Mailman 3 September 2011 - Cplusplus-sig

boost::python embedding error - runtime - Mac OS X - 1.38
by Igor Karpov Oct. 1, 2014

Oct. 1, 2014

Hi, I am trying to run the following program: #include <iostream> #include <Python.h> #include <boost/python.hpp> using namespace boost::python; using namespace std; int main(int argc, char** argv) { { // Using Python/C Py_Initialize(); PyObject* main_module = PyImport_ImportModule("__main__"); PyObject* globals = PyEval_GetGlobals(); PyObject* locals = PyEval_GetLocals(); PyRun_SimpleString("print 'Hello World, from Python/C!'\n"); Py_Finalize(); } { // Using boost::python Py_Initialize(); object main_module = import("__main__"); object main_namespace = main_module.attr("__dict__"); try { object ignored = exec("print 'Hello World, from boost::python!'\n", main_namespace); Py_Finalize(); } catch (error_already_set const& e) { PyErr_Print(); return 1; } } return 0; } on Linux (both x86 and x86_64), with Ubuntu's default version of Boost (1.34.1) installed, I am able to do so as expected: $ ./embed Hello World, from Python/C! Hello World, from boost::python! on Mac OS X, I get a runtime error: $ ./embed Hello World, from Python/C! Bus Error Running with gdb, this is the stack trace: Hello World, from Python/C! Program received signal EXC_BAD_ACCESS, Could not access memory. Reason: KERN_PROTECTION_FAILURE at address: 0x00000000 0x00000000 in ?? () (gdb) where #0 0x00000000 in ?? () #1 0x00bb618f in PyEval_GetGlobals () #2 0x00bce0dd in PyImport_Import () #3 0x00bce2b0 in PyImport_ImportModule () #4 0x008127d4 in boost::python::import (name=@0xbffff4a8) at libs/python/src/import.cpp:20 #5 0x00004446 in main (argc=1, argv=0xbffff510) at embed.cc:22 For this Mac, boost version is latest (1.38) and python version is: Python 2.5.4 (r254:67917, Dec 23 2008, 14:57:27) [GCC 4.0.1 (Apple Computer, Inc. build 5363)] on darwin Is this a bug? I attached the source and the cmake file I am using to build, for convenience. Thanks, --Igor.

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wrapped std::vector slice affecting items in a python list
by babak April 2, 2012

April 2, 2012

Hi, I've come across some unusual behaviour that I was hoping some one might be able to explain to me. In python I have a list and a wrapped std::vector<MyCustomType> (using the vector_indexing_suite) where the list contains the items in the vector. If I slice insert items into the vector then the items in the python list change. The attached example illustrates this. Is this behaviour expected ? I'm quite confused as to what's going on so any help clarifying this would be greatly appreciated. thanks, Babak http://boost.2283326.n4.nabble.com/file/n3738972/test.py test.py http://boost.2283326.n4.nabble.com/file/n3738972/source.cpp source.cpp -- View this message in context: http://boost.2283326.n4.nabble.com/wrapped-std-vector-slice-affecting-items… Sent from the Python - c++-sig mailing list archive at Nabble.com.

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[Boost.Python v3] Conversions and Registries
by Jim Bosch Oct. 6, 2011

