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Upload your first scene

When uploading raw data to the Kognic Platform, you need to do so in the form of a scene. A scene is a collection of data from different sources, such as images, point clouds, and other sensor data. This guide will walk you through the process of uploading your first scene, either in 2D (camera only) or 3D (camera and LiDAR/RADAR).

Prerequisites​

You have successfully followed the Quickstart guide and have the kognic-io library installed.

Uploading a 2D scene

To upload a 2D scene, you need to have the raw images available on your local machine (or create a callback for remote data). It is a two-step process:

  1. Build the scene object in Python
  2. Upload the scene object to the Kognic Platform

Below follows examples for a few different cases.

from kognic.io.client import KognicIOClient
from kognic.io.model.scene.cameras import Cameras, Frame
from kognic.io.model.scene.resources import Image

# 1. Build scene object
scene = Cameras(
    external_id="my-first-scene",
    frame=Frame(images=[Image(filename="path/to/image.jpg")])
)

# 2. Upload scene
client = KognicIOClient()
scene_uuid = client.cameras.create(scene).scene_uuid
print("Scene uploaded, got uuid:", scene_uuid)

Uploading a 2D/3D scene

To upload a 2D/3D scene, you need to have the raw images and point clouds available on your local machine (or create a callback for remote data). In addition you need to have calibration data available. It is a three-step process:

  1. Create a calibration
  2. Build the scene object in Python, referencing the calibration from the previous step
  3. Upload the scene object to the Kognic Platform

Below follows examples for a few different cases.

from kognic.io.client import KognicIOClient
from kognic.io.model.calibration import SensorCalibration, PinholeCalibration, LidarCalibration
from kognic.io.model.scene.lidars_and_cameras import LidarsAndCameras, Frame
from kognic.io.model.scene.resources import Image, PointCloud

client = KognicIOClient()

# 1. Create calibration (see calibration section for more details)
sensor_calibration = SensorCalibration(
    external_id = "my-first-calibration",
    calibration = {
        "CAM": PinholeCalibration(...),
        "lidar": LidarCalibration(...)
    }
)
created_calibration = client.calibration.create_calibration(sensor_calibration)

# 2. Build scene object
scene = LidarsAndCameras(
    external_id=f"my-first-scene",
    calibration_id = created_calibration.id,
    frame=Frame(
        images=[Image(sensor_name = "CAM", filename="path/to/image.jpg")],
        point_clouds=[PointCloud(sensor_name = "lidar", filename="path/to/pointcloud.pcd")]
    )
)

# 3. Upload scene
scene_uuid = client.lidars_and_cameras.create(scene).scene_uuid
print("Scene uploaded, got uuid:", scene_uuid)
note

Multiple point clouds is also supported, but not shown in the examples above since that requires a bit more data. See the Motion Compensation section for more details.

Uploading using ZOD Data

We have exemplar code and a tutorial for uploading scenes using Zenseact Open Dataset (ZOD) data, including 2D, 3D, and aggregated 3D scenes. Check out the guide document and exemplar code here!

If you have the ZOD data downloaded, and have Kognic API credentials, the examples will run out of the box to create functional scenes!

What happens now?​

The response given when creating the scene, the scene_uuid, is used to refer to the scene in the future. You can now check the status of the scene via the Platform or the Python client.

Go to the Data Orchestration tab in the Kognic Platform and search for the scene by its UUID or use any other suitable search criteria. The status is shown in a column in the scenes list.

Note that you may need to contact Kognic to get access to Data Orchestration in case you do not see it.

You may see the scene initially in the processing state while Kognic performs a little background processing, or in the ready or failed state after processing completes.