.. role:: raw-html-m2r(raw)
   :format: html


Building standalone version of Edge Video Analytics Microservice
================================================================

The standalone version of *Edge Video Analytics Microservice Overview* does not contain any EII library dependencies. And as a result of this, this mode does not support the gst-udfloader plugin.

Steps to bulid the standalone image
-----------------------------------


* Clone the *Edge Video Analytics Microservice Overview* repository
  .. code-block:: bash

      $ git clone https://github.com/intel-innersource/applications.services.esh.edge-video-analytics-microservice.git
      $ cd applications.services.esh.edge-video-analytics-microservice

* Run the ``build.sh`` script to build container image
  .. code-block:: bash

      $ export TAG="1.0.0-standalone"
      $ docker/standalone/build.sh $TAG

Steps to run the standalone image
---------------------------------


* Pull the container image from registry
  .. code-block:: bash

     $ export TAG="1.0.0-standalone"
     $ docker pull intel/edge_video_analytics_microservice:${TAG}

* Run the image
  .. code-block:: bash

     $ docker run -itd --rm \
        --privileged \
        --device=/dev:/dev \
        --device-cgroup-rule='c 189:* rmw' \
        --device-cgroup-rule='c 209:* rmw' \
        --group-add 109 \
        -p 8080:8080 \
        -p 8554:8554 \
        -e ENABLE_RTSP=true \
        -e RTSP_PORT=8554 \
        -e ENABLE_WEBRTC=true \
        -e WEBRTC_SIGNALING_SERVER=ws://localhost:8443 \
        -e RUN_MODE=EVA \
        -e DETECTION_DEVICE=CPU \
        -e CLASSIFICATION_DEVICE=CPU \
        intel/edge_video_analytics_microservice:${TAG}

Adding custom pipelines and models
----------------------------------

   Custom pipelines and models can be made available to the pipeline server by mounting them into to container image.


* 
  Prepare models

     Use the model downloader `available here <https://github.com/dlstreamer/pipeline-server/tree/main/tools/model_downloader>`_ to download new models. Point ``MODEL_DIR`` to the directory containing the new models. The following section assumes that the new models are available under ``$(pwd)/models``.

  .. code-block:: bash

      $ export MODEL_DIR=$(pwd)/models

* 
  Prepare pipelines

    Use `these docs <https://github.com/dlstreamer/pipeline-server/blob/main/docs/defining_pipelines.md>`_ to get started with defining new pipelines. Once the new pipelines have been defined, point ``PIPELINE_DIR`` to the directory containing the new pipelines. The following section assumes that the new pipelines are available under ``$(pwd)/pipelines``.

  .. code-block:: bash

      $ export PIPELINE_DIR=$(pwd)/pipelines

* Run the image with new models and pipelines mounted into the container
  .. code-block:: bash

     $ docker run -itd --rm \
        --privileged \
        --device=/dev:/dev \
        --device-cgroup-rule='c 189:* rmw' \
        --device-cgroup-rule='c 209:* rmw' \
        --group-add 109 \
        -p 8080:8080 \
        -p 8554:8554 \
        -e ENABLE_RTSP=true \
        -e RTSP_PORT=8554 \
        -e ENABLE_WEBRTC=true \
        -e WEBRTC_SIGNALING_SERVER=ws://localhost:8443 \
        -e RUN_MODE=EVA \
        -e DETECTION_DEVICE=CPU \
        -e CLASSIFICATION_DEVICE=CPU \
        -v ${MODEL_DIR}:/home/pipeline-server/models \
        -v ${PIPELINE_DIR}:/home/pipeline-server/pipelines \
        intel/edge_video_analytics_microservice:${TAG}
  ## Starting pipelines
* 
  We can trigger pipelines using the *pipeline server's* REST endpoints, here is an example cURL command, the output is available as a RTSP stream at *rtsp://\ :raw-html-m2r:`<host ip>`\ /pipeline-server*

  .. code-block:: bash

      $ curl localhost:8080/pipelines/object_classification/vehicle_attributes -X POST -H \
        'Content-Type: application/json' -d \
        '{
           "source": {
              "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true",
              "type": "uri"
           },
           "destination": {
              "metadata": {
                 "type": "file",
                 "path": "/tmp/results.jsonl",
                 "format": "json-lines"
              },
              "frame": {
                 "type": "rtsp",
                 "path": "pipeline-server"
              }
           },
           "parameters": {
              "detection-device": "CPU"
           }
        }'

  ## **\ *gencamsrc*\ ** sample pipeline


  * Add the pipeline give below under ``$(pwd)/pipelines/cameras/gencam/pipeline.json``
    .. code-block:: json

        {
           "type": "GStreamer",
           "template": [ "gencamsrc serial=<serial no> name=source", " ! videoconvert", " ! video/x-raw,format=BGR", " ! appsink name=destination" ],
           "description": "Pipeline"
        }

* Run the container as root, with environment var ``GENICAM=Matrix_Vision`` set and make sure ``/run/udev`` is mounted.
  .. code-block:: bash

     $ docker run -it --rm \
        -u 0 \
        --privileged \
        --device=/dev:/dev \
        --device-cgroup-rule='c 189:* rmw' \
        --device-cgroup-rule='c 209:* rmw' \
        --group-add 109 \
        -p 8080:8080 \
        -p 8554:8554 \
        -e ENABLE_RTSP=true \
        -e RTSP_PORT=8554 \
        -e ENABLE_WEBRTC=true \
        -e WEBRTC_SIGNALING_SERVER=ws://localhost:8443 \
        -e RUN_MODE=EVA \
        -e DETECTION_DEVICE=CPU \
        -e CLASSIFICATION_DEVICE=CPU \
        -e GENICAM="Matrix_Vision" \
        -e GST_DEBUG="1,gencamsrc:2" \
        -v ${PIPELINE_DIR}:/home/pipeline-server/pipelines \
        -v /run/udev:/run/udev:ro \
        intel/edge_video_analytics_microservice:${TAG}


  * Trigger the pipeline using the below cURL command, the output is available as a RTSP stream at *rtsp://\ :raw-html-m2r:`<host ip>`\ /pipeline-server*
    .. code-block::

       curl localhost:8080/pipelines/cameras/gencam -X POST -H \
        'Content-Type: application/json' -d \
        '{
           "source": {
              "element": "gencamsrc",
              "type": "gst"
           },
           "destination": {
              "metadata": {
                 "type": "file",
                 "path": "/tmp/results.jsonl",
                 "format": "json-lines"
              },
              "frame": {
                 "type": "rtsp",
                 "path": "pipeline-server"
              }
           }
        }'