Oct. 6, 2011

I'd like to restart the discussion on how conversions will work in Boost.Python v3. Here's the starting point: I'd like to see support for static, template-based conversions. These would be defined by [partial-]specializing a traits class, and I tend to think they should only be invoked after attempting all registry-based conversions. Users would have to include the same headers in groups of interdependent modules to avoid specializing the same traits class multiple times in different ways; I can't think of a way to protect them from this, but template-based specializations are a sufficiently advanced featured that I'm comfortable leaving it up to users to avoid this problem. We've had some discussion of allowing different modules to have different registries (in addition to a global registry shared across modules). Leaving aside implementation questions, I have a little survey for interested parties: 1) Under what circumstances would you want a conversion that is completely limited to a specific module (or a group of modules that explicitly declare it)? 2) Under what circumstances would you want a conversion to look in a module-specific registry, and then fall back to the global registry? 3) Considering that we will have a "best-match" overloading system, what should take precedence, an inexact match in a module-specific registry, or an exact match in a global registry? (Clearly this is a moot point for to-Python conversion). Finally, can anyone give me a reason why having a global registry can lead to a violation of the "One Definition Rule"? This was alluded to many times in the earlier discussion, and there's no doubt that a global registry may lead to unexpected (from a given module's perspective) behavior - but I do not understand the implication that the global registry can result in formally undefined behavior by violating the ODR. Thanks! Jim

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New Major-Release Boost.Python Development
by Jim Bosch Oct. 4, 2011

Oct. 4, 2011

I'd like to start work on a new major release of Boost.Python. While the library is currently well-maintained in terms of bugfixes, I get the sense that neither the original developers nor the current maintainer have the time or inclination to work on new features. I'd also like to propose some changes that are slightly backwards-incompatible, as well as some that mess with the internals to an extent that I'd feel better about doing it outside Boost itself, to make it easier for adventurous users to play with the new version without affecting people who depend on having an extremely stable library in Boost. To that end, I'm inclined to copy the library to somewhere else (possibly the boost sandbox, but more likely a separate site), work on it, produce some minor releases, and re-submit it to Boost for review. Perhaps the external site would continue on as the home of more fine-grained releases, or maybe we would fully reintegrate with Boost at that point (especially if Boost addresses some of its own project management and release control issues by that point, which I know is being discussed but to my knowledge doesn't really have a timeline yet). I am willing to take the lead on this project; I have a number of features that exist as extensions in the boost sandbox already that would work better if they could be more fully integrated into the Boost.Python core, and I think I have the necessary understanding of the full code base to coordinate things. I'd like to save a full discussion of what features a new version would include for another thread, but I am hoping other people on the list might volunteer some time to work on aspects they have coded up elsewhere - I know many such extensions exist. So I have a few questions for anyone who's paying attention: - For the original Boost.Python developers and current maintainers, and other people familiar with developing Boost libraries: do you have any preference on how to approach this? I don't want to step on any toes, especially toes attached to people who are responsible for the excellent library we already have. - For other Boost.Python experts on this list: do you have existing code or development time you'd like to contribute? Thanks! Jim Bosch

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bjam: How to determine python version for installation ?
by Avi Bahra Sept. 29, 2011

Sept. 29, 2011

As a part of installation of my extension I need determine the python extension ( without having to hard code it) Currently you can specify the python version, but I need a way of determining which python version is being used, when we specify: 'using python ;' Is this possible ? Best regards, Ta, Avi

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How to Tell Bjam about a Static Lib it needs to build the extension
by Kevin Hughes Sept. 28, 2011

Sept. 28, 2011

Hi, I think I am getting very close to successfully using boost python for the first time. All I need now is to tell Bjam about a static library my program depends on. I can't figure out how to do this in my JamRoot file. Thanks

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Boost Python class won't import
by Kevin Hughes Sept. 23, 2011

