detectron2使用教程20200824_1
2024-04-18 20:15:50
下载
https://github.com/facebookresearch/detectron2detectron2-master.zip
我下载的detectron2版本是0.2.1detectron2_ubuntu.tar.gz就不用测试了,只是安装detectron2环境重点参考detectron2_train(ubuntu).tar.gzInstallation
深度学习目标检测框架 detectron2 安装流程
https://zhuanlan.zhihu.com/p/1068537151.新建虚拟环境mydetection_python3.7.3
#新建虚拟环境
conda create --name mydetection_python3.7.3 python=3.7.3source ~/anaconda3/bin/activate mydetection_python3.7.3cd /home/liuhao/PycharmProjects/Python_3rdparty#虽然/Detectron2_download/detectron2-master/docs/requirements.txt写的是torch-1.5.0和torchvision-0.6.0
pip install ./torchvision0.5.0/*.whl #对应torch-1.4.0pip install ./opencv-python3.4.6/*.whl#安装tensorboard
pip install ./tensorflow2.0.0/cachetools-4.1.1-py3-none-any.whl ./tensorflow2.0.0/rsa-4.6-py3-none-any.whl ./tensorflow2.0.0/pyasn1-0.4.8-py2.py3-none-any.whl ./tensorflow2.0.0/pyasn1_modules-0.2.8-py2.py3-none-any.whl ./tensorflow2.0.0/zipp-3.1.0-py3-none-any.whl ./tensorflow2.0.0/importlib_metadata-1.7.0-py2.py3-none-any.whl ./tensorflow2.0.0/oauthlib-3.1.0-py2.py3-none-any.whl ./tensorflow2.0.0/chardet-3.0.4-py2.py3-none-any.whl ./tensorflow2.0.0/idna-2.10-py2.py3-none-any.whl ./tensorflow2.0.0/urllib3-1.25.9-py2.py3-none-any.whl ./tensorflow2.0.0/requests_oauthlib-1.3.0-py2.py3-none-any.whl ./tensorflow2.0.0/requests-2.24.0-py2.py3-none-any.whl ./tensorflow2.0.0/Werkzeug-1.0.1-py2.py3-none-any.whl ./tensorflow2.0.0/six-1.15.0-py2.py3-none-any.whl ./tensorflow2.0.0/google_auth_oauthlib-0.4.1-py2.py3-none-any.whl ./tensorflow2.0.0/wheel-0.34.2-py2.py3-none-any.whl ./tensorflow2.0.0/Markdown-3.2.2-py3-none-any.whl ./tensorflow2.0.0/absl_py-0.9.0-py3-none-any.whl ./tensorflow2.0.0/grpcio-1.30.0-cp37-cp37m-manylinux2010_x86_64.whl ./tensorflow2.0.0/protobuf-3.12.2-cp37-cp37m-manylinux1_x86_64.whl ./tensorflow2.0.0/setuptools-47.3.1-py3-none-any.whl ./tensorflow2.0.0/google_auth-1.18.0-py2.py3-none-any.whl ./tensorflow2.0.0/numpy-1.19.0-cp37-cp37m-manylinux2010_x86_64.whl ./tensorflow2.0.0/tensorboard-2.0.2-py3-none-any.whlpip install fvcore -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install cython -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install pycocotools -i https://pypi.tuna.tsinghua.edu.cn/simple/(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~/PycharmProjects/Python_3rdparty$ pip show tensorboard
Name: tensorboard
Version: 2.0.2
Summary: TensorBoard lets you watch Tensors Flow
Home-page: https://github.com/tensorflow/tensorboard
Author: Google Inc.
