Error - a lot of buffers are being dropped - when running ds sdk python sample + IPcamera on nano

Hi there.

I have installed the Deep stream SDK 4.0.2 and when I run the python example it takes more than minutes to initialize and after some seconds I am seeing the below messages in the terminal and my test application is hanging.

Frame Number= 1647 Number of Objects= 0 Vehicle_count= 0 Person_count= 0
Warning: gst-core-error-quark: A lot of buffers are being dropped. (13): gstbasesink.c(2902): gst_base_sink_is_too_late (): /GstPipeline:pipeline0/GstEglGlesSink:nvvideo-renderer:
There may be a timestamping problem, or this computer is too slow.
Warning: gst-core-error-quark: A lot of buffers are being dropped. (13): gstbasesink.c(2902): gst_base_sink_is_too_late (): /GstPipeline:pipeline0/GstEglGlesSink:nvvideo-renderer:
There may be a timestamping problem, or this computer is too slow.
Frame Number= 1648 Number of Objects= 0 Vehicle_count= 0 Person_count= 0.

Highly appreciated your help.

Me too, i got the same error

WARNING: gst-core-error-quark: A lot of buffers are being dropped. (13): gstbasesink.c(2902):

gst_base_sink_is_too_late (): /GstPipeline:pipeline0/GstEglGlesSink:nvvideo-renderer

Hi,
https://github.com/NVIDIA-AI-IOT/deepstream_python_apps/tree/master/apps
For information, please share which test app you run and hit the issue.

I run the deepstream-test3 with the RTSP Camera
At the first time, streaming run well, but after a sec, streaming got an error as shown

WARNING: gst-core-error-quark: A lot of buffers are being dropped. (13): gstbasesink.c(2902):

gst_base_sink_is_too_late (): /GstPipeline:pipeline0/GstEglGlesSink:nvvideo-renderer

and then i search it, and i found this link

https://github.com/NVIDIA-AI-IOT/deepstream_python_apps/blob/master/FAQ.md

Pipeline unable to perform at real time

I dont get it

Thanks for your reply.

I am using deepstream-test3 with the RTSP Camera.

yesterday I was trying with MP4 file and it was fine.

but when it comes to RTSP after a minute we are getting the above error and app almost hang stage then we need to kill the exe in task manager.

kind Note:
there is a TYPO error in deepstream-test3 python sample code like below

MUXER_OUTPUT_WIDTH=1920
MUXER_OUTPUT_HEIGHT=80 //should be 1080

Hi,
Please apply the modification to deepstream_test_3.py and try again.
1 set ‘live-source=1’ to streammux

streammux.set_property('live-source', 1)

2 Replace nveglglesink with nvoverlaysink and set ‘sync=0’

- transform=Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
+ transform=Gst.ElementFactory.make("queue", "queue")
- sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
+ sink = Gst.ElementFactory.make("nvoverlaysink", "nvvideo-renderer")
+ sink.set_property('sync', 0)

3 Apply the setting of Nano to config file
https://devtalk.nvidia.com/default/topic/1058597/deepstream-sdk/-nano-deepstream-test3-app-not-working-as-expected-for-multiple-video-source/post/5368352/#5368352

1 Like

Thank you Mr. DaneLLL for your respone,

I’ve tried the code, and i got a new problem here, the program running well without error, but stream video didn’t show.

These “nvoverlaysink” hide the streaming.

Hi,
Probably nvoverlaysink hasn’t been ported yet. Please keep nveglglessink and set sync=0

@DaneLLL I can definitely confirm your patch to work. I was having the same issues with 3 simultaneous MP4 files. Lost buffers, hanging display. The queue, the “live-source”, the sink change and the sync=0 did it.

Thanks

EDIT: Since I have learned this is important to mention: JP 4.4, Deep SDK 5.0.1, Jetson Nano 4G

EDIT 2: But in the end in my use case it is just sufficient to add sink.set_property('sync', 0) and let the rest unchanged. But the overlay is faster.

