Upload ZOD data

2min
This tutorial will guide you through uploading different scene types﻿ using the Zenseact Open Dataset (ZOD). The purpose of this page is to show you some of the steps that might be needed to convert recordings into Kognic scenes.
1. To follow along in this guide you need to download the data from Zenseact Open Dataset. The data should be structured like this:
Text
zod
├── sequences
│   ├── 000000
│   ├── 000002
│   ├── ...
└── trainval-sequences-mini.json
zod
├── sequences
│   ├── 000000
│   ├── 000002
│   ├── ...
└── trainval-sequences-mini.json
﻿
2. You will also need to install the ZOD Python package from PyPI, which provides some abstractions for reading the data:
Bash
pip install zod
pip install zod
﻿
3. You need to have a Kognic account and the Kognic Python client installed. If you have not done this yet, read the Quickstart﻿ guide.
This guide follows the process of uploading scenes using ZOD data, using the example code from the Kognic IO ZOD examples repository which contains the complete source files for all of the snippets in this page. The examples are runnable, if you have the data available and have Kognic authentication set up.
Cameras Sequence
Lidars and Cameras Sequence
Aggregated Lidars and Cameras Sequence
Our example code initialises a Kognic IO Client at the top level, then creates the scene from ZOD data for (potentially) multiple scenes at once using a function.
Python
if __name__ == "__main__":
    client = KognicIOClient()

    upload_cameras_sequence_scenes(
        zod_path=Path("/path/to/zod"),  # change me
        zod_version="mini",
        client=client,
        max_nr_scenes=1,
        max_nr_frames=10,
        dryrun=False,
    )
if __name__ == "__main__":
    client = KognicIOClient()

    upload_cameras_sequence_scenes(
        zod_path=Path("/path/to/zod"),  # change me
        zod_version="mini",
        client=client,
        max_nr_scenes=1,
        max_nr_frames=10,
        dryrun=False,
    )
﻿
This example follows the same broad structure as the cameras-only sequence with the addition of:
A LiDAR sensor
A caliration﻿, to allow projection between 2D and 3D coordinate systems﻿.
Conversion of point clouds from ZOD's packed NumPy arrays.
Conversion of ego poses for each frame.
As before we initialise a Kognic IO Client at the top-level, then create the scene from ZOD data for (potentially) multiple scenes at once using a function.
This example follows the same structure as the LiDAR-and-camera sequence example.
﻿Aggregated scenes﻿ are a special case of LiDAR + camera sequence scenes where the LiDAR data is aggregated across frames into a single pointcloud. This gives a dense, static pointcloud that represents the entire scene across all frames.
Aggregated scenes may be created by providing a pointcloud on every frame and allowing the Kognic platform to handle aggregation, or, they may be pre-aggregated and uploaded by specifying a pointcloud on the first frame, then nothing on subsequent frames.
In the case of ZOD data, we only have per-frame pointclouds, so the example uploads a pointcloud on every frame and leaves aggregation to the platform. As such it is very similar to the LiDAR-and-camera sequence example, except that:
1. The scene type is different: AggregatedLidarsAndCamerasSequence instead of LidarsAndCamerasSequence.
Python
def convert_scene(zod_sequence: ZodSequence, external_id: str, max_nr_frames: int) -> AggregatedLidarsAndCamerasSequence:
    frames = convert_frames(zod_sequence, max_nr_frames)
    return AggregatedLidarsAndCamerasSequence(external_id=external_id, frames=frames, calibration_id="<to be set later>")
def convert_scene(zod_sequence: ZodSequence, external_id: str, max_nr_frames: int) -> AggregatedLidarsAndCamerasSequence:
    frames = convert_frames(zod_sequence, max_nr_frames)
    return AggregatedLidarsAndCamerasSequence(external_id=external_id, frames=frames, calibration_id="<to be set later>")
﻿
2. The Frames are of an aggregated-scene specific type
Python
frames.append(
    ALCSFrame(
        relative_timestamp=ns_to_ms(frame_ts_ns) - start_ts_ms,
        frame_id=str(frame_ts_ns),
        images=[convert_zod_camera_frame_to_image(camera_frame)],
        point_clouds=[point_cloud],
        ego_vehicle_pose=convert_to_ego_vehicle_pose(ego_pose),
        unix_timestamp=frame_ts_ns,
    )
)
frames.append(
    ALCSFrame(
        relative_timestamp=ns_to_ms(frame_ts_ns) - start_ts_ms,
        frame_id=str(frame_ts_ns),
        images=[convert_zod_camera_frame_to_image(camera_frame)],
        point_clouds=[point_cloud],
        ego_vehicle_pose=convert_to_ego_vehicle_pose(ego_pose),
        unix_timestamp=frame_ts_ns,
    )
)
﻿
3. Ego pose data is required
Otherwise the two approaches are very similar - refer to the Lidars and Cameras Sequence tab.
Updated 19 Dec 2024
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Aggregated Lidars and Cameras Sequence
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Upload ZOD data

Prerequisites & Dependencies
