• Jetson AGX Xavier
• DeepStream 6.0.1
• JetPack 4.6.1
I`m trying to test the example, but it doesn`t work…
i followed github document, fit version, check pyds.so in lib folder… but i can`t find what is wrong.
how can i slove this problem??
Can you run gst-inspect-1.0 nvstreammux ?
This means that gstreamer is unable to find the plugin named nvstreammux. So in your python code when you try to create the plugin, it is unable to create it and returns a null value instead.
Can you check
-
gst-inspect-1.0 -b(This should list out any blacklisted plugins. Could be due to inconsistent dependencies) - Clear the gstreamer cache
rm -r ~/.cache/gstreamer-1.0and try to rungst-inspect-1.0 nvstreammuxagain and see if it works
1 Like
It is also possible that your deepstream installation has some problem.
You can try to use docker instead.
To verify if the docker setting will work you can try this comand:
docker run --rm --runtime nvidia nvcr.io/nvidia/deepstream-l4t:6.0.1-samples gst-inspect-1.0 nvstreammux`
NOTE: The command above will download a ~1-2 Gb docker image.leehag1224@ubuntu:~$ gst-inspect-1.0 nvstreammux
Factory Details:
Rank primary (256)
Long-name Stream multiplexer
Klass Generic
Description N-to-1 pipe stream multiplexing
Author NVIDIA Corporation. Post on Deepstream for Tesla forum for any queries @ https://devtalk.nvidia.com/default/board/209/
Plugin Details:
Name nvdsgst_multistream
Description NVIDIA Multistream mux/demux plugin
Filename /usr/lib/aarch64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_multistream.so
Version 6.0.1
License Proprietary
Source module nvmultistream
Binary package NVIDIA Multistream Plugins
Origin URL http://nvidia.com/
GObject
+----GInitiallyUnowned
+----GstObject
+----GstElement
+----GstNvStreamMux
Pad Templates:
SINK template: 'sink_%u'
Availability: On request
Capabilities:
video/x-raw(memory:NVMM)
format: { (string)NV12, (string)RGBA, (string)I420 }
width: [ 1, 2147483647 ]
height: [ 1, 2147483647 ]
framerate: [ 0/1, 2147483647/1 ]
SRC template: 'src'
Availability: Always
Capabilities:
video/x-raw(memory:NVMM)
format: { (string)NV12, (string)RGBA, (string)I420 }
width: [ 1, 2147483647 ]
height: [ 1, 2147483647 ]
framerate: [ 0/1, 2147483647/1 ]
Element has no clocking capabilities.
Element has no URI handling capabilities.
Pads:
SRC: 'src'
Pad Template: 'src'
Element Properties:
name : The name of the object
flags: readable, writable
String. Default: "nvstreammux0"
parent : The parent of the object
flags: readable, writable
Object of type "GstObject"
batch-size : Maximum number of buffers in a batch
flags: readable, writable
Unsigned Integer. Range: 0 - 1024 Default: 0
batched-push-timeout: Timeout in microseconds to wait after the first buffer is available
to push the batch even if the complete batch is not formed.
Set to -1 to wait infinitely
flags: readable, writable
Integer. Range: -1 - 2147483647 Default: -1
width : Width of each frame in output batched buffer. This property MUST be set.
flags: readable, writable
Unsigned Integer. Range: 0 - 4294967295 Default: 0
height : Height of each frame in output batched buffer. This property MUST be set.
flags: readable, writable
Unsigned Integer. Range: 0 - 4294967295 Default: 0
enable-padding : Maintain input aspect ratio when scaling by padding with black bands.
flags: readable, writable
Boolean. Default: false
gpu-id : Set GPU Device ID
flags: readable, writable
Unsigned Integer. Range: 0 - 4294967295 Default: 0
live-source : Boolean property to inform muxer that sources are live.
