Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.0
• JetPack Version (valid for Jetson only)
• TensorRT Version 8.1
• NVIDIA GPU Driver Version (valid for GPU only) 470
• 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) python3 deepstream_test1_rtsp_in_rtsp_out.py -i file:///opt/nvidia/deepstream/deepstream-6.0/samples/streams/sample_720p.h264
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
I am using sample python rtsp in and out code and tried to access tensormeta data, but every frame it is showing empty.
Is tensormeta able to access using nvinfer plugin not using nvinferserver.
I have enables output-tensor-meta in pgie config file like below both the ways.
output-tensor-meta=true
output-tensor-meta=1
these is the sample code below:
#!/usr/bin/env python3
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 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 sys
sys.path.append("../")
import pyds
from common.bus_call import bus_call
from common.is_aarch_64 import is_aarch64
import platform
import math
import time
from ctypes import *
import gi
gi.require_version("Gst", "1.0")
gi.require_version("GstRtspServer", "1.0")
from gi.repository import GObject, Gst, GstRtspServer, GLib
import configparser
import argparse
from common.FPS import GETFPS
import numpy as np
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 = 0
pgie_classes_str = ["Vehicle", "TwoWheeler", "Person", "RoadSign"]
# tiler_sink_pad_buffer_probe will extract metadata received on OSD sink pad
# and update params for drawing rectangle, object information etc.
def tiler_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 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
l_user_meta = obj_meta.obj_user_meta_list
# print(dir(l_user_meta))
# Extract object level meta data from NvDsAnalyticsObjInfo
while l_user_meta:
print('usermeta')
try:
user_meta = pyds.NvDsUserMeta.cast(l_user_meta.data)
if user_meta.base_meta.meta_type == pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META:
print(user_meta.base_meta.meta_type)
tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
layer = pyds.get_nvds_LayerInfo(tensor_meta, 0)
# Convert the tensor output meta to a numpy array containing the fingerprint
ptr = ctypes.cast(pyds.get_ptr(layer.buffer), ctypes.POINTER(ctypes.c_float))
probs = np.array(np.ctypeslib.as_array(ptr, shape=(layer.dims.numElements,)), copy=True)
print(probs)
except StopIteration:
break
try:
l_obj = l_obj.next
except StopIteration:
break
# 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],
# )
# 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
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
for i in range(0, len(args)):
fps_streams["stream{0}".format(i)] = GETFPS(i)
number_sources = len(args)
# 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]
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)
print("Creating Pgie \n ")
if gie=="nvinfer":
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
else:
pgie = Gst.ElementFactory.make("nvinferserver", "primary-inference")
if pgie:
print("created nvinferserver")
if not pgie:
sys.stderr.write(" Unable to create pgie \n")
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")
nvvidconv_postosd = Gst.ElementFactory.make(
"nvvideoconvert", "convertor_postosd")
if not nvvidconv_postosd:
sys.stderr.write(" Unable to create nvvidconv_postosd \n")
# 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)
# Make the payload-encode video into RTP packets
if codec == "H264":
rtppay = Gst.ElementFactory.make("rtph264pay", "rtppay")
print("Creating H264 rtppay")
elif codec == "H265":
rtppay = Gst.ElementFactory.make("rtph265pay", "rtppay")
print("Creating H265 rtppay")
if not rtppay:
sys.stderr.write(" Unable to create rtppay")
# Make the UDP sink
updsink_port_num = 5400
sink = Gst.ElementFactory.make("udpsink", "udpsink")
if not sink:
sys.stderr.write(" Unable to create udpsink")
sink.set_property("host", "224.224.255.255")
sink.set_property("port", updsink_port_num)
sink.set_property("async", False)
sink.set_property("sync", 1)
streammux.set_property("width", 1920)
streammux.set_property("height", 1080)
streammux.set_property("batch-size", 1)
streammux.set_property("batched-push-timeout", 4000000)
if gie=="nvinfer":
pgie.set_property("config-file-path", "dstest1_pgie_config.txt")
else:
pgie.set_property("config-file-path", "dstest1_pgie_inferserver_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)
print("Adding elements to Pipeline \n")
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)
pipeline.add(pgie)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
pipeline.add(nvvidconv_postosd)
pipeline.add(caps)
pipeline.add(encoder)
pipeline.add(rtppay)
pipeline.add(sink)
streammux.link(pgie)
pgie.link(nvvidconv)
nvvidconv.link(tiler)
tiler.link(nvosd)
nvosd.link(nvvidconv_postosd)
nvvidconv_postosd.link(caps)
caps.link(encoder)
encoder.link(rtppay)
rtppay.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)
tiler_src_sink_pad=pgie.get_static_pad("src")
if not tiler_src_sink_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
tiler_src_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_src_pad_buffer_probe, 0)
# Start streaming
rtsp_port_num = 8554
server = GstRtspServer.RTSPServer.new()
server.props.service = "%d" % rtsp_port_num
server.attach(None)
factory = GstRtspServer.RTSPMediaFactory.new()
factory.set_launch(
'( udpsrc name=pay0 port=%d buffer-size=524288 caps="application/x-rtp, media=video, clock-rate=90000, encoding-name=(string)%s, payload=96 " )'
% (updsink_port_num, codec)
)
factory.set_shared(True)
server.get_mount_points().add_factory("/ds-test", factory)
print(
"\n *** DeepStream: Launched RTSP Streaming at rtsp://localhost:%d/ds-test ***\n\n"
% rtsp_port_num
)
# start play back and listen to events
print("Starting pipeline \n")
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except BaseException:
pass
# cleanup
pipeline.set_state(Gst.State.NULL)
def parse_args():
parser = argparse.ArgumentParser(description='RTSP Output Sample Application Help ')
parser.add_argument("-i", "--input",
help="Path to input H264 elementry stream", nargs="+", default=["a"], required=True)
parser.add_argument("-g", "--gie", default="nvinfer",
help="choose GPU inference engine type nvinfer or nvinferserver , default=nvinfer", choices=['nvinfer','nvinferserver'])
parser.add_argument("-c", "--codec", default="H264",
help="RTSP Streaming Codec H264/H265 , default=H264", choices=['H264','H265'])
parser.add_argument("-b", "--bitrate", default=4000000,
help="Set the encoding bitrate ", type=int)
# Check input arguments
if len(sys.argv)==1:
parser.print_help(sys.stderr)
sys.exit(1)
args = parser.parse_args()
global codec
global bitrate
global stream_path
global gie
gie = args.gie
codec = args.codec
bitrate = args.bitrate
stream_path = args.input
return stream_path
if __name__ == '__main__':
stream_path = parse_args()
sys.exit(main(stream_path))