smplh.ipynb

export_mano.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],
        hands_components=output["hands_components"],
        hands_mean=output["hands_mean"],
        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"],
    )

export_smplh.py

import os
import argparse
import pickle
import numpy as np


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"].row,
        J_regressor_cols=output["J_regressor"].col,
        kintree_table=output["kintree_table"],
        posedirs=output["posedirs"],
        weights=output["weights"],
        f=output["f"],
    )

get_verts.py

import sys

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

from webuser.smpl_handpca_wrapper_HAND_only import load_model as load_model_mano
from webuser.smpl_handpca_wrapper import load_model
import numpy as np


def make_verts_mano(model, seed=0):
    m = load_model_mano(model)

    np.random.seed(seed)
    m.betas[:] = np.random.rand(m.betas.size) * 0.03
    # m.pose[:] = np.random.rand(m.pose.size) * 1.0
    m.pose[:3] = [0.0, 0.0, 0.0]
    m.pose[3:] = [
        -0.42671473,
        -0.85829819,
        -0.50662164,
        +1.97374622,
        -0.84298473,
        -1.29958491,
    ]

    return m.r


def make_verts_smplh(model, seed=0):
    m = load_model(model)

    np.random.seed(seed)
    m.betas[:] = np.random.rand(m.betas.size) * 0.03
    # m.pose[:] = np.random.rand(m.pose.size) * 1.0
    m.pose[:] = [
        -0.17192541,
        +0.36310464,
        +0.05572387,
        -0.42836206,
        -0.00707548,
        +0.03556427,
        +0.18696896,
        -0.22704364,
        -0.39019834,
        +0.20273526,
        +0.07125099,
        +0.07105988,
        +0.71328310,
        -0.29426986,
        -0.18284189,
        +0.72134655,
        +0.07865227,
        +0.08342645,
        +0.00934835,
        +0.12881420,
        -0.02610217,
        -0.15579594,
        +0.25352553,
        -0.26097519,
        -0.04529948,
        -0.14718626,
        +0.52724564,
        -0.07638319,
        +0.03324086,
        +0.05886086,
        -0.05683995,
        -0.04069042,
        +0.68593617,
        -0.75870686,
        -0.08579930,
        -0.55086359,
        -0.02401033,
        -0.46217096,
        -0.03665799,
        +0.12397343,
        +0.10974685,
        -0.41607569,
        -0.26874970,
        +0.40249335,
        +0.21223768,
        +0.03365140,
        -0.05243080,
        +0.16074013,
        +0.13433811,
        +0.10414972,
        -0.98688595,
        -0.17270103,
        +0.29374368,
        +0.61868383,
        +0.00458329,
        -0.15357027,
        +0.09531648,
        -0.10624117,
        +0.94679869,
        -0.26851003,
        +0.58547889,
        -0.13735695,
        -0.39952280,
        -0.16598853,
        -0.14982575,
        -0.27937399,
        +0.12354536,
        -0.55101035,
        -0.41938681,
        +0.52238684,
        -0.23376718,
        -0.29814804,
        -0.42671473,
        -0.85829819,
        -0.50662164,
        +1.97374622,
        -0.84298473,
        -1.29958491,
    ]

    return m.r


verts = {
    "left": make_verts_mano("models/MANO_LEFT.pkl"),
    "right": make_verts_mano("models/MANO_RIGHT.pkl"),
    "female": make_verts_smplh("models/SMPLH_female.pkl"),
    "male": make_verts_smplh("models/SMPLH_male.pkl"),
}
np.savez_compressed("verts", **verts)