I have the same error , my python script is python-deepstream app1 adjusted for csi camera, When doing the said changes this error occurs. AttributeError: ‘NoneType’ object has no attribute ‘set_property’ where the object is sink

Hi,
For csi camera you should use nvarguscamerasrc. May refer to
Multiple nvarguscamera sources in Python not linked - #26 by socieboy

The link you have sent me has unfortunately been deleted,

My pipeline works, just not effectively its slow and has too many buffers

Setup;
Jetson Nano 4GB Ram
Deepstream 5.0.1

import sys
sys.path.append('../')
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

import pyds


MAX_DISPLAY_LEN=64
PGIE_CLASS_ID_TOOTHBRUSH = 80
PGIE_CLASS_ID_HAIR_DRYER = 79
PGIE_CLASS_ID_TEDDY_BEAR = 78
PGIE_CLASS_ID_SCISSORS = 77
PGIE_CLASS_ID_VASE = 76
PGIE_CLASS_ID_CLOCK = 75
PGIE_CLASS_ID_BOOK = 74
PGIE_CLASS_ID_REFRIGERATOR = 73
PGIE_CLASS_ID_SINK = 72
PGIE_CLASS_ID_TOASTER = 71
PGIE_CLASS_ID_OVEN = 70
PGIE_CLASS_ID_MICROWAVE = 69
PGIE_CLASS_ID_CELL_PHONE = 68
PGIE_CLASS_ID_KEYBOARD = 67
PGIE_CLASS_ID_REMOTE = 66
PGIE_CLASS_ID_MOUSE = 65
PGIE_CLASS_ID_LAPTOP = 64
PGIE_CLASS_ID_TVMONITOR = 63
PGIE_CLASS_ID_TOILET = 62
PGIE_CLASS_ID_DININGTABLE= 61
PGIE_CLASS_ID_BED = 60
PGIE_CLASS_ID_POTTEDPLANT = 59
PGIE_CLASS_ID_SOFA = 58
PGIE_CLASS_ID_CHAIR = 57
PGIE_CLASS_ID_CAKE = 56
PGIE_CLASS_ID_DONUT = 55
PGIE_CLASS_ID_PIZZA = 54
PGIE_CLASS_ID_HOT_DOG = 53
PGIE_CLASS_ID_CARROT = 52
PGIE_CLASS_ID_BROCCOLI = 51
PGIE_CLASS_ID_ORANGE = 50
PGIE_CLASS_ID_SANDWICH = 49
PGIE_CLASS_ID_APPLE = 48
PGIE_CLASS_ID_BANANA = 47
PGIE_CLASS_ID_BOWL = 46
PGIE_CLASS_ID_SPOON = 45
PGIE_CLASS_ID_KNIFE = 44
PGIE_CLASS_ID_FORK = 43
PGIE_CLASS_ID_CUP = 42
PGIE_CLASS_ID_WINE_GLASS = 41
PGIE_CLASS_ID_BOTTLE = 40
PGIE_CLASS_ID_TENNIS_RACKET = 39
PGIE_CLASS_ID_SURFBOARD = 38
PGIE_CLASS_ID_SKATEBOARD = 37
PGIE_CLASS_ID_BASEBALL_GLOVE = 36
PGIE_CLASS_ID_BASEBALL_BAT = 35
PGIE_CLASS_ID_KITE = 34
PGIE_CLASS_ID_SPORTS_BALL = 33
PGIE_CLASS_ID_SNOWBOARD = 32
PGIE_CLASS_ID_SKIS = 31
PGIE_CLASS_ID_FRISBEE = 30
PGIE_CLASS_ID_SUITCASE = 29
PGIE_CLASS_ID_TIE = 28
PGIE_CLASS_ID_HANDBAG = 27
PGIE_CLASS_ID_UMBRELLA = 26
PGIE_CLASS_ID_BACKPACK = 25
PGIE_CLASS_ID_UMBRELLA = 24
PGIE_CLASS_ID_GIRAFFE = 23
PGIE_CLASS_ID_ZEBRA = 22
PGIE_CLASS_ID_BEAR = 21
PGIE_CLASS_ID_ELEPHANT = 20
PGIE_CLASS_ID_COW = 19
PGIE_CLASS_ID_SHEEP = 18
PGIE_CLASS_ID_HORSE = 17
PGIE_CLASS_ID_DOG = 16
PGIE_CLASS_ID_CAT = 15
PGIE_CLASS_ID_BIRD = 14
PGIE_CLASS_ID_BENCH = 13
PGIE_CLASS_ID_PARKING_METER = 12
PGIE_CLASS_ID_STOP_SIGN = 11
PGIE_CLASS_ID_FIRE_HYDRANT = 10
PGIE_CLASS_ID_TRAFFIC_LIGHT = 9
PGIE_CLASS_ID_BOAT = 8
PGIE_CLASS_ID_TRUCK = 7
PGIE_CLASS_ID_TRAIN = 6
PGIE_CLASS_ID_BUS = 5
PGIE_CLASS_ID_AEROPLANE = 4
PGIE_CLASS_ID_MOTORBIKE = 3
PGIE_CLASS_ID_VEHICLE = 2
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 0

