yoloV5 crowded student 检测的传统筛选流程

1 安装yolov5-filter

apt-get update
apt-get install zip
apt-get install unzip

conda install x264 ffmpeg -c conda-forge -y

cd /home
git clone https://gitee.com/YFwinston/yolov5-filter.git
cd yolov5-filter
pip install -r requirements.txt
pip install opencv-python-headless==4.1.2.30

mkdir -p /root/.config/Ultralytics/
cp /user-data/yolov5File/crowdhuman_vbody_yolov5m.pt /home/yolov5-filter/crowdhuman_vbody_yolov5m.pt 
cp /user-data/yolov5File/Arial.ttf /root/.config/Ultralytics/Arial.ttf

cd /home/yolov5-filter
mkdir chooseVideoFrameYolov5
mkdir chooseVideoFrame

在chooseVideoFrame中上传待检测的图片

2 开始检测

如果之前检测过，则要先做清除

cd /home/yolov5-filter

cd /home/yolov5-filter
rm -r ./chooseVideoFrameYolov5/*
python ./detect.py --source ./chooseVideoFrame/ --save-txt --save-conf  --weights ./crowdhuman_vbody_yolov5m.pt --hide-labels --line-thickness 2 --project ./chooseVideoFrameYolov5

3 yoloV5转via

cd /home/yolov5-filter
python yolo2via.py --yoloLabel_dir ./chooseVideoFrameYolov5/exp/labels --image_dir ./chooseVideoFrame

4 传统筛选

cd /home/yolov5-filter
mkdir -p ./chooseVideoFrameYolov5/newExp/
mkdir -p ./visualize
rm -r  ./chooseVideoFrameYolov5/newExp/*
rm -r ./visualize/*
python filter.py --label_dir ./chooseVideoFrameYolov5/exp/labels --image_dir ./chooseVideoFrame/ --newExp_dir ./chooseVideoFrameYolov5/newExp/

rm x.zip
zip -r x.zip ./visualize/

cd /home/yolov5-filter
mkdir -p ./chooseVideoFrameYolov5/newExp/
mkdir -p ./visualize
rm -r  ./chooseVideoFrameYolov5/newExp/*
rm -r ./visualize/*
python filter2.py --label_dir ./chooseVideoFrameYolov5/exp/labels --image_dir ./chooseVideoFrame/ --newExp_dir ./chooseVideoFrameYolov5/newExp/ --r_wh 0.15 --r_area1 0.7 --r_area2 0.7 --r_area3 0.5

5 筛选后转via

cd /home/yolov5-filter
python yolo2via.py --yoloLabel_dir ./chooseVideoFrameYolov5/newExp/ --image_dir ./chooseVideoFrame --labelName_dir detections.json

cd /home/yolov5-filter
zip -r  chooseVideoFrame.zip ./chooseVideoFrame