Deep3DFaceRecon 2D图像转3D模型实战

本案例通过Deep3DFaceRecon_pytorch实现

前置文档：

https://github.com/sicxu/Deep3DFaceRecon_pytorch

https://blog.csdn.net/flyfish1986/article/details/121861086

本文是在本地没有gpu硬件的支持下的实现方案，并不具体描述部署过程，部署过程建议看上面两个文档地址

准备工程文件

将下载好的Deep3DFaceRecon_pytorch工程文件（详见https://blog.csdn.net/flyfish1986/article/details/121861086中百度网盘的地址）

解压到本地（这个工程已经包含20epoch模型，不用再去谷歌网盘下载了）

项目根目录创建environment.sh

apt-get update && apt-get install -y --no-install-recommends \
    pkg-config \
    libglvnd0 \
    libgl1 \
    libglx0 \
    libegl1 \
    libgles2 \
    libglvnd-dev \
    libgl1-mesa-dev \
    libegl1-mesa-dev \
    libgles2-mesa-dev \
    cmake \
    curl \
    libsm6 \
    libxext6 \
    libxrender-dev

# export PYTHONDONTWRITEBYTECODE=1
export PYTHONUNBUFFERED=1

# for GLEW
export LD_LIBRARY_PATH=/usr/lib:$LD_LIBRARY_PATH

# nvidia-container-runtime
export NVIDIA_VISIBLE_DEVICES=all
export NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics

# Default pyopengl to EGL for good headless rendering support
export PYOPENGL_PLATFORM=egl

cp docker/10_nvidia.json /usr/share/glvnd/egl_vendor.d/10_nvidia.json

pip install --upgrade pip
pip install ninja imageio imageio-ffmpeg

pip install trimesh==3.9.20 -i https://pypi.douban.com/simple
pip install dominate==2.6.0 -i https://pypi.douban.com/simple
pip install kornia==0.5.5 -i https://pypi.douban.com/simple
pip install scikit-image==0.16.2 -i https://pypi.douban.com/simple
pip install numpy==1.18.1 -i https://pypi.douban.com/simple
pip install matplotlib==2.2.5 -i https://pypi.douban.com/simple
pip install opencv-python==3.4.9.33 -i https://pypi.douban.com/simple
pip install tensorboard==1.15.0 -i https://pypi.douban.com/simple
pip install tensorflow==1.15.0 -i https://pypi.douban.com/simple
pip install kornia==0.5.5 -i https://pypi.douban.com/simple
pip install nvdiffrast==0.2.7 -i https://pypi.douban.com/simple
pip install ninja -i https://pypi.douban.com/simple

这一步主要是初始化环境，包含：安装python库等

修改文件util/nvdiffrast.py：

# if self.glctx is None:
# self.glctx = dr.RasterizeGLContext(device=device)
# print("create glctx on device cuda:%d"%device.index)
if self.glctx is None:
    self.glctx = dr.RasterizeCudaContext(device=device)
    print("create glctx on device cuda:%d"%device.index)