flags: readable, writable
Boolean. Default: false
num-surfaces-per-frame: Max number of surfaces per frame
flags: readable, writable
Unsigned Integer. Range: 1 - 4 Default: 1
nvbuf-memory-type : Type of NvBufSurface Memory to be allocated for output buffers
flags: readable, writable, changeable only in NULL or READY state
Enum "GstNvBufMemoryType" Default: 0, "nvbuf-mem-default"
(0): nvbuf-mem-default - Default memory allocated, specific to particular platform
(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory
(2): nvbuf-mem-cuda-device - Allocate Device cuda memory
(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory
(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson
compute-hw : Compute Scaling HW
flags: readable, writable, controllable
Enum "GstNvComputeHWType" Default: 0, "Default"
(0): Default - Default, GPU for Tesla, VIC for Jetson
(1): GPU - GPU
(2): VIC - VIC
interpolation-method: Set interpolation methods
flags: readable, writable, controllable
Enum "GstNvInterpolationMethod" Default: 1, "Bilinear"
(0): Nearest - Nearest
(1): Bilinear - Bilinear
(2): Algo-1 - GPU - Cubic, VIC - 5 Tap
(3): Algo-2 - GPU - Super, VIC - 10 Tap
(4): Algo-3 - GPU - LanzoS, VIC - Smart
(5): Algo-4 - GPU - Ignored, VIC - Nicest
(6): Default - GPU - Nearest, VIC - Nearest
buffer-pool-size : Maximum number of buffers from muxer's output pool
flags: readable, writable
Unsigned Integer. Range: 4 - 1024 Default: 4
attach-sys-ts : If set to TRUE, system timestamp will be attached as ntp timestamp.
If set to FALSE, ntp timestamp from rtspsrc, if available, will be attached.
flags: readable, writable
Boolean. Default: true
sync-inputs : Boolean property to force sychronization of input frames.
flags: readable, writable
Boolean. Default: false
max-latency : Additional latency in live mode to allow upstream to take longer to produce buffers for the current position (in nanoseconds)
flags: readable, writable
Unsigned Integer. Range: 0 - 4294967295 Default: 0
frame-num-reset-on-eos: Reset frame numbers to 0 for a source from which EOS is received (For debugging purpose only)
flags: readable, writable
Boolean. Default: false
oohh… it works! than, is the problem solved??
If you see this, means its working. You should be able to run the code.
leehag1224@ubuntu:/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-test1$ python3 deepstream_test_1.py ../../../../samples/streams/sample_qHD.h264
Creating Pipeline
Creating Source
Creating H264Parser
Creating Decoder
Creating EGLSink
Playing file ../../../../samples/streams/sample_qHD.h264
Adding elements to Pipeline
Traceback (most recent call last):
File "deepstream_test_1.py", line 250, in <module>
sys.exit(main(sys.argv))
File "deepstream_test_1.py", line 208, in main
pipeline.add(sinkpad = streammux.get_request_pad("sink_0"))
TypeError: Gst.Bin.add() got an unexpected keyword argument 'sinkpad'
leehag1224@ubuntu:/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-test1$
noooooooooooooooo…
solved ‘unable to ~~’, but i got new error…
This seems weird.
Can you upload your code here please?
you mean “deepstream_test_1.py” code??
Yes. It doesn’t seem to match the deepstream_test1.py on their git deepstream_python_apps/deepstream_test_1.py at master · NVIDIA-AI-IOT/deepstream_python_apps · GitHub
ok!
