/
mrcal-pywrap.c
4322 lines (3805 loc) · 184 KB
/
mrcal-pywrap.c
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// Copyright (c) 2017-2023 California Institute of Technology ("Caltech"). U.S.
// Government sponsorship acknowledged. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
#define NPY_NO_DEPRECATED_API NPY_API_VERSION
#include <stdbool.h>
#include <Python.h>
#include <structmember.h>
#include <numpy/arrayobject.h>
#include <signal.h>
#include <dogleg.h>
#if (CHOLMOD_VERSION > (CHOLMOD_VER_CODE(2,2))) && (CHOLMOD_VERSION < (CHOLMOD_VER_CODE(4,0)))
#include <cholmod_function.h>
#endif
#include "mrcal.h"
#include "mrcal-image.h"
#include "stereo.h"
#define IS_NULL(x) ((x) == NULL || (PyObject*)(x) == Py_None)
#define BARF(fmt, ...) PyErr_Format(PyExc_RuntimeError, "%s:%d %s(): "fmt, __FILE__, __LINE__, __func__, ## __VA_ARGS__)
// Python is silly. There's some nuance about signal handling where it sets a
// SIGINT (ctrl-c) handler to just set a flag, and the python layer then reads
// this flag and does the thing. Here I'm running C code, so SIGINT would set a
// flag, but not quit, so I can't interrupt the solver. Thus I reset the SIGINT
// handler to the default, and put it back to the python-specific version when
// I'm done
#define SET_SIGINT() struct sigaction sigaction_old; \
do { \
if( 0 != sigaction(SIGINT, \
&(struct sigaction){ .sa_handler = SIG_DFL }, \
&sigaction_old) ) \
{ \
BARF("sigaction() failed"); \
goto done; \
} \
} while(0)
#define RESET_SIGINT() do { \
if( 0 != sigaction(SIGINT, \
&sigaction_old, NULL )) \
BARF("sigaction-restore failed"); \
} while(0)
#define PERCENT_S_COMMA(s,n) "'%s',"
#define COMMA_LENSMODEL_NAME(s,n) , mrcal_lensmodel_name_unconfigured( &(mrcal_lensmodel_t){.type = MRCAL_##s} )
#define VALID_LENSMODELS_FORMAT "(" MRCAL_LENSMODEL_LIST(PERCENT_S_COMMA) ")"
#define VALID_LENSMODELS_ARGLIST MRCAL_LENSMODEL_LIST(COMMA_LENSMODEL_NAME)
#define CHECK_CONTIGUOUS(x) do { \
if( !PyArray_IS_C_CONTIGUOUS(x) ) \
{ \
BARF("All inputs must be c-style contiguous arrays (" #x ")"); \
return false; \
} } while(0)
#define COMMA ,
#define ARG_DEFINE( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) pytype name = initialvalue;
#define ARG_LIST_DEFINE(name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) pytype name,
#define ARG_LIST_CALL( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) name,
#define NAMELIST( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) #name ,
#define PARSECODE( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) parsecode
#define PARSEARG( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) parseprearg &name,
#define FREE_PYARRAY( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) Py_XDECREF(name_pyarrayobj);
#define PYMETHODDEF_ENTRY(function_prefix, name, args) {#name, \
(PyCFunction)function_prefix ## name, \
args, \
function_prefix ## name ## _docstring}
#define CHECK_LAYOUT( name, pytype, initialvalue, parsecode, parseprearg, name_pyarrayobj, npy_type, dims_ref) \
{ \
const int dims[] = dims_ref; \
int ndims = (int)sizeof(dims)/(int)sizeof(dims[0]); \
if(!_check_layout( #name, (PyArrayObject*)name_pyarrayobj, (int)npy_type, #npy_type, dims, ndims, #dims_ref, true )) \
goto done; \
}
static bool _check_layout(const char* name,
PyArrayObject* pyarrayobj,
int npy_type,
const char* npy_type_string,
const int* dims_ref,
int Ndims_ref,
const char* dims_ref_string,
bool do_check_for_contiguity)
{
if(!IS_NULL(pyarrayobj))
{
if( Ndims_ref > 0 )
{
if( PyArray_NDIM(pyarrayobj) != Ndims_ref )
{
BARF("'%s' must have exactly %d dims; got %d",
name,
Ndims_ref, PyArray_NDIM(pyarrayobj));
return false;
}
for(int i=0; i<Ndims_ref; i++)
if(dims_ref[i] >= 0 && dims_ref[i] != PyArray_DIMS(pyarrayobj)[i])
{
BARF("'%s' must have dimensions '%s' where <0 means 'any'. Dims %d got %ld instead",
