Issue with smart recording duration

**• Hardware Platform --------> L4
**• DeepStream Version ---------> 7.0
• TensorRT Version -----------> 8.5
**• NVIDIA GPU Driver Version -----> 550.67

Hello @Fiona.Chen ,
I only can able to record a maximum duration of 10 to 11 minutes. I tried to record a duration of more than 13 minutes which is not working(means it is recording only 20-30 seconds and then giving sr-done signal). can you please check and confirm how much duration I can record actually?

@Fiona.Chen
@snehashish.debnath

Thanks

Which sample are you testing or referring to? what is the media pipeline? are you testing live stream or local file? how did call NvDsSRStart?

I am testing our code(not sample deepstream app) with an RTSP stream.
below is how Iam giving start-sr signal.

case1:
nvurisrcbin.set_property("smart-rec-cache", 600)
nvurisrcbin.emit('start-sr', sessionid, 300, 300, sr_user_context_buf)

case1 is working fine. I can able to record 5 minutes back from the current time and 5 minutes front from the current time.

case2:
nvurisrcbin.set_property("smart-rec-cache", 1200)
nvurisrcbin.emit('start-sr', sessionid, 600, 600, sr_user_context_buf)

case2 is not working . It is recording only 20-30 seconds and giving 'sr-done` signal.

#!/usr/bin/env python3

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2023 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('../')
from pathlib import Path
import gi
import configparser
import argparse
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from ctypes import *
import time
import sys
import math
import platform

from common.platform_info import PlatformInfo
from common.bus_call import bus_call
from common.FPS import PERF_DATA
import time, threading
import pyds
import ctypes

no_display = False
silent = False
file_loop = False
perf_data = None

MAX_DISPLAY_LEN=64
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
MUXER_OUTPUT_WIDTH=1920
MUXER_OUTPUT_HEIGHT=1080
MUXER_BATCH_TIMEOUT_USEC = 33000
TILED_OUTPUT_WIDTH=1280
TILED_OUTPUT_HEIGHT=720
GST_CAPS_FEATURES_NVMM="memory:NVMM"
OSD_PROCESS_MODE= 0
OSD_DISPLAY_TEXT= 1
pgie_classes_str= ["Vehicle", "TwoWheeler", "Person","RoadSign"]
list_for_nvurisrcbin = []



class SRUserContext(ctypes.Structure):
    _fields_ = [
        ("sessionid", ctypes.c_int),
        ("name", ctypes.c_char * 32)
    ]

# pgie_src_pad_buffer_probe  will extract metadata received on tiler sink pad
# and update params for drawing rectangle, object information etc.
def pgie_src_pad_buffer_probe(pad,info,u_data):
    frame_number=0
    num_rects=0
    got_fps = False
    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
        l_obj=frame_meta.obj_meta_list
        num_rects = frame_meta.num_obj_meta
        obj_counter = {
        PGIE_CLASS_ID_VEHICLE:0,
        PGIE_CLASS_ID_PERSON:0,
        PGIE_CLASS_ID_BICYCLE:0,
        PGIE_CLASS_ID_ROADSIGN:0
        }
        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
        if not silent:
            print("Frame Number=", frame_number, "Number of Objects=",num_rects,"Vehicle_count=",obj_counter[PGIE_CLASS_ID_VEHICLE],"Person_count=",obj_counter[PGIE_CLASS_ID_PERSON])

        # 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

    return Gst.PadProbeReturn.OK



def cb_newpad(decodebin, decoder_src_pad,data):
    print("In cb_newpad\n")
    caps=decoder_src_pad.get_current_caps()
    if not caps:
        caps = decoder_src_pad.query_caps()
    gststruct=caps.get_structure(0)
    gstname=gststruct.get_name()
    source_bin=data
    features=caps.get_features(0)

