Deepstream-nvdsanalytics

hi, i want to modify the demo code to let it can use csi camera, but when i running the script, it give me a error, please tell me how to modify the code:

environment

Jetpack 4.6.4
ubuntu 18.04
python 3.6.9
module jetson nano 4GB

code

this is my code modify from deepstream_nvdsanalytics.py

#!/usr/bin/env python3

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2020-2021 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.
################################################################################

import sys
sys.path.append('../')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from gi.repository import GLib
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 GETFPS

import pyds

fps_streams={}

MAX_DISPLAY_LEN=64
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
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
pgie_classes_str= ["Vehicle", "TwoWheeler", "Person","RoadSign"]

# nvanlytics_src_pad_buffer_probe  will extract metadata received on nvtiler sink pad
# and update params for drawing rectangle, object information etc.
def nvanalytics_src_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:
        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
        obj_counter = {
        PGIE_CLASS_ID_VEHICLE:0,
        PGIE_CLASS_ID_PERSON:0,
        PGIE_CLASS_ID_BICYCLE:0,
        PGIE_CLASS_ID_ROADSIGN:0
        }
        print("#"*50)
        while l_obj:
            try: 
                # Note that l_obj.data needs a cast to pyds.NvDsObjectMeta
                # The casting is done by pyds.NvDsObjectMeta.cast()
                obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            l_user_meta = obj_meta.obj_user_meta_list
            # Extract object level meta data from NvDsAnalyticsObjInfo
            while l_user_meta:
                try:
                    user_meta = pyds.NvDsUserMeta.cast(l_user_meta.data)
                    if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSOBJ.USER_META"):             
                        user_meta_data = pyds.NvDsAnalyticsObjInfo.cast(user_meta.user_meta_data)
                        if user_meta_data.dirStatus: print("Object {0} moving in direction: {1}".format(obj_meta.object_id, user_meta_data.dirStatus))                    
                        if user_meta_data.lcStatus: print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus))
                        if user_meta_data.ocStatus: print("Object {0} overcrowding status: {1}".format(obj_meta.object_id, user_meta_data.ocStatus))
                        if user_meta_data.roiStatus: print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus))
                except StopIteration:
                    break

                try:
                    l_user_meta = l_user_meta.next
                except StopIteration:
                    break
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break
    
        # Get meta data from NvDsAnalyticsFrameMeta
        l_user = frame_meta.frame_user_meta_list
        while l_user:
            try:
                user_meta = pyds.NvDsUserMeta.cast(l_user.data)
                if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSFRAME.USER_META"):
                    user_meta_data = pyds.NvDsAnalyticsFrameMeta.cast(user_meta.user_meta_data)
                    if user_meta_data.objInROIcnt: print("Objs in ROI: {0}".format(user_meta_data.objInROIcnt))                    
                    if user_meta_data.objLCCumCnt: print("Linecrossing Cumulative: {0}".format(user_meta_data.objLCCumCnt))
                    if user_meta_data.objLCCurrCnt: print("Linecrossing Current Frame: {0}".format(user_meta_data.objLCCurrCnt))
                    if user_meta_data.ocStatus: print("Overcrowding status: {0}".format(user_meta_data.ocStatus))
            except StopIteration:
                break
            try:
                l_user = l_user.next
            except StopIteration:
                break
        
        print("Frame Number=", frame_number, "stream id=", frame_meta.pad_index, "Number of Objects=",num_rects,"Vehicle_count=",obj_counter[PGIE_CLASS_ID_VEHICLE],"Person_count=",obj_counter[PGIE_CLASS_ID_PERSON])
        # Get frame rate through this probe
        fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
        try:
            l_frame=l_frame.next
        except StopIteration:
            break
        print("#"*50)

    return Gst.PadProbeReturn.OK



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.
    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):
    print("Decodebin child added:", name, "\n")
    if(name.find("decodebin") != -1):
        Object.connect("child-added",decodebin_child_added,user_data)

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 create_source_bin(index):
    print("Creating source bin")

    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 CSI camera.
    camera_src=Gst.ElementFactory.make("nvarguscamerasrc", "camera-source")
    if not camera_src:
        sys.stderr.write(" Unable to create uri decode bin \n")

    caps_filter = Gst.ElementFactory.make("capsfilter", "filter")
    if not caps_filter:
        sys.stderr.write(" Unable to create capsfilter \n")
    caps = Gst.Caps.from_string("video/x-raw(memory:NVMM), width=(int)1280, height=(int)720, format=(string)NV12, framerate=(fraction)30/1")
    caps_filter.set_property("caps", caps)

