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ZED SDK - Object Detection

This sample shows how to detect custom objects using the official Pytorch implementation of YOLOv8 from a ZED camera and ingest them into the ZED SDK to extract 3D informations and tracking for each objects.

Getting Started

Setting up

  • Install yolov8 using pip
pip install ultralytics

Run the program

NOTE: The ZED v1 is not compatible with this module

python detector.py --weights yolov8m.pt # [--img_size 512 --conf_thres 0.1 --svo path/to/file.svo]

Features

  • The camera point cloud is displayed in a 3D OpenGL view
  • 3D bounding boxes around detected objects are drawn
  • Objects classes and confidences can be changed

Training your own model

This sample can use any model trained with YOLOv8, including custom trained one. For a getting started on how to trained a model on a custom dataset with YOLOv5, see here https://docs.ultralytics.com/tutorials/train-custom-datasets/

Support

If you need assistance go to our Community site at https://community.stereolabs.com/