从Python访问OpenCV CUDA函数(无PyCUDA) [英] Accessing OpenCV CUDA Functions from Python (No PyCUDA)
本文介绍了从Python访问OpenCV CUDA函数(无PyCUDA)的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在编写一个使用OpenCV的Python绑定进行标记检测和其他图像处理的Python应用程序.我想使用OpenCV的CUDA模块来加速应用程序的某些部分，并在它们的.hpp文件中注意到，它们似乎正在使用针对Python和Java的OpenCV导出宏.但是，即使我正在构建OpenCV WITH_CUDA=ON，我似乎也无法访问那些CUDA函数.
是否有必要使用诸如PyCUDA之类的包装器来访问GPU功能(例如cudaarithm中的阈值)?或者，如果我在Python代码中调用 cv2.threshold()(而不是基于CPU的常规实现)，是否已经使用了这些CUDA加速功能?
CV_EXPORTS double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
我在cv2中看到的子模块如下:
- 错误
- 阿鲁科
- 细节
- 鱼眼
- flann
- instr
- ml
- ocl
- ogl
- videostab
cv2.cuda，cv2.gpu和cv2.cudaarithm都以AttributeError返回.
我正在运行的用于构建OpenCV的CMake指令如下:
cmake -DOPENCV_EXTRA_MODULES_PATH=/usr/local/lib/opencv_contrib/modules/ \
-D WITH_CUDA=ON -D CUDA_FAST_MATH=1 \
-D ENABLE_PRECOMPILED_HEADERS=OFF \
-D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF \
-D BUILD_opencv_java=OFF \
-DBUILD_opencv_bgsegm=OFF -DBUILD_opencv_bioinspired=OFF -DBUILD_opencv_ccalib=OFF -DBUILD_opencv_cnn_3dobj=OFF -DBUILD_opencv_contrib_world=OFF -DBUILD_opencv_cvv=OFF -DBUILD_opencv_datasets=OFF -DBUILD_openc
v_dnn=OFF -DBUILD_opencv_dnns_easily_fooled=OFF -DBUILD_opencv_dpm=OFF -DBUILD_opencv_face=OFF -DBUILD_opencv_fuzzy=OFF -DBUILD_opencv_hdf=OFF -DBUILD_opencv_line_descriptor=OFF -DBUILD_opencv_matlab=OFF -DBUILD_o
pencv_optflow=OFF -DBUILD_opencv_plot=OFF -DBUILD_opencv_README.md=OFF -DBUILD_opencv_reg=OFF -DBUILD_opencv_rgbd=OFF -DBUILD_opencv_saliency=OFF -DBUILD_opencv_sfm=OFF -DBUILD_opencv_stereo=OFF -DBUILD_opencv_str
uctured_light=OFF -DBUILD_opencv_surface_matching=OFF -DBUILD_opencv_text=OFF -DBUILD_opencv_tracking=OFF -DBUILD_opencv_viz=OFF -DBUILD_opencv_xfeatures2d=OFF -DBUILD_opencv_ximgproc=OFF -DBUILD_opencv_xobjdetect
=OFF -DBUILD_opencv_xphoto=OFF ..
解决方案
因此，正如在@NAmorim的答案和注释线程中所确认的，没有可访问的Python绑定到OpenCV的各种CUDA模块. /p>
我可以通过使用 Cython 来访问所需的CUDA函数并实现必要的逻辑以将我的Python对象(主要是NumPy数组)转换为OpenCV C/C ++来克服此限制.对象并返回.
工作代码
我首先编写了一个Cython定义文件GpuWrapper.pxd.该文件的目的是引用外部C/C ++类和方法，例如我感兴趣的CUDA方法.
