从 Python 访问 OpenCV CUDA 函数（无 PyCUDA）

Question

我正在编写一个 Python 应用程序，它使用 OpenCV 的 Python 绑定来进行标记检测和其他图像处理。我想使用 OpenCV 的 CUDA 模块来 CUDA 加速我的应用程序的某些部分，并在他们的 .hpp 文件中注意到他们似乎正在使用 Python 和 Java 的 OpenCV 导出宏。但是，我似乎无法访问那些 CUDA 功能，即使我正在构建 OpenCV WITH_CUDA=ON 。

是否需要使用诸如 PyCUDA 之类的包装器才能访问 GPU 功能，例如 cudaarithm 中的阈值？或者，如果我在 Python 代码中调用 cv2.threshold() （而不是常规的基于 CPU 的实现），这些 CUDA 加速函数是否已经在使用？

 CV_EXPORTS double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());

我看到的 cv2 的子模块如下：

• 错误
• 阿鲁科
• 细节
• 鱼眼
• 弗兰
• 指令
• 毫升
• ocl
• 奥格
• 视频标签

cv2.cuda ， cv2.gpu 和 cv2.cudaarithm 都返回 AttributeError

我正在运行以构建 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 ..

原文由 ostrumvulpes 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

因此，正如@NAmorim 的答案和评论线程中所证实的那样， OpenCV 的各种 CUDA 模块没有可访问的 Python 绑定。

通过使用 Cython 访问我需要的 CUDA 函数并实现必要的逻辑来将我的 Python 对象（主要是 NumPy 数组）转换为 OpenCV C/C++ 对象并返回，我能够绕过这个限制。

工作代码

我首先写了一个 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)

我们还需要将 Python 对象转换为 OpenCV 对象的能力。我从 OpenCV 的 modules/python/src2/cv2.cpp 复制了前几百行。您可以在下面的附录中找到该代码。

我们终于可以编写 Cython 包装器方法来调用 OpenCV 的 CUDA 函数了！这是 Cython 实现文件的一部分， 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

在运行设置脚本对代码进行 cythonize 和编译后（见附录），我们可以将 GpuWrapper 作为 Python 模块导入并运行 cudaWarpPerspectiveWrapper 。这允许我通过 CUDA 运行代码，只有 0.34722222222222854% 的不匹配——非常令人兴奋！

参考文献（最多只能发布 2 个）

• 将 ndarray 转换为 cv::Mat 的最简单方法是什么？
• 为使用 OpenCV 的 C++ 代码编写 Python 绑定

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)
)

原文由 ostrumvulpes 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

或者，如果我在 Python 代码中调用 cv2.threshold()（而不是常规的基于 CPU 的实现），这些 CUDA 加速函数是否已经被使用

不，您必须从 GPU 加速模块显式调用它们。调用 cv2.threshold() 将只运行 CPU 版本。

由于 OpenCV 的 Python API 包含 C++ 函数，因此检查 C++ API 通常会提供有关函数/模块所在位置的有用提示。

例如，通过 此 转换指南，您可以看到从 OpenCV 2.X 到 3.X 的 API 更改。在这里，OpenCV 3.X 上的 GPU 模块可以通过以前版本的 cv2.cuda 和 cv2.gpu 访问。而3.X中的cuda模块又分为几个小块：

• cuda - CUDA 加速的计算机视觉
• cudaarithm - 矩阵运算
• cudabgsegm - 背景分割
• cudacodec - 视频编码/解码
• cudafeatures2d - 特征检测和描述
• cudafilters - 图像过滤
• cudaimgproc - 图像处理
• cudalegacy - 旧版支持
• cudaoptflow - 光流
• cudastereo - 立体声通信
• cudawarping - 图像变形
• cudev - 设备层

您应该在 cv2 中搜索这些模块。

原文由 NAmorim 发布，翻译遵循 CC BY-SA 3.0 许可协议