I’m trying to modify the deep stream python example deepstream_nvdsanalytics (for DS version 6.0) to write the output to a file. When I start everything, I get the following error after the 4th frame:
[...]
##################################################
Object 2 roi status: ['RF']
Objs in ROI: {'RF': 1}
Linecrossing Cumulative: {'Exit': 0}
Linecrossing Current Frame: {'Exit': 0}
Frame Number= 4 stream id= 0 Number of Objects= 8 Vehicle_count= 5 Person_count= 3
##################################################
Error: gst-stream-error-quark: Internal data stream error. (1): gstqueue.c(988): gst_queue_handle_sink_event (): /GstPipeline:pipeline0/GstQueue:queue3:
streaming stopped, reason not-linked (-1)
Exiting app
[NvMultiObjectTracker] De-initialized
This is the modified version of the deepstream_nvdsanalytics.py file. I commented out things that I don’t need anymore (like EGL sink creation) and tried to add the necessary pipeline elements.
#!/usr/bin/env python3
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2020-2021 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 argparse
import sys
sys.path.append('../')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from gi.repository import GLib
from ctypes import *
import time
import sys
import math
import platform
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import GETFPS
import pyds
fps_streams={}
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=4000000
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"]
# nvanlytics_src_pad_buffer_probe will extract metadata received on nvtiler sink pad
# and update params for drawing rectangle, object information etc.
def nvanalytics_src_pad_buffer_probe(pad,info,u_data):
frame_number=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:
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
}
print("#"*50)
while l_obj:
try:
# Note that l_obj.data needs a cast to pyds.NvDsObjectMeta
# The casting is done by pyds.NvDsObjectMeta.cast()
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
obj_counter[obj_meta.class_id] += 1
l_user_meta = obj_meta.obj_user_meta_list
# Extract object level meta data from NvDsAnalyticsObjInfo
while l_user_meta:
try:
user_meta = pyds.NvDsUserMeta.cast(l_user_meta.data)
if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSOBJ.USER_META"):
user_meta_data = pyds.NvDsAnalyticsObjInfo.cast(user_meta.user_meta_data)
if user_meta_data.dirStatus: print("Object {0} moving in direction: {1}".format(obj_meta.object_id, user_meta_data.dirStatus))
if user_meta_data.lcStatus: print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus))
if user_meta_data.ocStatus: print("Object {0} overcrowding status: {1}".format(obj_meta.object_id, user_meta_data.ocStatus))
if user_meta_data.roiStatus: print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus))
except StopIteration:
break
try:
l_user_meta = l_user_meta.next
except StopIteration:
break
try:
l_obj=l_obj.next
except StopIteration:
break
# Get meta data from NvDsAnalyticsFrameMeta
l_user = frame_meta.frame_user_meta_list
while l_user:
try:
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSFRAME.USER_META"):
user_meta_data = pyds.NvDsAnalyticsFrameMeta.cast(user_meta.user_meta_data)
if user_meta_data.objInROIcnt: print("Objs in ROI: {0}".format(user_meta_data.objInROIcnt))
if user_meta_data.objLCCumCnt: print("Linecrossing Cumulative: {0}".format(user_meta_data.objLCCumCnt))
if user_meta_data.objLCCurrCnt: print("Linecrossing Current Frame: {0}".format(user_meta_data.objLCCurrCnt))
if user_meta_data.ocStatus: print("Overcrowding status: {0}".format(user_meta_data.ocStatus))
except StopIteration:
break
try:
l_user = l_user.next
except StopIteration:
break
print("Frame Number=", frame_number, "stream id=", frame_meta.pad_index, "Number of Objects=",num_rects,"Vehicle_count=",obj_counter[PGIE_CLASS_ID_VEHICLE],"Person_count=",obj_counter[PGIE_CLASS_ID_PERSON])
# Get frame rate through this probe
fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
try:
l_frame=l_frame.next
except StopIteration:
break
print("#"*50)
return Gst.PadProbeReturn.OK
def cb_newpad(decodebin, decoder_src_pad,data):
print("In cb_newpad\n")
caps=decoder_src_pad.get_current_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)
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.