name, dims_ref_string,
i, PyArray_DIMS(pyarrayobj)[i]);
return false;
}
}
if( npy_type >= 0 )
{
if( PyArray_TYPE(pyarrayobj) != npy_type )
{
BARF("'%s' must have type: %s",
name, npy_type_string);
return false;
}
if( do_check_for_contiguity &&
!PyArray_IS_C_CONTIGUOUS(pyarrayobj) )
{
BARF("'%s' must be c-style contiguous",
name);
return false;
}
}
}
return true;
}
// adds a reference to P,I,X, unless an error is reported
static PyObject* csr_from_cholmod_sparse( PyObject* P,
PyObject* I,
PyObject* X )
{
// I do the Python equivalent of this;
// scipy.sparse.csr_matrix((data, indices, indptr))
PyObject* result = NULL;
PyObject* module = NULL;
PyObject* method = NULL;
PyObject* args = NULL;
if(NULL == (module = PyImport_ImportModule("scipy.sparse")))
{
BARF("Couldn't import scipy.sparse. I need that to represent J");
goto done;
}
if(NULL == (method = PyObject_GetAttrString(module, "csr_matrix")))
{
BARF("Couldn't find 'csr_matrix' in scipy.sparse");
goto done;
}
// Here I'm assuming specific types in my cholmod arrays. I tried to
// _Static_assert it, but internally cholmod uses void*, so I can't do that
PyObject* MatrixDef = PyTuple_Pack(3, X, I, P);
args = PyTuple_Pack(1, MatrixDef);
Py_DECREF(MatrixDef);
if(NULL == (result = PyObject_CallObject(method, args)))
goto done; // reuse already-set error
// Testing code to dump out a dense representation of this matrix to a file.
// Can compare that file to what this function returns like this:
// Jf = np.fromfile("/tmp/J_17014_444.dat").reshape(17014,444)
// np.linalg.norm( Jf - J.toarray() )
// {
// #define P(A, index) ((unsigned int*)((A)->p))[index]
// #define I(A, index) ((unsigned int*)((A)->i))[index]
// #define X(A, index) ((double* )((A)->x))[index]
// char logfilename[128];
// sprintf(logfilename, "/tmp/J_%d_%d.dat",(int)Jt->ncol,(int)Jt->nrow);
// FILE* fp = fopen(logfilename, "w");
// double* Jrow;
// Jrow = malloc(Jt->nrow*sizeof(double));
// for(unsigned int icol=0; icol<Jt->ncol; icol++)
// {
// memset(Jrow, 0, Jt->nrow*sizeof(double));
// for(unsigned int i=P(Jt, icol); i<P(Jt, icol+1); i++)
// {
// int irow = I(Jt,i);
// double x = X(Jt,i);
// Jrow[irow] = x;
// }
// fwrite(Jrow,sizeof(double),Jt->nrow,fp);
// }
// fclose(fp);
// free(Jrow);
// #undef P
// #undef I
// #undef X
// }
done:
Py_XDECREF(module);
Py_XDECREF(method);
Py_XDECREF(args);
return result;
}
// A container for a CHOLMOD factorization
typedef struct {
PyObject_HEAD
// if(inited_common), the "common" has been initialized
// if(factorization), the factorization has been initialized
//
// So to use the object we need inited_common && factorization
bool inited_common;
cholmod_common common;
cholmod_factor* factorization;
// optimizer_callback should return it
// and I should have two solve methods:
} CHOLMOD_factorization;
// stolen from libdogleg
static int cholmod_error_callback(const char* s, ...)
{
va_list ap;
va_start(ap, s);
int ret = vfprintf(stderr, s, ap);
va_end(ap);
fprintf(stderr, "\n");
return ret;
}
// for my internal C usage
static void _CHOLMOD_factorization_release_internal(CHOLMOD_factorization* self)
{
if( self->factorization )
{
cholmod_free_factor(&self->factorization, &self->common);
self->factorization = NULL;
}
if( self->inited_common )
cholmod_finish(&self->common);
self->inited_common = false;
}
// for my internal C usage
static bool
_CHOLMOD_factorization_init_from_cholmod_sparse(CHOLMOD_factorization* self, cholmod_sparse* Jt)
{
if( !self->inited_common )
{
if( !cholmod_start(&self->common) )
{
BARF("Error trying to cholmod_start");
return false;
}
self->inited_common = true;
// stolen from libdogleg
// I want to use LGPL parts of CHOLMOD only, so I turn off the supernodal routines. This gave me a
// 25% performance hit in the solver for a particular set of optical calibration data.
