Deepstream-6.3 secondary-reinfer-interval property in sgie not working

Please provide complete information as applicable to your setup.

• Hardware Platform (Jetson / GPU) GPU RTX 4060 Laptop
• DeepStream Version 6.3
• TensorRT Version 8.5.3
• NVIDIA GPU Driver Version (valid for GPU only) 530.41.03
• Issue Type( questions, new requirements, bugs) bugs
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing) run deepstream python app test2 application with setting secondary-reinfer-interval=10 in all the sgies config txt
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
Setting secondary-reinfer-interval in all sgie config txt that are classifier in deepstream-test2 python apps but I think there is no impact of this property but on Gst-nvinfer — DeepStream 6.3 Release documentation it is mentioned
secondary-reinfer-interval

Re-inference interval for objects, in frames

Integer, ≥0

secondary-reinfer-interval=15

Detector & Classifier
Secondary

Kindly explain it is bit confusing.

how did you know there in no impact? nvinfer plugin and low-level are opensource. you can add some log to check.

Hi Fanzh,

Thanks for the reply. I have already mentioned " How to reproduce the issue ?" Can you check it and explain
Meanwhile I will try to provide the logs.

I mean, how did you observe there is no impact of this property. I need to reproduce this issue by your test steps. Thanks!

I ran deepstream-test2 python app with secondary-reinfer-interval=10 in all the secondary inference configs but still the there is no improvement in the fps if I ran on fakesink and with nveglglesink I also don’t see any changes in in the way car colour model type being displayed. Basically setting secondary-reinfer-interval=10
should have made the inference on an object once then after ten frame right?
But the car color model and type were displayed similar to without setting this property

yes, the inference results will be saved, the follow nine frames will not be inferred. the app will get the output from the previous saved results.

I tested test2, but the app has no fps statistics. could you share your running log? if using nveglglesink, please set its property sync to false.

1 Like

I have modified deepstream test app2 for printing fps still by setting the secondary-reinfer-interval=10 in all the secondary inference configs but still the there is no improvement in the fps and done sync 0.

#!/usr/bin/env python3

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################

import sys
sys.path.append('../')
import platform
import configparser


import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.is_aarch_64 import is_aarch64
from common.FPS import PERF_DATA
from common.bus_call import bus_call

import pyds

perf_data = None

PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3

def osd_sink_pad_buffer_probe(pad,info,u_data):
    frame_number=0
    #Intiallizing object counter with 0.
    obj_counter = {
        PGIE_CLASS_ID_VEHICLE:0,
        PGIE_CLASS_ID_PERSON:0,
        PGIE_CLASS_ID_BICYCLE:0,
        PGIE_CLASS_ID_ROADSIGN:0
    }
    num_rects=0
    gst_buffer = info.get_buffer()
    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    # Retrieve batch metadata from the gst_buffer
    # Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
    # C address of gst_buffer as input, which is obtained with hash(gst_buffer)
    batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
    l_frame = batch_meta.frame_meta_list
    while l_frame is not None:
        try:
            # Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
            # The casting is done by pyds.NvDsFrameMeta.cast()
            # The casting also keeps ownership of the underlying memory
            # in the C code, so the Python garbage collector will leave
            # it alone.
            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
        except StopIteration:
            break

        frame_number=frame_meta.frame_num
        num_rects = frame_meta.num_obj_meta
        l_obj=frame_meta.obj_meta_list
        while l_obj is not None:
            try:
                # Casting l_obj.data to pyds.NvDsObjectMeta
                obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break

        # Acquiring a display meta object. The memory ownership remains in
        # the C code so downstream plugins can still access it. Otherwise
        # the garbage collector will claim it when this probe function exits.
        display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
        display_meta.num_labels = 1
        py_nvosd_text_params = display_meta.text_params[0]
        # Setting display text to be shown on screen
        # Note that the pyds module allocates a buffer for the string, and the
        # memory will not be claimed by the garbage collector.
        # Reading the display_text field here will return the C address of the
        # allocated string. Use pyds.get_string() to get the string content.
        py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Vehicle_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_VEHICLE], obj_counter[PGIE_CLASS_ID_PERSON])

        # Now set the offsets where the string should appear
        py_nvosd_text_params.x_offset = 10
        py_nvosd_text_params.y_offset = 12

        # Font , font-color and font-size
        py_nvosd_text_params.font_params.font_name = "Serif"
        py_nvosd_text_params.font_params.font_size = 10
        # set(red, green, blue, alpha); set to White
        py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)

