mpiigaze的安装过程一
mpiigaze链接
mpiigaze应该不是作者本人写的，而是社区工作者的杰作，对原论文Appearance-Based Gaze Estimation in the Wild的代码进行的一些复现
1.创建conda环境
2.问题
Building wheels for collected packages: dlib
Building wheel for dlib (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for dlib (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [41 lines of output]
running bdist_wheel
running build
running build_ext
================================================================================
================================================================================
================================================================================
CMake is not installed on your system!
Or it is possible some broken copy of cmake is installed on your system.
It is unfortunately very common for python package managers to include
broken copies of cmake. So if the error above this refers to some file
path to a cmake file inside a python or anaconda or miniconda path then you
should delete that broken copy of cmake from your computer.
Instead, please get an official copy of cmake from one of these known good
sources of an official cmake:
- cmake.org (this is how windows users should get cmake)
- apt install cmake (for Ubuntu or Debian based systems)
- yum install cmake (for Redhat or CenOS based systems)
On a linux machine you can run `which cmake` to see what cmake you are
actually using. If it tells you it's some cmake from any kind of python
packager delete it and install an official cmake.
More generally, cmake is not installed if when you open a terminal window
and type
cmake --version
you get an error. So you can use that as a very basic test to see if you
have cmake installed. That is, if cmake --version doesn't run from the
same terminal window from which you are reading this error message, then
you have not installed cmake. Windows users should take note that they
need to tell the cmake installer to add cmake to their PATH. Since you
can't run commands that are not in your PATH. This is how the PATH works
on Linux as well, but failing to add cmake to the PATH is a particularly
common problem on windows and rarely a problem on Linux.
================================================================================
================================================================================
================================================================================
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for dlib
Failed to build dlib
ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (dlib)
问题是系统上没有安装 CMake，或者安装的 CMake 是损坏的/不正确的版本，导致 dlib 库无法成功构建。
dlib 是一个 C++ 库，它提供了很多机器学习算法，包括人脸检测和地标检测等功能。当你在 Python 中安装 dlib 时，它需要先编译 C++ 代码，而这个编译过程依赖于 CMake。
第一种安装是：sudo apt install cmake这个需要sudo权限
然而我没有sudo权限，
[sudo] password for zhouy24:
zhouy24 is not in the sudoers file. This incident will be reported.
所以使用不需要sudo的方式：
由于我使用conda创建环境了，所以 使用命令：conda install -c anaconda cmake 指定从 anaconda 频道安装，这通常更稳定。
(mpiigaze) zhouy24@RL-DSlab:~/zhouy24Files/mpiigaze/pytorch_mpiigaze$ conda install -c anaconda cmake
Channels:
- anaconda
- conda-forge
- defaults
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /home/zhouy24/miniconda3/envs/mpiigaze
added / updated specs:
- cmake
The following packages will be downloaded:
package | build
---------------------------|-----------------
c-ares-1.19.1 | h5eee18b_0 114 KB anaconda
cmake-3.31.2 | h27e300b_0 21.9 MB anaconda
expat-2.7.1 | h6a678d5_0 198 KB anaconda
krb5-1.21.3 | h8a1dbc1_1 1.4 MB anaconda
libcurl-8.14.1 | h31d0fb7_0 473 KB anaconda
libedit-3.1.20230828 | h5eee18b_0 191 KB anaconda
libev-4.33 | h7f8727e_1 106 KB anaconda
libnghttp2-1.57.0 | h2d74bed_0 705 KB anaconda
libsqlite-3.46.0 | hde9e2c9_0 845 KB conda-forge
libssh2-1.11.1 | h251f7ec_0 294 KB anaconda
libuv-1.48.0 | h5eee18b_0 1.1 MB anaconda
libxcb-1.17.0 | h9b100fa_0 407 KB anaconda
libzlib-1.2.13 | h4ab18f5_6 60 KB conda-forge
lz4-c-1.9.4 | h6a678d5_1 161 KB anaconda