self->common.supernodal = 0;
// I want all output to go to STDERR, not STDOUT
#if (CHOLMOD_VERSION <= (CHOLMOD_VER_CODE(2,2)))
self->common.print_function = cholmod_error_callback;
#elif (CHOLMOD_VERSION < (CHOLMOD_VER_CODE(4,0)))
CHOLMOD_FUNCTION_DEFAULTS ;
CHOLMOD_FUNCTION_PRINTF(&self->common) = cholmod_error_callback;
#else
SuiteSparse_config_printf_func_set(cholmod_error_callback);
#endif
}
self->factorization = cholmod_analyze(Jt, &self->common);
if(self->factorization == NULL)
{
BARF("cholmod_analyze() failed");
return false;
}
if( !cholmod_factorize(Jt, self->factorization, &self->common) )
{
BARF("cholmod_factorize() failed");
return false;
}
if(self->factorization->minor != self->factorization->n)
{
BARF("Got singular JtJ!");
return false;
}
return true;
}
static int
CHOLMOD_factorization_init(CHOLMOD_factorization* self, PyObject* args, PyObject* kwargs)
{
// Any existing factorization goes away. If this function fails, we lose the
// existing factorization, which is fine. I'm placing this on top so that
// __init__() will get rid of the old state
_CHOLMOD_factorization_release_internal(self);
// error by default
int result = -1;
char* keywords[] = {"J", NULL};
PyObject* Py_J = NULL;
PyObject* module = NULL;
PyObject* csr_matrix_type = NULL;
PyObject* Py_shape = NULL;
PyObject* Py_nnz = NULL;
PyObject* Py_data = NULL;
PyObject* Py_indices = NULL;
PyObject* Py_indptr = NULL;
PyObject* Py_has_sorted_indices = NULL;
PyObject* Py_h = NULL;
PyObject* Py_w = NULL;
if( !PyArg_ParseTupleAndKeywords(args, kwargs,
"|O:CHOLMOD_factorization.__init__",
keywords, &Py_J))
goto done;
if( Py_J == NULL )
{
// Success. Nothing to do
result = 0;
goto done;
}
if(NULL == (module = PyImport_ImportModule("scipy.sparse")))
{
BARF("Couldn't import scipy.sparse. I need that to represent J");
goto done;
}
if(NULL == (csr_matrix_type = PyObject_GetAttrString(module, "csr_matrix")))
{
BARF("Couldn't find 'csr_matrix' in scipy.sparse");
goto done;
}
if(!PyObject_IsInstance(Py_J, csr_matrix_type))
{
BARF("Argument J is must have type scipy.sparse.csr_matrix");
goto done;
}
#define GETATTR(x) \
if(NULL == (Py_ ## x = PyObject_GetAttrString(Py_J, #x))) \
{ \
BARF("Couldn't get J." # x); \
goto done; \
}
GETATTR(shape);
GETATTR(nnz);
GETATTR(data);
GETATTR(indices);
GETATTR(indptr);
GETATTR(has_sorted_indices);
if(!PySequence_Check(Py_shape))
{
BARF("J.shape should be an iterable");
goto done;
}
int lenshape = PySequence_Length(Py_shape);
if( lenshape != 2 )
{
if(lenshape < 0)
BARF("Failed to get len(J.shape)");
else
BARF("len(J.shape) should be exactly 2, but instead got %d", lenshape);
goto done;
}
Py_h = PySequence_GetItem(Py_shape, 0);
if(Py_h == NULL)
{
BARF("Error getting J.shape[0]");
goto done;
}
Py_w = PySequence_GetItem(Py_shape, 1);
if(Py_w == NULL)
{
BARF("Error getting J.shape[1]");
goto done;
}
long nnz;
if(PyLong_Check(Py_nnz)) nnz = PyLong_AsLong(Py_nnz);
else
{
BARF("Error interpreting nnz as an integer");
goto done;
}
long h;
if(PyLong_Check(Py_h)) h = PyLong_AsLong(Py_h);
else
{
BARF("Error interpreting J.shape[0] as an integer");
