graspnetAPI.utils package

Subpackages

Submodules

graspnetAPI.utils.config module

graspnetAPI.utils.config.get_config()[source]

  • return the config dict

graspnetAPI.utils.eval_utils module

graspnetAPI.utils.eval_utils.collision_detection(grasp_list, model_list, dexnet_models, poses, scene_points, outlier=0.05, empty_thresh=10, return_dexgrasps=False)[source]

Input:

  • grasp_list: [(k1, 17), (k2, 17), …, (kn, 17)] in camera coordinate

  • model_list: [(N1, 3), (N2, 3), …, (Nn, 3)] in camera coordinate

  • dexnet_models: [GraspableObject3D,] in object coordinate

  • poses: [(4, 4),] from model coordinate to camera coordinate

  • scene_points: (Ns, 3) in camera coordinate

  • outlier: float, used to compute workspace mask

  • empty_thresh: int, ‘num_inner_points < empty_thresh’ means empty grasp

  • return_dexgrasps: bool, return grasps in dex-net format while True

Output:

  • collsion_mask_list: [(k1,), (k2,), …, (kn,)]

  • empty_mask_list: [(k1,), (k2,), …, (kn,)]

  • dexgrasp_list: [[ParallelJawPtGrasp3D,],] in object coordinate

graspnetAPI.utils.eval_utils.compute_closest_points(A, B)[source]

Input:

  • A: (N, 3)

  • B: (M, 3)

Output:

  • indices: (N,) closest point index in B for each point in A

graspnetAPI.utils.eval_utils.compute_point_distance(A, B)[source]

Input: - A: (N, 3)

  • B: (M, 3)

Output: - dists: (N, M)

graspnetAPI.utils.eval_utils.create_table_points(lx, ly, lz, dx=0, dy=0, dz=0, grid_size=0.01)[source]

Input: - lx: - ly: - lz: Output: - numpy array of the points with shape (-1, 3).

graspnetAPI.utils.eval_utils.eval_grasp(grasp_group, models, dexnet_models, poses, config, table=None, voxel_size=0.008, TOP_K=50)[source]

Input:

  • grasp_group: GraspGroup instance for evaluation.

  • models: in model coordinate

  • dexnet_models: models in dexnet format

  • poses: from model to camera coordinate

  • config: dexnet config.

  • table: in camera coordinate

  • voxel_size: float of the voxel size.

  • TOP_K: int of the number of top grasps to evaluate.

graspnetAPI.utils.eval_utils.get_grasp_score(grasp, obj, fc_list, force_closure_quality_config)[source]
graspnetAPI.utils.eval_utils.get_scene_name(num)[source]

Input: - num: int of the scene number.

Output: - string of the scene name.

graspnetAPI.utils.eval_utils.load_dexnet_model(data_path)[source]

Input:

  • data_path: path to load .obj & .sdf files

Output: - obj: dexnet model

graspnetAPI.utils.eval_utils.parse_posevector(posevector)[source]

Input: - posevector: list of pose Output: - obj_idx: int of the index of object. - mat: numpy array of shape (4, 4) of the 6D pose of object.

graspnetAPI.utils.eval_utils.topk_grasps(grasps, k=10)[source]

Input:

  • grasps: (N, 17)

  • k: int

Output:

  • topk_grasps: (k, 17)

graspnetAPI.utils.eval_utils.transform_points(points, trans)[source]

Input:

  • points: (N, 3)

  • trans: (4, 4)

Output: - points_trans: (N, 3)

graspnetAPI.utils.eval_utils.voxel_sample_points(points, voxel_size=0.008)[source]

Input:

  • points: (N, 3)

Output:

  • points: (n, 3)

graspnetAPI.utils.pose module

class graspnetAPI.utils.pose.Pose(id, x, y, z, alpha, beta, gamma)[source]

Bases: object

get_id()[source]

Output:

  • return the id of this object

get_mat_4x4()[source]

Output:

  • Convert self.x, self.y, self.z, self.alpha, self.beta and self.gamma into mat_4x4 pose

get_quat()[source]

Output:

