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stwind/dsdfr_amass_crowd.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"Gl8wdGYDqdqR",
"BkRiUMuWqfmD",
"fARlbaQHv0jP",
"BNPl2Xmrv2GJ",
"EpFdWXEhv3Oh",
"tar7vAszv6NG",
"MzLRNEoAv8cj",
"h15TWWAlv-26",
"GhspSyAlv__3"
],
"machine_shape": "hm",
"gpuType": "L4",
"authorship_tag": "ABX9TyPZdO5OidGX25EW7T+ijrCT",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/stwind/63d44d2cee9199ad4f86487cafaf125e/dsdfr_amass_crowd.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!nvidia-smi --query-gpu=name,memory.total --format=csv,noheader"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_HIQjkq8vkkV",
"outputId": "d6eb1b35-a4ee-489b-e8a1-a7c7b77fc109"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"NVIDIA L4, 23034 MiB\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Setup"
],
"metadata": {
"id": "ke5CyTejqcrZ"
}
},
{
"cell_type": "markdown",
"source": [
"### Dependencies"
],
"metadata": {
"id": "Gl8wdGYDqdqR"
}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Cf0WHN3opajA",
"outputId": "81a2db48-11de-44c7-de38-a8e0e9dc2353"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 MB\u001b[0m \u001b[31m210.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m256.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m286.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.6/37.6 MB\u001b[0m \u001b[31m304.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m708.6/708.6 kB\u001b[0m \u001b[31m323.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.9/66.9 kB\u001b[0m \u001b[31m266.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m290.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m286.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m270.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m247.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m270.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m279.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m293.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m322.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m313.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m309.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.15.2 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m377.4/377.4 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for bl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip install --no-cache-dir -Uq matplotlib pillow scipy einops ffmpeg-python trimesh smplx mitsuba==3.6.0 fastsweep\n",
"!pip install -q \"git+https://github.com/stwind/bl.git\""
]
},
{
"cell_type": "markdown",
"source": [
"#### OpenVDB"
],
"metadata": {
"id": "d-5Gi6OBvdc0"
}
},
{
"cell_type": "code",
"source": [
"!apt-get update -yqq && DEBIAN_FRONTEND=noninteractive apt-get install -yqq --no-install-recommends libjemalloc-dev"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B966Gepwvc-x",
"outputId": "ba93eacb-5172-4935-f16e-5a70af4c9ba4"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n",
"Selecting previously unselected package libjemalloc2:amd64.\n",
"(Reading database ... 124947 files and directories currently installed.)\n",
"Preparing to unpack .../libjemalloc2_5.2.1-4ubuntu1_amd64.deb ...\n",
"Unpacking libjemalloc2:amd64 (5.2.1-4ubuntu1) ...\n",
"Selecting previously unselected package libjemalloc-dev.\n",
"Preparing to unpack .../libjemalloc-dev_5.2.1-4ubuntu1_amd64.deb ...\n",
"Unpacking libjemalloc-dev (5.2.1-4ubuntu1) ...\n",
"Setting up libjemalloc2:amd64 (5.2.1-4ubuntu1) ...\n",
"Setting up libjemalloc-dev (5.2.1-4ubuntu1) ...\n",
"Processing triggers for man-db (2.10.2-1) ...\n",
"Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n",
"/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -Uq conan nanobind"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xIn_lUjjvm2o",
"outputId": "721b6fa8-b66d-47c1-f76c-e1f9c81c5654"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/485.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m481.3/485.7 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.7/485.7 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.9/236.9 kB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for conan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for patch-ng (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!wget -qc --show-progress https://github.com/AcademySoftwareFoundation/openvdb/archive/refs/tags/v12.0.0.tar.gz && tar -zxf v12.0.0.tar.gz"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u3uGoHndvoEF",
"outputId": "56925ca6-930b-4aea-daa1-27150997aa79"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"v12.0.0.tar.gz [ <=> ] 4.47M 8.04MB/s in 0.6s \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%bash\n",
"\n",
"set -euxo pipefail\n",
"\n",
"cat << EOF > /content/conanfile.txt\n",
"[requires]\n",
"boost/1.86.0\n",
"\n",
"[generators]\n",
"CMakeDeps\n",
"CMakeToolchain\n",
"EOF\n",
"\n",
"conan profile detect --force -vquiet\n",
"conan install /content --output-folder=/content/build --build=missing -vquiet"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lypCC0lxvp7E",
"outputId": "56cee131-b8be-4e21-a21d-943b6e6fc8c2"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[settings]\n",
"arch=x86_64\n",
"build_type=Release\n",
"compiler=gcc\n",
"compiler.cppstd=gnu17\n",
"compiler.libcxx=libstdc++11\n",
"compiler.version=11\n",
"os=Linux\n",
"\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"+ cat\n",
"+ conan profile detect --force -vquiet\n",
"+ conan install /content --output-folder=/content/build --build=missing -vquiet\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!cd openvdb-12.0.0 && \\\n",
" cmake -B build \\\n",
" -DCMAKE_TOOLCHAIN_FILE=/content/build/conan_toolchain.cmake \\\n",
" -Dnanobind_DIR=$(python -m nanobind --cmake_dir) \\\n",
" -DUSE_NUMPY=ON \\\n",
" -DOPENVDB_BUILD_PYTHON_MODULE=ON -DOPENVDB_BUILD_VDB_RENDER=ON . && \\\n",
" cmake --build build -- -j$(nproc) && \\\n",
" cmake --install build"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TbWBvPVLvrNG",
"outputId": "a4b55e60-3bed-4e60-8595-1a753df05572"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-- Using Conan toolchain: /content/build/conan_toolchain.cmake\n",
"-- Conan toolchain: Defining architecture flag: -m64\n",
"-- Conan toolchain: C++ Standard 17 with extensions ON\n",
"-- The CXX compiler identification is GNU 11.4.0\n",
"-- Detecting CXX compiler ABI info\n",
"-- Detecting CXX compiler ABI info - done\n",
"-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
"-- Detecting CXX compile features\n",
"-- Detecting CXX compile features - done\n",
"-- CMake Build Type: Release\n",
"-- Found PkgConfig: /usr/bin/pkg-config (found version \"1.8.0\")\n",
"-- Configuring for OpenVDB Version 12.0.0\n",
"-- Found Python: /usr/local/bin/python (found suitable version \"3.11.11\", minimum required is \"3.10\") found components: Development Interpreter Development.Module Development.Embed\n",
"-- Configuring for OpenVDB ABI Version 12\n",
"-- ----------------------------------------------------\n",
"-- ------------- Configuring OpenVDBCore --------------\n",
"-- ----------------------------------------------------\n",
"-- Conan: Component target declared 'Boost::diagnostic_definitions'\n",
"-- Conan: Component target declared 'Boost::disable_autolinking'\n",
"-- Conan: Component target declared 'Boost::dynamic_linking'\n",
"-- Conan: Component target declared 'Boost::headers'\n",
"-- Conan: Component target declared 'Boost::boost'\n",
"-- Conan: Component target declared 'boost::_libboost'\n",
"-- Conan: Component target declared 'Boost::atomic'\n",
"-- Conan: Component target declared 'Boost::charconv'\n",
"-- Conan: Component target declared 'Boost::container'\n",
"-- Conan: Component target declared 'Boost::context'\n",
"-- Conan: Component target declared 'Boost::date_time'\n",
"-- Conan: Component target declared 'Boost::exception'\n",
"-- Conan: Component target declared 'Boost::math'\n",
"-- Conan: Component target declared 'Boost::program_options'\n",
"-- Conan: Component target declared 'Boost::regex'\n",
"-- Conan: Component target declared 'Boost::serialization'\n",
"-- Conan: Component target declared 'Boost::stacktrace'\n",
"-- Conan: Component target declared 'Boost::system'\n",
"-- Conan: Component target declared 'Boost::timer'\n",
"-- Conan: Component target declared 'Boost::chrono'\n",
"-- Conan: Component target declared 'Boost::coroutine'\n",
"-- Conan: Component target declared 'Boost::filesystem'\n",
"-- Conan: Component target declared 'Boost::json'\n",
"-- Conan: Component target declared 'Boost::math_c99'\n",
"-- Conan: Component target declared 'Boost::math_c99f'\n",
"-- Conan: Component target declared 'Boost::math_c99l'\n",
"-- Conan: Component target declared 'Boost::math_tr1'\n",
"-- Conan: Component target declared 'Boost::math_tr1f'\n",
"-- Conan: Component target declared 'Boost::math_tr1l'\n",
"-- Conan: Component target declared 'Boost::random'\n",
"-- Conan: Component target declared 'Boost::stacktrace_addr2line'\n",
"-- Conan: Component target declared 'Boost::stacktrace_backtrace'\n",
"-- Conan: Component target declared 'Boost::stacktrace_basic'\n",
"-- Conan: Component target declared 'Boost::stacktrace_from_exception'\n",
"-- Conan: Component target declared 'Boost::stacktrace_noop'\n",
"-- Conan: Component target declared 'Boost::test'\n",
"-- Conan: Component target declared 'Boost::url'\n",
"-- Conan: Component target declared 'Boost::wserialization'\n",
"-- Conan: Component target declared 'Boost::fiber'\n",
"-- Conan: Component target declared 'Boost::graph'\n",
"-- Conan: Component target declared 'Boost::iostreams'\n",
"-- Conan: Component target declared 'Boost::nowide'\n",
"-- Conan: Component target declared 'Boost::prg_exec_monitor'\n",
"-- Conan: Component target declared 'Boost::process'\n",
"-- Conan: Component target declared 'Boost::test_exec_monitor'\n",
"-- Conan: Component target declared 'Boost::thread'\n",
"-- Conan: Component target declared 'Boost::wave'\n",
"-- Conan: Component target declared 'Boost::contract'\n",
"-- Conan: Component target declared 'Boost::fiber_numa'\n",
"-- Conan: Component target declared 'Boost::locale'\n",
"-- Conan: Component target declared 'Boost::log'\n",
"-- Conan: Component target declared 'Boost::type_erasure'\n",
"-- Conan: Component target declared 'Boost::unit_test_framework'\n",
"-- Conan: Component target declared 'Boost::log_setup'\n",
"-- Conan: Target declared 'boost::boost'\n",
"-- Conan: Target declared 'ZLIB::ZLIB'\n",
"-- Conan: Target declared 'BZip2::BZip2'\n",
"-- Conan: Including build module from '/root/.conan2/p/bzip23c098e896e3ea/p/lib/cmake/conan-official-bzip2-variables.cmake'\n",
"-- Conan: Target declared 'libbacktrace::libbacktrace'\n",
"\u001b[0mCMake Deprecation Warning at openvdb/openvdb/CMakeLists.txt:118 (message):\n",
" Support for Boost versions < 1.82 is deprecated and will be removed.\n",
"\n",
"\u001b[0m\n",
"-- Found Blosc: /usr/lib/x86_64-linux-gnu/libblosc.so (found suitable version \"1.21.1\", minimum required is \"1.17.0\")\n",
"-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
"-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
"-- Found Threads: TRUE\n",
"-- ----------------------------------------------------\n",
"-- ------------ Configuring OpenVDBPython -------------\n",
"-- ----------------------------------------------------\n",
"-- ----------------------------------------------------\n",
"-- ----------- Configuring OpenVDBBinaries ------------\n",
"-- ----------------------------------------------------\n",
"-- Found Jemalloc: /usr/lib/x86_64-linux-gnu/libjemalloc.so\n",
"-- Configuring done (1.3s)\n",
"-- Generating done (0.1s)\n",
"-- Build files have been written to: /content/openvdb-12.0.0/build\n",
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"-- Installing: /usr/local/include/openvdb/tree/LeafNode.h\n",
"-- Installing: /usr/local/include/openvdb/tree/LeafNodeBool.h\n",
"-- Installing: /usr/local/include/openvdb/tree/LeafNodeMask.h\n",
"-- Installing: /usr/local/include/openvdb/tree/NodeManager.h\n",
"-- Installing: /usr/local/include/openvdb/tree/NodeUnion.h\n",
"-- Installing: /usr/local/include/openvdb/tree/RootNode.h\n",
"-- Installing: /usr/local/include/openvdb/tree/Tree.h\n",
"-- Installing: /usr/local/include/openvdb/tree/TreeIterator.h\n",
"-- Installing: /usr/local/include/openvdb/tree/ValueAccessor.h\n",
"-- Installing: /usr/local/include/openvdb/util/Assert.h\n",
"-- Installing: /usr/local/include/openvdb/util/CpuTimer.h\n",
"-- Installing: /usr/local/include/openvdb/util/ExplicitInstantiation.h\n",
"-- Installing: /usr/local/include/openvdb/util/Formats.h\n",
"-- Installing: /usr/local/include/openvdb/util/logging.h\n",
