Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) - GeForce RTX 2080 Ti
• DeepStream Version - 6.2
• TensorRT Version - 8.5.2
• NVIDIA GPU Driver Version (valid for GPU only) - 535.54.03
Hi!
I want to save a frame and i get:
0:00:10.081002309 1426136 0x2ca7180 WARN nvinfer gstnvinfer.cpp:1730:gst_nvinfer_process_objects:<secondary2-nvinference-engine> Untracked objects in metadata. Cannot infer on untracked objects in asynchronous mode.
0:00:10.081317869 1426136 0x2ca7180 WARN nvinfer gstnvinfer.cpp:1730:gst_nvinfer_process_objects:<secondary3-nvinference-engine> Untracked objects in metadata. Cannot infer on untracked objects in asynchronous mode.
0:00:10.081419306 1426136 0x2ca7180 WARN nvinfer gstnvinfer.cpp:1730:gst_nvinfer_process_objects:<secondary4-nvinference-engine> Untracked objects in metadata. Cannot infer on untracked objects in asynchronous mode.
Segmentation fault (core dumped)
This is my code:
import sys
sys.path.append('../')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GLib
from gi.repository import GLib
from ctypes import *
import time
import sys
import math
import random
import platform
from common.is_aarch_64 import is_aarch64
import argparse
from common.FPS import PERF_DATA
from common.bus_call import bus_call
import io
import pyds
from PIL import Image
import numpy as np
import cv2
import os
perf_data = None
OSD_PROCESS_MODE = 0
OSD_DISPLAY_TEXT = 1
MAX_DISPLAY_LEN=64
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
SGIE_CLASS_ID_FACE = 0
SGIE_CLASS_ID_LP = 1
PRIMARY_DETECTOR_UID = 1
SECONDARY_DETECTOR_UID = 2
SECOND_DETECTOR_IS_SECONDARY = 1
MUXER_OUTPUT_WIDTH=1920
MUXER_OUTPUT_HEIGHT=1080
MUXER_BATCH_TIMEOUT_USEC=4000000
TILED_OUTPUT_WIDTH=1280
TILED_OUTPUT_HEIGHT=720
GPU_ID = 0
MAX_NUM_SOURCES = 1
SINK_ELEMENT = "nveglglessink"
PGIE_CONFIG_FILE = "dstest2_pgie_config.txt"
TRACKER_CONFIG_FILE = "dstest2_tracker_config2.txt"
SGIE1_CONFIG_FILE = "dstest2_sgie1_config_license_face.txt"
SGIE2_CONFIG_FILE = "dstest2_sgie1_config.txt"
SGIE3_CONFIG_FILE = "dstest2_sgie2_config.txt"
SGIE4_CONFIG_FILE = "dstest2_sgie3_config.txt"
CONFIG_GPU_ID = "gpu-id"
CONFIG_GROUP_TRACKER = "tracker"
CONFIG_GROUP_TRACKER_WIDTH = "tracker-width"
CONFIG_GROUP_TRACKER_HEIGHT = "tracker-height"
CONFIG_GROUP_TRACKER_LL_CONFIG_FILE = "ll-config-file"
CONFIG_GROUP_TRACKER_LL_LIB_FILE = "ll-lib-file"
CONFIG_GROUP_TRACKER_ENABLE_BATCH_PROCESS = "enable-batch-process"
g_num_sources = 0
g_source_id_list = [0] * MAX_NUM_SOURCES
g_eos_list = [False] * MAX_NUM_SOURCES
g_source_enabled = [False] * MAX_NUM_SOURCES
g_source_bin_list = [None] * MAX_NUM_SOURCES
# pgie_classes_str= ["Vehicle", "TwoWheeler", "Person","RoadSign"]
# sgie_classes_str = ["Face", "License Plate"]
# frame_number = 0
uri = ""
loop = None
pipeline = None
streammux = None
sink = None
pgie = None
sgie1 = None
sgie2 = None
sgie3 = None
sgie4 = None
nvvideoconvert = None
nvosd = None
tiler = None
tracker = None
trackersec = None
def osd_sink_pad_buffer_probe(pad, info, u_data):
# This time we maintain two separate counters for primary and secondary objects
primary_obj_counter = {
PGIE_CLASS_ID_PERSON: 0,
PGIE_CLASS_ID_VEHICLE: 0,
}
secondary_obj_counter = {
SGIE_CLASS_ID_FACE: 0,
SGIE_CLASS_ID_LP: 0,
}
frame_number = 0
# global frame_number
global SECOND_DETECTOR_IS_SECONDARY
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return Gst.PadProbeReturn.OK
# Retrieve batch metadata from the 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:
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
l_obj = frame_meta.obj_meta_list
source_id = frame_meta.source_id
frame_number = frame_meta.frame_num
while l_obj is not None:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
