566 lines
17 KiB
C++
566 lines
17 KiB
C++
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#include <Python.h>
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#include <numpy/arrayobject.h>
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#include "grid_subsampling/grid_subsampling.h"
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#include <string>
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// docstrings for our module
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// *************************
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static char module_docstring[] = "This module provides an interface for the subsampling of a batch of stacked pointclouds";
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static char subsample_docstring[] = "function subsampling a pointcloud";
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static char subsample_batch_docstring[] = "function subsampling a batch of stacked pointclouds";
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// Declare the functions
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// *********************
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static PyObject *cloud_subsampling(PyObject* self, PyObject* args, PyObject* keywds);
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static PyObject *batch_subsampling(PyObject *self, PyObject *args, PyObject *keywds);
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// Specify the members of the module
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// *********************************
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static PyMethodDef module_methods[] =
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{
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{ "subsample", (PyCFunction)cloud_subsampling, METH_VARARGS | METH_KEYWORDS, subsample_docstring },
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{ "subsample_batch", (PyCFunction)batch_subsampling, METH_VARARGS | METH_KEYWORDS, subsample_batch_docstring },
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{NULL, NULL, 0, NULL}
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};
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// Initialize the module
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// *********************
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static struct PyModuleDef moduledef =
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{
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PyModuleDef_HEAD_INIT,
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"grid_subsampling", // m_name
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module_docstring, // m_doc
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-1, // m_size
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module_methods, // 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_grid_subsampling(void)
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{
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import_array();
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return PyModule_Create(&moduledef);
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}
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// Definition of the batch_subsample method
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// **********************************
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static PyObject* batch_subsampling(PyObject* self, PyObject* args, PyObject* keywds)
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{
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// Manage inputs
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// *************
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// Args containers
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PyObject* points_obj = NULL;
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PyObject* features_obj = NULL;
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PyObject* classes_obj = NULL;
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PyObject* batches_obj = NULL;
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// Keywords containers
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static char* kwlist[] = { "points", "batches", "features", "classes", "sampleDl", "method", "max_p", "verbose", NULL };
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float sampleDl = 0.1;
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const char* method_buffer = "barycenters";
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int verbose = 0;
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int max_p = 0;
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// Parse the input
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if (!PyArg_ParseTupleAndKeywords(args, keywds, "OO|$OOfsii", kwlist, &points_obj, &batches_obj, &features_obj, &classes_obj, &sampleDl, &method_buffer, &max_p, &verbose))
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{
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PyErr_SetString(PyExc_RuntimeError, "Error parsing arguments");
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return NULL;
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}
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// Get the method argument
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string method(method_buffer);
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// Interpret method
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if (method.compare("barycenters") && method.compare("voxelcenters"))
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{
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PyErr_SetString(PyExc_RuntimeError, "Error parsing method. Valid method names are \"barycenters\" and \"voxelcenters\" ");
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return NULL;
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}
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// Check if using features or classes
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bool use_feature = true, use_classes = true;
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if (features_obj == NULL)
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use_feature = false;
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if (classes_obj == NULL)
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use_classes = false;
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// Interpret the input objects as numpy arrays.
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PyObject* points_array = PyArray_FROM_OTF(points_obj, NPY_FLOAT, NPY_IN_ARRAY);
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PyObject* batches_array = PyArray_FROM_OTF(batches_obj, NPY_INT, NPY_IN_ARRAY);
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PyObject* features_array = NULL;
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PyObject* classes_array = NULL;
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if (use_feature)
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features_array = PyArray_FROM_OTF(features_obj, NPY_FLOAT, NPY_IN_ARRAY);
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if (use_classes)
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classes_array = PyArray_FROM_OTF(classes_obj, NPY_INT, NPY_IN_ARRAY);
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// Verify data was load correctly.
