Integrate a Camera SDK into the Pallet Defect Detection Application#

This guide explains how to create a custom Docker image based on the DL Streamer Pipeline Server with Gencamsrc support, using either the Balluff SDK or the pylon SDK. It supports Balluff, Basler, and other GenICam-compatible cameras connected over USB and GigE interfaces.

Note: You may observe a watermark in the camera feed when testing with a non-Balluff camera, as it is the free version.

Prerequisites#

System Requirements

Clone and Build the Docker Image#

Step 1: Base Image and User Setup#

Download the edge-ai-libraries source and go to the dlstreamer-pipeline-server folder.

git clone https://github.com/open-edge-platform/edge-ai-libraries.git -b main
cd edge-ai-libraries/microservices/dlstreamer-pipeline-server

Step 2: Create the Docker Image#

Create a Dockerfile inside your dlstreamer-pipeline-server directory with the content shown for your SDK.

Balluff SDK

Create a Dockerfile named BalluffDockerfile.

FROM intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24

USER root

RUN apt-get update && apt-get install -y wget gnupg cmake gstreamer1.0-plugins-base libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev g++ libxcb-cursor0 vim && apt-get clean && rm -rf /var/lib/apt/lists/*

COPY ./plugins/camera/src-gst-gencamsrc /home/pipeline-server/src-gst-gencamsrc

RUN cd /home/pipeline-server/src-gst-gencamsrc && cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j$(nproc) && cmake --install build && ldconfig

# For Ubuntu24 intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24 base image
RUN apt-get update && apt-get install -y libwxgtk-webview3.2-dev

# For Ubuntu 22 with intel/dlstreamer-pipeline-server:3.1.0-ubuntu22, uncomment the line below and comment the above line
# RUN apt-get update && apt-get install -y libwxgtk-webview3.0-gtk3-dev

RUN mkdir /home/pipeline-server/Balluff_Impact_Acquire_V3
RUN wget https://assets-2.balluff.com/mvIMPACT_Acquire/3.0.0/install_mvGenTL_Acquire.sh -P /home/pipeline-server/Balluff_Impact_Acquire_V3
RUN wget https://assets-2.balluff.com/mvIMPACT_Acquire/3.0.0/mvGenTL_Acquire-x86_64_ABI2-3.0.0.tgz -P /home/pipeline-server/Balluff_Impact_Acquire_V3
RUN cd Balluff_Impact_Acquire_V3 && chmod +x ./install_mvGenTL_Acquire.sh && ./install_mvGenTL_Acquire.sh -u3v -gev -u

ENV MVIMPACT_ACQUIRE_DIR="/opt/mvIMPACT_Acquire" \
   MVIMPACT_ACQUIRE_DATA_DIR="/opt/mvIMPACT_Acquire/data" \
   GENICAM_ROOT="/opt/mvIMPACT_Acquire/runtime" \
   MVIMPACT_ACQUIRE_FAVOUR_SYSTEMS_LIBUSB="1" \
   GENICAM_GENTL64_PATH=/opt/Impact_Acquire/lib/x86_64 \
   GST_PLUGIN_PATH=/opt/intel/dlstreamer/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:/opt/intel/dlstreamer/gstreamer/lib/:/usr/lib/x86_64-linux-gnu/gstreamer-1.0:/usr/local/lib/gstreamer-1.0

USER intelmicroserviceuser

pylon SDK

Create a Dockerfile named BaslerDockerfile.

