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paper code home video

目录

paper code home video

Step1.创建新的nvidia-docker container名为MotionCatch

Step2.在此docker下安装anaconda3 

出现问题1：提示网络连接失败

Checking Internet connectivity,Please make sure you are connected to the internet

Step3.按照安装官方文档在MotionCatch进行配置

 1. 创建conda环境

出现问题2：

You may need to closed and restart your shell after running 'code init'

2. 安装PyTorch

3. 获取AlphaPose源码

4. 安装

出现问题3:Subprocess.CalledProcessedError:Command '[Which','g++']'returned non-zero exit status 1.

出现问题4:error No such file or directory :':usr/local/cuda:/usr/local/cuda/bin/nvcc'

出现问题5：

.../compiler_compat/id:cannot find -LOSMesa : No such  file or directory

.../compiler_compat/id:cannot find -LGL : No such  file or directory

.../compiler_compat/id:cannot find -LGLU : No such  file or directory

出现问题6：Setup script exited with 

Beginning with Matplotlib 3.6 ,Python 3.8 above is required

 出现问题7:Import error:cannot import name 'get_installed_dostribution' from 'pip_internal.utils.misc'(/home/anaconda3/envs/alphapose/lib/python3.7/site-packages/pip/_internal/utils/misc.py)

Step4.目标检测模型配置 

1.下载yolov3-spp.weights

2.下载这个模型halpe26_fast_res50_256x192.pth

出现问题8：

ImportError: libgthread-2.0.so.0: cannot open shared object file: No such file or directory

3.下载第二个模型halpe136_fast50_regression_256x192.pth

4.下载前两个multi_domain_fast50_dcn_combined_256x192.pt和 multi_domain_fast50_regression_256x192.pth

Step5.姿态追踪模型配置  

1首先尝试第一个模型 下载这个

 2 尝试第二个模型 下载第一个

Conda list

Step1.创建新的nvidia-docker container名为MotionCatch

nvidia-docker run -it --name MotionCatch nvidia/cuda:11.0.3-base-ubuntu18.04 /bin/bash

Step2.在此docker下安装anaconda3 

下载版本为Anaconda3-5.3.0-Linux-x86_64.sh 安装在MotionCatch内

docker inspect --format="{{.Id}}"  MotionCatch # 查询该容器的container_id
docker cp /home/sqy/Software_Anzhuang/Anaconda3-5.3.0-Linux-x86_64.sh 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/home# 移动到home下
bash Anaconda3-5.3.0-Linux-x86_64.sh

出现问题1：提示网络连接失败

Checking Internet connectivity,Please make sure you are connected to the internet

​解决方法:这是vscode相关的，不影响anaconda 直接输入no

安装结束，配置环境变量

apt-get update
apt-get upgrade
apt-get install vim
vi ~/.bashrc
export export PATH="/home/anaconda3/bin:$PATH"# conda init里有这条指令，复制下来添加到末尾,路径为安装路径
source ~/.bashrc

检查安装是否成功

conda -V #显示conda 4.5.11

注意，conda卸载只需要把anaconda3文件夹整个删除，再把bashrc里刚才添加的export语句删除即可

(Tips:中间我看了下磁盘空间，觉得snapd不爽 卸载了snapd，虽然这和论文没关系：)

sudo apt-get purge snapd
sudo apt autoremove --purge snapd

Step3.按照安装官方文档在MotionCatch进行配置

 1. 创建conda环境

conda create -n alphapose python=3.7 -y
conda activate alphapose

出现问题2：

You may need to closed and restart your shell after running 'code init'

原因：shell未激活,执行conda init是没用的

解决办法： source activate

(Tips：执行conda update conda后若出现conda info -e出错的情况

An unexpected error has occurred.conda has prepared the above report)

