双目立体视觉PyTorch & ZED 3D人体识别与追踪 (上)

Posted 2022-01-20 Techblog of HaoWANG

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项目地址：GitHub - stereolabs/zed-examples: ZED SDK Example projects

官方文档：Stereolabs Docs: API Reference, Tutorials, and Integration

How to Use PyTorch with ZED | Stereolabshttps://www.stereolabs.com/docs/pytorch/github项目地址：3D Mask R-CNN using the ZED and Pytorch

GitHub - stereolabs/zed-pytorch: 3D Object detection using the ZED and Pytorchhttps://github.com/stereolabs/zed-pytorch

3D Mask R-CNN using the ZED and Pytorch

The ZED SDK can be interfaced with Pytorch for adding 3D localization of custom objects detected with Mask-RCNN. In this Python 3 sample, we will show you how to detect, segmente, classify and locate objects in 3D space using the ZED stereo camera and Pytorch.

ROS ZED SDK: Getting IMU and Sensor Data in ROS | Stereolabshttps://www.stereolabs.com/docs/ros/sensor-data/

1. 准备工作

Python API默认安装与源码编译

安装ZED Python API

其实在执行ZED SDK install 代码中./ZED_SDK_Linux_*.run时，在安装的最后，安装会提醒你是否希望安装ZED Python API，选Y的话会发现可能安装不上。当然，如果你安装上了，恭喜你，安好了，在python3中import pyzed如果没问题那就真的安好了，下面的东西对你就没用了，享受你的ZED使用吧！

但是我是没有办法安装上的，主要原因讲过了，stereolabs下行带宽不行。

针对这种情形的同学，可以上github上下载zed-python-api，工程的位置是下面这个链接：

GitHub - stereolabs/zed-python-api: Python API for the ZED SDKhttps://github.com/stereolabs/zed-python-api
下载之后，执行以下几句指令：
cd <workspace>(这里填上你解压zed-python-api的路径，进入zed-python-api文件夹中包含requirements.txt的地方)
pip3 install -r requirements.txt
python3 setup.py build
python3 setup.py install
执行之后，如果没有报错应该就没问题了，在python3下import pyzed没报错就是安装完成了。

pip安装出错

pip install报错：Missing dependencies for SOCKS support解决方法：

在使用pip install -r requirements.txt命令时，报错：
InvalidSchema: Missing dependencies for SOCKS support.

解决方法
依次输入下面命令：
unset all_proxy
unset ALL_PROXY
Install missing dependencies:
pip install pysocks
Reset proxy
source ~/.bashrc

安装CUDA与cuDNN

Linux查看 CUDA 版本

方法一：
nvcc --version
或
nvcc -V
如果 nvcc 没有安装，那么用方法二。

方法二：

去安装目录下查看：
cat /usr/local/cuda/version.txt
查看 cuDNN 版本
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
如果没有，那么可能没有安装 cuDNN。

如果是为了使用 PyTorch/TensorFlow，在 Linux 服务器上推荐使用 conda 安装，使用 conda 可以很方便安装 PyTorch/TensorFlow 以及对应版本的 CUDA 和 cuDNN。

安装方法：

ZED SDK已经默认安装了CUDA10.2，安装cuDNN步骤如下：

CUDNN安装
切换到cudnn文件所在目录，通过tar -xzvf cudnn-10.2-linux-x64-v7.6.5.32.tgz命令解压文件，会得到一个cuda文件夹，逐一执行下面的命令进行cudnn的安装。
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
完成后，通过下面的命令查看安装情况，如果结果如下图逐行显示版本号，则安装成功。
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
至此，在Ubuntu18.04上CUDA和CUDNN安装完成。

按照官方教程，在虚拟环境中安装Pytorch1.5并进行GPU训练测试，结果如下，成功使用CUDA训练。

2. 基础环境配置

Dependencies

The 3D Object Detection project depends on the following libraries:

ZED SDK Installation

Install the ZED SDKhttps://www.stereolabs.com/developers/release/ and the ZED Python APIhttps://www.stereolabs.com/docs/getting-started/python-development/

1. Pytorch Installation

Using Conda (recommended，推荐方法)

The CUDA version must match the one used for the ZED SDK, in that case CUDA 10.0. A dedicated environment can be created to setup Pytorch, but don't forget to activate it, especially when installing MaskRCNN.

conda create --name pytorch1 -y
conda activate pytorch1
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
conda install --yes --file requirements.txt

Note: Do not forget to install Python API inside your current environment.

等待安装成功即可

Using Pip

pip3 install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
pip3 install -r requirements.txt

For more information please refer to this page Start Locally | PyTorch.

2. Apex Installation

We make use of NVIDIA's Apex API. To install it, run the following:

$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ python setup.py install

3. Mask R-CNN Installation

Setup Mask R-CNN. If you're using a conda environment, make sure it is still active before running the following commands.

安装时报错: error: identifier “AT_CHECK“ is undefined

解决办法

将报错行中的AT_CHECK替换为TORCH_CHECK即可

参考：https://github.com/facebookresearch/maskrcnn-benchmark/issues/1248

https://github.com/facebookresearch/maskrcnn-benchmark/issues/1248https://github.com/facebookresearch/maskrcnn-benchmark/issues/1248

重新编译安装即可

$ git clone https://github.com/facebookresearch/maskrcnn-benchmark.git
$ cd maskrcnn-benchmark
$ python setup.py install

GitHub - facebookresearch/maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.https://github.com/facebookresearch/maskrcnn-benchmark

Highlights

maskrcnn-benchmark has been deprecated. Please see detectron2, which includes implementations for all models in maskrcnn-benchmark

This project aims at providing the necessary building blocks for easily creating detection and segmentation models using PyTorch 1.0.

PyTorch 1.0: RPN, Faster R-CNN and Mask R-CNN implementations that matches or exceeds Detectron accuracies
Very fast: up to 2x faster than Detectron and 30% faster than mmdetection during training. See MODEL_ZOO.md for more details.
Memory efficient: uses roughly 500MB less GPU memory than mmdetection during training
Multi-GPU training and inference
Mixed precision training: trains faster with less GPU memory on NVIDIA tensor cores.
Batched inference: can perform inference using multiple images per batch per GPU
CPU support for inference: runs on CPU in inference time. See our webcam demo for an example
Provides pre-trained models for almost all reference Mask R-CNN and Faster R-CNN configurations with 1x schedule.

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