YOLov4-tiny is used as the main detector and Resnet50 is used as the secondary detector,but only the detection results of YOLOv4-tiny can be displayed.
config_color.txt
[property]
gpu-id=0
net-scale-factor=1
model-engine-file=/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/Resnet_color.engine
labelfile-path=/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/DeepStream/labels_color.txt
force-implicit-batch-dim=1
batch-size=1
model-color-format=0
0=FP32, 1=INT8, 2=FP16 mode
network-mode=1
process-mode=2
is-classifier=1
classifier-async-mode=1
classifier-threshold=0.51
input-object-min-width=128
input-object-min-height=128
operate-on-gie-id=2
operate-on-class-ids=1
num-detected-classes=4
cluster-mode=2
deeepstream_app_config.txt
[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
#gie-kitti-output-dir=streamscl
[tiled-display]
enable=0
rows=2
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
[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
uri=file:/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/test.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=1
source-id=0
gpu-id=0
[sink1]
enable=0
type=3
#1=mp4 2=mkv
container=1
#1=h264 2=h265 3=mpeg4
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=0
bitrate=4000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0
output-file=out.mp4
source-id=0
[sink2]
enable=0
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming
type=4
source-id=1
#1=h264 2=h265
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=1
bitrate=4000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0
set below properties in case of RTSPStreaming
rtsp-port=8554
udp-port=5400
[sink3]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming
type=4
source-id=1
#1=h264 2=h265
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=1
bitrate=4000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0
set below properties in case of RTSPStreaming
rtsp-port=8555
udp-port=5401
[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=1280
height=720
##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=/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/yolov4-tiny-40000.engine
labelfile-path=labels.txt
#batch-size=1
#Required by the app for OSD, not a plugin property
bbox-border-color0=1;0;1;1
bbox-border-color1=0;0;1;1
bbox-border-color2=0;1;1;1
bbox-border-color3=0;1;1;1
interval=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV4.txt
[tracker]
enable=1
For the case of NvDCF tracker, tracker-width and tracker-height must be a multiple of 32, respectively
tracker-width=608
tracker-height=608
#ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_iou.so
#ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_nvdcf.so
ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_klt.so
#ll-config-file required for IOU only
#ll-config-file=iou_config.txt
gpu-id=0
[secondary-gie0]
enable=1
model-engine-file=/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/Resnet_color.engine
gpu-id=0
batch-size=1
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=1;
config-file=config_color.txt
[osd]
enable=1
gpu-id=0
border-width=1
text-size=12
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
[tests]
file-loop=1
Using winsys: x11
WARNING: [TRT]: Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
0:00:14.728392443 15617 0xf5dcb00 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1701> [UID = 2]: deserialized trt engine from :/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/Resnet_color.engine
INFO: [Implicit Engine Info]: layers num: 2
0 INPUT kFLOAT input 3x32x32
1 OUTPUT kFLOAT output 4
0:00:14.728692615 15617 0xf5dcb00 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1805> [UID = 2]: Use deserialized engine model: /home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/Resnet_color.engine
0:00:14.795305483 15617 0xf5dcb00 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<secondary_gie_0> [UID 2]: Load new model:/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/DeepStream/config_color.txt sucessfully
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_klt.so
gstnvtracker: Optional NvMOT_RemoveStreams not implemented
gstnvtracker: Batch processing is OFF
gstnvtracker: Past frame output is OFF
0:00:16.618311362 15617 0xf5dcb00 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1701> [UID = 1]: deserialized trt engine from :/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/yolov4-tiny-40000.engine
INFO: [FullDims Engine Info]: layers num: 3
0 INPUT kFLOAT input 3x416x416 min: 1x3x416x416 opt: 8x3x416x416 Max: 8x3x416x416
1 OUTPUT kFLOAT boxes 2535x1x4 min: 0 opt: 0 Max: 0
2 OUTPUT kFLOAT confs 2535x2 min: 0 opt: 0 Max: 0
0:00:16.618529930 15617 0xf5dcb00 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1805> [UID = 1]: Use deserialized engine model: /home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/yolov4-tiny-40000.engine
0:00:16.654006311 15617 0xf5dcb00 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<primary_gie> [UID 1]: Load new model:/home/nano/Desktop/yolov4-tiny/pytorch-YOLOv4/DeepStream/config_infer_primary_yoloV4.txt sucessfully
Runtime commands:
h: Print this help
q: Quit
p: Pause
r: Resume
**PERF: FPS 0 (Avg)
**PERF: 0.00 (0.00)
** INFO: <bus_callback:181>: Pipeline ready
Opening in BLOCKING MODE
NvMMLiteOpen : Block : BlockType = 260
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 260
** INFO: <bus_callback:167>: Pipeline running
KLT Tracker Init
**PERF: 33.36 (33.10)
**PERF: 29.81 (31.14)
**PERF: 29.74 (30.64)
**PERF: 29.93 (30.47)