goto done;
}
long w;
if(PyLong_Check(Py_w)) w = PyLong_AsLong(Py_w);
else
{
BARF("Error interpreting J.shape[1] as an integer");
goto done;
}
#define CHECK_NUMPY_ARRAY(x, dtype) \
if( !PyArray_Check((PyArrayObject*)Py_ ## x) ) \
{ \
BARF("J."#x " must be a numpy array"); \
goto done; \
} \
if( 1 != PyArray_NDIM((PyArrayObject*)Py_ ## x) ) \
{ \
BARF("J."#x " must be a 1-dimensional numpy array. Instead got %d dimensions", \
PyArray_NDIM((PyArrayObject*)Py_ ## x)); \
goto done; \
} \
if( PyArray_TYPE((PyArrayObject*)Py_ ## x) != dtype ) \
{ \
BARF("J."#x " must have dtype: " #dtype); \
goto done; \
} \
if( !PyArray_IS_C_CONTIGUOUS((PyArrayObject*)Py_ ## x) ) \
{ \
BARF("J."#x " must live in contiguous memory"); \
goto done; \
}
CHECK_NUMPY_ARRAY(data, NPY_FLOAT64);
CHECK_NUMPY_ARRAY(indices, NPY_INT32);
CHECK_NUMPY_ARRAY(indptr, NPY_INT32);
// OK, the input looks good. I guess I can tell CHOLMOD about it
// My convention is to store row-major matrices, but CHOLMOD stores
// col-major matrices. So I keep the same data, but tell CHOLMOD that I'm
// storing Jt and not J
cholmod_sparse Jt = {
.nrow = w,
.ncol = h,
.nzmax = nnz,
.p = PyArray_DATA((PyArrayObject*)Py_indptr),
.i = PyArray_DATA((PyArrayObject*)Py_indices),
.x = PyArray_DATA((PyArrayObject*)Py_data),
.stype = 0, // not symmetric
.itype = CHOLMOD_INT,
.xtype = CHOLMOD_REAL,
.dtype = CHOLMOD_DOUBLE,
.sorted = PyObject_IsTrue(Py_has_sorted_indices),
.packed = 1
};
if(!_CHOLMOD_factorization_init_from_cholmod_sparse(self, &Jt))
goto done;
result = 0;
done:
if(result != 0)
_CHOLMOD_factorization_release_internal(self);
Py_XDECREF(module);
Py_XDECREF(csr_matrix_type);
Py_XDECREF(Py_shape);
Py_XDECREF(Py_nnz);
Py_XDECREF(Py_data);
Py_XDECREF(Py_indices);
Py_XDECREF(Py_indptr);
Py_XDECREF(Py_has_sorted_indices);
Py_XDECREF(Py_h);
Py_XDECREF(Py_w);
return result;
#undef GETATTR
#undef CHECK_NUMPY_ARRAY
}
static void CHOLMOD_factorization_dealloc(CHOLMOD_factorization* self)
{
_CHOLMOD_factorization_release_internal(self);
Py_TYPE(self)->tp_free((PyObject*)self);
}
static PyObject* CHOLMOD_factorization_str(CHOLMOD_factorization* self)
{
if(!(self->inited_common && self->factorization))
return PyUnicode_FromString("No factorization given");
return PyUnicode_FromFormat("Initialized with a valid factorization. N=%d",
self->factorization->n);
}
static PyObject*
CHOLMOD_factorization_solve_xt_JtJ_bt(CHOLMOD_factorization* self, PyObject* args, PyObject* kwargs)
{
cholmod_dense* M = NULL;
cholmod_dense* Y = NULL;
cholmod_dense* E = NULL;
// error by default
PyObject* result = NULL;
PyObject* Py_out = NULL;
char* keywords[] = {"bt", NULL};
PyObject* Py_bt = NULL;
if(!(self->inited_common && self->factorization))
{
BARF("No factorization has been computed");
goto done;
}
if( !PyArg_ParseTupleAndKeywords(args, kwargs,
"O:CHOLMOD_factorization.solve_xt_JtJ_bt",
keywords, &Py_bt))
goto done;
if( Py_bt == NULL || !PyArray_Check((PyArrayObject*)Py_bt) )
{
BARF("bt must be a numpy array");
goto done;
}
int ndim = PyArray_NDIM((PyArrayObject*)Py_bt);
if( ndim < 1 )
