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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// The MIT License (MIT)
//
// Copyright (c) 2018-2021 www.open3d.org
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
// IN THE SOFTWARE.
// ----------------------------------------------------------------------------
#include <string>
#include <unordered_map>
#include "open3d/t/geometry/PointCloud.h"
#include "open3d/t/io/ImageIO.h"
#include "open3d/t/io/PointCloudIO.h"
#include "open3d/t/io/TriangleMeshIO.h"
#include "pybind/docstring.h"
#include "pybind/t/io/io.h"
namespace open3d {
namespace t {
namespace io {
// IO functions have similar arguments, thus the arg docstrings may be shared
static const std::unordered_map<std::string, std::string>
map_shared_argument_docstrings = {
{"filename", "Path to file."},
// Write options
{"compressed",
"Set to ``True`` to write in compressed format."},
{"format",
"The format of the input file. When not specified or set as "
"``auto``, the format is inferred from file extension name."},
{"remove_nan_points",
"If true, all points that include a NaN are removed from "
"the PointCloud."},
{"remove_infinite_points",
"If true, all points that include an infinite value are "
"removed from the PointCloud."},
{"quality", "Quality of the output file."},
{"write_ascii",
"Set to ``True`` to output in ascii format, otherwise binary "
"format will be used."},
{"write_vertex_normals",
"Set to ``False`` to not write any vertex normals, even if "
"present on the mesh."},
{"write_vertex_colors",
"Set to ``False`` to not write any vertex colors, even if "
"present on the mesh."},
{"write_triangle_uvs",
"Set to ``False`` to not write any triangle uvs, even if "
"present on the mesh. For ``obj`` format, mtl file is saved "
"only when ``True`` is set."},
// Entities
{"config", "AzureKinectSensor's config file."},
{"pointcloud", "The ``PointCloud`` object for I/O."},
{"mesh", "The ``TriangleMesh`` object for I/O."},
{"line_set", "The ``LineSet`` object for I/O."},
{"image", "The ``Image`` object for I/O."},
{"voxel_grid", "The ``VoxelGrid`` object for I/O."},
{"trajectory",
"The ``PinholeCameraTrajectory`` object for I/O."},
{"intrinsic", "The ``PinholeCameraIntrinsic`` object for I/O."},
{"parameters",
"The ``PinholeCameraParameters`` object for I/O."},
{"pose_graph", "The ``PoseGraph`` object for I/O."},
{"feature", "The ``Feature`` object for I/O."},
{"print_progress",
"If set to true a progress bar is visualized in the console."},
};
void pybind_class_io(py::module &m_io) {
// open3d::t::geometry::Image
m_io.def(
"read_image",
[](const std::string &filename) {
py::gil_scoped_release release;
geometry::Image image;
ReadImage(filename, image);
return image;
},
"Function to read image from file.", "filename"_a);
docstring::FunctionDocInject(m_io, "read_image",
map_shared_argument_docstrings);
m_io.def(
"write_image",
[](const std::string &filename, const geometry::Image &image,
int quality) {
py::gil_scoped_release release;
return WriteImage(filename, image, quality);
},
"Function to write Image to file.", "filename"_a, "image"_a,
"quality"_a = kOpen3DImageIODefaultQuality);
docstring::FunctionDocInject(m_io, "write_image",
map_shared_argument_docstrings);
// open3d::t::geometry::PointCloud
m_io.def(
"read_point_cloud",
[](const std::string &filename, const std::string &format,
bool remove_nan_points, bool remove_infinite_points,
bool print_progress) {
py::gil_scoped_release release;
t::geometry::PointCloud pcd;
ReadPointCloud(filename, pcd,
{format, remove_nan_points,
remove_infinite_points, print_progress});
return pcd;
},
"Function to read PointCloud with tensor attributes from file.",
"filename"_a, "format"_a = "auto", "remove_nan_points"_a = false,
"remove_infinite_points"_a = false, "print_progress"_a = false);
docstring::FunctionDocInject(m_io, "read_point_cloud",
map_shared_argument_docstrings);
m_io.def(
"write_point_cloud",
[](const std::string &filename,
const t::geometry::PointCloud &pointcloud, bool write_ascii,
bool compressed, bool print_progress) {
py::gil_scoped_release release;