#!/usr/bin/env python3
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2021 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
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
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
#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
}
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):
# 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
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("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():
transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
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])
streammux.set_property('width', 1920)
streammux.set_property('height', 1080)
streammux.set_property('batch-size', 1)
streammux.set_property('batched-push-timeout', 4000000)
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(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)
if is_aarch64():
nvosd.link(transform)
transform.link(sink)
else:
nvosd.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)
# 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))
here, deepstream_test1.py code
i didn`t modify it
I see. Can you change your line no 208 from
pipeline.add(sinkpad = streammux.get_request_pad("sink_0"))
To
sinkpad = streammux.get_request_pad("sink_0")
1 Like
leehag1224@ubuntu:/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-test1$ python3 deepstream_test_1.py ../../../../samples/streams/sample_720p.h264
Creating Pipeline
Creating Source
Creating H264Parser
Creating Decoder
Creating EGLSink
Playing file ../../../../samples/streams/sample_720p.h264
Adding elements to Pipeline
Traceback (most recent call last):
File "deepstream_test_1.py", line 251, in <module>
sys.exit(main(sys.argv))
File "deepstream_test_1.py", line 209, in main
sinkpad = streammux.get_request__pad("sink_0")
AttributeError: 'GstNvStreamMux' object has no attribute 'get_request__pad'
i tried this, so got this one. umm…
There is an extra underscore in this get_request__pad. It should be get_request_pad
oohhh… i missed it…
so i modify it and try
leehag1224@ubuntu:/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-test1$ python3 deepstream_test_1.py ../../../../samples/streams/sample_720p.h264
Creating Pipeline
Creating Source
Creating H264Parser
Creating Decoder
Creating EGLSink
Playing file ../../../../samples/streams/sample_720p.h264
Adding elements to Pipeline
(python3:18010): GStreamer-WARNING **: 15:35:55.655: Trying to link elements onscreendisplay and nvegl-transform that don't share a common ancestor: nvegl-transform hasn't been added to a bin or pipeline, and onscreendisplay is in pipeline0
(python3:18010): GStreamer-WARNING **: 15:35:55.656: Trying to link elements onscreendisplay and nvegl-transform that don't share a common ancestor: nvegl-transform hasn't been added to a bin or pipeline, and onscreendisplay is in pipeline0
Starting pipeline
Opening in BLOCKING MODE
0:00:00.540843938 18010 0x1fdd8c70 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> 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
ERROR: Deserialize engine failed because file path: /opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/apps/deepstream-test1/../../../../samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine open error
0:00:02.999987020 18010 0x1fdd8c70 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1889> [UID = 1]: deserialize engine from file :/opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/apps/deepstream-test1/../../../../samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine failed
0:00:03.019470284 18010 0x1fdd8c70 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1996> [UID = 1]: deserialize backend context from engine from file :/opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/apps/deepstream-test1/../../../../samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine failed, try rebuild
0:00:03.019560367 18010 0x1fdd8c70 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1914> [UID = 1]: Trying to create engine from model files
ERROR: Serialize engine failed because of file path: /opt/nvidia/deepstream/deepstream-6.0/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine opened error
0:00:48.455985704 18010 0x1fdd8c70 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1942> [UID = 1]: failed to serialize cude engine to file: /opt/nvidia/deepstream/deepstream-6.0/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.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
0:00:48.621075685 18010 0x1fdd8c70 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Error: gst-stream-error-quark: Internal data stream error. (1): gstbasesrc.c(3055): gst_base_src_loop (): /GstPipeline:pipeline0/GstFileSrc:file-source:
streaming stopped, reason not-linked (-1)
got this one. better than before, but still the example doesn`t work
Trying to link elements onscreendisplay and nvegl-transform that don't share a common ancestor: nvegl-transform hasn't been added to a bin or pipeline, and onscreendisplay is in pipeline0
This should not appear.
Can you change your if at line 184 to
if is_aarch64():
pipeline.add(transform)
nvosd.link(transform)
transform.link(sink)
there are one thing i want to ask
leehag1224@ubuntu:/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-test1$ nvcc --version
bash: nvcc: command not found
if i install jetpack, deepstream properly, the version should come out, right?
nvcc is not the deepstream version. For nvcc to work you will need to install cuda-toolkit.
For Jetpack 4.6.1 CUDA version should be 10.2 I think
i see
i change the code if is_aarch64():~
leehag1224@ubuntu:/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-test1$ python3 deepstream_test_1.py ../../../../samples/streams/sample_720p.h264
Creating Pipeline
Creating Source
Creating H264Parser
Creating Decoder
Traceback (most recent call last):
File "deepstream_test_1.py", line 255, in <module>
sys.exit(main(sys.argv))
File "deepstream_test_1.py", line 186, in main
pipeline.add(transform)
NameError: name 'transform' is not defined
and NameError: name ‘transform’ is not defined