deepin-ocr/3rdparty/opencv-4.5.4/modules/dnn/misc/python/pyopencv_dnn.hpp

220 lines
6.8 KiB
C++
Raw Normal View History

#ifdef HAVE_OPENCV_DNN
typedef dnn::DictValue LayerId;
typedef std::vector<dnn::MatShape> vector_MatShape;
typedef std::vector<std::vector<dnn::MatShape> > vector_vector_MatShape;
template<>
bool pyopencv_to(PyObject *o, dnn::DictValue &dv, const ArgInfo& info)
{
CV_UNUSED(info);
if (!o || o == Py_None)
return true; //Current state will be used
else if (PyLong_Check(o))
{
dv = dnn::DictValue((int64)PyLong_AsLongLong(o));
return true;
}
else if (PyInt_Check(o))
{
dv = dnn::DictValue((int64)PyInt_AS_LONG(o));
return true;
}
else if (PyFloat_Check(o))
{
dv = dnn::DictValue(PyFloat_AsDouble(o));
return true;
}
else
{
std::string str;
if (getUnicodeString(o, str))
{
dv = dnn::DictValue(str);
return true;
}
}
return false;
}
template<typename T>
PyObject* pyopencv_from(const dnn::DictValue &dv)
{
if (dv.size() > 1)
{
std::vector<T> vec(dv.size());
for (int i = 0; i < dv.size(); ++i)
vec[i] = dv.get<T>(i);
return pyopencv_from_generic_vec(vec);
}
else
return pyopencv_from(dv.get<T>());
}
template<>
PyObject* pyopencv_from(const dnn::DictValue &dv)
{
if (dv.isInt()) return pyopencv_from<int>(dv);
if (dv.isReal()) return pyopencv_from<float>(dv);
if (dv.isString()) return pyopencv_from<String>(dv);
CV_Error(Error::StsNotImplemented, "Unknown value type");
return NULL;
}
template<>
PyObject* pyopencv_from(const dnn::LayerParams& lp)
{
PyObject* dict = PyDict_New();
for (std::map<String, dnn::DictValue>::const_iterator it = lp.begin(); it != lp.end(); ++it)
{
CV_Assert(!PyDict_SetItemString(dict, it->first.c_str(), pyopencv_from(it->second)));
}
return dict;
}
template<>
PyObject* pyopencv_from(const std::vector<dnn::Target> &t)
{
return pyopencv_from(std::vector<int>(t.begin(), t.end()));
}
class pycvLayer CV_FINAL : public dnn::Layer
{
public:
pycvLayer(const dnn::LayerParams &params, PyObject* pyLayer) : Layer(params)
{
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
PyObject* args = PyTuple_New(2);
CV_Assert(!PyTuple_SetItem(args, 0, pyopencv_from(params)));
CV_Assert(!PyTuple_SetItem(args, 1, pyopencv_from(params.blobs)));
o = PyObject_CallObject(pyLayer, args);
Py_DECREF(args);
PyGILState_Release(gstate);
if (!o)
CV_Error(Error::StsError, "Failed to create an instance of custom layer");
}
static void registerLayer(const std::string& type, PyObject* o)
{
std::map<std::string, std::vector<PyObject*> >::iterator it = pyLayers.find(type);
if (it != pyLayers.end())
it->second.push_back(o);
else
pyLayers[type] = std::vector<PyObject*>(1, o);
}
static void unregisterLayer(const std::string& type)
{
std::map<std::string, std::vector<PyObject*> >::iterator it = pyLayers.find(type);
if (it != pyLayers.end())
{
if (it->second.size() > 1)
it->second.pop_back();
else
pyLayers.erase(it);
}
}
static Ptr<dnn::Layer> create(dnn::LayerParams &params)
{
std::map<std::string, std::vector<PyObject*> >::iterator it = pyLayers.find(params.type);
if (it == pyLayers.end())
CV_Error(Error::StsNotImplemented, "Layer with a type \"" + params.type +
"\" is not implemented");
CV_Assert(!it->second.empty());
return Ptr<dnn::Layer>(new pycvLayer(params, it->second.back()));
}
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
const int,
std::vector<std::vector<int> > &outputs,
std::vector<std::vector<int> > &) const CV_OVERRIDE
{
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
PyObject* args = PyList_New(inputs.size());
for(size_t i = 0; i < inputs.size(); ++i)
PyList_SetItem(args, i, pyopencv_from_generic_vec(inputs[i]));
PyObject* res = PyObject_CallMethodObjArgs(o, PyString_FromString("getMemoryShapes"), args, NULL);
Py_DECREF(args);
PyGILState_Release(gstate);
if (!res)
CV_Error(Error::StsNotImplemented, "Failed to call \"getMemoryShapes\" method");
CV_Assert(pyopencv_to_generic_vec(res, outputs, ArgInfo("", 0)));
return false;
}
virtual void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
PyObject* args = pyopencv_from(inputs);
PyObject* res = PyObject_CallMethodObjArgs(o, PyString_FromString("forward"), args, NULL);
Py_DECREF(args);
if (!res)
CV_Error(Error::StsNotImplemented, "Failed to call \"forward\" method");
std::vector<Mat> pyOutputs;
CV_Assert(pyopencv_to(res, pyOutputs, ArgInfo("", 0)));
Py_DECREF(res);
PyGILState_Release(gstate);
CV_Assert(pyOutputs.size() == outputs.size());
for (size_t i = 0; i < outputs.size(); ++i)
{
CV_Assert(pyOutputs[i].size == outputs[i].size);
CV_Assert(pyOutputs[i].type() == outputs[i].type());
pyOutputs[i].copyTo(outputs[i]);
}
}
private:
// Map layers types to python classes.
static std::map<std::string, std::vector<PyObject*> > pyLayers;
PyObject* o; // Instance of implemented python layer.
};
std::map<std::string, std::vector<PyObject*> > pycvLayer::pyLayers;
static PyObject *pyopencv_cv_dnn_registerLayer(PyObject*, PyObject *args, PyObject *kw)
{
const char *keywords[] = { "type", "class", NULL };
char* layerType;
PyObject *classInstance;
if (!PyArg_ParseTupleAndKeywords(args, kw, "sO", (char**)keywords, &layerType, &classInstance))
return NULL;
if (!PyCallable_Check(classInstance)) {
PyErr_SetString(PyExc_TypeError, "class must be callable");
return NULL;
}
pycvLayer::registerLayer(layerType, classInstance);
dnn::LayerFactory::registerLayer(layerType, pycvLayer::create);
Py_RETURN_NONE;
}
static PyObject *pyopencv_cv_dnn_unregisterLayer(PyObject*, PyObject *args, PyObject *kw)
{
const char *keywords[] = { "type", NULL };
char* layerType;
if (!PyArg_ParseTupleAndKeywords(args, kw, "s", (char**)keywords, &layerType))
return NULL;
pycvLayer::unregisterLayer(layerType);
dnn::LayerFactory::unregisterLayer(layerType);
Py_RETURN_NONE;
}
#endif // HAVE_OPENCV_DNN