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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "open3d/ml/contrib/GridSubsampling.h"
#include "pybind/core/tensor_converter.h"
#include "pybind/docstring.h"
#include "pybind/ml/contrib/contrib.h"
#include "pybind/open3d_pybind.h"
#include "pybind/pybind_utils.h"
namespace open3d {
namespace ml {
namespace contrib {
const py::tuple SubsampleBatch(py::array points,
py::array batches,
utility::optional<py::array> features,
utility::optional<py::array> classes,
float sampleDl,
const std::string& method,
int max_p,
int verbose) {
std::vector<PointXYZ> original_points;
std::vector<PointXYZ> subsampled_points;
std::vector<int> original_batches;
std::vector<int> subsampled_batches;
std::vector<float> original_features;
std::vector<float> subsampled_features;
std::vector<int> original_classes;
std::vector<int> subsampled_classes;
// Fill original_points.
core::Tensor points_t = core::PyArrayToTensor(points, true).Contiguous();
if (points_t.GetDtype() != core::Float32) {
utility::LogError("points must be np.float32.");
}
if (points_t.NumDims() != 2 || points_t.GetShape()[1] != 3) {
utility::LogError("points must have shape (N, 3), but got {}.",
points_t.GetShape().ToString());
}
int64_t num_points = points_t.NumElements() / 3;
original_points = std::vector<PointXYZ>(
reinterpret_cast<PointXYZ*>(points_t.GetDataPtr()),
reinterpret_cast<PointXYZ*>(points_t.GetDataPtr()) + num_points);
// Fill original batches.
core::Tensor batches_t = core::PyArrayToTensor(batches, true).Contiguous();
if (batches_t.GetDtype() != core::Int32) {
utility::LogError("batches must be np.int32.");
}
if (batches_t.NumDims() != 1) {
utility::LogError("batches must have shape (NB,), but got {}.",
batches_t.GetShape().ToString());
}
int64_t num_batches = batches_t.GetShape()[0];
if (static_cast<int64_t>(batches_t.Sum({0}).Item<int32_t>()) !=
num_points) {
utility::LogError("batches got {} points, but points got {} points.",
batches_t.Sum({0}).Item<int32_t>(), num_points);
}
original_batches = batches_t.ToFlatVector<int32_t>();
if (verbose) {
utility::LogInfo("Got {} batches with a total of {} points as inputs.",
num_batches, num_points);
}
// Fill original_features.
int64_t feature_dim = -1;
if (features.has_value()) {
core::Tensor features_t =
core::PyArrayToTensor(features.value(), true).Contiguous();
if (features_t.GetDtype() != core::Float32) {
utility::LogError("features must be np.float32.");
}
if (features_t.NumDims() != 2) {
utility::LogError("features must have shape (N, d), but got {}.",
features_t.GetShape().ToString());
}
if (features_t.GetShape()[0] != num_points) {
utility::LogError(
"features's shape {} is not compatible with "
"points's shape {}, their first dimension must "
"be equal.",
features_t.GetShape().ToString(),
points_t.GetShape().ToString());
}
feature_dim = features_t.GetShape()[1];
original_features = features_t.ToFlatVector<float>();
}
// Fill original_classes.
if (classes.has_value()) {
core::Tensor classes_t =
core::PyArrayToTensor(classes.value(), true).Contiguous();
if (classes_t.GetDtype() != core::Int32) {
utility::LogError("classes must be np.int32.");
}
if (classes_t.NumDims() != 1) {
utility::LogError("classes must have shape (N,), but got {}.",
classes_t.GetShape().ToString());
}
if (classes_t.GetShape()[0] != num_points) {
utility::LogError(
"classes's shape {} is not compatible with "
"points's shape {}, their first dimension must "
"be equal.",
classes_t.GetShape().ToString(),
points_t.GetShape().ToString());
}
original_classes = classes_t.ToFlatVector<int32_t>();
}
// Call function.
batch_grid_subsampling(
original_points, subsampled_points, original_features,
subsampled_features, original_classes, subsampled_classes,
original_batches, subsampled_batches, sampleDl, max_p);
// Wrap result subsampled_points. Data will be copied.
