VideoFigure¶

class VideoFigure(video_object, geometry, frame_index, key=None, class_id=None, labeler_login=None, updated_at=None, created_at=None, track_id=None, smart_tool_input=None, priority=None, status=None)[source]¶

Bases: object

Figure in video annotation: geometry tied to a VideoObject at a frame index. Immutable.

Parameters:

video_object¶: VideoObject for this figure.
geometry¶: Geometry (Rectangle, Polygon, etc.).
frame_index : int¶: Index of frame to which figure belongs.
key : uuid.UUID, optional¶: UUID key. Auto-generated if not provided.
class_id : int, optional¶: ID of VideoObject class.
labeler_login : str, optional¶: Login of user who created the figure.
updated_at : str, optional¶: Last modification timestamp.
created_at : str, optional¶: Creation timestamp.
track_id : str, optional¶: Track ID for tracking.
smart_tool_input : dict, optional¶: Smart Tool parameters from labeling.
priority : int, optional¶: Figure draw order (higher = on top).
status=None¶: Labeling status (manual, corrected, etc.).

Usage Example:

import supervisely as sly

obj_class_car = sly.ObjClass('car', sly.Rectangle)
video_obj_car = sly.VideoObject(obj_class_car)
fr_index = 7
geometry = sly.Rectangle(0, 0, 100, 100)
video_figure_car = sly.VideoFigure(video_obj_car, geometry, fr_index)
video_figure_car_json = video_figure_car.to_json()
print(video_figure_car_json)

Methods

`clone`	Makes a copy of VideoFigure with new fields, if fields are given, otherwise it will use fields of the original VideoFigure.
`from_json`	Convert a json dict to VideoFigure.
`get_meta`	Get metadata for the video figure.
`key`	Figure key.
`to_json`	Convert the VideoFigure to a json dict.
`validate_bounds`	Checks if given image with given size contains a figure.

Attributes

`frame_index`	Frame index of the current VideoFigure.
`geometry`	Geometry of the current VideoFigure.
`parent_object`	VideoObject of current VideoFigure.
`priority`	Priority of the figure (position of the figure relative to other overlapping or underlying figures).
`smart_tool_input`	Smart Tool parameters that were used for labeling.
`status`	Labeling status.
`video_object`	VideoObject of current VideoFigure.

classmethod from_json(data, objects, frame_index, key_id_map=None)[source]¶

Convert a json dict to VideoFigure. Read more about Supervisely format.

Parameters:

data : dict¶: Dict in json format.
objects¶: VideoObjectCollection object.
frame_index : int¶: Index of Frame to which VideoFigure belongs.
key_id_map=None¶: KeyIdMap object.

Raises:

RuntimeError – if video object ID and video object key are None, if video object key and key_id_map are None, if video object with given id not found in key_id_map

Returns:

VideoFigure object

Return type:

VideoFigure

Usage Example:

import supervisely as sly

# Create VideoFigure from json we use data from example to_json(see above)
new_video_figure = sly.VideoFigure.from_json(video_figure_json, sly.VideoObjectCollection([video_obj_car]), fr_index)

clone(video_object=None, geometry=None, frame_index=None, key=None, class_id=None, labeler_login=None, updated_at=None, created_at=None, track_id=None, smart_tool_input=None, priority=None, status=None)[source]¶

Makes a copy of VideoFigure with new fields, if fields are given, otherwise it will use fields of the original VideoFigure.

Parameters:

video_object=None¶: VideoObject object.
geometry=None¶: Label Geometry object.
frame_index : int, optional¶: Index of Frame to which VideoFigure belongs.
key_id_map : KeyIdMap, optional: KeyIdMap object.
class_id : int, optional¶: ID of ObjClass to which VideoFigure belongs.
labeler_login : str, optional¶: Login of the user who created VideoFigure.
updated_at : str, optional¶: Date and Time when VideoFigure was modified last. Date Format: Year:Month:Day:Hour:Minute:Seconds. Example: ‘2021-01-22T19:37:50.158Z’.
created_at : str, optional¶: Date and Time when VideoFigure was created. Date Format is the same as in “updated_at” parameter.
track_id : str, optional¶: ID of the track to which VideoFigure belongs.
smart_tool_input : dict, optional¶: Smart Tool parameters that were used for labeling.
priority : int, optional¶: Priority of the figure (position of the figure relative to other overlapping or underlying figures).
status=None¶: Sets labeling status. Specifies if the VideoFigure was created by NN model, manually or created by NN and then manually corrected.

Returns:

VideoFigure object

Return type:

VideoFigure

Usage Example:

import supervisely as sly

obj_class_car = sly.ObjClass('car', sly.Rectangle)
video_obj_car = sly.VideoObject(obj_class_car)
fr_index = 7
geometry = sly.Rectangle(0, 0, 100, 100)
video_figure_car = sly.VideoFigure(video_obj_car, geometry, fr_index)

obj_class_bus = sly.ObjClass('bus', sly.Rectangle)
video_obj_bus = sly.VideoObject(obj_class_bus)
fr_index_bus = 15
geometry_bus = sly.Rectangle(0, 0, 500, 600)

# Remember that VideoFigure object is immutable, and we need to assign new instance of VideoFigure to a new variable
video_figure_bus = video_figure_car.clone(video_object=video_obj_bus, geometry=geometry_bus, frame_index=fr_index_bus)
print(video_figure_bus.to_json())
# Output: {
#     "key": "c2f501e94f42483ebd202697608e8d26",
#     "objectKey": "942c79137b4547c59193276317f73897",
#     "geometryType": "rectangle",
#     "geometry": {
#         "points": {
#             "exterior": [
#                 [
#                     0,
#                     0
#                 ],
#                 [
#                     600,
#                     500
#                 ]
#             ],
#             "interior": []
#         }
#     }
# }

get_meta()[source]¶

Get metadata for the video figure.