解决办法：把anaconda里的lib文件夹 libffi.so.7文件进行软连接更改 ln -s libffi.so.6 libffi.so.7即可

2. 安装PyTorch

conda install pytorch torchvision cudatoolkit=11.3 -c pytorch

3. 获取AlphaPose源码

git clone https://github.com/MVIG-SJTU/AlphaPose.git
cd AlphaPose

4. 安装

# 以下两条环境变量在宿主机和docker bashrc设置,添加完毕后生效
vi ~/.bashrc 
export PATH="/usr/local/cuda/bin/:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda/lib64/:$LD_LIBRARY_PATH"
source ~/.bashrc
 
# 以下两条在docker安装
python -m pip install cython -i  https://pypi.tuna.tsinghua.edu.cn/simple
sudo apt-get install libyaml-dev
 
# 仅在Ubuntu18.04进行此设置
locale-gen C.UTF-8
# 如果locale-gen指令未被查到
sudo apt-get install locales
export LANG=C.UTF-8
 
# 最后执行
python setup.py build develop

出现问题3:Subprocess.CalledProcessedError:Command '[Which','g++']'returned non-zero exit status 1.

出错原因：未安装g++ 

解决办法：apt-get install g++ 

出现问题4:error No such file or directory :':usr/local/cuda:/usr/local/cuda/bin/nvcc'

解决办法：在docker安装CUDA后，并执行export CUDA_HOME=/usr/local/cuda。重新执行python setup.py build develop

出现问题5：

.../compiler_compat/id:cannot find -LOSMesa : No such  file or directory

.../compiler_compat/id:cannot find -LGL : No such  file or directory

.../compiler_compat/id:cannot find -LGLU : No such  file or directory

解决办法：安装缺失的三个动态库 并设置为当前虚拟环境的软连接

apt install libglu1-mesa
apt install -y libosmesa-dev
ln -s /usr/lib/x86_64-linux-gnu/libGL.so.1 /home/anaconda3/envs/alphapose/lib/libGL.so
ln -s /usr/lib/x86_64-linux-gnu/libGLU.so.1 /home/anaconda3/envs/alphapose/lib/libGLU.so
ln -s /usr/lib/x86_64-linux-gnu/libGLX_mesa.so.0.0.0 /home/anaconda3/envs/alphapose/lib/libOSMesa.so

注意：rm -rf build后再执行 python setup.py build develop

出现问题6：Setup script exited with 

Beginning with Matplotlib 3.6 ,Python 3.8 above is required

解决办法：缺什么安装什么，其他错误也是一样（注意加上清华源）

pip install matplotlib -i  https://pypi.tuna.tsinghua.edu.cn/simple

 出现问题7:Import error:cannot import name 'get_installed_dostribution' from 'pip_internal.utils.misc'(/home/anaconda3/envs/alphapose/lib/python3.7/site-packages/pip/_internal/utils/misc.py)

 解决办法：降级pip

python -m pip install pip==21.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

好用的pip下载指令

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn numpy 

编译完成： 

Step4.目标检测模型配置 

1.下载yolov3-spp.weights

和 yolox-l

分别拷贝到detector/yolo/data和detector/yolox/data(需要自己创建一个data文件夹)

docker cp yolov3-spp.weights 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/detector/yolo/data
 
docker cp yolox_x.pth 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/detector/yolox/data

 原始数据

2.下载这个模型halpe26_fast_res50_256x192.pth

(2 3 4的模型都放到/pretrained_models下)

docker cp halpe26_fast_res50_256x192.pth 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/pretrained_models

python scripts/demo_inference.py --cfg configs/halpe_26/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/halpe26_fast_res50_256x192.pth --indir examples/demo/ --save_img

(想用yolox-l做检测器，请在上述指令加上此参数：--detector yolox-x ) 

出现问题8：

ImportError: libgthread-2.0.so.0: cannot open shared object file: No such file or directory

解决方法：

apt-get install libglib2.0-0

直接跑通

docker cp 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/examples/res/vis ./

查看结果：

3.下载第二个模型halpe136_fast50_regression_256x192.pth

docker cp halpe136_fast50_regression_256x192.pth 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/pretrained_models

python scripts/demo_inference.py --cfg configs/halpe_136/resnet/256x192_res50_lr1e-3_2x-regression.yaml --checkpoint pretrained_models/halpe136_fast50_regression_256x192.pth --indir examples/demo/ --save_img

