open3d.ml.torch.datasets.augment.ObjdetAugmentation#
- class open3d.ml.torch.datasets.augment.ObjdetAugmentation(cfg, seed=None)#
Class consisting different augmentation for Object Detection
- ObjectRangeFilter(data, pcd_range)#
Filter Objects in the given range.
- ObjectSample(data, db_boxes_dict, sample_dict)#
Increase frequency of objects in a pointcloud.
Randomly place objects in a pointcloud from a database of all objects within the dataset. Checks collision with existing objects.
- Parameters:
data – Input data dict with keys (‘point’, ‘bounding_boxes’, ‘calib’).
db_boxes_dict – dict for different objects.
sample_dict – dict for number of objects to sample.
- PointShuffle(data)#
Shuffle Pointcloud.
- __init__(cfg, seed=None)#
- augment(data, attr, seed=None)#
Augment object detection data.
- Available augmentations are:
ObjectSample: Insert objects from ground truth database. ObjectRangeFilter: Filter pointcloud from given bounds. PointShuffle: Shuffle the pointcloud.
- Parameters:
data – A dictionary object returned from the dataset class.
attr – Attributes for current pointcloud.
- Returns:
Augmented data dictionary.
- static in_range_bev(box_range, box)#
- load_gt_database(pickle_path, min_points_dict, sample_dict)#
Load ground truth object database.
- Parameters:
pickle_path – Path of pickle file generated using scripts/collect_bbox.py.
min_points_dict – A dictionary to filter objects based on number of points inside. Format of dict {‘class_name’: num_points}.
sample_dict – A dictionary to decide number of objects to sample. Format of dict {‘class_name’: num_instance}