        # Text background color
        py_nvosd_text_params.set_bg_clr = 1
        # set(red, green, blue, alpha); set to Black
        py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
        # Using pyds.get_string() to get display_text as string
        # print(pyds.get_string(py_nvosd_text_params.display_text))
        pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)

        # Update frame rate through this probe
        stream_index = "stream{0}".format(frame_meta.pad_index)
        global perf_data
        perf_data.update_fps(stream_index)

        try:
            l_frame=l_frame.next
        except StopIteration:
            break
    #past tracking meta data
    l_user=batch_meta.batch_user_meta_list
    while l_user is not None:
        try:
            # Note that l_user.data needs a cast to pyds.NvDsUserMeta
            # The casting is done by pyds.NvDsUserMeta.cast()
            # The casting also keeps ownership of the underlying memory
            # in the C code, so the Python garbage collector will leave
            # it alone
            user_meta=pyds.NvDsUserMeta.cast(l_user.data)
        except StopIteration:
            break
        if(user_meta and user_meta.base_meta.meta_type==pyds.NvDsMetaType.NVDS_TRACKER_PAST_FRAME_META):
            try:
                # Note that user_meta.user_meta_data needs a cast to pyds.NvDsPastFrameObjBatch
                # The casting is done by pyds.NvDsPastFrameObjBatch.cast()
                # The casting also keeps ownership of the underlying memory
                # in the C code, so the Python garbage collector will leave
                # it alone
                pPastFrameObjBatch = pyds.NvDsPastFrameObjBatch.cast(user_meta.user_meta_data)
            except StopIteration:
                break
            # for trackobj in pyds.NvDsPastFrameObjBatch.list(pPastFrameObjBatch):
            #     print("streamId=",trackobj.streamID)
            #     print("surfaceStreamID=",trackobj.surfaceStreamID)
            #     for pastframeobj in pyds.NvDsPastFrameObjStream.list(trackobj):
            #         print("numobj=",pastframeobj.numObj)
            #         print("uniqueId=",pastframeobj.uniqueId)
            #         print("classId=",pastframeobj.classId)
            #         print("objLabel=",pastframeobj.objLabel)
            #         for objlist in pyds.NvDsPastFrameObjList.list(pastframeobj):
            #             print('frameNum:', objlist.frameNum)
            #             print('tBbox.left:', objlist.tBbox.left)
            #             print('tBbox.width:', objlist.tBbox.width)
            #             print('tBbox.top:', objlist.tBbox.top)
            #             print('tBbox.right:', objlist.tBbox.height)
            #             print('confidence:', objlist.confidence)
            #             print('age:', objlist.age)
        try:
            l_user=l_user.next
        except StopIteration:
            break
    return Gst.PadProbeReturn.OK	

def main(args):
    # Check input arguments
    if(len(args)<2):
        sys.stderr.write("usage: %s <h264_elementary_stream>\n" % args[0])
        sys.exit(1)

    global perf_data
    perf_data = PERF_DATA(len(args)-1)   

    # Standard GStreamer initialization

    Gst.init(None)

    # Create gstreamer elements
    # Create Pipeline element that will form a connection of other elements
    print("Creating Pipeline \n ")
    pipeline = Gst.Pipeline()

    if not pipeline:
        sys.stderr.write(" Unable to create Pipeline \n")

    # Source element for reading from the file
    print("Creating Source \n ")
    source = Gst.ElementFactory.make("filesrc", "file-source")
    if not source:
        sys.stderr.write(" Unable to create Source \n")

    # Since the data format in the input file is elementary h264 stream,
    # we need a h264parser
    print("Creating H264Parser \n")
    h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
    if not h264parser:
        sys.stderr.write(" Unable to create h264 parser \n")

    # Use nvdec_h264 for hardware accelerated decode on GPU
    print("Creating Decoder \n")
    decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
    if not decoder:
        sys.stderr.write(" Unable to create Nvv4l2 Decoder \n")

    # Create nvstreammux instance to form batches from one or more sources.
    streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
    if not streammux:
        sys.stderr.write(" Unable to create NvStreamMux \n")

    # Use nvinfer to run inferencing on decoder's output,
    # behaviour of inferencing is set through config file
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")

    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
    if not tracker:
        sys.stderr.write(" Unable to create tracker \n")

    sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
    if not sgie1:
        sys.stderr.write(" Unable to make sgie1 \n")


    sgie2 = Gst.ElementFactory.make("nvinfer", "secondary2-nvinference-engine")
    if not sgie2:
        sys.stderr.write(" Unable to make sgie2 \n")

    sgie3 = Gst.ElementFactory.make("nvinfer", "secondary3-nvinference-engine")
    if not sgie3:
        sys.stderr.write(" Unable to make sgie3 \n")

