geomapi.tools

• geomapi.tools.alignmenttools
  • geomapi.tools.alignmenttools.match
    • Match
      • Match.__init__()
      • Match.node1
      • Match.node2
      • Match.matchError
      • Match.matchAmount
      • Match.transformation
      • Match.get_matches()
      • Match.get_transformation()
    • Match2d
      • Match2d.__init__()
      • Match2d.get_matches()
      • Match2d.get_transformation()
      • Match2d.get_pnp_pose()
      • Match2d.get_reference_scaling_factor()
      • Match2d.set_scaling_factor()
      • Match2d.get_image2_pos()
    • Match3d
      • Match3d.__init__()
      • Match3d.get_matches()
    • PoseEstimation
      • PoseEstimation.referenceSessionId
      • PoseEstimation.__init__()
      • PoseEstimation.transformation
      • PoseEstimation.matches
      • PoseEstimation.method
      • PoseEstimation.get_confidence()
    • match_session()
    • match_incremental()
    • match_crossref()
    • match_raycast()
    • match_fgr()
    • match_super4pcs()
    • get_best_session_match()
    • get_best_matches()
  • geomapi.tools.alignmenttools.params
  • find_close_sessions()
  • get_weighted_pose()
  • estimate_session_position()
  • execute_fast_global_registration()
  • execute_global_registration()
  • weighted_average_quaternions()
  • calibrate_camera()
• geomapi.tools.combinationtools
  • combine_geometry()
  • check_point_inside_mesh()
  • get_invisible_points()
  • mesh_to_pcd()
  • get_points_in_hull()
  • get_indices_in_hull()
  • filter_pcd_by_distance()
  • filter_geometry_by_distance()
• geomapi.tools.inspectiontools
  • calculate_distance_between_images()
  • filter_images_by_overlap()
  • project_lineset_on_image()
  • crop_image_within_lineset()
• geomapi.tools.machinelearningtools
  • compute_balance_classes()
  • depth_map_to_hha()
  • processDepthImage()
  • getPointCloudFromZ()
  • computeNormalsSquareSupport()
  • filterItChopOff()
  • mutiplyIt()
  • invertIt()
  • getYDir()
  • getYDirHelper()
  • getRMatrix()
  • rotatePC()
• geomapi.tools.progresstools
  • create_voxel_block_grid_and_raytrace()
  • subdivide_pcd_per_box()
  • subdivide_pcd_per_octree()
  • pcd_to_octree()
  • capture_image_and_depth_viewer()
  • assign_point_cloud_information()
  • filter_img_classifcation_by_neighbors()
  • remap_color_images_to_masks()
  • project_meshes_to_rgbd_images()
  • project_pcd_to_rgbd_images()
  • get_average_cartesian_transform_ortho()
  • create_xy_grids()
  • volume_mesh_BIM()
  • volume_theoretical_BIM()
  • calculate_completion()
  • color_BIMNode()
  • remove_edges_volume_calculation()
  • color_pointcloud_by_height()
  • create_xy_grid()
  • get_mesh_intersections()
  • get_bim_intersections()
  • get_mesh_intersectionsBIM()
  • get_scene_intersections()
  • get_rays_raycast()
  • get_mesh_intersection_with_grid()
  • get_top_lineset_from_meshes()
  • determine_percentage_of_coverage()
• geomapi.tools.validationtools
  • plot_pano_positions()
  • get_heading()
  • get_zenit()
  • navvis_csv_to_nodes()
  • get_boundingbox_of_list_of_geometries()
  • match_BIM_points()
  • compute_LOA()
  • plot_histogram()
  • color_point_cloud_by_LOA()
  • color_point_cloud_by_distance()
  • csv_by_LOA()
  • excel_by_LOA()
  • color_BIMNode()
  • cad_show_lines()
  • sample_pcd_from_linesets()
  • get_linesets_inliers_in_box()
  • create_selection_box_from_image_boundary_points()
  • AlignmentPose
    • AlignmentPose.__init__()
  • PanoPose
    • PanoPose.__init__()
    • PanoPose.heading
    • PanoPose.zenit
    • PanoPose.get_direction_to_other_pose()
  • PanoPoseCollection
    • PanoPoseCollection.__init__()
    • PanoPoseCollection.headings
    • PanoPoseCollection.zenits
    • PanoPoseCollection.box
    • PanoPoseCollection.transform()
    • PanoPoseCollection.plot()
  • read_leica_pano_poses_xml()
  • read_navvis_pano_poses_csv()
  • read_navvis_alignment_xml()
  • plot_pose_collections()
  • plot_pose_collections_3D()
  • decode_depthmap()
  • get_loaclasses_from_ifcclass()
  • get_ifcclasses_from_loaclass()
  • create_default_loa_graph()
  • parse_loa_excel()
  • get_loa_class_per_bimnode()

