To help you get started, we've collected the most common ways that pytransform3d is being used within popular public projects.

rock-learning / pytransform3d / examples / plot_camera.py View on Github

We can see the camera frame and the world frame. There is a grid of points from
which we know the world coordinates. If we know the location and orientation of
the camera in the world, we can easily compute the location of the points on
the image.
"""
print(__doc__)


import numpy as np
import matplotlib.pyplot as plt
from pytransform3d.rotations import matrix_from_euler_xyz
from pytransform3d.transformations import transform_from, plot_transform
from pytransform3d.camera import make_world_grid, world2image


cam2world = transform_from(matrix_from_euler_xyz([np.pi - 1, 0.2, 0.2]),
                           [0.2, -1, 0.5])
focal_length = 0.0036
sensor_size = (0.00367, 0.00274)
image_size = (640, 480)

world_grid = make_world_grid()
image_grid = world2image(world_grid, cam2world, sensor_size, image_size,
                         focal_length)

plt.figure(figsize=(12, 5))
ax = plt.subplot(121, projection="3d")
ax.view_init(elev=30, azim=-70)
ax.set_xlim((-1, 1))
ax.set_ylim((-1, 1))
ax.set_zlim((-1, 1))
ax.set_xlabel("X")

View more ways to use pytransform3d

pytransform3d

A Python library for transformations in three dimensions.

pytransform3d offers...

• operations like concatenation and inversion for most common representations of rotation (orientation) and translation (position)
• conversions between those representations
• clear documentation of transformation conventions
• tight coupling with matplotlib to quickly visualize (or animate) transformations
• the TransformManager which manages complex chains of transformations (with export to graph visualization as PNG, additionally requires pydot)
• the TransformEditor which allows to modify transformations graphically (additionally requires PyQt4/5)
• the UrdfTransformManager which is able to load transformations from URDF files (additionally requires lxml)
• a matplotlib-like interface to Open3D's visualizer to display and animate geometries and transformations (additionally requires Open3D)

pytransform3d is used in various domains, for example:

• specifying motions of a robot
• learning robot movements from human demonstration
• sensor fusion for human pose estimation
• collision detection for robots

The API documentation can be found here.

I gave a talk at EuroSciPy 2023 about pytransform3d. Slides are available here.

Installation

Use pip to install the package from PyPI:

pip install pytransform3d[all]

or conda:

conda install -c conda-forge pytransform3d

Take a look at the installation instructions in the documentation for more details.

Gallery

The following plots and visualizations have been generated with pytransform3d. The code for most examples can be found in the documentation.

Left: Nao robot with URDF from Bullet3. Right: Kuka iiwa. The animation is based on pytransform3d's visualization interface to Open3D.

Visualizations based on Open3D.

Various plots based on Matplotlib.

Transformation editor based on Qt.

Example

This is just one simple example. You can find more examples in the subfolder examples/.

import numpy as np
import matplotlib.pyplot as plt
from pytransform3d import rotations as pr
from pytransform3d import transformations as pt
from pytransform3d.transform_manager import TransformManager


rng = np.random.default_rng(0)

ee2robot = pt.transform_from_pq(
    np.hstack((np.array([0.4, -0.3, 0.5]),
               pr.random_quaternion(rng))))
cam2robot = pt.transform_from_pq(
    np.hstack((np.array([0.0, 0.0, 0.8]), pr.q_id)))
object2cam = pt.transform_from(
    pr.active_matrix_from_intrinsic_euler_xyz(np.array([0.0, 0.0, -0.5])),
    np.array([0.5, 0.1, 0.1]))

tm = TransformManager()
tm.add_transform("end-effector", "robot", ee2robot)
tm.add_transform("camera", "robot", cam2robot)
tm.add_transform("object", "camera", object2cam)

ee2object = tm.get_transform("end-effector", "object")

ax = tm.plot_frames_in("robot", s=0.1)
ax.set_xlim((-0.25, 0.75))
ax.set_ylim((-0.5, 0.5))
ax.set_zlim((0.0, 1.0))
plt.show()

Documentation

The API documentation can be found here.

The documentation can be found in the directory doc. To build the documentation, run e.g. (on linux):

cd doc
make html

The HTML documentation is now located at doc/build/html/index.html. Execute the following command in the main folder of the repository to install the dependencies:

pip install -e .[doc]

Tests

You can use pytest to run the tests of this project in the root directory:

pytest

A coverage report will be located at htmlcov/index.html. Note that you have to install pytest to run the tests and pytest-cov to obtain the code coverage report.

Contributing

If you wish to report bugs, please use the issue tracker at Github. If you would like to contribute to pytransform3d, just open an issue or a pull request. The target branch for pull requests is the develop branch. The development branch will be merged to main for new releases. If you have questions about the software, you should ask them in the discussion section.

The recommended workflow to add a new feature, add documentation, or fix a bug is the following:

• Push your changes to a branch (e.g. feature/x, doc/y, or fix/z) of your fork of the pytransform3d repository.
• Open a pull request to the latest development branch. There is usually an open merge request from the latest development branch to the main branch.
• When the latest development branch is merged to the main branch, a new release will be made.

Note that there is a checklist for new features.

It is forbidden to directly push to the main branch. Each new version has its own development branch from which a pull request will be opened to the main branch. Only the maintainer of the software is allowed to merge a development branch to the main branch.

License

The library is distributed under the 3-Clause BSD license.

Citation

If you use pytransform3d for a scientific publication, I would appreciate citation of the following paper:

Fabisch, A. (2019). pytransform3d: 3D Transformations for Python. Journal of Open Source Software, 4(33), 1159,

Bibtex entry:

@article{Fabisch2019,
  doi = {10.21105/joss.01159},
  url = {https://doi.org/10.21105/joss.01159},
  year = {2019},
  publisher = {The Open Journal},
  volume = {4},
  number = {33},
  pages = {1159},
  author = {Alexander Fabisch},
  title = {pytransform3d: 3D Transformations for Python},
  journal = {Journal of Open Source Software}
}