FROM intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24

USER root

RUN apt-get update && apt-get install -y wget gnupg cmake gstreamer1.0-plugins-base libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev g++ libxcb-cursor0 vim && apt-get clean && rm -rf /var/lib/apt/lists/*

RUN cd /tmp && wget https://downloads-ctf.baslerweb.com/dg51pdwahxgw/16EbjATpV78LtOFUQ1VpJM/ab3db40227afb59df3eb1cccf0c5addc/pylon-7.5.0.15658-linux-x86_64_debs.tar.gz &&     tar -xvzf pylon-7.5.0.15658-linux-x86_64_debs.tar.gz && rm pylon-7.5.0.15658-linux-x86_64_debs.tar.gz && dpkg -i pylon_7.5.0.15658-deb0_amd64.deb || apt-get install -fy && rm pylon_7.5.0.15658-deb0_amd64.deb

RUN cd /tmp && wget https://github.com/basler/gst-plugin-pylon/releases/download/v1.0.0/gst-plugin-pylon_1.0.0-1.ubuntu-24.04_amd64.deb && dpkg -i gst-plugin-pylon_1.0.0-1.ubuntu-24.04_amd64.deb && rm gst-plugin-pylon_1.0.0-1.ubuntu-24.04_amd64.deb

COPY ./plugins/camera/src-gst-gencamsrc /home/pipeline-server/src-gst-gencamsrc

RUN cd /home/pipeline-server/src-gst-gencamsrc && cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j$(nproc) && cmake --install build && ldconfig

ENV GENICAM_GENTL64_PATH=/opt/pylon/lib/gentlproducer/gtl \
   GST_PLUGIN_PATH=/opt/intel/dlstreamer/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:/opt/intel/dlstreamer/gstreamer/lib/:/usr/lib/x86_64-linux-gnu/gstreamer-1.0:/usr/local/lib/gstreamer-1.0

USER intelmicroserviceuser

Step 3: Build the Docker Image#

Run the following command to build the image.

Balluff SDK

docker build -t intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24-gencamsrc-balluff -f BalluffDockerfile .

pylon SDK

docker build -t intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24-gencamsrc-basler -f BaslerDockerfile .

This command builds your Docker image using the steps defined above.

Step 4: Verify the Image#

After the build completes, inside dlstreamer-pipeline-server/docker directory, update .env and start the container.

Balluff SDK

Update .env with:

DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24-gencamsrc-balluff

pylon SDK

Update .env with:

DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24-gencamsrc-basler

docker compose up -d

Step 5: Run a Test Pipeline to Get Camera Output#

Check the camera serial number and update it in the command below.

The output will be written to a file in the /tmp directory.

Balluff SDK

Replace <balluff-camera-serial> with your Balluff camera serial number.

docker exec -it dlstreamer-pipeline-server bash
$ gst-launch-1.0 gencamsrc serial=<balluff-camera-serial> pixel-format=bayerrggb name=source ! bayer2rgb ! videoscale ! video/x-raw, width=1920,height=1080 ! videoconvert ! queue ! jpegenc ! avimux ! filesink location=/tmp/gencam_balluff_output.avi

Verify that /tmp/gencam_balluff_output.avi has the captured content.

pylon SDK

Replace <basler-camera-serial> with your Basler camera serial number.

docker exec -it dlstreamer-pipeline-server bash
$ gst-launch-1.0 gencamsrc serial=<basler-camera-serial> pixel-format=bayerrggb name=source ! bayer2rgb ! videoscale ! video/x-raw, width=1920,height=1080 ! videoconvert ! queue ! jpegenc ! avimux ! filesink location=/tmp/gencam_basler_output.avi

Verify that /tmp/gencam_basler_output.avi has the captured content.

Deploy the Pallet Defect Detection (PDD) Application Using Live Camera#

This section provides detailed, step-by-step instructions for setting up and deploying the Pallet Defect Detection (PDD) pipeline using a live camera feed.

Step 1: Set Up the Environment#

git clone https://github.com/open-edge-platform/edge-ai-suites.git -b main
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision
cp .env_pallet-defect-detection .env

Step 2: Configure the .env File#

Update the .env file with the image you built and modify any other required variables.

Balluff SDK

DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24-gencamsrc-balluff

pylon SDK

DLSTREAMER_PIPELINE_SERVER_IMAGE=intel/dlstreamer-pipeline-server:2026.1.0-ubuntu24-gencamsrc-basler

Step 3: Run the Setup Script#

Execute the setup script to initialize project directories and configurations.

./setup.sh

Step 4: Update the Pipeline Configuration#

Update the pipeline in ./apps/pallet-defect-detection/configs/pipeline-server-config.json to use the camera.