"-- Installing: /usr/local/include/openvdb/util/MapsUtil.h\n",
"-- Installing: /usr/local/include/openvdb/util/Name.h\n",
"-- Installing: /usr/local/include/openvdb/util/NodeMasks.h\n",
"-- Installing: /usr/local/include/openvdb/util/NullInterrupter.h\n",
"-- Installing: /usr/local/include/openvdb/util/PagedArray.h\n",
"-- Installing: /usr/local/include/openvdb/util/Util.h\n",
"-- Installing: /usr/local/include/openvdb/thread/Threading.h\n",
"-- Installing: /usr/local/lib/python3.11/dist-packages/openvdb.cpython-311-x86_64-linux-gnu.so\n",
"-- Set non-toolchain portion of runtime path of \"/usr/local/lib/python3.11/dist-packages/openvdb.cpython-311-x86_64-linux-gnu.so\" to \"/usr/local/lib:/root/.conan2/p/zlib9780dc2008618/p/lib:/root/.conan2/p/boost8d9c445f1bf77/p/lib:/root/.conan2/p/bzip23c098e896e3ea/p/lib\"\n",
"-- Installing: /usr/local/bin/vdb_print\n",
"-- Set non-toolchain portion of runtime path of \"/usr/local/bin/vdb_print\" to \"/usr/local/lib:/root/.conan2/p/zlib9780dc2008618/p/lib:/root/.conan2/p/boost8d9c445f1bf77/p/lib:/root/.conan2/p/bzip23c098e896e3ea/p/lib\"\n",
"-- Installing: /usr/local/bin/vdb_render\n",
"-- Set non-toolchain portion of runtime path of \"/usr/local/bin/vdb_render\" to \"/usr/local/lib:/root/.conan2/p/zlib9780dc2008618/p/lib:/root/.conan2/p/boost8d9c445f1bf77/p/lib:/root/.conan2/p/bzip23c098e896e3ea/p/lib\"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### Commons"
],
"metadata": {
"id": "BkRiUMuWqfmD"
}
},
{
"cell_type": "code",
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import os\n",
"import math\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"import PIL\n",
"import matplotlib.font_manager as fm\n",
"import locale\n",
"from fastprogress import progress_bar\n",
"from einops import rearrange, reduce, repeat, einsum\n",
"\n",
"locale.getpreferredencoding = lambda: \"UTF-8\"\n",
"\n",
"COLORS = {\n",
" \"red\": np.array([0.79215686, 0.14901961, 0.14901961]),\n",
" \"blue\": np.array([0.08683021, 0.41940383, 0.71699529]),\n",
"}\n",
"COLORS.update({f\"gray{k:02d}\": np.array([k,k,k])*.01 for k in np.arange(5,100,5)})\n",
"\n",
"def mpl_theme(gray=COLORS['gray50'], stroke_width=.1, fontsize=7,\n",
" facecolor=COLORS['gray10']):\n",
" ## category20: https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#category20\n",
" cat20 = mpl.cycler(color=[\"1f77b4\",\"ff7f0e\",\"2ca02c\",\"d62728\",\"9467bd\",\"8c564b\",\"e377c2\",\"7f7f7f\",\"bcbd22\",\"17becf\",\n",
" \"aec7e8\",\"ffbb78\",\"98df8a\",\"ff9896\",\"c5b0d5\",\"c49c94\",\"f7b6d2\",\"c7c7c7\", \"dbdb8d\", \"9edae5\"])\n",
" return {\n",
" \"font.size\": fontsize,\n",
" \"text.color\": gray,\n",
"\n",
" \"figure.dpi\": 100,\n",
" \"figure.facecolor\": facecolor,\n",
" \"figure.frameon\": False,\n",
" \"figure.figsize\": (5, 3),\n",
" \"figure.titlesize\": \"x-large\",\n",
" \"figure.titleweight\": \"bold\",\n",
" \"figure.constrained_layout.use\": True,\n",
" \"figure.constrained_layout.w_pad\": 0.05,\n",
" \"figure.constrained_layout.h_pad\": 0.05,\n",
" \"figure.constrained_layout.wspace\": 0.03,\n",
" \"figure.constrained_layout.hspace\": 0.03,\n",
"\n",
" \"axes.labelcolor\": gray,\n",
" \"axes.labelpad\": 8,\n",
" \"axes.labelsize\": \"large\",\n",
" \"axes.labelweight\": \"normal\",\n",
" \"axes.spines.left\": False,\n",
" \"axes.spines.bottom\": False,\n",
" \"axes.spines.top\": False,\n",
" \"axes.spines.right\": False,\n",
" \"axes.facecolor\": facecolor,\n",
" \"axes.edgecolor\": gray,\n",
" \"axes.linewidth\": stroke_width,\n",
" \"axes.axisbelow\": True,\n",
" \"axes.xmargin\": 0.02,\n",
" \"axes.ymargin\": 0.02,\n",
" \"axes.zmargin\": 0.02,\n",
" \"axes.prop_cycle\": cat20,\n",
" \"axes.titlepad\": 8,\n",
" \"axes.titlesize\": \"large\",\n",
" \"axes.titleweight\": 500,\n",
" \"axes.grid\": True,\n",
" \"axes.grid.axis\": \"both\",\n",
"\n",
" \"axes3d.grid\": False,\n",
" \"axes3d.xaxis.panecolor\": COLORS['gray15'],\n",
" \"axes3d.yaxis.panecolor\": COLORS['gray20'],\n",
" \"axes3d.zaxis.panecolor\": COLORS['gray25'],\n",
"\n",
" \"ytick.right\": False,\n",
" \"ytick.color\": gray,\n",
" \"ytick.major.width\": stroke_width,\n",
" \"ytick.minor.left\": False,\n",
" \"xtick.minor.visible\": True,\n",
" \"xtick.minor.top\": False,\n",
" \"xtick.minor.bottom\": False,\n",
" \"xtick.color\": gray,\n",
" \"xtick.major.width\": stroke_width,\n",
"\n",
" \"grid.color\": gray,\n",
" \"grid.linewidth\": stroke_width,\n",
" \"grid.linestyle\": \"-\",\n",
" \"legend.fancybox\": False,\n",
" \"legend.edgecolor\": '0.3',\n",
" \"legend.framealpha\": 0.7,\n",
" \"legend.handletextpad\": 0.8,\n",
"\n",
" \"lines.linewidth\": 0.7\n",
" }\n",
"\n",
"def add_mpl_font(fname):\n",
" if fname not in [fe.fname for fe in fm.fontManager.ttflist]:\n",
" fm.fontManager.addfont(fname)\n",
"\n",
"def setup_overpass():\n",
" folder = \"fonts\"\n",
" os.makedirs(folder, exist_ok=True)\n",
" for style in [\"Regular\", \"Italic\", \"SemiBold\", \"SemiBoldItalic\", \"Bold\", \"BoldItalic\"]:\n",
" ttf = f\"Overpass-{style}.ttf\"\n",
" !wget -qc \"https://github.com/RedHatOfficial/Overpass/raw/master/fonts/ttf/{ttf}\" -O \"{folder}/{ttf}\"\n",
" add_mpl_font(f\"{folder}/{ttf}\")\n",
" mpl.rcParams['font.sans-serif'].insert(0, \"Overpass\")\n",
"\n",
"def setup_quicksand():\n",
" folder = \"fonts\"\n",
" os.makedirs(folder, exist_ok=True)\n",
" for style in [\"Bold\", \"Light\", \"Medium\", \"Regular\"]:\n",
" ttf = f\"Quicksand-{style}.ttf\"\n",
" !wget -qc \"https://github.com/andrew-paglinawan/QuicksandFamily/raw/refs/heads/master/fonts/statics/{ttf}\" -O \"{folder}/{ttf}\"\n",
" add_mpl_font(f\"{folder}/{ttf}\")\n",
" mpl.rcParams['font.sans-serif'].insert(0, \"Quicksand\")\n",
"\n",
"# setup_overpass()\n",
"setup_quicksand()\n",
"\n",
"plt.style.use([\"dark_background\", mpl_theme()])"
],
"metadata": {
"id": "eYqnOg4NqgX2"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import sys\n",
"import io\n",
"import bz2\n",
"import ffmpeg\n",
"import requests\n",
"import subprocess\n",
"import IPython.display as ipd\n",
"import ipywidgets as widgets\n",
"from scipy import linalg\n",
"from fastprogress import progress_bar\n",
"from einops import rearrange, reduce, repeat\n",
"from base64 import b64encode\n",
"from zipfile import ZipFile\n",
"from contextlib import contextmanager\n",
"from mpl_toolkits.mplot3d.art3d import Line3DCollection, Poly3DCollection\n",
"\n",
"class Output(object):\n",
" def __init__(self):\n",
" self.out = widgets.Output()\n",
"\n",
" def display(self):\n",
" display(self.out)\n",
" return self\n",
"\n",
" def clear(self):\n",
" self.out.clear_output()\n",
" return self.out\n",
"\n",
" def close(self):\n",
" return self.out.close()\n",
"\n",
"def to_single_rgb(img):\n",
" img = np.asarray(img)\n",
" if len(img.shape) == 4: # take first frame from animations\n",
" return img[0,:,:,:]\n",
" if len(img.shape) == 2: # convert gray to rgb\n",
" return img[:,:,np.newaxis].repeat(3, 2)\n",
" if img.shape[-1] == 4: # drop alpha\n",
" return img[:,:,:3]\n",
" else:\n",
" return img\n",
"\n",
"def imread(url, size=None, mode=None):\n",
" if url.startswith(('http:', 'https:')):\n",
" resp = requests.get(url)\n",
" if resp.status_code != 200:\n",
" return None\n",
"\n",
" f = io.BytesIO(resp.content)\n",
" else:\n",
" f = url\n",
" img = PIL.Image.open(f)\n",
" if size is not None:\n",
" img.thumbnail((size, size), PIL.Image.Resampling.LANCZOS)\n",
" if mode is not None:\n",
" img = img.convert(mode)\n",
" return img\n",
"\n",
"def imshow(img, fmt='png', retina=True, zoom=None):\n",
" if isinstance(img, str):\n",
" display(ipd.Image(filename=img, retina=retina))\n",
" return\n",
"\n",
" if len(img.shape) == 3 and img.shape[-1] == 1:\n",
" img = img.squeeze()\n",
" if img.dtype == np.float32:\n",
" img = img * 255.0\n",
" img = np.uint8(img.clip(0, 255))\n",
" if fmt in ('jpeg', 'jpg'):\n",
" img = to_single_rgb(img)\n",
"\n",
" image = PIL.Image.fromarray(img)\n",
" height, width = img.shape[:2]\n",
" if zoom is not None:\n",
" width *= zoom\n",
" height *= zoom\n",
" retina = zoom == 1\n",
" if zoom < 1:\n",
" image.resize((int(width), int(height)))\n",
"\n",
" data = io.BytesIO()\n",
" image.save(data, fmt)\n",
" display(ipd.Image(data=data.getvalue(),width=width, height=height,retina=retina))\n",
"\n",
"def find_rectangle(n, ratio=1):\n",
" ny = int((n / ratio) ** .5)\n",
" return ny, math.ceil(n / ny)\n",
"\n",
"def make_mosaic(imgs, nx=None, ny=None, gap=0):\n",
" n, h, w = imgs.shape[:3]\n",
" has_channels = len(imgs.shape) > 3\n",
"\n",
" if nx is None and ny is None:\n",
" ny, nx = find_rectangle(n)\n",
" elif ny is None:\n",
" ny = math.ceil(n / nx)\n",
" elif nx is None:\n",
" nx = math.ceil(n / ny)\n",
"\n",
" sh, sw = h + gap, w + gap\n",
" shape = (ny * sh - gap, nx * sw - gap)\n",
" if has_channels:\n",
" shape += (imgs.shape[-1],)\n",
"\n",
" canvas = np.zeros(shape, dtype=imgs.dtype)\n",
" for i, x in enumerate(imgs):\n",
" iy, ix = divmod(i, nx)\n",
" canvas[iy * sh:iy * sh + h, ix * sw:ix * sw + w] = x\n",
" return canvas\n",
"\n",
"def ffprobe_video(path):\n",
" probe = ffmpeg.probe(path)\n",
" return next(s for s in probe['streams'] if s['codec_type'] == 'video')\n",
"\n",
"def read_frame(path, frame_no):\n",
" cap = cv2.VideoCapture(path)\n",
" cap.set(cv2.CAP_PROP_POS_FRAMES, frame_no)\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" raise RuntimeError(f\"Faild reading frame {frame_no} from {path}\")\n",
" return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
"\n",
"def read_frames(path, start=0, num=None):\n",
" cap = cv2.VideoCapture(path)\n",
" n_frames = num or int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
" cap.set(cv2.CAP_PROP_POS_FRAMES, start)\n",
" for i in range(n_frames):\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" raise RuntimeError(f\"Faild reading frame {i} from {path}\")\n",
" yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
"\n",
"def read_video_frames(path):\n",
" info = ffprobe_video(path)\n",
" out, _ = ffmpeg.input(path).output('pipe:', format='rawvideo', pix_fmt='rgb24').run(capture_stdout=True)\n",
" return np.frombuffer(out, np.uint8).reshape([-1, info['height'], info['width'], 3])\n",
"\n",
"def show_video(path):\n",
" vcap = cv2.VideoCapture(path)\n",
" width = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
" with open(path, \"r+b\") as f:\n",
" url = f\"data:video/mp4;base64,{b64encode(f.read()).decode()}\"\n",
" return ipd.HTML(f\"\"\"<video autoplay=\"autoplay\" width={width} controls loop><source src=\"{url}\"></video>\"\"\")\n",
"\n",
"def write_video(frames, size, path=\"__temp__.mp4\", fps=30,\n",
" preset=\"veryfast\", args=[]):\n",
" height, width = size\n",
" command = ['ffmpeg','-v','error','-f','rawvideo','-vcodec','rawvideo',\n",
" '-pix_fmt','rgb24','-s',f'{width}x{height}','-r', f'{fps}',\n",
" '-i', '-',\n",
" \"-movflags\", \"+faststart\", \"-preset\", preset,\n",
" \"-g\", \"30\", \"-bf\",\"2\",\"-c:v\", \"libx264\",\"-profile:v\", \"high\",\n",
" '-an', '-vcodec','h264','-pix_fmt','yuv420p', *args, '-y', path]\n",
" with subprocess.Popen(command, stdin=subprocess.PIPE, stderr=subprocess.PIPE) as proc:\n",
" with proc.stdin as stdin:\n",
" for image in frames:\n",
" data = image.tobytes()\n",
" if stdin.write(data) != len(data):\n",
" proc.wait()\n",
" stderr = proc.stderr\n",
" assert stderr is not None\n",
" s = stderr.read().decode()\n",
" raise RuntimeError(f\"Error writing '{path}': {s}\")\n",
" return path\n",
"\n",
"def read_video(path):\n",
" command = ['ffmpeg','-v','error','-nostdin','-i',path,'-vcodec','rawvideo',\n",
" '-f','image2pipe','-pix_fmt','rgb24','-vsync','vfr','-']\n",
"\n",
" info = ffprobe_video(path)\n",
" num_bytes = info['height'] * info['width'] * 3 * np.dtype(np.uint8).itemsize\n",
" with subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) as proc:\n",
" stdout = proc.stdout\n",
" assert stdout is not None\n",
" data = stdout.read(num_bytes)\n",
" while data is not None and len(data) == num_bytes:\n",
" image = np.frombuffer(data, dtype=np.uint8)\n",
" yield image.reshape(info['height'], info['width'], 3)\n",
" data = stdout.read(num_bytes)\n",
"\n",
"def sdiv(a, b, nan=0, posinf=0, neginf=0):\n",
" return np.nan_to_num(a / b, nan=nan, posinf=posinf, neginf=neginf)\n",
"\n",
"def topk(x, n):\n",
" return np.argpartition(x, -n)[-n:]\n",
"\n",
"def norm(a, b, x, **kw):\n",
" return sdiv(x - a, b - a, **kw)\n",
"\n",
"def norm_v(x, **kw):\n",
" a, b = x.min(), x.max()\n",
" return sdiv(x - a, b - a, **kw)\n",
"\n",
"def normalize(x, keepdims=True, axis=-1, **kw):\n",
" return sdiv(x, np.linalg.norm(x, keepdims=keepdims, axis=axis), **kw)\n",
"\n",
"def nudge(x, v=0, eps=1e-12):\n",
" return np.where(np.isclose(np.abs(x), v, atol=eps), np.where(x - v >= 0, eps, -eps), x)\n",
"\n",
"def linspace_m(start, stop, n):\n",
