README.md

gvaattachroi Sample

This sample demonstrates gvaattachroi element and ability to define the area of interest on which the inference should be performed. This feature is particularly useful when the focus is on processing only a specific part of an image, for example, monitoring traffic on a road in a city camera feed, rather than the surrounding areas. It can also be beneficial when dealing with large images where running inference on the entire image may lead to inaccurate results.

How It Works

The sample utilizes GStreamer command-line tool gst-launch-1.0 which can build and run GStreamer pipeline described in a string format. The string contains a list of GStreamer elements separated by exclamation mark !, each element may have properties specified in the format property=value.

This sample builds GStreamer pipeline of the following elements

• filesrc or urisourcebin or v4l2src for input from file/URL/web-camera
• decodebin3 for video decoding
• videoconvert for converting video frame into different color formats
• gvaattachroi for defining the areas of interest (one or more) in the input image
• gvadetect uses for roi object detection and marking objects with labels
• gvawatermark for points and theirs connections visualization
• autovideosink for rendering output video into screen

NOTE: sync=false property in autovideosink element disables real-time synchronization so pipeline runs as fast as possible

Model

The sample use YOLOv8s model from Ultralytics. The instructions assume Intel® DL Streamer framework is installed on the local system along with Intel® OpenVINO™ model downloader and converter tools, as described here: Tutorial.

For yolov8s model it is also necessary to install the ultralytics python package:

pip install ultralytics

Use the download_public_models.sh script found in the top-level samples directory. This allows you to download the full suite of YOLO models or select an individual model from the options presented above. Select the yolov8s model by executing the command:

./download_public_models.sh yolov8s

NOTE: Remember to set the MODELS_PATH environment variable, which is needed by both the script that downloads the model and the script that runs the sample.

Running

Run sample:

./gvaattachroi_sample.sh [INPUT_VIDEO] [DEVICE] [SINK_ELEMENT] [ROI_COORDS]

The sample takes three command-line optional parameters:

1. [INPUT_VIDEO] to specify input video file. The input could be
   • local video file
   • web camera device (ex. /dev/video0)
   • RTSP camera (URL starting with rtsp://) or other streaming source (ex URL starting with http://) If parameter is not specified, the sample by default streams video example from HTTPS link (utilizing urisourcebin element) so requires internet conection.
2. [DEVICE] to specify device for detection and classification. Please refer to OpenVINO™ toolkit documentation for supported devices. https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_Supported_Devices.html You can find what devices are supported on your system by running following OpenVINO™ toolkit sample: https://docs.openvinotoolkit.org/latest/openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README.html Default value: CPU
3. [SINK_ELEMENT] to choose between different output formats:
   • file (default)
   • display - render
   • fps - FPS only
   • json - json file with metadata
   • display-and-json - render and json file
4. [ROI_COORDS] to manually define the coordinates at which the ROI should be located format: x_top_left,y_top_left,x_bottom_right,y_bottom_right example: 100,150,200,300 If not defined, the roi list file will be used. You can also edit the roi list file file to add more ROIs or change the coordinates of the current ones.

Example:

./gvaattachroi_sample.sh "https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4" GPU file 100,150,200,300

See also

• Samples overview