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Camera Management Example App Service

Use the Camera Management Example application service to auto discover and connect to nearby ONVIF and USB based cameras. This application will also control cameras via commands, create inference pipelines for the camera video streams and publish inference results to MQTT broker.

This app uses EdgeX compose, Edgex Onvif Camera device service, Edgex USB Camera device service, Edgex MQTT device service and Edge Video Analytics Microservice.

Install Dependencies

Environment

This example has been tested with a relatively modern Linux environment - Ubuntu 20.04 and later

Install Docker

Install Docker from the official repository as documented on the Docker site.

Configure Docker

To enable running Docker commands without the preface of sudo, add the user to the Docker group.

Warning

The docker group grants root-level privileges to the user. For details on how this impacts security in your system, see Docker Daemon Attack Surface.

  1. Create Docker group: 

    sudo groupadd docker

    Note

    If the group already exists, groupadd outputs a message: groupadd: group docker already exists. This is OK.

  2. Add User to group: 

    sudo usermod -aG docker $USER

  3. Restart your computer for the changes to take effect.

Verify Docker

To verify the Docker installation, run hello-world:

docker run hello-world
A Hello from Docker! greeting indicates successful installation.

Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
2db29710123e: Pull complete 
Digest: sha256:10d7d58d5ebd2a652f4d93fdd86da8f265f5318c6a73cc5b6a9798ff6d2b2e67
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.
...

Install Docker Compose

Install Docker Compose from the official repository as documented on the Docker Compose site.

Install Golang

Install Golang from the official Golang website.

Install Tools

Install build tools:

sudo apt install build-essential

Steps for running this example:

1. Start the EdgeX Core Services and Device Services.

  1. Clone edgex-compose from github.com. 

    git clone https://github.com/edgexfoundry/edgex-compose.git

  2. Navigate to the edgex-compose directory: 

    cd edgex-compose

  3. Checkout the latest release (v3.1): 

    git checkout v3.1

  4. Navigate to the compose-builder subdirectory: 

    cd compose-builder/

  5. (Optional) Update the add-device-usb-camera.yml file:

    Note

    This step is only required if you plan on using USB cameras.

    a. Add enable rtsp server and the rtsp server hostname environment variables to the device-usb-camera service, where your-local-ip-address is the ip address of the machine running the device-usb-camera service.

    Snippet from add-device-usb-camera.yml

    services:
  device-usb-camera:
    environment:
      DRIVER_RTSPSERVERMODE: "internal"
      DRIVER_RTSPSERVEREXECUTABLE: "./rtsp-simple-server"
      DRIVER_RTSPSERVERHOSTNAME: "your-local-ip-address"

    b. Under the ports section, find the entry for port 8554 and change the host_ip from 127.0.0.1 to either 0.0.0.0 or the ip address you put in the previous step.

  6. Clone the EdgeX Examples repository : 

    git clone https://github.com/edgexfoundry/edgex-examples.git

  7. Navigate to the edgex-examples directory: 

    cd edgex-examples

  8. Checkout the latest release (v3.1): 

    git checkout v3.1

  9. Navigate to the application-services/custom/camera-management directory 

    cd application-services/custom/camera-management

  10. Configure device-mqtt service to send Edge Video Analytics Microservice inference results into Edgex via MQTT

    a. Copy the entire evam-mqtt-edgex folder into edgex-compose/compose-builder directory.

    b. Add this information into the add-device-mqtt.yml file in the edgex-compose/compose-builder directory.

    Snippet from add-device-mqtt.yml

    services:
  device-mqtt:
    ...
    environment:
      DEVICE_DEVICESDIR: /evam-mqtt-edgex/devices
      DEVICE_PROFILESDIR: /evam-mqtt-edgex/profiles
      MQTTBROKERINFO_INCOMINGTOPIC: "incoming/data/#"
      MQTTBROKERINFO_USETOPICLEVELS: "true"
      ...
    ...  
    volumes:
      # example: - /home/github.com/edgexfoundry/edgex-compose/compose-builder/evam-mqtt-edgex:/evam-mqtt-edgex
      - <add-absolute-path-of-your-edgex-compose-builder-here-example-above>/evam-mqtt-edgex:/evam-mqtt-edgex

    c. Add this information into the add-mqtt-broker-mosquitto.yml file in the edgex-compose/compose-builder directory.

    Snippet from add-mqtt-broker-mosquitto.yml

    services:
  mqtt-broker:
    ...
    ports:
      ...
      - "59001:9001"
    ...
    volumes:
      # example: - /home/github.com/edgexfoundry/edgex-compose/compose-builder/evam-mqtt-edgex:/evam-mqtt-edgex
      - <add-absolute-path-of-your-edgex-compose-builder-here>/evam-mqtt-edgex/mosquitto.conf:/mosquitto-no-auth.conf:ro

    Note

    Please note that both the services in this file need the absolute path to be inserted for their volumes.

  11. Run the following command to start all the Edgex services.

    Note

    The ds-onvif-camera parameter can be omitted if no Onvif cameras are present, or the ds-usb-camera parameter can be omitted if no usb cameras are present.

    make run no-secty ds-mqtt mqtt-broker ds-onvif-camera ds-usb-camera

2. Start Edge Video Analytics Microservice running for inference.

  1. Open cloned edgex-examples repo and navigate to the edgex-examples/application-services/custom/camera-management directory: 

    cd edgex-examples/application-services/custom/camera-management

  2. Run this once to download edge-video-analytics into the edge-video-analytics sub-folder, download models, and patch pipelines 

    make install-edge-video-analytics

3. Build and run the example application service

3.1 (Optional) Configure Onvif Camera Credentials.