" return np.linspace(start, stop, n, endpoint=False) + (stop - start) * .5 / n\n",
"\n",
"def indices_m(dims, shape, dtype=\"u4\"):\n",
" return tuple(np.meshgrid(*[np.round(linspace_m(0, d, s)).astype(dtype)\n",
" for d, s in zip(dims, shape)],\n",
" indexing='ij'))\n",
"\n",
"def saturate(x):\n",
" return np.clip(x, 0, 1)\n",
"\n",
"def lerp(a, b, t):\n",
" return a * (1.0 - t) + b * t\n",
"\n",
"def step(v, x):\n",
" return np.where(x < v, 0, 1)\n",
"\n",
"def window(x, a, b):\n",
" return step(a, x) * step(x, b)\n",
"\n",
"def satnorm(x, a, b):\n",
" return saturate(norm(x, a, b))\n",
"\n",
"def smoothstep(x):\n",
" return x * x * (3 - 2 * x)\n",
"\n",
"def smootherstep(x):\n",
" return x * x * x * (x * (x * 6 - 15) + 10)\n",
"\n",
"def cubic(a, b, c, d, t):\n",
" \"\"\"https://www.desmos.com/calculator/waof4r6avv\"\"\"\n",
" s = 1. - t\n",
" return s * s * (s * a + 3 * t * b) + t * t * (3 * s * c + t * d)\n",
"\n",
"def plt_show(pin=mpl.rcParams['savefig.pad_inches']):\n",
" with plt.rc_context({'savefig.pad_inches': pin}):\n",
" plt.show()\n",
"\n",
"def fig_image(fig=None, transparent=False, bbox_inches=None,\n",
" dpi=mpl.rcParams[\"figure.dpi\"]*2):\n",
" fig = fig or plt.gcf()\n",
"\n",
" buf = io.BytesIO()\n",
" fig.savefig(buf, format=\"png\", pad_inches=0, bbox_inches=bbox_inches,\n",
" facecolor=fig.get_facecolor(), dpi=dpi,transparent=transparent)\n",
" buf.seek(0)\n",
" data = np.frombuffer(buf.getvalue(), dtype=np.uint8)\n",
" buf.close()\n",
" plt.close(fig)\n",
"\n",
" code = cv2.COLOR_BGRA2RGBA if transparent else cv2.COLOR_BGR2RGB\n",
" return cv2.cvtColor(cv2.imdecode(data, cv2.IMREAD_UNCHANGED), code)\n",
"\n",
"def plt_savefig(name, pad_inches=mpl.rcParams['savefig.pad_inches'],\n",
" bbox_inches=0,facecolor='auto',\n",
" dpi=mpl.rcParams[\"figure.dpi\"]*2,close=True,**kw):\n",
" plt.savefig(name,\n",
" pad_inches=pad_inches,\n",
" bbox_inches=bbox_inches,\n",
" facecolor=facecolor,\n",
" dpi=dpi,**kw)\n",
" if close:\n",
" plt.close()\n",
"\n",
"class Flex(object):\n",
" def __init__(self, ratios, gap, size=None):\n",
" n, s = len(ratios), sum(ratios)\n",
" self.ratios = ratios\n",
" self.gap = gap\n",
" space = gap * n / s if size is None else gap * n / (size - gap * (n - 1))\n",
" self.h = dict(nrows=1, ncols=n, width_ratios=ratios, wspace=space)\n",
" self.v = dict(nrows=n, ncols=1, height_ratios=ratios, hspace=space)\n",
" self.size = s + gap * (n - 1) if size is None else size\n",
"\n",
"def ax_lim(mn, mx, ax=None, margin=.1):\n",
" ax = ax or plt.gca()\n",
" ax.set_xlim(mn[0], mx[0])\n",
" ax.set_ylim(mn[1], mx[1])\n",
" if len(mn) > 2:\n",
" ax.set_zlim(mn[2], mx[2])\n",
"\n",
"def ax_spines(sides=[\"left\",\"right\",\"bottom\",\"top\"], ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" ax.spines[sides].set(**kw)\n",
"\n",
"def ax_lines(lines, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" ax.add_collection(mpl.collections.LineCollection(lines,**kw))\n",
"\n",
"def ax_line3d(lines, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" return ax.add_collection(Line3DCollection(lines, **kw))\n",
"\n",
"def ax_poly3d(verts, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" return ax.add_collection(Poly3DCollection(verts, **kw))\n",
"\n",
"def ax_trisurf(v, f, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" ax.plot_trisurf(v[:,0],v[:,1],v[:,2],triangles=f, **kw)\n",
"\n",
"def ax_box2(mn, mx, ax=None):\n",
" ax = ax or plt.gca()\n",
" ax.set(xlim=(mn[0],mx[0]),ylim=(mn[1],mx[1]),aspect='equal')\n",
"\n",
"def ax_box3(mn, mx, ax=None):\n",
" ax = ax or plt.gca()\n",
" ax.set(xlim=(mn[0],mx[0]),ylim=(mn[1],mx[1]),zlim=(mn[2],mx[2]),box_aspect=mx-mn)\n",
"\n",
"def ax_axis_lines(ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" ax.xaxis.line.set(**kw)\n",
" ax.yaxis.line.set(**kw)\n",
" ax.zaxis.line.set(**kw)\n",
"\n",
"def ax_scatter(pts, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" return ax.scatter(*[pts[...,i] for i in range(pts.shape[-1])], **kw)\n",
"\n",
"def lowess(x, y, f=2. / 3., iter=3):\n",
" \"\"\"https://gist.github.com/agramfort/850437\n",
" lowess(x, y, f=2./3., iter=3) -> yest\n",
" Lowess smoother: Robust locally weighted regression.\n",
" The lowess function fits a nonparametric regression curve to a scatterplot.\n",
" The arrays x and y contain an equal number of elements; each pair\n",
" (x[i], y[i]) defines a data point in the scatterplot. The function returns\n",
" the estimated (smooth) values of y.\n",
" The smoothing span is given by f. A larger value for f will result in a\n",
" smoother curve. The number of robustifying iterations is given by iter. The\n",
" function will run faster with a smaller number of iterations.\n",
" \"\"\"\n",
" n = len(x)\n",
" r = int(math.ceil(f * n))\n",
" h = [np.sort(np.abs(x - x[i]))[r] for i in range(n)]\n",
" w = np.clip(np.abs((x[:, None] - x[None, :]) / h), 0.0, 1.0)\n",
" w = (1 - w ** 3) ** 3\n",
" yest = np.zeros(n)\n",
" delta = np.ones(n)\n",
" for iteration in range(iter):\n",
" for i in range(n):\n",
" weights = delta * w[:, i]\n",
" b = np.array([np.sum(weights * y), np.sum(weights * y * x)])\n",
" A = np.array([[np.sum(weights), np.sum(weights * x)],\n",
" [np.sum(weights * x), np.sum(weights * x * x)]])\n",
" beta = linalg.solve(A, b)\n",
" yest[i] = beta[0] + beta[1] * x[i]\n",
"\n",
" residuals = y - yest\n",
" s = np.median(np.abs(residuals))\n",
" delta = np.clip(residuals / (6.0 * s), -1, 1)\n",
" delta = (1 - delta ** 2) ** 2\n",
"\n",
" return yest\n",
"\n",
"def plot_metrics(metrics, groups=None, title=\"Metrics\", lowess=False):\n",
" groups = groups or [list(metrics.keys())]\n",
" n = len(groups)\n",
" ny = math.ceil(n / 2)\n",
" fig = plt.figure(figsize=(8 if n > 1 else 4, 2 * ny))\n",
"\n",
" for i, group in enumerate(groups, 1):\n",
" ax = fig.add_subplot(ny, 2 if n > 1 else 1, i)\n",
" for k in group:\n",
" x, y = np.arange(len(metrics[k])), metrics[k]\n",
" alpha = max(0.3, min(1, (1000 - len(x)) / 1000))\n",
" ax.plot(x, y, alpha=alpha, label=k, marker='.', markeredgewidth=0,lw=.5,ms=5)\n",
" if np.any(np.min(y) - y[0] > (np.max(y) - np.min(y)) * 0.01):\n",
" ax.set_ylim(np.min(y), y[0])\n",
" if lowess and len(y) >= 9:\n",
" ax.plot(x, lowess(x, y, f=0.25, iter=3), linestyle='-', alpha=0.8, label=k + \".lowess\", lw=2)\n",
" ax.legend(loc='lower left')\n",
" ax.grid(axis='x')\n",
"\n",
" fig.suptitle(title)\n",
" plt.show()\n",
"\n",
"def plot_tfevents_vals(vals, groups=None, **kwargs):\n",
" groups = groups or [vals.keys()]\n",
" keys = {k for g in groups for k in g}\n",
" metrics = {k: np.array([v.value for v in vs]) for k, vs in vals.items() if k in keys}\n",
" keys1 = set(metrics.keys())\n",
" groups1 = list(filter(None, [[k for k in g if k in keys1] for g in groups]))\n",
" plot_metrics(metrics, groups=groups1, **kwargs)\n",
"\n",
"\n",
"def sph2cart(sph):\n",
" az, el, r = rearrange(sph, \"... d -> d ...\")\n",
" c = np.cos(el)\n",
" return rearrange(np.stack((c * np.cos(az), c * np.sin(az), np.sin(el)) * r), \"d ... -> ... d\")\n",
"\n",
"def cart2sph(cart):\n",
" x, y, z = cart[...,0], cart[...,1], cart[...,2]\n",
" az, el = np.arctan2(y, x), np.arctan2(z, np.hypot(x, y))\n",
" r = np.sqrt(x ** 2 + y ** 2 + z ** 2)\n",
" return np.column_stack((az, el, r))\n",
"\n",
"def iter_batch(xs, bs, drop_last=True):\n",
" n = len(xs) // bs\n",
" for i in range(n):\n",
" yield xs[i*bs:(i+1)*bs]\n",
" if not drop_last:\n",
" yield xs[n*bs:]\n",
"\n",
"@contextmanager\n",
"def stdout_redirected(to=os.devnull):\n",
" '''\n",
" https://blender.stackexchange.com/a/270199\n",
" '''\n",
" fd = sys.stdout.fileno()\n",
"\n",
" ##### assert that Python and C stdio write using the same file descriptor\n",
" ####assert libc.fileno(ctypes.c_void_p.in_dll(libc, \"stdout\")) == fd == 1\n",
"\n",
" def _redirect_stdout(to):\n",
" sys.stdout.close() # + implicit flush()\n",
" os.dup2(to.fileno(), fd) # fd writes to 'to' file\n",
" sys.stdout = os.fdopen(fd, 'w') # Python writes to fd\n",
"\n",
" with os.fdopen(os.dup(fd), 'w') as old:\n",
" with open(to, 'w') as f:\n",
" _redirect_stdout(to=f)\n",
" try:\n",
" yield # allow code to be run with the redirected stdout\n",
" finally:\n",
" _redirect_stdout(to=old) # restore stdout. buffering and flags such as CLOEXEC may be different\n",
"\n",
"def unpack_bz2(src_path):\n",
" data = bz2.BZ2File(src_path).read()\n",
" dst_path = src_path[:-4]\n",
" with open(dst_path, 'wb') as fp:\n",
" fp.write(data)\n",
" return dst_path\n",
"\n",
"def make_zip(files, target, filename=os.path.basename):\n",
" with ZipFile(target, 'w') as f:\n",
" for p in files:\n",
" f.write(p, filename(p))\n",
" return target"
],
"metadata": {
"id": "vVsM87gGqiF7"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Prepare"
],
"metadata": {
"id": "CYrxLatDqjYA"
}
},
{
"cell_type": "code",
"source": [
"import smplx\n",
"import torch\n",
"import trimesh\n",
"from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection\n",
"from scipy.signal import argrelmin, argrelmax, windows, convolve\n",
"\n",
"def comp_dims(ndim, axis):\n",
" return tuple(i for i in range(ndim) if i not in (axis, ndim + axis))\n",
"\n",
"def m44_translation(t):\n",
" m = np.eye(4, dtype=t.dtype)\n",
" m[:3,3] = t\n",
" return m\n",
"\n",
"def m44_scale_n(n, dtype=\"f4\"):\n",
" m = np.eye(4, dtype=dtype)\n",
" m[:3,:3] *= n\n",
" return m\n",
"\n",
"def homo_coord(v):\n",
" pad = [(0,0) for _ in range(v.ndim-1)] + [(0,1)]\n",
" return np.pad(v, pad, constant_values=1)\n",
"\n",
"def m44_mulv3(m, v):\n",
" return einsum(m, homo_coord(v), \"i j,... j->... i\")[...,:3]\n",
"\n",
"def norm_scale_mat(v, axis=-1):\n",
" return m44_scale_n(1. / np.linalg.norm(v, axis=axis).max())\n",
"\n",
"def norm_mat(v, axis=-1):\n",
" t = -v.mean(comp_dims(v.ndim, axis))\n",
" return norm_scale_mat(v + t, axis=axis) @ m44_translation(t)\n",
"\n",
"def ax_box2(mn, mx, ax=None):\n",
" ax = ax or plt.gca()\n",
" ax.set(xlim=(mn[0],mx[0]),ylim=(mn[1],mx[1]),box_aspect=1)\n",
"\n",
"def ax_box3(mn, mx, ax=None):\n",
" ax = ax or plt.gca()\n",
" ax.set(xlim=(mn[0],mx[0]),\n",
" ylim=(mn[1],mx[1]),\n",
" zlim=(mn[2],mx[2]),\n",
" box_aspect=mx-mn)\n",
"\n",
"def ax_panes3(ax=None):\n",
" ax = ax or plt.gca()\n",
" ax.xaxis.pane.set(fc=(.2,.2,.2,.5),lw=0)\n",
" ax.yaxis.pane.set(fc=(.25,.25,.25,.5),lw=0)\n",
" ax.zaxis.pane.set(fc=(.3,.3,.3,.5),lw=0)\n",
"\n",
"def ax_poly(verts, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" ax.add_collection(mpl.collections.PolyCollection(verts, **kw))\n",
"\n",
"def ax_line3d(lines, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" return ax.add_collection(Line3DCollection(lines, **kw))\n",
"\n",
"def ax_poly3d(verts, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" return ax.add_collection(Poly3DCollection(verts, **kw))\n",
"\n",
"def ax_scatter(pts, ax=None, **kw):\n",
" ax = ax or plt.gca()\n",
" return ax.scatter(*[pts[...,i] for i in range(pts.shape[-1])], **kw)\n",
"\n",
"def ax_rects(rects, ax=None, **kw):\n",
" h = rects[:,2:] * .5\n",
" a, b = rects[:,:2] - h, rects[:,:2] + h\n",
" verts = np.stack((a,np.c_[a[:,0],b[:,1]],b,np.c_[b[:,0],a[:,1]]),1)\n",
" ax_poly(verts, ax=ax, **kw)\n",
"\n",
"def amass_forward(model, data, start=0, end=None, bs=128):\n",
" s = start\n",
" e = end or int(data['mocap_frame_rate']*data[\"mocap_time_length\"])\n",
"\n",
" vertices, joints = [], []\n",
" with torch.inference_mode():\n",
" for i in range(math.ceil((e - s) / bs)):\n",
" a, b = s + i * bs, min(s + (i + 1) * bs, e)\n",
" output = model(betas=torch.FloatTensor(repeat(data['betas'][:model.num_betas],\"...->n ...\",n=b - a)),\n",
" transl=torch.FloatTensor(data[\"trans\"][a:b]),\n",
" expression=repeat(model.expression,\"m ...->(m n) ...\",n=b-a),\n",
" global_orient=torch.FloatTensor(data['root_orient'][a:b]),\n",
" body_pose=torch.FloatTensor(data['pose_body'][a:b]),\n",
" jaw_pose=torch.FloatTensor(data['pose_jaw'][a:b]),\n",
" leye_pose=torch.FloatTensor(data['pose_eye'][a:b,:3]),\n",
" reye_pose=torch.FloatTensor(data['pose_eye'][a:b,3:]),\n",
" left_hand_pose=torch.FloatTensor(data['pose_hand'][a:b,:45]),\n",
" right_hand_pose=torch.FloatTensor(data['pose_hand'][a:b,45:]),\n",
" return_verts=True)\n",
" vertices.append(output.vertices.numpy())\n",
" joints.append(output.joints.numpy())\n",
" return np.concatenate(vertices), np.concatenate(joints)\n",
"\n",
"def init_pose(model, data):\n",
" with torch.inference_mode():\n",
" output = model(betas=torch.FloatTensor(data['betas'][None,:model.num_betas]),\n",
" transl=torch.FloatTensor(data[\"trans\"][:1]),\n",
" global_orient=torch.FloatTensor(data['root_orient'][:1]))\n",
" return output.vertices.numpy()[0], output.joints.numpy()[0]\n",
"\n",
"def get_salients(joints, j_init, wsize=121):\n",