    # Need to check if the pad created by the decodebin is for video and not
    # audio.
    print("gstname=",gstname)
    if(gstname.find("video")!=-1):
        # Link the decodebin pad only if decodebin has picked nvidia
        # decoder plugin nvdec_*. We do this by checking if the pad caps contain
        # NVMM memory features.
        print("features=",features)
        if features.contains("memory:NVMM"):
            # Get the source bin ghost pad
            bin_ghost_pad=source_bin.get_static_pad("src")
            if not bin_ghost_pad.set_target(decoder_src_pad):
                sys.stderr.write("Failed to link decoder src pad to source bin ghost pad\n")
        else:
            sys.stderr.write(" Error: Decodebin did not pick nvidia decoder plugin.\n")

def decodebin_child_added(child_proxy,Object,name,user_data):
    print("Decodebin child added:", name, "\n")
    if(name.find("decodebin") != -1):
        Object.connect("child-added",decodebin_child_added,user_data)

    if "source" in name:
        source_element = child_proxy.get_by_name("source")
        if source_element.find_property('drop-on-latency') != None:
            Object.set_property("drop-on-latency", True)



def create_source_bin(index,uri):
    print("Creating source bin")

    # Create a source GstBin to abstract this bin's content from the rest of the
    # pipeline
    bin_name="source-bin-%02d" %index
    print(bin_name)
    nbin=Gst.Bin.new(bin_name)
    if not nbin:
        sys.stderr.write(" Unable to create source bin \n")

    # Source element for reading from the uri.
    # We will use decodebin and let it figure out the container format of the
    # stream and the codec and plug the appropriate demux and decode plugins.
    if not file_loop:
        # use nvurisrcbin to enable file-loop
        print("HEY DEB I CAME INSIDE FILE LOOP --------------------------------> I don't know what to do ")
        uri_decode_bin=Gst.ElementFactory.make("nvurisrcbin", "uri-decode-bin" + str(index))
        # uri_decode_bin.set_property("file-loop", 1)
        uri_decode_bin.set_property("cudadec-memtype", 0)
    else:
        uri_decode_bin=Gst.ElementFactory.make("nvurisrcbin", "uri-decode-bin")
    if not uri_decode_bin:
        sys.stderr.write(" Unable to create uri decode bin \n")
    uri_decode_bin.set_property("smart-record", 2)
    uri_decode_bin.set_property("smart-rec-dir-path", ".")
    uri_decode_bin.set_property("smart-rec-cache", 600) # enable smart record.
    uri_decode_bin.set_property("source-id",index)
    uri_decode_bin.connect("sr-done", record_done, nbin)

    # We set the input uri to the source element
    uri_decode_bin.set_property("uri",uri)
    global list_for_nvurisrcbin
    list_for_nvurisrcbin.append(uri_decode_bin)
    # Connect to the "pad-added" signal of the decodebin which generates a
    # callback once a new pad for raw data has beed created by the decodebin
    uri_decode_bin.connect("pad-added",cb_newpad,nbin)
    uri_decode_bin.connect("child-added",decodebin_child_added,nbin)

    # We need to create a ghost pad for the source bin which will act as a proxy
    # for the video decoder src pad. The ghost pad will not have a target right
    # now. Once the decode bin creates the video decoder and generates the
    # cb_newpad callback, we will set the ghost pad target to the video decoder
    # src pad.
    Gst.Bin.add(nbin,uri_decode_bin)
    bin_pad=nbin.add_pad(Gst.GhostPad.new_no_target("src",Gst.PadDirection.SRC))
    if not bin_pad:
        sys.stderr.write(" Failed to add ghost pad in source bin \n")
        return None
    return nbin

def start_sr_function(element):
    print("readyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy")
    sessionid = pyds.get_native_ptr(pyds.alloc_buffer(4))
    print(f"sessionid {sessionid}")
    sr_user_context_size = ctypes.sizeof(SRUserContext)
    sr_user_context_buf = pyds.get_native_ptr(pyds.alloc_buffer(sr_user_context_size))
    sr_user_context = pyds.SRUserContext.cast(sr_user_context_buf)
    sr_user_context.sessionid = 42
    sr_user_context.name = "sr-demo " + element.get_name()
    print(f"sr_user_context_buf {sr_user_context_buf} {element.get_name()}")
    print('******start sr*****')
    element.emit('start-sr', sessionid, 300, 300, sr_user_context_buf)
    pyds.free_gbuffer(sessionid)
    return True