Different tools to Manage RDF data.

geomapi.tools.graph_to_nodes(graphPath: str = None, graph: Graph = None, subjects: List = None, **kwargs) → List[Node]

Convert a graphPath to a set of Nodes.

Args:

0. graphPath (str): absolute path to .ttl RDF Graph

1. kwargs (Any)

Returns:

A list of pointcloudnodes, imagenodes, meshnodes, bimnodes, orthonodes with metadata

geomapi.tools.e57xml_to_pointcloud_nodes(xmlPath: str, **kwargs) → List[PointCloudNode]

Parse XML file that is created with E57lib e57xmldump.exe.

E57 XML file structure e57Root

>data3D

>vectorChild

>pose

>rotation >translation

>cartesianBounds >guid >name >points recordCount

>images2D

Args:

path (string): e57 xml file path e.g. “D:Data2018-06 Werfopvolging Academiestraat Gentweek 22PCDweek 22 lidar_CC.xml” **kwargs: All extra arguments are applied to all nodes

Returns:

A list of pointcloudnodes with the xml metadata

geomapi.tools.xml_to_image_nodes(path: str, subjects: List = None, skip: int = None, filterByFolder: bool = False, **kwargs) → List[ImageNode]

Parse XML file that is created with https://www.agisoft.com/.

Args:

1.xmlPath (string or Path): xml file path e.g. “D:/Data/cameras.xml” 2.subjects (List[str]): a list of labels, matching with the camera labels, that you want filtered. 2.skip (int, Optional): select every nth image from the xml. Defaults to None. 3.filterByFolder (bool, Optional): Filter imgNodes based on the images in the folder or not. Defaults to False.

Returns:

A list of ImageNodes with the xml metadata

geomapi.tools.e57_to_pointcloud_nodes(e57Path, subjects=None, percentage: float = None, loadResource=False, multiProcessingTreshold=10, **kwargs) → List[PointCloudNode]

geomapi.tools.dxf_to_lineset_nodes(dxfPath: str | Path, **kwargs) → List[LineSetNode]

Parse a dxf file to a list of LineSetNodes. created by CAD software

NOTE: be aware of the scale factor!

Args:

• dxfPath(str): absolute path to .dxf file

• **kwargs: additional arguments to pass to the Line

Returns:

List[LineSetNode]

geomapi.tools.dxf_to_ortho_nodes(dxfPath: str | Path, name_filter: str = None, **kwargs) → List[OrthoNode]

Parse a DXF file into a list of OrthoNode objects., created by Metashape

Args:

dxfPath (str | Path): Path to the DXF file. name_filter (str, optional): If provided, only include entities matching this name. **kwargs: Additional arguments passed to OrthoNode.

Returns:

List[OrthoNode]

geomapi.tools.ifc_to_bim_nodes(path: str, classes: str = None, guids: list = None, types: List = None, getResource: bool = True, **kwargs) → List[BIMNode]

Parse ifc file to a list of BIMNodes, one for each ifcElement.

NOTE: classes are not case sensitive. It is advised to solely focus on IfcBuildingElement classes or inherited classes as these typically have geometry representations that can be used by GEOMAPI.

NOTE: If you intend to parse 1000+ elements, use the multithreading of the entire file instead and filter the BIMNodes afterwards as it will be faster.

WARNING: IfcOpenShell struggles with some ifc serializations. In our experience, IFC4 serializations is more robust.

Args:

1. ifcPath (string): absolute ifc file path e.g. “D:/myifc.ifc”

2. classes (string, optional): ifcClasses e.g. ‘IfcBeam, IfcColumn, IfcWall, IfcSlab’. Defaults to ‘IfcBuildingElement’.

