PythonOpenCV.C

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// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//
// JeVois Smart Embedded Machine Vision Toolkit - Copyright (C) 2016 by Laurent Itti, the University of Southern
// California (USC), and iLab at USC. See http://iLab.usc.edu and http://jevois.org for information about this project.
//
// This file is part of the JeVois Smart Embedded Machine Vision Toolkit.  This program is free software; you can
// redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software
// Foundation, version 2.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
// without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public
// License for more details.  You should have received a copy of the GNU General Public License along with this program;
// if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
//
// Contact information: Laurent Itti - 3641 Watt Way, HNB-07A - Los Angeles, CA 90089-2520 - USA.
// Tel: +1 213 740 3527 - itti@pollux.usc.edu - http://iLab.usc.edu - http://jevois.org
// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*! \file */
 
// This file is derived from https://github.com/Algomorph/pyboostcvconverter
 
/*
 * CV3BoostConverter.cpp
 *
 *  Created on: May 21, 2015
 *      Author: Gregory Kramida
 *   Copyright: 2015 Gregory Kramida
 The MIT License (MIT)
 
 Copyright (c) 2014 Gregory Kramida
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
 documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
 rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
 persons to whom the Software is furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
 Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
 WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
 COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
 OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
*/
 
#define NO_IMPORT_ARRAY
#define PY_ARRAY_UNIQUE_SYMBOL pbcvt_ARRAY_API
 
#include <jevois/Core/PythonOpenCV.H>
 
namespace pbcvt
{
  PyObject * opencv_error = nullptr;
  
  using namespace cv;
  //===================   ERROR HANDLING     =========================================================
  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;
  }
 
  //===================   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;
  };
  
  enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };
  
  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, cv::AccessFlag flags, cv::UMatUsageFlags usageFlags) const
      {
        if (data != nullptr)
        {
          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, cv::AccessFlag accessFlags, cv::UMatUsageFlags usageFlags) const
      { return stdAllocator->allocate(u, accessFlags, usageFlags); }
      
      void deallocate(UMatData* u) const
      {
        if (u)
        {
          PyEnsureGIL gil;
          PyObject* o = (PyObject*) u->userdata;
          Py_XDECREF(o);
          delete u;
        }
      }
      
      const MatAllocator* stdAllocator;
  };
  
  //===================   ALLOCATOR INITIALIZTION   ==================================================
  NumpyAllocator g_numpyAllocator;
  
  //===================   STANDALONE CONVERTER FUNCTIONS     =========================================
  
  PyObject* fromMatToNDArray(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;
  }
  
  Mat fromNDArrayToMat(PyObject* o)
  {
    cv::Mat m;
    bool allowND = true;
    if (!PyArray_Check(o))
    {
      failmsg("argument is not a numpy array");
      if (!m.data) m.allocator = &g_numpyAllocator;
    }
    else
    {
      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 || type == NPY_LONG) {
          needcopy = needcast = true;
          new_typenum = NPY_INT;
          type = CV_32S;
        } else {
          failmsg("Argument data type is not supported");
          m.allocator = &g_numpyAllocator;
          return m;
        }
      }
 
#ifndef CV_MAX_DIM
      const int CV_MAX_DIM = 32;
#endif
 
      int ndims = PyArray_NDIM(oarr);
      if (ndims >= CV_MAX_DIM) {
        failmsg("Dimensionality of argument is too high");
        if (!m.data) m.allocator = &g_numpyAllocator;
        return m;
      }
      
      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
        if ((i == ndims - 1 && (size_t) _strides[i] != elemsize) || (i < ndims - 1 && _strides[i] < _strides[i + 1]))
          needcopy = true;
      }
      
      if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2]) needcopy = true;
      
      if (needcopy)
      {
        if (needcast)
        {
          o = PyArray_Cast(oarr, new_typenum);
          oarr = (PyArrayObject*) o;
        }
        else
        {
          oarr = PyArray_GETCONTIGUOUS(oarr);
          o = (PyObject*) oarr;
        }
        
