Contents
========


* `Contents <#contents>`__

  * `GStreamer Video Analytics <#gstreamer-video-analytics>`__

GStreamer Video Analytics
-------------------------

The VideoIngestion module supports the usage of the Gstreamer Video Analytics (GVA) plugins with the GStreamer ingestor. `GVA <https://github.com/openvinotoolkit/dlstreamer_gst>`_ is
a collection of the GStreamer elements and it enables the CNN model-based video analytics capabilities such as object detection, classification, and recognition in the GStreamer framework.

Refer to `CustomUdfs-GVASafetyGearIngestion </IEdgeInsights/README.html>`_ for more information on the GVA-based CustomUdf container added for the SafetyGear sample.

The GVA use case configurations with different cameras are as follows:


* Video file-Gstreamer ingestor with GVA elements

.. code-block:: javascript

     {
       "type": "gstreamer",
       "pipeline": "multifilesrc loop=TRUE stop-index=0 location=./test_videos/<VIDEO_FILE> ! h264parse ! decodebin ! videoconvert ! video/x-raw,format=BGR ! gvadetect model=models/<DETECTION_MODEL> ! appsink"
     }

.. note:: 
   To use GVA with a video file using the ``multifilesrc`` element, copy the video file to ``[WORKDIR]/IEdgeInsights/VideoIngestion/test_videos`` and provide the location of the video file in the GStreamer pipeline.



* Generic plugin-Gstreamer ingestor with GVA elements

.. code-block:: javascript

     {
      "type": "gstreamer",
      "pipeline": "gencamsrc serial=<DEVICE_SERIAL_NUMBER> pixel-format=<PIXEL_FORMAT> ! vaapipostproc format=bgrx ! gvadetect model=models/<DETECTION_MODEL> ! videoconvert !  video/x-raw,format=BGR ! appsink"
     }


* RTSP camera-Gstreamer ingestor with GVA elements

.. code-block:: javascript

     {
      "type": "gstreamer",
      "pipeline": "rtspsrc location=\"rtsp://<USERNAME>:<PASSWORD>@<RTSP_CAMERA_IP>:<PORT>/<FEED>\" latency=100 ! rtph264depay ! h264parse ! vaapih264dec ! vaapipostproc format=bgrx ! gvadetect model=models/<DETECTION_MODEL> ! videoconvert ! video/x-raw,format=BGR ! appsink"
     }


* USB camera-Gstreamer ingestor with GVA elements

.. code-block:: javascript

     {
      "type": "gstreamer",
      "pipeline": "v4l2src ! decodebin ! videoconvert ! video/x-raw,format=BGR ! gvadetect model=models/<DETECTION_MODEL> ! appsink"
     }


* RTSP simulated-Gstreamer ingestor with GVA elements

.. code-block:: javascript

     {
      "type": "gstreamer",
      "pipeline": "rtspsrc location=\"rtsp://<SOURCE_IP>:<PORT>/<FEED>\" latency=100 ! rtph264depay ! h264parse ! vaapih264dec ! vaapipostproc format=bgrx ! gvadetect model=models/<DETECTION_MODEL> ! videoconvert ! video/x-raw,format=BGR ! appsink"
     }


* 
  For generic full frame inference, use the ``gvainference`` element. For more information, refer to `gvainference <https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvainference>`_.

  The following is an example pipeline to run the PCB classification using the ``gvainference`` element. When you run the following pipeline, you may not see the PCB defects and bounding boxes because there is no provision to provide a reference image and config ROI (which the PCB classifier expects) with the gvaelements.

.. code-block:: javascript

     {
      "type": "gstreamer",
      "pipeline": "rtspsrc location=\"rtsp://<SOURCE_IP>:<PORT>/<FEED>\" latency=100 ! rtph264depay ! h264parse ! vaapih264dec ! gvainference device=CPU model=common/video/udfs/python/pcb/ref/model_2.xml ! vaapipostproc format=bgrx height=600 width=600 ! videoconvert ! video/x-raw,format=BGR ! appsink",
     }

.. note:: 


   * The GVA elements can only be used with the ``gstreamer`` ingestor.
   * The ``gvadetect`` element cannot be used to run the PCB classification(\ ``[WORK_DIR]/IEdgeInsights/common/video/udfs/python/pcb/ref``\ ) pipeline.
   * Using the device property of the ``gvadetect`` and ``gvaclassify`` elements you can set CPU, GPU, or HDDL device to use with the GVA elements.
   * By default, the device property is set to CPU.
   * VideoIngestion does not support inferencing with ``MYRIAD (NCS2)`` and ``HDDL`` devices.
   * Start the HDDL daemon on the host machine by following the steps mentioned in the [Using video accelerators] section in the `../../README.md </IEdgeInsights/README.html#use-video-accelerators-in-ingestion-and-analytics-containers>`_.
     The following is an example pipeline to run a sample detection model using the GVA plugins on an HDDL device(ensure that the detection model IR files are copied to the models path):

   .. code-block:: javascript

       {
        "type": "gstreamer",
        "pipeline": "rtspsrc location=\"rtsp://<SOURCE_IP>:<PORT>/<FEED>\" latency=100 ! rtph264depay ! h264parse ! vaapih264dec ! vaapipostproc format=bgrx ! gvadetect device=GPU  model=models/<DETECTION_MODEL> ! videoconvert ! video/x-raw,format=BGR ! appsink"
       }