{
    "name": "pallet_defect_detection",
    "source": "gstreamer",
    "queue_maxsize": 50,
    "pipeline": "gencamsrc serial=<camera id> pixel-format=bayerrggb name=source ! bayer2rgb ! videoscale ! video/x-raw, width=640, height=480 ! videoconvert ! gvadetect name=detection model-instance-id=inst0 ! gvametaconvert add-empty-results=true name=metaconvert ! queue ! gvafpscounter ! gvawatermark ! appsink name=destination"
}

Replace <camera id> with the camera ID connected over USB or GigE.

Step 5: Configure docker-compose.yml (Optional, if Testing with a GigE Network Camera)#

When testing with a GigE camera, adjust the docker-compose.yml configuration for all services.

Add network_mode set to "host".
Remove the networks section.

The configuration for each service should look like this.

services:
  service_name:
    .
    .
    network_mode: "host"
    # networks:
    #   - industrial-edge-vision

Additionally, add the following entries to the /etc/hosts file on the host machine.

0.0.1       dlstreamer-pipeline-server
0.0.1       prometheus
0.0.1       mediamtx-server
0.0.1       minio
0.0.1       otel-collector
0.0.1       mqtt-broker

Step 6: Launch the Containers#

Start all the required services using Docker Compose.

Note: If you are running multiple instances of the application, start the services using ./run.sh up instead.

docker compose up -d

Step 7: Modify the Payload File#

Edit edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/apps/pallet-defect-detection/payload.json and remove the source section so that it looks like this.

[
    {
        "pipeline": "pallet_defect_detection",
        "payload": {
            "destination": {
                "frame": {
                    "type": "webrtc",
                    "peer-id": "pdd",
                    "overlay": false
                }
            },
            "parameters": {
                "detection-properties": {
                    "model": "/home/pipeline-server/resources/models/pallet-defect-detection/deployment/Detection/model/model.xml",
                    "device": "CPU"
                }
            }
        }
    }
]

Step 8: Start the Sample Pipeline#

Run the sample script to start the pipeline.

./sample_start.sh -p pallet_defect_detection

Step 9: Access the Web Interface#

Open a browser and navigate to:

https://<HOST_IP>/mediamtx/pdd/

Replace <HOST_IP> with the IP address configured in your .env file.

Note: If you are running multiple instances of the application, ensure to provide NGINX_HTTPS_PORT number in the URL for the application instance, i.e., replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>. If you are running a single instance and using an NGINX_HTTPS_PORT other than the default 443, replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>.

Troubleshooting#

Balluff SDK

For initial configuration and advanced configuration of the Balluff camera, use the company-provided visualization tool ImpactAcquire, which is part of the Balluff SDK.
Instructions to install the Balluff SDK on the host can be found in the Balluff SDK Installation Guide.

pylon SDK

For initial configuration and advanced configuration of the Basler camera, use the company-provided visualization tool pylonviewer, which is part of the pylon SDK.
Instructions to install the pylon SDK on the host can be found in the pylon SDK Installation Guide.
If you face any issues related to camera detection over USB, refer to the pylon Troubleshooting Guide.

Integrate a Camera SDK into the Pallet Defect Detection Application#

Prerequisites#

Clone and Build the Docker Image#

Step 1: Base Image and User Setup#

Step 2: Create the Docker Image#

Step 3: Build the Docker Image#

Step 4: Verify the Image#

Step 5: Run a Test Pipeline to Get Camera Output#

Deploy the Pallet Defect Detection (PDD) Application Using Live Camera#

Step 1: Set Up the Environment#

Step 2: Configure the .env File#

Step 3: Run the Setup Script#

Step 4: Update the Pipeline Configuration#

Step 5: Configure docker-compose.yml (Optional, if Testing with a GigE Network Camera)#

Step 6: Launch the Containers#

Step 7: Modify the Payload File#

Step 8: Start the Sample Pipeline#

Step 9: Access the Web Interface#

Troubleshooting#

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