467 lines
14 KiB
C
467 lines
14 KiB
C
/*
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* This program is free software; you can redistribute it and/or
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* modify it under the terms of the GNU General Public License
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* as published by the Free Software Foundation; either version 2
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* of the License, or (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software Foundation,
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* Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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*/
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/** \file
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* \ingroup mathutils
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*
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* This file defines the 'mathutils.kdtree' module, a general purpose module to access
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* blenders kdtree for 3d spatial lookups.
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*/
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#include <Python.h>
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#include "MEM_guardedalloc.h"
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#include "BLI_kdtree.h"
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#include "BLI_utildefines.h"
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#include "../generic/py_capi_utils.h"
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#include "../generic/python_utildefines.h"
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#include "mathutils.h"
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#include "mathutils_kdtree.h" /* own include */
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#include "BLI_strict_flags.h"
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typedef struct {
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PyObject_HEAD
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KDTree_3d *obj;
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uint maxsize;
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uint count;
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uint count_balance; /* size when we last balanced */
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} PyKDTree;
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/* -------------------------------------------------------------------- */
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/* Utility helper functions */
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static void kdtree_nearest_to_py_tuple(const KDTreeNearest_3d *nearest, PyObject *py_retval)
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{
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BLI_assert(nearest->index >= 0);
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BLI_assert(PyTuple_GET_SIZE(py_retval) == 3);
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PyTuple_SET_ITEMS(py_retval,
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Vector_CreatePyObject(nearest->co, 3, NULL),
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PyLong_FromLong(nearest->index),
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PyFloat_FromDouble(nearest->dist));
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}
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static PyObject *kdtree_nearest_to_py(const KDTreeNearest_3d *nearest)
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{
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PyObject *py_retval;
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py_retval = PyTuple_New(3);
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kdtree_nearest_to_py_tuple(nearest, py_retval);
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return py_retval;
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}
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static PyObject *kdtree_nearest_to_py_and_check(const KDTreeNearest_3d *nearest)
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{
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PyObject *py_retval;
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py_retval = PyTuple_New(3);
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if (nearest->index != -1) {
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kdtree_nearest_to_py_tuple(nearest, py_retval);
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}
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else {
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PyC_Tuple_Fill(py_retval, Py_None);
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}
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return py_retval;
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}
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/* -------------------------------------------------------------------- */
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/* KDTree */
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/* annoying since arg parsing won't check overflow */
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#define UINT_IS_NEG(n) ((n) > INT_MAX)
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static int PyKDTree__tp_init(PyKDTree *self, PyObject *args, PyObject *kwargs)
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{
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uint maxsize;
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const char *keywords[] = {"size", NULL};
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if (!PyArg_ParseTupleAndKeywords(args, kwargs, "I:KDTree", (char **)keywords, &maxsize)) {
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return -1;
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}
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if (UINT_IS_NEG(maxsize)) {
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PyErr_SetString(PyExc_ValueError, "negative 'size' given");
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return -1;
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}
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self->obj = BLI_kdtree_3d_new(maxsize);
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self->maxsize = maxsize;
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self->count = 0;
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self->count_balance = 0;
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return 0;
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}
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static void PyKDTree__tp_dealloc(PyKDTree *self)
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{
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BLI_kdtree_3d_free(self->obj);
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Py_TYPE(self)->tp_free((PyObject *)self);
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}
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PyDoc_STRVAR(py_kdtree_insert_doc,
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".. method:: insert(co, index)\n"
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"\n"
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" Insert a point into the KDTree.\n"
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"\n"
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" :arg co: Point 3d position.\n"
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" :type co: float triplet\n"
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" :arg index: The index of the point.\n"
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" :type index: int\n");
