pycram.utils ============ .. py:module:: pycram.utils .. autoapi-nested-parse:: Implementation of helper functions and classes for internal usage only. Functions: _block -- wrap multiple statements into a single block. Classes: GeneratorList -- implementation of generator list wrappers. Classes ------- .. autoapisummary:: pycram.utils.bcolors pycram.utils.GeneratorList pycram.utils.suppress_stdout_stderr pycram.utils.RayTestUtils pycram.utils.ClassPropertyDescriptor Functions --------- .. autoapisummary:: pycram.utils.get_rays_from_min_max pycram.utils.chunks pycram.utils._apply_ik pycram.utils.axis_angle_to_quaternion pycram.utils.adjust_camera_pose_based_on_target pycram.utils.get_quaternion_between_camera_and_target pycram.utils.transform_vector_using_pose pycram.utils.apply_quaternion_to_pose pycram.utils.get_quaternion_between_two_vectors pycram.utils.get_axis_angle_between_two_vectors pycram.utils.wxyz_to_xyzw pycram.utils.xyzw_to_wxyz pycram.utils.wxyz_to_xyzw_arr pycram.utils.xyzw_to_wxyz_arr pycram.utils.classproperty pycram.utils.is_iterable pycram.utils.lazy_product Module Contents --------------- .. py:function:: get_rays_from_min_max(min_bound: typing_extensions.Sequence[float], max_bound: typing_extensions.Sequence[float], step_size_in_meters: float = 0.01) -> numpy.ndarray Get rays from min and max bounds as an array of start and end 3D points. Note: The rays are not steped in the x direction as the rays are cast parallel to the x-axis. Example: >>> min_bound = [0, 0, 0] >>> max_bound = [1, 2, 3] >>> rays = get_rays_from_min_max(min_bound, max_bound, 1) >>> rays.shape (6, 3, 2) >>> rays array([ [[0. , 1. ], [0. , 0. ], [0. , 0. ]], [[0. , 1. ], [0. , 0. ], [1.5, 1.5]], [[0. , 1. ], [0. , 0. ], [3. , 3. ]], [[0. , 1. ], [2. , 2. ], [0. , 0. ]], [[0. , 1. ], [2. , 2. ], [1.5, 1.5]], [[0. , 1. ], [2. , 2. ], [3. , 3. ]] ]) :param min_bound: The minimum bound of the rays, a sequence of 3 floats. :param max_bound: The maximum bound of the rays, a sequence of 3 floats. :param step_size_in_meters: The step size in meters between the rays. :return: The rays as an array of shape (n, 3, 2) where n is number of rays, 3 is because each point has x, y, and z, and 2 is for the start and end points of the rays. .. py:function:: chunks(lst: typing_extensions.List, n: int) -> typing_extensions.List Yield successive n-sized chunks from lst. :param lst: The list from which chunks should be yielded :param n: Size of the chunks :return: A list of size n from lst .. py:class:: bcolors Color codes which can be used to highlight Text in the Terminal. For example, for warnings. Usage: Firstly import the class into the file. print(f'{bcolors.WARNING} Some Text {bcolors.ENDC}') .. py:attribute:: HEADER :value: '\x1b[95m' .. py:attribute:: OKBLUE :value: '\x1b[94m' .. py:attribute:: OKCYAN :value: '\x1b[96m' .. py:attribute:: OKGREEN :value: '\x1b[92m' .. py:attribute:: WARNING :value: '\x1b[93m' .. py:attribute:: FAIL :value: '\x1b[91m' .. py:attribute:: ENDC :value: '\x1b[0m' .. py:attribute:: BOLD :value: '\x1b[1m' .. py:attribute:: UNDERLINE :value: '\x1b[4m' .. py:function:: _apply_ik(robot: pycram.world_concepts.world_object.Object, pose_and_joint_poses: typing_extensions.Tuple[pycram.datastructures.pose.PoseStamped, typing_extensions.Dict[str, float]]) -> None Apllies a list of joint poses calculated by an inverse kinematics solver to a robot :param robot: The robot the joint poses should be applied on :param pose_and_joint_poses: The base pose and joint states as returned by the ik solver :return: None .. py:class:: GeneratorList(generator: typing_extensions.Callable) Implementation of generator list wrappers. Generator lists store the elements of a generator, so these can be fetched multiple times. Methods: get -- get the element at a specific index. has -- check if an element at a specific index exists. .. py:attribute:: _generated :value: [] .. py:method:: get(index: int = 0) Get the element at a specific index or raise StopIteration if it doesn't exist. Arguments: index -- the index to get the element of. .. py:method:: has(index: int) -> bool Check if an element at a specific index exists and return True or False. Arguments: index -- the index to check for. .. py:function:: axis_angle_to_quaternion(axis: typing_extensions.List, angle: float) -> typing_extensions.Tuple Convert axis-angle to quaternion. :param axis: (x, y, z) tuple representing rotation axis. :param angle: