dsyolo: add deepstream yolo support (still got issue with rtp/h265, but

ok for rtp/h264)

- [How to configure h265 stream?](marcoslucianops/DeepStream-Yolo#600)
- [Yolov8s no bounding box on default settings #597](marcoslucianops/DeepStream-Yolo#597)

utils/dsyolo/labels.txt

@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+dining table
+toilet
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

utils/dsyolo/source_config_yolov4.txt

@@ -0,0 +1,73 @@
+[application]
+enable-perf-measurement=1
+perf-measurement-interval-sec=5
+
+[tiled-display]
+enable=1
+rows=1
+columns=1
+width=1920
+height=1080
+gpu-id=0
+#nvbuf-memory-type
+#(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
+#type=3
+#uri=rtp://0.0.0.0:5000
+#uri=rtsp://127.0.0.1:8554/my_stream
+type=2
+uri=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h265.mp4
+num-sources=1
+gpu-id=0
+cudadec-memtype=0
+
+[sink0]
+enable=1
+#Type - 1=FakeSink 2=EglSink 3=File 4=UDPSink 5=nvdrmvideosink 6=MsgConvBroker
+type=2
+sync=0
+gpu-id=0
+nvbuf-memory-type=0
+
+[osd]
+enable=1
+gpu-id=0
+border-width=5
+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
+live-source=0
+batch-size=1
+batched-push-timeout=40000
+width=1920
+height=1080
+enable-padding=0
+nvbuf-memory-type=0
+
+[primary-gie]
+enable=1
+gpu-id=0
+gie-unique-id=1
+nvbuf-memory-type=0
+config-file=yolov4_infer_primary.txt
+
+[tests]
+file-loop=0

utils/dsyolo/yolov4_infer_primary.txt

@@ -0,0 +1,30 @@
+[property]
+gpu-id=0
+net-scale-factor=0.0039215697906911373
+model-color-format=0
+custom-network-config=yolov4.cfg
+model-file=yolov4.weights
+model-engine-file=model_b1_gpu0_fp32.engine
+#int8-calib-file=calib.table
+labelfile-path=labels.txt
+batch-size=1
+network-mode=0
+num-detected-classes=80
+interval=3
+gie-unique-id=1
+process-mode=1
+network-type=0
+cluster-mode=2
+maintain-aspect-ratio=0
+symmetric-padding=1
+force-implicit-batch-dim=0
+workspace-size=2000
+parse-bbox-func-name=NvDsInferParseYolo
+#parse-bbox-func-name=NvDsInferParseYoloCuda
+custom-lib-path=libnvdsinfer_custom_impl_Yolo.so
+engine-create-func-name=NvDsInferYoloCudaEngineGet
+
+[class-attrs-all]
+nms-iou-threshold=0.45
+pre-cluster-threshold=0.25
+topk=300