from libcpp cimport bool
from cpython.ref cimport PyObject
# References PyObject to OpenCV object conversion code borrowed from OpenCV's own conversion file, cv2.cpp
cdef extern from 'pyopencv_converter.cpp':
cdef PyObject* pyopencv_from(const Mat& m)
cdef bool pyopencv_to(PyObject* o, Mat& m)
cdef extern from 'opencv2/imgproc.hpp' namespace 'cv':
cdef enum InterpolationFlags:
INTER_NEAREST = 0
cdef enum ColorConversionCodes:
COLOR_BGR2GRAY
cdef extern from 'opencv2/core/core.hpp':
cdef int CV_8UC1
cdef int CV_32FC1
cdef extern from 'opencv2/core/core.hpp' namespace 'cv':
cdef cppclass Size_[T]:
Size_() except +
Size_(T width, T height) except +
T width
T height
ctypedef Size_[int] Size2i
ctypedef Size2i Size
cdef cppclass Scalar[T]:
Scalar() except +
Scalar(T v0) except +
cdef extern from 'opencv2/core/core.hpp' namespace 'cv':
cdef cppclass Mat:
Mat() except +
void create(int, int, int) except +
void* data
int rows
int cols
cdef extern from 'opencv2/core/cuda.hpp' namespace 'cv::cuda':
cdef cppclass GpuMat:
GpuMat() except +
void upload(Mat arr) except +
void download(Mat dst) const
cdef cppclass Stream:
Stream() except +
cdef extern from 'opencv2/cudawarping.hpp' namespace 'cv::cuda':
cdef void warpPerspective(GpuMat src, GpuMat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& stream)
# Function using default values
cdef void warpPerspective(GpuMat src, GpuMat dst, Mat M, Size dsize, int flags)
我们还需要具有将Python对象转换为OpenCV对象的功能.我从OpenCV的modules/python/src2/cv2.cpp复制了前几百行.您可以在附录的下面找到该代码.
我们终于可以编写我们的Cython包装器方法来调用OpenCV的CUDA函数了！这是Cython实施文件GpuWrapper.pyx的一部分.
import numpy as np # Import Python functions, attributes, submodules of numpy
cimport numpy as np # Import numpy C/C++ API
def cudaWarpPerspectiveWrapper(np.ndarray[np.uint8_t, ndim=2] _src,
np.ndarray[np.float32_t, ndim=2] _M,
_size_tuple,
int _flags=INTER_NEAREST):
# Create GPU/device InputArray for src
cdef Mat src_mat
cdef GpuMat src_gpu
pyopencv_to(<PyObject*> _src, src_mat)
src_gpu.upload(src_mat)
# Create CPU/host InputArray for M
cdef Mat M_mat = Mat()
pyopencv_to(<PyObject*> _M, M_mat)
# Create Size object from size tuple
# Note that size/shape in Python is handled in row-major-order -- therefore, width is [1] and height is [0]
cdef Size size = Size(<int> _size_tuple[1], <int> _size_tuple[0])
# Create empty GPU/device OutputArray for dst
cdef GpuMat dst_gpu = GpuMat()
warpPerspective(src_gpu, dst_gpu, M_mat, size, INTER_NEAREST)
# Get result of dst
cdef Mat dst_host
dst_gpu.download(dst_host)
cdef np.ndarray out = <np.ndarray> pyopencv_from(dst_host)
return out
运行安装程序脚本以进行cythonize和编译此代码(请参阅附录)后，我们可以将GpuWrapper导入为Python模块并运行cudaWarpPerspectiveWrapper.这使我可以通过CUDA运行代码，并且只有0.34722222222222854854％的不匹配-真是令人兴奋！
引用(最多只能发布2个)
附录
pyopencv_converter.cpp
#include <Python.h>
#include "numpy/ndarrayobject.h"
#include "opencv2/core/core.hpp"
static PyObject* opencv_error = 0;
// === FAIL MESSAGE ====================================================================================================
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;
}
struct ArgInfo
{
const char * name;
bool outputarg;
// more fields may be added if necessary
ArgInfo(const char * name_, bool outputarg_)
: name(name_)
, outputarg(outputarg_) {}
// to match with older pyopencv_to function signature
operator const char *() const { return name; }
};
// === THREADING =======================================================================================================
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;
};
// === ERROR HANDLING ==================================================================================================
#define ERRWRAP2(expr) \
try \
{ \
PyAllowThreads allowThreads; \
expr; \
} \
catch (const cv::Exception &e) \
{ \
PyErr_SetString(opencv_error, e.what()); \
return 0; \
}
// === USING NAMESPACE CV ==============================================================================================
using namespace cv;
// === NUMPY ALLOCATOR =================================================================================================
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;
}
UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const
{
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);
}
bool allocate(UMatData* u, int accessFlags, UMatUsageFlags usageFlags) const
{
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;
};
// === ALLOCATOR INITIALIZATION ========================================================================================