rotation angle in degree :return: The quaternion representing the axis angle .. py:class:: suppress_stdout_stderr Bases: :py:obj:`object` A context manager for doing a "deep suppression" of stdout and stderr in Python, i.e. will suppress all prints, even if the print originates in a compiled C/Fortran sub-function. This will not suppress raised exceptions, since exceptions are printed to stderr just before a script exits, and after the context manager has exited (at least, I think that is why it lets exceptions through). Copied from https://stackoverflow.com/questions/11130156/suppress-stdout-stderr-print-from-python-functions .. py:attribute:: null_fds .. py:attribute:: save_fds .. py:method:: __enter__() .. py:method:: __exit__(*_) .. py:function:: adjust_camera_pose_based_on_target(cam_pose: pycram.datastructures.pose.PoseStamped, target_pose: pycram.datastructures.pose.PoseStamped, camera_description: pycram.robot_description.CameraDescription) -> pycram.datastructures.pose.PoseStamped Adjust the given cam_pose orientation such that it is facing the target_pose, which partly depends on the front_facing_axis of the that is defined in the camera_description. :param cam_pose: The camera pose. :param target_pose: The target pose. :param camera_description: The camera description. :return: The adjusted camera pose. .. py:function:: get_quaternion_between_camera_and_target(cam_pose: pycram.datastructures.pose.PoseStamped, target_pose: pycram.datastructures.pose.PoseStamped, camera_description: pycram.robot_description.CameraDescription) -> numpy.ndarray Get the quaternion between the camera and the target. :param cam_pose: The camera pose. :param target_pose: The target pose. :param camera_description: The camera description. :return: The quaternion between the camera and the target. .. py:function:: transform_vector_using_pose(vector: typing_extensions.Sequence, pose) -> numpy.ndarray Transform a vector using a pose. :param vector: The vector. :param pose: The pose. :return: The transformed vector. .. py:function:: apply_quaternion_to_pose(pose: pycram.datastructures.pose.PoseStamped, quaternion: numpy.ndarray) -> pycram.datastructures.pose.PoseStamped Apply a quaternion to a pose. :param pose: The pose. :param quaternion: The quaternion. :return: The new pose. .. py:function:: get_quaternion_between_two_vectors(v1: numpy.ndarray, v2: numpy.ndarray) -> numpy.ndarray Get the quaternion between two vectors. :param v1: The first vector. :param v2: The second vector. :return: The quaternion between the two vectors. .. py:function:: get_axis_angle_between_two_vectors(v1: numpy.ndarray, v2: numpy.ndarray) -> typing_extensions.Tuple[numpy.ndarray, float] Get the axis and angle between two vectors. :param v1: The first vector. :param v2: The second vector. :return: The axis and angle between the two vectors. .. py:class:: RayTestUtils(ray_test_batch: typing_extensions.Callable, object_id_to_name: typing_extensions.Dict = None) .. py:attribute:: local_transformer .. py:attribute:: ray_test_batch .. py:attribute:: object_id_to_name :value: None .. py:method:: get_images_for_target(cam_pose: pycram.datastructures.pose.PoseStamped, camera_description: pycram.robot_description.CameraDescription, camera_frame: str, size: int = 256, camera_min_distance: float = 0.1, camera_max_distance: int = 3, plot: bool = False) -> typing_extensions.List[numpy.ndarray] Note: The returned color image is a repeated depth image in 3 channels. .. py:method:: construct_segmentation_mask_from_ray_test_object_ids(object_ids: typing_extensions.List[int], size: int) -> numpy.ndarray :staticmethod: Construct a segmentation mask from the object ids returned by the ray test. :param object_ids: The object ids. :param size: The size of the grid. :return: The segmentation mask. .. py:method:: construct_depth_image_from_ray_test_distances(distances: typing_extensions.List[float], size: int) -> numpy.ndarray :staticmethod: Construct a depth image from the distances returned by the ray test. :param distances: The distances. :param size: The size of the grid. :return: The depth image. .. py:method:: construct_color_image_from_depth_image(depth_image: numpy.ndarray) -> numpy.ndarray :staticmethod: Construct a color image from the depth image. :param depth_image: The depth image. :return: The color image. .. py:method:: get_camera_rays_start_positions(camera_description: pycram.robot_description.CameraDescription, camera_frame: str, camera_pose: pycram.datastructures.pose.PoseStamped, size: int, camera_min_distance: float) -> numpy.ndarray .. py:method:: get_camera_rays_start_pose(camera_description: pycram.robot_description.CameraDescription, camera_frame: str, camera_pose: pycram.datastructures.pose.PoseStamped, camera_min_distance: float) -> pycram.datastructures.pose.PoseStamped Get the start position of the camera rays, which is the camera pose shifted by the minimum distance of the camera. :param camera_description: The camera description. :param camera_frame: The camera tf frame. :param camera_pose: The camera pose. :param camera_min_distance: The minimum distance from which the camera can see. .. py:method:: get_camera_rays_end_positions(camera_description: pycram.robot_description.CameraDescription, camera_frame: str, camera_pose: pycram.datastructures.pose.PoseStamped, size: int, camera_max_distance: float = 3.0) -> numpy.ndarray Get the end positions of the camera rays. :param camera_description: The camera description. :param camera_frame: The camera frame. :param camera_pose: The camera pose. :param size: The size of the grid. :param camera_max_distance: The maximum distance of the camera. :return: The end positions of the camera rays. .. py:method:: transform_points_from_camera_frame_to_world_frame(camera_pose: pycram.datastructures.pose.PoseStamped, camera_frame: str, points: numpy.ndarray) -> numpy.ndarray :staticmethod: Transform points from the camera frame to the world frame. :param camera_pose: The camera pose. :param camera_frame: The camera frame. :param points: The points to transform. :return: The transformed points. .. py:method:: get_end_positions_of_rays_from_angles_and_distance(vertical_angles: numpy.ndarray, horizontal_angles: numpy.ndarray, distance: float) -> numpy.ndarray :staticmethod: Get the end positions of the rays from the angles and the distance. :param vertical_angles: The vertical angles of the rays. :param horizontal_angles: The horizontal angles of the rays. :param distance: The distance of the rays. :return: The end positions of the rays. .. py:method:: construct_grid_of_camera_rays_angles(camera_description: pycram.robot_description.CameraDescription, size: int) -> typing_extensions.Tuple[numpy.ndarray, numpy.ndarray] :staticmethod: Construct a 2D grid of camera rays angles. :param camera_description: The camera description. :param size: The size of the grid. :return: The 2D grid of the horizontal and the vertical angles of the camera rays. .. py:method:: plot_segmentation_mask(segmentation_mask, object_id_to_name: typing_extensions.Dict[int, str] = None) :staticmethod: Plot the segmentation mask with different colors for each object. :param segmentation_mask: The segmentation mask. :param object_id_to_name: The mapping from object id to object name. .. py:method:: plot_depth_image(depth_image) :staticmethod: .. py:function:: wxyz_to_xyzw(wxyz: typing_extensions.List[float]) -> typing_extensions.List[float] Convert a quaternion from WXYZ to XYZW format. .. py:function:: xyzw_to_wxyz(xyzw: typing_extensions.List[float]) -> typing_extensions.List[float] Convert a quaternion from XYZW to WXYZ format. :param xyzw: The quaternion in XYZW format. .. py:function:: wxyz_to_xyzw_arr(wxyz: numpy.ndarray) -> numpy.ndarray Convert a quaternion from WXYZ to XYZW format. :param wxyz: The quaternion in WXYZ format. .. py:function:: xyzw_to_wxyz_arr(xyzw: numpy.ndarray) -> numpy.ndarray Convert a quaternion from XYZW to WXYZ format. :param xyzw: The quaternion in XYZW format. .. py:class:: ClassPropertyDescriptor(fget, fset=None) A helper that can be used to define properties of a class like the built-in ones but does not require the class to be instantiated. .. py:attribute:: fget .. py:attribute:: fset :value: None .. py:method:: __get__(obj, klass=None) .. py:method:: __set__(obj, value) .. py:method:: setter(func) .. py:function:: classproperty(func) .. py:function:: is_iterable(obj: typing_extensions.Any) -> bool Checks if the given object is iterable. :param obj: The object that should be checked :return: True if the object is iterable, False otherwise .. py:function:: lazy_product(*iterables: typing_extensions.Iterable) -> typing_extensions.Iterable[typing_extensions.Tuple] Lazily generate the cartesian product of the iterables. :param iterables: Iterable of iterables to construct product for. :return: Iterable of tuples in the cartesian product.