l_class = obj_meta.classifier_meta_list
if obj_meta.unique_component_id == PRIMARY_DETECTOR_UID:
if obj_meta.class_id in [PGIE_CLASS_ID_PERSON, PGIE_CLASS_ID_VEHICLE]:
primary_obj_counter[obj_meta.class_id] += 1
elif obj_meta.unique_component_id == SECONDARY_DETECTOR_UID:
if obj_meta.class_id in [SGIE_CLASS_ID_FACE, SGIE_CLASS_ID_LP]:
secondary_obj_counter[obj_meta.class_id] += 1
# print(obj_meta.parent.class_id)
# A doua conditie este pentru ca detecteaza uneori gresit(poate gasi fata la oglinda masinii, de ex)
if obj_meta.class_id == 0 and obj_meta.parent.class_id == 2:
print('Face detected at Tracking ID:', obj_meta.parent.object_id)
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
# 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 obj_meta.class_id == 1 and obj_meta.parent.class_id == 0:
# l_class_parent = obj_meta.parent.classifier_meta_list
# color = 'undetected'
# model = 'undetected'
# vehicle_type = 'undetected'
# if l_class_parent is not None:
# while l_class_parent is not None:
# class_meta_parent = pyds.NvDsClassifierMeta.cast(l_class_parent.data)
# # top = int(obj_meta.rect_params.top)
# # left = int(obj_meta.rect_params.left)
# # width = int(obj_meta.rect_params.width)
# # height = int(obj_meta.rect_params.height)
# n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
# # 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 class_meta_parent.unique_component_id == 3:
# l_label_parent = class_meta_parent.label_info_list
# label_color = pyds.NvDsLabelInfo.cast(l_label_parent.data)
# color = label_color.result_label if label_color.result_label else 'undetected'
# if class_meta_parent.unique_component_id == 4:
# l_label_parent = class_meta_parent.label_info_list
# label_model = pyds.NvDsLabelInfo.cast(l_label_parent.data)
# model = label_model.result_label if label_model.result_label else 'undetected'
# if class_meta_parent.unique_component_id == 5:
# l_label_parent = class_meta_parent.label_info_list
# label_vehicle_type = pyds.NvDsLabelInfo.cast(l_label_parent.data)
# vehicle_type = label_vehicle_type.result_label if label_vehicle_type.result_label else 'undetected'
# l_class_parent = l_class_parent.next
# print('License Plate detected for vehicle with Tracking ID: {}, Color: {}, Model: {}, Type: {}'
# .format(obj_meta.parent.object_id, color, model, vehicle_type))
l_obj = l_obj.next
# print("Source ID:", source_id,
# "Frame Number:", frame_number,
# "Person Count:", primary_obj_counter[PGIE_CLASS_ID_PERSON],
# "Vehicle Count:", primary_obj_counter[PGIE_CLASS_ID_VEHICLE],
# "Face Count:", secondary_obj_counter[SGIE_CLASS_ID_FACE],
# "License Plate Count:", secondary_obj_counter[SGIE_CLASS_ID_LP])
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def get_label_names_from_file(filepath):
""" Read a label file and convert it to string list """
f = io.open(filepath, "r")
labels = f.readlines()
labels = [elm[:-1] for elm in labels]
f.close()
return labels
def sgie_src_pad_buffer_probe(pad, info, u_data):
label_names = get_label_names_from_file("/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/labels.txt")
gst_buffer = info.get_buffer()
if not gst_buffer:
logger.error("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 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
num_rects = frame_meta.num_obj_meta
l_obj = frame_meta.obj_meta_list
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
classifier_meta = pyds.nvds_acquire_classifier_meta_from_pool(batch_meta) # type of pyds.NvDsClassifierMeta
l_obj_user_meta = obj_meta.obj_user_meta_list
while l_obj_user_meta is not None:
try:
user_meta = pyds.NvDsUserMeta.cast(l_obj_user_meta.data)
tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
# Boxes in the tensor meta should be in network resolution which is
# found in tensor_meta.network_info. Use this info to scale boxes to
# the input frame resolution.
# layers_info = []
for i in range(tensor_meta.num_output_layers):
layer = pyds.get_nvds_LayerInfo(tensor_meta, i)
ptr = ctypes.cast(pyds.get_ptr(layer.buffer), ctypes.POINTER(ctypes.c_float))
v = np.ctypeslib.as_array(ptr, shape=(106,))
list1 = v.tolist()
dispaly_text = ""
for i in range(len(list1)):
if( list1[i]> 0.4):
if 'personal' in label_names[i]:
dispaly_text += label_names[i] + ":" + str(round(list1[i],2)) +"\n"
#Add label to classifier
label_info = pyds.nvds_acquire_label_info_meta_from_pool(batch_meta) # type of NvDsLabelInfo
label_info.num_classes-106
label_info.label_id=i
label_info.result_class_id=i
label_info.result_label=label_names[i]
label_info.result_prob=list1[i]
pyds.nvds_add_label_info_meta_to_classifier(classifier_meta,label_info)
# logger.info("DETECTION %s", dispaly_text.replace("\n",""))
txt_params = obj_meta.text_params
rect_params = obj_meta.rect_params
txt_params.x_offset = int(rect_params.left+rect_params.width)
txt_params.y_offset = max(0, int(rect_params.top) - 10)
txt_params.display_text = pyds.get_string(txt_params.display_text) + "\n" + dispaly_text
except StopIteration:
continue
try:
l_obj_user_meta=l_obj_user_meta.next
except StopIteration:
break
pyds.nvds_add_classifier_meta_to_object(obj_meta, classifier_meta)
except StopIteration:
continue
try:
l_obj = l_obj.next
except StopIteration:
break
try:
l_frame = l_frame.next
except StopIteration:
break
#logger.info("---------------------------------------------------------------")
return Gst.PadProbeReturn.OK
def cb_newpad(decodebin, decoder_src_pad, data):
"""
The function is called when a new pad is created by the decodebin.
The function checks if the new pad is for video and not audio.
If the new pad is for video, the function checks if the pad caps contain NVMM memory features.
If the pad caps contain NVMM memory features, the function links the decodebin pad to the source bin
ghost pad.
If the pad caps do not contain NVMM memory features, the function prints an error message.
:param decodebin: The decodebin element that is creating the new pad
:param decoder_src_pad: The source pad created by the decodebin element
:param data: This is the data that was passed to the callback function. In this case, it is the
source_bin
"""
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):
"""
If the child added to the decodebin is another decodebin, connect to its child-added signal. If the
child added is a source, set its drop-on-latency property to True.
:param child_proxy: The child element that was added to the decodebin
:param Object: The object that emitted the signal
:param name: The name of the element that was added
:param user_data: This is a pointer to the data that you want to pass to the callback function
"""
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):
"""
It creates a GstBin, adds a uridecodebin to it, and connects the uridecodebin's pad-added signal to
a callback function
:param index: The index of the source bin
:param uri: The URI of the video file to be played
:return: A bin with a uri decode bin and a ghost pad.
"""
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 make_element(element_name, i):
"""
Creates a Gstreamer element with unique name
Unique name is created by adding element type and index e.g. `element_name-i`
Unique name is essential for all the element in pipeline otherwise gstreamer will throw exception.
:param element_name: The name of the element to create
:param i: the index of the element in the pipeline
:return: A Gst.Element object
"""
element = Gst.ElementFactory.make(element_name, element_name)
if not element:
sys.stderr.write(" Unable to create {0}".format(element_name))
element.set_property("name", "{0}-{1}".format(element_name, str(i)))
return element
def main(args, requested_pgie=None, config=None, disable_probe=False):
global g_num_sources
global g_source_bin_list
global uri
global loop
global pipeline
global streammux
global sink
global pgie
global sgie1
global sgie2
global sgie3
global nvvideoconvert
global nvosd
global tiler
global tracker
global trackersec
input_sources = args
number_sources = len(input_sources)
global perf_data
perf_data = PERF_DATA(number_sources)
# 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 = input_sources[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")
# pipeline.add(queue1)
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 nvtracker2 \n ")
trackersec = Gst.ElementFactory.make("nvtracker", "trackersec")
if not trackersec:
sys.stderr.write(" Unable to create tracker \n")
print("Creating sgie1 \n ")
sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
if not sgie1:
sys.stderr.write(" Unable to make sgie1 \n")
print("Creating sgie2 \n ")
sgie2 = Gst.ElementFactory.make("nvinfer", "secondary2-nvinference-engine")
if not sgie2:
sys.stderr.write(" Unable to make sgie2 \n")
print("Creating sgie3 \n ")
sgie3 = Gst.ElementFactory.make("nvinfer", "secondary3-nvinference-engine")
if not sgie3:
sys.stderr.write(" Unable to make sgie3 \n")
print("Creating sgie4 \n ")