Author-email: packages@tensorflow.org
License: Apache 2.0
Location: /home/liuhao/anaconda3/envs/mydetection_python3.7.3/lib/python3.7/site-packages
Requires: setuptools, numpy, google-auth, protobuf, werkzeug, google-auth-oauthlib, wheel, absl-py, markdown, grpcio, six, requests
Required-by:
(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~/PycharmProjects/Python_3rdparty$
/detectron2-master/docs/requirements.txt 仅作为参考，直接进行下一步安装，缺啥需要的库就安装一下
termcolor
numpy
tqdm
docutils==0.16
# https://github.com/sphinx-doc/sphinx/commit/7acd3ada3f38076af7b2b5c9f3b60bb9c2587a3d
git+git://github.com/sphinx-doc/sphinx.git@7acd3ada3f38076af7b2b5c9f3b60bb9c2587a3d
recommonmark==0.6.0
sphinx_rtd_theme
mock
matplotlib
termcolor
yacs
tabulate
cloudpickle
Pillow==6.2.2
future
requests
six
git+git://github.com/facebookresearch/fvcore.git
https://download.pytorch.org/whl/cpu/torch-1.5.0%2Bcpu-cp37-cp37m-linux_x86_64.whl
https://download.pytorch.org/whl/cpu/torchvision-0.6.0%2Bcpu-cp37-cp37m-linux_x86_64.whl
2.安装detectron2
#在/Detectron2_download/detectron2-master文件夹下面打开终端source ~/anaconda3/bin/activate mydetection_python3.7.3python3 ./setup.py develop #我用的这个
或者pip install -e . #可能需要联网下载安装future;#==========================================================================
#会在/home/liuhao/anaconda3/envs/mydetection_python3.7.3/lib/python3.7/site-packages/生成:
easy-install.pth:
/home/liuhao/PycharmProjects/DeepLearning_liuhao/Detectron2_download/detectron2-masterdetectron2.egg-link#会在/home/liuhao/PycharmProjects/DeepLearning_liuhao/CenterNet_download/DCNv2/生成:
build文件夹(里面有_C.cpython-37m-x86_64-linux-gnu.so)
detectron2.egg-info文件夹......Building wheels for collected packages: fvcoreBuilding wheel for fvcore (setup.py) ... doneCreated wheel for fvcore: filename=fvcore-0.1.2.post20200912-py3-none-any.whl size=43994 sha256=6847c087ed04ebd1952672263a971c8b08fb170792ebee3d90fc33b426010712Stored in directory: /home/liuhao/.cache/pip/wheels/f7/93/d9/8b0c812517efbb9d22a48b6755a8aed255207d9dbde7f68228
Successfully built fvcore
Installing collected packages: yacs, tabulate, cloudpickle, mock, portalocker, fvcore, future, pydot, detectron2Running setup.py develop for detectron2
Successfully installed cloudpickle-1.6.0 detectron2 future-0.18.2 fvcore-0.1.2.post20200912 mock-4.0.2 portalocker-2.0.0 pydot-1.4.1 tabulate-0.8.7 yacs-0.1.8
(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~/PycharmProjects/DeepLearning_liuhao/Detectron2_liuhao/detectron2-master$pip show detectron2
(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~$ pip show detectron2
Name: detectron2
Version: 0.2.1
Summary: Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
Home-page: https://github.com/facebookresearch/detectron2
Author: FAIR
Author-email: None
License: UNKNOWN
Location: /home/liuhao/PycharmProjects/DeepLearning_liuhao/Detectron2_download/detectron2-master
Requires: termcolor, Pillow, yacs, tabulate, cloudpickle, matplotlib, mock, tqdm, tensorboard, fvcore, pycocotools, future, pydot
Required-by:
(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~$
Quick Start
#在/Detectron2_download/detectron2-master文件夹下面打开终端#在mydetectron2_python3.7.3环境中
source ~/anaconda3/bin/activate mydetection_python3.7.3查看帮助
(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~/PycharmProjects/DeepLearning_liuhao/Detectron2_liuhao/detectron2-master$ python3 ./demo/demo.py -h
usage: demo.py [-h] [--config-file FILE] [--webcam][--video-input VIDEO_INPUT] [--input INPUT [INPUT ...]][--output OUTPUT] [--confidence-threshold CONFIDENCE_THRESHOLD][--opts ...]Detectron2 demo for builtin modelsoptional arguments:-h, --help show this help message and exit--config-file FILE path to config file--webcam Take inputs from webcam.--video-input VIDEO_INPUTPath to video file.--input INPUT [INPUT ...]A list of space separated input images; or a singleglob pattern such as 'directory/*.jpg'--output OUTPUT A file or directory to save output visualizations. Ifnot given, will show output in an OpenCV window.--confidence-threshold CONFIDENCE_THRESHOLDMinimum score for instance predictions to be shown--opts ... Modify config options using the command-line 'KEYVALUE' pairs
(mydetection_python3.7.3) liuhao@liuhao-Lenovo:~/PycharmProjects/DeepLearning_liuhao/Detectron2_liuhao/detectron2-master$
1.下载配置文件和权重文件
在/maskrcnn-benchmark-models/文件夹里面在/model_zoo/文件夹里面2.拷贝配置文件
#必须要使用yaml文件，里面还有其他设置#===========================================================================
#1.执行一下下面命令，立马结束，会生成/home/liuhao/.torch/fvcore_cache/detectron2/ImageNetPretrained/MSRA/文件夹
python3 ./demo/demo.py --config-file ./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input ../detectron2_liuhao/input/huaxue.jpg#2.