NumpyAllocator g_numpyAllocator;
// === CONVERTOR FUNCTIONS =============================================================================================
template<typename T> static
bool pyopencv_to(PyObject* obj, T& p, const char* name = "<unknown>");
template<typename T> static
PyObject* pyopencv_from(const T& src);
enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };
// special case, when the convertor needs full ArgInfo structure
static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo info)
{
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-- )
{
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
// the _sizes[i] > 1 is needed to avoid spurious copies when NPY_RELAXED_STRIDES is set
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 (info.outputarg)
{
failmsg("Layout of the output array %s is incompatible with cv::Mat (step[ndims-1] != elemsize or step[1] != elemsize*nchannels)", info.name);
return false;
}
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;
}
template<>
bool pyopencv_to(PyObject* o, Mat& m, const char* name)
{
return pyopencv_to(o, m, ArgInfo(name, 0));
}
template<>
PyObject* pyopencv_from(const 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;
}
setupGpuWrapper.py
import subprocess
import os
import numpy as np
from distutils.core import setup, Extension
from Cython.Build import cythonize
from Cython.Distutils import build_ext
"""
Run setup with the following command:
```
python setupGpuWrapper.py build_ext --inplace
```
"""
# Determine current directory of this setup file to find our module
CUR_DIR = os.path.dirname(__file__)
# Use pkg-config to determine library locations and include locations
opencv_libs_str = subprocess.check_output("pkg-config --libs opencv".split()).decode()
opencv_incs_str = subprocess.check_output("pkg-config --cflags opencv".split()).decode()
# Parse into usable format for Extension call
opencv_libs = [str(lib) for lib in opencv_libs_str.strip().split()]
opencv_incs = [str(inc) for inc in opencv_incs_str.strip().split()]
extensions = [
Extension('GpuWrapper',
sources=[os.path.join(CUR_DIR, 'GpuWrapper.pyx')],
language='c++',
include_dirs=[np.get_include()] + opencv_incs,
extra_link_args=opencv_libs)
]
setup(
cmdclass={'build_ext': build_ext},
name="GpuWrapper",
ext_modules=cythonize(extensions)
)
I am writing a Python application that uses OpenCV's Python bindings to do marker detection and other image processing. I would like to use OpenCV's CUDA modules to CUDA-accelerate certain parts of my application, and noticed in their .hpp files that they seem to be using the OpenCV export macros for Python and Java. However, I do not seem to be able to access those CUDA functions, even though I am building OpenCV WITH_CUDA=ON.
Is it necessary to use a wrapper such as PyCUDA in order to access the GPU functions, such as threshold in cudaarithm? Or, are these CUDA-accelerated functions already being used if I call cv2.threshold() in my Python code (rather than the regular, CPU-based implementation)?
CV_EXPORTS double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
The submodules I see for cv2 are the following:
- Error
- aruco
- detail
- fisheye
- flann
- instr
- ml
- ocl
- ogl
- videostab
cv2.cuda, cv2.gpu, and cv2.cudaarithm all return with an AttributeError.
The CMake instruction I am running to build OpenCV is as follows:
cmake -DOPENCV_EXTRA_MODULES_PATH=/usr/local/lib/opencv_contrib/modules/ \
-D WITH_CUDA=ON -D CUDA_FAST_MATH=1 \
-D ENABLE_PRECOMPILED_HEADERS=OFF \
-D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF \
-D BUILD_opencv_java=OFF \
-DBUILD_opencv_bgsegm=OFF -DBUILD_opencv_bioinspired=OFF -DBUILD_opencv_ccalib=OFF -DBUILD_opencv_cnn_3dobj=OFF -DBUILD_opencv_contrib_world=OFF -DBUILD_opencv_cvv=OFF -DBUILD_opencv_datasets=OFF -DBUILD_openc
v_dnn=OFF -DBUILD_opencv_dnns_easily_fooled=OFF -DBUILD_opencv_dpm=OFF -DBUILD_opencv_face=OFF -DBUILD_opencv_fuzzy=OFF -DBUILD_opencv_hdf=OFF -DBUILD_opencv_line_descriptor=OFF -DBUILD_opencv_matlab=OFF -DBUILD_o
pencv_optflow=OFF -DBUILD_opencv_plot=OFF -DBUILD_opencv_README.md=OFF -DBUILD_opencv_reg=OFF -DBUILD_opencv_rgbd=OFF -DBUILD_opencv_saliency=OFF -DBUILD_opencv_sfm=OFF -DBUILD_opencv_stereo=OFF -DBUILD_opencv_str
uctured_light=OFF -DBUILD_opencv_surface_matching=OFF -DBUILD_opencv_text=OFF -DBUILD_opencv_tracking=OFF -DBUILD_opencv_viz=OFF -DBUILD_opencv_xfeatures2d=OFF -DBUILD_opencv_ximgproc=OFF -DBUILD_opencv_xobjdetect
=OFF -DBUILD_opencv_xphoto=OFF ..