  • Convert self.alpha, self.beta, self.gamma into self.quat

get_translation()[source]

Output:

  • Convert self.x, self.y, self.z into self.translation

graspnetAPI.utils.pose.pose_from_pose_vector(pose_vector)[source]

Input:

  • pose_vector: A list in the format of [id,x,y,z,alpha,beta,gamma]

Output:

  • A pose class instance

graspnetAPI.utils.pose.pose_list_from_pose_vector_list(pose_vector_list)[source]

Input:

  • Pose vector list defined in xmlhandler.py

Output:

  • list of poses.

graspnetAPI.utils.rotation module

Author: chenxi-wang Transformation matrices from/to viewpoints and dexnet gripper params.

graspnetAPI.utils.rotation.batch_viewpoint_params_to_matrix(batch_towards, batch_angle)[source]

Input:

  • towards: numpy array towards vectors with shape (n, 3).

  • angle: numpy array of in-plane rotations (n, ).

Output:

  • numpy array of the rotation matrix with shape (n, 3, 3).

graspnetAPI.utils.rotation.dexnet_params_to_matrix(binormal, angle)[source]

Input:

  • binormal: numpy array of shape (3,).

  • angle: float of the angle.

Output:

  • numpy array of shape (3, 3) of the rotation matrix.

graspnetAPI.utils.rotation.matrix_to_dexnet_params(matrix)[source]

Input:

  • numpy array of shape (3, 3) of the rotation matrix.

Output:

  • binormal: numpy array of shape (3,).

  • angle: float of the angle.

graspnetAPI.utils.rotation.rotation_matrix(alpha, beta, gamma)[source]

Input:

  • alpha: float of alpha angle.

  • beta: float of beta angle.

  • gamma: float of the gamma angle.

Output:

  • numpy array of shape (3, 3) of rotation matrix.

graspnetAPI.utils.rotation.viewpoint_params_to_matrix(towards, angle)[source]

Input:

  • towards: numpy array towards vector with shape (3,).

  • angle: float of in-plane rotation.

Output:

  • numpy array of the rotation matrix with shape (3, 3).

graspnetAPI.utils.trans3d module

graspnetAPI.utils.trans3d.get_mat(x, y, z, alpha, beta, gamma)[source]

Calls get_mat() to get the 4x4 matrix

graspnetAPI.utils.trans3d.get_pose(pose)[source]
graspnetAPI.utils.trans3d.pos_quat_to_pose_4x4(pos, quat)[source]

pose = pos_quat_to_pose_4x4(pos, quat) Convert pos and quat into pose, 4x4 format

Args:

pos: length-3 position quat: length-4 quaternion

Returns:

pose: numpy array, 4x4

graspnetAPI.utils.trans3d.pose_4x4_to_pos_quat(pose)[source]

Convert pose, 4x4 format into pos and quat

Args:

pose: numpy array, 4x4

Returns:

pos: length-3 position

quat: length-4 quaternion

graspnetAPI.utils.utils module

class graspnetAPI.utils.utils.CameraInfo(width, height, fx, fy, cx, cy, scale)[source]

Bases: object

Author: chenxi-wang Camera intrinsics for point cloud generation.

graspnetAPI.utils.utils.batch_center_depth(depths, centers, open_points, upper_points)[source]

Input:

  • depths: numpy array of the depths.

  • centers: numpy array of the center points of shape(-1, 2).

  • open_points: numpy array of the open points of shape(-1, 2).

  • upper_points: numpy array of the upper points of shape(-1, 2).

Output:

  • depths: numpy array of the grasp depth of shape (-1).

graspnetAPI.utils.utils.batch_framexy_depth_2_xyz(pixel_x, pixel_y, depth, camera)[source]

Input:

  • pixel_x: numpy array of int of the pixel x coordinate. shape: (-1,)

  • pixel_y: numpy array of int of the pixle y coordicate. shape: (-1,)

  • depth: numpy array of float of depth. The unit is millimeter. shape: (-1,)

  • camera: string of type of camera. “realsense” or “kinect”.

