A Python implementation of Yolov5 to detect head or helmet in the wild in Jetson Xavier nx and Jetson nano. In Jetson Xavier Nx, it can achieve 33 FPS.
You can see video play in BILIBILI, or YOUTUBE.
if you have problem in this project, you can see this artical.
If you want to try to train your own model, you can see yolov5-helmet-detection-python. Follow the readme to get your own model.
You can get the dataset from this aistudio url. And the head & helmet detect project pdpd version can be found in this url. It is an amazing project.
This pro needs dataset like
../datasets/coco128/images/im0.jpg #image
../datasets/coco128/labels/im0.txt #label
Download the dataset and unzip it.
unzip annnotations.zip
unzip images.zip
You can get this.
├── dataset
├── annotations
│ ├── fire_000001.xml
│ ├── fire_000002.xml
│ ├── fire_000003.xml
│ | ...
├── images
│ ├── fire_000001.jpg
│ ├── fire_000003.jpg
│ ├── fire_000003.jpg
│ | ...
├── label_list.txt
├── train.txt
└── valid.txt
You should turn xml files to txt files. You also can see this.
Open script/sw2yolo.py
, Change save_path
to your own save path,root
as your data path, and list_file
as val_list.txt
and train_list.txt
path.
list_file = "./val_list.txt"
xmls_path,imgs_path = get_file_path(list_file)
# 将train_list中的xml 转成 txt, img放到img中
save_path = './data/yolodata/fire/cocolike/val/'
root = "./data/yolodata/fire/"
train_img_root = root
Then you need script/yolov5-split-label-img.py
to split img and txt file.
mkdir images
mkdir lables
mv ./train/images/* ./images/train
mv ./train/labels/* ./labels/train
mv ./val/iamges/* ./images/val
mv ./val/lables/* ./lables/val
Finally You can get this.
├── cocolike
├── lables
│ ├── val
│ ├── fire_000001.xml
| ├── ...
│ ├── train
│ ├── fire_000002.xml
| ├── ...
│
├── images
│ ├── val
│ ├── fire_000001.jpg
| ├── ...
│ ├── train
│ ├── fire_000003.jpg
| ├── ...
├── label_list.txt
├── train.txt
└── valid.txt
{porject}/yolov5/data/
add your own yaml files like helmet.yaml
.
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco128 downloads here
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: /home/data/tbw_data/face-dataset/yolodata/helmet/cocolike/ # dataset root dir
train: images/train # train images (relative to 'path') 128 images
val: images/val # val images (relative to 'path') 128 images
test: # test images (optional)
# Classes
nc: 2 # number of classes
names: ['head','helmet'] # class names
Change {project}/train.py
's data path as your own data yaml path.
Change batch-size
as a suitable num. Change device if you have 2 or more gpu devices.
Then
python train.py
Use detect.py
to test.
python detect.py --source ./data/yolodata/helmet/cocolike/images --weights ./runs/train/exp/weights/best.pt
You can see {project}/runs/detect/
has png results.
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。