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if (points_array == NULL)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Error converting input points to numpy arrays of type float32");
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return NULL;
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}
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if (batches_array == NULL)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Error converting input batches to numpy arrays of type int32");
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return NULL;
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}
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if (use_feature && features_array == NULL)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Error converting input features to numpy arrays of type float32");
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return NULL;
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}
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if (use_classes && classes_array == NULL)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Error converting input classes to numpy arrays of type int32");
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return NULL;
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}
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// Check that the input array respect the dims
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if ((int)PyArray_NDIM(points_array) != 2 || (int)PyArray_DIM(points_array, 1) != 3)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : points.shape is not (N, 3)");
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return NULL;
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}
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if ((int)PyArray_NDIM(batches_array) > 1)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : batches.shape is not (B,) ");
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return NULL;
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}
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if (use_feature && ((int)PyArray_NDIM(features_array) != 2))
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : features.shape is not (N, d)");
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return NULL;
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}
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if (use_classes && (int)PyArray_NDIM(classes_array) > 2)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : classes.shape is not (N,) or (N, d)");
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return NULL;
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}
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// Number of points
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int N = (int)PyArray_DIM(points_array, 0);
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// Number of batches
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int Nb = (int)PyArray_DIM(batches_array, 0);
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// Dimension of the features
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int fdim = 0;
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if (use_feature)
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fdim = (int)PyArray_DIM(features_array, 1);
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//Dimension of labels
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int ldim = 1;
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if (use_classes && (int)PyArray_NDIM(classes_array) == 2)
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ldim = (int)PyArray_DIM(classes_array, 1);
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// Check that the input array respect the number of points
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if (use_feature && (int)PyArray_DIM(features_array, 0) != N)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : features.shape is not (N, d)");
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return NULL;
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}
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if (use_classes && (int)PyArray_DIM(classes_array, 0) != N)
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{
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Py_XDECREF(points_array);
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Py_XDECREF(batches_array);
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Py_XDECREF(classes_array);
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Py_XDECREF(features_array);
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PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : classes.shape is not (N,) or (N, d)");
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return NULL;
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}
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// Call the C++ function
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// *********************
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// Create pyramid
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if (verbose > 0)
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cout << "Computing cloud pyramid with support points: " << endl;
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// Convert PyArray to Cloud C++ class
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vector<PointXYZ> original_points;
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vector<int> original_batches;
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vector<float> original_features;
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vector<int> original_classes;
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original_points = vector<PointXYZ>((PointXYZ*)PyArray_DATA(points_array), (PointXYZ*)PyArray_DATA(points_array) + N);
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original_batches = vector<int>((int*)PyArray_DATA(batches_array), (int*)PyArray_DATA(batches_array) + Nb);
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if (use_feature)
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original_features = vector<float>((float*)PyArray_DATA(features_array), (float*)PyArray_DATA(features_array) + N * fdim);
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if (use_classes)
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original_classes = vector<int>((int*)PyArray_DATA(classes_array), (int*)PyArray_DATA(classes_array) + N * ldim);
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// Subsample
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vector<PointXYZ> subsampled_points;
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vector<float> subsampled_features;
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vector<int> subsampled_classes;
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vector<int> subsampled_batches;
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batch_grid_subsampling(original_points,
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subsampled_points,
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original_features,
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subsampled_features,
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original_classes,
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subsampled_classes,
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original_batches,