#在/home/liuhao/.torch/fvcore_cache/detectron2/ImageNetPretrained/MSRA/下面执行
ln -s /home/liuhao/PycharmProjects/DeepLearning_liuhao/Detectron2_download/maskrcnn-benchmark-models/R-50.pkl ./3.测试四个功能(对应四个权重文件)
注意/Detectron2_download/detectron2-master/detectron2/data/datasets/builtin_meta.py里面的COCO_CATEGORIES
#===========================================================================
#1 目标检测
#这种方式才会在/home/liuhao/.torch/fvcore_cache/detectron2/生成对应文件夹,并拷贝对应权重文件到这个文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml --input=../detectron2_liuhao/input/input.jpg --opts MODEL.WEIGHTS detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137257794/model_final_280758.pkl#这种方式不会生成文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/input.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_280758.pklpython3 ./demo/demo.py --config-file=./configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/1.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_280758.pkl#===========================================================================
#2 实例分割
#这种方式才会在/home/liuhao/.torch/fvcore_cache/detectron2/生成对应文件夹,并拷贝对应权重文件到这个文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../detectron2_liuhao/input/input.jpg --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl#这种方式不会生成文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/input.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pklpython3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/1.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#===========================================================================
#3 关键点检测
#这种方式才会在/home/liuhao/.torch/fvcore_cache/detectron2/生成对应文件夹,并拷贝对应权重文件到这个文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml --input=../detectron2_liuhao/input/input.jpg --opts MODEL.WEIGHTS detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl#这种方式不会生成文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/input.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_a6e10b.pklpython3 ./demo/demo.py --config-file=./configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/1.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_a6e10b.pkl#===========================================================================
#4 全景分割
#这种方式才会在/home/liuhao/.torch/fvcore_cache/detectron2/生成对应文件夹,并拷贝对应权重文件到这个文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml --input=../detectron2_liuhao/input/input.jpg --opts MODEL.WEIGHTS detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514519/model_final_c10459.pkl#这种方式不会生成文件夹
python3 ./demo/demo.py --config-file=./configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml --input=../code_liuhao/input/input.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_c10459.pklpython3 ./demo/demo.py --config-file=./configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml --input=../code_liuhao/input/1.jpg --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_c10459.pkl#摄像头
将--input ../code_liuhao/input/huaxue.jpg换成--webcampython3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --webcam --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#视频
将--input=../code_liuhao/input/huaxue.jpg换成--video-input=../detectron2_liuhao/input/road.mp4python3 ./demo/demo.py --config-file=./configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml --video-input=../../科比0.4秒绝杀热火.mp4 --confidence-threshold=0.8 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_a6e10b.pkl4.设置–output，生成.png图片
#=======================================================
#检测图片1.jpg
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/1.jpg --confidence-threshold=0.8 --output=../code_liuhao/output/result_1 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#=======================================================
