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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.</span>
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<h1 id="Render-a-textured-mesh">Render a textured mesh<a class="anchor-link" href="#Render-a-textured-mesh"></a></h1><p>This tutorial shows how to:</p>
<ul>
<li>load a mesh and textures from an <code>.obj</code> file. </li>
<li>set up a renderer </li>
<li>render the mesh </li>
<li>vary the rendering settings such as lighting and camera position</li>
<li>use the batching features of the pytorch3d API to render the mesh from different viewpoints</li>
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<h2 id="0.-Install-and-Import-modules">0. Install and Import modules<a class="anchor-link" href="#0.-Install-and-Import-modules"></a></h2>
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<p>If <code>torch</code>, <code>torchvision</code> and <code>pytorch3d</code> are not installed, run the following cell:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pip install torch torchvision
<span class="o">!</span>pip install <span class="s1">'git+https://github.com/facebookresearch/pytorch3d.git@stable'</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">skimage.io</span> <span class="k">import</span> <span class="n">imread</span>
<span class="c1"># Util function for loading meshes</span>
<span class="kn">from</span> <span class="nn">pytorch3d.io</span> <span class="k">import</span> <span class="n">load_objs_as_meshes</span>
<span class="c1"># Data structures and functions for rendering</span>
<span class="kn">from</span> <span class="nn">pytorch3d.structures</span> <span class="k">import</span> <span class="n">Meshes</span><span class="p">,</span> <span class="n">Textures</span>
<span class="kn">from</span> <span class="nn">pytorch3d.renderer</span> <span class="k">import</span> <span class="p">(</span>
<span class="n">look_at_view_transform</span><span class="p">,</span>
<span class="n">OpenGLPerspectiveCameras</span><span class="p">,</span>
<span class="n">PointLights</span><span class="p">,</span>
<span class="n">DirectionalLights</span><span class="p">,</span>
<span class="n">Materials</span><span class="p">,</span>
<span class="n">RasterizationSettings</span><span class="p">,</span>
<span class="n">MeshRenderer</span><span class="p">,</span>
<span class="n">MeshRasterizer</span><span class="p">,</span>
<span class="n">TexturedSoftPhongShader</span>
<span class="p">)</span>
<span class="c1"># add path for demo utils functions </span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">''</span><span class="p">))</span>
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<p>If using <strong>Google Colab</strong>, fetch the utils file for plotting image grids:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/master/docs/tutorials/utils/plot_image_grid.py
<span class="kn">from</span> <span class="nn">plot_image_grid</span> <span class="k">import</span> <span class="n">image_grid</span>
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<p>OR if running <strong>locally</strong> uncomment and run the following cell:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from utils import image_grid</span>
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<h3 id="1.-Load-a-mesh-and-texture-file">1. Load a mesh and texture file<a class="anchor-link" href="#1.-Load-a-mesh-and-texture-file"></a></h3><p>Load an <code>.obj</code> file and it's associated <code>.mtl</code> file and create a <strong>Textures</strong> and <strong>Meshes</strong> object.</p>
<p><strong>Meshes</strong> is a unique datastructure provided in PyTorch3D for working with batches of meshes of different sizes.</p>
<p><strong>Textures</strong> is an auxillary datastructure for storing texture information about meshes.</p>
<p><strong>Meshes</strong> has several class methods which are used throughout the rendering pipeline.</p>
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<p>If running this notebook using <strong>Google Colab</strong>, run the following cell to fetch the mesh obj and texture files and save it at the path <code>data/cow_mesh</code>:
If running locally, the data is already available at the correct path.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>mkdir -p data/cow_mesh
<span class="o">!</span>wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj
<span class="o">!</span>wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl
<span class="o">!</span>wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Setup</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda:0"</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="c1"># Set paths</span>
<span class="n">DATA_DIR</span> <span class="o">=</span> <span class="s2">"./data"</span>
<span class="n">obj_filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="s2">"cow_mesh/cow.obj"</span><span class="p">)</span>
<span class="c1"># Load obj file</span>
<span class="n">mesh</span> <span class="o">=</span> <span class="n">load_objs_as_meshes</span><span class="p">([</span><span class="n">obj_filename</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">texture_image</span><span class="o">=</span><span class="n">mesh</span><span class="o">.</span><span class="n">textures</span><span class="o">.</span><span class="n">maps_padded</span><span class="p">()</span>
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<h4 id="Let's-visualize-the-texture-map">Let's visualize the texture map<a class="anchor-link" href="#Let's-visualize-the-texture-map"></a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">texture_image</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">);</span>
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<h2 id="2.-Create-a-renderer">2. Create a renderer<a class="anchor-link" href="#2.-Create-a-renderer"></a></h2><p>A renderer in PyTorch3D is composed of a <strong>rasterizer</strong> and a <strong>shader</strong> which each have a number of subcomponents such as a <strong>camera</strong> (orthographic/perspective). Here we initialize some of these components and use default values for the rest.</p>