Output:

x, y, z: numpy array of float of x, y and z coordinates in camera frame. The unit is millimeter.

graspnetAPI.utils.utils.batch_key_point_2_rotation(centers_xyz, open_points_xyz, upper_points_xyz)[source]

Input:

  • centers_xyz: numpy array of the center points of shape (-1, 3).

  • open_points_xyz: numpy array of the open points of shape (-1, 3).

  • upper_points_xyz: numpy array of the upper points of shape (-1, 3).

Output:

  • rotations: numpy array of the rotation matrix of shape (-1, 3, 3).

graspnetAPI.utils.utils.batch_key_points_2_tuple(key_points, scores, object_ids, camera)[source]

Input:

  • key_points: np.array(-1,4,3) of grasp key points, definition is shown in key_points.png

  • scores: numpy array of batch grasp scores.

  • camera: string of ‘realsense’ or ‘kinect’.

Output:

  • np.array([center_x,center_y,open_x,open_y,height])

graspnetAPI.utils.utils.batch_rgbdxyz_2_rgbxy_depth(points, camera)[source]

Input:

  • points: np.array(-1,3) of the points in camera frame

  • camera: string of the camera type

Output:

  • coords: float of xy in pixel frame [-1, 2]

  • depths: float of the depths of pixel frame [-1]

graspnetAPI.utils.utils.center_depth(depths, center, open_point, upper_point)[source]

Input:

  • depths: numpy array of the depths.

  • center: numpy array of the center point.

  • open_point: numpy array of the open point.

  • upper_point: numpy array of the upper point.

Output:

  • depth: float of the grasp depth.

graspnetAPI.utils.utils.create_axis(length, grid_size=0.01)[source]
graspnetAPI.utils.utils.create_mesh_box(width, height, depth, dx=0, dy=0, dz=0)[source]

Author: chenxi-wang Create box instance with mesh representation.

graspnetAPI.utils.utils.create_point_cloud_from_depth_image(depth, camera, organized=True)[source]
graspnetAPI.utils.utils.create_table_cloud(width, height, depth, dx=0, dy=0, dz=0, grid_size=0.01)[source]

Author: chenxi-wang

Input:

  • width/height/depth: float, table width/height/depth along x/z/y-axis in meters

  • dx/dy/dz: float, offset along x/y/z-axis in meters

  • grid_size: float, point distance along x/y/z-axis in meters

Output:

  • open3d.geometry.PointCloud

graspnetAPI.utils.utils.dexnet_params_to_matrix(binormal, angle)[source]

Author: chenxi-wang

Input:

  • binormal: numpy array of shape (3,).

  • angle: float of the angle.

Output:

  • numpy array of shape (3, 3) of the rotation matrix.

graspnetAPI.utils.utils.find_scene_by_model_id(dataset_root, model_id_list)[source]
graspnetAPI.utils.utils.framexy_depth_2_xyz(pixel_x, pixel_y, depth, camera)[source]

Input:

  • pixel_x: int of the pixel x coordinate.

  • pixel_y: int of the pixle y coordicate.

  • depth: float of depth. The unit is millimeter.

  • camera: string of type of camera. “realsense” or “kinect”.

Output:

  • x, y, z: float of x, y and z coordinates in camera frame. The unit is millimeter.

graspnetAPI.utils.utils.generate_scene(scene_idx, anno_idx, return_poses=False, align=False, camera='realsense')[source]
graspnetAPI.utils.utils.generate_scene_model(dataset_root, scene_name, anno_idx, return_poses=False, align=False, camera='realsense')[source]

Author: chenxi-wang

Input:

  • dataset_root: str, graspnet dataset root

  • scene_name: str, name of scene folder, e.g. scene_0000

  • anno_idx: int, frame index from 0-255

  • return_poses: bool, return object ids and 6D poses if set to True

  • align: bool, transform to table coordinates if set to True

  • camera: str, camera name (realsense or kinect)

Output:

  • list of open3d.geometry.PointCloud.

graspnetAPI.utils.utils.generate_scene_pointcloud(dataset_root, scene_name, anno_idx, align=False, camera='kinect')[source]

Author: chenxi-wang

Input:

  • dataset_root: str, graspnet dataset root

  • scene_name: str, name of scene folder, e.g. scene_0000

  • anno_idx: int, frame index from 0-255

  • align: bool, transform to table coordinates if set to True

  • camera: str, camera name (realsense or kinect)

Output:

  • open3d.geometry.PointCloud.

graspnetAPI.utils.utils.generate_views(N, phi=0.6180339887498949, center=array([0.0, 0.0, 0.0], dtype=float32), R=1)[source]

Author: chenxi-wang View sampling on a sphere using Febonacci lattices.