#检测图片input.jpg
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/input.jpg --confidence-threshold=0.8 --output=../code_liuhao/output/result_input --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#=======================================================
#检测图片huaxue.jpg
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/huaxue.jpg --confidence-threshold=0.8 --output=../code_liuhao/output/result_huaxue --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#=======================================================
#检测图片liuhao01.jpeg
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/liuhao01.jpeg --confidence-threshold=0.8 --output=../code_liuhao/output/result_liuhao01 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#=======================================================
#检测图片liuhao02.jpeg
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/liuhao02.jpeg --confidence-threshold=0.8 --output=../code_liuhao/output/result_liuhao02 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl#=======================================================
#检测图片liuhao03.jpeg
python3 ./demo/demo.py --config-file=./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input=../code_liuhao/input/liuhao03.jpeg --confidence-threshold=0.8 --output=../code_liuhao/output/result_liuhao03 --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkl5.demo脚本说明.py
#用pycharm打开/Detectron2_liuhao/detectron2_liuhao/文件夹
#使用mydetectron2_python3.7.3环境python3 ./demo脚本说明.py6.demo自己写的脚本.py(相比于demo脚本说明.py比较简单)
Detectron2安装测试(这里面包含了代码测试)
https://blog.csdn.net/happyday_d/article/details/103484213
#用pycharm打开/Detectron2_liuhao/detectron2_liuhao/文件夹
#使用mydetectron2_python3.7.3环境python3 ./demo自己写的脚本.py参考网址
detectron2（目标检测框架）无死角玩转-00：目录
https://blog.csdn.net/weixin_43013761/article/details/104006026接下来，我会为大家无死角的解析detectron2(基于pytorch的目标检测框架)，之前的文章，如下(以下是我工作的所有项目，每一个项目都是，我都做了百分百的详细解读，随着项目增多，为了方便不臃肿，所以给出以下链接)
视觉工作项目-为后来的你，提供一份帮助！我相信，关于detectron2(基于pytorch的目标检测框架)的讲解，我的这一系列博客或许不是国内最早的，但是肯定是最详细的，废话就不多说了，我们直接开始吧！（01）detectron2（目标检测框架）无死角玩转-01：框架安装，以及demo测试：https://blog.csdn.net/weixin_43013761/article/details/104022615（02）detectron2（目标检测框架）无死角玩转-02：训练，测试coco数据：https://blog.csdn.net/weixin_43013761/article/details/104027656（03）detectron2（目标检测框架）无死角玩转-03：配置config解析-实时更新：https://blog.csdn.net/weixin_43013761/article/details/104030359（04）detectron2（目标检测框架）无死角玩转-04：训练自己的数据：https://blog.csdn.net/weixin_43013761/article/details/104032429（05）detectron2（目标检测框架）无死角玩转-05：源码详解（1）-总体架构分析：https://blog.csdn.net/weixin_43013761/article/details/104043605（06）detectron2（目标检测框架）无死角玩转-06：源码详解（2）-Trainer继承关系，Hook：https://blog.csdn.net/weixin_43013761/article/details/104092658（07）detectron2（目标检测框架）无死角玩转-07：源码详解（3）-模型构建-RetinaNet为例：https://blog.csdn.net/weixin_43013761/article/details/104098263（08）detectron2（目标检测框架）无死角玩转-08：源码详解（4）-数据预处理，数据增强：https://blog.csdn.net/weixin_43013761/article/details/104106792（09）detectron2（目标检测框架）无死角玩转-09：源码详解（5）-anchor生成：https://blog.csdn.net/weixin_43013761/article/details/104194978（10）detectron2（目标检测框架）无死角玩转-10：源码详解（6）-anchor的使用，loss计算：https://blog.csdn.net/weixin_43013761/article/details/104714022#======================================================================================
detectron2（目标检测框架）无死角玩转-01：框架安装，以及demo测试（winds + linux）
https://blog.csdn.net/weixin_43013761/article/details/104022615#在/detectron2-master文件夹下面打开终端
source ~/anaconda3/bin/activate mydetectron2_python3.7.3python3 ./demo/demo.py --config-file ./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input ../code_liuhao/input/input1.jpg --opts MODEL.WEIGHTS ../../Detectron2_download/model_zoo/model_final_f10217.pkldetectron2使用教程20200824_1相关推荐
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