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This repository contains a Python script for real-time object tracking using YOLOv10 and OpenCV. It allows you to select an object in a video frame, track it, and display its coordinates in real-time.
- Real-time object tracking using YOLOv10 and OpenCV.
- User-friendly interface for selecting objects in video frames.
- Real-time display of object coordinates.
- 360Β° video support for enhanced video experiences.

easydict==1.13
gdown==5.2.0
ipdb==0.13.13
motmetrics==1.4.0
numpy==1.26.4
opencv_python==4.8.1.78
pandas==1.5.3
PyYAML==6.0.1
scipy==1.11.4
torch==2.3.0
torchvision==0.18.0
torchreid==0.2.5
scikit-learn==1.5.2
scikit-image==0.24.0
ultralytics==8.3.15
tensorboard==2.18.0
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Clone this repository to your local machine:
git clone https://github.com/MAVERICK-VF142/Object_tracking_in_360_video.git -
Install the required dependencies:
pip install -r requirements.txt -
Download the YOLOv10 model weights (
yolov10e.pt) and place them in the root directory of this repository.
-
Run the script
app.py:python app.py -
Select an object in the video frame by clicking on it. The script will track the selected object and display its coordinates in real-time.
Contributions are welcome! Here's how you can contribute:
-
Fork the repository to your GitHub account.
-
Clone the forked repository to your local machine:
git clone https://github.com/your-username/Object_tracking_in_360_video.git -
Create a new branch for your feature or bug fix:
git checkout -b feature-nameReplace
feature-namewith a descriptive name for your feature or bug fix. -
Make your changes and commit them:
git add . git commit -m "Description of your changes" -
Push your changes to your forked repository:
git push origin feature-name -
Create a pull request from your forked repository to the main repository's
masterbranch. Note: Please ensure your pull request adheres to the repository's contribution guidelines. Thank you for contributing to this project!
This project is licensed under the MIT License - see the LICENSE file for details.