    # We need to create a ghost pad for the source bin which will act as a proxy
    # for the camera source src pad. The ghost pad will not have a target right
    # now. Once the camera source creates the src pad, we will set the ghost pad target.
    Gst.Bin.add(nbin, camera_src)
    Gst.Bin.add(nbin, caps_filter)
    camera_src.link(caps_filter)
    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):
    # Check input arguments
    if len(args) < 2:
        sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN]\n" % args[0])
        sys.exit(1)

    for i in range(0,len(args)-1):
        fps_streams["stream{0}".format(i)]=GETFPS(i)
    number_sources=len(args)-1

    # 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):
        print("Creating source_bin ",i," \n ")
        uri_name=args[i+1]
        if uri_name.find("rtsp://") == 0 :
            is_live = True
        #source_bin=create_source_bin(i, uri_name)
        source_bin=create_source_bin(i)
        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")
    queue6=Gst.ElementFactory.make("queue","queue6")
    queue7=Gst.ElementFactory.make("queue","queue7")
    pipeline.add(queue1)
    pipeline.add(queue2)
    pipeline.add(queue3)
    pipeline.add(queue4)
    pipeline.add(queue5)
    pipeline.add(queue6)
    pipeline.add(queue7)

    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 nvdsanalytics \n ")
    nvanalytics = Gst.ElementFactory.make("nvdsanalytics", "analytics")
    if not nvanalytics:
        sys.stderr.write(" Unable to create nvanalytics \n")
    nvanalytics.set_property("config-file", "config_nvdsanalytics.txt")
    #nvanalytics.set_property("config-file", "config_nvdsanalytics_c02.txt")

    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(is_aarch64()):
        print("Creating transform \n ")
        transform=Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
        if not transform:
            sys.stderr.write(" Unable to create transform \n")

    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)

    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', number_sources)
    streammux.set_property('batched-push-timeout', 4000000)
    pgie.set_property('config-file-path', "dsnvanalytics_pgie_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)

    #Set properties of tracker
    config = configparser.ConfigParser()
    config.read('dsnvanalytics_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 key == 'enable-past-frame' :
            tracker_enable_past_frame = config.getint('tracker', key)
            tracker.set_property('enable_past_frame', tracker_enable_past_frame)

    print("Adding elements to Pipeline \n")
    pipeline.add(pgie)
    pipeline.add(tracker)
    pipeline.add(nvanalytics)
    pipeline.add(tiler)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)

    if is_aarch64():
        pipeline.add(transform)
    pipeline.add(sink)

    # We link elements in the following order:
    # sourcebin -> streammux -> nvinfer -> nvtracker -> nvdsanalytics ->
    # nvtiler -> nvvideoconvert -> nvdsosd -> sink
    print("Linking elements in the Pipeline \n")
    streammux.link(queue1)
    queue1.link(pgie)
    pgie.link(queue2)
    queue2.link(tracker)
    tracker.link(queue3)
    queue3.link(nvanalytics)
    nvanalytics.link(queue4)
    queue4.link(tiler)
    tiler.link(queue5)
    queue5.link(nvvidconv)
    nvvidconv.link(queue6)
    queue6.link(nvosd)
    if is_aarch64():
        nvosd.link(queue7)
        queue7.link(transform)
        transform.link(sink)
    else:
        nvosd.link(queue7)
        queue7.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)
    nvanalytics_src_pad=nvanalytics.get_static_pad("src")
    if not nvanalytics_src_pad:
        sys.stderr.write(" Unable to get src pad \n")
    else:
        nvanalytics_src_pad.add_probe(Gst.PadProbeType.BUFFER, nvanalytics_src_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)

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

zs@zs-desktop:/dsPyApps/deepstream-nvdsanalytics$ python3 deepstream_nvdsanalytics.py 0
Creating Pipeline

Creating streamux

Creating source_bin 0

Creating source bin
source-bin-00
Creating Pgie

Creating nvtracker

Creating nvdsanalytics

Creating tiler

Creating nvvidconv

Creating nvosd

Creating transform

Creating EGLSink

Adding elements to Pipeline

Linking elements in the Pipeline

Now playing…
1 : 0
Starting pipeline

Using winsys: x11
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
gstnvtracker: Batch processing is ON
gstnvtracker: Past frame output is OFF
[NvMultiObjectTracker] Initialized
0:00:00.547629270 9319 0x17d17c70 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1161> [UID = 1]: Warning, OpenCV has been deprecated. Using NMS for clustering instead of cv::groupRectangles with topK = 20 and NMS Threshold = 0.5
0:00:05.997022445 9319 0x17d17c70 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1900> [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-6.0/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
INFO: [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x368x640
1 OUTPUT kFLOAT conv2d_bbox 16x23x40
2 OUTPUT kFLOAT conv2d_cov/Sigmoid 4x23x40

