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<h1 id="项目使用硬件"><a href="#项目使用硬件" class="headerlink" title="项目使用硬件"></a>项目使用硬件</h1><ul>
<li>Jetson Xavier NX </li>
</ul>
<h1 id="项目需求"><a href="#项目需求" class="headerlink" title="项目需求"></a>项目需求</h1><ul>
<li>人脸检测</li>
<li>人脸识别</li>
<li>目标跟踪 </li>
</ul>
<h1 id="CUDA-cuDNN及系统安装"><a href="#CUDA-cuDNN及系统安装" class="headerlink" title="CUDA/cuDNN及系统安装"></a>CUDA/cuDNN及系统安装</h1><p>买的成品，出厂预装了最新的系统<br>查询确认CUDA/cuDNN</p>
<blockquote>
<p>nvcc -V</p>
</blockquote>
<h1 id="远程控制"><a href="#远程控制" class="headerlink" title="远程控制"></a>远程控制</h1><p>VNC配了几次也没配置上，直接安装了toDesk</p>
<h1 id="python-环境配置"><a href="#python-环境配置" class="headerlink" title="python 环境配置"></a>python 环境配置</h1><h2 id="miniforge-安装"><a href="#miniforge-安装" class="headerlink" title="miniforge 安装"></a>miniforge 安装</h2><p>Jetson(aarch64)暂时无法安装anaconda，使用miniforge替代。<br>miniforge release版本下载地址：<a href="https://github.com/conda-forge/miniforge/releases" target="_blank" rel="noopener">https://github.com/conda-forge/miniforge/releases</a><br><strong>我下载的版本为 Mambaforge-4.11.0-4-Linux-aarch64.sh</strong><br>下载后进行安装</p>
<blockquote>
<p>sh Mambaforge-4.11.0-4-Linux-aarch64.sh</p>
</blockquote>
<h2 id="虚拟环境创建"><a href="#虚拟环境创建" class="headerlink" title="虚拟环境创建"></a>虚拟环境创建</h2><p><strong>yolov5 pytorch 在x86上已经支持python3.9，但是Jetson上测试遇到部分问题，安装3.6版本实测正常</strong></p>
<blockquote>
<p>conda create -n pytorch python=3.6</p>
</blockquote>
<p>后面所有安装均在虚拟环境中完成</p>
<h1 id="torch-torchvision-安装"><a href="#torch-torchvision-安装" class="headerlink" title="torch/torchvision 安装"></a>torch/torchvision 安装</h1><h2 id="查询系统-JetPack-版本"><a href="#查询系统-JetPack-版本" class="headerlink" title="查询系统 JetPack 版本"></a>查询系统 JetPack 版本</h2><p>方法一</p>
<blockquote>
<p>sudo systemctl restart jetson_stats.service<br>sudo jtop 6-INFO</p>
</blockquote>
<p>方法二</p>
<blockquote>
<p>cat /etc/nv_tegra_release</p>
</blockquote>
<h2 id="torch-安装"><a href="#torch-安装" class="headerlink" title="torch 安装"></a>torch 安装</h2><p>根据查询到的JetPack版本到官网下载对应的pytorch<br>下载地址：<a href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048" target="_blank" rel="noopener">https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048</a><br><strong>我下载的版本为 v1.10.0</strong><br>安装</p>
<blockquote>
<p>sudo apt-get install python3-pip libopenblas-base libopenmpi-dev<br>pip install Cython<br>pip install numpy torch-1.10.0-cp36-cp36m-linux_aarch64.whl</p>
</blockquote>
<h2 id="torchvision-安装"><a href="#torchvision-安装" class="headerlink" title="torchvision 安装"></a>torchvision 安装</h2><p>下载pytorch对应torchvision版本，具体对应信息为<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">... 前面的史前版本省略了 ...</span><br><span class="line">PyTorch v1.8 - torchvision v0.9.0</span><br><span class="line">PyTorch v1.9 - torchvision v0.10.0</span><br><span class="line">PyTorch v1.10 - torchvision v0.11.1</span><br></pre></td></tr></table></figure></p>