查看结果

4.下载前两个multi_domain_fast50_dcn_combined_256x192.pt和 multi_domain_fast50_regression_256x192.pth

docker cp multi_domain_fast50_dcn_combined_256x192.pth 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/pretrained_models
 
 docker cp multi_domain_fast50_regression_256x192.pth 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/pretrained_models

python scripts/demo_inference.py --cfg configs/halpe_coco_wholebody_136/resnet/256x192_res50_lr1e-3_2x-regression.yaml --checkpoint pretrained_models/multi_domain_fast50_regression_256x192.pth --indir examples/demo/ --save_img
 
python scripts/demo_inference.py --cfg configs/halpe_coco_wholebody_136/resnet/256x192_res50_lr1e-3_2x-dcn-combined.yaml --checkpoint pretrained_models/multi_domain_fast50_dcn_combined_256x192.pth --indir examples/demo/ --save_img

Step5.姿态追踪模型配置  

1首先尝试第一个模型 下载这个

模型移动到/root/AlphaPose/trackers/weights(自己创建weights目录)

docker cp osnet_ain_x1_0_msmt17_256x128_amsgrad_ep50_lr0.0015_coslr_b64_fb10_softmax_labsmth_flip_jitter.pth 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/trackers/weights

视频移动到 /root/AlphaPose/examples/video_demo

docker cp 1.mp4 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/examples/video_demo

(另外，可以更改/AlphaPose/trackers/tracker_cfg.py中的cfg.arch和cfg.loadmodel来更改模型)

跑模型 

python scripts/demo_inference.py --cfg configs/halpe_26/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/halpe26_fast_res50_256x192.pth --video examples/video_demo/1.mp4 --outdir examples/res/vis_video --save_video --pose_track

出现问题9:执行卡住CUDA out of memory

可能的原因：内存不够 

因为在执行之前可以执行的图片样例 出现报错

此问题尚未解决 但是可以通过宿主机重启docker重新执行之前的图片样例

 2 尝试第二个模型 下载第一个

 模型移动到/root/AlphaPose/detector/tracker/data(需要自己创建data)

 docker cp jde.1088x608.uncertainty.pt 24520982511f3b8d72adea23a0113817c49d8c991ade2edb3c6d6fa43305ca5e:/root/AlphaPose/detector/tracker/data

python scripts/demo_inference.py --cfg configs/halpe_26/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/halpe26_fast_res50_256x192.pth --video examples/video_demo/1.mp4 --outdir examples/res/vis_video --save_video --detector tracker

