Deepstream使用

DeepStream介绍

deepstream是nvidia官方推出的一个数据流处理工具包,可以很方便的实现对视频的解码、推理等工作,高效的完成图像分类、目标检测、识别和跟踪等任务。
deepstream是在gstreamer的基础上开发的,因此使用deepstream需要一定的c语言基础和gstreamer基础。deepstream可以看成是一个壳,里面集成了gstreamer和tensorrt等工具,我们可以使用deepstream完成视频的解码、传输、神经网络推理、结果的渲染等工作。

安装步骤

$ sudo apt install \
    libssl1.0.0 \
    libgstreamer1.0-0 \
    gstreamer1.0-tools \
    gstreamer1.0-plugins-good \
    gstreamer1.0-plugins-bad \
    gstreamer1.0-plugins-ugly \
    gstreamer1.0-libav \
    libgstrtspserver-1.0-0 \
    libjansson4=2.11-1
$ sudo apt-get install librdkafka1=0.11.3-1build1
$ tar -xpvf deepstream_sdk_v4.0.2_jetson.tbz2
$ cd deepstream_sdk_v4.0.2_jetson
$ sudo tar -xvpf binaries.tbz2 -C /
$ sudo ./install.sh
$ sudo ldconfig

插件配置

参考deepstream_sdk_v4.0.2_jetson/samples/configs/deepstream-app/下的配置文件:

  • source30_1080p_resnet_dec_infer_tiled_display_int8.txt:演示具有主要推理功能的30个流解码。(仅适用于dGPU和Jetson AGX Xavier平台。)
  • source4_1080p_resnet_dec_infer_tiled_display_int8.txt:演示具有主要推理,对象跟踪和三个不同辅助分类器的四个流解码。(仅适用于dGPU和Jetson AGX Xavier平台。)
  • source4_1080p_resnet_dec_infer_tracker_sgie_tiled_display_int8_gpu1.txt:在GPU 1上针对主要推理,对象跟踪和三个不同的二级分类器演示四个流解码(对于具有多个GPU卡的系统)。仅适用于dGPU平台。
  • config_infer_primary.txt:将 nvinfer元素配置为主要检测器。
  • config_infer_secondary_carcolor.txt, config_infer_secondary_carmake.txt, config_infer_secondary_vehicletypes.txt:将 nvinfer元素配置为辅助分类器。
  • iou_config.txt:配置一个低级的IOU(联合路口)跟踪器。
  • source1_usb_dec_infer_resnet_int8.txt:演示一台USB摄像机作为输入。
  • source1_csi_dec_infer_resnet_int8.txt:演示一个CSI摄像机作为输入;仅限于Jetson。
  • source2_csi_usb_dec_infer_resnet_int8.txt:演示一台CSI摄像机和一台USB摄像机作为输入;仅限于Jetson。
  • source6_csi_dec_infer_resnet_int8.txt:演示六个CSI摄像机作为输入;仅限于Jetson。
  • source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt:演示8解码+推断+跟踪器;仅适用于Jetson Nano。
  • source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_tx1.txt:演示8解码+推断+跟踪器;仅适用于Jetson TX1。
  • source12_1080p_dec_infer-resnet_tracker_tiled_display_fp16_tx2.txt:演示12个解码+推断+跟踪器;仅适用于Jetson TX2。

视频输入

camera

  • USB摄像头
[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=1
camera-width=1280
camera-height=720
camera-fps-n=30
camera-fps-d=1
camera-v4l2-dev-node=0
  • CSI摄像头
[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=CSI
type=5
camera-width=1280
camera-height=720
camera-fps-n=30
camera-fps-d=1
camera-csi-sensor-id=0

videofile

4个相同文件,用MultiURI

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=3
uri=file://../../streams/sample_1080p_h264.mp4
num-sources=4
#drop-frame-interval=2
gpu-id=0
# (0): memtype_device   - Memory type Device
# (1): memtype_pinned   - Memory type Host Pinned
# (2): memtype_unified  - Memory type Unified
cudadec-memtype=0

media stream

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=4
uri=rtsp://admin:admin123@192.168.1.106:554/cam/realmonitor?channel=1&subtype=0
num-sources=1
#drop-frame-interval=2
gpu-id=0
# (0): memtype_device   - Memory type Device
# (1): memtype_pinned   - Memory type Host Pinned
# (2): memtype_unified  - Memory type Unified
cudadec-memtype=0

多路USB

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=3
uri=file://../../streams/sample_1080p_h264.mp4
num-sources=4
#drop-frame-interval=2
gpu-id=0
# (0): memtype_device   - Memory type Device
# (1): memtype_pinned   - Memory type Host Pinned
# (2): memtype_unified  - Memory type Unified
cudadec-memtype=0

多路CSI

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=CSI
type=5
camera-csi-sensor-id=0
camera-width=1280
camera-height=720
camera-fps-n=30
camera-fps-d=1

[source1]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=CSI
type=5
camera-csi-sensor-id=1
camera-width=1280
camera-height=720
camera-fps-n=30
camera-fps-d=1

[source2]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=CSI
type=5
camera-csi-sensor-id=2
camera-width=1280
camera-height=720
camera-fps-n=30
camera-fps-d=1

[source3]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=CSI
type=5
camera-csi-sensor-id=3
camera-width=1280
camera-height=720
camera-fps-n=30
camera-fps-d=1