    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")

    # Create OSD to draw on the converted RGBA buffer
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")

    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")

    # Finally render the osd output
    if is_aarch64():
        print("Creating nv3dsink \n")
        sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
        if not sink:
            sys.stderr.write(" Unable to create nv3dsink \n")
    else:
        print("Creating EGLSink \n")
        sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
        if not sink:
            sys.stderr.write(" Unable to create egl sink \n")

    print("Playing file %s " %args[1])
    source.set_property('location', args[1])
    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', 1)
    streammux.set_property('batched-push-timeout', 4000000)
    sink.set_property("sync",0)

    #Set properties of pgie and sgie
    pgie.set_property('config-file-path', "dstest2_pgie_config.txt")
    sgie1.set_property('config-file-path', "dstest2_sgie1_config.txt")
    sgie2.set_property('config-file-path', "dstest2_sgie2_config.txt")
    sgie3.set_property('config-file-path', "dstest2_sgie3_config.txt")

    #Set properties of tracker
    config = configparser.ConfigParser()
    config.read('dstest2_tracker_config.txt')
    config.sections()

    for key in config['tracker']:
        if key == 'tracker-width' :
            tracker_width = config.getint('tracker', key)
            tracker.set_property('tracker-width', tracker_width)
        if key == 'tracker-height' :
            tracker_height = config.getint('tracker', key)
            tracker.set_property('tracker-height', tracker_height)
        if key == 'gpu-id' :
            tracker_gpu_id = config.getint('tracker', key)
            tracker.set_property('gpu_id', tracker_gpu_id)
        if key == 'll-lib-file' :
            tracker_ll_lib_file = config.get('tracker', key)
            tracker.set_property('ll-lib-file', tracker_ll_lib_file)
        if key == 'll-config-file' :
            tracker_ll_config_file = config.get('tracker', key)
            tracker.set_property('ll-config-file', tracker_ll_config_file)

    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    pipeline.add(h264parser)
    pipeline.add(decoder)
    pipeline.add(streammux)
    pipeline.add(pgie)
    pipeline.add(tracker)
    pipeline.add(sgie1)
    pipeline.add(sgie2)
    pipeline.add(sgie3)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)

    # we link the elements together
    # file-source -> h264-parser -> nvh264-decoder ->
    # nvinfer -> nvvidconv -> nvosd -> video-renderer
    print("Linking elements in the Pipeline \n")
    source.link(h264parser)
    h264parser.link(decoder)

    sinkpad = streammux.get_request_pad("sink_0")
    if not sinkpad:
        sys.stderr.write(" Unable to get the sink pad of streammux \n")
    srcpad = decoder.get_static_pad("src")
    if not srcpad:
        sys.stderr.write(" Unable to get source pad of decoder \n")
    srcpad.link(sinkpad)
    streammux.link(pgie)
    pgie.link(tracker)
    tracker.link(sgie1)
    sgie1.link(sgie2)
    sgie2.link(sgie3)
    sgie3.link(nvvidconv)
    nvvidconv.link(nvosd)
    nvosd.link(sink)


    # create and event loop and feed gstreamer bus mesages to it
    loop = GLib.MainLoop()

    bus = pipeline.get_bus()
    bus.add_signal_watch()
    bus.connect ("message", bus_call, loop)

    # Lets add probe to get informed of the meta data generated, we add probe to
    # the sink pad of the osd element, since by that time, the buffer would have
    # had got all the metadata.
    osdsinkpad = nvosd.get_static_pad("sink")
    if not osdsinkpad:
        sys.stderr.write(" Unable to get sink pad of nvosd \n")
    else:
        osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)
        GLib.timeout_add(1000, perf_data.perf_print_callback)


    print("Starting pipeline \n")
    
    # start play back and listed to events
    pipeline.set_state(Gst.State.PLAYING)
    try:
      loop.run()
    except:
      pass

    # cleanup
    pipeline.set_state(Gst.State.NULL)

if __name__ == '__main__':
    sys.exit(main(sys.argv))


There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

nvinfer plugin and low-level lib is openousrce. you can add log in should_infer_object of opt\nvidia\deepstream\deepstream-6.4\sources\gst-plugins\gst-nvinfer\gstnvinfer.cpp to check if the current frame is inferred by sgie. even secondary-reinfer-interval is set to10, there is other logics which leads to inference. for example,

    if ((history->last_inferred_coords.width *
          history->last_inferred_coords.height * (1 +
            REINFER_AREA_THRESHOLD)) <
        (obj_meta->rect_params.width * obj_meta->rect_params.height))
      should_reinfer = TRUE;

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