pointtree.visualization
Tools for visualizing processing results.
- pointtree.visualization.color_instance_segmentation(
- point_cloud: DataFrame,
- instance_id_column: str,
- target_instance_id_column: str | None = None,
- fp_ids: List[int] | None = None,
- fn_ids: List[int] | None = None,
Sets the color of each point based on its instance ID.
- Parameters:
point_cloud – Point cloud to be colored.
instance_id_column – Name of the column containing the instance IDs.
target_instance_id_column – Name of the column containing the ground truth instance IDs.
fp_ids – List of the predicted tree IDs that represent false positives. Defaults to
None, which means that false positives are not colored by a dedicated color.fn_ids – List of the ground truth tree IDs that represents false negatives. Defaults to
None, which means that false negatives are not colored by a dedicated color.
- Returns:
Point cloud with added or modified “r”, “g”, “b”, “a” attributes.
- Return type:
pandas.DataFrame
- pointtree.visualization.color_semantic_segmentation(
- point_cloud: DataFrame,
- classes_to_colors: Dict[int, str],
- semantic_segmentation_column: str = 'classification',
Sets the color of each point based on its semantic class.
- Parameters:
point_cloud – Point cloud to be colored.
classes_to_colors – Mapping of class IDs to hex color codes.
semantic_segmentation_column – Name of the column containing semantic class IDs. Defaults to
"classification".
- Returns:
Point cloud with added or modified “r”, “g”, “b”, “a” attributes.
- pointtree.visualization.save_tree_map(
- image: ndarray,
- output_path: str,
- *,
- is_label_image: bool = False,
- crown_borders: ndarray | None = None,
- border_mask: ndarray | None = None,
- seed_mask: ndarray | None = None,
- core_mask: ndarray | None = None,
- tree_positions: ndarray | None = None,
- crown_positions: ndarray | None = None,
- trunk_positions: ndarray | None = None,
Saves a 2D map showing canopy height models, tree positions etc. as image file.