{
BARF("bt must be at least a 1-dimensional numpy array. Instead got %d dimensions",
ndim);
goto done;
}
int Nstate = (int)PyArray_DIMS((PyArrayObject*)Py_bt)[ndim-1];
int Nrhs = (int)PyArray_SIZE((PyArrayObject*)Py_bt) / Nstate;
if( self->factorization->n != (unsigned)Nstate )
{
BARF("bt must be a 2-dimensional numpy array with %d cols (that's what the factorization has). Instead got %d cols",
self->factorization->n,
Nstate);
goto done;
}
if( PyArray_TYPE((PyArrayObject*)Py_bt) != NPY_FLOAT64 )
{
BARF("bt must have dtype=float");
goto done;
}
if( !PyArray_IS_C_CONTIGUOUS((PyArrayObject*)Py_bt) )
{
BARF("bt must live in contiguous memory");
goto done;
}
// Alright. b looks good-enough to use
if( 0 == Nrhs )
{
// Degenerate input (0 columns). Just return it, and I'm done
result = Py_bt;
Py_INCREF(result);
goto done;
}
cholmod_dense b = {
.nrow = Nstate,
.ncol = Nrhs,
.nzmax = Nrhs * Nstate,
.d = Nstate,
.x = PyArray_DATA((PyArrayObject*)Py_bt),
.xtype = CHOLMOD_REAL,
.dtype = CHOLMOD_DOUBLE };
Py_out = PyArray_SimpleNew(ndim,
PyArray_DIMS((PyArrayObject*)Py_bt),
NPY_DOUBLE);
if(Py_out == NULL)
{
BARF("Couldn't allocate Py_out");
goto done;
}
cholmod_dense out = {
.nrow = Nstate,
.ncol = Nrhs,
.nzmax = Nrhs * Nstate,
.d = Nstate,
.x = PyArray_DATA((PyArrayObject*)Py_out),
.xtype = CHOLMOD_REAL,
.dtype = CHOLMOD_DOUBLE };
M = &out;
Y = NULL;
E = NULL;
if(!cholmod_solve2( CHOLMOD_A, self->factorization,
&b, NULL,
&M, NULL, &Y, &E,
&self->common))
{
BARF("cholmod_solve2() failed");
goto done;
}
if( M != &out )
{
BARF("cholmod_solve2() reallocated out! We leaked memory");
goto done;
}
Py_INCREF(Py_out);
result = Py_out;
done:
if(E != NULL) cholmod_free_dense (&E, &self->common);
if(Y != NULL) cholmod_free_dense (&Y, &self->common);
Py_XDECREF(Py_out);
return result;
}
static PyObject*
CHOLMOD_factorization_rcond(CHOLMOD_factorization* self,
PyObject* NPY_UNUSED(args))
{
if(!(self->inited_common && self->factorization))
{
BARF("No factorization has been computed");
return NULL;
}
return PyFloat_FromDouble(cholmod_rcond( self->factorization,
&self->common));
}
static const char CHOLMOD_factorization_docstring[] =
#include "CHOLMOD_factorization.docstring.h"
;
static const char CHOLMOD_factorization_solve_xt_JtJ_bt_docstring[] =
#include "CHOLMOD_factorization_solve_xt_JtJ_bt.docstring.h"
;
static const char CHOLMOD_factorization_rcond_docstring[] =
#include "CHOLMOD_factorization_rcond.docstring.h"
;
static PyMethodDef CHOLMOD_factorization_methods[] =
{
PYMETHODDEF_ENTRY(CHOLMOD_factorization_, solve_xt_JtJ_bt, METH_VARARGS | METH_KEYWORDS),
PYMETHODDEF_ENTRY(CHOLMOD_factorization_, rcond, METH_NOARGS),
{}
};
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wmissing-braces"
// PyObject_HEAD_INIT throws
// warning: missing braces around initializer []
// This isn't mine to fix, so I'm ignoring it
static PyTypeObject CHOLMOD_factorization_type =
{
PyObject_HEAD_INIT(NULL)
.tp_name = "mrcal.CHOLMOD_factorization",
.tp_basicsize = sizeof(CHOLMOD_factorization),
.tp_new = PyType_GenericNew,
