Deepstream cannot be displayed on the screen

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

I’m running deepstream with : deepstream-app -c deepstream_app_config_yoloV3.txt
but the results don’t show up on the screen
Here is my system configuration:
ubuntu 18.04
gpu :RTX2080Ti —only one gpu
display dirve : 440.59
deepstream_sdk:4.0
cuda:10.1
cudnn:7.6.5
tensorRT : 6.0.1.5

and i’m used “sudo ./NVIDIA-Linux-x86_64-440.59.run --no-opengl-files” to install my display dirve.and X_config was set during installation.

Here is the output of the deepstream:

optical@optical-Super-Server:~/deepstream_sdk_v4.0.2_x86_64/sources/objectDetector_Yolo$ deepstream-app -c deepstream_app_config_yoloV3.txt
libEGL warning: DRI2: failed to authenticate
Creating LL OSD context new
0:00:00.610830396 11417 0x561ce0baa6f0 INFO nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:initialize(): Trying to create engine from model files
Loading pre-trained weights…
Loading complete!
Total Number of weights read : 62001757
layer inp_size out_size weightPtr
(1) conv-bn-leaky 3 x 608 x 608 32 x 608 x 608 992
(2) conv-bn-leaky 32 x 608 x 608 64 x 304 x 304 19680
(3) conv-bn-leaky 64 x 304 x 304 32 x 304 x 304 21856
(4) conv-bn-leaky 32 x 304 x 304 64 x 304 x 304 40544
(5) skip 64 x 304 x 304 64 x 304 x 304 -
(6) conv-bn-leaky 64 x 304 x 304 128 x 152 x 152 114784
(7) conv-bn-leaky 128 x 152 x 152 64 x 152 x 152 123232
(8) conv-bn-leaky 64 x 152 x 152 128 x 152 x 152 197472
(9) skip 128 x 152 x 152 128 x 152 x 152 -
(10) conv-bn-leaky 128 x 152 x 152 64 x 152 x 152 205920
(11) conv-bn-leaky 64 x 152 x 152 128 x 152 x 152 280160
(12) skip 128 x 152 x 152 128 x 152 x 152 -
(13) conv-bn-leaky 128 x 152 x 152 256 x 76 x 76 576096
(14) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 609376
(15) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 905312
(16) skip 256 x 76 x 76 256 x 76 x 76 -
(17) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 938592
(18) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 1234528
(19) skip 256 x 76 x 76 256 x 76 x 76 -
(20) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 1267808
(21) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 1563744
(22) skip 256 x 76 x 76 256 x 76 x 76 -
(23) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 1597024
(24) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 1892960
(25) skip 256 x 76 x 76 256 x 76 x 76 -
(26) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 1926240
(27) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 2222176
(28) skip 256 x 76 x 76 256 x 76 x 76 -
(29) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 2255456
(30) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 2551392
(31) skip 256 x 76 x 76 256 x 76 x 76 -
(32) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 2584672
(33) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 2880608
(34) skip 256 x 76 x 76 256 x 76 x 76 -
(35) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 2913888
(36) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 3209824
(37) skip 256 x 76 x 76 256 x 76 x 76 -
(38) conv-bn-leaky 256 x 76 x 76 512 x 38 x 38 4391520
(39) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 4523616
(40) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 5705312
(41) skip 512 x 38 x 38 512 x 38 x 38 -
(42) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 5837408
(43) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 7019104
(44) skip 512 x 38 x 38 512 x 38 x 38 -
(45) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 7151200
(46) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 8332896
(47) skip 512 x 38 x 38 512 x 38 x 38 -
(48) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 8464992
(49) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 9646688
(50) skip 512 x 38 x 38 512 x 38 x 38 -
(51) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 9778784
(52) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 10960480
(53) skip 512 x 38 x 38 512 x 38 x 38 -
(54) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 11092576
(55) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 12274272
(56) skip 512 x 38 x 38 512 x 38 x 38 -
