# Change Deep Learning Streamer Pipeline

## Steps

DL Streamer Pipeline Server supports dynamic update of pipeline parameters using REST API.
Users are expected to provide the required placeholders in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/configs/default/config.json` to make it configurable at run-time
of DL Streamer Pipeline Server container.

In case users want to update the default pipeline, they need to update the same in the
configuration file loaded by DL Streamer Pipeline Server. Users can mount updated config files
from host systems on to DL Streamer Pipeline Server containers by updating
`[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/docker-compose.yml`.
To pull the image and start the container, refer to the steps mentioned [here](../get-started.md#pull-the-image-and-start-container).

Refer to the below snippets:

```sh
    volumes:
      # Volume mount [WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/configs/default/config.json to config file that DL Streamer Pipeline Server container loads."
      - "../configs/default/config.json:/home/pipeline-server/config.json"
```
As an example we are creating `video-ingestion and resize` pipeline. We need to update the
`pipeline` key in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/configs/default/config.json`
as shown below. It will create a DL Streamer pipeline that reads the user provided video file,
decodes it, and resizes it to 1280x720.
```sh
"pipeline": "{auto_source} ! decodebin ! videoscale ! video/x-raw, width=1280,height=720 ! gvametapublish name=destination ! appsink name=appsink",
```

> **Note:** If needed users can change pipeline name by updating `name` key in `config.json`.
> If user is updating this field, accordingly endpoint in curl request needs to be changed to
> `<SERVER-IP>:<PORT>/pipelines/user_defined_pipelines/<NEW-NAME>`. In this example, we are
> only changing the pipeline.

Once updated, the user needs to restart DL Streamer Pipeline Server containers to reflect this
change. Run these commands from the `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/` folder.
> **Note:** To get you started, instruction to create sample docker compose file is available [here](../get-started.md#pull-the-image-and-start-container)

```sh
docker compose down

docker compose up
```

The above steps will restart DL Streamer Pipeline Server and load the `video-ingestion and resize`
pipeline. Now, to start this pipeline, run below Curl request. It will start the DL Streamer
pipeline that reads the `classroom.avi` video file with resolution of 1920x1080 and after
resizing to 1280x720, it will stream over RTSP. Users can view this on any media player, e.g.
vlc, ffplay, etc.

RTSP Stream will be accessible at `rtsp://<SYSTEM_IP_ADDRESS>:8554/classroom-video-streaming`.

```sh
curl 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/classroom.avi",
                    "type": "uri"
                },
                "destination": {
                   "metadata": {
                        "type": "file",
                        "path": "/tmp/results.jsonl",
                        "format": "json-lines"
                    },
                    "frame": {
                        "type": "rtsp",
                        "path": "classroom-video-streaming"
                    }
                }
}'
```