int64_t num_subsampled_points =
static_cast<int64_t>(subsampled_points.size());
core::Tensor subsampled_points_t(
reinterpret_cast<float*>(subsampled_points.data()),
{num_subsampled_points, 3}, core::Float32);
// Wrap result subsampled_batches. Data will be copied.
int64_t num_subsampled_batches =
static_cast<int64_t>(subsampled_batches.size());
core::Tensor subsampled_batches_t = core::Tensor(
subsampled_batches, {num_subsampled_batches}, core::Int32);
if (static_cast<int64_t>(subsampled_batches_t.Sum({0}).Item<int32_t>()) !=
num_subsampled_points) {
utility::LogError(
"subsampled_batches got {} points, but subsampled_points got "
"{} points.",
subsampled_batches_t.Sum({0}).Item<int32_t>(),
num_subsampled_points);
}
if (verbose) {
utility::LogInfo("Subsampled to {} batches with a total of {} points.",
num_subsampled_batches, num_subsampled_points);
}
// Wrap result subsampled_features. Data will be copied.
core::Tensor subsampled_features_t;
if (features.has_value()) {
if (subsampled_features.size() % num_subsampled_points != 0) {
utility::LogError(
"Error: subsampled_points.size() {} is not a "
"multiple of num_subsampled_points {}.",
subsampled_points.size(), num_subsampled_points);
}
int64_t subsampled_feature_dim =
static_cast<int64_t>(subsampled_features.size()) /
num_subsampled_points;
if (feature_dim != subsampled_feature_dim) {
utility::LogError(
"Error: input feature dim {} does not match "
"the subsampled feature dim {}.",
feature_dim, subsampled_feature_dim);
}
subsampled_features_t = core::Tensor(
subsampled_features, {num_subsampled_points, feature_dim},
core::Float32);
}
// Wrap result subsampled_classes. Data will be copied.
core::Tensor subsampled_classes_t;
if (classes.has_value()) {
if (static_cast<int64_t>(subsampled_classes.size()) !=
num_subsampled_points) {
utility::LogError(
"Error: subsampled_classes.size() {} != "
"num_subsampled_points {}.",
subsampled_classes.size(), num_subsampled_points);
}
subsampled_classes_t = core::Tensor(
subsampled_classes, {num_subsampled_points}, core::Int32);
}
if (features.has_value() && classes.has_value()) {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_batches_t),
core::TensorToPyArray(subsampled_features_t),
core::TensorToPyArray(subsampled_classes_t));
} else if (features.has_value()) {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_batches_t),
core::TensorToPyArray(subsampled_features_t));
} else if (classes.has_value()) {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_batches_t),
core::TensorToPyArray(subsampled_classes_t));
} else {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_batches_t));
}
}
const py::object Subsample(py::array points,
utility::optional<py::array> features,
utility::optional<py::array> classes,
float sampleDl,
int verbose) {
std::vector<PointXYZ> original_points;
std::vector<PointXYZ> subsampled_points;
std::vector<float> original_features;
std::vector<float> subsampled_features;
std::vector<int> original_classes;
std::vector<int> subsampled_classes;
// Fill original_points.
core::Tensor points_t = core::PyArrayToTensor(points, true).Contiguous();
if (points_t.GetDtype() != core::Float32) {
utility::LogError("points must be np.float32.");
}
if (points_t.NumDims() != 2 || points_t.GetShape()[1] != 3) {
utility::LogError("points must have shape (N, 3), but got {}.",
points_t.GetShape().ToString());
}
int64_t num_points = points_t.NumElements() / 3;
original_points = std::vector<PointXYZ>(
reinterpret_cast<PointXYZ*>(points_t.GetDataPtr()),
reinterpret_cast<PointXYZ*>(points_t.GetDataPtr()) + num_points);
if (verbose) {
utility::LogInfo("Got {} points as inputs.", num_points);
}
// Fill original_features.