def record_done(nvurisrcbin, recordingInfo, user_ctx, user_data):
    print('******sr done*****')
    sr = pyds.SRUserContext.cast(hash(user_ctx))
    print(f"session id {sr.sessionid} -- name {sr.name}")
    pyds.free_buffer(hash(user_ctx))

def main(args, requested_pgie=None, config=None, disable_probe=False):
    global perf_data
    perf_data = PERF_DATA(len(args))

    number_sources=len(args)

    platform_info = PlatformInfo()
    # 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()
    is_live = False

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

    pipeline.add(streammux)
    for i in range(number_sources):
        print("Creating source_bin ",i," \n ")
        uri_name=args[i]
        if uri_name.find("rtsp://") == 0 :
            is_live = True
        source_bin=create_source_bin(i, uri_name)
        if not source_bin:
            sys.stderr.write("Unable to create source bin \n")
        pipeline.add(source_bin)
        padname="sink_%u" %i
        sinkpad= streammux.get_request_pad(padname) 
        if not sinkpad:
            sys.stderr.write("Unable to create sink pad bin \n")
        srcpad=source_bin.get_static_pad("src")
        if not srcpad:
            sys.stderr.write("Unable to create src pad bin \n")
        srcpad.link(sinkpad)
    queue1=Gst.ElementFactory.make("queue","queue1")
    queue2=Gst.ElementFactory.make("queue","queue2")
    queue3=Gst.ElementFactory.make("queue","queue3")
    queue4=Gst.ElementFactory.make("queue","queue4")
    queue5=Gst.ElementFactory.make("queue","queue5")
    pipeline.add(queue1)
    pipeline.add(queue2)
    pipeline.add(queue3)
    pipeline.add(queue4)
    pipeline.add(queue5)

    nvdslogger = None

    print("Creating Pgie \n ")
    if requested_pgie != None and (requested_pgie == 'nvinferserver' or requested_pgie == 'nvinferserver-grpc') :
        pgie = Gst.ElementFactory.make("nvinferserver", "primary-inference")
    elif requested_pgie != None and requested_pgie == 'nvinfer':
        pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    else:
        pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")

    if not pgie:
        sys.stderr.write(" Unable to create pgie :  %s\n" % requested_pgie)

    if disable_probe:
        # Use nvdslogger for perf measurement instead of probe function
        print ("Creating nvdslogger \n")
        nvdslogger = Gst.ElementFactory.make("nvdslogger", "nvdslogger")

    print("Creating tiler \n ")
    tiler=Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
    if not tiler:
        sys.stderr.write(" Unable to create tiler \n")
    print("Creating nvvidconv \n ")
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")
    print("Creating nvosd \n ")
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")
    nvosd.set_property('process-mode',OSD_PROCESS_MODE)
    nvosd.set_property('display-text',OSD_DISPLAY_TEXT)

    if file_loop:
        if platform_info.is_integrated_gpu():
            # Set nvbuf-memory-type=4 for aarch64 for file-loop (nvurisrcbin case)
            streammux.set_property('nvbuf-memory-type', 4)
        else:
            # Set nvbuf-memory-type=2 for x86 for file-loop (nvurisrcbin case)
            streammux.set_property('nvbuf-memory-type', 2)

    if no_display:
        print("Creating Fakesink \n")
        sink = Gst.ElementFactory.make("fakesink", "fakesink")
        sink.set_property('enable-last-sample', 0)
        sink.set_property('sync', 0)
    else:
        if platform_info.is_integrated_gpu():
            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")

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

    if is_live:
        print("At least one of the sources is live")
        streammux.set_property('live-source', 1)

