smpl.ipynb

Dockerfile

FROM quay.io/pypa/manylinux2014_x86_64

RUN curl https://bootstrap.pypa.io/pip/2.7/get-pip.py | python && \
    pip install opencv-python-headless==3.4.8.29 chumpy

# docker build --platform linux/x86_64 -t smpl .

export.py

import os
import argparse
import pickle
import numpy as np


def csc_cols(m):
    col_start = m.indptr[:-1]
    col_end = m.indptr[1:]

    return np.concatenate(
        [
            np.full(end - start, i, dtype=int)
            for i, (start, end) in enumerate(zip(col_start, col_end))
        ]
    )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("file", type=str)
    args = parser.parse_args()

    with open(args.file, "rb") as f:
        data = pickle.load(f)

    output = {}
    for key, data in data.iteritems():
        if "chumpy" in str(type(data)):
            output[key] = np.array(data)
        else:
            output[key] = data

    np.savez_compressed(
        os.path.splitext(args.file)[0],
        shapedirs=output["shapedirs"],
        v_template=output["v_template"],
        J_regressor_data=output["J_regressor"].data,
        J_regressor_rows=output["J_regressor"].indices,
        J_regressor_cols=csc_cols(output["J_regressor"]),
        kintree_table=output["kintree_table"],
        posedirs=output["posedirs"],
        weights=output["weights"],
        f=output["f"],
    )

gen_verts.py

import sys

sys.path.insert(0, ".")

from smpl_webuser.serialization import load_model
import numpy as np


def make_verts(model, seed=0):
    np.random.seed(seed)

    m = load_model(model)
    m.pose[:] = np.random.rand(m.pose.size) * 0.2
    m.betas[:] = np.random.rand(m.betas.size) * 0.03

    return m.r


verts = {
    "f": make_verts("models/basicmodel_f_lbs_10_207_0_v1.1.0.pkl"),
    "m": make_verts("models/basicmodel_m_lbs_10_207_0_v1.1.0.pkl"),
    "n": make_verts(model="models/basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl"),
}
np.savez_compressed("verts", **verts)

