【Intel®RealSense™SDK2.0】在【Linux】环境配置记录

Intel® RealSense™ SDK 2.0 官网

系统环境

ubuntu 18.04

建conda环境

conda create -n real_sense python&＃61;3.7
conda activate real_sense

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple scikit-build
pip3 install opencv-contrib-python -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install numpy
pip3 install PyQt5 -i https://pypi.douban.com/simple
pip3 install PyQt5-tools -i https://pypi.douban.com/simple

Real_Sense SDK 2.0 Ubuntu X86/AMD64 的 Debian 安装包

官方Linux 发行版SDK安装指南
Linux Ubuntu 编译安装

1. 使 Ubuntu 保持最新&＃xff1a;

   更新 Ubuntu 发行版&＃xff0c;包括获取最新的稳定内核&＃xff1a;
   sudo apt-get update && sudo apt-get upgrade && sudo apt-get dist-upgrade

2. 安装软件包&＃xff1a;
   注册服务器的公钥&＃xff1a;

   sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE

   如果仍然无法检索到公钥&＃xff0c;请检查并指定代理设置&＃xff1a;export http_proxy&＃61;“http://:”
   &＃xff0c;然后重新运行该命令。请参阅以下链接中的其他方法。

3. 将服务器添加到存储库列表中&＃xff1a;

   sudo add-apt-repository “deb https://librealsense.intel.com/Debian/apt-repo $(lsb_release -cs) main” -u

   安装库&＃xff08;如果升级包&＃xff0c;请参阅下面的部分&＃xff09;&＃xff1a;

   sudo apt-get install librealsense2-dkms
   sudo apt-get install librealsense2-utils

   以上两行将部署 librealsense2 udev 规则&＃xff0c;构建和激活内核模块&＃xff0c;运行时库和可执行演示和工具。

   可选择安装开发人员和调试包&＃xff1a;安装包后&＃xff0c;您可以

   sudo apt-get install librealsense2-dev
   sudo apt-get install librealsense2-dbg

   重新连接英特尔实感深度摄像头并运行&＃xff1a;realsense-viewer以验证安装。

Python库的安装和调用

官方Python Wrapper

pip install pyrealsense2

官方例程&＃xff08;已修改&＃xff09;

# First import the libraryimport pyrealsense2 as rsimport numpy as np# Create a context object. This object owns the handles to all connected realsense devicespipeline &＃61; rs.pipeline()pipeline.start()try:while True:# Create a pipeline object. This object configures the streaming camera and owns it&＃39;s handleframes &＃61; pipeline.wait_for_frames()depth &＃61; frames.get_depth_frame()depth_data &＃61; depth.as_frame().get_data()np_image &＃61; np.asanyarray(depth_data)if not depth:continue# Print a simple text-based representation of the image, by breaking it into 10x20 pixel regions and approximating the coverage of pixels within one metercoverage &＃61; [0] * 64for y in range(480):for x in range(640): # 每行检测640个点dist &＃61; depth.get_distance(x, y) # 在给定的像素处提供以米为单位的深度if 0 < dist and dist < 1:coverage[x // 10] &＃43;&＃61; 1 # x上每10个点对应一个要打印的深度记录if y % 20 &＃61;&＃61; 19: # 每20个y打印一次line &＃61; ""for c in coverage:line &＃43;&＃61; " .:nhBXWW"[c // 25] # 根据深度记录打印不同字符coverage &＃61; [0] * 64print(line)finally:pipeline.stop()&＃96;&＃96;&＃96;## 参考文献&＃xff1a;
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