<p>In this example we will first create a <strong>renderer</strong> which uses a <strong>perspective camera</strong>, a <strong>point light</strong> and applies <strong>phong shading</strong>. Then we learn how to vary different components using the modular API.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Initialize an OpenGL perspective camera.</span>
<span class="c1"># With world coordinates +Y up, +X left and +Z in, the front of the cow is facing the -Z direction. </span>
<span class="c1"># So we move the camera by 180 in the azimuth direction so it is facing the front of the cow. </span>
<span class="n">R</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">look_at_view_transform</span><span class="p">(</span><span class="mf">2.7</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">180</span><span class="p">)</span>
<span class="n">cameras</span> <span class="o">=</span> <span class="n">OpenGLPerspectiveCameras</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">R</span><span class="o">=</span><span class="n">R</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">)</span>
<span class="c1"># Define the settings for rasterization and shading. Here we set the output image to be of size</span>
<span class="c1"># 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1</span>
<span class="c1"># and blur_radius=0.0. We also set bin_size and max_faces_per_bin to None which ensure that </span>
<span class="c1"># the faster coarse-to-fine rasterization method is used. Refer to rasterize_meshes.py for </span>
<span class="c1"># explanations of these parameters. Refer to docs/notes/renderer.md for an explanation of </span>
<span class="c1"># the difference between naive and coarse-to-fine rasterization. </span>
<span class="n">raster_settings</span> <span class="o">=</span> <span class="n">RasterizationSettings</span><span class="p">(</span>
<span class="n">image_size</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
<span class="n">blur_radius</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">faces_per_pixel</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">bin_size</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="c1"># this setting controls whether naive or coarse-to-fine rasterization is used</span>
<span class="n">max_faces_per_bin</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># this setting is for coarse rasterization</span>
<span class="p">)</span>
<span class="c1"># Place a point light in front of the object. As mentioned above, the front of the cow is facing the </span>
<span class="c1"># -z direction. </span>
<span class="n">lights</span> <span class="o">=</span> <span class="n">PointLights</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.0</span><span class="p">]])</span>
<span class="c1"># Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will </span>
<span class="c1"># interpolate the texture uv coordinates for each vertex, sample from a texture image and </span>
<span class="c1"># apply the Phong lighting model</span>
<span class="n">renderer</span> <span class="o">=</span> <span class="n">MeshRenderer</span><span class="p">(</span>
<span class="n">rasterizer</span><span class="o">=</span><span class="n">MeshRasterizer</span><span class="p">(</span>
<span class="n">cameras</span><span class="o">=</span><span class="n">cameras</span><span class="p">,</span>
<span class="n">raster_settings</span><span class="o">=</span><span class="n">raster_settings</span>
<span class="p">),</span>
<span class="n">shader</span><span class="o">=</span><span class="n">TexturedSoftPhongShader</span><span class="p">(</span>
<span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
<span class="n">cameras</span><span class="o">=</span><span class="n">cameras</span><span class="p">,</span>
<span class="n">lights</span><span class="o">=</span><span class="n">lights</span>
<span class="p">)</span>
<span class="p">)</span>
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<h2 id="3.-Render-the-mesh">3. Render the mesh<a class="anchor-link" href="#3.-Render-the-mesh"></a></h2>
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<p>The light is in front of the object so it is bright and the image has specular highlights.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">images</span> <span class="o">=</span> <span class="n">renderer</span><span class="p">(</span><span class="n">mesh</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
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<h2 id="4.-Move-the-light-behind-the-object-and-re-render">4. Move the light behind the object and re-render<a class="anchor-link" href="#4.-Move-the-light-behind-the-object-and-re-render"></a></h2><p>We can pass arbirary keyword arguments to the <code>rasterizer</code>/<code>shader</code> via the call to the <code>renderer</code> so the renderer does not need to be reinitialized if any of the settings change/</p>
<p>In this case, we can simply update the location of the lights and pass them into the call to the renderer.</p>
<p>The image is now dark as there is only ambient lighting, and there are no specular highlights.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Now move the light so it is on the +Z axis which will be behind the cow. </span>
<span class="n">lights</span><span class="o">.</span><span class="n">location</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">+</span><span class="mf">1.0</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)[</span><span class="kc">None</span><span class="p">]</span>
<span class="n">images</span> <span class="o">=</span> <span class="n">renderer</span><span class="p">(</span><span class="n">mesh</span><span class="p">,</span> <span class="n">lights</span><span class="o">=</span><span class="n">lights</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
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<h2 id="5.-Rotate-the-object,-modify-the-material-properties-or-light-properties">5. Rotate the object, modify the material properties or light properties<a class="anchor-link" href="#5.-Rotate-the-object,-modify-the-material-properties-or-light-properties"></a></h2><p>We can also change many other settings in the rendering pipeline. Here we:</p>
