OpenCV如何为具有不同参数的函数确定python绑定？ OpenCV中，我们有两个C++函数，不同的参数 void cv::text::detectRegions ( InputArray image, const Ptr< ERFilter > & er_filter1, const Ptr< ERFilter > & er_filter2, std::vector< std::vector< Point > > & regions ) void cv::text::detectRegions ( InputArray image, const Ptr< ERFilter > & er_filter1, const Ptr< ERFilter > & er_filter2, std::vector< Rect > & groups_rects, int method = ERGROUPING_ORIENTATION_HORIZ, const String & filename = String(), float minProbability = (float) 0.5 ) < Python绑定如何决定执行哪个C++功能？< P>手动绑定，因此您需要检查OpenCV中的Python绑定代码，以知道使用哪个函数。

以下是OpenCV在我的机器上为

detectRegions

函数创建的Python代码。Python函数签名是：

cv2.text.detectRegions（image，er_filter1，er_filter2[，method[，filename[，minProbability]]）->groups\u rects

如果仔细查看代码，您将看到根据您提供的参数数量和类型调用了两个不同版本的

cv:：text:：detectRegions

static PyObject* pyopencv_cv_text_detectRegions(PyObject* , PyObject* args, PyObject* kw)
{
    using namespace cv::text;

    {
    PyObject* pyobj_image = NULL;
    Mat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_vector_Point regions;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, regions));
        return pyopencv_from(regions);
    }
    }
    PyErr_Clear();

    {
    PyObject* pyobj_image = NULL;
    UMat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_vector_Point regions;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, regions));
        return pyopencv_from(regions);
    }
    }
    PyErr_Clear();

    {
    PyObject* pyobj_image = NULL;
    Mat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_Rect groups_rects;
    int method=ERGROUPING_ORIENTATION_HORIZ;
    PyObject* pyobj_filename = NULL;
    String filename;
    float minProbability=(float)0.5;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", "method", "filename", "minProbability", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO|iOf:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2, &method, &pyobj_filename, &minProbability) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) &&
        pyopencv_to(pyobj_filename, filename, ArgInfo("filename", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, groups_rects, method, filename, minProbability));
        return pyopencv_from(groups_rects);
    }
    }
    PyErr_Clear();

    {
    PyObject* pyobj_image = NULL;
    UMat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_Rect groups_rects;
    int method=ERGROUPING_ORIENTATION_HORIZ;
    PyObject* pyobj_filename = NULL;
    String filename;
    float minProbability=(float)0.5;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", "method", "filename", "minProbability", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO|iOf:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2, &method, &pyobj_filename, &minProbability) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) &&
        pyopencv_to(pyobj_filename, filename, ArgInfo("filename", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, groups_rects, method, filename, minProbability));
        return pyopencv_from(groups_rects);
    }
    }