" d = np.linalg.norm(joints[1:] - joints[:1],axis=-1).mean(-1)\n",
" win = windows.hann(wsize)\n",
" d = convolve(d, win / win.sum(), mode='same')\n",
"\n",
" idxs = np.sort(np.r_[argrelmax(d)[0], argrelmin(d)[0]])\n",
"\n",
" d_mean = np.linalg.norm(joints[idxs]-j_init,axis=-1).mean(-1)\n",
" return d, idxs[np.argmax(d_mean > .1).clip(0):]\n",
"\n",
"def xy_rects(verts):\n",
" pts = verts[...,:2]\n",
" mn, mx = pts.min(1), pts.max(1)\n",
" return np.concatenate((lerp(mn, mx, .5), mx-mn),-1)\n",
"\n",
"def rect_points(rects):\n",
" cx, cy = rects[:,0], rects[:,1]\n",
" hw, hh = rects[:,2] * .5, rects[:,3] * .5\n",
" return np.c_[cx - hw, cx + hw, cy - hh, cy + hh]\n",
"\n",
"def rect_intersect(pts):\n",
" l, r, b, t = pts.take(0,-1),pts.take(1,-1),pts.take(2,-1),pts.take(3,-1)\n",
" isec = ~((r[:,None] < l[None]) |\n",
" (l[:,None] > r[None]) |\n",
" (b[:,None] > t[None]) |\n",
" (t[:,None] < b[None]))\n",
" idx = np.arange(len(pts))\n",
" isec[idx,idx] = False\n",
" return isec\n",
"\n",
"def rect_remove_overlaps(rects, n_iter=1000):\n",
" r_0 = rects.copy()\n",
" for _ in range(n_iter):\n",
" pts = rect_points(r_0)\n",
" isec = rect_intersect(pts)\n",
" xranks, yranks = np.argsort(r_0[:,0]), np.argsort(r_0[:,1])\n",
" l, r, b, t = pts.take(0,-1),pts.take(1,-1),pts.take(2,-1),pts.take(3,-1)\n",
"\n",
" r_1 = r_0.copy()\n",
" for i in np.random.permutation(len(r_0)):\n",
" p1 = np.argwhere(isec[i]).squeeze()\n",
" p0 = np.full_like(p1, i)\n",
" xr0, yr0, xr1, yr1 = xranks[p0], yranks[p0], xranks[p1], yranks[p1]\n",
" order_x, order_y = xr0 < xr1, yr0 < yr1\n",
" osize_x = np.where(order_x, np.abs(l[p1] - r[p0]), np.abs(l[p0] - r[p1]))\n",
" osize_y = np.where(order_y, np.abs(b[p1] - t[p0]), np.abs(b[p0] - t[p1]))\n",
" order_size = osize_x < osize_y\n",
" off = np.where(order_size[...,None],\n",
" np.c_[osize_x, np.zeros_like(osize_x)],\n",
" np.c_[np.zeros_like(osize_y), osize_y]) * .5\n",
" sgn = np.where(order_size, np.where(order_x, -1, 1), np.where(order_y, -1, 1))[...,None]\n",
" r_1[p0,:2] = r_0[p0,:2] + sgn * off\n",
" r_1[p1,:2] = r_0[p1,:2] - sgn * off\n",
"\n",
" r_0 = r_1\n",
" return r_0[:,:2] - rects[:,:2]\n",
"\n",
"def decluster(verts, jnts, n_iter):\n",
" rects = xy_rects(verts)\n",
"\n",
" off = rect_remove_overlaps(rects, n_iter)\n",
" rects1 = np.c_[rects[:,:2] + off, rects[:,2:]]\n",
"\n",
" rects1[:,:2] -= lerp(rects1[:,:2].min(0), rects1[:,:2].max(0), .5)\n",
"\n",
" mi = np.linalg.norm(rects1[:,:2],axis=-1).argmin()\n",
" rects1[:,:2] -= rects1[mi,:2]\n",
"\n",
" off = np.pad(rects1[:,:2]-rects[:,:2],[(0,0),(0,1)])[:,None]\n",
" return verts + off, jnts + off, rects1, mi\n",
"\n",
"def face_orient_xy(joint):\n",
" face = joint[22:25,:2]\n",
" v = normalize(lerp(face[1],face[2],.5) - face[0])\n",
" return np.arctan2(v[1],v[0])\n",
"\n",
"def as_segments(pts):\n",
" return rearrange([pts[:,:-1],pts[:,1:]],\"k n m ...->n m k ...\")\n",
"\n",
"def closed(pts, axis=0):\n",
" tail = np.expand_dims(pts.take(0,axis),axis=axis)\n",
" return np.concatenate((pts,tail),axis)\n",
"\n",
"def body_lines(joints, parents):\n",
" return np.stack((joints[:,1:55], joints[:,parents[1:]]),2)\n",
"\n",
"def face_lines(joints):\n",
" eb_l, eb_r = joints[:,76:81], joints[:,81:86]\n",
" nose_1, nose_2 = joints[:,86:90], joints[:,90:95]\n",
" nose_3, nose_4 = joints[:,89:91], joints[:,[89,94]]\n",
" eye_l, eye_r = closed(joints[:,95:101],1), closed(joints[:,101:107],1)\n",
" lip_1, lip_2 = closed(joints[:,107:119],1), closed(joints[:,119:127],1)\n",
" cntr = joints[:,127:]\n",
" segs = [as_segments(l) for l in [eb_l,eb_r,nose_1,nose_2,nose_3,nose_4,eye_l,eye_r,lip_1,lip_2,cntr]]\n",
" return np.concatenate(segs, 1)\n",
"\n",
"def finger_lines(joints):\n",
" left = rearrange([joints[:,[54,42,45,51,48]], joints[:,71:76]],\"k n m ...->n m k ...\")\n",
" right = rearrange([joints[:,[39,27,30,36,33]], joints[:,66:71]],\"k n m ...->n m k ...\")\n",
" return np.concatenate((left,right), 1)"
],
"metadata": {
"id": "3bN8JZfdqkGh"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/gdrive')\n",
"\n",
"root = \"/content/gdrive/MyDrive/human\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YdE2qpLuE1kO",
"outputId": "d9a9da2f-304c-4e8b-e7d7-a5bd542812a9"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/gdrive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model = smplx.create(f\"{root}/SMPLX_FEMALE.npz\", \"smplx\",\n",
" use_pca=False, flat_hand_mean=True, use_face_contour=True,gender='female')"
],
"metadata": {
"id": "Kfi3kFtwqvnC"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"name = \"StefanosKoullapis_Reggaeton_C3D\"\n",
"\n",
"data = np.load(f\"{name}_stageii.npz\",allow_pickle=True)\n",
"vertices, joints = amass_forward(model, data)\n",
"\n",
"v_init, j_init = init_pose(model, data)\n",
"d, idxs = get_salients(joints, j_init)\n",
"\n",
"verts, jnts = vertices[idxs], joints[idxs]\n",
"\n",
"verts1, jnts1, rects1, i_m = decluster(verts, jnts, int(idxs.shape[0] * 5))\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot()\n",
"ax_rects(rects1,fc='none',lw=.2,ec='gainsboro')\n",
"ax_scatter(jnts1[:,0,:2],s=3,lw=0,c='tab:red')\n",
"mn, mx = rects1[:,:2].min(0) * 1.2, rects1[:,:2].max(0) * 1.2\n",
"ax.set(xlim=(mn[0],mx[0]),ylim=(mn[1],mx[1]),box_aspect=1)\n",
"plt_show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 326
},
"id": "OYGaxjriqxZy",
"outputId": "d0f47ed7-bbee-4217-f8cc-dc0ab6ffdf47"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 500x300 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"image/png": {
"width": 311,
"height": 309
}
}
}
]
},
{
"cell_type": "code",
"source": [
"nmat = norm_scale_mat(verts1)\n",
"verts2, jnts2 = m44_mulv3(nmat, verts1), m44_mulv3(nmat, jnts1)\n",
"orient = face_orient_xy(jnts2[i_m])\n",
"\n",
"os.makedirs(f\"{name}/plys\")\n",
"for i, vs in zip(idxs, verts2):\n",
" with open(f\"{name}/plys/{i:05d}.ply\", \"wb\") as f:\n",
" f.write(trimesh.exchange.ply.export_ply(trimesh.Trimesh(vs,model.faces)))\n",
"\n",
"with open(f\"00000.ply\", \"wb\") as f:\n",
" f.write(trimesh.exchange.ply.export_ply(trimesh.Trimesh(v_init,model.faces)))"
],
"metadata": {
"id": "AUGNghasq-P9"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from glob import glob\n",
"\n",
"os.makedirs(f\"{root}/{name}\")\n",
"make_zip(glob(f\"{name}/plys/*.ply\"), f\"{root}/{name}/plys.zip\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "hjngOI82I9P6",
"outputId": "443a91a4-3332-4dac-98f5-600a2118202b"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content/gdrive/MyDrive/human/Vasso_Bachata_01/plys.zip'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"np.savez_compressed(f\"{root}/{name}/data\",\n",
" orient=orient, i_m=i_m, joints=jnts2, parents=model.parents)"
],
"metadata": {
"id": "BY-vCBqLJz8B"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Blender"
],
"metadata": {
"id": "HdpKO500sImw"
}
},
{
"cell_type": "code",
"source": [
"## https://polyhaven.com/a/brown_photostudio_02\n",
"!wget -qc --show-progress https://dl.polyhaven.org/file/ph-assets/HDRIs/exr/4k/brown_photostudio_02_4k.exr"
],
"metadata": {
"id": "ax-OpuMzsJV8",
"outputId": "9932b42a-0bbc-4c38-fb5b-c987f03680f1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"brown_photostudio_0 100%[===================>] 19.23M --.-KB/s in 0.1s \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import bpy\n",
"import bmesh\n",
"import mathutils as mu\n",
"import bl\n",
"import trimesh\n",
"from bpy import context as C, data as D\n",
"from glob import glob\n",
"\n",
"def render_image(alpha=C.scene.render.film_transparent):\n",
" bpy.ops.render.render(write_still=True)\n",
" img = cv2.imread(C.scene.render.filepath, cv2.IMREAD_UNCHANGED if alpha else cv2.IMREAD_COLOR)\n",
" return cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA if alpha else cv2.COLOR_BGR2RGB)\n",
"\n",
"def read_ply(fn):\n",
" with open(fn, \"rb\") as f:\n",
" mesh = trimesh.exchange.ply.load_ply(f)\n",
" return mesh\n",
"\n",
"def load_ply(D, fn, name=None):\n",
" m = read_ply(fn)\n",
" name = name or os.path.splitext(os.path.basename(fn))[0]\n",
" return bl.mesh.add(D, name, m['vertices'], m['faces'])\n",
"\n",
"def add_material_mesh(name, roughness=1, ao_dist=1, ao_color=(.1,.1,.1)):\n",
" mat = bl.material.add(D, name)\n",
" tree = mat.node_tree\n",
" tree.nodes.clear()\n",
"\n",
" bsdf = bl.node_tree.add_node(tree, \"ShaderNodeBsdfPrincipled\", {\n",
" \"Base Color\": (.8, .8, .8, 1),\n",
" \"Roughness\": roughness,\n",
" \"Subsurface Weight\": 0,\n",
" \"Subsurface Scale\": .05,\n",
" \"Coat Weight\": .3,\n",
" \"Coat Roughness\": 1,\n",
" \"Sheen Weight\": .3,\n",
" \"Sheen Roughness\": 1,\n",
" })\n",
"\n",
" ao = bl.node_tree.add_node(tree, \"ShaderNodeAmbientOcclusion\", {\"Distance\": ao_dist})\n",
" mix = bl.node_tree.add_node(tree, \"ShaderNodeMixShader\")\n",
" diffuse = bl.node_tree.add_node(tree, \"ShaderNodeBsdfDiffuse\", {\n",
" \"Color\": (*ao_color,1),\n",
" \"Roughness\": roughness\n",
" })\n",
" out = bl.node_tree.add_node(tree, \"ShaderNodeOutputMaterial\")\n",
"\n",
" bl.node_tree.connect(tree, [\n",
" (ao.outputs[\"Color\"], mix.inputs[\"Fac\"]),\n",
" (diffuse.outputs[\"BSDF\"], mix.inputs[1]),\n",
" (bsdf.outputs[\"BSDF\"], mix.inputs[2]),\n",
" (mix.outputs[\"Shader\"], out.inputs[\"Surface\"])\n",
" ])\n",
"\n",
" return mat\n",
"\n",
"def add_node_group_lights(coll):\n",
" group = bl.node_group.add(D, \"lights\", {\n",
" \"Target\": {\n",
" \"in_out\": \"INPUT\",\n",
" \"socket_type\": \"NodeSocketVector\",\n",
" \"attrs\": {\n",
" \"subtype\": \"XYZ\"\n",
" }\n",
" }\n",
" })\n",
" group.links.clear()\n",
"\n",
" iop = bl.node_tree.add_node(group, \"GeometryNodeInstanceOnPoints\", {\n",
" \"Pick Instance\": True\n",
" })\n",
" ci = bl.node_tree.add_node(group, \"GeometryNodeCollectionInfo\", {\n",
" \"Collection\": coll,\n",
" \"Separate Children\": True,\n",
" \"Reset Children\": True,\n",
" })\n",
" pos = bl.node_tree.add_node(group, \"GeometryNodeInputPosition\")\n",
" sub = bl.node_tree.add_node(group, \"ShaderNodeVectorMath\", attrs={\"operation\": \"SUBTRACT\"})\n",
" artv = bl.node_tree.add_node(group, \"FunctionNodeAlignRotationToVector\")\n",
"\n",
" bl.node_tree.connect(group, [\n",
" (group.nodes[\"Group Input\"].outputs[\"Geometry\"], iop.inputs[\"Points\"]),\n",
" (iop.outputs[\"Instances\"], group.nodes[\"Group Output\"].inputs[\"Geometry\"]),\n",
" (ci.outputs[\"Instances\"], iop.inputs[\"Instance\"]),\n",
" (pos.outputs[\"Position\"], sub.inputs[0]),\n",
" (group.nodes[\"Group Input\"].outputs[\"Target\"], sub.inputs[1]),\n",
" (sub.outputs[\"Vector\"], artv.inputs[\"Vector\"]),\n",
" (artv.outputs[\"Rotation\"], iop.inputs[\"Rotation\"]),\n",
" ])\n",
"\n",
" return group\n",
"\n",
"def add_node_group_smooth_mat(mat):\n",
" group = bl.node_group.add(D, \"volume_mesh\", {\n",
" \"Origin\": {\n",
" \"in_out\": \"OUTPUT\",\n",
" \"socket_type\": \"NodeSocketVector\",\n",
" \"attrs\": {\n",
" \"attribute_domain\": \"INSTANCE\",\n",
" \"subtype\": \"TRANSLATION\"\n",
" }\n",
" }\n",
" })\n",
"\n",
" smooth = group.nodes.new(\"GeometryNodeSetShadeSmooth\")\n",
" set_mat = group.nodes.new(\"GeometryNodeSetMaterial\")\n",
" set_mat.inputs[\"Material\"].default_value = mat\n",
"\n",
" bl.node_tree.connect(group, [\n",
" (group.nodes[\"Group Input\"].outputs[\"Geometry\"], smooth.inputs[\"Geometry\"]),\n",
" (smooth.outputs[\"Geometry\"], set_mat.inputs[\"Geometry\"]),\n",
" (set_mat.outputs[\"Geometry\"], group.nodes[\"Group Output\"].inputs[\"Geometry\"])\n",
" ])\n",
"\n",
" return group\n",
"\n",
"def render_mesh_scene(folder, orient, size=(960,360)):\n",
" bpy.ops.wm.read_homefile(use_empty=True)\n",
" bl.scene.setup(C.scene, size=size, samples=128)\n",
" bl.cycles.setup(C.preferences)\n",
"\n",
" C.scene.world = bl.world.add(D, \"World\")\n",
" bl.world.use_environment(C.scene.world, bl.image.open(D, \"brown_photostudio_02_4k.exr\"),\n",
" rotation=(0,np.pi/4,np.pi/6), strength=.2)\n",
"\n",
" ng = add_node_group_smooth_mat(add_material_mesh(\"mesh\"))\n",
" coll = D.collections.new(\"meshes\")\n",
" for f in glob(f\"{folder}/plys/*.ply\"):\n",
" mesh = load_ply(D, f)\n",
" obj = D.objects.new(mesh.name, mesh)\n",
" coll.objects.link(obj)\n",
" bl.object.add_node_group_modifier(obj, \"Volume Mesh\", ng)\n",
"\n",
" min_z = min(bl.mesh.get_aabb(obj.data)[0,2] for obj in coll.objects) * .6\n",
" max_z = max(bl.mesh.get_aabb(obj.data)[1,2] for obj in coll.objects)\n",
"\n",
" instance = bl.object.add_collection_instance(D, coll)\n",
" C.scene.collection.objects.link(instance)\n",
" instance.rotation_euler.z = -orient\n",
" instance.location.z = -min_z\n",
"\n",
" camera = C.scene.camera = D.objects.new('Camera', bl.camera.add_ortho(D))\n",
" camera.data.ortho_scale = -.0028 * len(coll.objects) + 1.2\n",
" C.scene.collection.objects.link(camera)\n",