0:00:05.998228591 9319 0x17d17c70 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2004> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-6.0/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
0:00:06.008117081 9319 0x17d17c70 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus: [UID 1]: Load new model:dsnvanalytics_pgie_config.txt sucessfully
GST_ARGUS: Creating output stream
CONSUMER: Waiting until producer is connected…
GST_ARGUS: Available Sensor modes :
GST_ARGUS: 3264 x 2464 FR = 21.000000 fps Duration = 47619048 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 3264 x 1848 FR = 28.000001 fps Duration = 35714284 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1920 x 1080 FR = 29.999999 fps Duration = 33333334 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1640 x 1232 FR = 29.999999 fps Duration = 33333334 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1280 x 720 FR = 59.999999 fps Duration = 16666667 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: 1280 x 720 FR = 120.000005 fps Duration = 8333333 ; Analog Gain range min 1.000000, max 10.625000; Exposure Range min 13000, max 683709000;

GST_ARGUS: Running with following settings:
Camera index = 0
Camera mode = 5
Output Stream W = 1280 H = 720
seconds to Run = 0
Frame Rate = 120.000005
GST_ARGUS: Setup Complete, Starting captures for 0 seconds
GST_ARGUS: Starting repeat capture requests.
CONSUMER: Producer has connected; continuing.
Error: gst-stream-error-quark: Internal data stream error. (1): gstbasesrc.c(3055): gst_base_src_loop (): /GstPipeline:pipeline0/GstBin:source-bin-00/GstNvArgusCameraSrc:camera-source:
streaming stopped, reason not-linked (-1)
Exiting app

[NvMultiObjectTracker] De-initialized
GST_ARGUS: Cleaning up
CONSUMER: Done Success
GST_ARGUS: Done Success

  1. what is the DeepStream version?
  2. from the log, the camera 's 120fps is selected. it is inconsistent with capsfilter “framerate=(fraction)30/1”.

DeepStream version: 6.0.1
yes, i see the difference, but i dont know how to let it right

can you try removing “framerate=(fraction)30/1” or using “framerate=(fraction)60/1” or using “framerate=(fraction)120/1”?

yes, but the log look like …
i think my csi source not into the pipeline or not link with other element

you can use gst-launch to debug the pipeline first. then port to the python code. please refer to the following pipeline.
gst-launch-1.0 nvarguscamerasrc sensor-id=0 num-buffers=200 ! ‘video/x-raw(memory:NVMM), width=1280, height=720, framerate=30/1, format=NV12’ ! mux.sink_0 nvstreammux name=mux width=1280 height=720 batch-size=1 ! fakesink

I have tried since yesterday in CLI and python code, but give me a error:

/GstPipeline:pipeline0/GstNvStreamMux:mux:Input buffer number of surfaces (0) must be equal to mux->num_surfaces_per_frame (1)
	Set nvstreammux property num-surfaces-per-frame appropriately

then, i set the num_surfaces_per_frame = 1, but not run

could you share your CLI which gave error “…:mux:Input buffer number of …” ?

such you give me the referment, but num-buffers=40000 and surfaces=1, if i remember right:
gst-launch-1.0 nvarguscamerasrc sensor-id=0 num-buffers=40000 ! 'video/x-raw(memory:NVMM),width=1280,height=720,framerate=30/1,format=NV12,surfaces=1' ! mux.sink_0 nvstreammux name=mux width=1280 height=720 batch-size=1 ! fakesink

testing my local camera, the CLI works fink. I can see the output video after replacing fakesink with nv3dsink.

ow, next i thinking must check my camera and replace it, then i will tell you my conclusion

Sorry for the late reply, Is this still an DeepStream issue to support? Thanks!

I used a different scheme , then it was able to use the csi camera, next
will show the code tomorrow if have time . thinks.

Thanks for the update, Is this still an DeepStream issue to support? Thanks! please open a new topic if having other DeepStream problems.

So sorry, The reply time is a little late, This is the modified code, It can use csi camera:

'''
this method is ok, can use csi camera
'''
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.
    # use nvurisrcbin to enable file-loop
    uri_decode_bin=Gst.ElementFactory.make("nvarguscamerasrc", "src-elem")
    uri_decode_bin.set_property("sensor-id", index)
    uri_decode_bin.set_property("bufapi-version", True)
    if not uri_decode_bin:
        sys.stderr.write(" Unable to create uri decode bin \n")

    return uri_decode_bin

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