mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
Summary: A fairly big refactor of the texturing API with some breaking changes to how textures are defined. Main changes: - There are now 3 types of texture classes: `TexturesUV`, `TexturesAtlas` and `TexturesVertex`. Each class: - has a `sample_textures` function which accepts the `fragments` from rasterization and returns `texels`. This means that the shaders will not need to know the type of the mesh texture which will resolve several issues people were reporting on GitHub. - has a `join_batch` method for joining multiple textures of the same type into a batch Reviewed By: gkioxari Differential Revision: D21067427 fbshipit-source-id: 4b346500a60181e72fdd1b0dd89b5505c7a33926
605 lines
17 KiB
Plaintext
605 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "_Ip8kp4TfBLZ"
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},
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"outputs": [],
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"source": [
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"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "kuXHJv44fBLe"
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},
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"source": [
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"# Render a textured mesh\n",
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"\n",
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"This tutorial shows how to:\n",
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"- load a mesh and textures from an `.obj` file. \n",
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"- set up a renderer \n",
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"- render the mesh \n",
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"- vary the rendering settings such as lighting and camera position\n",
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"- use the batching features of the pytorch3d API to render the mesh from different viewpoints"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "Bnj3THhzfBLf"
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},
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"source": [
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"## 0. Install and Import modules"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "okLalbR_g7NS"
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},
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"source": [
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"If `torch`, `torchvision` and `pytorch3d` are not installed, run the following cell:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 717
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},
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"colab_type": "code",
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"id": "musUWTglgxSB",
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"outputId": "16d1a1b2-3f7f-43ed-ca28-a4d236cc0572"
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},
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"outputs": [],
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"source": [
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"!pip install torch torchvision\n",
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"!pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "nX99zdoffBLg"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch\n",
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"import matplotlib.pyplot as plt\n",
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"from skimage.io import imread\n",
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"\n",
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"# Util function for loading meshes\n",
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"from pytorch3d.io import load_objs_as_meshes, load_obj\n",
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"\n",
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"# Data structures and functions for rendering\n",
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"from pytorch3d.structures import Meshes, Textures\n",
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"from pytorch3d.renderer import (\n",
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" look_at_view_transform,\n",
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" OpenGLPerspectiveCameras, \n",
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" PointLights, \n",
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" DirectionalLights, \n",
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" Materials, \n",
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" RasterizationSettings, \n",
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" MeshRenderer, \n",
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" MeshRasterizer, \n",
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" SoftPhongShader\n",
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")\n",
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"\n",
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"# add path for demo utils functions \n",
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"import sys\n",
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"import os\n",
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"sys.path.append(os.path.abspath(''))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "Lxmehq6Zhrzv"
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},
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"source": [
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"If using **Google Colab**, fetch the utils file for plotting image grids:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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},
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"colab_type": "code",
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"id": "HZozr3Pmho-5",
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"outputId": "be5eb60d-5f65-4db1-cca0-44ee68c8f5fd"
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},
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"outputs": [],
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"source": [
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"!wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/master/docs/tutorials/utils/plot_image_grid.py\n",
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"from plot_image_grid import image_grid"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "g4B62MzYiJUM"
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},
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"source": [
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"OR if running **locally** uncomment and run the following cell:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "paJ4Im8ahl7O"
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},
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"outputs": [],
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"source": [
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"# from utils import image_grid"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "5jGq772XfBLk"
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},
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"source": [
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"### 1. Load a mesh and texture file\n",
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"\n",
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"Load an `.obj` file and it's associated `.mtl` file and create a **Textures** and **Meshes** object. \n",
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"\n",
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"**Meshes** is a unique datastructure provided in PyTorch3D for working with batches of meshes of different sizes. \n",
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"\n",
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"**Textures** is an auxillary datastructure for storing texture information about meshes. \n",
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"\n",
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"**Meshes** has several class methods which are used throughout the rendering pipeline."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "a8eU4zo5jd_H"
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},
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"source": [
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"If running this notebook using **Google Colab**, run the following cell to fetch the mesh obj and texture files and save it at the path `data/cow_mesh`:\n",
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"If running locally, the data is already available at the correct path. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 578
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},