(57) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 12406368
(58) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 13588064
(59) skip 512 x 38 x 38 512 x 38 x 38 -
(60) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 13720160
(61) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 14901856
(62) skip 512 x 38 x 38 512 x 38 x 38 -
(63) conv-bn-leaky 512 x 38 x 38 1024 x 19 x 19 19624544
(64) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 20150880
(65) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 24873568
(66) skip 1024 x 19 x 19 1024 x 19 x 19 -
(67) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 25399904
(68) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 30122592
(69) skip 1024 x 19 x 19 1024 x 19 x 19 -
(70) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 30648928
(71) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 35371616
(72) skip 1024 x 19 x 19 1024 x 19 x 19 -
(73) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 35897952
(74) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 40620640
(75) skip 1024 x 19 x 19 1024 x 19 x 19 -
(76) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 41146976
(77) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 45869664
(78) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 46396000
(79) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 51118688
(80) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 51645024
(81) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 56367712
(82) conv-linear 1024 x 19 x 19 255 x 19 x 19 56629087
(83) yolo 255 x 19 x 19 255 x 19 x 19 56629087
(84) route - 512 x 19 x 19 56629087
(85) conv-bn-leaky 512 x 19 x 19 256 x 19 x 19 56761183
(86) upsample 256 x 19 x 19 256 x 38 x 38 -
(87) route - 768 x 38 x 38 56761183
(88) conv-bn-leaky 768 x 38 x 38 256 x 38 x 38 56958815
(89) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 58140511
(90) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 58272607
(91) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 59454303
(92) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 59586399
(93) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 60768095
(94) conv-linear 512 x 38 x 38 255 x 38 x 38 60898910
(95) yolo 255 x 38 x 38 255 x 38 x 38 60898910
(96) route - 256 x 38 x 38 60898910
(97) conv-bn-leaky 256 x 38 x 38 128 x 38 x 38 60932190
(98) upsample 128 x 38 x 38 128 x 76 x 76 -
(99) route - 384 x 76 x 76 60932190
(100) conv-bn-leaky 384 x 76 x 76 128 x 76 x 76 60981854
(101) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 61277790
(102) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 61311070
(103) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 61607006
(104) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 61640286
(105) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 61936222
(106) conv-linear 256 x 76 x 76 255 x 76 x 76 62001757
(107) yolo 255 x 76 x 76 255 x 76 x 76 62001757
Output blob names :
yolo_83
yolo_95
yolo_107
Total number of layers: 257
Total number of layers on DLA: 0
Building the TensorRT Engine…
0:00:05.708908905 11417 0x561ce0baa6f0 WARN nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:log(): TensorRT was linked against cuBLAS 10.2.0 but loaded cuBLAS 10.1.0
0:02:01.146147529 11417 0x561ce0baa6f0 WARN nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:log(): TensorRT was linked against cuBLAS 10.2.0 but loaded cuBLAS 10.1.0
Building complete!
0:02:03.215855239 11417 0x561ce0baa6f0 INFO nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:generateTRTModel(): Storing the serialized cuda engine to file at /home/optical/deepstream_sdk_v4.0.2_x86_64/sources/objectDetector_Yolo/model_b1_int8.engine
Deserialize yoloLayerV3 plugin: yolo_83
Deserialize yoloLayerV3 plugin: yolo_95
Deserialize yoloLayerV3 plugin: yolo_107
0:02:03.978093499 11417 0x561ce0baa6f0 WARN nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:log(): TensorRT was linked against cuBLAS 10.2.0 but loaded cuBLAS 10.1.0
0:02:03.981819471 11417 0x561ce0baa6f0 WARN nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:log(): TensorRT was linked against cuBLAS 10.2.0 but loaded cuBLAS 10.1.0