smpl.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "7Qu00vwK6Hpp",
        "bC-N2qBH6P7B"
      ],
      "authorship_tag": "ABX9TyMs7KW5oGvzzfo880OR9Zt3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/stwind/2aa7a9d0da91f675902f30b2fbf08d22/smpl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "cNoqqGsQ6Ghi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dependencies"
      ],
      "metadata": {
        "id": "7Qu00vwK6Hpp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ad6dDUxh6FKq",
        "outputId": "3b424023-f007-4b8d-e5ef-38c53be41085"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m47.4 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[31m107.3 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"
          ]
        }
      ],
      "source": [
        "!pip install --no-cache-dir -Uq matplotlib pillow scipy einops ffmpeg-python"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Commons"
      ],
      "metadata": {
        "id": "bC-N2qBH6P7B"
      }
    },
    {
      "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",
        "\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": "qzkgyzZV6PWT"
      },
      "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",
        "\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_frame(ax):\n",
        "    ax.spines[[\"left\",\"right\",\"bottom\",\"top\"]].set_visible(True)\n",
        "    ax.grid(False)\n",
        "    ax.set(xticks=[],yticks=[])\n",
        "\n",
        "def ax_frames(axs):\n",
        "    for ax in axs.flat: ax_frame(ax)\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 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": "Jtbw8fdj6SE8"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SMPL"
      ],
      "metadata": {
        "id": "79bR2Dup6TXT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import csc_matrix\n",
        "\n",
        "def rodrigues(k):\n",
        "    theta = np.linalg.norm(k, axis=-1, keepdims=True)\n",
        "    c, s = np.cos(theta), np.sin(theta)\n",
        "    k = k / theta\n",
        "\n",
        "    K = np.zeros((k.shape[0], 3, 3))\n",
        "    x, y, z = k.take(0,-1), k.take(1,-1), k.take(2,-1)\n",
        "    K[:,0,1], K[:,0,2] = -z, y\n",
        "    K[:,1,0], K[:,1,2] = z, -x\n",
        "    K[:,2,0], K[:,2,1] = -y, x\n",
        "\n",
        "    return np.eye(3) + s[...,None] * K + (1 - c[...,None]) * einsum(K,K,\"... a b,... b c->... a c\")\n",
        "\n",
        "def mat44(r, t):\n",
        "    m = np.zeros((4,4),dtype=r.dtype)\n",
        "    m[:3,:3], m[:3,3], m[-1,-1] = r, t, 1\n",
        "    return m\n",
        "\n",
        "def rigid(R, J, kin):\n",
        "    mats = np.zeros((kin.shape[1], 4, 4), dtype=R.dtype)\n",
        "    mats[0] = mat44(R[0], J[0])\n",
        "    for i, p in enumerate(kin[0, 1:], 1):\n",
        "        mats[i] = mats[p].dot(mat44(R[i], J[i] - J[p]))\n",
        "\n",
        "    m = np.zeros_like(mats)\n",
        "    m[...,3] = einsum(mats, np.pad(J, [(0,0),(0,1)]), \"... m n,... n->... m\")\n",
        "    return mats - m\n",
        "\n",
        "def save_obj(fn, verts, faces):\n",
        "    with open(fn, \"w\") as f:\n",
        "        for x, y, z in verts:\n",
        "            f.write(f\"v {x:.8f} {y:.8f} {z:.8f}\\n\")\n",
        "        for a, b, c in faces + 1:\n",
        "            f.write(f\"f {a} {b} {c}\\n\")\n",
        "\n",
        "class SMPL(object):\n",
        "    def __init__(self, path):\n",
        "        data = np.load(path)\n",
        "        for k in [\"shapedirs\",\"v_template\",\"kintree_table\",\"posedirs\",\"f\",\"weights\"]:\n",
        "            setattr(self, k, data[k])\n",
        "        self.J_regressor = csc_matrix(\n",
        "            (data[\"J_regressor_data\"],(data[\"J_regressor_rows\"],data[\"J_regressor_cols\"])),\n",
        "            shape=(self.kintree_table.shape[1], self.shapedirs.shape[0]))\n",
        "\n",
        "    def __call__(self, theta, betas):\n",
        "        v_shaped = self.shapedirs.dot(betas) + self.v_template\n",
        "\n",
        "        R = rodrigues(theta)\n",
        "        G = rigid(R, self.J_regressor @ v_shaped, self.kintree_table)\n",
        "        T = einsum(self.weights, G, \"n k,k ...->n ...\")\n",
        "\n",
        "        v_posed = v_shaped + self.posedirs.dot((R[1:] - np.eye(3)).ravel())\n",
        "        h = np.pad(v_posed, [(0,0),(0,1)], constant_values=1)\n",
        "        return einsum(T, h, \"... r c,... c->... r\")[:,:3]\n",
        "\n",
        "    @property\n",
        "    def n_theta(self):\n",
        "        return self.kintree_table.shape[1]\n",
        "\n",
        "    @property\n",
        "    def n_beta(self):\n",
        "        return self.shapedirs.shape[-1]"
      ],
      "metadata": {
        "id": "35vdY8pT6pmQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6XMbL4w-6UrB",
        "outputId": "c7723b31-ad1e-4559-cf56-fb77e56b764b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "root = \"/content/gdrive/MyDrive/human/smpl\"\n",
        "\n",
        "verts = np.load(f\"{root}/verts.npz\")\n",
        "models = {\n",
        "    \"f\": SMPL(f\"{root}/basicmodel_f_lbs_10_207_0_v1.1.0.npz\"),\n",
        "    \"m\": SMPL(f\"{root}/basicmodel_m_lbs_10_207_0_v1.1.0.npz\"),\n",
        "    \"n\": SMPL(f\"{root}/basicmodel_neutral_lbs_10_207_0_v1.1.0.npz\"),\n",
        "}"
      ],
      "metadata": {
        "id": "_qMqK64y64eW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "\n",
        "theta = np.random.rand(models[\"f\"].n_theta, 3) * 0.2\n",
        "betas = np.random.rand(models[\"f\"].n_beta) * .03\n",
        "\n",
        "np.testing.assert_allclose(verts[\"f\"], models[\"f\"](theta, betas))\n",
        "np.testing.assert_allclose(verts[\"m\"], models[\"m\"](theta, betas))\n",
        "np.testing.assert_allclose(verts[\"n\"], models[\"n\"](theta, betas))"
      ],
      "metadata": {
        "id": "ecGYErQB7Vmi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(9,3))\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 1, projection='3d', proj_type=\"ortho\")\n",
        "vs = models[\"f\"](theta, betas)\n",
        "ax.plot_trisurf(vs[:,0],vs[:,1],vs[:,2], triangles=models[\"f\"].f)\n",
        "ax.set_title(\"female\")\n",
        "ax.set_xlim(-1,1)\n",
        "ax.set_ylim(-1,1)\n",
        "ax.set_zlim(-1,1)\n",
        "ax.set_box_aspect((1,1,1))\n",
        "ax.view_init(elev=20, azim=30, vertical_axis='y')\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2, projection='3d', proj_type=\"ortho\")\n",
        "vs = models[\"m\"](theta, betas)\n",
        "ax.plot_trisurf(vs[:,0],vs[:,1],vs[:,2], triangles=models[\"m\"].f)\n",
        "ax.set_title(\"male\")\n",
        "ax.set_xlim(-1,1)\n",
        "ax.set_ylim(-1,1)\n",
        "ax.set_zlim(-1,1)\n",
        "ax.set_box_aspect((1,1,1))\n",
        "ax.view_init(elev=20, azim=30, vertical_axis='y')\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3, projection='3d', proj_type=\"ortho\")\n",
        "vs = models[\"n\"](theta, betas)\n",
        "ax.plot_trisurf(vs[:,0],vs[:,1],vs[:,2], triangles=models[\"n\"].f)\n",
        "ax.set_title(\"neutral\")\n",
        "ax.set_xlim(-1,1)\n",
        "ax.set_ylim(-1,1)\n",
        "ax.set_zlim(-1,1)\n",
        "ax.set_box_aspect((1,1,1))\n",
        "ax.view_init(elev=20, azim=30, vertical_axis='y')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "ob1eGgC17e8t",
        "outputId": "6b8ee96b-6512-4c47-b0f3-4bb0965a006a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x300 with 3 Axes>"
            ],
            "image/png": 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\n"
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            "image/png": {
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              "height": 315
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        }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f0LCugFR7hZc"
      },
      "execution_count": null,
      "outputs": []
    }
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}