Raises:

ValueError: ‘No valid ifcPath.’

Returns:

List[BIMNode]

geomapi.tools.ifc_to_nodes_multiprocessing(path: str, **kwargs) → List[BIMNode]

Returns the contents of geometry elements in an ifc file as BIMNodes. This method is 3x faster than other parsing methods due to its multi-threading. However, only the entire ifc can be parsed.

WARNING: IfcOpenShell strugles with some ifc serializations. In our experience, IFC4 serializations is more robust.

Args:

ifcPath (str | Path): ifc file path e.g. “D:/myifc.ifc”

Raises:

ValueError: ‘No valid ifcPath.’

Returns:

List[BIMNode]

geomapi.tools.navvis_csv_to_pano_nodes(csvPath: Path, directory: Path = None, includeDepth: bool = True, depthPath: Path = None, skip: int = 1, **kwargs) → List[PanoNode]

Parse Navvis csv file and return a list of PanoNodes with the csv metadata.

Args:

• csvPath (Path): csv file path e.g. “D:/Data/pano/pano-poses.csv”

• skip (int, Optional): select every nth image from the xml. Defaults to None.

• Path (Path, Optional): path to the pano directory. Defaults to None.

• includeDepth (bool, Optional): include depth images. Defaults to True.

• depthPath (Path, Optional): path to the depth images. Defaults to None.

• kwargs: additional keyword arguments for the PanoNode instances

Returns:

• A list of PanoNodes with the csv metadata

geomapi.tools.select_nodes_k_nearest_neighbors(nodes: List[Node], center: ndarray = [0, 0, 0], k: int = 10)

Select k nearest nodes based on Euclidean distance between centroids.

geomapi.tools.select_nodes_within_radius(nodes, center, radius)

geomapi.tools.select_nodes_within_bounding_box(nodes: List[Node], bbox: open3d.geometry.OrientedBoundingBox, margin: List[float] = [0, 0, 0]) → List[Node]

Select the nodes whose centers lie inside the given bounding box.

Args:

nodes: List of node objects. bbox: An open3d.geometry.OrientedBoundingBox (or AxisAlignedBoundingBox). margin: List of margins [u, v, w] to expand the bounding box.

Returns:

List of nodes inside the bounding box, or None if no nodes are inside.

geomapi.tools.select_nodes_within_convex_hull(nodes: List[Node], hull: open3d.geometry.TriangleMesh) → List[Node]

Select the nodes whose centers lie inside the given convex hull.

Args:

nodes: List of node objects. hull: An open3d.geometry.TriangleMesh representing a convex hull.

Returns:

List of nodes inside the convex hull, or None if no nodes are inside.

geomapi.tools.select_nodes_intersecting_bounding_box(nodes: List[Node], bbox: open3d.geometry.OrientedBoundingBox, margin: List[float] = [0, 0, 0]) → List[Node]

Select the nodes whose bounding box intersects with the given bounding box.

Args:

nodes: List of node objects. bbox: An open3d.geometry.OrientedBoundingBox. margin: List of margins [u, v, w] to expand the bounding box.

Returns:

List of nodes whose bounding box intersects the given bounding box.

geomapi.tools.select_nodes_intersecting_convex_hull(nodes: List[Node], hull: open3d.geometry.TriangleMesh) → List[Node]

Select the nodes whose convex hull intersects with the given convex hull.

Args:

nodes: List of node objects. hull: An open3d.geometry.TriangleMesh.

Returns:

List of nodes whose convex hull intersects the given convex hull.

geomapi.tools.nodes_to_graph(nodelist: List[Node], path: str = None, overwrite: bool = False, save: bool = False, base: URIRef = None) → Graph

Convert list of nodes to an RDF graph.

Args:

• nodelist (List[Node])

• graphPath (str, optional): path that serves as the basepath for all path information in the graph. This is also the storage location of the graph.

• overwrite (bool, optional): Overwrite the existing graph triples. Defaults to False.

• save (bool, optional): Save the Graph to file. Defaults to False.

Returns:

Graph

geomapi.tools.navvis_decode_depthmap(depth_image)

Function to decode the depthmaps generated by the navvis processing

Args:

• None

Returns:

• np.array: Depthmap