np::ndarray cleanVOC( const np::ndarray& lbl ) {
unsigned char * plbl = (unsigned char *)lbl.get_data();
np::ndarray r = np::zeros( lbl.get_nd(), lbl.get_shape(), np::dtype::get_builtin<short>() );
short * pr = (short *)r.get_data();
for( int i=0; i<lbl.shape(0)*lbl.shape(1); i++ )
pr[i] = (plbl[i]>250)?-2:(short)plbl[i]-1;
return r;
}
/**************** other ****************/
static np::ndarray extractPatch( const np::ndarray &a, const RMatrixXi & l, const VectorXi & s ) {
// Check the input
if( !(a.get_flags() & np::ndarray::C_CONTIGUOUS) )
throw std::invalid_argument( "Contiguous array required!" );
if( l.cols() != a.get_nd() )
throw std::invalid_argument("location dimension does not match matrix");
if( l.cols() != s.size() )
throw std::invalid_argument("patch size and locations have different dimension");
for( int i=0; i<l.rows(); i++ )
for( int j=0; j<s.size(); j++ )
if( l(i,j)<0 || l(i,j)+s[j] > a.shape(j) )
throw std::invalid_argument("location out of range! Dimension "+std::to_string(j)+" : "+std::to_string(l(i,j))+" + "+std::to_string(s[j])+" ( 0 .. "+std::to_string(a.shape(j))+" )");
const int itemsize = a.get_dtype().get_itemsize();
// Create the output array
std::vector<Py_intptr_t> out_size;
out_size.push_back( l.rows() );
for( int i=0; i<s.size(); i++ )
if( s[i]>1 )
out_size.push_back( s[i] );
np::ndarray res = np::empty( out_size.size(), out_size.data(), a.get_dtype() );
// Prepare the patches
const int ls = s[s.size()-1]; // Radius of the last dimension
std::vector<int> patch_offsets(1,0);
for( int d=0; d<s.size()-1; d++ ) {
std::vector<int> opo = patch_offsets;
patch_offsets.clear();
for( int i=0; i<s[d]; i++ )
for( int o: opo )
patch_offsets.push_back( a.shape(d+1)*(o+i) );
}
const char * pa = a.get_data();
char * pres = res.get_data();
for( int i=0,k=0; i<l.rows(); i++ ) {
int o=0;
for( int d=0; d<s.size(); d++ )
o = a.shape(d)*o + l(i,d);
// Extract the patches
for( int po: patch_offsets ) {
memcpy( pres+k*itemsize, pa+(po+o)*itemsize, ls*itemsize );
k += ls;
}
}
return res;
}
vcl::tools::shared_ptr<vcl::matrix<SCALARTYPE, F> >
matrix_init_ndarray(const np::ndarray& array, const vcl::context& ctx)
{
int d = array.get_nd();
if (d != 2) {
PyErr_SetString(PyExc_TypeError, "Can only create a matrix from a 2-D array!");
bp::throw_error_already_set();
}
ndarray_wrapper<SCALARTYPE> wrapper(array);
vcl::matrix<SCALARTYPE, F>* mat = new vcl::matrix<SCALARTYPE, F>(wrapper.size1(), wrapper.size2(), ctx);
vcl::copy(wrapper, (*mat));
return vcl::tools::shared_ptr<vcl::matrix<SCALARTYPE, F> >(mat);
}
vcl::tools::shared_ptr<std::vector<SCALARTYPE> >
std_vector_init_ndarray(const np::ndarray& array)
{
int d = array.get_nd();
if (d != 1) {
PyErr_SetString(PyExc_TypeError, "Can only create a vector from a 1-D array!");
bp::throw_error_already_set();
}
vcl::vcl_size_t s = (vcl::vcl_size_t) array.shape(0);
std::vector<SCALARTYPE> *v = new std::vector<SCALARTYPE>(s);
for (vcl::vcl_size_t i=0; i < s; ++i)
(*v)[i] = bp::extract<SCALARTYPE>(array[i]);
return vcl::tools::shared_ptr<std::vector<SCALARTYPE> >(v);
}
vcl::tools::shared_ptr<vcl::vector<SCALARTYPE> >
vcl_vector_init_ndarray(const np::ndarray& array, const vcl::context& ctx)
{
int d = array.get_nd();
if (d != 1) {
PyErr_SetString(PyExc_TypeError, "Can only create a vector from a 1-D array!");
bp::throw_error_already_set();
}
vcl::vcl_size_t s = (vcl::vcl_size_t) array.shape(0);
std::vector<SCALARTYPE> cpu_vector(s);
vcl::vector<SCALARTYPE> *v = new vcl::vector<SCALARTYPE>(s, ctx);
for (vcl::vcl_size_t i=0; i < s; ++i)
cpu_vector[i] = bp::extract<SCALARTYPE>(array[i]);
vcl::copy(cpu_vector.begin(), cpu_vector.end(), v->begin());
return vcl::tools::shared_ptr<vcl::vector<SCALARTYPE> >(v);
}