<ul>
<li>change the <strong>viewing angle</strong> of the camera</li>
<li>change the <strong>position</strong> of the point light</li>
<li>change the <strong>material reflectance</strong> properties of the mesh</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Rotate the object by increasing the elevation and azimuth angles</span>
<span class="n">R</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">look_at_view_transform</span><span class="p">(</span><span class="n">dist</span><span class="o">=</span><span class="mf">2.7</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">azim</span><span class="o">=-</span><span class="mi">150</span><span class="p">)</span>
<span class="n">cameras</span> <span class="o">=</span> <span class="n">OpenGLPerspectiveCameras</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">R</span><span class="o">=</span><span class="n">R</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">)</span>
<span class="c1"># Move the light location so the light is shining on the cow's face. </span>
<span class="n">lights</span><span class="o">.</span><span class="n">location</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.0</span><span class="p">]],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="c1"># Change specular color to green and change material shininess </span>
<span class="n">materials</span> <span class="o">=</span> <span class="n">Materials</span><span class="p">(</span>
<span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
<span class="n">specular_color</span><span class="o">=</span><span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span>
<span class="n">shininess</span><span class="o">=</span><span class="mf">10.0</span>
<span class="p">)</span>
<span class="c1"># Re render the mesh, passing in keyword arguments for the modified components.</span>
<span class="n">images</span> <span class="o">=</span> <span class="n">renderer</span><span class="p">(</span><span class="n">mesh</span><span class="p">,</span> <span class="n">lights</span><span class="o">=</span><span class="n">lights</span><span class="p">,</span> <span class="n">materials</span><span class="o">=</span><span class="n">materials</span><span class="p">,</span> <span class="n">cameras</span><span class="o">=</span><span class="n">cameras</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
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<h2 id="6.-Batched-Rendering">6. Batched Rendering<a class="anchor-link" href="#6.-Batched-Rendering"></a></h2><p>One of the core design choices of the PyTorch3D API is to support <strong>batched inputs for all components</strong>.
The renderer and associated components can take batched inputs and <strong>render a batch of output images in one forward pass</strong>. We will now use this feature to render the mesh from many different viewpoints.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Set batch size - this is the number of different viewpoints from which we want to render the mesh.</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">20</span>
<span class="c1"># Create a batch of meshes by repeating the cow mesh and associated textures. </span>
<span class="c1"># Meshes has a useful `extend` method which allows us do this very easily. </span>
<span class="c1"># This also extends the textures. </span>
<span class="n">meshes</span> <span class="o">=</span> <span class="n">mesh</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
<span class="c1"># Get a batch of viewing angles. </span>
<span class="n">elev</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
<span class="n">azim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
<span class="c1"># All the cameras helper methods support mixed type inputs and broadcasting. So we can </span>
<span class="c1"># view the camera from the same distance and specify dist=2.7 as a float,</span>
<span class="c1"># and then specify elevation and azimuth angles for each viewpoint as tensors. </span>
<span class="n">R</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <span class="n">look_at_view_transform</span><span class="p">(</span><span class="n">dist</span><span class="o">=</span><span class="mf">2.7</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">)</span>
<span class="n">cameras</span> <span class="o">=</span> <span class="n">OpenGLPerspectiveCameras</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">R</span><span class="o">=</span><span class="n">R</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="n">T</span><span class="p">)</span>
<span class="c1"># Move the light back in front of the cow which is facing the -z direction.</span>
<span class="n">lights</span><span class="o">.</span><span class="n">location</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.0</span><span class="p">]],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We can pass arbirary keyword arguments to the rasterizer/shader via the renderer</span>
<span class="c1"># so the renderer does not need to be reinitialized if any of the settings change.</span>
<span class="n">images</span> <span class="o">=</span> <span class="n">renderer</span><span class="p">(</span><span class="n">meshes</span><span class="p">,</span> <span class="n">cameras</span><span class="o">=</span><span class="n">cameras</span><span class="p">,</span> <span class="n">lights</span><span class="o">=</span><span class="n">lights</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">image_grid</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">rows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">rgb</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<h2 id="7.-Conclusion">7. Conclusion<a class="anchor-link" href="#7.-Conclusion"></a></h2><p>In this tutorial we learnt how to <strong>load</strong> a textured mesh from an obj file, initialize a PyTorch3D datastructure called <strong>Meshes</strong>, set up an <strong>Renderer</strong> consisting of a <strong>Rasterizer</strong> and a <strong>Shader</strong>, and modify several components of the rendering pipeline.</p>
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