PointcloudEpisodeAnnotation¶
- class PointcloudEpisodeAnnotation[source]¶
Bases:
objectPointcloudEpisodeAnnotation for point cloud episodes.
PointcloudEpisodeAnnotationobject is immutable.- Parameters
- frames_count : int, optional
Number of PointcloudEpisodeFrame objects.
- objects : PointcloudEpisodeObjectCollection, optional
PointcloudEpisodeObjectCollection object
- frames : PointcloudEpisodeFrameCollection, optional
PointcloudEpisodeFrameCollection object
- tags : PointcloudEpisodeTagCollection, optional
PointcloudEpisodeTagCollection object
- description : str, optional
Description text
- key : uuid.UUID, optional
uuid class object
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap # PointcloudEpisodeAnnotation example 1 pointcloud_episodes_ann = sly.PointcloudEpisodeAnnotation() print(pointcloud_episodes_ann.to_json()) # Output: { # "description": "", # "frames": [], # "framesCount": None, # "key": "494f67984d714c1eaf7a65e5df289ac6", # "objects": [], # "tags": [] # } # PointcloudEpisodeAnnotation example 2 pointcloud_id = 19481098 key_id_map = KeyIdMap() pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ann_json = api.pointcloud_episode.annotation.download(pcd_info.dataset_id) ann = sly.PointcloudEpisodeAnnotation.from_json( data=ann_json, project_meta=project_meta, key_id_map=key_id_map )
Methods
Makes a copy of PointcloudEpisodeAnnotation with new fields, if fields are given, otherwise it will use fields of the original PointcloudEpisodeAnnotation.
Create a PointcloudEpisodeAnnotation object from a JSON representation.
Retrieve figures associated with a specific frame in a PointcloudEpisodeAnnotation.
Retrieve objects associated with a specific frame in a PointcloudEpisodeAnnotation.
Retrieve tags associated with a specific frame in a PointcloudEpisodeAnnotation.
Check whether point cloud episodes annotation contains objects or tags, or not.
PointcloudEpisodeAnnotation key value.
Loads json file and converts it to PointcloudEpisodeAnnotation.
Convert PointcloudEpisodeAnnotation to JSON format.
Attributes
Description text for PointcloudEpisodeAnnotation object.
PointcloudFigure objects.
PointcloudEpisodeFrameCollection collection.
Number of frames.
PointcloudEpisodeObject objects collection.
PointcloudEpisodeTag objects collection.
-
clone(frames_count=
None, objects=None, frames=None, tags=None, description='')[source]¶ Makes a copy of PointcloudEpisodeAnnotation with new fields, if fields are given, otherwise it will use fields of the original PointcloudEpisodeAnnotation.
- Parameters
- frames_count : int, optional
Number of PointcloudEpisodeFrame objects
- objects : PointcloudEpisodeObjectCollection, optional
PointcloudEpisodeObjectCollection object
- frames : PointcloudEpisodeFrameCollection, optional
PointcloudEpisodeFrameCollection object
- tags : PointcloudEpisodeTagCollection, optional
PointcloudEpisodeTagCollection object
- description : str, optional
Description text
- Return type
- Returns
PointcloudAnnotation class object
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) key_id_map = KeyIdMap() pointcloud_id = 19481098 pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ann_json = api.pointcloud_episode.annotation.download(pcd_info.dataset_id) ann = sly.PointcloudAnnotation.from_json( data=ann_json, project_meta=project_meta, key_id_map=key_id_map ) obj_class_car = sly.ObjClass('car', sly.Cuboid) pointcloud_obj_car = sly.PointcloudEpisodeObject(obj_class_car) new_objects = ann.objects.add(pointcloud_obj_car) new_ann = ann.clone(objects=new_objects) print(new_ann.to_json()) # Output: # { # 'datasetId': 60988, # 'description': '', # 'frames': [{'figures': [{'classId': None, # 'createdAt': '2023-03-16T06:38:45.004Z', # 'description': '', # 'geometry': {'dimensions': {'x': 2.3652234, # 'y': 23.291742, # 'z': 3.326648}, # 'position': {'x': 86.29707472161449, # 'y': -14.472597682830635, # 'z': 0.8842007608554671}, # 'rotation': {'x': 0, # 'y': 0, # 'z': -1.6962800995995606}}, # 'geometryType': 'cuboid_3d', # 'id': 87536496, # 'labelerLogin': 'almaz', # 'objectId': 5531328, # 'updatedAt': '2023-03-16T06:38:45.004Z'}], # 'index': 0, # 'pointCloudId': 19481098}], # 'index': 1, # 'pointCloudId': 19481100}, # ...], # 'framesCount': 54, # 'objects': [{ # 'classId': 666944, # 'classTitle': 