<p><strong>我下载的为 v0.11.1</strong><br>安装</p>
<blockquote>
<p>sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev<br>git clone –branch v0.11.1 <a href="https://github.com/pytorch/vision" target="_blank" rel="noopener">https://github.com/pytorch/vision</a> torchvision<br>cd torchvision<br>export BUILD_VERSION=0.11.1<br>python setup.py install –user</p>
</blockquote>
<h2 id="环境验证"><a href="#环境验证" class="headerlink" title="环境验证"></a>环境验证</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> torch</span><br><span class="line"><span class="keyword">import</span> torchvision</span><br><span class="line"></span><br><span class="line">print(torchvision.__version__)</span><br><span class="line">print(torch.__version__)</span><br><span class="line">print(<span class="string">'CUDA available: '</span> + str(torch.cuda.is_available()))</span><br><span class="line">print(<span class="string">'cuDNN version: '</span> + str(torch.backends.cudnn.version()))</span><br><span class="line">a = torch.cuda.FloatTensor(<span class="number">2</span>).zero_()</span><br><span class="line">print(<span class="string">'Tensor a = '</span> + str(a))</span><br><span class="line">b = torch.randn(<span class="number">2</span>).cuda()</span><br><span class="line">print(<span class="string">'Tensor b = '</span> + str(b))</span><br><span class="line">c = a + b</span><br><span class="line">print(<span class="string">'Tensor c = '</span> + str(c))</span><br></pre></td></tr></table></figure>
<h1 id="yolov5-环境配置"><a href="#yolov5-环境配置" class="headerlink" title="yolov5 环境配置"></a>yolov5 环境配置</h1><p>最开始原本打算用yolov5检测出人再进行识别，但是效果不是太理想。<br>项目地址：<a href="https://github.com/ultralytics/yolov5" target="_blank" rel="noopener">https://github.com/ultralytics/yolov5</a><br>我是直接下载最新的release版本，解压后修改requirements.txt文件<br>注释掉torch和torchvision后进行安装</p>
<blockquote>
<p>pip install -r requirements.txt</p>
</blockquote>
<p>此时matplotlib安装可能会报版本错误，可以使用conda进行安装</p>
<blockquote>
<p>conda install matplotlib</p>
</blockquote>
<p>安装完成后进行测试</p>
<blockquote>
<p>python detect.py</p>
</blockquote>
<h1 id="OpenCV-Gstreamer-安装"><a href="#OpenCV-Gstreamer-安装" class="headerlink" title="OpenCV/Gstreamer 安装"></a>OpenCV/Gstreamer 安装</h1><p>由于要使用网络摄像头，实测RTSP拉流延迟较高，无论PC还是Jetson。<br>使用OpenCV开启Gstreamer<br>安装脚本参考：<a href="https://github.com/AastaNV/JEP/blob/master/script/install_opencv4.5.0_Jetson.sh" target="_blank" rel="noopener">https://github.com/AastaNV/JEP/blob/master/script/install_opencv4.5.0_Jetson.sh</a></p>
<blockquote>
<p>sh install_opencv4.5.0_Jetson.sh</p>
</blockquote>
<p><strong>我安装的 4.5.5 版本</strong></p>
<blockquote>
<p>注：安装后延迟有改善，由之前的5s延迟改善到0.5s左右，jtop中可以看到JetsonNX已开启NVDEC硬件解码。图像像素太大，RAM吃紧，fps只有10-15fps。</p>
</blockquote>