    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', number_sources)
    streammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)
    if requested_pgie == "nvinferserver" and config != None:
        pgie.set_property('config-file-path', config)
    elif requested_pgie == "nvinferserver-grpc" and config != None:
        pgie.set_property('config-file-path', config)
    elif requested_pgie == "nvinfer" and config != None:
        pgie.set_property('config-file-path', config)
    else:
        pgie.set_property('config-file-path', "dstest3_pgie_config.txt")
    pgie_batch_size=pgie.get_property("batch-size")
    if(pgie_batch_size != number_sources):
        print("WARNING: Overriding infer-config batch-size",pgie_batch_size," with number of sources ", number_sources," \n")
        pgie.set_property("batch-size",number_sources)
    tiler_rows=int(math.sqrt(number_sources))
    tiler_columns=int(math.ceil((1.0*number_sources)/tiler_rows))
    tiler.set_property("rows",tiler_rows)
    tiler.set_property("columns",tiler_columns)
    tiler.set_property("width", TILED_OUTPUT_WIDTH)
    tiler.set_property("height", TILED_OUTPUT_HEIGHT)
    sink.set_property("qos",0)

    print("Adding elements to Pipeline \n")
    pipeline.add(pgie)
    if nvdslogger:
        pipeline.add(nvdslogger)
    pipeline.add(tiler)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)

    print("Linking elements in the Pipeline \n")
    streammux.link(queue1)
    queue1.link(pgie)
    pgie.link(queue2)
    if nvdslogger:
        queue2.link(nvdslogger)
        nvdslogger.link(tiler)
    else:
        queue2.link(tiler)
    tiler.link(queue3)
    queue3.link(nvvidconv)
    nvvidconv.link(queue4)
    queue4.link(nvosd)
    nvosd.link(queue5)
    queue5.link(sink)   

    # create an 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)
    pgie_src_pad=pgie.get_static_pad("src")
    if not pgie_src_pad:
        sys.stderr.write(" Unable to get src pad \n")
    else:
        if not disable_probe:
            pgie_src_pad.add_probe(Gst.PadProbeType.BUFFER, pgie_src_pad_buffer_probe, 0)
            # perf callback function to print fps every 5 sec
            GLib.timeout_add(5000, perf_data.perf_print_callback)

    # List the sources
    print("Now playing...")
    for i, source in enumerate(args):
        print(i, ": ", source)

    print("Starting pipeline \n")
    # start play back and listed to events		
    pipeline.set_state(Gst.State.PLAYING)
    global list_for_nvurisrcbin
    for uribin in list_for_nvurisrcbin:
        timer_id = GLib.timeout_add_seconds(600, start_sr_function, uribin)
    try:
        loop.run()
    except:
        pass
    # cleanup
    print("Exiting app\n")
    pipeline.set_state(Gst.State.NULL)

def parse_args():

    parser = argparse.ArgumentParser(prog="deepstream_test_3",
                    description="deepstream-test3 multi stream, multi model inference reference app")
    parser.add_argument(
        "-i",
        "--input",
        help="Path to input streams",
        nargs="+",
        metavar="URIs",
        default=["a"],
        required=True,
    )
    parser.add_argument(
        "-c",
        "--configfile",
        metavar="config_location.txt",
        default=None,
        help="Choose the config-file to be used with specified pgie",
    )
    parser.add_argument(
        "-g",
        "--pgie",
        default=None,
        help="Choose Primary GPU Inference Engine",
        choices=["nvinfer", "nvinferserver", "nvinferserver-grpc"],
    )
    parser.add_argument(
        "--no-display",
        action="store_true",
        default=True,
        dest='no_display',
        help="Disable display of video output",
    )
    parser.add_argument(
        "--file-loop",
        action="store_true",
        default=False,
        dest='file_loop',
        help="Loop the input file sources after EOS",
    )
    parser.add_argument(
        "--disable-probe",
        action="store_true",
        default=False,
        dest='disable_probe',
        help="Disable the probe function and use nvdslogger for FPS",
    )
    parser.add_argument(
        "-s",
        "--silent",
        action="store_true",
        default=False,
        dest='silent',
        help="Disable verbose output",
    )
    # Check input arguments
    if len(sys.argv) == 1:
        parser.print_help(sys.stderr)
        sys.exit(1)
    args = parser.parse_args()