'Car', # 'createdAt': '2023-03-16T06:38:44.934Z', # 'datasetId': 60988, # 'entityId': None, # 'id': 5531324, # 'labelerLogin': 'almaz', # 'tags': [], # 'updatedAt': '2023-03-16T06:38:44.934Z'} # { # 'classTitle': 'Car', # 'createdAt': '2023-03-16T06:38:44.934Z', # 'key': 'fc149a8f3e3a413c807a6b4ba474645c', # 'labelerLogin': 'almaz', # 'tags': [], # 'updatedAt': '2023-03-16T06:38:44.934Z # }], # 'tags': [] # } # Output: { # "description": "", # "figures": [], # "key": "2cc443272aca4cfa9c4f404614938aa7", # "objects": [ # { # "classTitle": "Pole", # "createdAt": "2023-03-16T06:38:44.934Z", # "key": "eff2ec5e3cda47968f45bc51b36a0dc1", # "labelerLogin": "almaz", # "tags": [], # "updatedAt": "2023-03-16T06:38:44.934Z" # }, # { # "classTitle": "Tram", # "createdAt": "2023-03-16T06:38:44.934Z", # "key": "6baa92e09ceb413ba8fbfcfae74be1c7", # "labelerLogin": "almaz", # "tags": [], # "updatedAt": "2023-03-16T06:38:44.934Z" # }, # { # "classTitle": "car", # "key": "6b1bced23061437b8ddbcdd267548c96", # "tags": [] # } # ], # "tags": [] # }
-
classmethod from_json(data, project_meta, key_id_map=
None)[source]¶ Create a PointcloudEpisodeAnnotation object from a JSON representation.
- Parameters
- data : Dict
JSON data representing the PointcloudEpisodeAnnotation.
- project_meta : ProjectMeta
Project metadata.
- Returns
PointcloudEpisodeAnnotation object
- Return type
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap key_id_map = KeyIdMap() pointcloud_id = 19481098 pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ann_json = api.pointcloud_episode.annotation.download(pcd_info.dataset_id) ann = sly.PointcloudEpisodeAnnotation.from_json( data=ann_json, project_meta=project_meta, key_id_map=key_id_map )
- get_figures_on_frame(frame_index)[source]¶
Retrieve figures associated with a specific frame in a PointcloudEpisodeAnnotation.
- Parameters
- frame_index : int
The index of the frame for which figures need to be retrieved.
- Returns
List of PointcloudFigure objects containing the retrieved figures associated with the specified frame.
- Return type
List[PointcloudFigure]
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap key_id_map = KeyIdMap() pointcloud_id = 19481098 pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ann_json = api.pointcloud_episode.annotation.download(pcd_info.dataset_id) ann = sly.PointcloudEpisodeAnnotation.from_json( data=ann_json, project_meta=project_meta, key_id_map=key_id_map ) frame_index = 0 figures_on_frame = ann.get_figures_on_frame(frame_index) print(figures_on_frame) # Output: # [<supervisely.pointcloud_annotation.pointcloud_figure.PointcloudFigure object at 0x7fc83895a4d0>, # <supervisely.pointcloud_annotation.pointcloud_figure.PointcloudFigure object at 0x7fc83895a810>, # <supervisely.pointcloud_annotation.pointcloud_figure.PointcloudFigure object at 0x7fc8389410d0>]
- get_objects_on_frame(frame_index)[source]¶
Retrieve objects associated with a specific frame in a PointcloudEpisodeAnnotation.
- Parameters
- frame_index : int
The index of the frame for which objects need to be retrieved.
- Returns
PointcloudEpisodeObjectCollection containing the retrieved objects associated with the specified frame.
- Return type
- Raises
ValueError – If no frame with the given frame_index exists in the annotation.
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap key_id_map = KeyIdMap() pointcloud_id = 19481098 pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ann_json = api.pointcloud_episode.annotation.download(pcd_info.dataset_id) ann = sly.PointcloudEpisodeAnnotation.from_json( data=ann_json, project_meta=project_meta, key_id_map=key_id_map ) frame_index = 0 objects_on_frame = ann.get_objects_on_frame(frame_index) print(objects_on_frame.to_json()) # Output: # [ # { # "key": "687784c3d4d64ec4811948fec245514a", # "classTitle": "Tram", # "tags": [], # "labelerLogin": "almaz", # "updatedAt": "2023-03-16T06:38:44.934Z", # "createdAt": "2023-03-16T06:38:44.934Z" # }, # { # "key": "b8b23b6712444f0fbfb320b0b4acd09a", # "classTitle": "Car", # "tags": [], # "labelerLogin": "almaz", # "updatedAt": "2023-03-16T06:38:44.934Z", # "createdAt": "2023-03-16T06:38:44.934Z" # } # ]
- get_tags_on_frame(frame_index)[source]¶
Retrieve tags associated with a specific frame in a PointcloudEpisodeAnnotation.