pgie_classes_str= ["Toothbrush", "Hair dryer", "Teddy bear","Scissors","Vase", "Clock", "Book","Refrigerator", "Sink", "Toaster","Oven","Microwave", "Cell phone", "Keyboard","Remote", "Mouse", "Laptop","Tvmonitor","Toilet", "Diningtable", "Bed","Pottedplant", "Sofa", "Chair","Cake","Donut", "Pizza", "Hot dog","Carrot", "Broccli", "Orange","Sandwich","Apple", "Banana", "Bowl","Spoon", "Knife", "Fork","Cup","Wine Glass", "Bottle", "Tennis racket","Surfboard", "Skateboard", "Baseball glove","Baseball bat","Kite", "Sports ball", "Snowboard","Skis", "Frisbee", "Suitcase","Tie","Handbag", "Umbrella", "Backpack","Giraffe", "Zebra", "Bear","Elephant","Cow", "Sheep", "Horse","Dog", "Cat", "Bird","Bench","Parking meter", "Stop sign", "Fire hydrant","Traffic light", "Boat", "Truck","Train","Bus", "Areoplane", "Motorbike","Car", "Bicycle", "Person"]


def osd_sink_pad_buffer_probe(pad,info,u_data):
    frame_number=0
    #Intiallizing object counter with 0.
    obj_counter = {
        PGIE_CLASS_ID_TOOTHBRUSH:0,
        PGIE_CLASS_ID_HAIR_DRYER:0,
        PGIE_CLASS_ID_TEDDY_BEAR:0,
        PGIE_CLASS_ID_SCISSORS:0,
        PGIE_CLASS_ID_VASE:0,
        PGIE_CLASS_ID_CLOCK:0,
        PGIE_CLASS_ID_BOOK:0,
        PGIE_CLASS_ID_REFRIGERATOR:0,
        PGIE_CLASS_ID_SINK:0,
        PGIE_CLASS_ID_TOASTER:0,
        PGIE_CLASS_ID_OVEN:0,
        PGIE_CLASS_ID_MICROWAVE:0,
        PGIE_CLASS_ID_CELL_PHONE:0,
        PGIE_CLASS_ID_KEYBOARD:0,
        PGIE_CLASS_ID_REMOTE:0,
        PGIE_CLASS_ID_MOUSE:0,
        PGIE_CLASS_ID_LAPTOP:0,
        PGIE_CLASS_ID_TVMONITOR:0,
        PGIE_CLASS_ID_TOILET:0,
        PGIE_CLASS_ID_DININGTABLE:0,
        PGIE_CLASS_ID_BED:0,
        PGIE_CLASS_ID_POTTEDPLANT:0,
        PGIE_CLASS_ID_SOFA:0,
        PGIE_CLASS_ID_CHAIR:0,
        PGIE_CLASS_ID_CAKE:0,
        PGIE_CLASS_ID_DONUT:0,
        PGIE_CLASS_ID_PIZZA:0,
        PGIE_CLASS_ID_HOT_DOG:0,
        PGIE_CLASS_ID_CARROT:0,
        PGIE_CLASS_ID_BROCCOLI:0,
        PGIE_CLASS_ID_ORANGE:0,
        PGIE_CLASS_ID_SANDWICH:0,
        PGIE_CLASS_ID_APPLE:0,
        PGIE_CLASS_ID_BANANA:0,
        PGIE_CLASS_ID_BOWL:0,
        PGIE_CLASS_ID_SPOON:0,
        PGIE_CLASS_ID_KNIFE:0,
        PGIE_CLASS_ID_FORK:0,
        PGIE_CLASS_ID_CUP:0,
        PGIE_CLASS_ID_WINE_GLASS:0,
        PGIE_CLASS_ID_BOTTLE:0,
        PGIE_CLASS_ID_TENNIS_RACKET:0,
        PGIE_CLASS_ID_SURFBOARD:0,
        PGIE_CLASS_ID_SKATEBOARD:0,
        PGIE_CLASS_ID_BASEBALL_GLOVE:0,
        PGIE_CLASS_ID_BASEBALL_BAT:0,
        PGIE_CLASS_ID_KITE:0,
        PGIE_CLASS_ID_SPORTS_BALL:0,
        PGIE_CLASS_ID_SNOWBOARD:0,
        PGIE_CLASS_ID_SKIS:0,
        PGIE_CLASS_ID_FRISBEE:0,
        PGIE_CLASS_ID_SUITCASE:0,
        PGIE_CLASS_ID_TIE:0,
        PGIE_CLASS_ID_HANDBAG:0,
        PGIE_CLASS_ID_UMBRELLA:0,
        PGIE_CLASS_ID_BACKPACK:0,
        PGIE_CLASS_ID_UMBRELLA:0,
        PGIE_CLASS_ID_GIRAFFE:0,
        PGIE_CLASS_ID_ZEBRA:0,
        PGIE_CLASS_ID_BEAR:0,
        PGIE_CLASS_ID_ELEPHANT:0,
        PGIE_CLASS_ID_COW:0,
        PGIE_CLASS_ID_SHEEP:0,
        PGIE_CLASS_ID_HORSE:0,
        PGIE_CLASS_ID_DOG:0,
        PGIE_CLASS_ID_CAT:0,
        PGIE_CLASS_ID_BIRD:0,
        PGIE_CLASS_ID_BENCH:0,
        PGIE_CLASS_ID_PARKING_METER:0,
        PGIE_CLASS_ID_STOP_SIGN:0,
        PGIE_CLASS_ID_FIRE_HYDRANT:0,
        PGIE_CLASS_ID_TRAFFIC_LIGHT:0,
        PGIE_CLASS_ID_BOAT:0,
        PGIE_CLASS_ID_TRUCK:0,
        PGIE_CLASS_ID_TRAIN:0,
        PGIE_CLASS_ID_BUS:0,
        PGIE_CLASS_ID_AEROPLANE:0,
        PGIE_CLASS_ID_MOTORBIKE:0,
        PGIE_CLASS_ID_VEHICLE:0,
        PGIE_CLASS_ID_BICYCLE:0,
        PGIE_CLASS_ID_PERSON: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.glist_get_nvds_frame_meta()
            # 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.glist_get_nvds_frame_meta(l_frame.data)
            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.glist_get_nvds_object_meta(l_obj.data)
                obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.0)
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break