" camera.location = mu.Vector((3,0,(max_z - min_z) * 2))\n",
" bl.object.look_at(camera, mu.Vector((0,0,(max_z - min_z) * .5)))\n",
"\n",
" bpy.ops.mesh.primitive_plane_add(size=100)\n",
" D.objects[\"Plane\"].hide_viewport = True\n",
" D.objects[\"Plane\"].is_shadow_catcher = True\n",
"\n",
" coll_lights = D.collections.new(\"Lights\")\n",
" coll_lights.hide_viewport = True\n",
" coll_lights.hide_render = True\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Back\", \"AREA\", \"DISK\", 40, 5), \"Light.Back\"))\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Fill\", \"AREA\", \"DISK\", 50, 3), \"Light.Fill\"))\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Key\", \"AREA\", \"DISK\", 20, 1), \"Light.Key\"))\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Top\", \"AREA\", \"DISK\", 10, 3), \"Light.Top\"))\n",
" C.scene.collection.children.link(coll_lights)\n",
"\n",
" mesh = D.meshes.new(\"Lights\")\n",
" mesh.from_pydata(np.array([[-3,3,5],[3,-3,3],[-3,-3,1],[0,0,5]],dtype=\"f4\"),[],[])\n",
" lights = D.objects.new('Lights', mesh)\n",
" C.scene.collection.objects.link(lights)\n",
"\n",
" bl.object.add_node_group_modifier(lights, \"Lights\", add_node_group_lights(coll_lights))\n",
"\n",
" return render_image()"
],
"metadata": {
"id": "-wjphgx-sWq3"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"imshow(render_mesh_scene(name, orient))"
],
"metadata": {
"id": "Ej35j_8v-fVZ",
"outputId": "2981fa2e-ab8d-4f4d-cb85-aee296c32831",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 197
}
},
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"image/png": {
"width": 480,
"height": 180
}
}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Mitsuba"
],
"metadata": {
"id": "fARlbaQHv0jP"
}
},
{
"cell_type": "markdown",
"source": [
"### Data"
],
"metadata": {
"id": "BNPl2Xmrv2GJ"
}
},
{
"cell_type": "code",
"source": [
"!wget -qc --show-progress https://rgl.s3.eu-central-1.amazonaws.com/media/papers/Vicini2022SDF_1.zip\n",
"!unzip -nqq Vicini2022SDF_1.zip -d Vicini2022SDF_1\n",
"!wget -qc --show-progress https://rgl.s3.eu-central-1.amazonaws.com/media/papers/Nicolet2021Large.zip\n",
"!unzip -nqq Nicolet2021Large.zip -d Nicolet2021Large"
],
"metadata": {
"id": "QYunOEwauB-v",
"outputId": "9a107aaa-4b95-4e9f-ff61-581843b79af5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Vicini2022SDF_1.zip 100%[===================>] 382.87M 23.9MB/s in 17s \n",
"Nicolet2021Large.zi 100%[===================>] 234.95M 20.7MB/s in 12s \n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### Utils"
],
"metadata": {
"id": "EpFdWXEhv3Oh"
}
},
{
"cell_type": "code",
"source": [
"def dot(a, b, axis=-1, keepdims=False):\n",
" return (a * b).sum(axis=axis, keepdims=keepdims)\n",
"\n",
"def quat_axis_angle(a, r):\n",
" r = (np.asarray(r) * .5)[...,None]\n",
" return np.concatenate((a * np.sin(r), np.cos(r)),-1)\n",
"\n",
"def quat_mul(a, b):\n",
" ax, ay, az, aw = rearrange(a, \"... d -> d ...\")\n",
" bx, by, bz, bw = rearrange(b, \"... d -> d ...\")\n",
" res = np.stack((\n",
" ax * bw + aw * bx + ay * bz - az * by,\n",
" ay * bw + aw * by + az * bx - ax * bz,\n",
" az * bw + aw * bz + ax * by - ay * bx,\n",
" aw * bw - ax * bx - ay * by - az * bz))\n",
" return rearrange(res, \"d ... -> ... d\")\n",
"\n",
"def quat_mul_v(q, v):\n",
" x, y, z = rearrange(v, \"... d -> d ...\")\n",
" qx, qy, qz, qw = rearrange(q, \"... d -> d ...\")\n",
" ix = qw * x + qy * z - qz * y\n",
" iy = qw * y + qz * x - qx * z\n",
" iz = qw * z + qx * y - qy * x\n",
" iw = qx * x + qy * y + qz * z\n",
" res = np.stack((ix * qw + iw * qx - iy * qz + iz * qy,\n",
" iy * qw + iw * qy - iz * qx + ix * qz,\n",
" iz * qw + iw * qz - ix * qy + iy * qx))\n",
" return rearrange(res, \"d ... -> ... d\")\n",
"\n",
"def orthogonal(v, m=.5, n=.5):\n",
" x, y, z = rearrange(v, \"... d -> d ...\")\n",
" res = np.stack((m * -y + n * -z, m * x, n * x))\n",
" return normalize(rearrange(res, \"d ... -> ... d\"))\n",
"\n",
"def quat_between(a, b):\n",
" w, q = dot(a, b), np.cross(a, b)\n",
" qw = w + np.sqrt(q[...,0] ** 2 + q[...,1] ** 2 + q[...,2] ** 2 + w ** 2)\n",
" qa = normalize(np.concatenate((q, qw[...,None]),-1))\n",
" qb = quat_axis_angle(orthogonal(a), np.full(a.shape[:-1],np.pi))\n",
" return np.where(w[...,None] != -1, qa, qb)\n",
"\n",
"def quat_lookat(vdir, rad=None):\n",
" rad = rad or np.full(vdir.shape[0], 0, dtype=\"f4\")\n",
" YZ = np.array([[0,1,0],[0,0,1]], dtype=vdir.dtype)\n",
" w = dot(vdir, YZ[1:],keepdims=True)\n",
" q = np.where(w == 1, np.array([[0,0,0,1]],dtype=vdir.dtype),\n",
" np.where(w == -1, quat_axis_angle(YZ[:1], [np.pi]), quat_between(YZ[1:], vdir)))\n",
" return quat_mul(quat_axis_angle(vdir, rad), q)\n",
"\n",
"def m44_rotation_axis(idx, theta, dtype=\"f4\"):\n",
" c, s = np.cos(theta), np.sin(theta)\n",
" a, b = (idx + 1) % 3, (idx + 2) % 3\n",
"\n",
" mat = np.eye(4, dtype=dtype)\n",
" mat[a, a], mat[b, b] = c, c\n",
" mat[a, b], mat[b, a] = -s, s\n",
" return mat\n",
"\n",
"def regular_points_sphere(steps=[1,4,8,4,1]):\n",
" pts = np.concatenate([sph2cart(np.c_[np.linspace(0, np.pi*2, n, endpoint=False), np.full(n, a), np.ones(n)])\n",
" for n, a in zip(steps,np.linspace(-np.pi/2,np.pi/2,len(steps)))])\n",
" return pts @ m44_rotation_axis(0, np.pi/2)[:3,:3].T\n",
"\n",
"def m33_rotation_axis(idx, theta, dtype=\"f4\"):\n",
" c, s = np.cos(theta), np.sin(theta)\n",
" a, b = (idx + 1) % 3, (idx + 2) % 3\n",
"\n",
" mat = np.eye(3, dtype=dtype)\n",
" mat[a, a], mat[b, b] = c, c\n",
" mat[a, b], mat[b, a] = -s, s\n",
" return mat"
],
"metadata": {
"id": "UAf2fHWBv39c"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Shape"
],
"metadata": {
"id": "tar7vAszv6NG"
}
},
{
"cell_type": "code",
"source": [
"import drjit as dr\n",
"import mitsuba as mi\n",
"\n",
"mi.set_variant('cuda_ad_rgb')\n",
"# mi.set_variant('llvm_ad_rgb')\n",
"\n",
"class Mesh(mi.Mesh):\n",
" def __init__(self, props=mi.Properties()):\n",
" verts = props.get(\"vertices\")\n",
" faces = props.get(\"faces\")\n",
" super().__init__(props.get(\"name\"), verts.shape[0] // 3, faces.shape[0] // 3, props, True, False)\n",
" params = mi.traverse(self)\n",
" params['vertex_positions'] = verts.array\n",
" params['faces'] = faces.array\n",
" params.update()\n",
" self.recompute_vertex_normals()\n",
"\n",
" def to_string(self):\n",
" return \"mesh\"\n",
"\n",
"mi.register_mesh(\"mesh\", lambda props: Mesh(props))\n",
"\n",
"class Grid3d(mi.Object):\n",
" def __init__(self, shape):\n",
" super().__init__()\n",
" self.texture = mi.Texture3f(shape, 1, use_accel=False)\n",
" self.to_world = mi.Transform4f().translate([-.5,-.5,-.5])\n",
" self.to_local = self.to_world.inverse()\n",
" self.aabb = mi.ScalarBoundingBox3f(mi.ScalarPoint3f(-.5,-.5,-.5),\n",
" mi.ScalarPoint3f(.5,.5,.5))\n",
"\n",
" def bbox(self, delta=.05):\n",
" return mi.BoundingBox3f(self.aabb.min - delta, self.aabb.max + delta)\n",
"\n",
" def eval(self, x):\n",
" return self.texture.eval_cubic(self.to_local @ x)[0]\n",
"\n",
" def eval_grad(self, x):\n",
" g = mi.Vector3f(self.texture.eval_cubic_grad(self.to_local @ x)[1][0])\n",
" return self.to_world @ mi.Normal3f(g.x, g.y, g.z)\n",
"\n",
" def eval_and_grad(self, x):\n",
" v, g = self.texture.eval_cubic_grad(self.to_local @ x)\n",
" g = mi.Vector3f(g[0])\n",
" g = self.to_world @ mi.Normal3f(g.x, g.y, g.z)\n",
" return mi.Float(v[0]), g\n",
"\n",
" def eval_all(self, x):\n",
" v, g, h = self.texture.eval_cubic_hessian(self.to_local @ x)\n",
" v, g, h = mi.Float(v[0]), mi.Vector3f(g[0]), mi.Matrix3f(h[0])\n",
"\n",
" mat = self.to_local.matrix\n",
" to_local3 = mi.Matrix3f([\n",
" [mat[0, 0], mat[0, 1], mat[0, 2]],\n",
" [mat[1, 0], mat[1, 1], mat[1, 2]],\n",
" [mat[2, 0], mat[2, 1], mat[2, 2]]\n",
" ])\n",
" g = mi.Vector3f(to_local3.T @ g)\n",
" h = to_local3.T @ h @ to_local3\n",
"\n",
" return v, dr.detach(v, True), g, dr.detach(g, True), h\n",
"\n",
" def traverse(self, callback):\n",
" callback.put_parameter(\"data\", self.texture.tensor(), mi.ParamFlags.Differentiable)\n",
"\n",
" def parameters_changed(self, keys):\n",
" self.texture.set_tensor(self.texture.tensor())"
],
"metadata": {
"id": "VGw9u1Cqv45F"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Warp"
],
"metadata": {
"id": "MzLRNEoAv8cj"
}
},
{
"cell_type": "code",
"source": [
"def bbox_distance_d(min_dist):\n",
" n = mi.Vector3f(0.0)\n",
" n[(min_dist.x < min_dist.y) & (min_dist.x < min_dist.z)] = mi.Vector3f(1, 0, 0)\n",
" n[(min_dist.y < min_dist.z) & (min_dist.y < min_dist.x)] = mi.Vector3f(0, 1, 0)\n",
" n[(min_dist.z < min_dist.x) & (min_dist.z < min_dist.y)] = mi.Vector3f(0, 0, 1)\n",
" return n\n",
"\n",
"def bbox_distance_inside_d(x, bbox):\n",
" bbox_max_dist, bbox_min_dist = dr.abs(bbox.max - x), dr.abs(bbox.min - x)\n",
" n = bbox_distance_d(dr.minimum(bbox_min_dist, bbox_max_dist))\n",
"\n",
" dist = dr.maximum(0.0, dr.minimum(dr.min(x - bbox.min), dr.min(bbox.max - x)))\n",
" dist_d = dr.select(dist > 0.0, n * dr.sign(bbox_max_dist - bbox_min_dist), 0.0)\n",
" return dist, dist_d\n",
"\n",
"def eval_trace_weight(ray, i, bbox, x, sdf_value, sdf_grad, hessian,\n",
" sil_weight_epsilon, sil_weight_offset, weight_power):\n",
" n_dot_d, n_dot_n = dr.dot(sdf_grad, ray.d), dr.dot(sdf_grad, sdf_grad)\n",
" dot_ratio = n_dot_d / n_dot_n\n",
" denom = sil_weight_epsilon + dr.abs(sdf_value) + sil_weight_offset * n_dot_d * dot_ratio\n",
" dist_weight = 1 / denom ** weight_power\n",
"\n",
" bbox_dist, bbox_dist_d = bbox_distance_inside_d(x, bbox)\n",
"\n",
" bbox_eps = 0.01\n",
" bbox_weight = dr.select(i > 0, dr.minimum(bbox_dist, bbox_eps) / bbox_eps, 1.0)\n",
" weight = dist_weight * bbox_weight\n",
"\n",
" bbox_weight_d = dr.select((i > 0) & (bbox_dist < bbox_eps), bbox_dist_d / bbox_eps, 0.0)\n",
" gradient = 2 * dot_ratio * (ray.d - dot_ratio * sdf_grad)\n",
" denom_d = dr.sign(sdf_value) * sdf_grad + sil_weight_offset * gradient @ hessian\n",
" dist_weight_d = -weight_power * dist_weight / denom * denom_d\n",
" weight_d = dist_weight * bbox_weight_d + bbox_weight * dist_weight_d\n",
"\n",
" return weight, weight_d\n",
"\n",
"@dr.syntax\n",
"def sphere_trace(sdf, ray, active,\n",
" trace_eps=1e-6,\n",
" sil_weight_epsilon=1e-6,\n",
" sil_weight_offset=.05,\n",
" weight_power=3,\n",
" extra_thresh=.05):\n",
" loop_record_state = dr.flag(dr.JitFlag.LoopRecord)\n",
" dr.set_flag(dr.JitFlag.LoopRecord, True)\n",
"\n",
" ray = mi.Ray3f(ray)\n",
" ray.d = dr.normalize(ray.d)\n",
"\n",
" bbox = sdf.bbox()\n",
" intersects_bbox, mint, maxt = bbox.ray_intersect(ray)\n",
" inside_bbox = bbox.contains(ray.o)\n",
" intersects_bbox &= (mint > 0) | inside_bbox\n",
" active = active & intersects_bbox\n",
"\n",
" ray.maxt = dr.minimum(maxt, ray.maxt)\n",
" trace_eps = trace_eps * dr.maximum(ray.maxt, 1)\n",
"\n",
" its_t = mi.Float(dr.inf)\n",
" t = dr.select(inside_bbox, 0.0, mint + 1e-5)\n",
" warp_t = mi.Float(0.0)\n",
" prev_surf_dist = mi.Float(0.0)\n",
" prev_sdf_grad_c = mi.Vector3f(0.0)\n",
" weight_sum = mi.Float(0.0)\n",
" mixed_sum_d = mi.Vector3f(0.0)\n",
" weight_d_sum = mi.Vector3f(0.0)\n",
" i = mi.Int32(0)\n",
" extra_weight_sum = mi.Float(0.0)\n",
" extra_weight_sum_d = mi.Vector3f(0.0)\n",
"\n",
" bbox_its_p = ray(t)\n",
" n = bbox_distance_d(dr.minimum(dr.abs(bbox.min - bbox_its_p), dr.abs(bbox.max - bbox_its_p)))\n",
" d_dot_n = dr.dot(ray.d, n)\n",
" t_d = mi.Vector3f(0.0)\n",
" t_d[~inside_bbox & (dr.abs(d_dot_n) > 0)] = -n / d_dot_n * t\n",
"\n",
" while active:\n",
" x = ray(t)\n",
" with dr.suspend_grad():\n",
" sdf_value, _, sdf_grad, _, hessian = sdf.eval_all(x)\n",
"\n",
" intersected = sdf_value < trace_eps\n",
" its_t[intersected] = t\n",
" surf_dist = dr.abs(sdf_value)\n",
" weight, weight_d = eval_trace_weight(ray, i, bbox, x, sdf_value, sdf_grad, hessian,\n",
" sil_weight_epsilon, sil_weight_offset, weight_power)\n",
"\n",
" inv_extra_w_den = 1 / dr.minimum(extra_thresh, surf_dist)\n",
" dist_difference = prev_surf_dist - surf_dist\n",
" extra_weight_sum += dr.select(dist_difference >= 0, dist_difference * inv_extra_w_den, 0.0)\n",
" extra_weight_sum = dr.minimum(extra_weight_sum, 1.0)\n",
"\n",
" curr_segment_value = dr.select(intersected, 0.0, surf_dist)\n",
" segment_length = 0.5 * (curr_segment_value + prev_surf_dist)\n",
" weight_increment = segment_length * weight * extra_weight_sum\n",
"\n",
" weight_sum = weight_sum + weight_increment\n",
" warp_t = warp_t + weight_increment * t\n",
"\n",
" weight_d = dr.fma(t, weight_d, dr.dot(ray.d, weight_d) * t_d)\n",
" sdf_grad_c = dr.fma(t, sdf_grad, dr.dot(ray.d, sdf_grad) * t_d)\n",
" segment_d = 0.5 * (sdf_grad_c + prev_sdf_grad_c)\n",
"\n",
" surf_dist_d = dr.sign(sdf_value) * sdf_grad_c\n",
" extra_w_d = (prev_sdf_grad_c - surf_dist_d) * inv_extra_w_den\n",
