Here is my sample code
obj_meta2=pyds.glist_get_nvds_object_meta(l_obj.data)
l_classifier= obj_meta2.classifier_meta_list
print("Step 6 ",l_classifier.data)
display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
display_meta.num_labels = 1
Tracker_id = obj_meta2.object_id
print("================ frame_meta.frame_num ======================",frame_meta.frame_num)
print("================ Tracker_id ======================",Tracker_id)
if l_classifier is not None:
classifier_meta=pyds.glist_get_nvds_classifier_meta(l_classifier.data)
l_label=classifier_meta.label_info_list
uid=classifier_meta.unique_component_id
numLabel=classifier_meta.num_labels
label_info=pyds.glist_get_nvds_label_info(l_label.data)
classifier_class = label_info.result_class_id
num_classes = label_info.num_classes
label_id = label_info.label_id
result_prob = label_info.result_prob
print("1 l_label :",l_label)
print("1 u id ------------ :",uid)
print("1 numLabel :",numLabel)
print("1 label_info :",label_info)
print("1 classifier_class:",classifier_class)
print("1 num_classes :",num_classes)
print("1 label_id :",label_id)
Could you please show us clearer information about what you are handling with, such as
- what task you are using DeepStream pipeline for,
- what inference model you are dealing with,
- what configurations you have done already, and finally
- what problems you have met?
Thank you very much.
Thanks @ersheng
My problem is I am not getting both classifier result on single frame
below is my gst pipeline linking
# 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):
#os.mkdir("/root/Smarg/Gateway_11May/Gateway_11May/Images/stream_"+str(i))
self.frame_count["stream_"+str(i)]=0
self.saved_count["stream_"+str(i)]=0
print("Creating source_bin ",i," \n ")
uri_name=args[i]
if uri_name.find("rtsp://") == 0 :
is_live = True
source_bin=self.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")
# which is easier to work with in Python.
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
if not sgie1:
sys.stderr.write(" Unable to make sgie1 \n")
sgie2 = Gst.ElementFactory.make("nvinfer", "secondary2-nvinference-engine")
if not sgie2:
sys.stderr.write(" Unable to make sgie2 \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 nvvidconv1 \n ")
#nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor_postosd")
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 nvosd \n ")
nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
if not nvosd:
sys.stderr.write(" Unable to create nvosd \n")
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")
codec = "H264"
bitrate=4000000
# 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 = 5401
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)
nvosd.set_property('process-mode', 1)
# print("Creating EGLSink \n")
# sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
# #sink = Gst.ElementFactory.make("nvoverlaysink", "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)
streammux.set_property('width', 960)
streammux.set_property('height', 1080)
streammux.set_property('batch-size',number_sources )
streammux.set_property('batched-push-timeout', 4000000)
print(os.getcwd())
pgie.set_property('config-file-path', "../config/people_config.txt")
sgie1.set_property('config-file-path', "../config/abc.txt")
sgie2.set_property('config-file-path', "../config/xyz.txt")
#pgie.set_property('interval', self.interval)
#pgie.set_property('interval', 10)
#sgie1.set_property('secondary-reinfer-interval', 10)
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", self.TILED_OUTPUT_WIDTH)
tiler.set_property("height", self.TILED_OUTPUT_HEIGHT)
#sink.set_property("sync", 0)
#Set properties of tracker
config = configparser.ConfigParser()
config.read('../config/mask_nomask_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 not is_aarch64():
# Use CUDA unified memory in the pipeline so frames
# can be easily accessed on CPU in Python.
print("Adding elements to Pipeline----------------",int(pyds.NVBUF_MEM_CUDA_UNIFIED))
#mem_type = int(pyds.NVBUF_MEM_CUDA_DEVICE)
mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
#mem_type = 3
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)
print("Adding elements to Pipeline \n")
print(“Adding elements to Pipeline \n”)
pipeline.add(pgie)
pipeline.add(tracker)
pipeline.add(sgie1)
pipeline.add(sgie2)
pipeline.add(tiler)
pipeline.add(nvvidconv)
#new added
pipeline.add(nvosd)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(encoder)
pipeline.add(rtppay)
if is_aarch64():
pipeline.add(transform)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(pgie)
pgie.link(tracker)
tracker.link(sgie2)
sgie2.link(sgie1)
pgie.link(nvvidconv)
nvvidconv.link(filter1)
filter1.link(tiler)
tiler.link(nvosd)
nvosd.link(nvvidconv1)
nvvidconv1.link(encoder)
encoder.link(rtppay)
if is_aarch64():
rtppay.link(transform)
transform.link(sink)
else:
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)
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_src_pad=pgie.get_static_pad("src")
# if not tiler_src_pad:
# sys.stderr.write(" Unable to get src pad \n")
# else:
# tiler_src_pad.add_probe(Gst.PadProbeType.BUFFER, self.tiler_src_pad_buffer_probe, 0)
tiler_sink_pad=tiler.get_static_pad("sink")
if not tiler_sink_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, self.tiler_sink_pad_buffer_probe, 0)
# osdsinkpad = nvosd.get_static_pad("sink")
# if not osdsinkpad:
# sys.stderr.write(" Unable to get sink pad of nvosd \n")
# osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, self.osd_sink_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)
The pipeline is wrong. Can you refer to DeepStream User Guide and sample codes to get the correct plugin information and pipeline samples?
https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html
https://gstreamer.freedesktop.org/documentation/tutorials/basic/debugging-tools.html