Set Up SceneScape with Basler GigE Camera and PTP Support#

Configure the Basler Camera with PTP Support#

The Basler camera only supports IEEE 1588v2 over UDP, so the switch and host must be configured for that profile. Follow the guides below before continuing:

Set Up SceneScape with Basler GigE Support#

Step 1: Clone SceneScape#

git clone https://github.com/open-edge-platform/scenescape
cd scenescape
git checkout 2026.1.0-rc1 -b 2026.1.0-rc1

Step 2: Build the DL Streamer Pipeline Server Image with Basler Support#

The standard DL Streamer Pipeline Server image does not include the Basler pylon SDK or the gencamsrc GStreamer plugin. Follow the instructions below to build a custom image:

Integrate Pylon SDK — Step 2: Create the Docker Image

Note: Patch gencamsrc to propagate the PTP timestamp before building the image. By default, gencamsrc discards the camera’s PTP hardware timestamp after setting the GStreamer buffer PTS. The following patch adds a GstReferenceTimestampMeta to each buffer so downstream elements such as gvapython can read the original camera timestamp before any clock correction occurs.

git -C /path/to/edge-ai-libraries apply \
  /path/to/deterministic-threat-detection/usecases/scenescape-deterministic-inference/basler/patches/genicam.patch

Step 3: Update the Docker Compose Image Reference#

After the image build completes, update sample_data/docker-compose-dl-streamer-example.yml so the queuing-video service uses your custom Basler-enabled image.

services:
  queuing-video:
    image: <your-basler-enabled-image>:<tag>

Step 4: Patch Docker Compose to Add a macvlan Network#

The Basler GigE camera is connected to the private TSN switch network, while Docker containers use a bridge network by default. A macvlan network bridges the two, giving the queuing-video container a routable address on the camera subnet, for the Basler GigE camera detection to work correctly.

Apply the patch to your SceneScape checkout:

git -C /path/to/scenescape apply \
  /path/to/deterministic-threat-detection/usecases/scenescape-deterministic-inference/basler/patches/macvlan_docker.patch

Note: Edit the patched docker-compose-dl-streamer-example.yml to set the correct host NIC name (default: enp5s0.1) and IP address (192.168.127.51) for the macvlan interface to match your network configuration.

Step 5: Patch `sscape_adapter` to Publish the PTP Timestamp#

The sscape_adapter.py patch extracts the GstReferenceTimestampMeta added by the patched gencamsrc and uses the camera PTP timestamp as the frame timestamp field in the SceneScape MQTT message. Without this patch, the adapter falls back to the post-decode software timestamp.

Apply the patch to your SceneScape checkout:

git -C /path/to/scenescape apply \
  /path/to/deterministic-threat-detection/usecases/scenescape-deterministic-inference/basler/patches/sscape_adapter.patch

Step 6: Configure the GStreamer Pipeline#

Update the pipeline definition in SceneScape to use gencamsrc as the source and insert the gvapython timestamp capture element before inference. Note down the serial number of your Basler camera and substitute it for <basler-camera-serial>:

gencamsrc serial=<basler-camera-serial> pixel-format=bayerrggb frame-rate=10 name=source ! bayer2rgb ! videoscale ! video/x-raw,width=1920,height=1080 ! videoconvert ! \
video/x-raw,format=BGR ! gvapython class=PostDecodeTimestampCapture function=processFrame module=/home/pipeline-server/user_scripts/gvapython/sscape/sscape_adapter.py name=timesync ! gvadetect model=/home/pipeline-server/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml model-proc=/home/pipeline-server/models/object_detection/person/person-detection-retail-0013.json ! gvametaconvert add-tensor-data=true name=metaconvert ! gvapython class=PostInferenceDataPublish function=processFrame module=/home/pipeline-server/user_scripts/gvapython/sscape/sscape_adapter.py name=datapublisher ! gvametapublish name=destination ! appsink sync=true

Tip: SceneScape tracking quality depends on the camera feed. You can either configure the camera for your real-world scene or point the camera at a monitor that plays the queuing demo video. The monitor-based setup is often the quickest way to validate Basler camera tracking behavior.