• Hardware Platform (Jetson / GPU) Jetson
• DeepStream Version 6.1.1
• JetPack Version (valid for Jetson only) 5.0.2
• TensorRT Version 8.4
• NVIDIA GPU Driver Version (valid for GPU only)
• 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 facedetect_tensor_meta.py -i file:///
I was trying to access tensor meta data from secondary inference, i am not good at C++ to write custom post processing, so i decided accessing tensormeta but both object tensormeta and frame tensormeta returned None.
i am using age and gender model from this genderage it is a single classification model which gives both gender and age.
i used face detection model from NVIDA NGC and it is detecting faces.
Please find the python code and sgie config file.
facedetect_tensor_meta.py
#!/usr/bin/env python3
from pathlib import Path
import sys
sys.path.append('../')
import platform
import configparser
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from gi.repository import GLib
from ctypes import *
import time
from datetime import datetime
import math
from common.FPS import GETFPS
import argparse
import pyds
import cv2
import numpy as np
from common.FPS import PERF_DATA
import pdb
fps_streams={}
perf_data = None
MAX_DISPLAY_LEN=64
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
# torch.set_printoptions(precision=20)
PGIE_CLASS_ID_FACE = 0
past_tracking_meta=[0]
alert_thresh = 60
def sgie_src_pad_buffer_probe(pad,info,u_data):
frame_number=0
obj_counter = {
PGIE_CLASS_ID_FACE:0
}
num_rects=0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
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:
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:
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
obj_counter[PGIE_CLASS_ID_FACE] += 1
l_user_meta = obj_meta.obj_user_meta_list
print("object", l_user_meta)
while l_user_meta:
try:
user_meta = pyds.NvDsUserMeta.cast(l_user_meta.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type
!= pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
print("Object meta Unique id: {}".format(tensor_meta.unique_id))
# access tensor meta data of objects.
try:
l_user_meta = l_user_meta.next
except StopIteration:
break
try:
l_obj=l_obj.next
except StopIteration:
break
l_user = frame_meta.frame_user_meta_list
print("frame", l_user)
while l_user is not None:
try:
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type != pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
print("Frame meta Unique id: {}".format(tensor_meta.unique_id))
try:
l_user = l_user.next
except StopIteration:
break
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]
py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Face_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_FACE])
py_nvosd_text_params.x_offset = 10
py_nvosd_text_params.y_offset = 12
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)
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
return Gst.PadProbeReturn.OK
def draw_bounding_boxes(image, box, label, id_, color):
x1 = int(box[0])
y1 = int(box[2])
x2 = int(box[1])
y2 = int(box[3])
image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2, cv2.LINE_4)
image = cv2.putText(image, label+ f'-{id_}', (x1 - 10, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255, 0), 2)
return image
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)
print("gstname=",gstname)
if(gstname.find("video")!=-1):
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")
if file_loop:
# use nvurisrcbin to enable file-loop
uri_decode_bin=Gst.ElementFactory.make("nvurisrcbin", "uri-decode-bin")
uri_decode_bin.set_property("file-loop", 1)
uri_decode_bin.set_property("cudadec-memtype", 0)
else:
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")
uri_decode_bin.set_property("uri",uri)
uri_decode_bin.connect("pad-added",cb_newpad,nbin)
uri_decode_bin.connect("child-added",decodebin_child_added,nbin)
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, requested_pgie=None, config=None, disable_probe=False):
global perf_data
perf_data = PERF_DATA(len(args))
number_sources=len(args)
Gst.init(None)
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 ")
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)
sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
if not sgie1:
sys.stderr.write(" Unable to make sgie1 \n")
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 is_aarch64():
# 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 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")
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', 4000000)
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', "config_infer_primary_facedetect.txt")
sgie1.set_property('config-file-path', "age_gender_sgi_tensormeta_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)
pipeline.add(sgie1)
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(sgie1)
sgie1.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)
sgie_src_pad=sgie1.get_static_pad("sink")
if not sgie_src_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
sgie_src_pad.add_probe(Gst.PadProbeType.BUFFER, sgie_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)
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=False,
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
silent = args.silent
file_loop = args.file_loop
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))
age_gender_sgie_cinfig.txt
################################################################################
# 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.
################################################################################
# Following properties are mandatory when engine files are not specified:
# int8-calib-file(Only in INT8)
# Caffemodel mandatory properties: model-file, proto-file, output-blob-names
# UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names
# ONNX: onnx-file
#
# Mandatory properties for detectors:
# num-detected-classes
#
# Optional properties for detectors:
# cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)
# custom-lib-path,
# parse-bbox-func-name
#
# Mandatory properties for classifiers:
# classifier-threshold, is-classifier
#
# Optional properties for classifiers:
# classifier-async-mode(Secondary mode only, Default=false)
#
# Optional properties in secondary mode:
# operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),
# input-object-min-width, input-object-min-height, input-object-max-width,
# input-object-max-height
#
# Following properties are always recommended:
# batch-size(Default=1)
#
# Other optional properties:
# net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
# model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,
# mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),
# custom-lib-path, network-mode(Default=0 i.e FP32)
#
# The values in the config file are overridden by values set through GObject
# properties.
[property]
gpu-id=0
net-scale-factor=1
onnx-file=./models/age_gender/genderage.onnx
model-engine-file=./models/age_gender/genderage.onnx_b1_gpu0_fp32.engine
force-implicit-batch-dim=0
batch-size=1
# 0=FP32 and 1=INT8 2=FP16 mode
network-mode=0
network-type=1 # 0: Detector 1: Classifier 2: Segmentation 3: Instance Segmentation
#input-object-min-width=64
#input-object-min-height=64
# 1=Primary 2=Secondary
process-mode=2
model-color-format=1
gpu-id=0
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=0
#is-classifier=1
output-blob-names=fc1
classifier-async-mode=0
classifier-threshold=0.51
#scaling-filter=0
#scaling-compute-hw=0
output-tensor-meta=1
output-tensor-meta=true