int64_t feature_dim = -1;
if (features.has_value()) {
core::Tensor features_t =
core::PyArrayToTensor(features.value(), true).Contiguous();
if (features_t.GetDtype() != core::Float32) {
utility::LogError("features must be np.float32.");
}
if (features_t.NumDims() != 2) {
utility::LogError("features must have shape (N, d), but got {}.",
features_t.GetShape().ToString());
}
if (features_t.GetShape()[0] != num_points) {
utility::LogError(
"features's shape {} is not compatible with "
"points's shape {}, their first dimension must "
"be equal.",
features_t.GetShape().ToString(),
points_t.GetShape().ToString());
}
feature_dim = features_t.GetShape()[1];
original_features = features_t.ToFlatVector<float>();
}
// Fill original_classes.
if (classes.has_value()) {
core::Tensor classes_t =
core::PyArrayToTensor(classes.value(), true).Contiguous();
if (classes_t.GetDtype() != core::Int32) {
utility::LogError("classes must be np.int32.");
}
if (classes_t.NumDims() != 1) {
utility::LogError("classes must have shape (N,), but got {}.",
classes_t.GetShape().ToString());
}
if (classes_t.GetShape()[0] != num_points) {
utility::LogError(
"classes's shape {} is not compatible with "
"points's shape {}, their first dimension must "
"be equal.",
classes_t.GetShape().ToString(),
points_t.GetShape().ToString());
}
original_classes = classes_t.ToFlatVector<int32_t>();
}
// Call function.
grid_subsampling(original_points, subsampled_points, original_features,
subsampled_features, original_classes, subsampled_classes,
sampleDl, verbose);
// Wrap result subsampled_points. Data will be copied.
int64_t num_subsampled_points =
static_cast<int64_t>(subsampled_points.size());
core::Tensor subsampled_points_t(
reinterpret_cast<float*>(subsampled_points.data()),
{num_subsampled_points, 3}, core::Float32);
if (verbose) {
utility::LogInfo("Subsampled to {} points.", num_subsampled_points);
}
// Wrap result subsampled_features. Data will be copied.
core::Tensor subsampled_features_t;
if (features.has_value()) {
if (subsampled_features.size() % num_subsampled_points != 0) {
utility::LogError(
"Error: subsampled_points.size() {} is not a "
"multiple of num_subsampled_points {}.",
subsampled_points.size(), num_subsampled_points);
}
int64_t subsampled_feature_dim =
static_cast<int64_t>(subsampled_features.size()) /
num_subsampled_points;
if (feature_dim != subsampled_feature_dim) {
utility::LogError(
"Error: input feature dim {} does not match "
"the subsampled feature dim {}.",
feature_dim, subsampled_feature_dim);
}
subsampled_features_t = core::Tensor(
subsampled_features, {num_subsampled_points, feature_dim},
core::Float32);
}
// Wrap result subsampled_classes. Data will be copied.
core::Tensor subsampled_classes_t;
if (classes.has_value()) {
if (static_cast<int64_t>(subsampled_classes.size()) !=
num_subsampled_points) {
utility::LogError(
"Error: subsampled_classes.size() {} != "
"num_subsampled_points {}.",
subsampled_classes.size(), num_subsampled_points);
}
subsampled_classes_t = core::Tensor(
subsampled_classes, {num_subsampled_points}, core::Int32);
}
if (features.has_value() && classes.has_value()) {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_features_t),
core::TensorToPyArray(subsampled_classes_t));
} else if (features.has_value()) {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_features_t));
} else if (classes.has_value()) {
return py::make_tuple(core::TensorToPyArray(subsampled_points_t),
core::TensorToPyArray(subsampled_classes_t));
} else {
return core::TensorToPyArray(subsampled_points_t);
}
}
void pybind_contrib_subsample_definitions(py::module& m_contrib) {
m_contrib.def("subsample", &Subsample, "points"_a,
"features"_a = py::none(), "classes"_a = py::none(),
"sampleDl"_a = 0.1, "verbose"_a = 0);
m_contrib.def("subsample_batch", &SubsampleBatch, "points"_a, "batches"_a,
"features"_a = py::none(), "classes"_a = py::none(),
"sampleDl"_a = 0.1, "method"_a = "barycenters", "max_p"_a = 0,
"verbose"_a = 0);
}
} // namespace contrib
} // namespace ml
} // namespace open3d
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