• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.4
• TensorRT Version 8.6.1.6-1+cuda12.0
• NVIDIA GPU Driver Version (valid for GPU only) 545.23.08
this is my output that i get using python code
actually i need the unique id to be mentioned also like this
here i used txt file code format
How do i do it and need some reference
What do you mean? Do you need the object id to be unique in all the streams in the batch?
What is your pipeline and configurations?
#!/usr/bin/env python3
################################################################################
################################################################################
import argparse
import sys
sys.path.append(‘…/’)
import gi
import configparser
gi.require_version(‘Gst’, ‘1.0’)
gi.require_version(‘GstRtspServer’, ‘1.0’)
from gi.repository import GLib, Gst, GstRtspServer
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 PERF_DATA
import numpy as np
import pyds
import cv2
import os
import os.path
from os import path
perf_data = None
frame_count = {}
saved_count = {}
global PGIE_CLASS_ID_PERSON
PGIE_CLASS_ID_PERSON = 0
PGIE_CLASS_ID_BAG = 1
global PGIE_CLASS_ID_FACE
PGIE_CLASS_ID_FACE = 2
MAX_DISPLAY_LEN = 64
MUXER_OUTPUT_WIDTH = 720
MUXER_OUTPUT_HEIGHT = 576
MUXER_BATCH_TIMEOUT_USEC = 33000
TILED_OUTPUT_WIDTH = 720
TILED_OUTPUT_HEIGHT = 576
GST_CAPS_FEATURES_NVMM = “memory:NVMM”
pgie_classes_str = [“Person”, “Bag”, “Face”]
MIN_CONFIDENCE = 0.3
MAX_CONFIDENCE = 0.4
#tiler_sink_pad_buffer_probe will extract metadata received on tiler sink pad
#and update params for drawing rectangle, object information etc.
def tiler_sink_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
is_first_obj = True
save_image = False
obj_counter = {
PGIE_CLASS_ID_PERSON: 0,
PGIE_CLASS_ID_BAG: 0,
PGIE_CLASS_ID_FACE: 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
#osd_rect_params = pyds.NvOSD_RectParams.cast(obj_meta.rect_params)
# Draw black patch to cover faces (class_id = 2), can change to other colors
if (obj_meta.class_id == PGIE_CLASS_ID_FACE):
obj_meta.rect_params.border_width = 0
obj_meta.rect_params.has_bg_color = 1
obj_meta.rect_params.bg_color.red = 0.0
obj_meta.rect_params.bg_color.green = 0.0
obj_meta.rect_params.bg_color.blue = 0.0
obj_meta.rect_params.bg_color.alpha = 0.2
elif (obj_meta.class_id == PGIE_CLASS_ID_PERSON ) :
obj_meta.rect_params.border_width = 0
obj_meta.rect_params.has_bg_color = 1
obj_meta.rect_params.bg_color.red = 0.0
obj_meta.rect_params.bg_color.green = 0.0
obj_meta.rect_params.bg_color.blue = 0.0
obj_meta.rect_params.bg_color.alpha = 0.4
# Periodically check for objects and save the annotated object to file.
if saved_count["stream_{}".format(frame_meta.pad_index)] % 10 == 0 and obj_meta.class_id == PGIE_CLASS_ID_FACE :
if is_first_obj:
is_first_obj = False
# Getting Image data using nvbufsurface
# the input should be address of buffer and batch_id
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
n_frame = crop_object(n_frame, obj_meta)
# convert python array into numpy array format in the copy mode.
frame_copy = np.array(n_frame, copy=True, order='C')
# convert the array into cv2 default color format
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_RGBA2BGRA)
if is_aarch64(): # If Jetson, since the buffer is mapped to CPU for retrieval, it must also be unmapped
pyds.unmap_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id) # The unmap call should be made after operations with the original array are complete.
# The original array cannot be accessed after this call.
save_image = True
try:
l_obj = l_obj.next
except StopIteration:
break
print("Frame Number=", frame_number, "Number of Objects=", num_rects, "Face_count=",
obj_counter[PGIE_CLASS_ID_FACE], "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)
if save_image:
img_path = "{}/stream_{}/frame_{}.jpg".format(folder_name, frame_meta.pad_index, frame_number)
cv2.imwrite(img_path, frame_copy)
saved_count["stream_{}".format(frame_meta.pad_index)] += 1
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def crop_object(image, obj_meta):
rect_params = obj_meta.rect_params
top = int(rect_params.top)
left = int(rect_params.left)
width = int(rect_params.width)
height = int(rect_params.height)
obj_name = pgie_classes_str[obj_meta.class_id]
crop_img = image[top:top+height, left:left+width]
return crop_img
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.
