Get Started Guide#

  • Time to Complete: 5 - 15 minutes

  • Programming Language: Python 3

Prerequisites#

Ensure that the following installations are present.

  • unzip. It can be installed using sudo apt-get install unzip

  • OS

    Python

    Ubuntu 22.04

    3.10

Quick try out#

Follow the steps in this section to quickly pull the latest pre-built Edge Video Analytics Microservice docker image followed by running a sample usecase.

Pull the image and start container#

  • Pull the image with the latest tag from dockerhub registry

      docker pull intel/edge-video-analytics-microservice:2.3.0

  • Create docker-compose.yml file with below contents inside the docker folder within your work directory ([EVAM_WORKDIR]/docker/). EVAM_WORKDIR is your host machine workspace:

     #
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 #

 services:
   edge-video-analytics-microservice:
     image: intel/edge-video-analytics-microservice:2.3.0
     hostname: edge-video-analytics-microservice
     container_name: edge-video-analytics-microservice
     read_only: true
     security_opt:
     - no-new-privileges
     privileged: false
     tty: true
     entrypoint: ["./run.sh"]
     ports:
       - '8080:8080'
       - '8554:8554'
     networks:
       - app_network
     environment:
       - ENABLE_RTSP=true
       - RTSP_PORT=8554
       - no_proxy=$no_proxy,${RTSP_CAMERA_IP}
       - http_proxy=$http_proxy
       - https_proxy=$https_proxy
       - RUN_MODE=EVA
       - GENICAM=Balluff
       - GST_DEBUG=1,gencamsrc:2
       # Default Detection and Classification Device
       - DETECTION_DEVICE=CPU
       - CLASSIFICATION_DEVICE=CPU
       - ADD_UTCTIME_TO_METADATA=true
       - LSFEATURE_NAME="EVAM"
       - HTTPS=false # Make it "true" to enable SSL/TLS secure mode, mount the generated certificates
       - MTLS_VERIFICATION=false # if HTTPS=true, enable/disable client certificate verification for mTLS
       # Model Registry Microservice
       - MR_VERIFY_CERT=/run/secrets/ModelRegistry_Server/ca-bundle.crt
       # Append pipeline name to a publisher topic
       - APPEND_PIPELINE_NAME_TO_PUBLISHER_TOPIC=false
       - REST_SERVER_PORT=8080
     volumes:
       # - "../configs/default/config.json:/home/pipeline-server/config.json"
       - vol_evam_pipeline_root:/var/cache/pipeline_root:uid=1999,gid=1999
       - "../certificates:/MqttCerts:ro"
       - "../Certificates/ssl_server/:/run/secrets/EdgeVideoAnalyticsMicroservice_Server:ro"
       - "../Certificates/model_registry/:/run/secrets/ModelRegistry_Server:ro"
       - "/run/udev:/run/udev:ro"
       - "/dev:/dev"
       - "/tmp:/tmp"
       - "./mr_models:/home/pipeline-server/mr_models:rw"
     group_add:
       - "109"
       - "110"
     device_cgroup_rules:
     - 'c 189:* rmw'
     - 'c 209:* rmw'
     - 'a 189:* rwm'
     devices:
     - "/dev:/dev"

 networks:
   app_network:
     driver: "bridge"

 volumes:
   vol_evam_pipeline_root:
     driver: local
     driver_opts:
       type: tmpfs
       device: tmpfs

  • Bring up the container

      docker compose up

Run default sample#

Once the container is up, we will send a pipeline request to EVAM to run a detection model on a warehouse video. Both the model and video are provided as default sample in the docker image.

We will send the below curl request to run the inference. It comprises of a source file path which is warehouse.avi, a destination, with metadata directed to a json fine in /tmp/resuts.jsonl and frames streamed over RTSP with id pallet-defect-detection. Additionally, we will also provide the GETi model path that would be used for detecting defective boxes on the video file.

Open another terminal and send the following curl request

    curl http://localhost:8080/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{
    "source": {
        "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
        "type": "uri"
    },
    "destination": {
        "metadata": {
            "type": "file",
            "path": "/tmp/results.jsonl",
            "format": "json-lines"
        },
        "frame": {
            "type": "rtsp",
            "path": "pallet-defect-detection"
        }
    },
    "parameters": {
        "detection-properties": {
            "model": "/home/pipeline-server/resources/models/geti/pallet_defect_detection/deployment/Detection/model/model.xml",
            "device": "CPU"
        }
    }
}'

The REST request will return a pipeline instance ID, which can be used as an identifier to query later the pipeline status or stop the pipeline instance. For example, a6d67224eacc11ec9f360242c0a86003.

  • To view the metadata, open another terminal and run the following command,

      tail -f /tmp/results.jsonl

  • RTSP Stream will be accessible at rtsp://<SYSTEM_IP_ADDRESS>:8554/pallet-defect-detection. Users can view this on any media player e.g. vlc (as a network stream), ffplay etc

    sample frame RTSP stream

To check the pipeline status and stop the pipeline send the following requests,

  • view the pipeline status that you triggered in the above step.

     curl --location -X GET http://localhost:8080/pipelines/status

  • stop a running pipeline instance,

     curl --location -X DELETE http://localhost:8080/pipelines/{instance_id}

Now you have successfully run the Edge Video Analytics Microservice container, sent a curl request to start a pipeline within the microservice which runs the Geti based pallet defect detection model on a sample warehouse video. Then, you have also looked into the status of the pipeline to see if everything worked as expected and eventually stopped the pipeline as well.

Summary#

In this get started guide, you learned how to start the EVAM service and start default pipeline.

Learn More#

  • For getting started with the deployment on k8s with helm chart, please go here

  • Understand the components, services, architecture, and data flow, in the Overview.

  • For more details on advanced configuration, usage of features refer to Detailed Usage

  • For more tutorials, refer to Tutorials How-to guide.

  • For more details on Intel Deep Learning Streamer visit this page.