Python Function Unit

The Python Function Unit is used to include arbitrary custom Python code in the pipeline. To work with pyfunc, custom code must be implemented by specifying NvDsPyFuncPlugin as the parent class, which exposes two methods: the first allows frames for each source to be handled separately, the second supports processing the frames for the whole batch.

Per-source processing, normally you want to use this method:

from savant.deepstream.pyfunc import NvDsPyFuncPlugin
class CustomProcessing(NvDsPyFuncPlugin):
  def process_frame(self, buffer: Gst.Buffer, frame_meta: NvDsFrameMeta):
    """Put custom frame processing code here."""

Per-batch processing:

from savant.deepstream.pyfunc import NvDsPyFuncPlugin
class CustomProcessing(NvDsPyFuncPlugin):
  def process_buffer(self, buffer: Gst.Buffer):
    """Process Gst.Buffer, parse DS batch manually."""

In addition to the above methods, NvDsPyFuncPlugin also allows you to override several auxiliary methods with which the code can respond to various pipeline events.

The on_start method is called when the pipeline starts processing data, once for all frames and sources. By default, it is defined as follows:

def on_start(self) -> bool:
    """Do on plugin start."""
    return True

It is important to pay attention to the return value: if it returns False or None, the pipeline will not start processing.

The on_stop method is similar to the on_start method: it is called once when the pipeline stops; defined by default like this:

def on_stop(self) -> bool:
    """Do on plugin stop."""
    return True

The on_event method is called for GStreamer events received on the sink pad of the Python Function Unit and makes it possible to process them:

def on_event(self, event: Gst.Event):
    """Do on event."""

It is an analogue of GstBaseTransform.do_sink_event(), however, it does not replace the basic implementation, but is called before it. Therefore, the callback does not require any return value.

When overriding this method, it is important to note that, by default, the NvDsPyFuncPlugin.on_event method handles GST_NVEVENT_PAD_ADDED, GST_NVEVENT_PAD_DELETED, and GST_NVEVENT_STREAM_EOS DeepStream events (gst-nvevent.h documentation), determining which source it refers to, and calls on_source_add, on_source_delete, and on_source_eos callbacks respectively. Therefore, when implementing your version of the event handler on the sink pad, it is worth including a call to the parent class methods in it.

The on_source_add and on_source_delete methods are called for GST_NVEVENT_PAD_ADDED and GST_NVEVENT_PAD_DELETED events arriving at the PyFunc sink pad, respectively. The purpose of these methods is to handle the situation in which a source is added or a source is deleted. Methods are useful for state management, such as creating and releasing the state resource allocated to a particular source.

def on_source_add(self, source_id: str):
    """On source add callback."""

def on_source_delete(self, source_id: str):
    """On source delete callback."""

The on_source_eos method is called for every GST_NVEVENT_STREAM_EOS event that arrives on the PyFunc sink pad. The purpose of this method is to handle the situation when a data stream corresponding to a particular source ends. The method can be used to free resources that are assigned to a specific source and are relevant within a single stream of the source. For example, delete information about the tracks of this stream.

def on_source_eos(self, source_id: str):
    """On source EOS event callback."""

Source addressing is achieved by reading frame_meta.source_id property, which corresponds to the identifier of the source defined by an external system ingesting frames.

The Python Function Unit is specified in the configuration by the pyfunc unit, specifying the required configuration parameters module and class_name, where:

  • module is a string indicating either the file system path to the user code file or a valid Python path to the code module.

  • class_name is a string with the name of the class that performs the processing.

A Python path example:

- element: pyfunc
  module: samples.traffic_meter.line_crossing
  class_name: LineCrossing

A filesystem path example:

- element: pyfunc
  module: /opt/savant/samples/traffic_meter/
  class_name: LineCrossing

Also, the pyfunc unit configuration allows setting an arbitrary set of user parameters through the kwargs key:

- element: pyfunc
  module: /opt/savant/samples/traffic_meter/
  class_name: LineCrossing
    config_path: /opt/savant/samples/traffic_meter/line_crossing.yml

Parameters defined with kwargs are available as pyfunc class instance attributes.