Can not run deepstream_test_1.py?

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

I use Docker container on PC with Deeepstream 6.2. I downloaded DS python app and run deepstream_test_1.py with modified code to not display video

python3 deepstream_test_1.py /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4

#!/usr/bin/env python3

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2022 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 os
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call

import pyds

PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3


def osd_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

        #Intiallizing object counter with 0.
        obj_counter = {
            PGIE_CLASS_ID_VEHICLE:0,
            PGIE_CLASS_ID_PERSON:0,
            PGIE_CLASS_ID_BICYCLE:0,
            PGIE_CLASS_ID_ROADSIGN:0
        }
        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)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.8) #0.8 is alpha (opacity)
            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):
    # Check input arguments
    if len(args) != 2:
        sys.stderr.write("usage: %s <media file or uri>\n" % args[0])
        sys.exit(1)

    # 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()

    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("filesrc", "file-source")
    if not source:
        sys.stderr.write(" Unable to create Source \n")

    # Since the data format in the input file is elementary h264 stream,
    # we need a h264parser
    print("Creating H264Parser \n")
    h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
    if not h264parser:
        sys.stderr.write(" Unable to create h264 parser \n")

    # Use nvdec_h264 for hardware accelerated decode on GPU
    print("Creating Decoder \n")
    decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
    if not decoder:
        sys.stderr.write(" Unable to create Nvv4l2 Decoder \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")

    # Use convertor to convert from NV12 to RGBA as required by nvosd
    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():
        print("Creating nv3dsink \n")
        sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
        if not sink:
            sys.stderr.write(" Unable to create nv3dsink \n")
    else:
        sink = Gst.ElementFactory.make("fakesink","fakesink")
        
        # print("Creating EGLSink \n")
        # sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
        # if not sink:
        #     sys.stderr.write(" Unable to create egl sink \n")

    print("Playing file %s " %args[1])
    source.set_property('location', args[1])
    if os.environ.get('USE_NEW_NVSTREAMMUX') != 'yes': # Only set these properties if not using new gst-nvstreammux
        streammux.set_property('width', 1920)
        streammux.set_property('height', 1080)
        streammux.set_property('batched-push-timeout', 4000000)
    
    streammux.set_property('batch-size', 1)
    pgie.set_property('config-file-path', "dstest1_pgie_config.txt")

    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    pipeline.add(h264parser)
    pipeline.add(decoder)
    pipeline.add(streammux)
    pipeline.add(pgie)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)

    # we link the elements together
    # file-source -> h264-parser -> nvh264-decoder ->
    # nvinfer -> nvvidconv -> nvosd -> video-renderer
    print("Linking elements in the Pipeline \n")
    source.link(h264parser)
    h264parser.link(decoder)

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

    # 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)

    # start play back and listen to events
    print("Starting pipeline \n")
    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))

But I got the error as follow

Creating Pipeline 
 
Creating Source 
 
Creating H264Parser 

Creating Decoder 

Playing file /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 
Adding elements to Pipeline 

Linking elements in the Pipeline 

Starting pipeline 

0:00:00.206146373 29767      0x2f43260 WARN                 nvinfer gstnvinfer.cpp:677:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1170> [UID = 1]: Warning, OpenCV has been deprecated. Using NMS for clustering instead of cv::groupRectangles with topK = 20 and NMS Threshold = 0.5
WARNING: [TRT]: CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
WARNING: [TRT]: CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
0:00:02.414181392 29767      0x2f43260 INFO                 nvinfer gstnvinfer.cpp:680:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1909> [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-6.2/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [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         

0:00:02.447430341 29767      0x2f43260 INFO                 nvinfer gstnvinfer.cpp:680:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2012> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-6.2/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine
0:00:02.449350296 29767      0x2f43260 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Error: gst-stream-error-quark: No valid frames found before end of stream (5): gstbaseparse.c(3657): gst_base_parse_loop (): /GstPipeline:pipeline0/GstH264Parse:h264-parser
nvstreammux: Successfully handled EOS for source_id=0

How I can fix it?

3

deepstream_test_1.py only support h264 raw data.Please replace with sample_720p.h264

If you want process mp4, need add qtdemux to pipeline

Thanks

4

Thanks. I can run with sample_720p.h264.