Sept. 23, 2011

Hello, I am trying to wrap a simple class that runs some image processing routines. I would like them to be script-able from python. I am not trying to pass complicated types, in fact almost all the functions take and return no arguments. Here is my wrapper code (generated with pyplusplus partially): #include "boost/python.hpp" #include "eventDetector.h" namespace bp = boost::python; BOOST_PYTHON_MODULE(pyplusplus){ { //::eventDetector typedef bp::class_< eventDetector > eventDetector_exposer_t; eventDetector_exposer_t eventDetector_exposer = eventDetector_exposer_t( "eventDetector", bp::init< params & >(( bp::arg("op") )) ); bp::scope eventDetector_scope( eventDetector_exposer ); bp::implicitly_convertible< params &, eventDetector >(); { //::eventDetector::detect typedef void ( ::eventDetector::*detect_function_type )( ) ; eventDetector_exposer.def( "detect" , detect_function_type( &::eventDetector::detect ) ); } { //::eventDetector::init typedef void ( ::eventDetector::*init_function_type )( ) ; eventDetector_exposer.def( "init" , init_function_type( &::eventDetector::init ) ); } { //::eventDetector::load typedef void ( ::eventDetector::*load_function_type )( ) ; eventDetector_exposer.def( "load" , load_function_type( &::eventDetector::load ) ); } { //::eventDetector::save typedef void ( ::eventDetector::*save_function_type )( ) ; eventDetector_exposer.def( "save" , save_function_type( &::eventDetector::save ) ); } { //::eventDetector::setGridSize typedef void ( ::eventDetector::*setGridSize_function_type )( int,int ) ; eventDetector_exposer.def( "setGridSize" , setGridSize_function_type( &::eventDetector::setGridSize ) , ( bp::arg("x"), bp::arg("y") ) ); } { //::eventDetector::setVideoCapture typedef bool ( ::eventDetector::*setVideoCapture_function_type )( ::std::string ) ; eventDetector_exposer.def( "setVideoCapture" , setVideoCapture_function_type( &::eventDetector::setVideoCapture ) , ( bp::arg("filename") ) ); } { //::eventDetector::setVideoCapture typedef bool ( ::eventDetector::*setVideoCapture_function_type )( int ) ; eventDetector_exposer.def( "setVideoCapture" , setVideoCapture_function_type( &::eventDetector::setVideoCapture ) , ( bp::arg("cam") ) ); } { //::eventDetector::train typedef void ( ::eventDetector::*train_function_type )( ) ; eventDetector_exposer.def( "train" , train_function_type( &::eventDetector::train ) ); } } bp::class_< params >( "params" ) ... large list of simple params...; } It builds fine using bjam but when I import into python I get an error message: libEventDetector.so: undefined symbol: _ZN13eventDetector4loadEv. I am really not very good with boost::python and I don't really know what to do. Help is appreciated, thank you!

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map_indexing_suite does not find to-python convertors
by Wichert Akkerman Sept. 22, 2011

Sept. 22, 2011

I am struggling to expose a map to python code. What I am doing is pretty simple: I have a map from an enum type to Glib::ustring, with a convertors registered to convert Python str and unicode instances to an ustring, and ustring to Python unicode. I've put the relevant code below. What I am running into is that the conversion from python to C++ types is failing with a "No Python class registered for C++ class Glib::ustring" error. This looks very similar to the FAQ entry "Why is my automatic to-python conversion not being found?" (http://www.boost.org/doc/libs/1_47_0/libs/python/doc/v2/faq.html#topythonco… ), and the workaround suggested there indeed works fine for class properties. I can't seem to find a similar trick for map values though. Is there a trick to get this working? Wichert. enum aspect_type { ... }; typedef std::map<aspect_type, Glib::ustring> aspect_map; struct ustring_to_python { static PyObject* convert(Glib::ustring const& s) { PyObject* unicode; unicode=PyUnicode_DecodeUTF8(s.data(), static_cast<Py_ssize_t>(s.size()), "ignore"); Py_INCREF(unicode); return unicode; } }; struct ustring_from_python { ustring_from_python() { converter::registry::push_back(&convertible,&construct, type_id<Glib::ustring>()); } static void* convertible(PyObject *obj) { if (PyUnicode_Check(obj) || PyString_Check(obj)) return obj; return 0; } static void construct(PyObject* obj, converter::rvalue_from_python_stage1_data *data) { PyObject *str = 0; char* value; if (PyUnicode_Check(obj)) { str = PyUnicode_AsUTF8String(obj); if (str==0) throw_error_already_set(); } value = PyString_AsString(obj); if (value==0) { Py_XDECREF(str); throw_error_already_set(); } void* storage = ((converter::rvalue_from_python_storage<Glib::ustring>*)data)->storage.bytes; new (storage) Glib::ustring(value); data->convertible = storage; Py_XDECREF(obj); } }; BOOST_PYTHON_MODULE(mymodule) { to_python_converter<Glib::ustring, ustring_to_python>(); ustring_from_python(); class_<Article::aspect_map>("AspectMap") .def(map_indexing_suite<aspect_map, true>()); }

