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yolov5_d415

https://github.com/jumbo-zczlbj0/yolov5_d415

Science Score: 44.0%

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    Low similarity (12.8%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: Jumbo-zczlbj0
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 39.2 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 4
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

Yolo-Object-Detection-With-Intel-Realsense-Camera

The algorithm does not change the YoloV5 source code, but creates a new python file: pyrealsense2_camera.py.

Install

1.Install cuda

URL：https://developer.nvidia.com/cuda-toolkit

2.Create YoloV5 environment && install requirements

conda create --name yolov5 python=3.10

conda activate yolov5

install pytorch URL：https://pytorch.org/

git clone https://github.com/Jumbo-zczlbj0/YoloV5_D415.git

cd YoloV5_D415

pip install -r requirements.txt

3.Install Intel RealSense SDK 2.0

Choose the version that suits you based on your computer system and install SDK-2：https://www.intelrealsense.com/sdk-2/

Install pyrealsense2：pip install pyrealsense2

4.Install other package

Sound module, used to play the name, confidence, and depth of the detected object

pip install pyttsx3

Multithreading module for parallel sound module and detection module

pip install threading

Try

Download and change the weight file path.(Weight link: https://github.com/ultralytics/yolov5)

python pyrealsense2_camera.py

Train YOLO V5

Dataset

workspace: mushroom28, project: mushroom-nksu4, version: 6

url: https://universe.roboflow.com/mushroom28/mushroom-nksu4/dataset/6

Train

Log in to the roboflow website and label your own dataset. Select YOLOV5 format to download and replace the .yaml file path in the code.(The yaml generated by the website contains the train and val paths, please replace them with your own paths) The official connection of yoloV5 has detailed tutorials and You can also use other label methods, such as labelme. Tutorials URL: https://docs.ultralytics.com/integrations/roboflow/?h=roboflow#upload-convert-and-label-data-for-yolov8-format

python train.py --img 640 --batch 64 --epochs 300 --data coco128.yaml --weights yolov5s.pt --cache

Running YOLOV5 using Realsense Camera on Jetson Nano Developer Kit

InstallA Jetson Nano - Ubuntu 20.04 image with OpenCV, TensorFlow and Pytorch

Tutorials URL: https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image

BalenaEtcher: https://etcher.balena.io/#download-etcher

1.Get a 32 GB (minimal) SD card with exFat to hold the image.

2.Download the image JetsonNanoUb20_3b.img.xz (8.7 GByte!) from our Sync.

3.Flash the image on the SD card with the Imager or balenaEtcher.

4.nsert the SD card in your Jetson Nano and enjoy.

Password: jetson

（The above content comes from Qengineering： https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image）

Install

1.Install exfat

sudo apt-get install exfat-fuse exfat-utils

2.Install pip

Download: https://bootstrap.pypa.io/get-pip.py

cd Downloads

python3 get-pip.py

3.Install SDK 2.0

Tutorials URL: https://github.com/IntelRealSense/librealsense/blob/master/doc/installation_jetson.md

4.Install environment

git clone https://github.com/Jumbo-zczlbj0/YoloV5_D415.git

cd YoloV5_D415

requirements_Jetson.txt add pyrealsense2 && pyttsx3

pip3 install -r requirements_Jetson.txt

5.Install espeak

sudo apt-get install espeak

Run

python3 pyrealsense2_camera.py

Owner

  • Login: Jumbo-zczlbj0
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

GitHub Events

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Dependencies

utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
requirements.txt pypi
  • Pillow >=10.0.1
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.22.2
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.147
requirements_Jetson.txt pypi
  • Pillow >=10.0.1
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.22.2
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • pyrealsense2 *
  • pyttsx3 *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.147
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==19.10.0
  • pip ==23.3
  • werkzeug >=3.0.1