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static PyObject *py_kdtree_insert(PyKDTree *self, PyObject *args, PyObject *kwargs)
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{
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PyObject *py_co;
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float co[3];
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int index;
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const char *keywords[] = {"co", "index", NULL};
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if (!PyArg_ParseTupleAndKeywords(args, kwargs, "Oi:insert", (char **)keywords, &py_co, &index)) {
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return NULL;
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}
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if (mathutils_array_parse(co, 3, 3, py_co, "insert: invalid 'co' arg") == -1) {
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return NULL;
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}
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if (index < 0) {
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PyErr_SetString(PyExc_ValueError, "negative index given");
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return NULL;
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}
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if (self->count >= self->maxsize) {
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PyErr_SetString(PyExc_RuntimeError, "Trying to insert more items than KDTree has room for");
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return NULL;
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}
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BLI_kdtree_3d_insert(self->obj, index, co);
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self->count++;
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Py_RETURN_NONE;
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}
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PyDoc_STRVAR(py_kdtree_balance_doc,
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".. method:: balance()\n"
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"\n"
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" Balance the tree.\n"
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"\n"
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".. note::\n"
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"\n"
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" This builds the entire tree, avoid calling after each insertion.\n");
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static PyObject *py_kdtree_balance(PyKDTree *self)
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{
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BLI_kdtree_3d_balance(self->obj);
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self->count_balance = self->count;
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Py_RETURN_NONE;
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}
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struct PyKDTree_NearestData {
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PyObject *py_filter;
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bool is_error;
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};
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static int py_find_nearest_cb(void *user_data, int index, const float co[3], float dist_sq)
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{
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UNUSED_VARS(co, dist_sq);
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struct PyKDTree_NearestData *data = user_data;
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PyObject *py_args = PyTuple_New(1);
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PyTuple_SET_ITEM(py_args, 0, PyLong_FromLong(index));
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PyObject *result = PyObject_CallObject(data->py_filter, py_args);
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Py_DECREF(py_args);
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if (result) {
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bool use_node;
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const int ok = PyC_ParseBool(result, &use_node);
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Py_DECREF(result);
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if (ok) {
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return (int)use_node;
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}
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}
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data->is_error = true;
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return -1;
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}
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PyDoc_STRVAR(py_kdtree_find_doc,
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".. method:: find(co, filter=None)\n"
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"\n"
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" Find nearest point to ``co``.\n"
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"\n"
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" :arg co: 3d coordinates.\n"
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" :type co: float triplet\n"
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" :arg filter: function which takes an index and returns True for indices to "
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"include in the search.\n"
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" :type filter: callable\n"
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" :return: Returns (:class:`Vector`, index, distance).\n"
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" :rtype: :class:`tuple`\n");
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static PyObject *py_kdtree_find(PyKDTree *self, PyObject *args, PyObject *kwargs)
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{
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PyObject *py_co, *py_filter = NULL;
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float co[3];
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KDTreeNearest_3d nearest;
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const char *keywords[] = {"co", "filter", NULL};
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if (!PyArg_ParseTupleAndKeywords(
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args, kwargs, "O|$O:find", (char **)keywords, &py_co, &py_filter)) {
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return NULL;
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}
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if (mathutils_array_parse(co, 3, 3, py_co, "find: invalid 'co' arg") == -1) {
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return NULL;
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}
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if (self->count != self->count_balance) {
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PyErr_SetString(PyExc_RuntimeError, "KDTree must be balanced before calling find()");
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return NULL;
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}
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nearest.index = -1;
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if (py_filter == NULL) {
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BLI_kdtree_3d_find_nearest(self->obj, co, &nearest);
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}
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else {
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struct PyKDTree_NearestData data = {0};