pthread-stubs-0.3 | h0ce48e5_1 5 KB anaconda
python-3.8.20 | he870216_0 24.3 MB anaconda
rhash-1.4.3 | hdbd6064_0 256 KB anaconda
sqlite-3.50.2 | hb25bd0a_1 1.6 MB anaconda
tk-8.6.14 | h993c535_1 3.4 MB anaconda
xorg-libx11-1.8.12 | h9b100fa_1 922 KB anaconda
xorg-libxau-1.0.12 | h9b100fa_0 14 KB anaconda
xorg-libxdmcp-1.1.5 | h9b100fa_0 20 KB anaconda
xorg-xorgproto-2024.1 | h5eee18b_1 560 KB anaconda
zlib-1.2.13 | h4ab18f5_6 91 KB conda-forge
zstd-1.5.6 | hc292b87_0 1.0 MB anaconda
------------------------------------------------------------
Total: 59.9 MB
The following NEW packages will be INSTALLED:
c-ares anaconda/linux-64::c-ares-1.19.1-h5eee18b_0
cmake anaconda/linux-64::cmake-3.31.2-h27e300b_0
expat anaconda/linux-64::expat-2.7.1-h6a678d5_0
krb5 anaconda/linux-64::krb5-1.21.3-h8a1dbc1_1
libcurl anaconda/linux-64::libcurl-8.14.1-h31d0fb7_0
libedit anaconda/linux-64::libedit-3.1.20230828-h5eee18b_0
libev anaconda/linux-64::libev-4.33-h7f8727e_1
libnghttp2 anaconda/linux-64::libnghttp2-1.57.0-h2d74bed_0
libssh2 anaconda/linux-64::libssh2-1.11.1-h251f7ec_0
libuv anaconda/linux-64::libuv-1.48.0-h5eee18b_0
libxcb anaconda/linux-64::libxcb-1.17.0-h9b100fa_0
lz4-c anaconda/linux-64::lz4-c-1.9.4-h6a678d5_1
pthread-stubs anaconda/linux-64::pthread-stubs-0.3-h0ce48e5_1
rhash anaconda/linux-64::rhash-1.4.3-hdbd6064_0
sqlite anaconda/linux-64::sqlite-3.50.2-hb25bd0a_1
xorg-libx11 anaconda/linux-64::xorg-libx11-1.8.12-h9b100fa_1
xorg-libxau anaconda/linux-64::xorg-libxau-1.0.12-h9b100fa_0
xorg-libxdmcp anaconda/linux-64::xorg-libxdmcp-1.1.5-h9b100fa_0
xorg-xorgproto anaconda/linux-64::xorg-xorgproto-2024.1-h5eee18b_1
zlib conda-forge/linux-64::zlib-1.2.13-h4ab18f5_6
zstd anaconda/linux-64::zstd-1.5.6-hc292b87_0
The following packages will be UPDATED:
tk conda-forge::tk-8.6.13-noxft_hd72426e~ --> anaconda::tk-8.6.14-h993c535_1
The following packages will be SUPERSEDED by a higher-priority channel:
python conda-forge::python-3.8.20-h4a871b0_2~ --> anaconda::python-3.8.20-he870216_0
The following packages will be DOWNGRADED:
libsqlite 3.50.3-hee844dc_0 --> 3.46.0-hde9e2c9_0
libzlib 1.3.1-hb9d3cd8_2 --> 1.2.13-h4ab18f5_6
Proceed ([y]/n)? y
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
验证安装：
cmake --version
如果在 Conda 环境中成功安装，当你激活该环境时，cmake --version 应该可以正常运行。停用环境后，cmake 命令可能就找不到了。
(mpiigaze) zhouy24@RL-DSlab:~/zhouy24Files/mpiigaze/pytorch_mpiigaze$ cmake --version
cmake version 3.31.2
之后使用pip下载命令：
(mpiigaze) zhouy24@RL-DSlab:~/zhouy24Files/mpiigaze/pytorch_mpiigaze$ pip install -r requirements.txt
除去一堆下载之外，最重要的是：
Building wheels for collected packages: dlib
Building wheel for dlib (pyproject.toml) ... done
Created wheel for dlib: filename=dlib-20.0.0-cp38-cp38-linux_x86_64.whl size=3973919 sha256=3a6b5f5508cafa89392278cba89fa01cd5e953506f7e96208b19651e9e405f53
Stored in directory: /home/zhouy24/.cache/pip/wheels/29/a3/42/e1f8773f2019449881db7ae7488211a3b3e97a72e21b944970
Successfully built dlib
Installing collected packages: pytz, mpmath, dlib, tzdata, typing-extensions, tqdm, termcolor, tabulate, sympy, six, pyyaml, protobuf, portalocker, Pillow, packaging, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, fsspec, filelock, yacs, triton, tensorboardX, scipy, python-dateutil, opencv-python, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, iopath, h5py, pandas, nvidia-cusolver-cu12, fvcore, torch, torchvision
Successfully installed MarkupSafe-2.1.5 Pillow-10.4.0 dlib-20.0.0 filelock-3.16.1 fsspec-2025.3.0 fvcore-0.1.5.post20221221 h5py-3.11.0 iopath-0.1.10 jinja2-3.1.6 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.9.86 nvidia-nvtx-cu12-12.1.105 opencv-python-4.12.0.88 packaging-25.0 pandas-2.0.3 portalocker-3.0.0 protobuf-5.29.5 python-dateutil-2.9.0.post0 pytz-2025.2 pyyaml-6.0.2 scipy-1.10.1 six-1.17.0 sympy-1.13.3 tabulate-0.9.0 tensorboardX-2.6.2.2 termcolor-2.4.0 torch-2.4.1 torchvision-0.19.1 tqdm-4.67.1 triton-3.0.0 typing-extensions-4.13.2 tzdata-2025.2 yacs-0.1.8