        _strides = PyArray_STRIDES(oarr);
      }
 
      for (int i = 0; i < ndims; i++) { size[i] = (int) _sizes[i]; step[i] = (size_t) _strides[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");
      } else {
        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 m;
  }
  
  //===================   BOOST CONVERTERS     =======================================================
  PyObject* matToNDArrayBoostConverter::convert(Mat const& 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;
  }
  
  matFromNDArrayBoostConverter::matFromNDArrayBoostConverter()
  {
    boost::python::converter::registry::push_back(convertible, construct, boost::python::type_id<Mat>());
  }
  
  // Check if PyObject is an array and can be converted to OpenCV matrix.
  void* matFromNDArrayBoostConverter::convertible(PyObject* object)
  {
    if (!PyArray_Check(object)) {
      return NULL;
    }
#ifndef CV_MAX_DIM
    const int CV_MAX_DIM = 32;
#endif
    PyArrayObject* oarr = (PyArrayObject*) object;
    
    int typenum = PyArray_TYPE(oarr);
    if (typenum != NPY_INT64 && typenum != NPY_UINT64 && typenum != NPY_LONG
        && typenum != NPY_UBYTE && typenum != NPY_BYTE
        && typenum != NPY_USHORT && typenum != NPY_SHORT
        && typenum != NPY_INT && typenum != NPY_INT32
        && typenum != NPY_FLOAT && typenum != NPY_DOUBLE) {
      return NULL;
    }
    int ndims = PyArray_NDIM(oarr); //data type not supported
    
    if (ndims >= CV_MAX_DIM) {
      return NULL; //too many dimensions
    }
    return object;
  }
  
  // Construct a Mat from an NDArray object.
  void matFromNDArrayBoostConverter::construct(PyObject* object,
                                               boost::python::converter::rvalue_from_python_stage1_data* data) {
    namespace python = boost::python;
    // Object is a borrowed reference, so create a handle indicting it is borrowed for proper reference counting.
    python::handle<> handle(python::borrowed(object));
    
    // Obtain a handle to the memory block that the converter has allocated for the C++ type.
    typedef python::converter::rvalue_from_python_storage<Mat> storage_type;
    void* storage = reinterpret_cast<storage_type*>(data)->storage.bytes;
    
    // Allocate the C++ type into the converter's memory block, and assign its handle to the converter's convertible
    // variable.  The C++ container is populated by passing the begin and end iterators of the python object to the
    // container's constructor.
    PyArrayObject* oarr = (PyArrayObject*) object;
    
    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) {
      needcopy = needcast = true;
      new_typenum = NPY_INT;
      type = CV_32S;
    }
    
#ifndef CV_MAX_DIM
    const int CV_MAX_DIM = 32;
#endif
    int ndims = PyArray_NDIM(oarr);
    
    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
      if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
          || (i < ndims - 1 && _strides[i] < _strides[i + 1]))
        needcopy = true;
    }
    
    if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
      needcopy = true;
    
    if (needcopy) {
      
      if (needcast) {
        object = PyArray_Cast(oarr, new_typenum);
        oarr = (PyArrayObject*) object;
      } else {
        oarr = PyArray_GETCONTIGUOUS(oarr);
        object = (PyObject*) oarr;
      }
      
      _strides = PyArray_STRIDES(oarr);
    }
    
    for (int i = 0; i < ndims; i++) {
      size[i] = (int) _sizes[i];
      step[i] = (size_t) _strides[i];
    }
    
    // handle degenerate case
    if (ndims == 0) {
      size[ndims] = 1;
      step[ndims] = elemsize;
      ndims++;
    }
    
    if (ismultichannel) {
      ndims--;
      type |= CV_MAKETYPE(0, size[2]);
    }
    if (!needcopy) {
      Py_INCREF(object);
    }
    
    cv::Mat* m = new (storage) cv::Mat(ndims, size, type, PyArray_DATA(oarr), step);
    m->u = g_numpyAllocator.allocate(object, ndims, size, type, step);
    m->allocator = &g_numpyAllocator;
    m->addref();
    data->convertible = storage;
  }
 
}           //end namespace pbcvt