open-mmlab labelImg mmdetection

• 数据标柱工具介绍
• • labelImg
  • • 安装(两种方法都适用于linux和mac上的conda虚拟环境下安装)
    • • pip包安装
      • 从源码安装
• mmdetection
• • 配置环境
  • 运行
• mmsegmentation

git地址: https://github.com/tzutalin/labelImg

根据git上介绍，有两种安装方法：

pip包安装（linux上最简单的安装方法），安装命令及使用命令如下：

# 环境准备
conda create -n labelImg_1 python=3.7
conda activate labelImg_1
# linux安装
pip3 install labelImg
# linux使用vnc打开可视化界面。命令行输入如下：
labelImg
#
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

使用pip list查看安装包

pip list
Package    Version
---------- ---------
certifi    2021.10.8
labelImg   1.8.6
lxml       4.6.3
pip        21.0.1
PyQt5      5.15.5
PyQt5-Qt5  5.15.2
PyQt5-sip  12.9.0
setuptools 58.0.4
wheel      0.37.0

软件截图如下

下载源代码，准备虚拟环境

# 环境准备
conda create -n labelImg_2 python==3.7
conda activate labelImg_2
pip install PyQt5
pip install lxml
pyrcc5 -o libs/resources.py resources.qrc
# 下载
git clone https://github.com/tzutalin/labelImg
cd labelImg
# 使用
python3 labelImg.py
#
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

使用pip list查看安装包

pip list
Package    Version
---------- ---------
certifi    2021.10.8
lxml       4.6.3
pip        21.2.2
PyQt5      5.15.5
PyQt5-Qt5  5.15.2
PyQt5-sip  12.9.0
setuptools 58.0.4
wheel      0.37.0

软件截图如下

git地址: https://github.com/open-mmlab/mmdetection

git clone https://github.com/open-mmlab/mmdetection

下载到服务器，更新到本地，用pycharm编辑器打开（参考https://blog.csdn.net/fighting_Kitty/article/details/121023315）

找到readme中Installation，get_started.md中详细写了环境安装，主要命令如下：

# 创建虚拟环境
conda create -n openmmlab python=3.7 -y
conda activate openmmlab
# 安装PyTorch and torchvision
#（按照自己的cuda版本（nvcc -V）
# 去pytorch官网上选https://pytorch.org/get-started/previous-versions/）
# CUDA 11.0
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# Install MMDetection（自动或者手动，自动简单，手动适合调试研究代码）
pip install openmim
mim install mmdet

# 手动
pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 torchaudio==0.7.0  -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/1.7.1+cu110/index.htm

附：cuda查看nvcc -V

nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:38_PDT_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.TC445_37.28540450_0

mkdir checkpoints
cd checkpoints
wget https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
wget https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py

代码

from mmdet.apis import init_detector, inference_detector

config_file = '../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
# url: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
checkpoint_file = '../checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
device = 'cuda:0'
# init a detector
model = init_detector(config_file, checkpoint_file, device=device)
# inference the demo image
inference_detector(model, '../demo/demo.jpg')

git地址: https://github.com/open-mmlab/mmdetection

git clone https://github.com/open-mmlab/mmsegmentation