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data.py_filter = py_filter;
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data.is_error = false;
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BLI_kdtree_3d_find_nearest_cb(self->obj, co, py_find_nearest_cb, &data, &nearest);
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if (data.is_error) {
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return NULL;
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}
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}
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return kdtree_nearest_to_py_and_check(&nearest);
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}
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PyDoc_STRVAR(py_kdtree_find_n_doc,
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".. method:: find_n(co, n)\n"
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"\n"
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" Find nearest ``n`` points to ``co``.\n"
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"\n"
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" :arg co: 3d coordinates.\n"
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" :type co: float triplet\n"
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" :arg n: Number of points to find.\n"
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" :type n: int\n"
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" :return: Returns a list of tuples (:class:`Vector`, index, distance).\n"
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" :rtype: :class:`list`\n");
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static PyObject *py_kdtree_find_n(PyKDTree *self, PyObject *args, PyObject *kwargs)
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{
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PyObject *py_list;
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PyObject *py_co;
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float co[3];
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KDTreeNearest_3d *nearest;
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uint n;
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int i, found;
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const char *keywords[] = {"co", "n", NULL};
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if (!PyArg_ParseTupleAndKeywords(args, kwargs, "OI:find_n", (char **)keywords, &py_co, &n)) {
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return NULL;
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}
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if (mathutils_array_parse(co, 3, 3, py_co, "find_n: invalid 'co' arg") == -1) {
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return NULL;
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}
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if (UINT_IS_NEG(n)) {
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PyErr_SetString(PyExc_RuntimeError, "negative 'n' given");
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return NULL;
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}
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if (self->count != self->count_balance) {
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PyErr_SetString(PyExc_RuntimeError, "KDTree must be balanced before calling find_n()");
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return NULL;
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}
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nearest = MEM_mallocN(sizeof(KDTreeNearest_3d) * n, __func__);
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found = BLI_kdtree_3d_find_nearest_n(self->obj, co, nearest, n);
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py_list = PyList_New(found);
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for (i = 0; i < found; i++) {
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PyList_SET_ITEM(py_list, i, kdtree_nearest_to_py(&nearest[i]));
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}
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MEM_freeN(nearest);
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return py_list;
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}
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PyDoc_STRVAR(py_kdtree_find_range_doc,
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".. method:: find_range(co, radius)\n"
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"\n"
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" Find all points within ``radius`` of ``co``.\n"
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"\n"
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" :arg co: 3d coordinates.\n"
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" :type co: float triplet\n"
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" :arg radius: Distance to search for points.\n"
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" :type radius: float\n"
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" :return: Returns a list of tuples (:class:`Vector`, index, distance).\n"
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" :rtype: :class:`list`\n");
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static PyObject *py_kdtree_find_range(PyKDTree *self, PyObject *args, PyObject *kwargs)
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{
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PyObject *py_list;
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PyObject *py_co;
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float co[3];
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KDTreeNearest_3d *nearest = NULL;
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float radius;
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int i, found;
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const char *keywords[] = {"co", "radius", NULL};
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if (!PyArg_ParseTupleAndKeywords(
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args, kwargs, "Of:find_range", (char **)keywords, &py_co, &radius)) {
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return NULL;
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}
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if (mathutils_array_parse(co, 3, 3, py_co, "find_range: invalid 'co' arg") == -1) {
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return NULL;
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}
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if (radius < 0.0f) {
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PyErr_SetString(PyExc_RuntimeError, "negative radius given");
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return NULL;
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}
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if (self->count != self->count_balance) {
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PyErr_SetString(PyExc_RuntimeError, "KDTree must be balanced before calling find_range()");
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return NULL;
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}
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found = BLI_kdtree_3d_range_search(self->obj, co, &nearest, radius);
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py_list = PyList_New(found);
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for (i = 0; i < found; i++) {
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PyList_SET_ITEM(py_list, i, kdtree_nearest_to_py(&nearest[i]));
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}
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if (nearest) {
