The Purpose of this repository is to create a Deepstream/Triton-Server sample application that utilizes YOLOv7 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 - YOLOV7.

Follow the steps on below link to Start Deepstream
Deepstream - YOLOV7.

To use this sample application, follow the steps below from within the container:

The sample application is installed on /deepstream_python_apps/apps/deepstream-yolov7-triton-server-rtsp-out/

This sample application supports input streaming from both file:// and rtsp:// sources. It processes the input and streams the output via RTSP at the following URL: rtsp://localhost:8554/ds-test

python3 deepstream_yolov7-triton-server_rtsp_out.py -i <input_files> -m <model> -c <codec> -b <bitrate> [--rtsp-ts]

#example
python3 deepstream_yolov7-triton-server_rtsp_out.py  \
-i file:///videos/input.mp4 rtsp://myserver:554  \
-m  yolov7_qat \
-c H264   

• -i, --input: Path to input file:// or rtsp:// elementary stream. Multiple input files can be provided.
• -m, --model: Choose between 'yolov7' or 'yolov7_qat'. (Default: 'yolov7_qat')
• -c, --codec: RTSP Streaming Codec. Choose between H264 and H265. (Default: H264)
• -b, --bitrate: Set the encoding bitrate. (Default: 4000000)
• --rtsp-ts: Attach NTP timestamp from RTSP source. (Default: False)