<h1 id="face-recognition-安装"><a href="#face-recognition-安装" class="headerlink" title="face_recognition 安装"></a>face_recognition 安装</h1><h2 id="dlib-安装"><a href="#dlib-安装" class="headerlink" title="dlib 安装"></a>dlib 安装</h2><p>安装参考：<a href="https://forums.developer.nvidia.com/t/import-dlib-on-jetson-nx-got-illegal-instruction-core-dumped/193413/17" target="_blank" rel="noopener">https://forums.developer.nvidia.com/t/import-dlib-on-jetson-nx-got-illegal-instruction-core-dumped/193413/17</a><br><strong>实测19.21版本可以正常运行，19.23有点问题没有深究</strong></p>
<blockquote>
<p>sudo apt-get install python3-pip<br>sudo apt-get install libjpeg-dev</p>
<p>wget <a href="http://dlib.net/files/dlib-19.21.tar.bz2" target="_blank" rel="noopener">http://dlib.net/files/dlib-19.21.tar.bz2</a><br>tar jxvf dlib-19.21.tar.bz2<br>cd dlib-19.21/<br>mkdir build<br>cd build/<br>cmake ..<br>cmake –build .<br>cd ../<br>sudo python setup.py install</p>
</blockquote>
<h2 id="face-recognition-安装-1"><a href="#face-recognition-安装-1" class="headerlink" title="face_recognition 安装"></a>face_recognition 安装</h2><blockquote>
<p>sudo pip install face_recognition</p>
</blockquote>
<h1 id="人脸模型"><a href="#人脸模型" class="headerlink" title="人脸模型"></a>人脸模型</h1><p>至此，项目需要的软件环境基本已经安装完成。<br>人懒没有训练人脸模型，试了下face_recognition自带的人脸检测，人脸像素稍微变小后可能无法正常检测。找了狗东开源的人脸模型 <a href="https://github.com/JDAI-CV/FaceX-Zoo" target="_blank" rel="noopener">https://github.com/JDAI-CV/FaceX-Zoo</a> ，在3050TI的PC上检测iphone11拍摄的视频能达到30fps左右，合适像素下也能准确检测出人脸。<br>只不过JetsonNX下同一视频进行人脸检测能达到10-15fps，加上人脸识别后约为8-10fps(加上RTSP流后RAM不足，fps还会降低)。<br>最后找了一个轻量级模型 <a href="https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/README_CN.md" target="_blank" rel="noopener">https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/README_CN.md</a> ，先压缩图像进行人脸检测，根据检测出的人脸bbox坐标换算后再原图中截取出图片进入人脸识别，最终在导入RTSP视频流1920x1080像素下进行检测/识别后约8-12fps。</p>
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<h1 id="谐波减速机"><a href="#谐波减速机" class="headerlink" title="谐波减速机"></a>谐波减速机</h1><p><a href="https://en.wikipedia.org/wiki/Harmonic_drive" target="_blank" rel="noopener">Harmonic drive</a><br>减速比: (柔轮齿数 - 刚轮齿数) / 柔轮齿数</p>
<hr>
<h1 id="建模"><a href="#建模" class="headerlink" title="建模"></a>建模</h1><h2 id="摆线减速机-1"><a href="#摆线减速机-1" class="headerlink" title="摆线减速机"></a>摆线减速机</h2><p>Solidworks<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">X = (R*cos(t))-(Rr*cos(t+arctan(sin((1-N)*t)/((R/EN)-cos((1-N)*t)))))-(E*cos(N*t)) </span><br><span class="line">Y = (-R*sin(t))+(Rr*sin(t+arctan(sin((1-N)*t)/((R/EN)-cos((1-N)*t)))))+(E*sin(N*t)) </span><br><span class="line">X : (-10/360)*(2*pi) </span><br><span class="line">Y : (190/360)*(2*pi) </span><br><span class="line"></span><br><span class="line">R = 10, E = 0.75, Rr = 1.5, N = 10</span><br></pre></td></tr></table></figure></p>
<blockquote>
<p>R: 转子半径<br>E: 输入轴与转子中心的偏心距<br>Rr: 齿销(轧辊)半径<br>N: 齿销(轧辊)数量 </p>
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