_NONE;
}
Mat temp, *p = (Mat*)&m;
if(!p->u || p->allocator != &g_numpyAllocator){
temp.allocator = &g_numpyAllocator;
ERRWRAP2(m.copyTo(temp));
p = &temp;
}
PyObject* o = (PyObject*)p->u->userdata;
Py_INCREF(o);
return o;
}
/*
* Python bindings, module openvslam
* Minimum requirement for openvslam operation: some functions in classes system and config.
* Classes, functions and arguments keep their original openvslam names.
*/
using namespace openvslam;
PYBIND11_MODULE(openvslam, m){
NDArrayConverter::init_numpy();
py::class_<config, std::shared_ptr<config>>(m, "config")
.def(py::init<const std::string&>(), py::arg("config_file_path"))
.def(py::init<const YAML::Node&, const std::string&>(), py::arg("yaml_node"), py::arg("config_file_path") = "");
py::class_<openvslam::system>(m, "system")
// Init & finish
.def(py::init<const std::shared_ptr<config>&, const std::string&>(), py::arg("cfg"), py::arg("vocab_file_path"))
.def("startup", &system::startup, py::arg("need_initialize") = true)
.def("shutdown", &system::shutdown)
// Feed image
.def("feed_monocular_frame", &system::feed_monocular_frame, py::arg("img"), py::arg("timestamp"), py::arg("mask") = cv::Mat{})
.def("feed_stereo_frame", &system::feed_stereo_frame, py::arg("left_img"), py::arg("right_img"), py::arg("timestamp"), py::arg("mask") = cv::Mat{})
.def("feed_RGBD_frame", &system::feed_RGBD_frame, py::arg("rgb_img"), py::arg("depthmap"), py::arg("timestamp"), py::arg("mask") = cv::Mat{})
// Map save & load
.def("load_map_database", &system::load_map_database, py::arg("path"))
.def("save_map_database", &system::save_map_database, py::arg("path"))
.def("save_frame_trajectory", &system::save_frame_trajectory, py::arg("path"), py::arg("format"))
.def("save_keyframe_trajectory", &system::save_keyframe_trajectory, py::arg("path"), py::arg("format"))
// System controls
.def("mapping_module_is_enabled", &system::mapping_module_is_enabled)
.def("enable_mapping_module", &system::enable_mapping_module)
.def("disable_mapping_module", &system::disable_mapping_module)
.def("loop_detector_is_enabled", &system::loop_detector_is_enabled)
.def("enable_loop_detector", &system::enable_loop_detector)
.def("disable_loop_detector", &system::disable_loop_detector)
.def("loop_BA_is_running", &system::loop_BA_is_running)
.def("abort_loop_BA", &system::abort_loop_BA)
.def("tracker_is_paused", &system::tracker_is_paused)
.def("pause_tracker", &system::pause_tracker)
.def("resume_tracker", &system::resume_tracker)
.def("reset_is_requested", &system::reset_is_requested)
.def("request_reset", &system::request_reset)
.def("terminate_is_requested", &system::terminate_is_requested)
.def("request_terminate", &system::request_terminate)
;
}