Note

This step is only required if you have Onvif cameras. Currently, this example app is limited to supporting only 1 username/password combination for all Onvif cameras.

Note

Please follow the instructions for the Edgex Onvif Camera device service in order to connect your Onvif cameras to EdgeX.

Modify the res/configuration.yaml file

InsecureSecrets:
  onvifauth:
    SecretName: onvifauth
    SecretData:
      username: "<username>"
      password: "<password>"

Export environment variable overrides 

export WRITABLE_INSECURESECRETS_ONVIFAUTH_SECRETDATA_USERNAME="<username>"
export WRITABLE_INSECURESECRETS_ONVIFAUTH_SECRETDATA_PASSWORD="<password>"

3.2 (Optional) Configure USB Camera RTSP Credentials.

Note

This step is only required if you have USB cameras.

Note

Please follow the instructions for the Edgex USB Camera device service in order to connect your USB cameras to EdgeX.

Modify the res/configuration.yaml file

InsecureSecrets:
  rtspauth:
    SecretName: rtspauth
    SecretData:
      username: "<username>"
      password: "<password>"

Export environment variable overrides 

export WRITABLE_INSECURESECRETS_RTSPAUTH_SECRETDATA_USERNAME="<username>"
export WRITABLE_INSECURESECRETS_RTSPAUTH_SECRETDATA_PASSWORD="<password>"

3.3 Configure Default Pipeline

Initially, all new cameras added to the system will start the default analytics pipeline as defined in the configuration file below. The desired pipeline can be changed or the feature can be disabled by setting the DefaultPipelineName and DefaultPipelineVersion to empty strings.

Modify the res/configuration.yaml file with the name and version of the default pipeline to use when a new device is added to the system.

Note

These values can be left empty to disable the feature.

AppCustom:
  DefaultPipelineName: object_detection # Name of the default pipeline used when a new device is added to the system; can be left blank to disable feature
  DefaultPipelineVersion: person # Version of the default pipeline used when a new device is added to the system; can be left blank to disable feature

3.4 Build and run

  1. Make sure you are at the root of this example app 

    cd edgex-examples/application-services/custom/camera-management

  2. Build the docker image 

    make docker

  3. Start the docker compose services in the background for both EVAM and Camera Management App 

    docker compose up -d

Note

If you would like to view the logs for these services, you can use docker compose logs -f. To stop the services, use docker compose down.

Note

The port for EVAM result streams has been changed from 8554 to 8555 to avoid conflicts with the device-usb-camera service.

Using the App

Visit http://localhost:59750 to access the app.

homepage

Figure 1: Homepage for the Camera Management app

Camera Position

You can control the position of supported cameras using ptz commands.

camera-position

  • Use the arrows to control the direction of the camera movement.
  • Use the magnifying glass icons to control the camera zoom.

Start an Edge Video Analytics Pipeline

This section outlines how to start an analytics pipeline for inferencing on a specific camera stream.

camera

  1. Select a camera out of the drop down list of connected cameras.
    select-camera

  2. Select a video stream out of the drop down list of connected cameras.
    select-profile

  3. Select a analytics pipeline out of the drop down list of connected cameras.
    select-pipeline

  4. Click the Start Pipeline button.

Running Pipelines

Once the pipeline is running, you can view the pipeline and its status.

default pipelines state

Expand a pipeline to see its status. This includes important information such as elapsed time, latency, frames per second, and elapsed time.
select-camera

In the terminal where you started the app, once the pipeline is started, this log message will pop up. 

level=INFO ts=2022-07-11T22:26:11.581149638Z app=app-camera-management source=evam.go:115 msg="View inference results at 'rtsp://<SYSTEM_IP_ADDRESS>:8555/<device name>'"

Use the URI from the log to view the camera footage with analytics overlayed. 

ffplay 'rtsp://<SYSTEM_IP_ADDRESS>:8555/<device name>'

Example Output:

example analytics

Figure 2: analytics stream with overlay

If you want to stop the stream, press the red square:

stop pipeline

Figure 3: the red square to shut down the pipeline

API Log

The API log shows the status of the 5 most recent calls and commands that the management has made. This includes important information from the responses, including camera information or error messages.

API Logs

Expand a log item to see the response

Good response: good api response
Bad response: bad api response

Inference Events

inference events default

To view the inference events in a json format, click the Stream Events button.

inference events

Inference results in Edgex

To view inference results in Edgex, open Edgex UI http://localhost:4000, click on the DataCenter tab and view data streaming under Event Data Streamby clicking on the Start button.

inference events

Next steps

A custom app service can be used to analyze this inference data and take action based on the analysis.

Video Example

A brief video demonstration of building and using the device service:

Warning

This video was created with a previous release. Some new features may not be depicted in this video, and there might be some extra steps needed to configure the service.

Additional Development

Warning

The following steps are only useful for developers who wish to make modifications to the code and the Web-UI.

Development and Testing of UI

1. Build the production web-ui

This builds the web ui into the web-ui/dist folder, which is what is served by the app service on port 59750. 

make web-ui

2. Serve the Web-UI in hot-reload mode

This will serve the web ui in hot reload mode on port 4200 which will recompile and update anytime you make changes to a file. It is useful for rapidly testing changes to the UI. 

make serve-ui

Open your browser to http://localhost:4200