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subsampled_batches,
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sampleDl,
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max_p);
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// Check result
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if (subsampled_points.size() < 1)
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{
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PyErr_SetString(PyExc_RuntimeError, "Error");
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return NULL;
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}
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// Manage outputs
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// **************
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// Dimension of input containers
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npy_intp* point_dims = new npy_intp[2];
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point_dims[0] = subsampled_points.size();
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point_dims[1] = 3;
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npy_intp* feature_dims = new npy_intp[2];
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feature_dims[0] = subsampled_points.size();
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feature_dims[1] = fdim;
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npy_intp* classes_dims = new npy_intp[2];
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classes_dims[0] = subsampled_points.size();
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classes_dims[1] = ldim;
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npy_intp* batches_dims = new npy_intp[1];
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batches_dims[0] = Nb;
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// Create output array
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PyObject* res_points_obj = PyArray_SimpleNew(2, point_dims, NPY_FLOAT);
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PyObject* res_batches_obj = PyArray_SimpleNew(1, batches_dims, NPY_INT);
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PyObject* res_features_obj = NULL;
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PyObject* res_classes_obj = NULL;
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PyObject* ret = NULL;
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// Fill output array with values
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size_t size_in_bytes = subsampled_points.size() * 3 * sizeof(float);
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memcpy(PyArray_DATA(res_points_obj), subsampled_points.data(), size_in_bytes);
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size_in_bytes = Nb * sizeof(int);
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memcpy(PyArray_DATA(res_batches_obj), subsampled_batches.data(), size_in_bytes);
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if (use_feature)
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{
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size_in_bytes = subsampled_points.size() * fdim * sizeof(float);
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res_features_obj = PyArray_SimpleNew(2, feature_dims, NPY_FLOAT);
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memcpy(PyArray_DATA(res_features_obj), subsampled_features.data(), size_in_bytes);
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}
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if (use_classes)
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{
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size_in_bytes = subsampled_points.size() * ldim * sizeof(int);
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res_classes_obj = PyArray_SimpleNew(2, classes_dims, NPY_INT);
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memcpy(PyArray_DATA(res_classes_obj), subsampled_classes.data(), size_in_bytes);
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}
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// Merge results
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if (use_feature && use_classes)
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ret = Py_BuildValue("NNNN", res_points_obj, res_batches_obj, res_features_obj, res_classes_obj);
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else if (use_feature)
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ret = Py_BuildValue("NNN", res_points_obj, res_batches_obj, res_features_obj);
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else if (use_classes)
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ret = Py_BuildValue("NNN", res_points_obj, res_batches_obj, res_classes_obj);
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else
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ret = Py_BuildValue("NN", res_points_obj, res_batches_obj);
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// Clean up
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// ********
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Py_DECREF(points_array);
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Py_DECREF(batches_array);
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Py_XDECREF(features_array);
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Py_XDECREF(classes_array);
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return ret;
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}
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||
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// Definition of the subsample method
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||
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// ****************************************
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||
|
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|
||
|
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static PyObject* cloud_subsampling(PyObject* self, PyObject* args, PyObject* keywds)
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||
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{
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||
|
|
|
||
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// Manage inputs
|
||
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// *************
|
||
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|
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// Args containers
|
||
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PyObject* points_obj = NULL;
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PyObject* features_obj = NULL;
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PyObject* classes_obj = NULL;
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||
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|
||
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// Keywords containers
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||
|
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static char* kwlist[] = { "points", "features", "classes", "sampleDl", "method", "verbose", NULL };
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||
|
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float sampleDl = 0.1;
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|
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const char* method_buffer = "barycenters";
