UPLOAD DATA
Guides
Upload your first scene
3min
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).
You have successfully followed the Quickstart guide and have the kognic-io library installed. For users with access to multiple workspaces you need to specify a workspace to upload data too.
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:
- Build the scene object in Python
- Upload the scene object to the Kognic Platform
Below follows examples for a few different cases.
One Image
Multiple Images
Sequence
Python
1from kognic.io.client import KognicIOClient
2from kognic.io.model.scene.cameras import Cameras, Frame
3from kognic.io.model.scene.resources import Image
4
5# 1. Build scene object
6scene = Cameras(
7 external_id="my-first-scene",
8 frame=Frame(images=[Image(filename="path/to/image.jpg")])
9)
10
11# 2. Upload scene
12client = KognicIOClient()
13scene_uuid = client.cameras.create(scene).scene_uuid
14print("Scene uploaded, got uuid:", scene_uuid)

Python
1from kognic.io.client import KognicIOClient
2from kognic.io.model.scene.cameras import Cameras, Frame
3from kognic.io.model.scene.resources import Image
4
5# 1. Build scene object
6scene = Cameras(
7 external_id="my-first-scene",
8 frame=Frame(
9 images=[
10 # Sensor names must be unique
11 Image(sensor_name = "CAM1", filename="path/to/image1.jpg"),
12 Image(sensor_name = "CAM2", filename="path/to/image2.jpg")
13 ],
14 )
15)
16
17# 2. Upload scene
18client = KognicIOClient()
19scene_uuid = client.cameras.create(scene).scene_uuid
20print("Scene uploaded, got uuid:", scene_uuid)

Python
1from kognic.io.client import KognicIOClient
2from kognic.io.model.scene.cameras_sequence import CamerasSequence, Frame
3from kognic.io.model.scene.resources import Image
4
5# 1. Build scene object
6scene = CamerasSequence(
7 external_id="my-first-scene",
8 frames=[
9 # Relative timestamps must be unique and strictly increasing
10 Frame(
11 relative_timestamp=0,
12 frame_id="1",
13 images=[Image(filename="path/to/image1.jpg")],
14 ),
15 Frame(
16 relative_timestamp=100,
17 frame_id="2",
18 images=[Image(filename="path/to/image2.jpg")],
19 ),
20 Frame(
21 relative_timestamp=200,
22 frame_id="3",
23 images=[Image(filename="path/to/image3.jpg")],
24 ),
25 ]
26)
27
28# 2. Upload scene
29client = KognicIOClient()
30scene_uuid = client.cameras_sequence.create(scene).scene_uuid
31print("Scene uploaded, got uuid:", scene_uuid)


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:
- Build the scene object in Python, referencing the calibration from the previous step
- Upload the scene object to the Kognic Platform
Below follows examples for a few different cases.
One Image
Multiple Images
Sequence
Python
1from kognic.io.client import KognicIOClient
2from kognic.io.model.calibration import SensorCalibration, PinholeCalibration, LidarCalibration
3from kognic.io.model.scene.lidars_and_cameras import LidarsAndCameras, Frame
4from kognic.io.model.scene.resources import Image, PointCloud
5
6client = KognicIOClient()
7
8# 1. Create calibration (see calibration section for more details)
9sensor_calibration = SensorCalibration(
10 external_id = "my-first-calibration",
11 calibration = {
12 "CAM": PinholeCalibration(...),
13 "lidar": LidarCalibration(...)
14 }
15)
16created_calibration = client.calibration.create_calibration(sensor_calibration)
17
18# 2. Build scene object
19scene = LidarsAndCameras(
20 external_id=f"my-first-scene",
21 calibration_id = created_calibration.id,
22 frame=Frame(
23 images=[Image(sensor_name = "CAM", filename="path/to/image.jpg")],
24 point_clouds=[PointCloud(sensor_name = "lidar", filename="path/to/pointcloud.pcd")]
25 )
26)
27
28# 3. Upload scene
29scene_uuid = client.lidars_and_cameras.create(scene).scene_uuid
30print("Scene uploaded, got uuid:", scene_uuid)

Python
1from kognic.io.client import KognicIOClient
2from kognic.io.model.calibration import SensorCalibration, PinholeCalibration, LidarCalibration
3from kognic.io.model.scene.lidars_and_cameras import LidarsAndCameras, Frame
4from kognic.io.model.scene.resources import Image, PointCloud
5
6client = KognicIOClient()
7
8# 1. Create calibration (see calibration section for more details)
9sensor_calibration = SensorCalibration(
10 external_id = "my-first-calibration",
11 calibration = {
12 "CAM1": PinholeCalibration(...),
13 "CAM2": PinholeCalibration(...),
14 "lidar": LidarCalibration(...)
15 }
16)
17created_calibration = client.calibration.create_calibration(sensor_calibration)
18
19# 2. Build scene object
20scene = LidarsAndCameras(
21 external_id="my-first-scene",
22 calibration_id = created_calibration.id,
23 frame=Frame(
24 images=[
25 Image(sensor_name = "CAM1", filename="path/to/image1.jpg"),
26 Image(sensor_name = "CAM2", filename="path/to/image2.jpg"),
27 ],
28 point_clouds=[PointCloud(sensor_name = "lidar", filename="path/to/pointcloud.pcd")]
29 )
30)
31
32# 3. Upload scene
33client = KognicIOClient()
34scene_uuid = client.lidars_and_cameras.create(scene).scene_uuid
35print("Scene uploaded, got uuid:", scene_uuid)

Python
1from kognic.io.client import KognicIOClient
2from kognic.io.model.calibration import SensorCalibration, PinholeCalibration, LidarCalibration
3from kognic.io.model.scene.lidars_and_cameras_sequence import LidarsAndCamerasSequence, Frame
4from kognic.io.model.scene.resources import Image, PointCloud
5
6client = KognicIOClient()
7
8# 1. Create calibration (see calibration section for more details)
9calibration = { "CAM": PinholeCalibration(...), "lidar": LidarCalibration(...) }
10sensor_calibration = SensorCalibration(
11 external_id = "my-first-calibration",
12 calibration = {
13 "CAM": PinholeCalibration(...),
14 "lidar": LidarCalibration(...)
15 }
16)
17created_calibration = client.calibration.create_calibration(sensor_calibration)
18
19# 2. Build scene object
20scene = LidarsAndCamerasSequence(
21 external_id="my-first-scene",
22 calibration_id = created_calibration.id,
23 frames=[
24 # Relative timestamps must be unique and strictly increasing
25 Frame(
26 relative_timestamp=0,
27 frame_id="1",
28 images=[Image(sensor_name = "CAM", filename="path/to/image1.jpg")],
29 point_clouds=[PointCloud(sensor_name = "lidar", filename="path/to/pointcloud1.pcd")]
30 ),
31 Frame(
32 relative_timestamp=100,
33 frame_id="2",
34 images=[Image(sensor_name = "CAM", filename="path/to/image2.jpg")],
35 point_clouds=[PointCloud(sensor_name = "lidar", filename="path/to/pointcloud2.pcd")]
36 ),
37 Frame(
38 relative_timestamp=200,
39 frame_id="3",
40 images=[Image(sensor_name = "CAM", filename="path/to/image3.jpg")],
41 point_clouds=[PointCloud(sensor_name = "lidar", filename="path/to/pointcloud3.pcd")]
42 ),
43 ]
44)
45
46# 3. Upload scene
47scene_uuid = client.lidars_and_cameras_sequence.create(scene).scene_uuid
48print("Scene uploaded, got uuid:", scene_uuid)

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.

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!

