The Purpose of this repository is to create a DeepStream/Triton-Server sample application that utilizes yolov7, yolov7-qat, yolov9 yolov9-qat models to perform inference on video files or RTSP streams. It then showcases the output on an RTSP URL, providing a straightforward demonstration of end-to-end AI processing.
Follow this steps to Use this Sample App
Follow the steps on below link to Start Triton Server
Triton Server - YOLO.
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Start Nvidia Container
nvcr.io/nvidia/deepstream:6.4-triton-multiarch
Install Deepstream Python Bindings./user_deepstream_python_apps_install.sh --version 1.1.10 -
Install custom parse lib
NvDsInferYolov9EfficientNMSfor Gst-nvinferserver
Clone this repository nvdsinfer_yolo_efficient_nms
The custom library is built and installed using the provided Makefile. -
Install Application for DeepStream 6.4
ds-6.4-ts-yolo-rtsp-outFollow the steps on below link to Build DeepStream
DeepStream - YOLO -
Using Sample Application
ds-6.4-ts-yolo-rtsp-outTo use this sample application, follow the steps below from within the container:
The sample application is installed on
/deepstream_python_apps/apps/ds-6.4-ts-yolo-rtsp-out/This sample application supports input streaming from both
file://andrtsp://sources. It processes the input and streams the output via RTSP at the following URL:rtsp://localhost:8554/ds-testpython3 ds-6.4-ts-yolo-rtsp-out.py -i <input_files> -m <model> -c <codec> -b <bitrate> [--rtsp-ts] #example python3 ds-6.4-ts-yolo-rtsp-out.py \ -i file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \ -m yolov9-c \ -c H264
-i,--input: Path to inputfile://orrtsp://elementary stream. Multiple input files can be provided.-m,--model: Choice ['yolov7','yolov7x','yolov7-qat','yolov7x-qat','yolov9-c','yolov9-e','yolov9-c-qat','yolov9-e-qat'].-c,--codec: RTSP Streaming Codec. Choose betweenH264andH265. (Default:H264)-b,--bitrate: Set the encoding bitrate. (Default:4000000)--rtsp-ts: Attach NTP timestamp from RTSP source. (Default:False)