    return NULL;
}

静态PyObject*pyopencv\u cv\u text\u检测区域（PyObject*，PyObject*args，PyObject*kw）
{
使用名称空间cv:：text；
{
PyObject*pyobj_image=NULL；
Mat图像；
PyObject*pyobj_er_filter1=NULL；
Ptr er_过滤器1；
PyObject*pyobj_er_filter2=NULL；
Ptr er_过滤器2；
矢量点区域；
const char*关键字[]={“image”，“er_filter1”，“er_filter2”，NULL}；
if（PyArg_ParseTupleAndKeywords（args，kw，“OOO:detectRegions”，（char**）关键字，&pyobj_图像，&pyobj_er_过滤器1，&pyobj_er_过滤器2）&&
pyopencv_to（pyobj_图像，图像，ArgInfo（“图像”，0））&&
pyopencv_to（pyobj_er_filter1，er_filter1，ArgInfo（“er_filter1”，0））&&
pyopencv_to（pyobj_er_filter2，er_filter2，ArgInfo（“er_filter2”，0）））
{
ERRWRAP2（cv:：text:：detectRegions（图像、er_过滤器1、er_过滤器2、区域））；
从（区域）返回pyopencv_；
}
}
PyErr_Clear（）；
{
PyObject*pyobj_image=NULL；
UMat图像；
PyObject*pyobj_er_filter1=NULL；
Ptr er_过滤器1；
PyObject*pyobj_er_filter2=NULL；
Ptr er_过滤器2；
矢量点区域；
const char*关键字[]={“image”，“er_filter1”，“er_filter2”，NULL}；
if（PyArg_ParseTupleAndKeywords（args，kw，“OOO:detectRegions”，（char**）关键字，&pyobj_图像，&pyobj_er_过滤器1，&pyobj_er_过滤器2）&&
pyopencv_to（pyobj_图像，图像，ArgInfo（“图像”，0））&&
pyopencv_to（pyobj_er_filter1，er_filter1，ArgInfo（“er_filter1”，0））&&
pyopencv_to（pyobj_er_filter2，er_filter2，ArgInfo（“er_filter2”，0）））
{
ERRWRAP2（cv:：text:：detectRegions（图像、er_过滤器1、er_过滤器2、区域））；
从（区域）返回pyopencv_；
}
}
PyErr_Clear（）；
{
PyObject*pyobj_image=NULL；
Mat图像；
PyObject*pyobj_er_filter1=NULL；
Ptr er_过滤器1；
PyObject*pyobj_er_filter2=NULL；
Ptr er_过滤器2；
向量组；
int method=ERGROUPING\u ORIENTATION\u HORIZ；
PyObject*pyobj_filename=NULL；
字符串文件名；
浮动概率=（浮动）0.5；
const char*关键字[]={“image”，“er_filter1”，“er_filter2”，“method”，“filename”，“minProbability”，NULL}；
if（PyArg_ParseTupleAndKeywords（args，kw，“OOO|iOf:detectRegions”，（char**）关键字，&pyobj_图像，&pyobj_er_过滤器1，&pyobj_er_过滤器2，&method，&pyobj_文件名，&minProbability）&&
pyopencv_to（pyobj_图像，图像，ArgInfo（“图像”，0））&&
pyopencv_to（pyobj_er_filter1，er_filter1，ArgInfo（“er_filter1”，0））&&
pyopencv_to（pyobj_er_filter2，er_filter2，ArgInfo（“er_filter2”，0））&&
pyopencv_to（pyobj_文件名，文件名，ArgInfo（“文件名”，0）））
{
ERRWRAP2（cv:：text:：detectRegions（图像、er_过滤器1、er_过滤器2、组、方法、文件名、最小概率））；
从（组矩形）返回pyopencv；
}
}
PyErr_Clear（）；
{
PyObject*pyobj_image=NULL；
UMat图像；
PyObject*pyobj_er_filter1=NULL；
Ptr er_过滤器1；
PyObject*pyobj_er_filter2=NULL；
Ptr er_过滤器2；
向量组；
int method=ERGROUPING\u ORIENTATION\u HORIZ；
PyObject*pyobj_filename=NULL；
字符串文件名；
浮动概率=（浮动）0.5；
const char*关键字[]={“image”，“er_filter1”，“er_filter2”，“method”，“filename”，“minProbability”，NULL}；
if（PyArg_ParseTupleAndKeywords（args，kw，“OOO|iOf:detectRegions”，（char**）关键字，&pyobj_图像，&pyobj_er_过滤器1，&pyobj_er_过滤器2，&method，&pyobj_文件名，&minProbability）&&
pyopencv_to（pyobj_图像，图像，ArgInfo（“图像”，0））&&
pyopencv_to（pyobj_er_filter1，er_filter1，ArgInfo（“er_filter1”，0））&&
pyopencv_to（pyobj_er_filter2，er_filter2，ArgInfo（“er_filter2”，0））&&
pyopencv_to（pyobj_文件名，文件名，ArgInfo（“文件名”，0）））
{
ERRWRAP2（cv:：text:：detectRegions（图像、er_过滤器1、er_过滤器2、组、方法、文件名、最小概率））；
从（组矩形）返回pyopencv；
}
}
返回NULL；
}

第二个函数自动完整函数签名如下：

cv2.text.detectRegions（image，er_filter1，er_filter2[，method[，filename[，minProbability]]]->groups\u rects

。两个函数仍然存在歧义。这两个函数的python定义相同。

static PyObject* pyopencv_cv_text_detectRegions(PyObject* , PyObject* args, PyObject* kw)
{
    using namespace cv::text;

    {
    PyObject* pyobj_image = NULL;
    Mat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_vector_Point regions;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, regions));
        return pyopencv_from(regions);
    }
    }
    PyErr_Clear();

    {
    PyObject* pyobj_image = NULL;
    UMat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_vector_Point regions;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, regions));
        return pyopencv_from(regions);
    }
    }
    PyErr_Clear();

    {
    PyObject* pyobj_image = NULL;
    Mat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_Rect groups_rects;
    int method=ERGROUPING_ORIENTATION_HORIZ;
    PyObject* pyobj_filename = NULL;
    String filename;
    float minProbability=(float)0.5;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", "method", "filename", "minProbability", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO|iOf:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2, &method, &pyobj_filename, &minProbability) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) &&
        pyopencv_to(pyobj_filename, filename, ArgInfo("filename", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, groups_rects, method, filename, minProbability));
        return pyopencv_from(groups_rects);
    }
    }
    PyErr_Clear();

    {
    PyObject* pyobj_image = NULL;
    UMat image;
    PyObject* pyobj_er_filter1 = NULL;
    Ptr<ERFilter> er_filter1;
    PyObject* pyobj_er_filter2 = NULL;
    Ptr<ERFilter> er_filter2;
    vector_Rect groups_rects;
    int method=ERGROUPING_ORIENTATION_HORIZ;
    PyObject* pyobj_filename = NULL;
    String filename;
    float minProbability=(float)0.5;

    const char* keywords[] = { "image", "er_filter1", "er_filter2", "method", "filename", "minProbability", NULL };
    if( PyArg_ParseTupleAndKeywords(args, kw, "OOO|iOf:detectRegions", (char**)keywords, &pyobj_image, &pyobj_er_filter1, &pyobj_er_filter2, &method, &pyobj_filename, &minProbability) &&
        pyopencv_to(pyobj_image, image, ArgInfo("image", 0)) &&
        pyopencv_to(pyobj_er_filter1, er_filter1, ArgInfo("er_filter1", 0)) &&
        pyopencv_to(pyobj_er_filter2, er_filter2, ArgInfo("er_filter2", 0)) &&
        pyopencv_to(pyobj_filename, filename, ArgInfo("filename", 0)) )
    {
        ERRWRAP2(cv::text::detectRegions(image, er_filter1, er_filter2, groups_rects, method, filename, minProbability));
        return pyopencv_from(groups_rects);
    }
    }

    return NULL;
}