Label set
A label set is a collection of labels. Create a new label set if you want to upload model predictions and use features such as model-assisted labeling.
Create a new label set with client.add_labelset(). You need to specify the name and (optionally) the description of the new label set.
For best performance, create a new dataset and choose a suitable data category.
or| Name | Label status | Prio | Last updated | ||
|---|---|---|---|---|---|
| Labeled | 0.00 | 8 months ago | |||
| Labeled | 0.00 | 3 years ago |
Use client.upload_asset() to upload a local file to Segments.ai's asset storage service.
Add a sample with client.add_sample(). For each sample, specify a name and the required sample attributes. If you do not have URLs for your assets, upload the assets to Segments.ai first.
dataset = "segments/ouster-automotive-demo-data-cuboid"
name = "sample_point_cloud_sequence"
pc_urls = [
"https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/73b62f59-cb43-4625-aeb0-6e64ea42b28a.pcd",
"https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/73b62f59-cb43-4625-aeb0-6e64ea42b28a.pcd",
]
frames = [
{
"pcd": {"url": pc_urls[0], "type": "pcd"},
"name": "sample_point_cloud_1",
"timestamp": "1532402927647951",
"ego_pose": {
"position": {"x": -1180.8903791765097, "y": 411.3039349319818, "z": 0},
"heading": {
"qx": -0.0016977771610471074,
"qy": 0.011798001930183783,
"qz": -0.8201446642457809,
"qw": 0.5720320396729045,
},
},
"default_z": -1,
},
{
"pcd": {"url": pc_urls[1], "type": "pcd"},
"name": "sample_point_cloud_2",
"timestamp": "1532402928147847",
"ego_pose": {
"position": {"x": -1176.6769733781416, "y": 409.7431520488096, "z": 0},
"heading": {
"qx": -0.0013288675751650467,
"qy": 0.013917029415654661,
"qz": -0.818440972734323,
"qw": 0.5744205119534879,
},
},
"default_z": -1,
},
]
attributes = {"frames": frames}
sample = client.add_sample(dataset, name, attributes)Are you sure you want to delete this label set? All labels in the label set will be deleted as well.
Are you sure you want to delete 0 samples? The corresponding labels will be deleted as well.