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||
|
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int verbose = 0;
|
||
|
|
|
||
|
|
// Parse the input
|
||
|
|
if (!PyArg_ParseTupleAndKeywords(args, keywds, "O|$OOfsi", kwlist, &points_obj, &features_obj, &classes_obj, &sampleDl, &method_buffer, &verbose))
|
||
|
|
{
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Error parsing arguments");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
// Get the method argument
|
||
|
|
string method(method_buffer);
|
||
|
|
|
||
|
|
// Interpret method
|
||
|
|
if (method.compare("barycenters") && method.compare("voxelcenters"))
|
||
|
|
{
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Error parsing method. Valid method names are \"barycenters\" and \"voxelcenters\" ");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
// Check if using features or classes
|
||
|
|
bool use_feature = true, use_classes = true;
|
||
|
|
if (features_obj == NULL)
|
||
|
|
use_feature = false;
|
||
|
|
if (classes_obj == NULL)
|
||
|
|
use_classes = false;
|
||
|
|
|
||
|
|
// Interpret the input objects as numpy arrays.
|
||
|
|
PyObject* points_array = PyArray_FROM_OTF(points_obj, NPY_FLOAT, NPY_IN_ARRAY);
|
||
|
|
PyObject* features_array = NULL;
|
||
|
|
PyObject* classes_array = NULL;
|
||
|
|
if (use_feature)
|
||
|
|
features_array = PyArray_FROM_OTF(features_obj, NPY_FLOAT, NPY_IN_ARRAY);
|
||
|
|
if (use_classes)
|
||
|
|
classes_array = PyArray_FROM_OTF(classes_obj, NPY_INT, NPY_IN_ARRAY);
|
||
|
|
|
||
|
|
// Verify data was load correctly.
|
||
|
|
if (points_array == NULL)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Error converting input points to numpy arrays of type float32");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
if (use_feature && features_array == NULL)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Error converting input features to numpy arrays of type float32");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
if (use_classes && classes_array == NULL)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Error converting input classes to numpy arrays of type int32");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
// Check that the input array respect the dims
|
||
|
|
if ((int)PyArray_NDIM(points_array) != 2 || (int)PyArray_DIM(points_array, 1) != 3)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : points.shape is not (N, 3)");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
if (use_feature && ((int)PyArray_NDIM(features_array) != 2))
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : features.shape is not (N, d)");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (use_classes && (int)PyArray_NDIM(classes_array) > 2)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : classes.shape is not (N,) or (N, d)");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
// Number of points
|
||
|
|
int N = (int)PyArray_DIM(points_array, 0);
|
||
|
|
|
||
|
|
// Dimension of the features
|
||
|
|
int fdim = 0;
|
||
|
|
if (use_feature)
|
||
|
|
fdim = (int)PyArray_DIM(features_array, 1);
|
||
|
|
|
||
|
|
//Dimension of labels
|
||
|
|
int ldim = 1;
|
||
|
|
if (use_classes && (int)PyArray_NDIM(classes_array) == 2)
|
||
|
|
ldim = (int)PyArray_DIM(classes_array, 1);
|
||
|
|
|
||
|
|
// Check that the input array respect the number of points
|
||
|
|
if (use_feature && (int)PyArray_DIM(features_array, 0) != N)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : features.shape is not (N, d)");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
if (use_classes && (int)PyArray_DIM(classes_array, 0) != N)
|
||
|
|
{
|
||
|
|
Py_XDECREF(points_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Wrong dimensions : classes.shape is not (N,) or (N, d)");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
// Call the C++ function
|
||
|
|
// *********************
|
||
|
|
|
||
|
|
// Create pyramid
|
||
|
|
if (verbose > 0)
|
||
|
|
cout << "Computing cloud pyramid with support points: " << endl;
|
||
|
|
|
||
|
|
|
||
|
|
// Convert PyArray to Cloud C++ class
|
||
|
|
vector<PointXYZ> original_points;
|
||
|
|
vector<float> original_features;
|
||
|
|
vector<int> original_classes;
|
||
|
|
original_points = vector<PointXYZ>((PointXYZ*)PyArray_DATA(points_array), (PointXYZ*)PyArray_DATA(points_array) + N);
|
||
|
|
if (use_feature)
|
||
|
|
original_features = vector<float>((float*)PyArray_DATA(features_array), (float*)PyArray_DATA(features_array) + N * fdim);
|
||
|
|
if (use_classes)
|
||
|
|
original_classes = vector<int>((int*)PyArray_DATA(classes_array), (int*)PyArray_DATA(classes_array) + N * ldim);
|
||
|
|
|
||
|
|
// Subsample
|
||
|
|
vector<PointXYZ> subsampled_points;
|
||
|
|
vector<float> subsampled_features;
|
||
|
|
vector<int> subsampled_classes;
|
||
|
|
grid_subsampling(original_points,
|
||
|
|
subsampled_points,
|
||
|
|
original_features,
|
||
|
|
subsampled_features,
|
||
|
|
original_classes,
|
||
|
|
subsampled_classes,
|
||
|
|
sampleDl,
|
||
|
|
verbose);
|
||
|
|
|
||
|
|
// Check result
|
||
|
|
if (subsampled_points.size() < 1)
|
||
|
|
{
|
||
|
|
PyErr_SetString(PyExc_RuntimeError, "Error");
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
// Manage outputs
|
||
|
|
// **************
|
||
|
|
|
||
|
|
// Dimension of input containers
|
||
|
|
npy_intp* point_dims = new npy_intp[2];
|
||
|
|
point_dims[0] = subsampled_points.size();
|
||
|
|
point_dims[1] = 3;
|
||
|
|
npy_intp* feature_dims = new npy_intp[2];
|
||
|
|
feature_dims[0] = subsampled_points.size();
|
||
|
|
feature_dims[1] = fdim;
|
||
|
|
npy_intp* classes_dims = new npy_intp[2];
|
||
|
|
classes_dims[0] = subsampled_points.size();
|
||
|
|
classes_dims[1] = ldim;
|
||
|
|
|
||
|
|
// Create output array
|
||
|
|
PyObject* res_points_obj = PyArray_SimpleNew(2, point_dims, NPY_FLOAT);
|
||
|
|
PyObject* res_features_obj = NULL;
|
||
|
|
PyObject* res_classes_obj = NULL;
|
||
|
|
PyObject* ret = NULL;
|
||
|
|
|
||
|
|
// Fill output array with values
|
||
|
|
size_t size_in_bytes = subsampled_points.size() * 3 * sizeof(float);
|
||
|
|
memcpy(PyArray_DATA(res_points_obj), subsampled_points.data(), size_in_bytes);
|
||
|
|
if (use_feature)
|
||
|
|
{
|
||
|
|
size_in_bytes = subsampled_points.size() * fdim * sizeof(float);
|
||
|
|
res_features_obj = PyArray_SimpleNew(2, feature_dims, NPY_FLOAT);
|
||
|
|
memcpy(PyArray_DATA(res_features_obj), subsampled_features.data(), size_in_bytes);
|
||
|
|
}
|
||
|
|
if (use_classes)
|
||
|
|
{
|
||
|
|
size_in_bytes = subsampled_points.size() * ldim * sizeof(int);
|
||
|
|
res_classes_obj = PyArray_SimpleNew(2, classes_dims, NPY_INT);
|
||
|
|
memcpy(PyArray_DATA(res_classes_obj), subsampled_classes.data(), size_in_bytes);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
// Merge results
|
||
|
|
if (use_feature && use_classes)
|
||
|
|
ret = Py_BuildValue("NNN", res_points_obj, res_features_obj, res_classes_obj);
|
||
|
|
else if (use_feature)
|
||
|
|
ret = Py_BuildValue("NN", res_points_obj, res_features_obj);
|
||
|
|
else if (use_classes)
|
||
|
|
ret = Py_BuildValue("NN", res_points_obj, res_classes_obj);
|
||
|
|
else
|
||
|
|
ret = Py_BuildValue("N", res_points_obj);
|
||
|
|
|
||
|
|
// Clean up
|
||
|
|
// ********
|
||
|
|
|
||
|
|
Py_DECREF(points_array);
|
||
|
|
Py_XDECREF(features_array);
|
||
|
|
Py_XDECREF(classes_array);
|
||
|
|
|
||
|
|
return ret;
|
||
|
|
}
|