habitat_sim.scene.SemanticScene class

Static methods

def load_mp3d_house(file: str, scene: SemanticScene, rotation: numpy.ndarray[numpy.float32[4, 1]]) -> bool: Loads a SemanticScene from a Matterport3D House format file into passed SemanticScene.

def get_regions_for_point(self, point: magnum.Vector3) -> typing.List[int]: Compute all SemanticRegions which contain the point and return a list of indices for the regions in this SemanticScene.
def get_regions_for_points(self, points: typing.List[magnum.Vector3]) -> typing.List[typing.Tuple[int, float]]: “Compute SemanticRegion containment for a set of points. Return a sorted list of tuple pairs with each containing region index and the percentage of points contained by that region. In the case of nested regions, points are considered belonging to every region the point is found in.
def get_weighted_regions_for_point(self, point: magnum.Vector3) -> typing.List[typing.Tuple[int, float]]: “Find all SemanticRegions which contain the point and return a sorted list of tuple pairs of the region index and a score of that region, derived as 1 - (region_area/ttl_region_area) where ttl_region_area is the area of all the regions containing the point, so that smaller regions are weighted higher. If only one region contains the passed point, its weight will be 1.
def semantic_index_to_object_index(self, arg0: int, /) -> int

aabb: geo.BBox get
categories: typing.List[SemanticCategory] get: All semantic categories in the scene
levels: typing.List[SemanticLevel] get: All levels in the scene
objects: typing.List[SemanticObject] get: All object in the scene
regions: typing.List[SemanticRegion] get: All regions in the scene
semantic_index_map: typing.Dict[int, int] get