- Parameters
- frame_index : int
The index of the frame for which tags need to be retrieved.
- Returns
PointcloudEpisodeTagCollection containing the retrieved tags associated with the specified frame.
- Return type
- Raises
ValueError – If no frame with the given frame_index exists in the annotation.
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap key_id_map = KeyIdMap() pointcloud_id = 19481098 pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ann_json = api.pointcloud_episode.annotation.download(pcd_info.dataset_id) ann = sly.PointcloudEpisodeAnnotation.from_json( data=ann_json, project_meta=project_meta, key_id_map=key_id_map ) frame_index = 0 tags_on_frame = ann.get_tags_on_frame(frame_index) print(tags_on_frame) # Output: Tags: +-------+------------+-------+-------------+ | Name | Value type | Value | Frame range | +-------+------------+-------+-------------+ | color | any_string | red | [0, 0] | +-------+------------+-------+-------------+
- is_empty()[source]¶
Check whether point cloud episodes annotation contains objects or tags, or not.
- Returns
True if point cloud episodes annotation is empty, False otherwise.
- Return type
- Usage example
import supervisely as sly from supervisely.video_annotation.key_id_map import KeyIdMap address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) project_id = 18428 dataset_id = 60988 key_id_map = KeyIdMap() meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_json = api.pointcloud_episode.annotation.download(dataset_id) ann = sly.PointcloudEpisodeAnnotation.from_json(ann_json, meta, key_id_map) print(ann.is_empty()) # False
- key()[source]¶
PointcloudEpisodeAnnotation key value.
- Returns
Key value of point cloud episodes annotation object.
- Return type
- Usage example
import supervisely as sly ann = sly.PointcloudEpisodeAnnotation() print(ann.key()) # Output: 93ab6292-c661-4a53-b407-85ed34f5b68a'
-
classmethod load_json_file(path, project_meta, key_id_map=
None)[source]¶ Loads json file and converts it to PointcloudEpisodeAnnotation.
- Parameters
- path : str
Path to the json file.
- project_meta : ProjectMeta
Input
ProjectMeta.- key_id_map : KeyIdMap, optional
KeyIdMap object.
- Returns
PointcloudEpisodeAnnotation object
- Return type
- Usage example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) team_name = 'Vehicle Detection' workspace_name = 'Cities' project_name = 'London' project_id = 19441 project_meta_json = api.project.get_meta(project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) # Load json file path = "/home/admin/work/docs/my_dataset/ann/annotation.json" ann = sly.PointcloudEpisodeAnnotation.load_json_file(path, project_meta)
-
to_json(key_id_map=
None)[source]¶ Convert PointcloudEpisodeAnnotation to JSON format.
- Parameters
- key_id_map : KeyIdMap, optional
KeyIdMap object.
- Returns
PointcloudEpisodeAnnotation in JSON format.
- Return type
Dict
- Usage example
import supervisely as sly pointcloud_id = 19481098 pcd_info = api.pointcloud_episode.get_info_by_id(pointcloud_id) project_path = "Downloads/pointcloud_api/project" sly.PointcloudEpisodeProject.download( api=api, project_id=pcd_info.project_id, dest_dir=project_path, dataset_ids=[pcd_info.dataset_id], download_pointclouds=True, ) project_fs = sly.PointcloudEpisodeProject(project_path, sly.OpenMode.READ) project_meta_json = api.project.get_meta(pcd_info.project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) ds = project_fs.datasets.items()[0]: ann = ds.get_ann(project_meta) print(ann.to_json()) # Output: # { # 'datasetId': 60988, # 'description': '', # 'frames': [{'figures': [{'classId': None, # 'createdAt': '2023-03-16T06:38:45.004Z', # 'description': '', # 'geometry': {'dimensions': {'x': 2.3652234, # 'y': 23.291742, # 'z': 3.326648}, # 'position': {'x': 86.29707472161449, # 'y': -14.472597682830635, # 'z': 0.8842007608554671}, # 'rotation': {'x': 0, # 'y': 0, # 'z': -1.6962800995995606}}, # 'geometryType': 'cuboid_3d', # 'id': 87536496, # 'labelerLogin': 'almaz', # 'objectId': 5531328, # 'updatedAt': '2023-03-16T06:38:45.004Z'}], # 'index': 0, # 'pointCloudId': 19481098}], # 'index': 1, # 'pointCloudId': 19481100}, # ...], # 'framesCount': 54, # 'objects': [{'classId': 666944, # 'classTitle': 'Car', # 'createdAt': '2023-03-16T06:38:44.934Z', # 'datasetId': 60988, # 'entityId': None, # 'id': 5531324, # 'labelerLogin': 'almaz', # 'tags': [], # 'updatedAt': '2023-03-16T06:38:44.934Z'}], # 'tags': [] # }
- property description¶
Description text for PointcloudEpisodeAnnotation object.