Runtime commands:
h: Print this help
q: Quit

p: Pause
r: Resume

NOTE: To expand a source in the 2D tiled display and view object details, left-click on the source.
To go back to the tiled display, right-click anywhere on the window.

**PERF: FPS 0 (Avg)
**PERF: 0.00 (0.00)
** INFO: <bus_callback:189>: Pipeline ready

** INFO: <bus_callback:175>: Pipeline running

Creating LL OSD context new
cuGraphicsGLRegisterBuffer failed with error(304) gst_eglglessink_cuda_init texture = 1
0:02:04.509782553 11417 0x561ce07a8f70 WARN nvinfer gstnvinfer.cpp:1830:gst_nvinfer_output_loop:<primary_gie_classifier> error: Internal data stream error.
0:02:04.509808446 11417 0x561ce07a8f70 WARN nvinfer gstnvinfer.cpp:1830:gst_nvinfer_output_loop:<primary_gie_classifier> error: streaming stopped, reason not-negotiated (-4)
ERROR from primary_gie_classifier: Internal data stream error.
Debug info: gstnvinfer.cpp(1830): gst_nvinfer_output_loop (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie_classifier:
streaming stopped, reason not-negotiated (-4)
Quitting
ERROR from sink_bin_queue: Internal data stream error.
Debug info: gstqueue.c(988): gst_queue_handle_sink_event (): /GstPipeline:pipeline/GstBin:processing_bin_0/GstBin:sink_bin/GstQueue:sink_bin_queue:
streaming stopped, reason not-negotiated (-4)
ERROR from qtdemux0: Internal data stream error.
Debug info: qtdemux.c(6073): gst_qtdemux_loop (): /GstPipeline:pipeline/GstBin:multi_src_bin/GstBin:src_sub_bin0/GstURIDecodeBin:src_elem/GstDecodeBin:decodebin0/GstQTDemux:qtdemux0:
streaming stopped, reason not-negotiated (-4)
App run failed

my config_infer_primary_yoloV3.txt:

[property]
gpu-id=0
net-scale-factor=1
#0=RGB, 1=BGR
model-color-format=0
custom-network-config=yolov3.cfg
model-file=yolov3.weights
#model-engine-file=model_b1_int8.engine
labelfile-path=labels.txt
int8-calib-file=yolov3-calibration.table.trt5.1
network-mode=1
num-detected-classes=80
gie-unique-id=1
is-classifier=0
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseCustomYoloV3
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so

my deepstream_app_config_yoloV3.txt:

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
#gie-kitti-output-dir=streamscl

[tiled-display]
enable=1
rows=1
columns=1
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

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
uri=file://…/…/samples/streams/sample_1080p_h264.mp4
num-sources=1
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

[sink0]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File
type=2
sync=0
source-id=0
gpu-id=0
nvbuf-memory-type=0

[osd]
enable=1
gpu-id=0
border-width=1
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
nvbuf-memory-type=0

[streammux]
gpu-id=0
##Boolean property to inform muxer that sources are live
live-source=0
batch-size=1
##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=1920
height=1080
##Enable to maintain aspect ratio wrt source, and allow black borders, works
##along with width, height properties
enable-padding=0
nvbuf-memory-type=0

#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
gpu-id=0
#model-engine-file=model_b1_int8.engine
labelfile-path=labels.txt
batch-size=1
#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
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV3.txt

[tests]
file-loop=0

I want to display the results on the screen,What should I do? Thanks.

2

I used “ldd deepstream” result :