uri_decode_bin=Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
if not uri_decode_bin:
sys.stderr.write(" Unable to create uri decode bin \n")
# We set the input uri to the source element
uri_decode_bin.set_property("uri",uri)
# 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 main(args):
# Check input arguments
if len(args) < 2:
sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN]\n" % args[0])
sys.exit(1)
for i in range(0,len(args)-1):
fps_streams["stream{0}".format(i)]=GETFPS(i)
number_sources=len(args)-1
# Standard GStreamer initialization
GObject.threads_init()
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+1]
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")
queue6=Gst.ElementFactory.make("queue","queue6")
queue7=Gst.ElementFactory.make("queue","queue7")
pipeline.add(queue1)
pipeline.add(queue2)
pipeline.add(queue3)
pipeline.add(queue4)
pipeline.add(queue5)
pipeline.add(queue6)
pipeline.add(queue7)
print("Creating Pgie \n ")
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
sys.stderr.write(" Unable to create pgie \n")
print("Creating nvtracker \n ")
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
print("Creating nvdsanalytics \n ")
nvanalytics = Gst.ElementFactory.make("nvdsanalytics", "analytics")
if not nvanalytics:
sys.stderr.write(" Unable to create nvanalytics \n")
nvanalytics.set_property("config-file", "config_nvdsanalytics.txt")
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(is_aarch64()):
# print("Creating transform \n ")
# transform=Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
# if not transform:
# sys.stderr.write(" Unable to create transform \n")
# print("Creating EGLSink \n")
# sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
# if not sink:
# sys.stderr.write(" Unable to create egl sink \n")
# added for mp4-output
# Create a caps filter
caps = Gst.ElementFactory.make("capsfilter", "filter")
caps.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=I420"))
# Make the encoder
if codec == "H264":
encoder = Gst.ElementFactory.make("nvv4l2h264enc", "encoder")
print("Creating H264 Encoder")
elif codec == "H265":
encoder = Gst.ElementFactory.make("nvv4l2h265enc", "encoder")
print("Creating H265 Encoder")
if not encoder:
sys.stderr.write(" Unable to create encoder")
encoder.set_property('bitrate', bitrate)
if is_aarch64():
encoder.set_property('preset-level', 1)
encoder.set_property('insert-sps-pps', 1)
encoder.set_property('bufapi-version', 1)
codecparse = Gst.ElementFactory.make("h264parse", "h264_parse")
if not codecparse:
sys.stderr.write(" Unable to create codecparse \n")
mux = Gst.ElementFactory.make("mp4mux", "mux")
if not mux:
sys.stderr.write(" Unable to create mux \n")
sink = Gst.ElementFactory.make("filesink", "filesink")
if not sink:
sys.stderr.write(" Unable to create filesink \n")
sink.set_property('location', output_path)
sink.set_property("sync", 1)
sink.set_property("async", 0)
#################
if is_live:
print("Atleast 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', 4000000)
pgie.set_property('config-file-path', "dsnvanalytics_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)
#Set properties of tracker
config = configparser.ConfigParser()
config.read('dsnvanalytics_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)
if key == 'enable-batch-process' :
tracker_enable_batch_process = config.getint('tracker', key)
tracker.set_property('enable_batch_process', tracker_enable_batch_process)
if key == 'enable-past-frame' :
tracker_enable_past_frame = config.getint('tracker', key)
tracker.set_property('enable_past_frame', tracker_enable_past_frame)
print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tracker)
pipeline.add(nvanalytics)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
# if is_aarch64():
# pipeline.add(transform)
pipeline.add(caps)
pipeline.add(encoder)
pipeline.add(codecparse)
pipeline.add(mux)
pipeline.add(sink)
# We link elements in the following order:
# sourcebin -> streammux -> nvinfer -> nvtracker -> nvdsanalytics ->
# nvtiler -> nvvideoconvert -> nvdsosd -> sink
print("Linking elements in the Pipeline \n")
streammux.link(queue1)
queue1.link(pgie)
pgie.link(queue2)
queue2.link(tracker)
tracker.link(queue3)
queue3.link(nvanalytics)
nvanalytics.link(queue4)
queue4.link(tiler)
tiler.link(queue5)
queue5.link(nvvidconv)
nvvidconv.link(queue6)
queue6.link(nvosd)
nvosd.link(queue7)
queue7.link(caps)
caps.link(encoder)
encoder.link(codecparse)
codecparse.link(mux)
mux.link(sink)
# if is_aarch64():
# nvosd.link(queue7)
# queue7.link(transform)
# transform.link(sink)
# else:
# nvosd.link(queue7)
# queue7.link(sink)
# create an event loop and feed gstreamer bus mesages to it
loop = GObject.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect ("message", bus_call, loop)
nvanalytics_src_pad=nvanalytics.get_static_pad("src")
if not nvanalytics_src_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
nvanalytics_src_pad.add_probe(Gst.PadProbeType.BUFFER, nvanalytics_src_pad_buffer_probe, 0)
# List the sources
print("Now playing...")
for i, source in enumerate(args):
if (i != 0):
print(i, ": ", source)
print("Starting pipeline \n")
# start play back and listed to events
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# cleanup
print("Exiting app\n")
pipeline.set_state(Gst.State.NULL)
if __name__ == '__main__':
global codec
codec = "H264"
global bitrate
bitrate = 4000000
global output_path
output_path = "out.mp4"
sys.exit(main(sys.argv))
I didn’t modify the pgie or tracker files and used the sample_1080p_h264.mp4 file for input.
I’d really appreciate some help!
• Hardware Platform (Jetson / GPU)
Jetson Nano
• DeepStream Version
Deepstream 6.0
• JetPack Version (valid for Jetson only)
4.6
• TensorRT Version
8.2.1-1