.tp_init = (initproc)CHOLMOD_factorization_init,
.tp_dealloc = (destructor)CHOLMOD_factorization_dealloc,
.tp_methods = CHOLMOD_factorization_methods,
.tp_str = (reprfunc)CHOLMOD_factorization_str,
.tp_flags = Py_TPFLAGS_DEFAULT,
.tp_doc = CHOLMOD_factorization_docstring,
};
#pragma GCC diagnostic pop
// For the C code. Create a new Python CHOLMOD_factorization object from a C
// cholmod_sparse structure
static PyObject*
CHOLMOD_factorization_from_cholmod_sparse(cholmod_sparse* Jt)
{
PyObject* self = PyObject_CallObject((PyObject*)&CHOLMOD_factorization_type, NULL);
if(NULL == self)
return NULL;
if(!_CHOLMOD_factorization_init_from_cholmod_sparse((CHOLMOD_factorization*)self, Jt))
{
Py_DECREF(self);
return NULL;
}
return self;
}
static bool parse_lensmodel_from_arg(// output
mrcal_lensmodel_t* lensmodel,
// input
const char* lensmodel_cstring)
{
mrcal_lensmodel_from_name(lensmodel, lensmodel_cstring);
if( !mrcal_lensmodel_type_is_valid(lensmodel->type) )
{
switch(lensmodel->type)
{
case MRCAL_LENSMODEL_INVALID:
// this should never (rarely?) happen
BARF("Lens model '%s': error parsing",
lensmodel_cstring);
return false;
case MRCAL_LENSMODEL_INVALID_BADCONFIG:
BARF("Lens model '%s': error parsing the required configuration",
lensmodel_cstring);
return false;
case MRCAL_LENSMODEL_INVALID_MISSINGCONFIG:
BARF("Lens model '%s': missing the required configuration",
lensmodel_cstring);
return false;
case MRCAL_LENSMODEL_INVALID_TYPE:
BARF("Invalid lens model type was passed in: '%s'. Must be one of " VALID_LENSMODELS_FORMAT,
lensmodel_cstring
VALID_LENSMODELS_ARGLIST);
return false;
default:
BARF("Lens model '%s' produced an unexpected error: lensmodel->type=%d. This should never happen",
lensmodel_cstring,
(int)lensmodel->type);
return false;
}
return false;
}
return true;
}
static PyObject* lensmodel_metadata_and_config(PyObject* NPY_UNUSED(self),
PyObject* args)
{
PyObject* result = NULL;
char* lensmodel_string = NULL;
if(!PyArg_ParseTuple( args, "s", &lensmodel_string ))
goto done;
mrcal_lensmodel_t lensmodel;
if(!parse_lensmodel_from_arg(&lensmodel, lensmodel_string))
goto done;
mrcal_lensmodel_metadata_t meta = mrcal_lensmodel_metadata(&lensmodel);
#define MRCAL_ITEM_BUILDVALUE_DEF( name, type, pybuildvaluecode, PRIcode,SCNcode, bitfield, cookie) " s "pybuildvaluecode
#define MRCAL_ITEM_BUILDVALUE_VALUE(name, type, pybuildvaluecode, PRIcode,SCNcode, bitfield, cookie) , #name, cookie name
if(lensmodel.type == MRCAL_LENSMODEL_CAHVORE )
result = Py_BuildValue("{"
MRCAL_LENSMODEL_META_LIST(MRCAL_ITEM_BUILDVALUE_DEF, )
MRCAL_LENSMODEL_CAHVORE_CONFIG_LIST(MRCAL_ITEM_BUILDVALUE_DEF, )
"}"
MRCAL_LENSMODEL_META_LIST(MRCAL_ITEM_BUILDVALUE_VALUE, meta.)
MRCAL_LENSMODEL_CAHVORE_CONFIG_LIST(MRCAL_ITEM_BUILDVALUE_VALUE, lensmodel.LENSMODEL_CAHVORE__config.));
else if(lensmodel.type == MRCAL_LENSMODEL_SPLINED_STEREOGRAPHIC )
result = Py_BuildValue("{"
MRCAL_LENSMODEL_META_LIST(MRCAL_ITEM_BUILDVALUE_DEF, )
MRCAL_LENSMODEL_SPLINED_STEREOGRAPHIC_CONFIG_LIST(MRCAL_ITEM_BUILDVALUE_DEF, )
"}"
MRCAL_LENSMODEL_META_LIST(MRCAL_ITEM_BUILDVALUE_VALUE, meta.)