解决方案
So as confirmed in the answer and comment thread with @NAmorim, there are no accessible Python bindings to OpenCV's various CUDA modules.
I was able to get around this restriction by using Cython to gain access to the CUDA functions I needed and implementing the necessary logic to convert my Python objects (mainly NumPy arrays) to OpenCV C/C++ objects and back.
Working Code
I first wrote a Cython definition file, GpuWrapper.pxd. The purpose of this file is to reference external C/C++ classes and methods, such as the CUDA methods I am interested in.
from libcpp cimport bool
from cpython.ref cimport PyObject
# References PyObject to OpenCV object conversion code borrowed from OpenCV's own conversion file, cv2.cpp
cdef extern from 'pyopencv_converter.cpp':
cdef PyObject* pyopencv_from(const Mat& m)
cdef bool pyopencv_to(PyObject* o, Mat& m)
cdef extern from 'opencv2/imgproc.hpp' namespace 'cv':
cdef enum InterpolationFlags:
INTER_NEAREST = 0
cdef enum ColorConversionCodes:
COLOR_BGR2GRAY
cdef extern from 'opencv2/core/core.hpp':
cdef int CV_8UC1
cdef int CV_32FC1
cdef extern from 'opencv2/core/core.hpp' namespace 'cv':
cdef cppclass Size_[T]:
Size_() except +
Size_(T width, T height) except +
T width
T height
ctypedef Size_[int] Size2i
ctypedef Size2i Size
cdef cppclass Scalar[T]:
Scalar() except +
Scalar(T v0) except +
cdef extern from 'opencv2/core/core.hpp' namespace 'cv':
cdef cppclass Mat:
Mat() except +
void create(int, int, int) except +
void* data
int rows
int cols
cdef extern from 'opencv2/core/cuda.hpp' namespace 'cv::cuda':
cdef cppclass GpuMat:
GpuMat() except +
void upload(Mat arr) except +
void download(Mat dst) const
cdef cppclass Stream:
Stream() except +
cdef extern from 'opencv2/cudawarping.hpp' namespace 'cv::cuda':
cdef void warpPerspective(GpuMat src, GpuMat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& stream)
# Function using default values
cdef void warpPerspective(GpuMat src, GpuMat dst, Mat M, Size dsize, int flags)
We also need the ability to convert Python objects to OpenCV objects. I copied the first couple hundred lines from OpenCV's modules/python/src2/cv2.cpp. You can find that code below in the appendix.
We can finally write our Cython wrapper methods to call OpenCV's CUDA functions! This is part of the Cython implementation file, GpuWrapper.pyx.
import numpy as np # Import Python functions, attributes, submodules of numpy
cimport numpy as np # Import numpy C/C++ API
def cudaWarpPerspectiveWrapper(np.ndarray[np.uint8_t, ndim=2] _src,
np.ndarray[np.float32_t, ndim=2] _M,
_size_tuple,
int _flags=INTER_NEAREST):
# Create GPU/device InputArray for src
cdef Mat src_mat
cdef GpuMat src_gpu
pyopencv_to(<PyObject*> _src, src_mat)
src_gpu.upload(src_mat)
# Create CPU/host InputArray for M
cdef Mat M_mat = Mat()
pyopencv_to(<PyObject*> _M, M_mat)
# Create Size object from size tuple
# Note that size/shape in Python is handled in row-major-order -- therefore, width is [1] and height is [0]
cdef Size size = Size(<int> _size_tuple[1], <int> _size_tuple[0])
# Create empty GPU/device OutputArray for dst
cdef GpuMat dst_gpu = GpuMat()
warpPerspective(src_gpu, dst_gpu, M_mat, size, INTER_NEAREST)
# Get result of dst
cdef Mat dst_host
dst_gpu.download(dst_host)
cdef np.ndarray out = <np.ndarray> pyopencv_from(dst_host)
return out
After running a setup script to cythonize and compile this code (see apendix), we can import GpuWrapper as a Python module and run cudaWarpPerspectiveWrapper. This allowed me to run the code through CUDA with only a mismatch of 0.34722222222222854% -- quite exciting!