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.
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 not is_aarch64() and name.find(“nvv4l2decoder”) != -1:
# Use CUDA unified memory in the pipeline so frames
# can be easily accessed on CPU in Python.
Object.set_property(“cudadec-memtype”, 2)
# print(“Seting bufapi_version\n”)
# Object.set_property(“bufapi-version”, 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.
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(uri_inputs,codec,bitrate ):
# Check input arguments
number_sources = len(uri_inputs)
global perf_data
perf_data = PERF_DATA(number_sources)
global folder_name
folder_name = "out_crops"
if path.exists(folder_name):
sys.stderr.write("The output folder %s already exists. Please remove it first.\n" % folder_name)
sys.exit(1)
os.mkdir(folder_name)
print("Frames will be saved in ", folder_name)
# 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):
os.mkdir(folder_name + "/stream_" + str(i))
frame_count["stream_" + str(i)] = 0
saved_count["stream_" + str(i)] = 0
print("Creating source_bin ", i, " \n ")
uri_name = uri_inputs[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 ")
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
sys.stderr.write(" Unable to create pgie \n")
# Add nvvidconv1 and filter1 to convert the frames to RGBA
# which is easier to work with in Python.
print("Creating nvvidconv1 \n ")
nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
if not nvvidconv1:
sys.stderr.write(" Unable to create nvvidconv1 \n")
print("Creating filter1 \n ")
caps1 = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
filter1 = Gst.ElementFactory.make("capsfilter", "filter1")
if not filter1:
sys.stderr.write(" Unable to get the caps filter1 \n")
filter1.set_property("caps", caps1)
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)
print("Playing file {} ".format(uri_inputs))
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)
pgie.set_property('config-file-path', "config_infer_primary_peoplenet.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)
if not is_aarch64():
# Use CUDA unified memory in the pipeline so frames
# can be easily accessed on CPU in Python.
mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
streammux.set_property("nvbuf-memory-type", mem_type)
nvvidconv.set_property("nvbuf-memory-type", mem_type)
nvvidconv1.set_property("nvbuf-memory-type", mem_type)
tiler.set_property("nvbuf-memory-type", mem_type)
nvvidconv_postosd.set_property("nvbuf-memory-type", mem_type)
print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
pipeline.add(nvvidconv_postosd)
pipeline.add(caps)
pipeline.add(encoder)
pipeline.add(rtppay)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(pgie)
pgie.link(nvvidconv1)
nvvidconv1.link(filter1)
filter1.link(tiler)
tiler.link(nvvidconv)
nvvidconv.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 = GLib.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect("message", bus_call, loop)
# 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)
tiler_sink_pad = tiler.get_static_pad("sink")
if not tiler_sink_pad:
sys.stderr.write(" Unable to get sink pad \n")
else:
tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_sink_pad_buffer_probe, 0)
# perf callback function to print fps every 5 sec
GLib.timeout_add(5000, 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
print("Exiting app\n")
pipeline.set_state(Gst.State.NULL)
def parse_args():
parser = argparse.ArgumentParser(description='RTSP Output Sample Application Help ')
parser.add_argument("-i","--uri_inputs", metavar='N', type=str, nargs='+',
help='Path to inputs URI e.g. rtsp:// ... or file:// seperated by space')
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()
print("URI Inputs: " + str(args.uri_inputs ))
return args.uri_inputs , args.codec, args.bitrate
if name == ‘main’:
uri_inputs , out_codec, out_bitrate = parse_args()
sys.exit(main(uri_inputs, out_codec, out_bitrate ))
This is the code
i need to match the people from different video stream with same unique id
and i need to represent it in the output video stream
I think you have mentioned to use PGIE(people detector)+SGIE(face detect)+SGIE(Re-Identification) pipeline in Deepstream Multi Stream detection - Intelligent Video Analytics / DeepStream SDK - NVIDIA Developer Forums. Do you mean you don’t know how to construct such pipeline?
yes, i dont know how to get the unique id and match them with the other streams
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
This is the sample of get embedding vector(ReID) from the metadata after embedding model. deepstream_tao_apps/apps/tao_others/deepstream-mdx-perception-app at master · NVIDIA-AI-IOT/deepstream_tao_apps (github.com). Please be familiar with how to integrate Re-Identification model first. You need to parse the model output and add NVIDIA DeepStream SDK API Reference: NvDsEmbedding Struct Reference | NVIDIA Docs meta by yourself.
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