    stream_paths = args.input
    pgie = args.pgie
    config = args.configfile
    disable_probe = args.disable_probe
    global no_display
    global silent
    global file_loop
    # no_display = args.no_display
    no_display = True
    # silent = args.silent
    silent = True
    # file_loop = args.file_loop
    file_loop = True

    if config and not pgie or pgie and not config:
        sys.stderr.write ("\nEither pgie or configfile is missing. Please specify both! Exiting...\n\n\n\n")
        parser.print_help()
        sys.exit(1)
    if config:
        config_path = Path(config)
        if not config_path.is_file():
            sys.stderr.write ("Specified config-file: %s doesn't exist. Exiting...\n\n" % config)
            sys.exit(1)

    print(vars(args))
    return stream_paths, pgie, config, disable_probe

if __name__ == '__main__':
    stream_paths, pgie, config, disable_probe = parse_args()
    sys.exit(main(stream_paths, pgie, config, disable_probe))

my run command
python3 smart_record.py -i rtsp://xxxx/xxxx -c /opt/nvidia/deepstream/deepstream-7.0/sources/deepstream_python_apps/apps/deepstream-test3/dstest3_pgie_config.txt -g nvinfer

Iam trying to run the above attached smart recording sample app code. For every 10 minutes(600 seconds), Iam triggering start-sr for recording.
element.emit('start-sr', sessionid, 300, 300, sr_user_context_buf)
Iam keeping backtime=300 sec and fronttime=300 sec. It is recording 300 seconds back from current time, but only recording 20 seconds front from current time.

Can you please check and let me know @fanzh

Thanks.

please refer to this faq are-multiple-parallel-records-on-same-source-supported .

Noticing a big cache is set, after stopping a recording, please wait for a while, then start a new recording because the code needs time to fill the cache.

nvurisrcbin.set_property("smart-rec-cache", 600)
nvurisrcbin.emit('start-sr', sessionid, 300, 300, sr_user_context_buf)

As you mentioned, it is recording 300 seconds from the buffer cache. However, it only records 20 seconds from the current time and then sending the sr-done signal, even though I specified it to record 300 seconds from the current time.

nvurisrcbin.set_property("smart-rec-cache", 550)
nvurisrcbin.emit('start-sr', sessionid, 275, 275, sr_user_context_buf)

But in this scenario, It is recording 275 seconds from the buffer-cache and 275 seconds from the current time. no issue in this case.

please replicate these changes in your machine if possible.
Thanks.

If the time front from current time is inaccurate? there are two reasons.

  1. the low-level lib does not support overlapped recording. please refer to this topic. if stopping a recording, the cache will be almost empty.
  2. AYK, the video should start from I frame. please check the I frame interval.
  1. I am not trying to record any overlapped videos. Sequentially, Triggering start-sr signal.
  2. I-frame interval is 50

I have tested deepstream-app with smart record as well as our code.

In both codes, I found a limitation when recording from the current time (front time). If my front time < 300 seconds, it records the exact duration from the current time. However, if my front time >= 300 seconds, it only records around 20 seconds*

Please test from your side once if possible.

Thanks.

There are two durations. one is cache duration. there is no limit on the maximum duration of data I can cache. please refer to this faq. the other is duration after the start of recording, the limitation is 300 seconds. if the set value is bigger than 300, the set value will be changed to smart-rec-default-duration.
For your case, Here is a workaround.

nvurisrcbin.set_property("smart-rec-cache", 600)
nvurisrcbin.set_property("smart-rec-default-duration", 300)
nvurisrcbin.emit('start-sr', sessionid, 300, 300, sr_user_context_buf)
1 Like

The above answer is really helpful.

Is there any way to achieve more than 300 seconds after the start of recording?

Thanks @fanzh

If you want more than 300 seconds, you may set smart-rec-default-duration to your desired value. please refer to my last comment for the explanation.

1 Like