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opencv\build\x64\vc14\bin
opencv\build\x64\vc15\bin

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# 如果Git Bash没有默认运行Anaconda自带的base环境，则进行初始化操作，反之跳过该操作
conda init bash

# 查看环境变量，刚安装的Anaconda只有自带的第一个base环境
conda env list

# 配置环境名称
export myenv="python_3.8"

# 创建环境
conda create -n $myenv python=3.8 -y

# 激活环境
conda activate $myenv

# 删除环境，仅不需要该环境的时候执行该命令
conda env remove --name $myenv -y

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# 设置代理，根据需要进行设置
conda config --set proxy_servers.http http://127.0.0.1:10808

export myenv="python_3.8"

# 创建环境
conda create -n $myenv python=3.8 -y

# 设置复制环境变量
export myclone=${myenv}_clone

# 复制环境
conda create --name $myclone --clone $myenv

# 激活环境
conda activate $myenv

conda install -y pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 # -c pytorch

# Install the ultralytics package
pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple

# Clone the YOLOv5 repository
git clone https://github.com/ultralytics/yolov5

# Navigate to the cloned directory
cd yolov5

# Install required packages
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

# Traning配置的--cfg *.yaml与Run inference using a webcam的.pt模型对应
# --------------------------------Training
# Train YOLOv5n on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128

# Train YOLOv5s on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64

# Train YOLOv5m on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40

# Train YOLOv5l on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24

# Train YOLOv5x on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16

# --------------------------------Inference with detect.py
# Run inference using a webcam
python detect.py --weights yolov5s.pt --source 0

# Run inference on a local image file
python detect.py --weights yolov5s.pt --source img.jpg

# Run inference on a local video file
python detect.py --weights yolov5s.pt --source vid.mp4

# Run inference on a screen capture
python detect.py --weights yolov5s.pt --source screen

# Run inference on a directory of images
python detect.py --weights yolov5s.pt --source path/to/images/

# Run inference on a text file listing image paths
python detect.py --weights yolov5s.pt --source list.txt

# Run inference on a text file listing stream URLs
python detect.py --weights yolov5s.pt --source list.streams

# Run inference using a glob pattern for images
python detect.py --weights yolov5s.pt --source 'path/to/*.jpg'

# Run inference on a YouTube video URL
python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'

# Run inference on an RTSP, RTMP, or HTTP stream
python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'

# 取消代理
conda config --remove-key proxy_servers.http