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"colab_type": "code",
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"id": "tTm0cVuOjb1W",
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"outputId": "6cd7e2ec-65e1-4dcc-99e8-c347bc504f0a"
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},
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"outputs": [],
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"source": [
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"!mkdir -p data/cow_mesh\n",
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"!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj\n",
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"!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl\n",
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"!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "gi5Kd0GafBLl"
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},
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"outputs": [],
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"source": [
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"# Setup\n",
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"if torch.cuda.is_available():\n",
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" device = torch.device(\"cuda:0\")\n",
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" torch.cuda.set_device(device)\n",
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"else:\n",
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" device = torch.device(\"cpu\")\n",
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"\n",
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"# Set paths\n",
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"DATA_DIR = \"./data\"\n",
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"obj_filename = os.path.join(DATA_DIR, \"cow_mesh/cow.obj\")\n",
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"\n",
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"# Load obj file\n",
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"mesh = load_objs_as_meshes([obj_filename], device=device)\n",
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"texture_image=mesh.textures.maps_padded()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "5APAQs6-fBLp"
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},
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"source": [
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"#### Let's visualize the texture map"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 428
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},
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"colab_type": "code",
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"id": "YipUhrIHfBLq",
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"outputId": "48987b1d-5cc1-4c2a-cb3c-713d64f6a38d"
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(7,7))\n",
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"plt.imshow(texture_image.squeeze().cpu().numpy())\n",
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"plt.grid(\"off\");\n",
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"plt.axis('off');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "GcnG6XJ6fBLu"
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},
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"source": [
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"## 2. Create a renderer\n",
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"\n",
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"A renderer in PyTorch3D is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthographic/perspective). Here we initialize some of these components and use default values for the rest.\n",
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"\n",
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"In this example we will first create a **renderer** which uses a **perspective camera**, a **point light** and applies **phong shading**. Then we learn how to vary different components using the modular API. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "dX466mWnfBLv"
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},
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"outputs": [],
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"source": [
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"# Initialize an OpenGL perspective camera.\n",
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"# With world coordinates +Y up, +X left and +Z in, the front of the cow is facing the -Z direction. \n",
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"# So we move the camera by 180 in the azimuth direction so it is facing the front of the cow. \n",
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"R, T = look_at_view_transform(2.7, 0, 180) \n",
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"cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)\n",
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"\n",
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"# Define the settings for rasterization and shading. Here we set the output image to be of size\n",
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"# 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1\n",
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"# and blur_radius=0.0. We also set bin_size and max_faces_per_bin to None which ensure that \n",
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"# the faster coarse-to-fine rasterization method is used. Refer to rasterize_meshes.py for \n",
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"# explanations of these parameters. Refer to docs/notes/renderer.md for an explanation of \n",
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"# the difference between naive and coarse-to-fine rasterization. \n",
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"raster_settings = RasterizationSettings(\n",
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" image_size=512, \n",
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" blur_radius=0.0, \n",
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" faces_per_pixel=1, \n",
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")\n",
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"\n",
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"# Place a point light in front of the object. As mentioned above, the front of the cow is facing the \n",
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"# -z direction. \n",
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"lights = PointLights(device=device, location=[[0.0, 0.0, -3.0]])\n",
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"\n",
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"# Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will \n",
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"# interpolate the texture uv coordinates for each vertex, sample from a texture image and \n",
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"# apply the Phong lighting model\n",
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"renderer = MeshRenderer(\n",
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" rasterizer=MeshRasterizer(\n",
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" cameras=cameras, \n",
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" raster_settings=raster_settings\n",
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" ),\n",
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" shader=SoftPhongShader(\n",
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" device=device, \n",
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" cameras=cameras,\n",
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" lights=lights\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "KyOY5qXvfBLz"
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},
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"source": [
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"## 3. Render the mesh"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "8VkRA4qJfBL0"
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},
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"source": [
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"The light is in front of the object so it is bright and the image has specular highlights."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 592
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},
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"colab_type": "code",
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"id": "gBLZH8iUfBL1",
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"outputId": "cc3cd3f0-189e-4497-ce47-e64b4da542e8"
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},
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"outputs": [],
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"source": [
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"images = renderer(mesh)\n",