        # Acquiring a display meta object. The memory ownership remains in
        # the C code so downstream plugins can still access it. Otherwise
        # the garbage collector will claim it when this probe function exits.
        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]
        # Setting display text to be shown on screen
        # Note that the pyds module allocates a buffer for the string, and the
        # memory will not be claimed by the garbage collector.
        # Reading the display_text field here will return the C address of the
        # allocated string. Use pyds.get_string() to get the string content.
        py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Vehicle_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_VEHICLE], obj_counter[PGIE_CLASS_ID_PERSON])

        # Now set the offsets where the string should appear
        py_nvosd_text_params.x_offset = 10
        py_nvosd_text_params.y_offset = 12

        # Font , font-color and font-size
        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)

        # Text background color
        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)
        try:
            l_frame=l_frame.next
        except StopIteration:
            break
			
    return Gst.PadProbeReturn.OK	

def main(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()

    if not pipeline:
        sys.stderr.write(" Unable to create Pipeline \n")

    # Source element for reading from the file
    print("Creating Source \n ")
    source = Gst.ElementFactory.make("nvarguscamerasrc", "src-elem")
    if not source:
        sys.stderr.write(" Unable to create Source \n")

    # Converter to scale the image
    nvvidconv_src = Gst.ElementFactory.make("nvvideoconvert", "convertor_src")
    if not nvvidconv_src:
        sys.stderr.write(" Unable to create nvvidconv_src \n")

    # Caps for NVMM and resolution scaling
    caps_nvvidconv_src = Gst.ElementFactory.make("capsfilter", "nvmm_caps")
    if not caps_nvvidconv_src:
        sys.stderr.write(" Unable to create capsfilter \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")

    # Use nvinfer to run inferencing on decoder's output,
    # behaviour of inferencing is set through config file
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")

    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
    if not tracker:
        sys.stderr.write(" Unable to create tracker \n")

   
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")

    # Create OSD to draw on the converted RGBA buffer
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")

    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")