" extra_w_d = extra_w_d - dist_difference * dr.square(inv_extra_w_den) * dr.select(sdf_value < extra_thresh, surf_dist_d, 0.0)\n",
" extra_weight_sum_d += dr.select(dist_difference > 0.0, extra_w_d, 0.0)\n",
" extra_weight_sum_d[(extra_weight_sum >= 1.0) | (extra_weight_sum <= 0.0)] = 0.0\n",
" weight_d = weight * extra_weight_sum_d + weight_d * extra_weight_sum\n",
" weight *= extra_weight_sum\n",
"\n",
" weight_increment_d = dr.fma(weight, segment_d, weight_d * segment_length)\n",
" mixed_sum_d += dr.fma(weight_increment_d, t, weight * segment_length * t_d)\n",
" t_d = t_d + sdf_grad_c\n",
" weight_d_sum += weight_increment_d\n",
" i += 1\n",
" t += curr_segment_value\n",
" prev_surf_dist = surf_dist\n",
" prev_sdf_grad_c = sdf_grad_c\n",
" active &= (t <= ray.maxt) & (~intersected)\n",
"\n",
" refining = mi.Mask(dr.isfinite(its_t))\n",
" i = mi.Int32(0)\n",
" while refining:\n",
" min_dist = dr.detach(sdf.eval(ray(its_t)))\n",
" its_t[refining] += min_dist * (mi.Float(10) / mi.Float(10 + i))\n",
" refining &= (min_dist <= 0) | (min_dist > trace_eps)\n",
" i += 1\n",
" refining &= i < 10\n",
"\n",
" inv_weight_sum = 1 / weight_sum\n",
" warp_t = warp_t * inv_weight_sum\n",
" warp_t_d = (-warp_t * weight_d_sum + mixed_sum_d) * inv_weight_sum\n",
"\n",
" warp_weight = dr.clip(weight_sum, 0.0, 1.0)\n",
" warp_weight_d = dr.select((weight_sum > 0.0) & (weight_sum < 1.0), weight_d_sum, 0.0)\n",
"\n",
" invalid = (weight_sum < 1e-7) | ~intersects_bbox\n",
" warp_t[invalid] = dr.inf\n",
" warp_t_d[invalid] = 0.0\n",
" warp_weight[invalid] = 0.0\n",
" warp_weight_d[invalid] = 0.0\n",
"\n",
" dr.set_flag(dr.JitFlag.LoopRecord, loop_record_state)\n",
"\n",
" return its_t, warp_t, warp_t_d, warp_weight, warp_weight_d\n",
"\n",
"def compute_surface_interaction(sdf, ray, t):\n",
" si = dr.zeros(mi.SurfaceInteraction3f)\n",
" p = ray(t)\n",
"\n",
" sdf_value, sdf_grad = sdf.eval_and_grad(p)\n",
" t_diff = sdf_value / dr.detach(dr.dot(sdf_grad, -ray.d))\n",
" t = dr.replace_grad(mi.Float(t), t_diff)\n",
"\n",
" si.t = t\n",
" si.p = ray(t)\n",
" si.sh_frame.n = dr.normalize(sdf.eval_grad(si.p))\n",
" si.initialize_sh_frame()\n",
" si.n = si.sh_frame.n\n",
" si.wi = dr.select(si.is_valid(), si.to_local(-ray.d), -ray.d)\n",
" si.wavelengths = ray.wavelengths\n",
" si.dp_du = si.sh_frame.s\n",
" si.dp_dv = si.sh_frame.t\n",
" return si\n",
"\n",
"def outer_product(v, w):\n",
" return mi.Matrix3f(v.x * w.x, v.x * w.y, v.x * w.z,\n",
" v.y * w.x, v.y * w.y, v.y * w.z,\n",
" v.z * w.x, v.z * w.y, v.z * w.z)\n",
"\n",
"def normalize_sqr(x):\n",
" x2 = dr.squared_norm(x)\n",
" jac = mi.Matrix3f(1.0) / x2 - (2 / dr.square(x2)) * outer_product(x, x)\n",
" return x / x2, jac\n",
"\n",
"def warp_field_weight(sdf, x, d, sdf_value, sdf_grad, edge_eps):\n",
" bbox_dist, bbox_dist_d = bbox_distance_inside_d(x, sdf.bbox())\n",
" use_edge_eps = edge_eps <= bbox_dist\n",
" edge_eps_d = dr.select(use_edge_eps, mi.Vector3f(0.0), bbox_dist_d)\n",
" inv_edge_eps = 1 / dr.minimum(edge_eps, bbox_dist)\n",
" surf_dist = dr.abs(sdf_value)\n",
" fac = 1 - surf_dist * inv_edge_eps\n",
" w = dr.maximum(fac, 0.0)\n",
" w_d = -dr.sign(sdf_value) * sdf_grad * inv_edge_eps + surf_dist * dr.square(inv_edge_eps) * edge_eps_d\n",
" w_d = dr.select(fac >= 0.0, w_d, 0.0)\n",
" edge_eps_d = dr.select(use_edge_eps & (fac >= 0), surf_dist * dr.square(inv_edge_eps), 0.0)\n",
" return w, w_d, edge_eps_d\n",
"\n",
"class WarpField2D(mi.Object):\n",
" def __init__(self, sdf, edge_eps=0.05):\n",
" super().__init__()\n",
" self.sdf = sdf\n",
" self.max_reparam_depth = -1\n",
" self.edge_eps = dr.opaque(mi.Float, edge_eps)\n",
" self.clamping_thresh = 0.05\n",
"\n",
" def traverse(self, cb):\n",
" self.sdf.traverse(cb)\n",
"\n",
" def parameters_changed(self, keys):\n",
" self.sdf.parameters_changed(keys)\n",
"\n",
" def eval(self, x, ray_d, t, dt_dx, active, warp_weight=None, warp_weight_d=None):\n",
" active = active & dr.isfinite(t)\n",
"\n",
" sdf_value, _, sdf_normal, sdf_normal_d, h_mat = self.sdf.eval_all(x)\n",
"\n",
" sdf_normal_d_n, norm_jac = normalize_sqr(sdf_normal_d)\n",
" warp = -sdf_normal_d_n * sdf_value\n",
" jac = -norm_jac @ dr.detach(h_mat, True) * sdf_value - outer_product(sdf_normal_d_n, sdf_normal)\n",
"\n",
" weight, weight_grad, edge_eps_grad = warp_field_weight(self.sdf, dr.detach(x, True), dr.detach(ray_d, True),\n",
" dr.detach(sdf_value), dr.detach(sdf_normal),\n",
" self.edge_eps * dr.detach(t))\n",
" weight_grad += edge_eps_grad * ray_d * self.edge_eps\n",
"\n",
" weight_grad = weight_grad * warp_weight + weight * warp_weight_d\n",
" weight *= warp_weight\n",
"\n",
" weight = dr.detach(weight, True)\n",
" jac = outer_product(warp, weight_grad) + weight * jac\n",
" warp = warp * weight\n",
"\n",
" warp = dr.replace_grad(mi.Vector3f(0.0), warp)\n",
" warp = ray_d * dr.maximum(self.clamping_thresh, t) + warp\n",
" warp = dr.normalize(warp)\n",
"\n",
" proj_jac = (mi.Matrix3f(1.0) - outer_product(ray_d, ray_d)) @ jac\n",
" jac = proj_jac + proj_jac @ outer_product(ray_d, dt_dx / t)\n",
" div = jac[0, 0] + jac[1, 1] + jac[2, 2]\n",
"\n",
" active &= weight > 0\n",
" div = dr.select(active, div, 0.0)\n",
" warp = dr.replace_grad(ray_d, dr.select(active, warp, ray_d))\n",
" return warp, div\n",
"\n",
" def ray_test(self, ray, reparam=True, active=True):\n",
" with dr.suspend_grad():\n",
" its_t, warp_t, warp_t_d, warp_weight, warp_weight_d = sphere_trace(self.sdf, dr.detach(ray), active=active)\n",
"\n",
" div = mi.Float(1.0)\n",
" if reparam:\n",
" warp, div = self.eval(ray(warp_t), ray.d, t=warp_t, dt_dx=warp_t_d,\n",
" active=active, warp_weight=warp_weight, warp_weight_d=warp_weight_d)\n",
" ray.d = dr.replace_grad(ray.d, warp)\n",
" div = dr.replace_grad(mi.Float(1.0), div)\n",
"\n",
" return dr.isfinite(its_t), div\n",
"\n",
" def ray_intersect(self, ray, reparam=True, active=True):\n",
" with dr.suspend_grad():\n",
" its_t, warp_t, warp_t_d, warp_weight, warp_weight_d = sphere_trace(self.sdf, dr.detach(ray), active=active)\n",
"\n",
" div = mi.Float(1.0)\n",
" if reparam:\n",
" warp, div = self.eval(ray(warp_t), ray.d, t=warp_t, dt_dx=warp_t_d,\n",
" active=active, warp_weight=warp_weight, warp_weight_d=warp_weight_d)\n",
" ray.d = dr.replace_grad(ray.d, warp)\n",
" div = dr.replace_grad(mi.Float(1.0), div)\n",
"\n",
" si = compute_surface_interaction(self.sdf, ray, its_t)\n",
" return si, div"
],
"metadata": {
"id": "jMIxN0p6v8JR"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Integrator"
],
"metadata": {
"id": "h15TWWAlv-26"
}
},
{
"cell_type": "code",
"source": [
"class ReparamIntegrator(mi.SamplingIntegrator):\n",
" def __init__(self, props=mi.Properties()):\n",
" super().__init__(props)\n",
" self.antithetic_sampling = props.get('antithetic_sampling', False)\n",
" self.warp = props.get(\"warp\")\n",
" self.bsdf = props.get(\"bsdf\")\n",
"\n",
" def prepare(self, sensor, film_size, seed, spp):\n",
" sampler = sensor.sampler().clone()\n",
" if spp != 0:\n",
" sampler.set_sample_count(spp)\n",
" spp = sampler.sample_count()\n",
" sampler.set_samples_per_wavefront(spp)\n",
"\n",
" wavefront_size = dr.prod(film_size) * spp\n",
" wavefront_size_limit = 0x40000000 if mi.Float == dr.cuda.ad.Float else 0xffffffff\n",
" if wavefront_size > wavefront_size_limit:\n",
" raise Exception(f\"Wavefront {wavefront_size} exceeds {wavefront_size_limit}\")\n",
" sampler.seed(seed, wavefront_size)\n",
" return sampler, spp\n",
"\n",
" def render(self, scene, sensor=0, seed=0,\n",
" spp=0, develop=True, evaluate=True, mode=dr.ADMode.Primal):\n",
" if isinstance(sensor, int):\n",
" sensor = scene.sensors()[sensor]\n",
"\n",
" film = sensor.film()\n",
" film_size = film.crop_size()\n",
" border_size = film.rfilter().border_size()\n",
" if film.sample_border():\n",
" film_size += 2 * border_size\n",
" film.prepare([])\n",
"\n",
" sampler, spp = self.prepare(sensor, film_size, seed, spp)\n",
"\n",
" spp = sampler.sample_count()\n",
" idx = dr.arange(mi.UInt32, dr.prod(film_size) * spp)\n",
"\n",
" log_spp = dr.log2i(spp)\n",
" if 1 << log_spp == spp:\n",
" idx >>= dr.opaque(mi.UInt32, log_spp)\n",
" else:\n",
" idx //= dr.opaque(mi.UInt32, spp)\n",
"\n",
" pos = mi.Vector2i()\n",
" pos.y = idx // film_size[0]\n",
" pos.x = dr.fma(type(pos.y)(-film_size[0]), pos.y, idx)\n",
" if film.sample_border():\n",
" pos -= border_size\n",
" pos += mi.Vector2i(film.crop_offset())\n",
"\n",
" block = film.create_block()\n",
"\n",
" diff_scale_factor = dr.rsqrt(mi.ScalarFloat(spp))\n",
" active = mi.Bool(True)\n",
" r = sampler.next_2d(active)\n",
" if self.antithetic_sampling:\n",
" sampler2 = sampler.clone()\n",
" self.eval_sample(mode, scene, sensor, sampler, block, pos + r, diff_scale_factor, active)\n",
" if self.antithetic_sampling:\n",
" self.eval_sample(mode, scene, sensor, sampler2, block, pos - r + 1.0, diff_scale_factor, active)\n",
"\n",
" film.put_block(block)\n",
" return film.develop()\n",
"\n",
" def eval_sample(self, mode, scene, sensor, sampler, block, position_sample, diff_scale_factor, active):\n",
" aperture_sample = mi.Point2f(0.5)\n",
" if sensor.needs_aperture_sample():\n",
" aperture_sample = sampler.next_2d(active)\n",
" time = sensor.shutter_open()\n",
" if sensor.shutter_open_time() > 0:\n",
" time += sampler.next_1d(active) * sensor.shutter_open_time()\n",
"\n",
" wavelength_sample = sampler.next_1d(active)\n",
" adjusted_position = (position_sample - sensor.film().crop_offset()) / mi.Vector2f(sensor.film().crop_size())\n",
" ray, ray_weight = sensor.sample_ray_differential(time, wavelength_sample, adjusted_position, aperture_sample)\n",
" ray.scale_differential(diff_scale_factor)\n",
"\n",
" rgb, valid_ray, det = self.sample(mode, scene, sampler, ray, mi.Mask(active))\n",
"\n",
" it = dr.zeros(mi.Interaction3f)\n",
" it.p = ray.o + ray.d\n",
" ds, ray_weight = sensor.sample_direction(it, aperture_sample)\n",
" ray_weight = dr.select(ray_weight > 0.0, ray_weight / dr.detach(ray_weight), 1.0)\n",
" ray_weight = dr.replace_grad(type(ray_weight)(1.0), ray_weight)\n",
"\n",
" rgb = ray_weight * rgb\n",
" aovs = [rgb.x, rgb.y, rgb.z]\n",
" if block.channel_count() == 5:\n",
" aovs.append(dr.select(valid_ray, mi.Float(1.0), mi.Float(0.0)))\n",
" aovs.append(dr.replace_grad(mi.Float(1.0), det * ray_weight[0]))\n",
"\n",
" block.put(ds.uv, aovs, active)\n",
"\n",
" def render_backward(self, scene, params, grad_in, sensor=0, seed=0, spp=0):\n",
" image = self.render(scene=scene, sensor=sensor, seed=seed,\n",
" spp=spp, develop=True, evaluate=False, mode=dr.ADMode.Backward)\n",
" dr.backward_from(image * grad_in)\n",
"\n",
" def render_forward(self, scene, params, sensor=0, seed=0, spp=0):\n",
" image = self.render(scene=scene, sensor=sensor, seed=seed, spp=spp,\n",
" develop=True, evaluate=False, mode=dr.ADMode.Forward)\n",
" dr.forward_to(image)\n",
" return dr.grad(image)\n",
"\n",
" def traverse(self, cb):\n",
" self.warp.traverse(cb)\n",
" super().traverse(cb)\n",
"\n",
" def parameters_changed(self, keys):\n",
" self.warp.parameters_changed(keys)\n",
" super().parameters_changed(keys)\n",
"\n",
" def sample(self, mode, scene, sampler, ray, active):\n",
" reparametrize = mode != dr.ADMode.Primal\n",
"\n",
" si, det = self.warp.ray_intersect(ray, reparam=reparametrize, active=active)\n",
" valid_ray = (not self.hide_emitters) and scene.environment() is not None\n",
" valid_ray |= si.is_valid()\n",
"\n",
" throughput = mi.Spectrum(1.0) * det\n",
" result = throughput * dr.select(active, si.emitter(scene, active).eval(si, active), 0.0)\n",
"\n",
" active_e = active & si.is_valid() & mi.has_flag(self.bsdf.flags(), mi.BSDFFlags.Smooth)\n",
" with dr.suspend_grad():\n",
" ds, _ = scene.sample_emitter_direction(si, sampler.next_2d(active_e), False, active_e)\n",
"\n",
" active_e &= ds.pdf != 0.0\n",
"\n",
" shadow_ray = si.spawn_ray_to(ds.p)\n",
" shadow_ray.d = dr.detach(shadow_ray.d)\n",
" occluded, det_e = self.warp.ray_test(shadow_ray, reparam=reparametrize, active=active_e)\n",
"\n",
" si_e = dr.zeros(mi.SurfaceInteraction3f)\n",
" si_e.sh_frame.n = ds.n\n",
" si_e.initialize_sh_frame()\n",
" si_e.n = si_e.sh_frame.n\n",
" si_e.wi = -shadow_ray.d\n",
" si_e.wavelengths = ray.wavelengths\n",
"\n",
" emitter_val = dr.select(active_e, ds.emitter.eval(si_e, active_e) / ds.pdf, 0.)\n",
" bsdf_val = self.bsdf.eval(mi.BSDFContext(), si, si.to_local(shadow_ray.d), active_e)\n",
" nee_contrib = dr.select(~occluded, bsdf_val * emitter_val * det_e, 0.)\n",
"\n",
" result[active_e] += throughput * nee_contrib\n",
"\n",
" return dr.select(valid_ray, mi.Spectrum(result), 0.0), valid_ray, det\n",
"\n",
" def to_string(self):\n",
" return \"integrator\"\n",
"\n",
"mi.register_integrator(\"sdf_reparam_direct\", lambda props: ReparamIntegrator(props))"
],
"metadata": {
"id": "cMPqQrk5v_kU"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Trainer"