return WritePointCloud(
filename, pointcloud,
{write_ascii, compressed, print_progress});
},
"Function to write PointCloud with tensor attributes to file.",
"filename"_a, "pointcloud"_a, "write_ascii"_a = false,
"compressed"_a = false, "print_progress"_a = false);
docstring::FunctionDocInject(m_io, "write_point_cloud",
map_shared_argument_docstrings);
// open3d::geometry::TriangleMesh
m_io.def(
"read_triangle_mesh",
[](const std::string &filename, bool enable_post_processing,
bool print_progress) {
py::gil_scoped_release release;
t::geometry::TriangleMesh mesh;
open3d::io::ReadTriangleMeshOptions opt;
opt.enable_post_processing = enable_post_processing;
opt.print_progress = print_progress;
ReadTriangleMesh(filename, mesh, opt);
return mesh;
},
"Function to read TriangleMesh from file", "filename"_a,
"enable_post_processing"_a = false, "print_progress"_a = false);
docstring::FunctionDocInject(m_io, "read_triangle_mesh",
map_shared_argument_docstrings);
m_io.def(
"write_triangle_mesh",
[](const std::string &filename,
const t::geometry::TriangleMesh &mesh, bool write_ascii,
bool compressed, bool write_vertex_normals,
bool write_vertex_colors, bool write_triangle_uvs,
bool print_progress) {
py::gil_scoped_release release;
return WriteTriangleMesh(filename, mesh, write_ascii,
compressed, write_vertex_normals,
write_vertex_colors,
write_triangle_uvs, print_progress);
},
"Function to write TriangleMesh to file", "filename"_a, "mesh"_a,
"write_ascii"_a = false, "compressed"_a = false,
"write_vertex_normals"_a = true, "write_vertex_colors"_a = true,
"write_triangle_uvs"_a = true, "print_progress"_a = false);
docstring::FunctionDocInject(m_io, "write_triangle_mesh",
map_shared_argument_docstrings);
// DepthNoiseSimulator
py::class_<DepthNoiseSimulator> depth_noise_simulator(
m_io, "DepthNoiseSimulator",
R"(Simulate depth image noise from a given noise distortion model. The distortion model is based on *Teichman et. al. "Unsupervised intrinsic calibration of depth sensors via SLAM" RSS 2009*. Also see <http://redwood-data.org/indoor/dataset.html>__
Example::
import open3d as o3d
# Redwood Indoor LivingRoom1 (Augmented ICL-NUIM)
# http://redwood-data.org/indoor/
data = o3d.data.RedwoodIndoorLivingRoom1()
noise_model_path = data.noise_model_path
im_src_path = data.depth_paths[0]
depth_scale = 1000.0
# Read clean depth image (uint16)
im_src = o3d.t.io.read_image(im_src_path)
# Run noise model simulation
simulator = o3d.t.io.DepthNoiseSimulator(noise_model_path)
im_dst = simulator.simulate(im_src, depth_scale=depth_scale)
# Save noisy depth image (uint16)
o3d.t.io.write_image("noisy_depth.png", im_dst)
)");
depth_noise_simulator.def(py::init<const std::string &>(),
"noise_model_path"_a);
depth_noise_simulator.def("simulate", &DepthNoiseSimulator::Simulate,
"im_src"_a, "depth_scale"_a = 1000.0f,
"Apply noise model to a depth image.");
depth_noise_simulator.def(
"enable_deterministic_debug_mode",
&DepthNoiseSimulator::EnableDeterministicDebugMode,
"Enable deterministic debug mode. All normally distributed noise "
"will be replaced by 0.");
depth_noise_simulator.def_property_readonly(
"noise_model", &DepthNoiseSimulator::GetNoiseModel,
"The noise model tensor.");
docstring::ClassMethodDocInject(
m_io, "DepthNoiseSimulator", "__init__",
{{"noise_model_path",
"Path to the noise model file. See "
"http://redwood-data.org/indoor/dataset.html for the format. Or, "
"you may use one of our example datasets, e.g., "
"RedwoodIndoorLivingRoom1."}});
docstring::ClassMethodDocInject(
m_io, "DepthNoiseSimulator", "simulate",
{{"im_src",
"Source depth image, must be with dtype UInt16 or Float32, "
"channels==1."},
{"depth_scale",
"Scale factor to the depth image. As a sanity check, if the "
"dtype is Float32, the depth_scale must be 1.0. If the dtype is "
"is UInt16, the depth_scale is typically larger than 1.0, e.g. "
"it can be 1000.0."}});
docstring::ClassMethodDocInject(m_io, "DepthNoiseSimulator",
"enable_deterministic_debug_mode");
}
} // namespace io
} // namespace t
} // namespace open3d
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