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Boost Python wrapper for a c++ class that uses Opencv 2.3
by Kevin Hughes Sept. 21, 2011

Sept. 21, 2011

I am trying to create a python library from a class which uses opencv 2.3. I want to be able to pass numpy array's into the class where they will be converted into cv::Mat's processed then converted back to numpy array's and returned. Here is a simple test class I am working on to get this working before wrapping my own class. Currently I am just trying to receive a numpy array concert to a cv::Mat, process it and then write it to file. After this is working I will work on returning the processed array to python. Here is the simple class: foo.h : #include <opencv2/core/core.hpp> class Foo { public: Foo(); ~Foo(); cv::Mat image; void bar( cv::Mat in ); }; foo.cpp : #include "foo.h" Foo::Foo(){} Foo::~Foo(){} void Foo::bar( cv::Mat in) { image = in; cv::Canny( image, image, 50, 100 ); cv::imwrite("image.png", image); } And here is where I have attempted to wrap this class using boost::python (I am using code from the opencv source for the the numpy to mat conversion) wrap_foo.cpp #include <boost/python.hpp> #include <numpy/arrayobject.h> #include <opencv2/core/core.hpp> #include "foo.h" using namespace cv; namespace bp = boost::python; //// Wrapper Functions void bar(Foo& f, bp::object np); //// Converter Functions cv::Mat convertNumpy2Mat(bp::object np); //// Wrapper Functions void bar(Foo& f, bp::object np) { Mat img = convertNumpy2Mat(np); f.bar(img); return; } //// Boost Python Class BOOST_PYTHON_MODULE(lib) { bp::class_<Foo>("Foo") .def("bar", bar) ; } //// Converters cv::Mat convertNumpy2Mat(bp::object np) { Mat m; numpy_to_mat(np.ptr(),m); return m; } The numpy_to_mat function is from the opencv source (modules/python/src2/cv2.cpp). The full file has the function below what I wrote above. This code compiles with bjam just fine but the when I import into python it crashes. The error is this: libFoo.so: undefined symbol: _ZN2cv3Mat10deallocateEv. I have tried a number of different things but I can't get this to work.

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scriptable widget system
by István Csanády Sept. 13, 2011