Now I want to run with mp4 video, but I got error after adding qtdemux and linking

0:00:02.482843389 15501      0x3e71500 INFO                 nvinfer gstnvinfer.cpp:680:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2012> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-6.2/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine
0:00:02.484941317 15501      0x3e71500 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Error: gst-stream-error-quark: Internal data stream error. (1): qtdemux.c(6619): gst_qtdemux_loop (): /GstPipeline:pipeline0/GstQTDemux:qtdemux:
streaming stopped, reason not-linked (-1)

Here is my code

#!/usr/bin/env python3

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2022 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 os
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call

import pyds

PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3


def osd_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

        #Intiallizing object counter with 0.
        obj_counter = {
            PGIE_CLASS_ID_VEHICLE:0,
            PGIE_CLASS_ID_PERSON:0,
            PGIE_CLASS_ID_BICYCLE:0,
            PGIE_CLASS_ID_ROADSIGN:0
        }
        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)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.8) #0.8 is alpha (opacity)
            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):
    # Check input arguments
    if len(args) != 2:
        sys.stderr.write("usage: %s <media file or uri>\n" % args[0])
        sys.exit(1)

    # 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()

    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("filesrc", "file-source")
    if not source:
        sys.stderr.write(" Unable to create Source \n")

    ###############################################
    # dtdemux for mp4
    print("Creating Source \n ")
    qtdemux_mp4 = Gst.ElementFactory.make("qtdemux", "qtdemux")
    if not source:
        sys.stderr.write(" Unable to create qtdemux \n")
    
    ###############################################
    
    # Since the data format in the input file is elementary h264 stream,
    # we need a h264parser
    print("Creating H264Parser \n")
    h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
    if not h264parser:
        sys.stderr.write(" Unable to create h264 parser \n")

    # Use nvdec_h264 for hardware accelerated decode on GPU
    print("Creating Decoder \n")
    decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
    if not decoder:
        sys.stderr.write(" Unable to create Nvv4l2 Decoder \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")

    # Use convertor to convert from NV12 to RGBA as required by nvosd
    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():
        print("Creating nv3dsink \n")
        sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
        if not sink:
            sys.stderr.write(" Unable to create nv3dsink \n")
    else:
        sink = Gst.ElementFactory.make("fakesink", "fakesink")
        
        
        # print("Creating EGLSink \n")
        # sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
        # if not sink:
        #     sys.stderr.write(" Unable to create egl sink \n")

    print("Playing file %s " %args[1])
    source.set_property('location', args[1])
    if os.environ.get('USE_NEW_NVSTREAMMUX') != 'yes': # Only set these properties if not using new gst-nvstreammux
        streammux.set_property('width', 1920)
        streammux.set_property('height', 1080)
        streammux.set_property('batched-push-timeout', 4000000)
    
    streammux.set_property('batch-size', 1)
    pgie.set_property('config-file-path', "dstest1_pgie_config.txt")

    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    ######################################
    # for mp4
    pipeline.add(qtdemux_mp4)
    ######################################
    pipeline.add(h264parser)
    pipeline.add(decoder)
    pipeline.add(streammux)
    pipeline.add(pgie)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)

    # we link the elements together
    # file-source -> h264-parser -> nvh264-decoder ->
    # nvinfer -> nvvidconv -> nvosd -> video-renderer
    print("Linking elements in the Pipeline \n")
    # source.link(h264parser)
    ########################################
    # for mp4
    source.link(qtdemux_mp4)
    qtdemux_mp4.link(h264parser)
    
    ########################################
    h264parser.link(decoder)

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

    # 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)

    # start play back and listen to events
    print("Starting pipeline \n")
    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))
5

qtdemux can’t link h264parser directly. It’s linked in runtime

Here is a patch to process mp4 for deepstream_test_1.py
new.patch (2.4 KB)

This is not DeepStream problem, Please reference the document of Gstreamer

6

Thanks a lot. I will check it and reply you later.

7

Thanks a lot. It works properly.
Now I want to run inference on image and images. What examples can I follow?

8

deepstream-image-decode-test maybe which you want.

If you have another problem, please open a new topic.thanks.

9

Thanks.
It is C++ code. There is not Python code. I will check it.

10

This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.