Hi,
i use deepstream-sdk 4.0
i read one hikivision ip camera ---- rtsp(25fps) , get 25.04 fps without any infer
i read two hikivision ip cameras ---- rtsp(25fps) , get 25.04 fps 25.04 fps without any infer
i read four hikivision ip cameras ---- rtsp(25fps) , get 16.04 fps 16.04 fps 16.04 fps 16.04 fps without any infer
i read eight hikivision ip cameras ---- rtsp(25fps) , get 8.04 fps 8.04 fps 8.04 fps 8.04 fps 8.04 fps 8.04 fps 8.04 fps 8.04 fps without any infer
As you say in document , the camera is Parallel Processing by gstreamer. but why there is no infer, the processing speed is slow down?
i my opinion, if we read camrea in muti-threads and push to a queue, the speed will not be slow! i have no idea about this , can you give me any help??
i have sure that i have set the power model to 10W by
sudo nvpmodel -m 0
i get the memory used 2G and cpu rate is 15% ??? (why is so low??)
Hi,
Please share information about your device and attach your config file for reference. On Jetson platforms, you can run ‘sudo nvpmodel -m 0’ and ‘sudo jetson_clocks’ to get max performance. Also in config file, you may check if ‘live-source=1’ is set in [streammux].
Hi ,
i use jetson nano and deepstream sdk 4.0 i have sure that i have already do the power model and clock.
but when i use upto 4 cameras without any infer plugins. the mean fps will slow down, i am very confused about that.
here is my configure.
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[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
#gie-kitti-output-dir=streamscl
[ds-example]
enable=0
processing-width=1920
processing-height=1080
full-frame=1
unique-id=15
gpu-id=0
[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 4=RTSP 5=Camera (CSI)
type=4
uri=rtsp://admin:admin@192.168.0.64/Streaming/Channels/1
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 4=RTSP 5=Camera (CSI)
type=4
uri=rtsp://admin:admin@192.168.0.64/Streaming/Channels/1
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
[source2]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=Camera (CSI)
type=4
uri=rtsp://admin:admin@192.168.0.64/Streaming/Channels/1
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
[source3]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=Camera (CSI)
type=4
uri=rtsp://admin:admin@192.168.0.64/Streaming/Channels/1
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
[sink1]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File
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
[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=1
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=0
gpu-id=0
#model-engine-file=model_b1_fp32.engine
model-engine-file=model_b1_fp16.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
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV3_tiny.txt
interval=2
[tests]
file-loop=0
Hi,
Please also refer to source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt.
A few settings you may apply and try again:
[tiled-display]
rows=2 // rows*columns = source number
columns=2
[sink0]
type=5 // same as source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt
[streammux]
batch-size=4 // identical to source number
[primary-gie]
batch-size=4 // identical to source number
interval=4 // same as source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt
Please also check system loading through tegrastats