sgie4 = Gst.ElementFactory.make("nvinfer", "secondary4-nvinference-engine")
if not sgie4:
sys.stderr.write(" Unable to make sgie4 \n")
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
if not nvvidconv:
logger.error(" Unable to create nvvidconv \n")
# Create OSD to draw on the converted RGBA buffer
nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
if not nvosd:
logger.error(" Unable to create nvosd \n")
nvosd.set_property('process-mode',OSD_PROCESS_MODE)
nvosd.set_property('display-text',OSD_DISPLAY_TEXT)
nvvidconv_postosd = Gst.ElementFactory.make("nvvideoconvert", "convertor_postosd")
if not nvvidconv_postosd:
logger.error(" Unable to create nvvidconv_postosd \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)
tee=Gst.ElementFactory.make("tee", "nvsink-tee")
if not tee:
logger.error(" Unable to create tee \n")
tiler=Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
if not tiler:
logger.error(" Unable to create tiler \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)
# Finally render the osd output
updsink_port_num = 5400
sink = Gst.ElementFactory.make("udpsink", "udpsink")
if not sink:
logger.error(" Unable to create udpsink")
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 is_live:
print("Atleast one of the sources is live")
streammux.set_property('live-source', 1)
print("Creating nvstreamdemux \n ")
nvstreamdemux = Gst.ElementFactory.make("nvstreamdemux", "nvstreamdemux")
if not nvstreamdemux:
sys.stderr.write(" Unable to create nvstreamdemux \n")
if is_live:
print("Atleast one of the sources is live")
streammux.set_property("live-source", 1)
streammux.set_property("width", 960)
streammux.set_property("height", 540)
streammux.set_property("batch-size", number_sources)
streammux.set_property("batched-push-timeout", 4000000)
pgie.set_property("config-file-path", PGIE_CONFIG_FILE)
pgie.set_property("unique-id", PRIMARY_DETECTOR_UID)
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)
sgie1.set_property('config-file-path', SGIE1_CONFIG_FILE)
sgie1.set_property("unique-id", SECONDARY_DETECTOR_UID)
sgie1.set_property("process-mode", 2 if SECOND_DETECTOR_IS_SECONDARY else 1)
sgie2.set_property('config-file-path', SGIE2_CONFIG_FILE)
sgie2.set_property("unique-id", 3)
sgie2.set_property("process-mode", 2)
sgie3.set_property('config-file-path', SGIE3_CONFIG_FILE)
sgie3.set_property("unique-id", 4)
sgie3.set_property("process-mode", 2)
sgie4.set_property('config-file-path', SGIE4_CONFIG_FILE)
sgie4.set_property("unique-id", 5)
sgie4.set_property("process-mode", 2)
config = configparser.ConfigParser()
config.read(TRACKER_CONFIG_FILE)
config.sections()
for key in config['tracker']:
if key == 'tracker-width' :
tracker_width = config.getint('tracker', key)
tracker.set_property('tracker-width', tracker_width)
trackersec.set_property('tracker-width', tracker_width)
if key == 'tracker-height' :
tracker_height = config.getint('tracker', key)
tracker.set_property('tracker-height', tracker_height)
trackersec.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)
trackersec.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)
trackersec.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)
trackersec.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)
trackersec.set_property('enable_batch_process', tracker_enable_batch_process)
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)
#Set gpu IDs of tiler, nvvideoconvert, and nvosd
tiler.set_property("gpu_id", GPU_ID)
nvvidconv.set_property("gpu_id", GPU_ID)
nvvidconv_postosd.set_property("gpu_id", GPU_ID)
nvosd.set_property("gpu_id", GPU_ID)
#Set gpu ID of sink if not aarch64
if(not is_aarch64()):
sink.set_property("gpu_id", GPU_ID)
print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tracker)
pipeline.add(sgie1)
# pipeline.add(trackersec)
pipeline.add(sgie2)
pipeline.add(sgie3)
pipeline.add(sgie4)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
pipeline.add(tee)
pipeline.add(tiler)
pipeline.add(nvvidconv_postosd)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(pgie)
pgie.link(tracker)
tracker.link(sgie1)
sgie1.link(sgie2)
sgie2.link(sgie3)
sgie3.link(sgie4)
sgie4.link(nvvidconv)
nvvidconv.link(tiler)
tiler.link(nvosd)
nvosd.link(nvvidconv_postosd)
nvvidconv_postosd.link(sink)
print("Linking elements in the Pipeline \n")
sink.set_property("sync", 0)
sink.set_property("qos",0)
# 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)
sgiesrcpad = sgie4.get_static_pad("src")
sgiesrcpad.add_probe(Gst.PadProbeType.BUFFER, sgie_src_pad_buffer_probe, 0)
osdsinkpad = tiler.get_static_pad("sink")
osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)
# List the sources
print("Now playing...")
for i, source in enumerate(input_sources):
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_demux_multi_in_multi_out.py",
description="deepstream-demux-multi-in-multi-out takes multiple URI streams as input" \
"and uses `nvstreamdemux` to split batches and output separate buffer/streams")
parser.add_argument(
"-i",
"--input",
help="Path to input streams",
nargs="+",
metavar="URIs",
default=["a"],
required=True,
)
args = parser.parse_args()
stream_paths = args.input
return stream_paths
if __name__ == "__main__":
stream_paths = parse_args()
sys.exit(main(stream_paths))
i dont know if sgie_src_pad_buffer_probe its useful.