Input:

  • N: int, number of viewpoints.

  • phi: float, constant angle to sample views, usually 0.618.

  • center: numpy array of (3,), sphere center.

  • R: float, sphere radius.

Output:

  • numpy array of (N, 3), coordinates of viewpoints.

graspnetAPI.utils.utils.get_batch_key_points(centers, Rs, widths)[source]

Input:

  • centers: np.array(-1,3) of the translation

  • Rs: np.array(-1,3,3) of the rotation matrix

  • widths: np.array(-1) of the grasp width

Output:

  • key_points: np.array(-1,4,3) of the key point of the grasp

graspnetAPI.utils.utils.get_camera_intrinsic(camera)[source]

Input:

  • camera: string of type of camera, “realsense” or “kinect”.

Output:

  • numpy array of shape (3, 3) of the camera intrinsic matrix.

graspnetAPI.utils.utils.get_model_grasps(datapath)[source]

Author: chenxi-wang Load grasp labels from .npz files.

graspnetAPI.utils.utils.get_obj_pose_list(camera_pose, pose_vectors)[source]
graspnetAPI.utils.utils.key_point_2_rotation(center_xyz, open_point_xyz, upper_point_xyz)[source]

Input:

  • center_xyz: numpy array of the center point.

  • open_point_xyz: numpy array of the open point.

  • upper_point_xyz: numpy array of the upper point.

Output:

  • rotation: numpy array of the rotation matrix.

graspnetAPI.utils.utils.matrix_to_dexnet_params(matrix)[source]

Author: chenxi-wang

Input:

  • numpy array of shape (3, 3) of the rotation matrix.

Output:

  • binormal: numpy array of shape (3,).

  • angle: float of the angle.

graspnetAPI.utils.utils.parse_posevector(posevector)[source]

Author: chenxi-wang Decode posevector to object id and transformation matrix.

graspnetAPI.utils.utils.plot_axis(R, center, length, grid_size=0.01)[source]
graspnetAPI.utils.utils.plot_gripper_pro_max(center, R, width, depth, score=1, color=None)[source]

Author: chenxi-wang

Input:

  • center: numpy array of (3,), target point as gripper center

  • R: numpy array of (3,3), rotation matrix of gripper

  • width: float, gripper width

  • score: float, grasp quality score

Output:

  • open3d.geometry.TriangleMesh

graspnetAPI.utils.utils.rotation_matrix(rx, ry, rz)[source]

Author: chenxi-wang

Input:

  • rx/ry/rz: float, rotation angle along x/y/z-axis

Output:

  • numpy array of (3, 3), rotation matrix.

graspnetAPI.utils.utils.transform_matrix(tx, ty, tz, rx, ry, rz)[source]

Author: chenxi-wang

Input:

  • tx/ty/tz: float, translation along x/y/z-axis

  • rx/ry/rz: float, rotation angle along x/y/z-axis

Output:

  • numpy array of (4, 4), transformation matrix.

graspnetAPI.utils.utils.transform_points(points, trans)[source]

Author: chenxi-wang

Input:

  • points: numpy array of (N,3), point cloud

  • trans: numpy array of (4,4), transformation matrix

Output:

  • numpy array of (N,3), transformed points.

graspnetAPI.utils.utils.viewpoint_params_to_matrix(towards, angle)[source]

Author: chenxi-wang

Input:

  • towards: numpy array towards vector with shape (3,).

  • angle: float of in-plane rotation.