optical@optical-Super-Server:~/deepstream_sdk_v4.0.2_x86_64/sources/objectDetector_Yolo$ which deepstream-app
/usr/bin/deepstream-app
optical@optical-Super-Server:~/deepstream_sdk_v4.0.2_x86_64/sources/objectDetector_Yolo$ ldd /usr/bin/deepstream-app
linux-vdso.so.1 (0x00007ffd5495c000)
libnvds_utils.so => ///opt/nvidia/deepstream/deepstream-4.0/lib/libnvds_utils.so (0x00007fe403cb2000)
libnvdsgst_meta.so => ///opt/nvidia/deepstream/deepstream-4.0/lib/libnvdsgst_meta.so (0x00007fe403aae000)
libnvds_meta.so => ///opt/nvidia/deepstream/deepstream-4.0/lib/libnvds_meta.so (0x00007fe4038a6000)
libX11.so.6 => /usr/lib/x86_64-linux-gnu/libX11.so.6 (0x00007fe40356e000)
libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007fe4031d0000)
libgstrtspserver-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstrtspserver-1.0.so.0 (0x00007fe402f79000)
libgstrtp-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstrtp-1.0.so.0 (0x00007fe402d59000)
libgstreamer-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstreamer-1.0.so.0 (0x00007fe402a1e000)
libgstvideo-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0 (0x00007fe402785000)
libglib-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libglib-2.0.so.0 (0x00007fe40246e000)
libgobject-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgobject-2.0.so.0 (0x00007fe40221a000)
libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fe401e29000)
libnvinfer.so.6 => /home/optical/TensorRT-6.0.1.5/lib/libnvinfer.so.6 (0x00007fe3f4ae2000)
libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007fe3f48de000)
libxcb.so.1 => /usr/lib/x86_64-linux-gnu/libxcb.so.1 (0x00007fe3f46b6000)
/lib64/ld-linux-x86-64.so.2 (0x00007fe4040e0000)
libgstnet-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstnet-1.0.so.0 (0x00007fe3f449f000)
libgstbase-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstbase-1.0.so.0 (0x00007fe3f422a000)
libgstrtsp-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstrtsp-1.0.so.0 (0x00007fe3f4009000)
libgstsdp-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstsdp-1.0.so.0 (0x00007fe3f3df2000)
libgstapp-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstapp-1.0.so.0 (0x00007fe3f3be3000)
libgio-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgio-2.0.so.0 (0x00007fe3f3844000)
libgmodule-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgmodule-2.0.so.0 (0x00007fe3f3640000)
librt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007fe3f3438000)
libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007fe3f3219000)
liborc-0.4.so.0 => /usr/lib/x86_64-linux-gnu/liborc-0.4.so.0 (0x00007fe3f2f9d000)
libpcre.so.3 => /lib/x86_64-linux-gnu/libpcre.so.3 (0x00007fe3f2d2b000)
libffi.so.6 => /usr/lib/x86_64-linux-gnu/libffi.so.6 (0x00007fe3f2b23000)
libcudnn.so.7 => /usr/lib/x86_64-linux-gnu/libcudnn.so.7 (0x00007fe3d8fb6000)
libcublas.so.10 => /usr/lib/x86_64-linux-gnu/libcublas.so.10 (0x00007fe3d42fc000)
libcudart.so.10.1 => /usr/local/cuda-10.1/lib64/libcudart.so.10.1 (0x00007fe3d4080000)
libstdc++.so.6 => /usr/lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007fe3d3cf7000)
libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007fe3d3adf000)
libXau.so.6 => /usr/lib/x86_64-linux-gnu/libXau.so.6 (0x00007fe3d38db000)
libXdmcp.so.6 => /usr/lib/x86_64-linux-gnu/libXdmcp.so.6 (0x00007fe3d36d5000)
libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x00007fe3d34b8000)
libselinux.so.1 => /lib/x86_64-linux-gnu/libselinux.so.1 (0x00007fe3d3290000)
libresolv.so.2 => /lib/x86_64-linux-gnu/libresolv.so.2 (0x00007fe3d3075000)
libmount.so.1 => /lib/x86_64-linux-gnu/libmount.so.1 (0x00007fe3d2e21000)
libcublasLt.so.10 => /usr/lib/x86_64-linux-gnu/libcublasLt.so.10 (0x00007fe3d08c1000)
libbsd.so.0 => /lib/x86_64-linux-gnu/libbsd.so.0 (0x00007fe3d06ac000)
libblkid.so.1 => /lib/x86_64-linux-gnu/libblkid.so.1 (0x00007fe3d045f000)
libuuid.so.1 => /lib/x86_64-linux-gnu/libuuid.so.1 (0x00007fe3d0258000)
optical@optical-Super-Server:~/deepstream_sdk_v4.0.2_x86_64/sources/objectDetector_Yolo$

4

your issue is:
cuGraphicsGLRegisterBuffer failed with error(304) gst_eglglessink_cuda_init texture = 1
i noticed you install display driver without opengl,
" i’m used “sudo ./NVIDIA-Linux-x86_64-440.59.run --no-opengl-files” to install my display dirve"
you can choose reinstall driver without this option, or you can change sink type to Fakesink or File or RTSP streaming.

5

Well,Thanks,i reinstall driver with"sudo ./NVIDIA-Linux-x86_64-440.59.run",it can show now .

6

Now,i can run deepstream show result ,but ,when i used only one source like a mp4 video as input data ,result show FPS is 25. and used only one camera (usr the camera uri ) as input ,the FPS is 25.

when i used one mp4 video and one camera as input ,deep stream show the FPS of the video is also 25 ,but the FPS of the camera is only 0.3. the fps of camera is very low.

my config of source is ::

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
uri=file://…/…/samples/streams/sample_1080p_h264.mp4
num-sources=1
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

[source1]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
uri=rtsp://admin:password@192.168.1.77:554/h264/ch1/main/av_stream
num-sources=1
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

What should I do to get the video and camera FPS working.Can you give me some advice,Thanks.

7

If your rtsp camera have network latency, it will impact decoder and streammux latency, and streammux property live set to 1 will have improvements, also set sink sync to 0;
we have jitterbuffer property set which will improve the situation in upcoming release.