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MEM_freeN(nearest);
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}
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return py_list;
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}
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static PyMethodDef PyKDTree_methods[] = {
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{"insert", (PyCFunction)py_kdtree_insert, METH_VARARGS | METH_KEYWORDS, py_kdtree_insert_doc},
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{"balance", (PyCFunction)py_kdtree_balance, METH_NOARGS, py_kdtree_balance_doc},
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{"find", (PyCFunction)py_kdtree_find, METH_VARARGS | METH_KEYWORDS, py_kdtree_find_doc},
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{"find_n", (PyCFunction)py_kdtree_find_n, METH_VARARGS | METH_KEYWORDS, py_kdtree_find_n_doc},
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{"find_range",
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(PyCFunction)py_kdtree_find_range,
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METH_VARARGS | METH_KEYWORDS,
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py_kdtree_find_range_doc},
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{NULL, NULL, 0, NULL},
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};
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PyDoc_STRVAR(py_KDtree_doc,
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"KdTree(size) -> new kd-tree initialized to hold ``size`` items.\n"
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"\n"
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".. note::\n"
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"\n"
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" :class:`KDTree.balance` must have been called before using any of the ``find`` "
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"methods.\n");
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PyTypeObject PyKDTree_Type = {
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PyVarObject_HEAD_INIT(NULL, 0) "KDTree", /* tp_name */
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sizeof(PyKDTree), /* tp_basicsize */
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0, /* tp_itemsize */
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/* methods */
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(destructor)PyKDTree__tp_dealloc, /* tp_dealloc */
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(printfunc)NULL, /* tp_print */
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NULL, /* tp_getattr */
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NULL, /* tp_setattr */
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NULL, /* tp_compare */
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NULL, /* tp_repr */
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NULL, /* tp_as_number */
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NULL, /* tp_as_sequence */
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NULL, /* tp_as_mapping */
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NULL, /* tp_hash */
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NULL, /* tp_call */
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NULL, /* tp_str */
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NULL, /* tp_getattro */
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NULL, /* tp_setattro */
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NULL, /* tp_as_buffer */
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Py_TPFLAGS_DEFAULT, /* tp_flags */
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py_KDtree_doc, /* Documentation string */
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NULL, /* tp_traverse */
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NULL, /* tp_clear */
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NULL, /* tp_richcompare */
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0, /* tp_weaklistoffset */
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NULL, /* tp_iter */
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NULL, /* tp_iternext */
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(struct PyMethodDef *)PyKDTree_methods, /* tp_methods */
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NULL, /* tp_members */
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NULL, /* tp_getset */
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NULL, /* tp_base */
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NULL, /* tp_dict */
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NULL, /* tp_descr_get */
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NULL, /* tp_descr_set */
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0, /* tp_dictoffset */
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(initproc)PyKDTree__tp_init, /* tp_init */
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(allocfunc)PyType_GenericAlloc, /* tp_alloc */
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(newfunc)PyType_GenericNew, /* tp_new */
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(freefunc)0, /* tp_free */
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NULL, /* tp_is_gc */
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NULL, /* tp_bases */
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NULL, /* tp_mro */
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NULL, /* tp_cache */
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NULL, /* tp_subclasses */
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NULL, /* tp_weaklist */
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(destructor)NULL, /* tp_del */
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};
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PyDoc_STRVAR(py_kdtree_doc, "Generic 3-dimensional kd-tree to perform spatial searches.");
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static struct PyModuleDef kdtree_moduledef = {
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PyModuleDef_HEAD_INIT,
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"mathutils.kdtree", /* m_name */
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py_kdtree_doc, /* m_doc */
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0, /* m_size */
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NULL, /* m_methods */
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NULL, /* m_reload */
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NULL, /* m_traverse */
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NULL, /* m_clear */
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NULL, /* m_free */
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};
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PyMODINIT_FUNC PyInit_mathutils_kdtree(void)
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{
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PyObject *m = PyModule_Create(&kdtree_moduledef);
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if (m == NULL) {
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return NULL;
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}
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/* Register the 'KDTree' class */
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if (PyType_Ready(&PyKDTree_Type)) {
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return NULL;
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}
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PyModule_AddType(m, &PyKDTree_Type);
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return m;
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}
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