geomapi.tools.validationtools

validationtools - a Python library for validating objects.

geomapi.tools.validationtools.decode_depthmap(source: str, size=(8192, 4096), show=False) → array

Function to decode the depthmaps generated by the navvis processing.

Args:

source (str): Location of the PNG files containing the depthmap size (tuple, optional): size of the corresponding pano, by default 8192x4096 show (bool, optional): Show the pano. Defaults to False.

Returns:

np.array(MxNx3): decoded pano

geomapi.tools.validationtools.navvis_csv_to_nodes(csvPath: str, panoDirectory: str = None, includeDepth: bool = True, depthDirectory: str = None, skip: int = 1, filterByFolder=True, **kwargs) → List[PanoNode]

Parse a csv file from Navvis and output a serie of PanoNodes containing the image and depth information at a certain location.

Args:

csvPath (str): formatted nr, name, timestamp, X,Y,Z,w,x,y,z (as quaternion) panoDirectory (str, optional): path to the image (.png) includeDepth (bool, optional): Defaults to True. depthDirectory (str, optional): skip (int, optional): select every nth image from the xml. Defaults to None. filterByFolder (bool, optional): Filter Nodes based on the images in the folder or not. Defaults to True.

Returns:

List[PanoNode]: _description_

geomapi.tools.validationtools.get_boundingbox_of_list_of_geometries(geometries: List[open3d.geometry.PointCloud]) → array

Determines the global boundingbox of a group of Node containing geometries.

Args:

geometries (List[Nodes]): list of Nodes containing a resource of which the boundingbox must be determined”

Returns:

np.array[3x1]

geomapi.tools.validationtools.match_BIM_points(sources: List[open3d.geometry.PointCloud], references: List[open3d.geometry.PointCloud], referenceIds=None, resolution: float = 0.02, t00: float = 0.15, wd: float = 0.62, wn: float = 0.38, threshold: float = 0.7) → array

Determines the accuracy of a BIM model or object using a pointcloud

Args:

  1. sources(list(o3d.geometry.PointCloud)): The captured point clouds of the site.

  2. references(list(o3d.geometry.PointCloud)): A pointcloud per reference object

  3. referenceIds(list(str), optional): ID to be assigned to the points of the source point cloud

  4. resolution (float, optional): voxel size for the a voxel downsampling before the analysis default 0.02m

  5. t00(float): Threshold to which matching point will be searched default 0.15m

  6. wd (float): weight of the distances in the decision function default 0.62

  7. wn (float): weight of the normals in the decision function default 0.38

  8. threshold (float): minimum value to be seen as a match, default 0.7

Returns:

o3d.geometry.PointCloud: pointcloud used in the analysis linked to the identity and the distances of the points.

identityArray: array containing the matching label to the BIM for each point from the point cloud.

distances: array containing all the distances to the matching BIM for each point from the point cloud.

geomapi.tools.validationtools.compute_LOA(identities, distances, t00: float = 0.15, t10: float = 0.1, t20: float = 0.05, t30: float = 0.015, byElement: bool = False, limit: float = 0.95)

Function which uses distances and a linked identity array to determine the LOA percentages

Args:

identities (nx1-array): Array containing the identity of the distance between two matched points distances (nx1 array): Array containing the distances between two matched points t00 (float, optional): Maximum distance to be used in the analysis. Defaults to 0.15. t10 (float, optional): Upper bound of the LOA10 bracket. Defaults to 0.10. t20 (float, optional): Upper bound of the LOA20 bracket. Defaults to 0.05. t30 (float, optional): Upper bound of the LOA10 bracket. Defaults to 0.015. byElement (bool, optional): If the LOA must be computed per element of for the enitre cloud. Defaults to False. limit (float, optional): Percentage of inliers between two brackets needed to assign the LOA label. Defaults to 0.95.

Returns:

LOA: List of LOAs per element (id, [LOA10, LOA20, LOA30], label)

geomapi.tools.validationtools.plot_histogram(identities, distances, buckets: int = None, interval: float = None, dmax: float = 0.1, byElement=False, bins=None, directory=None, show=True)

Function to plot distances between the captured cloud and the reference cloud

Args:

identities (nx1-array): Array containing the identity of the distance between two matched points distances (nx1 array): Array containing the distances between two matched points buckets (int, optional): Number of intervals the data will be seperated. Defaults to None. interval (float, optional): distance between the upper and lower bound of an interval. Defaults to None. dmax (float, optional): Distances higher then this distance will be ignored. Defaults to 0.1. byElement (bool, optional): If the LOA must be computed per element of for the enitre cloud. Defaults to False. bins (1xn array): Can be used to describe custom bin boundries (intervals must be equal). Defaults to None. directory (path, optional): When provided the histograms will be saved in the form of a PNG to this directory. Defaults to None. show (bool, optional): When set on true the histograms will be visualized. Defaults to True.

geomapi.tools.validationtools.color_point_cloud_by_LOA(pointcloud: open3d.geometry.PointCloud, identities, distances, t00: float = 0.15, t10: float = 0.1, t20: float = 0.05, t30: float = 0.015, byElement: bool = False)

Colors each point by its computed LOA based on the distance between the matched points of the reference and the source cloud

Args:

pointcloud (o3d.geometry.PointCloud): Point cloud from the LOA determination or pointcloud matching its the returned indeces identities (nx1-array): Array containing the identity of the distance between two matched points distances (nx1 array): Array containing the distances between two matched points t00 (float, optional): Maximum distance to be used in the analysis. Defaults to 0.15. t10 (float, optional): Upper bound of the LOA10 bracket. Defaults to 0.10. t20 (float, optional): Upper bound of the LOA20 bracket. Defaults to 0.05. t30 (float, optional): Upper bound of the LOA10 bracket. Defaults to 0.015. byElement (bool, optional): If the LOA must be computed per element of for the enitre cloud. Defaults to False.