Conda list

# packages in environment at /home/anaconda3/envs/alphapose:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
_openmp_mutex             5.1                       1_gnu  
alphapose                 0.5.0+a94dae6             dev_0    <develop>
blas                      1.0                         mkl  
brotlipy                  0.7.0           py37h27cfd23_1003  
bzip2                     1.0.8                h7b6447c_0  
ca-certificates           2022.07.19           h06a4308_0  
certifi                   2022.9.14        py37h06a4308_0  
cffi                      1.15.1           py37h74dc2b5_0  
charset-normalizer        2.0.4              pyhd3eb1b0_0  
chumpy                    0.70                     pypi_0    pypi
cryptography              37.0.1           py37h9ce1e76_0  
cudatoolkit               11.3.1               h2bc3f7f_2  
cycler                    0.11.0                   pypi_0    pypi
cython                    0.29.32                  pypi_0    pypi
cython-bbox               0.1.3                    pypi_0    pypi
easydict                  1.10                     pypi_0    pypi
ffmpeg                    4.3                  hf484d3e_0    pytorch
fonttools                 4.37.3                   pypi_0    pypi
freetype                  2.11.0               h70c0345_0  
giflib                    5.2.1                h7b6447c_0  
gmp                       6.2.1                h295c915_3  
gnutls                    3.6.15               he1e5248_0  
halpecocotools            0.0.0                    pypi_0    pypi
idna                      3.3                pyhd3eb1b0_0  
intel-openmp              2021.4.0          h06a4308_3561  
jpeg                      9e                   h7f8727e_0  
jsonpatch                 1.32                     pypi_0    pypi
jsonpointer               2.3                      pypi_0    pypi
kiwisolver                1.4.4                    pypi_0    pypi
lame                      3.100                h7b6447c_0  
lcms2                     2.12                 h3be6417_0  
ld_impl_linux-64          2.38                 h1181459_1  
lerc                      3.0                  h295c915_0  
libdeflate                1.8                  h7f8727e_5  
libffi                    3.3                  he6710b0_2  
libgcc-ng                 11.2.0               h1234567_1  
libgomp                   11.2.0               h1234567_1  
libiconv                  1.16                 h7f8727e_2  
libidn2                   2.3.2                h7f8727e_0  
libpng                    1.6.37               hbc83047_0  
libstdcxx-ng              11.2.0               h1234567_1  
libtasn1                  4.16.0               h27cfd23_0  
libtiff                   4.4.0                hecacb30_0  
libunistring              0.9.10               h27cfd23_0  
libwebp                   1.2.2                h55f646e_0  
libwebp-base              1.2.2                h7f8727e_0  
lz4-c                     1.9.3                h295c915_1  
matplotlib                3.5.3                    pypi_0    pypi
mkl                       2021.4.0           h06a4308_640  
mkl-service               2.4.0            py37h7f8727e_0  
mkl_fft                   1.3.1            py37hd3c417c_0  
mkl_random                1.2.2            py37h51133e4_0  
munkres                   1.1.4                    pypi_0    pypi
natsort                   8.2.0                    pypi_0    pypi
ncurses                   6.3                  h5eee18b_3  
nettle                    3.7.3                hbbd107a_1  
numpy                     1.21.5           py37h6c91a56_3  
numpy-base                1.21.5           py37ha15fc14_3  
opencv-python             4.6.0.66                 pypi_0    pypi
opendr                    0.78                     pypi_0    pypi
openh264                  2.1.1                h4ff587b_0  
openssl                   1.1.1q               h7f8727e_0  
packaging                 21.3                     pypi_0    pypi
pillow                    9.2.0            py37hace64e9_1  
pip                       21.2                     pypi_0    pypi
protobuf                  3.20.1                   pypi_0    pypi
pycocotools               2.0.5                    pypi_0    pypi
pycparser                 2.21               pyhd3eb1b0_0  
pyopenssl                 22.0.0             pyhd3eb1b0_0  
pyparsing                 3.0.9                    pypi_0    pypi
pysocks                   1.7.1                    py37_1  
python                    3.7.13               h12debd9_0  
python-dateutil           2.8.2                    pypi_0    pypi
pytorch                   1.12.1          py3.7_cuda11.3_cudnn8.3.2_0    pytorch
pytorch-mutex             1.0                        cuda    pytorch
pyyaml                    6.0                      pypi_0    pypi
pyzmq                     24.0.1                   pypi_0    pypi
readline                  8.1.2                h7f8727e_1  
requests                  2.28.1           py37h06a4308_0  
scipy                     1.7.3                    pypi_0    pypi
setuptools                63.4.1           py37h06a4308_0  
six                       1.16.0             pyhd3eb1b0_1  
sqlite                    3.39.3               h5082296_0  
tensorboardx              2.5.1                    pypi_0    pypi
terminaltables            3.1.10                   pypi_0    pypi
timm                      0.1.20                   pypi_0    pypi
tk                        8.6.12               h1ccaba5_0  
torchvision               0.13.1               py37_cu113    pytorch
tornado                   6.2                      pypi_0    pypi
tqdm                      4.64.1                   pypi_0    pypi
typing_extensions         4.3.0            py37h06a4308_0  
urllib3                   1.26.11          py37h06a4308_0  
visdom                    0.1.8.9                  pypi_0    pypi
websocket-client          1.4.1                    pypi_0    pypi
wheel                     0.37.1             pyhd3eb1b0_0  
xz                        5.2.6                h5eee18b_0  
zlib                      1.2.12               h5eee18b_3  
zstd                      1.5.2                ha4553b6_0