Returns:

Dictionary with metadata for the video figure.

Return type:

Dict[str, int]

Usage Example:

import supervisely as sly

obj_class_car = sly.ObjClass('car', sly.Rectangle)
video_obj_car = sly.VideoObject(obj_class_car)
fr_index = 7
geometry = sly.Rectangle(0, 0, 100, 100)
video_figure_car = sly.VideoFigure(video_obj_car, geometry, fr_index)

print(video_figure_car.get_meta()) # {'frame': 7}

key()[source]¶

Figure key.

Returns:

Figure key.

Return type:

UUID

Usage Example:

key = video_figure_car.key
print(key) # 158e6cf4f4ac4c639fc6994aad127c16

to_json(key_id_map=None, save_meta=False)[source]¶

Convert the VideoFigure to a json dict. Read more about Supervisely format.

Parameters:

key_id_map=None¶: KeyIdMap object.
save_meta : bool, optional¶: Save frame index or not.

Returns:

Json format as a dict

Return type:

Dict

Usage Example:

import supervisely as sly

obj_class_car = sly.ObjClass('car', sly.Rectangle)
video_obj_car = sly.VideoObject(obj_class_car)
fr_index = 7
geometry = sly.Rectangle(0, 0, 100, 100)
video_figure_car = sly.VideoFigure(video_obj_car, geometry, fr_index)
video_figure_json = video_figure_car.to_json(save_meta=True)
print(video_figure_json)
# Output: {
#     "key": "591d0511ba28462c8cd657691743359c",
#     "objectKey": "e061bc50bd464c23a008b712d195570a",
#     "geometryType": "rectangle",
#     "geometry": {
#         "points": {
#             "exterior": [
#                 [
#                     0,
#                     0
#                 ],
#                 [
#                     100,
#                     100
#                 ]
#             ],
#             "interior": []
#         }
#     },
#     "meta": {"frame": 7},
#     "nnCreated": false,
#     "nnUpdated": false
# }

validate_bounds(img_size, _auto_correct=False)[source]¶

Checks if given image with given size contains a figure.

Parameters:

img_size : Tuple[int, int]¶: Size of the image (height, width).
_auto_correct : bool, optional¶: Correct the geometry of a shape if it is out of bounds or not.

:raises OutOfImageBoundsException: if figure is out of image bounds :returns: None :rtype: None

Usage Example:

import supervisely as sly

obj_class_car = sly.ObjClass('car', sly.Rectangle)
video_obj_car = sly.VideoObject(obj_class_car)
fr_index = 7
geometry = sly.Rectangle(0, 0, 100, 100)
video_figure_car = sly.VideoFigure(video_obj_car, geometry, fr_index)

im_size = (50, 200)
video_figure_car.validate_bounds(im_size)
# raise OutOfImageBoundsException("Figure is out of image bounds")

property frame_index : int¶

Frame index of the current VideoFigure.

Returns:

Index of Frame to which VideoFigure belongs

Return type:

int

Usage Example:

fr_index = video_figure_car.frame_index
print(fr_index) # 7

property geometry : supervisely.geometry.geometry.Geometry¶

Geometry of the current VideoFigure.

Returns:

Geometry object

Return type:

Geometry

Usage Example:

geometry = video_figure_car.geometry
print(geometry.to_json())
# Output: {
#     "points": {
#         "exterior": [
#             [
#                 0,
#                 0
#             ],
#             [
#                 100,
#                 100
#             ]
#         ],
#         "interior": []
#     }
# }

property parent_object : supervisely.video_annotation.video_object.VideoObject¶

VideoObject of current VideoFigure.

Returns:

VideoObject object

Return type:

VideoObject

Usage Example:

video_obj_car = video_figure_car.parent_object
print(video_obj_car.to_json())
# Output: {
#     "key": "d573c6f081544e3da20022d932b259c1",
#     "classTitle": "car",
#     "tags": []
# }

property priority¶: Priority of the figure (position of the figure relative to other overlapping or underlying figures).

property smart_tool_input¶

Smart Tool parameters that were used for labeling.

Example:

{
‘crop’: [[85.69912274538524, 323.07711452375236], [1108.5635719011857, 1543.1199742240174]], ‘visible’: True, ‘negative’: [], ‘positive’: [[597, 933], [474.5072466934964, 1381.6437133813354]]

}

property status : supervisely.annotation.label.LabelingStatus¶: Labeling status. Specifies if the VideoFigure was created by NN model, manually or created by NN and then manually corrected.

property video_object : supervisely.video_annotation.video_object.VideoObject¶

VideoObject of current VideoFigure.

Returns:

VideoObject object

Return type:

VideoObject

Usage Example:

video_obj_car = video_figure_car.video_object
print(video_obj_car.to_json())
# Output: {
#     "key": "d573c6f081544e3da20022d932b259c1",
#     "classTitle": "car",
#     "tags": []
# }