Returns:

o3d.geometry.PointCloud()

geomapi.tools.validationtools.color_point_cloud_by_distance(pointcloud: open3d.geometry.PointCloud, identities: array, distances: array, buckets: int = 5, dmax: float = 0.1, byElement: bool = False) → open3d.geometry.PointCloud

Colorizes the resulting point cloud of the LOA analysis in a gradient by distance between the matched points from the reference and the source (very slow).

NOTE: use sklearn to make this faster.

Args:

  1. pointcloud (o3d.geometry.PointCloud): Point cloud from the LOA determination or pointcloud matching its the returned indeces.

  2. identities (nx1-array): Array containing the identity of the distance between two matched points.

  3. distances (nx1 array): Array containing the distances between two matched points.

  4. buckets (int, optional): Number of intervals to be colored in. Defaults to 5.

  5. dmax (float, optional): Distances higher then this distance will be ignored. Defaults to 0.1m.

  6. byElement (bool, optional): If the LOA must be computed per element of for the enitre cloud. Defaults to False.

Returns:

o3d.geometry.PointCloud

geomapi.tools.validationtools.csv_by_LOA(directory: str, LOAs, visibility=None)

Function to report the LOA analysis in a csv file.

Args:

  1. directory (path): directory where the report must be saved.

  2. LOAs (_type_): results of the LOA computation.

  3. visibility (_type_, optional): array containing the per element visibility.

Returns:

returns true when succeded.

geomapi.tools.validationtools.excel_by_LOA(directory, LOAs, visibility=None)

Function to report the LOA analysis in an excel file

Args:

directory (path): directory where the report must be saved LOAs (_type_): results of the LOA computation visibility (_type_, optional): array containing the per element visibility

Returns:

returns true when succeded

geomapi.tools.validationtools.color_BIMNode(LOAs, BIMNodes: List[BIMNode])

Colors the BIM mesh geometries in the computed LOA color

Args:

LOAs (_type_): results of the LOA analysis BIMNodes (List[BIMNode]): List of the BIMNodes in the project

geomapi.tools.validationtools.cad_show_lines(dxf_path: str)
geomapi.tools.validationtools.sample_pcd_from_linesets(linesets: List[open3d.geometry.LineSet], step_size: float = 0.1) → Tuple[open3d.geometry.PointCloud, ndarray]

Sample a point cloud from a set of o3d.geometry.LineSet elements (color is inherited)

Args:

linesets (List[o3d.geometry.LineSet]): linesets to sample. step_size(float,optional):spacing between points. Defaults to 0.1m.

Returns:

Tuple[List[o3d.geometry.PointCloud],np.ndarray]: point_clouds, identityarray with integers of the origin of the points

geomapi.tools.validationtools.get_linesets_inliers_in_box(linesets: List[open3d.geometry.LineSet], box: open3d.geometry.OrientedBoundingBox, point_cloud: open3d.geometry.PointCloud, identityArray: ndarray) → List[open3d.geometry.LineSet]

Returns the segments of the linesets that have sampled pointcloud points falling within a certain bounding box. This function should be used together with:

1. vt.sample_pcd_from_linesets(linesets,step_size=0.1)2.vt.create_selection_box_from_image_boundary_points(n,roi,meshNode.resource,z=5)

Args:

linesets (List[o3d.geometry.LineSet]): linesets from which the segments will be selected box (o3d.geometry.OrientedBoundingBox): bounding box that is used to filter the point cloud points point_cloud (o3d.geometry.PointCloud): sampled points on the linesets identityArray (np.ndarray): array with integers that reflect which point cloud point belongs to which lineset

Returns:

List[o3d.geometry.LineSet]: _description_

geomapi.tools.validationtools.create_selection_box_from_image_boundary_points(n: ImageNode, roi: Tuple[int, int, int, int], mesh: open3d.geometry.TriangleMesh, z: float = 5) → open3d.geometry.OrientedBoundingBox

Create a selection box from an ImageNode, a region of interest (roi) and a mesh to raycast. A o3d.geometry.OrientedBoundingBox will be created on the location of the intersection of the rays with the mesh. The height of the box is determined by the offset of z in both positive and negative Z-direction

Args:

n (ImageNode): Imagenode used for the raycasting (internal and external camera paramters) roi (Tuple[int,int,int,int]): region of interest (rowMin,rowMax,columnMin,columnMax) mesh (o3d.geometry.TriangleMesh): mesh used for the raycasting z (float, optional): offset in height of the bounding box. Defaults to [-5m:5m].

Returns:

o3d.geometry.OrientedBoundingBox or None (if not all rays hit the mesh)