视频处理

物体检测

# config-file property is mandatory for any gie section.
# Other properties are optional and if set will override the properties set in
# the infer config file.
[primary-gie]
enable=1
model-engine-file=../../models/Primary_Detector/resnet10.caffemodel_b30_int8.engine
#Required to display the PGIE labels, should be added even when using config-file
#property
batch-size=4
#Required by the app for OSD, not a plugin property
bbox-border-color0=1;0;0;1
bbox-border-color1=0;1;1;1
bbox-border-color2=0;0;1;1
bbox-border-color3=0;1;0;1
interval=0
#Required by the app for SGIE, when used along with config-file property
gie-unique-id=1
config-file=config_infer_primary.txt

物体跟踪

[tracker]
enable=1
tracker-width=640
tracker-height=368
#tracker-width=480
#tracker-height=272

#ll-lib-file=/opt/nvidia/deepstream/deepstream-4.0/lib/libnvds_mot_iou.so
#ll-lib-file=/opt/nvidia/deepstream/deepstream-4.0/lib/libnvds_nvdcf.so
ll-lib-file=/opt/nvidia/deepstream/deepstream-4.0/lib/libnvds_mot_klt.so
#ll-config-file required for DCF/IOU only
#ll-config-file=tracker_config.yml
#ll-config-file=iou_config.txt
gpu-id=0
#enable-batch-process applicable to DCF only
enable-batch-process=1

检测后的具体分类

[secondary-gie0]
enable=1
model-engine-file=../../models/Secondary_VehicleTypes/resnet18.caffemodel_b16_int8.engine
gpu-id=0
batch-size=16
gie-unique-id=4
operate-on-gie-id=1
operate-on-class-ids=0;
config-file=config_infer_secondary_vehicletypes.txt

[secondary-gie1]
enable=1
model-engine-file=../../models/Secondary_CarColor/resnet18.caffemodel_b16_int8.engine
batch-size=16
gpu-id=0
gie-unique-id=5
operate-on-gie-id=1
operate-on-class-ids=0;
config-file=config_infer_secondary_carcolor.txt

[secondary-gie2]
enable=1
model-engine-file=../../models/Secondary_CarMake/resnet18.caffemodel_b16_int8.engine
batch-size=16
gpu-id=0
gie-unique-id=6
operate-on-gie-id=1
operate-on-class-ids=0;
config-file=config_infer_secondary_carmake.txt

视频输出

多路合并

单路

[tiled-display]
enable=1
rows=1
columns=1
width=1280
height=720

多路

[tiled-display]
enable=1
rows=4
columns=2
width=1280
height=720
gpu-id=0
#(0): nvbuf-mem-default - Default memory allocated, specific to particular platform
#(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory, applicable for Tesla
#(2): nvbuf-mem-cuda-device - Allocate Device cuda memory, applicable for Tesla
#(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory, applicable for Tesla
#(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson
nvbuf-memory-type=0

screen

[sink0]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 5=Overlay
type=5
sync=0
display-id=0
offset-x=0
offset-y=0
width=0
height=0
overlay-id=1
source-id=0

videofile

[sink1]
enable=1
type=3
#1=mp4 2=mkv
container=1
#1=h264 2=h265 3=mpeg4
codec=1
sync=0
bitrate=2000000
output-file=out.mp4
source-id=0

media stream

[sink2]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 5=Overlay
type=4
#1=h264 2=h265
codec=1
sync=0
bitrate=4000000
# set below properties in case of RTSPStreaming
rtsp-port=8554
udp-port=5400

vlc打开网络流 rtsp://192.168.0.118:8554/ds-test

osd

[osd]
enable=1
border-width=2
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Serif
show-clock=0
clock-x-offset=800
clock-y-offset=820
clock-text-size=12
clock-color=1;0;0;0

streammux

[streammux]
##Boolean property to inform muxer that sources are live
live-source=1
## 根据路数进行设置
batch-size=4 
##time out in usec, to wait after the first buffer is available
##to push the batch even if the complete batch is not formed
batched-push-timeout=40000
## Set muxer output width and height
width=1280
height=720

Sample例程

  • DeepStream Sample App /sources/apps/sample_apps/deepstream-app

说明:端到端示例演示了4级联神经网络(1个一级检测器和3个二级分类器)的多相机流,并显示平铺输出。

  • DeepStream Test 1 /sources/apps/sample_apps/deepstream-t
  • DeepStream Test 2 /sources/apps/sample_apps/deepstream-test2

说明:简单的应用程序,建立在test1之上,显示额外的属性,如跟踪和二级分类属性。

  • DeepStream Test 3 /sources/apps/sample_apps/deepstream-test3

说明:简单的应用程序,建立在test1的基础上,显示多个输入源和批处理使用nvstreammuxer。

  • DeepStream Test 4 /sources/apps/sample_apps/deepstream-test4

说明:这是在Test1示例的基础上构建的,演示了“nvmsgconv”和“nvmsgbroker”插件在物联网连接管道中的使用。对于test4,用户必须修改kafka代理连接字符串才能成功连接。需要安装分析服务器docker之前运行test4。DeepStream分析文档有关于设置分析服务器的更多信息。

  • FasterRCNN Object Detector /sources/objectDetector_FasterRCNN

说明:FasterRCNN物体探测器实例。

  • SSD Object Detector /sources/objectDetector_SSD

说明:SSD目标探测器实例。