References (can only post max of 2)
- What is the easiest way to convert ndarray into cv::Mat?
- Writing Python bindings for C++ code that use OpenCV
Appendix
pyopencv_converter.cpp
#include <Python.h>
#include "numpy/ndarrayobject.h"
#include "opencv2/core/core.hpp"
static PyObject* opencv_error = 0;
// === FAIL MESSAGE ====================================================================================================
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;
}
struct ArgInfo
{
const char * name;
bool outputarg;
// more fields may be added if necessary
ArgInfo(const char * name_, bool outputarg_)
: name(name_)
, outputarg(outputarg_) {}
// to match with older pyopencv_to function signature
operator const char *() const { return name; }
};
// === THREADING =======================================================================================================
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;
};
// === ERROR HANDLING ==================================================================================================
#define ERRWRAP2(expr) \
try \
{ \
PyAllowThreads allowThreads; \
expr; \
} \
catch (const cv::Exception &e) \
{ \
PyErr_SetString(opencv_error, e.what()); \
return 0; \
}
// === USING NAMESPACE CV ==============================================================================================
using namespace cv;
// === NUMPY ALLOCATOR =================================================================================================
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;
}
UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const
{
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);
}
bool allocate(UMatData* u, int accessFlags, UMatUsageFlags usageFlags) const
{
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;
};
// === ALLOCATOR INITIALIZATION ========================================================================================
NumpyAllocator g_numpyAllocator;
// === CONVERTOR FUNCTIONS =============================================================================================
template<typename T> static
bool pyopencv_to(PyObject* obj, T& p, const char* name = "<unknown>");
template<typename T> static
PyObject* pyopencv_from(const T& src);
enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };
// special case, when the convertor needs full ArgInfo structure
static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo info)
{
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-- )
{
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
// the _sizes[i] > 1 is needed to avoid spurious copies when NPY_RELAXED_STRIDES is set
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 (info.outputarg)
{
failmsg("Layout of the output array %s is incompatible with cv::Mat (step[ndims-1] != elemsize or step[1] != elemsize*nchannels)", info.name);
return false;
}
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;
}
template<>
bool pyopencv_to(PyObject* o, Mat& m, const char* name)
{
return pyopencv_to(o, m, ArgInfo(name, 0));
}
template<>
PyObject* pyopencv_from(const 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;
}
setupGpuWrapper.py
import subprocess
import os
import numpy as np
from distutils.core import setup, Extension
from Cython.Build import cythonize
from Cython.Distutils import build_ext
"""
Run setup with the following command:
```
python setupGpuWrapper.py build_ext --inplace
```
"""
# Determine current directory of this setup file to find our module
CUR_DIR = os.path.dirname(__file__)
# Use pkg-config to determine library locations and include locations
opencv_libs_str = subprocess.check_output("pkg-config --libs opencv".split()).decode()
opencv_incs_str = subprocess.check_output("pkg-config --cflags opencv".split()).decode()
# Parse into usable format for Extension call
opencv_libs = [str(lib) for lib in opencv_libs_str.strip().split()]
opencv_incs = [str(inc) for inc in opencv_incs_str.strip().split()]
extensions = [
Extension('GpuWrapper',
sources=[os.path.join(CUR_DIR, 'GpuWrapper.pyx')],
language='c++',
include_dirs=[np.get_include()] + opencv_incs,
extra_link_args=opencv_libs)
]
setup(
cmdclass={'build_ext': build_ext},
name="GpuWrapper",
ext_modules=cythonize(extensions)
)
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