],
"metadata": {
"id": "GhspSyAlv__3"
}
},
{
"cell_type": "code",
"source": [
"import trimesh\n",
"import openvdb as vdb\n",
"from fastsweep import redistance\n",
"from bisect import bisect_left\n",
"\n",
"def array_to_vdb_grid(data, size=256, grid_class=vdb.GridClass.LEVEL_SET, name=\"distance\"):\n",
" grid = vdb.FloatGrid()\n",
" grid.copyFromArray(data)\n",
" grid.gridClass = grid_class\n",
" grid.name = name\n",
" grid.transform = vdb.createLinearTransform(voxelSize=1/size)\n",
" return grid\n",
"\n",
"def vdb_grid_to_array(grid):\n",
" mn, mx = grid.evalLeafBoundingBox()\n",
" shape = (mx[0] - mn[0] + 1, mx[1] - mn[2] + 1, mx[2] - mn[2] + 1)\n",
" arr = np.empty(shape, dtype=\"f4\")\n",
" grid.copyToArray(arr)\n",
" return arr\n",
"\n",
"def read_ply_vf(path, scale=.3):\n",
" with open(path, \"rb\") as f:\n",
" mesh = trimesh.exchange.ply.load_ply(f)\n",
" verts = mesh['vertices'].astype(\"f4\")\n",
" verts = verts - verts.mean(0)\n",
" verts = verts / np.linalg.norm(verts,axis=-1).max() * scale\n",
" return verts, mesh[\"faces\"]\n",
"\n",
"def read_obj_vf(fn, scale=.3):\n",
" with open(fn, \"rb\") as f:\n",
" mesh = trimesh.exchange.obj.load_obj(f)['geometry'][fn]\n",
"\n",
" verts = mesh['vertices'].astype(\"f4\")\n",
" verts = verts - verts.mean(0)\n",
" verts = verts / np.linalg.norm(verts,axis=-1).max() * scale\n",
" return verts, mesh['faces']\n",
"\n",
"def pick(i, ts, vs):\n",
" return vs[bisect_left(ts, i)]\n",
"\n",
"def to_image(img):\n",
" return np.rint((img ** (1/2.2)).clip(0,1) * 255).astype(\"u1\")\n",
"\n",
"def make_film(size):\n",
" return mi.load_dict({'type': 'hdrfilm', 'width': size[0], 'height': size[1],\n",
" 'pixel_format': 'rgb', 'pixel_filter': {'type': 'gaussian'},\n",
" 'sample_border': True})\n",
"\n",
"def set_film_size(film, size):\n",
" params = mi.traverse(film)\n",
" params['size'] = size\n",
" params.update()\n",
" film.parameters_changed()\n",
"\n",
"def render_direct(verts, faces, envmap, sensors, seed=41):\n",
" scene = mi.load_dict({\n",
" 'type': 'scene',\n",
" 'integrator': {'type': 'direct'},\n",
" 'mesh': {\n",
" 'type': 'mesh',\n",
" \"name\": \"mesh\",\n",
" \"vertices\": mi.Float(verts.ravel()),\n",
" \"faces\": mi.UInt(faces.ravel()),\n",
" 'bsdf': {'type': 'twosided',\n",
" 'material': {'type': 'diffuse',\n",
" 'reflectance': {'type': 'rgb','value': (.8, .8, .8)}}}},\n",
" 'emitter': {'type': 'envmap','filename': envmap}})\n",
" return [mi.Bitmap(mi.render(scene, sensor=sensor, seed=i + seed, spp=1024))\n",
" for i, sensor in enumerate(sensors)]\n",
"\n",
"def make_sphere_sdf(shape, center=[0.5, 0.5, 0.5], radius=0.3):\n",
" z, y, x = np.meshgrid(np.linspace(0, 1, shape[0]),\n",
" np.linspace(0, 1, shape[1]),\n",
" np.linspace(0, 1, shape[2]), indexing='ij')\n",
" pts = np.stack([x.ravel(), y.ravel(), z.ravel()], axis=1)\n",
" dist = np.linalg.norm(pts - center, axis=-1) - radius\n",
" sdf = dist.reshape(shape).astype(np.float32)\n",
" return redistance(mi.TensorXf(sdf))\n",
"\n",
"def clip_gradient(val, r=1e-1):\n",
" grad = dr.grad(val)\n",
" dr.set_grad(val, dr.select(dr.isnan(grad), 0.0, dr.clip(grad, -r, r)))\n",
"\n",
"def upsample_sdf(sdf):\n",
" shape = 2 * mi.ScalarVector3i(sdf.texture.shape[:3])\n",
" with dr.suspend_grad():\n",
" z, y, x = dr.meshgrid(*[dr.linspace(mi.Float, -.5 + .5 / shape[i], .5 - .5 / shape[i], shape[i])\n",
" for i in range(3)],\n",
" indexing='ij')\n",
" out = sdf.eval(mi.Point3f(x, y, z))\n",
" return mi.TensorXf(out, (*shape, *sdf.texture.shape[3:]))\n",
"\n",
"def eval_box_sdf(x, p, extents, smoothing):\n",
" q = dr.abs(x - p) - extents\n",
" return dr.norm(dr.maximum(q, 0.0)) + dr.minimum(dr.maximum(q.x, dr.maximum(q.y, q.z)), 0.0) - smoothing\n",
"\n",
"def make_box_sdf(shape):\n",
" z, y, x = dr.meshgrid(dr.linspace(mi.Float, -0.5, 0.5, shape[0]),\n",
" dr.linspace(mi.Float, -0.5, 0.5, shape[1]),\n",
" dr.linspace(mi.Float, -0.5, 0.5, shape[2]), indexing='ij')\n",
" dist = eval_box_sdf(mi.Point3f(x, y, z), mi.Point3f(0), mi.Vector3f(0.49), 0.01)\n",
" return mi.TensorXf(dist, shape)\n",
"\n",
"def discrete_laplacian(data):\n",
" shape = data.shape\n",
"\n",
" def val(x, y, z):\n",
" a = dr.clip(z, 0, shape[2] - 1) * shape[1] * shape[0]\n",
" b = dr.clip(y, 0, shape[1] - 1) * shape[0]\n",
" c = dr.clip(x, 0, shape[0] - 1)\n",
"\n",
" return dr.gather(mi.Float, data.array, a + b + c)\n",
"\n",
" z, y, x = dr.meshgrid(*[dr.arange(mi.UInt, shape[i]) for i in range(3)], indexing='ij')\n",
" c = val(x, y, z)\n",
" v = val(x - 1, y, z) + val(x + 1, y, z) + val(x, y - 1, z) + val(x, y + 1, z) + val(x, y, z - 1) + val(x, y, z + 1)\n",
" return dr.sum(dr.square(c - v / 6.))\n",
"\n",
"def box_filter3(data):\n",
" shape = data.shape\n",
"\n",
" def val(x, y, z):\n",
" a = dr.clip(z, 0, shape[2] - 1) * shape[1] * shape[0]\n",
" b = dr.clip(y, 0, shape[1] - 1) * shape[0]\n",
" c = dr.clip(x, 0, shape[0] - 1)\n",
" return dr.gather(mi.Float, data.array, a + b + c)\n",
"\n",
" z, y, x = dr.meshgrid(*[dr.arange(mi.UInt, shape[i]) for i in range(3)], indexing='ij')\n",
" v = val(x-1,y-1,z-1) + val(x-1,y-1,z) + val(x-1,y-1,z+1)\n",
" v = v + val(x-1,y,z-1) + val(x-1,y,z) + val(x-1,y,z+1)\n",
" v = v + val(x-1,y+1,z-1) + val(x-1,y+1,z) + val(x-1,y+1,z+1)\n",
" v = v + val(x,y-1,z-1) + val(x,y-1,z) + val(x,y-1,z+1)\n",
" v = v + val(x,y,z-1) + val(x,y,z) + val(x,y,z+1)\n",
" v = v + val(x,y+1,z-1) + val(x,y+1,z) + val(x,y+1,z+1)\n",
" v = v + val(x+1,y-1,z-1) + val(x+1,y-1,z) + val(x+1,y-1,z+1)\n",
" v = v + val(x+1,y,z-1) + val(x+1,y,z) + val(x+1,y,z+1)\n",
" v = v + val(x+1,y+1,z-1) + val(x+1,y+1,z) + val(x+1,y+1,z+1)\n",
" return v / 27.\n",
"\n",
"def box_filter5(data):\n",
" shape = data.shape\n",
"\n",
" def val(x, y, z):\n",
" a = dr.clip(z, 0, shape[2] - 1) * shape[1] * shape[0]\n",
" b = dr.clip(y, 0, shape[1] - 1) * shape[0]\n",
" c = dr.clip(x, 0, shape[0] - 1)\n",
" return dr.gather(mi.Float, data.array, a + b + c)\n",
"\n",
" z, y, x = dr.meshgrid(*[dr.arange(mi.UInt, shape[i]) for i in range(3)], indexing='ij')\n",
" v = val(x-2,y-2,z-2)+val(x-2,y-2,z-1)+val(x-2,y-2,z)+val(x-2,y-2,z+1)+val(x-2,y-2,z+2)\n",
" v = v + val(x-2,y-1,z-2)+val(x-2,y-1,z-1)+val(x-2,y-1,z)+val(x-2,y-1,z+1)+val(x-2,y-1,z+2)\n",
" v = v + val(x-2,y,z-2)+val(x-2,y,z-1)+val(x-2,y,z)+val(x-2,y,z+1)+val(x-2,y,z+2)\n",
" v = v + val(x-2,y+1,z-2)+val(x-2,y+1,z-1)+val(x-2,y+1,z)+val(x-2,y+1,z+1)+val(x-2,y+1,z+2)\n",
" v = v + val(x-2,y+2,z-2)+val(x-2,y+2,z-1)+val(x-2,y+2,z)+val(x-2,y+2,z+1)+val(x-2,y+2,z+2)\n",
"\n",
" v = v + val(x-1,y-2,z-2)+val(x-1,y-2,z-1)+val(x-1,y-2,z)+val(x-1,y-2,z+1)+val(x-1,y-2,z+2)\n",
" v = v + val(x-1,y-1,z-2)+val(x-1,y-1,z-1)+val(x-1,y-1,z)+val(x-1,y-1,z+1)+val(x-1,y-1,z+2)\n",
" v = v + val(x-1,y,z-2)+val(x-1,y,z-1)+val(x-1,y,z)+val(x-1,y,z+1)+val(x-1,y,z+2)\n",
" v = v + val(x-1,y+1,z-2)+val(x-1,y+1,z-1)+val(x-1,y+1,z)+val(x-1,y+1,z+1)+val(x-1,y+1,z+2)\n",
" v = v + val(x-1,y+2,z-2)+val(x-1,y+2,z-1)+val(x-1,y+2,z)+val(x-1,y+2,z+1)+val(x-1,y+2,z+2)\n",
"\n",
" v = v + val(x,y-2,z-2)+val(x,y-2,z-1)+val(x,y-2,z)+val(x,y-2,z+1)+val(x,y-2,z+2)\n",
" v = v + val(x,y-1,z-2)+val(x,y-1,z-1)+val(x,y-1,z)+val(x,y-1,z+1)+val(x,y-1,z+2)\n",
" v = v + val(x,y,z-2)+val(x,y,z-1)+val(x,y,z)+val(x,y,z+1)+val(x,y,z+2)\n",
" v = v + val(x,y+1,z-2)+val(x,y+1,z-1)+val(x,y+1,z)+val(x,y+1,z+1)+val(x,y+1,z+2)\n",
" v = v + val(x,y+2,z-2)+val(x,y+2,z-1)+val(x,y+2,z)+val(x,y+2,z+1)+val(x,y+2,z+2)\n",
"\n",
" v = v + val(x+1,y-2,z-2)+val(x+1,y-2,z-1)+val(x+1,y-2,z)+val(x+1,y-2,z+1)+val(x+1,y-2,z+2)\n",
" v = v + val(x+1,y-1,z-2)+val(x+1,y-1,z-1)+val(x+1,y-1,z)+val(x+1,y-1,z+1)+val(x+1,y-1,z+2)\n",
" v = v + val(x+1,y,z-2)+val(x+1,y,z-1)+val(x+1,y,z)+val(x+1,y,z+1)+val(x+1,y,z+2)\n",
" v = v + val(x+1,y+1,z-2)+val(x+1,y+1,z-1)+val(x+1,y+1,z)+val(x+1,y+1,z+1)+val(x+1,y+1,z+2)\n",
" v = v + val(x+1,y+2,z-2)+val(x+1,y+2,z-1)+val(x+1,y+2,z)+val(x+1,y+2,z+1)+val(x+1,y+2,z+2)\n",
"\n",
" v = v + val(x+2,y-2,z-2)+val(x+2,y-2,z-1)+val(x+2,y-2,z)+val(x+2,y-2,z+1)+val(x+2,y-2,z+2)\n",
" v = v + val(x+2,y-1,z-2)+val(x+2,y-1,z-1)+val(x+2,y-1,z)+val(x+2,y-1,z+1)+val(x+2,y-1,z+2)\n",
" v = v + val(x+2,y,z-2)+val(x+2,y,z-1)+val(x+2,y,z)+val(x+2,y,z+1)+val(x+2,y,z+2)\n",
" v = v + val(x+2,y+1,z-2)+val(x+2,y+1,z-1)+val(x+2,y+1,z)+val(x+2,y+1,z+1)+val(x+2,y+1,z+2)\n",
" v = v + val(x+2,y+2,z-2)+val(x+2,y+2,z-1)+val(x+2,y+2,z)+val(x+2,y+2,z+1)+val(x+2,y+2,z+2)\n",
" return v / 125.\n",
"\n",
"def soft_grad3(val):\n",
" dr.set_grad(val, box_filter3(dr.grad(val)))\n",
"\n",
"def soft_grad5(val):\n",
" dr.set_grad(val, box_filter5(dr.grad(val)))\n",
"\n",
"def render_sdf(data, pos=(0,1,-2)):\n",
" scene = mi.load_dict({\n",
" \"type\": \"scene\",\n",
" 'integrator': {'type': 'direct'},\n",
" 'sensor': {\n",
" 'type': 'perspective',\n",
" 'to_world': mi.ScalarTransform4f().look_at(pos,(0, 0, 0),(0, 1, 0)),\n",
" 'film': {\n",
" 'type': 'hdrfilm',\n",
" 'width': 256, 'height': 256,\n",
" 'rfilter': { 'type': 'box' },\n",
" },\n",
" \"sampler\": {\n",
" \"type\": \"independent\",\n",
" \"sample_count\": 512\n",
" }\n",
" },\n",
" \"emitter\": {\n",
" \"type\": \"envmap\",\n",
" \"filename\": \"Nicolet2021Large/scenes/suzanne/textures/kloppenheim_06_2k.hdr\"\n",
" },\n",
" 'sdf': {\n",
" \"type\" : \"sdfgrid\",\n",
" 'to_world': mi.ScalarTransform4f().translate((-.5,-.5,-.5)),\n",
" \"normals\" : \"smooth\",\n",
" \"grid\": data,\n",
" 'bsdf': {'type': 'diffuse'}\n",
" }\n",
" })\n",
" return mi.util.convert_to_bitmap(mi.render(scene, seed=0))\n",
"\n",
"@dr.syntax\n",
"def mesh2sdf(verts, faces, res=256):\n",
" scene = mi.load_dict({\n",
" 'type': 'scene',\n",
" 'integrator': {'type': 'path'},\n",
" 'sensor': {'type': 'perspective'},\n",
" 'shape': {\n",
" 'type': 'mesh',\n",
" \"name\": \"mesh\",\n",
" \"vertices\": mi.Float(verts.ravel()),\n",
" \"faces\": mi.UInt(faces.ravel())}})\n",
" z, y, x = dr.meshgrid(*[dr.linspace(mi.Float, -.5 + .5 / res, .5 - .5 / res, res) for i in range(3)], indexing='ij')\n",
" ray = mi.Ray3f(mi.Point3f(x, y, z), dr.normalize(mi.Vector3f(0, 1, 0)))\n",
" si = scene.ray_intersect(ray)\n",
"\n",
" values = .5 - dr.select(si.is_valid() & (dr.dot(si.n, ray.d) > 0), 1.0, 0.0)\n",
" grid = redistance(mi.TensorXf(values, (res, res, res)))\n",
"\n",
" # Gather voxels close to surface\n",
" indices = dr.arange(mi.UInt, res ** 3)\n",
" near_surface_indices = mi.UInt(np.array(indices)[dr.abs(grid.array) < 1.0 / res])\n",
"\n",
" # For each index, get the world space ray origin\n",
" ray_o = dr.gather(mi.Point3f, ray.o, near_surface_indices)\n",
" angular_res = 16\n",
" n_angle_samples = angular_res ** 2\n",
" r = dr.arange(mi.Float, angular_res)\n",
" u, v = dr.meshgrid((r + 0.5) / angular_res, (r + 0.5) / angular_res)\n",
" uv = dr.tile(mi.Vector2f(u, v), dr.width(ray_o))\n",
" ray = mi.Ray3f(dr.repeat(ray_o, n_angle_samples), mi.warp.square_to_uniform_sphere(uv))\n",
"\n",
" # Trace these rays and find the minimum distance and modulate by sign\n",
" si = scene.ray_intersect(ray)\n",
" min_dist = dr.full(mi.Float, 100.0, dr.width(near_surface_indices))\n",
" j = dr.arange(mi.UInt32, dr.width(near_surface_indices))\n",
" i = mi.UInt32(0)\n",
" while i < n_angle_samples:\n",
" min_dist = dr.minimum(min_dist, dr.gather(mi.Float, si.t, j * n_angle_samples + i))\n",
" i += 1\n",
" min_dist = min_dist * dr.sign(dr.gather(type(grid.array), grid.array, near_surface_indices))\n",
" dr.scatter(grid.array, min_dist, near_surface_indices)\n",
" return redistance(grid)"
],
"metadata": {
"id": "-E3u2KzqwCpN"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from argparse import Namespace\n",
"from collections import defaultdict\n",
"\n",
"def make_refs(imgs):\n",
" rfilter = mi.scalar_rgb.load_dict({'type': 'gaussian'})\n",
" size = imgs[0].size().x\n",
" refs = {size: [mi.TensorXf(x) for x in imgs]}\n",
" while size > 4:\n",
" size //= 2\n",
" refs[size] = [mi.TensorXf(x.resample((size, size), rfilter)) for x in imgs]\n",
" return refs\n",
"\n",
"def train(args, data, film, sensors, ref_imgs, fine_iter=420,\n",
" lr=1e-2, lr_decay=1e-2,\n",
" reg=([128, 256], [1e-3, 1e-4, 1e-5])):\n",
" set_film_size(film, args.resolution // 2 ** len(args.render_upsample_iter))\n",
" for s in sensors: s.parameters_changed()\n",
"\n",
" scene = mi.load_dict({\n",
" 'type': 'scene',\n",
" 'integrator': {\n",
" 'type': 'sdf_reparam_direct',\n",
" 'warp': WarpField2D(Grid3d((args.size, args.size, args.size)), edge_eps=.01),\n",
" 'bsdf': {'type': 'twosided',\n",
" 'material': {'type': 'diffuse',\n",
" 'reflectance': {'type': 'rgb','value': (.8, .8, .8)}}}},\n",
" 'emitter': {'type': 'envmap','filename': args.envmap}})\n",
"\n",
" params = mi.traverse(scene)\n",
" sdf_key = 'integrator.data'\n",
" params.keep([sdf_key])\n",
"\n",
" opt = mi.ad.Adam(lr=lr, mask_updates=True)\n",
"\n",