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"plt.figure(figsize=(10, 10))\n",
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"plt.imshow(images[0, ..., :3].cpu().numpy())\n",
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"plt.grid(\"off\");\n",
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"plt.axis(\"off\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "k161XF3sfBL5"
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},
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"source": [
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"## 4. Move the light behind the object and re-render\n",
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"\n",
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"We can pass arbirary keyword arguments to the `rasterizer`/`shader` via the call to the `renderer` so the renderer does not need to be reinitialized if any of the settings change/\n",
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"\n",
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"In this case, we can simply update the location of the lights and pass them into the call to the renderer. \n",
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"\n",
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"The image is now dark as there is only ambient lighting, and there are no specular highlights."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "BdWkkeibfBL6"
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},
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"outputs": [],
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"source": [
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"# Now move the light so it is on the +Z axis which will be behind the cow. \n",
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"lights.location = torch.tensor([0.0, 0.0, +1.0], device=device)[None]\n",
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"images = renderer(mesh, lights=lights)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 592
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},
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"colab_type": "code",
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"id": "UmV3j1YffBL9",
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"outputId": "2e8edca0-5bd8-4a2f-a160-83c4b0520123"
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 10))\n",
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"plt.imshow(images[0, ..., :3].cpu().numpy())\n",
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"plt.grid(\"off\");\n",
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"plt.axis(\"off\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "t93aVotMfBMB"
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},
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"source": [
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"## 5. Rotate the object, modify the material properties or light properties\n",
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"\n",
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"We can also change many other settings in the rendering pipeline. Here we:\n",
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"\n",
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"- change the **viewing angle** of the camera\n",
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"- change the **position** of the point light\n",
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"- change the **material reflectance** properties of the mesh"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "4mYXYziefBMB"
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},
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"outputs": [],
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"source": [
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"# Rotate the object by increasing the elevation and azimuth angles\n",
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"R, T = look_at_view_transform(dist=2.7, elev=10, azim=-150)\n",
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"cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)\n",
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"\n",
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"# Move the light location so the light is shining on the cow's face. \n",
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"lights.location = torch.tensor([[2.0, 2.0, -2.0]], device=device)\n",
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"\n",
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"# Change specular color to green and change material shininess \n",
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"materials = Materials(\n",
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" device=device,\n",
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" specular_color=[[0.0, 1.0, 0.0]],\n",
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" shininess=10.0\n",
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")\n",
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"\n",
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"# Re render the mesh, passing in keyword arguments for the modified components.\n",
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"images = renderer(mesh, lights=lights, materials=materials, cameras=cameras)"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": null,
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|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
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|
"height": 592
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},
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"colab_type": "code",
|
|
"id": "rHIxIfh5fBME",
|
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"outputId": "1ca2d337-2983-478f-b3c9-d64b84ba1a31"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(10, 10))\n",
|
|
"plt.imshow(images[0, ..., :3].cpu().numpy())\n",
|
|
"plt.grid(\"off\");\n",
|
|
"plt.axis(\"off\");"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "17c4xmtyfBMH"
|
|
},
|
|
"source": [
|
|
"## 6. Batched Rendering\n",
|
|
"\n",
|
|
"One of the core design choices of the PyTorch3D API is to support **batched inputs for all components**. \n",
|
|
"The renderer and associated components can take batched inputs and **render a batch of output images in one forward pass**. We will now use this feature to render the mesh from many different viewpoints.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "CDQKebNNfBMI"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Set batch size - this is the number of different viewpoints from which we want to render the mesh.\n",
|
|
"batch_size = 20\n",
|
|
"\n",
|
|
"# Create a batch of meshes by repeating the cow mesh and associated textures. \n",
|
|
"# Meshes has a useful `extend` method which allows us do this very easily. \n",
|
|
"# This also extends the textures. \n",
|
|
"meshes = mesh.extend(batch_size)\n",
|
|
"\n",
|
|
"# Get a batch of viewing angles. \n",
|
|
"elev = torch.linspace(0, 180, batch_size)\n",
|
|
"azim = torch.linspace(-180, 180, batch_size)\n",
|
|
"\n",
|
|
"# All the cameras helper methods support mixed type inputs and broadcasting. So we can \n",
|
|
"# view the camera from the same distance and specify dist=2.7 as a float,\n",
|
|
"# and then specify elevation and azimuth angles for each viewpoint as tensors. \n",
|
|
"R, T = look_at_view_transform(dist=2.7, elev=elev, azim=azim)\n",
|
|
"cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)\n",
|
|
"\n",
|
|
"# Move the light back in front of the cow which is facing the -z direction.\n",
|
|
"lights.location = torch.tensor([[0.0, 0.0, -3.0]], device=device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "gyYJCwEDfBML"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# We can pass arbirary keyword arguments to the rasterizer/shader via the renderer\n",
|
|
"# so the renderer does not need to be reinitialized if any of the settings change.\n",
|
|
"images = renderer(meshes, cameras=cameras, lights=lights)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"image_grid(images.cpu().numpy(), rows=4, cols=5, rgb=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "t3qphI1ElUb5"
|
|
},
|
|
"source": [
|
|
"## 7. Conclusion\n",
|
|
"In this tutorial we learnt how to **load** a textured mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, and modify several components of the rendering pipeline. "
|
|
]
|
|
}
|
|
],
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"accelerator": "GPU",
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"anp_metadata": {
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"path": "fbsource/fbcode/vision/fair/pytorch3d/docs/tutorials/render_textured_meshes.ipynb"
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"name": "render_textured_meshes.ipynb",
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