MRCAL_LENSMODEL_SPLINED_STEREOGRAPHIC_CONFIG_LIST(MRCAL_ITEM_BUILDVALUE_VALUE, lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.));
else
result = Py_BuildValue("{"
MRCAL_LENSMODEL_META_LIST(MRCAL_ITEM_BUILDVALUE_DEF, )
"}"
MRCAL_LENSMODEL_META_LIST(MRCAL_ITEM_BUILDVALUE_VALUE, meta.));
Py_INCREF(result);
done:
return result;
}
static PyObject* knots_for_splined_models(PyObject* NPY_UNUSED(self),
PyObject* args)
{
PyObject* result = NULL;
PyArrayObject* py_ux = NULL;
PyArrayObject* py_uy = NULL;
char* lensmodel_string = NULL;
if(!PyArg_ParseTuple( args, "s", &lensmodel_string ))
goto done;
mrcal_lensmodel_t lensmodel;
if(!parse_lensmodel_from_arg(&lensmodel, lensmodel_string))
goto done;
if(lensmodel.type != MRCAL_LENSMODEL_SPLINED_STEREOGRAPHIC)
{
BARF( "This function works only with the MRCAL_LENSMODEL_SPLINED_STEREOGRAPHIC model. %s passed in",
lensmodel_string);
goto done;
}
{
double ux[lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.Nx];
double uy[lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.Ny];
if(!mrcal_knots_for_splined_models(ux,uy, &lensmodel))
{
BARF( "mrcal_knots_for_splined_models() failed");
goto done;
}
npy_intp dims[1];
dims[0] = lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.Nx;
py_ux = (PyArrayObject*)PyArray_SimpleNew(1, dims, NPY_DOUBLE);
if(py_ux == NULL)
{
BARF("Couldn't allocate ux");
goto done;
}
dims[0] = lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.Ny;
py_uy = (PyArrayObject*)PyArray_SimpleNew(1, dims, NPY_DOUBLE);
if(py_uy == NULL)
{
BARF("Couldn't allocate uy");
goto done;
}
memcpy(PyArray_DATA(py_ux), ux, lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.Nx*sizeof(double));
memcpy(PyArray_DATA(py_uy), uy, lensmodel.LENSMODEL_SPLINED_STEREOGRAPHIC__config.Ny*sizeof(double));
}
result = Py_BuildValue("OO", py_ux, py_uy);
done:
Py_XDECREF(py_ux);
Py_XDECREF(py_uy);
return result;
}
static PyObject* lensmodel_num_params(PyObject* NPY_UNUSED(self),
PyObject* args)
{
PyObject* result = NULL;
char* lensmodel_string = NULL;
if(!PyArg_ParseTuple( args, "s", &lensmodel_string ))
goto done;
mrcal_lensmodel_t lensmodel;
if(!parse_lensmodel_from_arg(&lensmodel, lensmodel_string))
goto done;
int Nparams = mrcal_lensmodel_num_params(&lensmodel);
result = Py_BuildValue("i", Nparams);
done:
return result;
}
static PyObject* supported_lensmodels(PyObject* NPY_UNUSED(self),
PyObject* NPY_UNUSED(args))
{
PyObject* result = NULL;
const char* const* names = mrcal_supported_lensmodel_names();
// I now have a NULL-terminated list of NULL-terminated strings. Get N
int N=0;
while(names[N] != NULL)
N++;
result = PyTuple_New(N);
if(result == NULL)
{
BARF("Failed PyTuple_New(%d)", N);
goto done;
}
for(int i=0; i<N; i++)
{
PyObject* name = Py_BuildValue("s", names[i]);
if( name == NULL )
{
BARF("Failed Py_BuildValue...");
Py_DECREF(result);
result = NULL;
goto done;
}
PyTuple_SET_ITEM(result, i, name);
}
done:
return result;
}
// just like PyArray_Converter(), but leave None as None
static
int PyArray_Converter_leaveNone(PyObject* obj, PyObject** address)
{
if(obj == Py_None)
{
*address = Py_None;
Py_INCREF(Py_None);
return 1;
}
return PyArray_Converter(obj,address);
}
// For various utility functions. Accepts ONE lens model, not N of them like the optimization function
#define LENSMODEL_ONE_ARGUMENTS(_, suffix) \
_(lensmodel ## suffix, char*, NULL, "s", , NULL, -1, {} ) \
_(intrinsics ## suffix, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, intrinsics ## suffix, NPY_DOUBLE, {-1 } )
#define ARGDEF_observations_point_triangulated(_) \
_(observations_point_triangulated, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, observations_point_triangulated, NPY_DOUBLE, {-1 COMMA 3 } )