Output:

  • numpy array of the rotation matrix with shape (3, 3).

graspnetAPI.utils.vis module

graspnetAPI.utils.vis.create_table_cloud(width, height, depth, dx=0, dy=0, dz=0, grid_size=0.01)[source]

Author: chenxi-wang

Input:

  • width/height/depth: float, table width/height/depth along x/z/y-axis in meters

  • dx/dy/dz: float, offset along x/y/z-axis in meters

  • grid_size: float, point distance along x/y/z-axis in meters

Output:

  • open3d.geometry.PointCloud

graspnetAPI.utils.vis.get_camera_parameters(camera='kinect')[source]

author: Minghao Gou

Input:

  • camera: string of type of camera: ‘kinect’ or ‘realsense’

Output:

  • open3d.camera.PinholeCameraParameters

graspnetAPI.utils.vis.vis6D(dataset_root, scene_name, anno_idx, camera, align_to_table=True, save_folder='save_fig', show=False, per_obj=False)[source]

Input:

  • dataset_root: str, graspnet dataset root

  • scene_name: str, name of scene folder, e.g. scene_0000

  • anno_idx: int, frame index from 0-255

  • camera: str, camera name (realsense or kinect)

  • align_to_table: bool, transform to table coordinates if set to True

  • save_folder: str, folder to save screen captures

  • show: bool, show visualization in open3d window if set to True

  • per_obj: bool, show pose of each object

graspnetAPI.utils.vis.visAnno(dataset_root, scene_name, anno_idx, camera, num_grasp=10, th=0.3, align_to_table=True, max_width=0.08, save_folder='save_fig', show=False, per_obj=False)[source]

Author: chenxi-wang

Input:

  • dataset_root: str, graspnet dataset root

  • scene_name: str, name of scene folder, e.g. scene_0000

  • anno_idx: int, frame index from 0-255

  • camera: str, camera name (realsense or kinect)

  • num_grasp: int, number of sampled grasps

  • th: float, threshold of friction coefficient

  • align_to_table: bool, transform to table coordinates if set to True

  • max_width: float, only visualize grasps with width<=max_width

  • save_folder: str, folder to save screen captures

  • show: bool, show visualization in open3d window if set to True

  • per_obj: bool, show grasps on each object

graspnetAPI.utils.vis.visObjGrasp(dataset_root, obj_idx, num_grasp=10, th=0.5, max_width=0.08, save_folder='save_fig', show=False)[source]

Author: chenxi-wang

Input:

  • dataset_root: str, graspnet dataset root

  • obj_idx: int, index of object model

  • num_grasp: int, number of sampled grasps

  • th: float, threshold of friction coefficient

  • max_width: float, only visualize grasps with width<=max_width

  • save_folder: str, folder to save screen captures

  • show: bool, show visualization in open3d window if set to True

graspnetAPI.utils.vis.vis_rec_grasp(rec_grasp_tuples, numGrasp, image_path, save_path, show=False)[source]

author: Minghao Gou

Input:

  • rec_grasp_tuples: np.array of rectangle grasps

  • numGrasp: int of total grasps number to show

  • image_path: string of path of the image

  • image_path: string of the path to save the image

  • show: bool of whether to show the image

Output:

  • no output but display the rectangle grasps in image

graspnetAPI.utils.xmlhandler module

graspnetAPI.utils.xmlhandler.empty_pose_vector(objectid)[source]
graspnetAPI.utils.xmlhandler.empty_pose_vector_list(objectidlist)[source]
graspnetAPI.utils.xmlhandler.getframeposevectorlist(objectidlist, is_resume, frame_number, xml_dir)[source]
graspnetAPI.utils.xmlhandler.getposevectorlist(objectidlist, is_resume, num_frame, frame_number, xml_dir)[source]
class graspnetAPI.utils.xmlhandler.xmlReader(xmlfilename)[source]

Bases: object

get_pose_list()[source]
getposevectorlist()[source]
gettop()[source]
showinfo()[source]
class graspnetAPI.utils.xmlhandler.xmlWriter(topfromreader=None)[source]

Bases: object

addobject(pose, objname, objpath, objid)[source]
objectlistfromposevectorlist(posevectorlist, objdir, objnamelist, objidlist)[source]
writexml(xmlfilename='scene.xml')[source]

Module contents