• Hardware Platform (GPU): Quadro P620
• DeepStream Version: 6.2
• TensorRT Version:8.5.2.2
• NVIDIA GPU Driver Version (valid for GPU only): 535.54.03
Hi,
Could you help me how to use software encoder like x264enc in deepstream apps, pls ?
I use deepstream-test1 to give it a try.
With nvv4l2h264enc, it works normally. But it will raise errors when using x264enc.
#!/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):
mem_type = 0
# 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")
nvvidconv.set_property("nvbuf-memory-type", mem_type)
# 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")
vidconv = Gst.ElementFactory.make(
"videoconvert", "convertor_postosd")
if not vidconv:
sys.stderr.write(" Unable to create nvvidconv_postosd \n")
# Make the encoder
encoder = Gst.ElementFactory.make("x264enc", "encoder")
print("Creating H264 Encoder")
if not encoder:
sys.stderr.write(" Unable to create encoder")
# Make parser
parser = Gst.ElementFactory.make("h264parse", "h264parse")
if not parser:
sys.stderr.write(" Unable to create h264parse")
# Make matroskamux
muxer = Gst.ElementFactory.make("matroskamux", "matroskamux")
if not muxer:
sys.stderr.write(" Unable to create matroskamux")
# Make the file sink to save video
sink = Gst.ElementFactory.make("filesink", "filesink")
if not sink:
sys.stderr.write(" Unable to create filesink")
sink.set_property("location", "recorded_swEnc.mkv")
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)
streammux.set_property("nvbuf-memory-type", mem_type)
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(vidconv)
pipeline.add(encoder)
pipeline.add(parser)
pipeline.add(muxer)
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(vidconv)
vidconv.link(encoder)
encoder.link(parser)
parser.link(muxer)
muxer.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))
Full log:
Creating Pipeline
Creating Source
Creating H264Parser
Creating Decoder
Creating H264 Encoder
Playing file /opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.h264
Adding elements to Pipeline
Linking elements in the Pipeline
Starting pipeline
0:00:00.158018171 817 0x39304d0 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
0:00:02.178786963 817 0x39304d0 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
0:00:02.190266292 817 0x39304d0 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
0:00:02.191661149 817 0x39304d0 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Frame Number=0 Number of Objects=12 Vehicle_count=8 Person_count=4
0:00:02.333412672 817 0x3835f00 WARN nvinfer gstnvinfer.cpp:2369:gst_nvinfer_output_loop:<primary-inference> error: Internal data stream error.
0:00:02.333422910 817 0x3835f00 WARN nvinfer gstnvinfer.cpp:2369:gst_nvinfer_output_loop:<primary-inference> error: streaming stopped, reason not-linked (-1)
Error: gst-stream-error-quark: Internal data stream error. (1): gstnvinfer.cpp(2369): gst_nvinfer_output_loop (): /GstPipeline:pipeline0/GstNvInfer:primary-inference:
streaming stopped, reason not-linked (-1)