#define ARGDEF_indices_point_triangulated_camintrinsics_camextrinsics(_) \
_(indices_point_triangulated_camintrinsics_camextrinsics,PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, indices_point_triangulated_camintrinsics_camextrinsics, NPY_INT32, {-1 COMMA 3 } )
#define OPTIMIZE_ARGUMENTS_REQUIRED(_) \
_(intrinsics, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, intrinsics, NPY_DOUBLE, {-1 COMMA -1 } ) \
_(lensmodel, char*, NULL, "s", , NULL, -1, {} ) \
_(imagersizes, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, imagersizes, NPY_INT32, {-1 COMMA 2 } )
// Defaults for do_optimize... MUST match those in ingest_packed_state()
//
// Accepting observed_pixel_uncertainty for backwards compatibility. It doesn't
// do anything anymore
#define OPTIMIZE_ARGUMENTS_OPTIONAL(_) \
_(extrinsics_rt_fromref, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, extrinsics_rt_fromref, NPY_DOUBLE, {-1 COMMA 6 } ) \
_(frames_rt_toref, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, frames_rt_toref, NPY_DOUBLE, {-1 COMMA 6 } ) \
_(points, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, points, NPY_DOUBLE, {-1 COMMA 3 } ) \
_(observations_board, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, observations_board, NPY_DOUBLE, {-1 COMMA -1 COMMA -1 COMMA 3 } ) \
_(indices_frame_camintrinsics_camextrinsics,PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, indices_frame_camintrinsics_camextrinsics, NPY_INT32, {-1 COMMA 3 } ) \
_(observations_point, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, observations_point, NPY_DOUBLE, {-1 COMMA 3 } ) \
_(indices_point_camintrinsics_camextrinsics,PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, indices_point_camintrinsics_camextrinsics, NPY_INT32, {-1 COMMA 3 } ) \
ARGDEF_observations_point_triangulated(_) \
ARGDEF_indices_point_triangulated_camintrinsics_camextrinsics(_) \
_(observed_pixel_uncertainty, double, -1.0, "d", , NULL, -1, {}) \
_(calobject_warp, PyArrayObject*, NULL, "O&", PyArray_Converter_leaveNone COMMA, calobject_warp, NPY_DOUBLE, {2}) \
_(Npoints_fixed, int, 0, "i", , NULL, -1, {}) \
_(do_optimize_intrinsics_core, int, -1, "p", , NULL, -1, {}) \
_(do_optimize_intrinsics_distortions, int, -1, "p", , NULL, -1, {}) \
_(do_optimize_extrinsics, int, -1, "p", , NULL, -1, {}) \
_(do_optimize_frames, int, -1, "p", , NULL, -1, {}) \
_(do_optimize_calobject_warp, int, -1, "p", , NULL, -1, {}) \
_(calibration_object_spacing, double, -1.0, "d", , NULL, -1, {}) \
_(point_min_range, double, -1.0, "d", , NULL, -1, {}) \
_(point_max_range, double, -1.0, "d", , NULL, -1, {}) \
_(verbose, int, 0, "p", , NULL, -1, {}) \
_(do_apply_regularization, int, 1, "p", , NULL, -1, {}) \
_(do_apply_regularization_unity_cam01,int, 0, "p", , NULL, -1, {}) \
_(do_apply_outlier_rejection, int, 1, "p", , NULL, -1, {}) \
_(imagepaths, PyObject*, NULL, "O", , NULL, -1, {})
/* imagepaths is in the argument list purely to make the
mrcal-show-residuals-board-observation tool work. The python code doesn't
actually touch it */
#define OPTIMIZER_CALLBACK_ARGUMENTS_OPTIONAL_EXTRA(_) \
_(no_jacobian, int, 0, "p", , NULL, -1, {}) \
_(no_factorization, int, 0, "p", , NULL, -1, {})
typedef enum {
OPTIMIZEMODE_OPTIMIZE,
OPTIMIZEMODE_CALLBACK,
OPTIMIZEMODE_DRTRRP_DB
} optimizemode_t;
static bool lensmodel_one_validate_args( // out
mrcal_lensmodel_t* mrcal_lensmodel,
// in
LENSMODEL_ONE_ARGUMENTS(ARG_LIST_DEFINE, )
bool do_check_layout)
{
if(do_check_layout)
{
LENSMODEL_ONE_ARGUMENTS(CHECK_LAYOUT, );
}
if(!parse_lensmodel_from_arg(mrcal_lensmodel, lensmodel))
return false;
int NlensParams = mrcal_lensmodel_num_params(mrcal_lensmodel);
int NlensParams_have = PyArray_DIMS(intrinsics)[PyArray_NDIM(intrinsics)-1];
if( NlensParams != NlensParams_have )