    # Finally render the osd output
    if is_aarch64():
        transform=Gst.ElementFactory.make("queue", "queue")

    print("Creating EGLSink \n")
    sink = Gst.ElementFactory.make("nvoverlaysink ", "nvvideo-renderer")
    sink.set_property('sync', 0)
    
    
    if not sink:
        sys.stderr.write(" Unable to create egl sink \n")

    source.set_property('bufapi-version', True)

    caps_nvvidconv_src.set_property('caps', Gst.Caps.from_string('video/x-raw(memory:NVMM), width=1280, height=720'))

    streammux.set_property('width', 1280)
    streammux.set_property('height', 720)
    streammux.set_property('batch-size', 1)
    streammux.set_property('batched-push-timeout', 4000000)
    streammux.set_property('live-source', 1)

    #Set properties of pgie and sgie
    pgie.set_property('config-file-path', "config_infer_primary_yoloV3.txt")


    tracker.set_property('tracker-width', 480)
    
    
    tracker.set_property('tracker-height', 272)
    
    
    tracker.set_property('gpu_id', 0)
    
    
    tracker.set_property('ll-lib-file', "/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_klt.so")
    
    
    tracker.set_property('enable_batch_process', 1)

    

    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    pipeline.add(nvvidconv_src)
    pipeline.add(caps_nvvidconv_src)
    pipeline.add(streammux)
    pipeline.add(pgie)
    pipeline.add(tracker)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)
    if is_aarch64():
        pipeline.add(transform)


    # we link the elements together
    print("Linking elements in the Pipeline \n")
    source.link(nvvidconv_src)
    nvvidconv_src.link(caps_nvvidconv_src)

    sinkpad = streammux.get_request_pad("sink_0")
    if not sinkpad:
        sys.stderr.write(" Unable to get the sink pad of streammux \n")
    srcpad = caps_nvvidconv_src.get_static_pad("src")
    if not srcpad:
        sys.stderr.write(" Unable to get source pad of source \n")
    srcpad.link(sinkpad)
    streammux.link(pgie)
    pgie.link(tracker)
    tracker.link(nvvidconv)
    nvvidconv.link(nvosd)
    if is_aarch64():
        nvosd.link(transform)
        transform.link(sink)

    else:
        nvosd.link(sink)


    # create and 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)

    # Lets add probe to get informed of the meta data generated, we add probe to
    # the sink pad of the osd element, since by that time, the buffer would have
    # had got all the metadata.
    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, osd_sink_pad_buffer_probe, 0)


    print("Starting pipeline \n")

    # start play back and listed to events
    pipeline.set_state(Gst.State.PLAYING)
    try:
      loop.run()
    except:
      pass

    # cleanup
    pipeline.set_state(Gst.State.NULL)

if __name__ == '__main__':
    sys.exit(main(sys.argv))

Is the number of objects to be detected too high ? Does that affect the speed ?

Hi,
The user shared a sample and looks like he deleted it. Sorry I did not notice it.

There are set_property() calls in the code. So only sink.set_property(‘sync’, 0) fails and other calls succeed? A bit strange only one function call fails.

The error as shown on the terminal when I run the script with recommended changes .

Creating Pipeline 
 
Creating Source 
 
Creating EGLSink 

Traceback (most recent call last):
  File "Yolo_app1.py", line 427, in <module>
    sys.exit(main(sys.argv))
  File "Yolo_app1.py", line 324, in main
    sink.set_property('sync', 0)
AttributeError: 'NoneType' object has no attribute 'set_property'

Note: This a edited script python deepstream-test app1 and NOT app3 for csi camera

This is embarrassing but I messed up with a additional space in the code after “nvoverlaysink”. Everything is working, but the live feed doesn’t show up anymore. So I changed nvoverlaysink back to nveglglessink to try to fix it, is to be expected ?

Hi,
By default it is

... ! nvegltransform ! nveglglessink

It should work by replacing with

... ! queue ! nvoverlaysink
1 Like

Yes, It is working but there is no video feed now, is this to be expected ?

Hi,

Do you mean you cannot see video playback on screen?

Yes, when changing the recommended settings there is no video playback but everything else is fine and message “the buffers are being dropped” doesn’t show up

Hi,
Please check if you can run gst-launch-1.0 command and see video preview:
Could not link nvarguscamerasrc0 to nvstreammux0 - #10 by DaneLLL