/Capturer

Live target grasping project implemented by hexapod robot

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Capturer based on YOLOv7

This is a project to Capturer using YOLOv7.

Video Presentation

Hexapod Robot

Web Code Repository

Installation

Docker environment (recommended)

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# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name capturer -it -v your_datasets_path/:/datasets/ -v your_code_path/:/capturer --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3

# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx

# pip install environment required packages
pip install seaborn thop

# pip install cpaturer required packages
pip install -r requirements.txt

# go to code folder
cd /capturer

Model Parameter Download

Run

Deploy the ActionSet File Config.ini on 24-Way steering geer control board and boot it.

Deploy the File connect_arduino_jetsonnano.py on Adruino Uno and boot it.

Deploy the Code Folder on Jetson Nano and run the following command:

python detect.py

Defalut Settings

  • weights: best.pt && best.onnx
  • source: 0 (default camera)
  • cfg: cfg/training/yovov7-snail.yaml
  • data: data/snail.yaml
  • img_size: 640