How to Use YOLO v8 with ZED in Python

Introduction #

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.

Installation #

ZED Yolo depends on the following libraries:

• ZED SDK and [Python API]
• Pytorch / YOLOv8 package
• OpenCV
• CUDA
• [Python 3]

ZED SDK #

Install the ZED SDK and Python API.

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/