- Returns
PointcloudEpisodeAnnotation description
- Return type
- Usage example
import supervisely as sly descr = 'example' ann = sly.PointcloudEpisodeAnnotation(description=descr) print(ann.description) # example
- property figures¶
PointcloudFigure objects.
- Returns
List of PointcloudFigure objects from PointcloudEpisodeAnnotation object.
- Return type
- Usage example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) project_id = 19441 project_meta_json = api.project.get_meta(project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) # Load json file path = "/home/admin/work/docs/my_dataset/ann/annotation.json" ann = sly.PointcloudEpisodeAnnotation.load_json_file(path, project_meta) for figure in ann.figures: print(figure.to_json()) # Output: # { # "geometry": { # "dimensions": { "x": 1.8, "y": 3.9, "z": 1.6 }, # "position": { "x": -3.4, "y": 28.9, "z": -0.7 }, # "rotation": { "x": 0.0, "y": 0, "z": -0.03 } # }, # "geometryType": "cuboid_3d", # "key": "01836c294c514250a11889f56cf210e9", # "objectKey": "737c4df19c0c4cccbc48cf69b72abe36" # }
- property frames¶
PointcloudEpisodeFrameCollection collection.
- Returns
PointcloudEpisodeFrameCollection object
- Return type
PointcloudEpisodeFrameCollection- Usage example
import supervisely as sly from supervisely.geometry.cuboid_3d import Cuboid3d, Vector3d from supervisely.pointcloud_annotation.pointcloud_episode_object_collection import PointcloudEpisodeObjectCollection obj_class_car = sly.ObjClass('car', Cuboid3d) pointcloud_obj_car = sly.PointcloudEpisodeObject(obj_class_car) objects = sly.PointcloudEpisodeObjectCollection([pointcloud_obj_car]) position, rotation, dimension = Vector3d(-3.4, 28.9, -0.7), Vector3d(0., 0, -0.03), Vector3d(1.8, 3.9, 1.6) cuboid = Cuboid3d(position, rotation, dimension) frame_index = 10 figure = sly.PointcloudFigure(pointcloud_obj_car, cuboid, frame_index=frame_index) frame = sly.PointcloudEpisodeFrame(frame_index, figures=[figure]) frames = sly.PointcloudEpisodeFrameCollection([frame]) pointcloud_episodes_ann = sly.PointcloudEpisodeAnnotation(frames_count, objects, frames) print(pointcloud_episodes_ann.frames.to_json()) # Output: # [ # { # "figures": [ # { # "geometry": { # "dimensions": { "x": 1.8, "y": 3.9, "z": 1.6 }, # "position": { "x": -3.4, "y": 28.9, "z": -0.7 }, # "rotation": { "x": 0.0, "y": 0, "z": -0.03 } # }, # "geometryType": "cuboid_3d", # "key": "030b9aafa97642e887e2be544ef7a7ee", # "objectKey": "95c473a6cff44afda127ffb40d2bac5b" # } # ], # "index": 0 # } # ]
- property frames_count¶
Number of frames.
- Returns
Frames count
- Return type
- Usage example
import supervisely as sly frames_count = 15 video_ann = sly.PointcloudEpisodeAnnotation(frames_count=frames_count) print(video_ann.frames_count) # Output: 15
- property objects¶
PointcloudEpisodeObject objects collection.
- Returns
PointcloudEpisodeObjectCollection object.
- Return type
PointcloudEpisodeObjectCollection
- Usage example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) project_id = 19441 project_meta_json = api.project.get_meta(project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) # Load json file path = "/home/admin/work/docs/my_dataset/ann/annotation.json" ann = sly.PointcloudEpisodeAnnotation.load_json_file(path, project_meta) objects = ann.objects
- property tags¶
PointcloudEpisodeTag objects collection.
- Returns
PointcloudEpisodeTagCollection object.
- Return type
- Usage example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) project_id = 19441 project_meta_json = api.project.get_meta(project_id) project_meta = sly.ProjectMeta.from_json(project_meta_json) # Load json file path = "/home/admin/work/docs/my_dataset/ann/annotation.json" ann = sly.PointcloudEpisodeAnnotation.load_json_file(path, project_meta) tags = ann.tags