" opt[sdf_key] = data\n",
" params.update(opt)\n",
"\n",
" bbox_sdf = make_box_sdf(opt[sdf_key].shape)\n",
" refs = make_refs(ref_imgs)\n",
"\n",
" seed = 0\n",
" metrics = defaultdict(list)\n",
" n_sensors = len(sensors)\n",
" for i in range(args.n_iter):\n",
" loss = mi.Float(0)\n",
" targets = refs[film.crop_size().x]\n",
" for j in range(n_sensors):\n",
" img = mi.render(scene, params=params, sensor=sensors[j],\n",
" seed=seed, spp=256,\n",
" seed_grad=seed + 1 + n_sensors, spp_grad=64)\n",
" seed += 1 + n_sensors\n",
"\n",
" view_loss = dr.mean(dr.abs(img - targets[j])) / n_sensors\n",
" dr.backward(view_loss)\n",
" loss += dr.detach(view_loss)\n",
"\n",
" reg_loss = discrete_laplacian(opt[sdf_key]) * pick(i, reg[0], reg[1])\n",
" dr.backward(reg_loss)\n",
" loss += dr.detach(reg_loss)\n",
"\n",
" clip_gradient(opt[sdf_key])\n",
" if i < fine_iter:\n",
" soft_grad5(opt[sdf_key])\n",
" soft_grad3(opt[sdf_key])\n",
" opt.step()\n",
"\n",
" opt.set_learning_rate({sdf_key: lr / (1. + lr_decay * i)})\n",
" opt[sdf_key] = mi.TensorXf(redistance(dr.maximum(opt[sdf_key], bbox_sdf)))[...,None]\n",
" dr.enable_grad(opt[sdf_key])\n",
"\n",
" if i in args.render_upsample_iter:\n",
" set_film_size(film, film.crop_size().x * 2)\n",
" for s in sensors: s.parameters_changed()\n",
"\n",
" params.update(opt)\n",
"\n",
" metric = {\"loss\": loss.numpy().item()}\n",
" for k, v in metric.items(): metrics[k].append(v)\n",
"\n",
" return scene, metrics, opt[sdf_key]"
],
"metadata": {
"id": "UlLDiMzDwZCU"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Train"
],
"metadata": {
"id": "1hOWaQYNa0gf"
}
},
{
"cell_type": "code",
"source": [
"def train_smplx(source, lr=3e-3, n_iter=1):\n",
" args = Namespace(\n",
" size=256,resolution=512,\n",
"\n",
" n_iter=n_iter,\n",
" render_upsample_iter=[64, 92, 128],\n",
" fine_iter=64,\n",
" lr=lr,\n",
"\n",
" envmap=\"Nicolet2021Large/scenes/suzanne/textures/kloppenheim_06_2k.hdr\")\n",
"\n",
" film = make_film((args.resolution, args.resolution))\n",
" pts = regular_points_sphere()\n",
" sensors = [mi.load_dict({'type': 'perspective','fov': 39.0,\n",
" 'to_world': mi.ScalarTransform4f().look_at(mi.ScalarPoint3f(p[0], p[1], p[2]) * 1.1,\n",
" mi.ScalarPoint3f(0, 0, 0),\n",
" mi.ScalarPoint3f(u[0],u[1],u[2])),\n",
" 'sampler': {'type': 'independent'},\n",
" 'film': film})\n",
" for p, u in zip(pts, quat_mul_v(quat_lookat(-pts),np.array([[0,1,0]])))]\n",
"\n",
" verts, faces = read_ply_vf(source)\n",
" ref_imgs = render_direct(verts, faces, args.envmap, sensors)\n",
"\n",
" verts, faces = read_ply_vf(\"00000.ply\")\n",
" sdf = mi.TensorXf(mesh2sdf(verts, faces)[...,None])\n",
" scene, metrics, out = train(args, sdf, film, sensors, ref_imgs, fine_iter=args.fine_iter, lr=args.lr)\n",
" return out"
],
"metadata": {
"id": "wAFKMHe_Hvqg"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from glob import glob\n",
"\n",
"os.makedirs(f\"{name}/vdbs\")\n",
"\n",
"for fn in progress_bar(glob(f\"{name}/plys/*.ply\")):\n",
" out = train_smplx(fn, n_iter=20)\n",
" fn_out = os.path.splitext(fn.split(\"/\")[-1])[0]\n",
" vdb.write(f\"{name}/vdbs/{fn_out}.vdb\", array_to_vdb_grid(data=out.numpy().squeeze()))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 57
},
"id": "GVUQW_eOKR0O",
"outputId": "d01ee23a-ed51-4653-8b7c-95a01f092149"
},
"execution_count": 22,
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <progress value='136' class='' max='221' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
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" <progress value='221' class='' max='221' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [221/221 2:10:33&lt;00:00]\n",
" </div>\n",
" "
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from random import choice\n",
"\n",
"fn = choice(glob(f\"{name}/vdbs/*.vdb\"))\n",
"\n",
"render_sdf(\n",
" mi.TensorXf(vdb_grid_to_array(vdb.read(fn,\"distance\"))[...,None]),\n",
" normalize(np.array((-1.,-1,0.))))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 271
},
"id": "VuV369O7NOYM",
"outputId": "1a086653-acb9-441f-8b31-55c8d5138bf1"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Bitmap[\n",
" pixel_format = rgb,\n",
" component_format = uint8,\n",
" size = [256, 256],\n",
" srgb_gamma = 1,\n",
" struct = Struct<3>[\n",
" uint8 R; // @0, normalized, gamma, premultiplied alpha\n",
" uint8 G; // @1, normalized, gamma, premultiplied alpha\n",
" uint8 B; // @2, normalized, gamma, premultiplied alpha\n",
" ],\n",
" data = [ 192 KiB of image data ]\n",
"]"
],
"text/html": [
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\"width=\"250vm\" />"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"!cp {name}/vdbs/*.vdb /content/gdrive/MyDrive/human/{name}/"
],
"metadata": {
"id": "A63ZiwxzNpac"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Render"
],
"metadata": {
"id": "JA5Uyawaa1nk"
}
},
{
"cell_type": "code",
"source": [
"def add_node_group(mat):\n",
" group = bl.node_group.add(D, \"volume_mesh\")\n",
"\n",
" v2m = bl.node_tree.add_node(group, \"GeometryNodeVolumeToMesh\", {\"Threshold\": 0})\n",
" smooth = bl.node_tree.add_node(group, \"GeometryNodeSetShadeSmooth\")\n",
" set_mat = bl.node_tree.add_node(group, \"GeometryNodeSetMaterial\", {\"Material\": mat})\n",
"\n",
" bl.node_tree.connect(group, [\n",
" (group.nodes[\"Group Input\"].outputs[\"Geometry\"], v2m.inputs[\"Volume\"]),\n",
" (v2m.outputs[\"Mesh\"], smooth.inputs[\"Geometry\"]),\n",
" (smooth.outputs[\"Geometry\"], set_mat.inputs[\"Geometry\"]),\n",
" (set_mat.outputs[\"Geometry\"], group.nodes[\"Group Output\"].inputs[\"Geometry\"])\n",
" ])\n",
"\n",
" return group\n",
"\n",
"def add_vdb_object(D, mesh, vol):\n",
" m = norm_mat(bl.mesh.get_vertices(D.meshes[mesh]))\n",
" s = 1. / m[0,0]\n",
"\n",
" t, s = mu.Vector((-m[:3,3] * s)), mu.Vector((s,s,s))\n",
" return bl.object.add(D, vol, f\"{vol.name}.vol\",\n",
" location=t - s / .6,\n",
" scale=(s * 2 / .6) * mu.Vector((1,-1,1)),\n",
" rotation_euler=(0,-np.pi/2,np.pi))\n",
"\n",
"def add_material_emission(name, strength=1, color=(1,1,1)):\n",
" mat = bl.material.add(D, name)\n",
" tree = mat.node_tree\n",
" tree.nodes.clear()\n",
"\n",
" emission = bl.node_tree.add_node(tree, \"ShaderNodeEmission\", {\n",
" \"Color\": (*color,1),\n",
" \"Strength\": strength\n",
" })\n",
" out = bl.node_tree.add_node(tree, \"ShaderNodeOutputMaterial\")\n",
" bl.node_tree.connect(tree, [\n",
" (emission.outputs[\"Emission\"], out.inputs[\"Surface\"]),\n",
" ])\n",
" return mat\n",
"\n",
"def add_material_diffuse(name, roughness=1, color=(.8,.8,.8)):\n",
" mat = bl.material.add(D, name)\n",
" tree = mat.node_tree\n",
" tree.nodes.clear()\n",
"\n",
" diffuse = bl.node_tree.add_node(tree, \"ShaderNodeBsdfDiffuse\", {\n",
" \"Color\": (*color,1),\n",
" \"Roughness\": roughness\n",
" })\n",
" out = bl.node_tree.add_node(tree, \"ShaderNodeOutputMaterial\")\n",
" bl.node_tree.connect(tree, [\n",
" (diffuse.outputs[\"BSDF\"], out.inputs[\"Surface\"]),\n",
" ])\n",
" return mat\n",
"\n",
"def add_node_group_instance(obj, mat, scale=.2):\n",
" g = bl.node_group.add(D, \"Instances\", {\n",
" \"ID\": {\n",
" \"in_out\": \"OUTPUT\",\n",
" \"socket_type\": \"NodeSocketInt\"\n",
" }\n",
" })\n",
"\n",
" oi = bl.node_tree.add_node(g, \"GeometryNodeObjectInfo\", {\n",
" \"Object\": obj,\n",
" }, {\"label\": \"Object\"})\n",
" iop = bl.node_tree.add_node(g, \"GeometryNodeInstanceOnPoints\", {\n",
" \"Scale\": (scale,scale,scale)\n",
" })\n",
" ca = bl.node_tree.add_node(g, \"GeometryNodeCaptureAttribute\", attrs={\n",
" \"domain\": \"POINT\",\n",
" })\n",
" ca.capture_items.new(\"INT\", \"ID\")\n",
" gnid = bl.node_tree.add_node(g, \"GeometryNodeInputID\")\n",
" ri = bl.node_tree.add_node(g, \"GeometryNodeRealizeInstances\")\n",
" sm = bl.node_tree.add_node(g, \"GeometryNodeSetMaterial\", {\"Material\": mat})\n",
"\n",
" bl.node_tree.connect(g, [\n",
" (oi.outputs[\"Geometry\"], iop.inputs[\"Instance\"]),\n",
" (g.nodes[\"Group Input\"].outputs[\"Geometry\"], ca.inputs[\"Geometry\"]),\n",
" (gnid.outputs[\"ID\"], ca.inputs[\"ID\"]),\n",
" (ca.outputs[\"Geometry\"], iop.inputs[\"Points\"]),\n",
" (iop.outputs[\"Instances\"], ri.inputs[\"Geometry\"]),\n",
" (ri.outputs[\"Geometry\"], sm.inputs[\"Geometry\"]),\n",
" (sm.outputs[\"Geometry\"], g.nodes[\"Group Output\"].inputs[\"Geometry\"]),\n",
" (ca.outputs[\"ID\"], g.nodes[\"Group Output\"].inputs[\"ID\"]),\n",
" ])\n",
"\n",
" return g\n",
"\n",
"def render_vol_scene(name, orient, lines, points, size=(1280,480)):\n",
" bpy.ops.wm.read_homefile(use_empty=True)\n",
" bl.scene.setup(C.scene, size=size, samples=1024, max_bounces=64, diffuse_bounces=24)\n",
" bl.cycles.setup(C.preferences)\n",
"\n",
" C.scene.world = bl.world.add(D, \"World\")\n",
" bl.world.use_environment(C.scene.world, bl.image.open(D, \"brown_photostudio_02_4k.exr\"),\n",
" rotation=(0,np.pi/4,np.pi/6), strength=.2)\n",
"\n",
" coll = D.collections.new(\"meshes\")\n",
" for f in glob(f\"{name}/plys/*.ply\"):\n",
" coll.objects.link(bl.object.add(D, load_ply(D, f)))\n",
"\n",
" min_z = min(bl.mesh.get_aabb(obj.data)[0,2] for obj in coll.objects) * .6\n",
" max_z = max(bl.mesh.get_aabb(obj.data)[1,2] for obj in coll.objects)\n",
"\n",
" camera = C.scene.camera = D.objects.new('Camera', bl.camera.add_ortho(D))\n",
" camera.data.ortho_scale = -.0028 * len(coll.objects) + 1.2\n",
" C.scene.collection.objects.link(camera)\n",
" camera.location = mu.Vector((3,0,(max_z - min_z) * 2))\n",
" bl.object.look_at(camera, mu.Vector((0,0,(max_z - min_z) * .5)))\n",
"\n",
" ng = add_node_group(add_material_mesh(\"mesh\"))\n",
" coll = D.collections.new(\"volumes\")\n",
" for f in glob(f\"{name}/vdbs/*.vdb\"):\n",
" name = os.path.splitext(os.path.basename(f))[0]\n",
" obj = add_vdb_object(D, name, bl.volume.add(D, f))\n",
" coll.objects.link(obj)\n",
" bl.object.add_node_group_modifier(obj, \"Volume Mesh\", ng)\n",
"\n",
" instance = bl.object.add_collection_instance(D, coll)\n",
" C.scene.collection.objects.link(instance)\n",
" instance.rotation_euler.z = -orient\n",
" instance.location.z = -min_z\n",
"\n",
" cu = bl.curve.add(D, lines, \"joints\", bevel_depth=5e-4, bevel_resolution=0)\n",
" cu.materials.append(add_material_diffuse(\"Diffuse\", color=mpl.colors.to_rgb(\"tab:orange\")[:3]))\n",
" obj = bl.object.add(D, cu)\n",
" C.scene.collection.objects.link(obj)\n",
" obj.rotation_euler.z = -orient\n",
" obj.location.z = -min_z\n",
"\n",
" bpy.ops.mesh.primitive_cube_add()\n",
" inst = D.objects[\"Cube\"]\n",
" inst.name = \"Object\"\n",
" inst.hide_viewport = True\n",
" inst.hide_render = True\n",
"\n",
" mesh = bl.mesh.add(D, \"Points\", points)\n",
" obj = bl.object.add(D, mesh)\n",
" obj.rotation_euler.z = -orient\n",
" obj.location.z = -min_z\n",
" g = add_node_group_instance(inst, add_material_diffuse(\"Diffuse\", color=COLORS['blue']), scale=.002)\n",
" mod = bl.object.add_node_group_modifier(obj, \"instance\", g)\n",
" mod[\"Socket_2_attribute_name\"] = \"InstanceId\"\n",
" C.scene.collection.objects.link(obj)\n",
"\n",
" bpy.ops.mesh.primitive_plane_add(size=100)\n",
" D.objects[\"Plane\"].hide_viewport = True\n",
" D.objects[\"Plane\"].is_shadow_catcher = True\n",
"\n",
" coll_lights = D.collections.new(\"Lights\")\n",
" coll_lights.hide_viewport = True\n",
" coll_lights.hide_render = True\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Back\", \"AREA\", \"DISK\", 40, 5), \"Light.Back\"))\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Fill\", \"AREA\", \"DISK\", 50, 3), \"Light.Fill\"))\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Key\", \"AREA\", \"DISK\", 20, 1), \"Light.Key\"))\n",
" coll_lights.objects.link(bl.object.add(D, bl.light.add(D, \"Top\", \"AREA\", \"DISK\", 10, 3), \"Light.Top\"))\n",
" C.scene.collection.children.link(coll_lights)\n",
"\n",
" mesh = D.meshes.new(\"Lights\")\n",
" mesh.from_pydata(np.array([[-3,3,5],[3,-3,3],[-3,-3,1],[0,0,5]],dtype=\"f4\"),[],[])\n",
" lights = D.objects.new('Lights', mesh)\n",
" C.scene.collection.objects.link(lights)\n",
"\n",
" bl.object.add_node_group_modifier(lights, \"Lights\", add_node_group_lights(coll_lights))\n",
"\n",
" return render_image()"
],
"metadata": {
"id": "muW9f0hCN6fg"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"lines = np.concatenate([body_lines(jnts2, model.parents), finger_lines(jnts2)], 1).reshape(-1,2,3)\n",
"points = jnts2[:,1:22].reshape(-1,3)\n",
"imshow(cv2.resize(render_vol_scene(name, orient, lines, points, size=(1920,720)),\n",
" None, fx=.5, fy=.5, interpolation=cv2.INTER_LANCZOS4))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 197
},
"id": "wZ6hWi1XcQwS",
"outputId": "0eddea70-5412-45cf-cd0a-f625ac7df284"
},
"execution_count": 31,
"outputs": [
{
"output_type": "display_data",
"data": {
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