Sept. 13, 2011

Hi All! I am developing a custom widget system. The widgets are written in C++, since the rendering requires very high performance. The widgets are loaded from xml files. A widget can contain several different UI elements, for example a slider, a textbox, a label, buttons, etc... Until now I used a simple but quite powerful solution to synchronize the UI values and the python object attributes. The concept is the following: C++ UI elements can have properties (see the code below). Properties can be bounded to a python object's attribute. If a property changes, it sets the python attribute, and after the setter ran, it gets the actual python value, and sets the property's value to it. (For better understanding: when a property changes because of some user input it rather suggests a value then set it. This is useful when for example you want a slider to be able to set only to odd values, or want to limit the availible values of a numeric text box. So this way you have full control of the "control UI elements" values.) From the other side: when you set an attribute of the python object the object checks if it is bounded to any C++ property, and if it is, then it sets the property to the value. Here are some code snippets: This is the C++ property: template <typename T> class Property : public PythonPropertyBinder { public: void bindToAttribute(PythonComponent* component,std::string name) { //Add a PyCFunction to the bindings of the python object. } void unbind() { //Remove the bindings. } T& operator= (const Property<T>& p) { return (*this = p.value); } T & operator = (const T &i) { if(!updating) { updating = true; if(component) { component->setAttribute(i, pythonAttributeName.c_str()); component->getAttribute(value,pythonAttributeName.c_str ()); PyErr_Print(); } else { value = i; } updating = false; notifyObservers(); return value; }else { return value; } } operator T const & () const { return value; } void addObserver(PythonPropertyBinderObserver* observer) { observers.insert(observer); } void removeObserver(PythonPropertyBinderObserver* observer) { observers.erase(observer); } Property(const T& v):Property() { value = v; } Property():updating(false),component(nullptr) { //set up stuff... } ~Property() { unbind(); } private: T value; std::string pythonAttributeName; //...not interesting }; And for example a slider looks like the following: class Slider : public View, public PythonPropertyBinderObserver { public: //... Property<double> value; Property<double> low; Property<double> high; //... }; The python class: class Component(object): def bind_to_attribute(self,attribute_name, cpp_property_setter): if not hasattr(self,attribute_name): return if self.binder_functions.has_key(attribute_name): self.binder_functions[attribute_name].add(cpp_property_setter) else: s = set() s.add(cpp_property_setter) self.binder_functions[attribute_name] = s def unbind_from_attribute(self,attribute_name, cpp_property_setter): if self.binder_functions.has_key(attribute_name): self.binder_functions[attribute_name].remove(cpp_property_setter ) def __setattr__(self,attributename,value): super(Component,self).__setattr__(attributename,value) self.sync_attribute(attributename) def sync_attribute(self,attributename): if(hasattr(self,'binder_functions') andself.binder_functions.has_key(attributename)): s = self.binder_functions[attributename] for cpp_property_setter in s: cpp_property_setter() ''' And several other methods... ''' So this way I can simply bind a UI element's property to a python attribute, and it is guaranteed that they will be the same (until a python property is not accessed by its setter...) For example: class Slider(Component): def get_value(self): return self.__value def set_value(self,value): if((value<=self.high and value>=self.low) or (value>=self.high andvalue<=self.low)): if self.integers_only: self.__value = round(value) else: self.__value = value self.run() def get_high(self): return self.__high def set_high(self,high): self.__high = high def get_low(self): return self.__low def set_low(self,low): self.low = low value = property(get_value,set_value) low = property(get_low,set_low) high = property(get_high,set_high) And then all I have to do from the C++ UI: Slider* slider = new Slider(Point(20, 30.), 195.); slider->value.bindToAttribute(controller->getModel(),"value"); slider->low.bindToAttribute(controller->getModel(),"low"); slider->high.bindToAttribute(controller->getModel(),"high"); So this way: - the python objects work like models - the values are synced - the script writer is not able access to any other properties of the UI elements - however one have total control of the values - all the widgets can work without binded to any UI elements - if I want to serialize the objects (and I want to), the PyCFunctions and other - runtime added - objects can be easily excluded - properties can be easily bounded and unbouded - I did not have to create any new types, such as MySyncedFloat, MySyncedString etc. - the script writer doesn't even have to know about that a value will be bounded to a UI element - the UI elements are not strongly connected to any python code - more UI elements can be bounded to a single python attribute for example I can bind an attribute to a slider's max value and to a numeric text box at the same time - this approach can be easily used with a graphical interface builder: one just have to drag and drop some UI elements to a widget, and tell the interface builder that a property of an element will be bounded to a python attribute. Then it can be exported to an XML and loaded runtime and bind the properties runtime. Cool. This approach worked very well. But... Few days ago our UI/UX designer created some new UI elements which I call "batched controllers". A batched controller is an array of same controllers for example sliders. The number of the sliders can be changed runtime for example when I click on a button a new slider is added. And this type of view can not be used with the previous approach, since a value is determined by two values: the ordinal of the slider and the value itself. So my question is: does anybody have any idea how to create something that works like I previously specified for these "batched controllers" (==properties with indices) István

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