smplh.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "R39kzdUz0zRr",
        "1NmaoMNK01Pp"
      ],
      "authorship_tag": "ABX9TyMabg/gslIGIzejbmB4hSc9",
      "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/ab87dd3948d0954bd670844917c09415/smplh.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": "DrK3wDyc0yhL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dependencies"
      ],
      "metadata": {
        "id": "R39kzdUz0zRr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzV9ztE00x-3",
        "outputId": "af00167b-f52e-4497-f75a-91847b08e954"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m3.0 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[31m120.7 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": "1NmaoMNK01Pp"
      }
    },
    {
      "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": "X_PVlQnP00Vv"
      },
      "execution_count": null,
      "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": "_hUggoLQ03IW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SMPL-H"
      ],
      "metadata": {
        "id": "Frw_FvFH04SF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import csc_matrix, coo_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.clip(1e-8)\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 MANO(object):\n",
        "    def __init__(self, path):\n",
        "        data = np.load(path)\n",
        "        for k in [\"shapedirs\",\"v_template\",\"kintree_table\",\"posedirs\",\"f\",\"weights\",\"hands_components\",\"hands_mean\"]:\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, coeffs, betas):\n",
        "        v_shaped = self.shapedirs.dot(betas) + self.v_template\n",
        "\n",
        "        fullpose = np.concatenate((coeffs[:3], self.hands_mean + coeffs[3:].dot(self.hands_components[:6]))).reshape(-1,3)\n",
        "        R = rodrigues(fullpose)\n",
        "\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_pose(self):\n",
        "        return 9\n",
        "\n",
        "    @property\n",
        "    def n_beta(self):\n",
        "        return self.shapedirs.shape[-1]\n",
        "\n",
        "class SMPLH(object):\n",
        "    def __init__(self, mano_l, mano_r, 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 = coo_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",
        "        self.mano_l = mano_l\n",
        "        self.mano_r = mano_r\n",
        "\n",
        "    def __call__(self, coeffs, betas):\n",
        "        selected_components = np.vstack((np.hstack((self.mano_l.hands_components[:6],\n",
        "                                                    np.zeros_like(self.mano_l.hands_components[:6]))),\n",
        "                                         np.hstack((np.zeros_like(self.mano_r.hands_components[:6]),\n",
        "                                                    self.mano_r.hands_components[:6]))))\n",
        "        hands_mean = np.concatenate((self.mano_l.hands_mean, self.mano_r.hands_mean))\n",
        "        fullpose = np.concatenate((coeffs[:66], hands_mean + coeffs[66:].dot(selected_components))).reshape(-1,3)\n",
        "\n",
        "        v_shaped = self.shapedirs.dot(betas) + self.v_template\n",
        "\n",
        "        R = rodrigues(fullpose)\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_pose(self):\n",
        "        return 78\n",
        "\n",
        "    @property\n",
        "    def n_beta(self):\n",
        "        return self.shapedirs.shape[-1]"
      ],
      "metadata": {
        "id": "Jx6YFJk405jL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4ZPXixm1EEw",
        "outputId": "18f77b11-af88-47ed-a8ef-99fb1c5a1a86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "root = \"/content/gdrive/MyDrive/human/smplh\"\n",
        "\n",
        "verts = np.load(f\"{root}/verts.npz\")\n",
        "\n",
        "mano_l = MANO(f\"{root}/MANO_LEFT.npz\")\n",
        "mano_r = MANO(f\"{root}/MANO_RIGHT.npz\")\n",
        "\n",
        "smplh_f = SMPLH(mano_l, mano_r, f\"{root}/SMPLH_female.npz\")\n",
        "smplh_m = SMPLH(mano_l, mano_r, f\"{root}/SMPLH_male.npz\")"
      ],
      "metadata": {
        "id": "zoZp3b6S1KU6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "\n",
        "betas = np.random.rand(mano_r.n_beta) * .03\n",
        "pose = np.array([0,0,0,-0.42671473, -0.85829819, -0.50662164, +1.97374622, -0.84298473, -1.29958491])\n",
        "\n",
        "np.testing.assert_allclose(mano_r(pose, betas), verts[\"right\"])\n",
        "np.testing.assert_allclose(mano_l(pose, betas), verts[\"left\"])\n",
        "\n",
        "fig = plt.figure()\n",
        "\n",
        "ax = fig.add_subplot(projection='3d', proj_type=\"ortho\")\n",
        "vs = mano_r(pose,betas)\n",
        "ax.plot_trisurf(vs[:,0],vs[:,1],vs[:,2], triangles=mano_r.f)\n",
        "ax.set_xlim(-.2,.2)\n",
        "ax.set_ylim(-.2,.2)\n",
        "ax.set_zlim(-.2,.2)\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": 330
        },
        "id": "w0fbgby21iEx",
        "outputId": "e8199189-ddb9-4386-f5a9-d67bf5236230"
      },
      "execution_count": null,
      "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": 320,
              "height": 313
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "\n",
        "betas = np.random.rand(smplh_f.n_beta) * .03\n",
        "pose = np.array([-0.17192541, +0.36310464, +0.05572387, -0.42836206, -0.00707548, +0.03556427,\n",
        "             +0.18696896, -0.22704364, -0.39019834, +0.20273526, +0.07125099, +0.07105988,\n",
        "             +0.71328310, -0.29426986, -0.18284189, +0.72134655, +0.07865227, +0.08342645,\n",
        "             +0.00934835, +0.12881420, -0.02610217, -0.15579594, +0.25352553, -0.26097519,\n",
        "             -0.04529948, -0.14718626, +0.52724564, -0.07638319, +0.03324086, +0.05886086,\n",
        "             -0.05683995, -0.04069042, +0.68593617, -0.75870686, -0.08579930, -0.55086359,\n",
        "             -0.02401033, -0.46217096, -0.03665799, +0.12397343, +0.10974685, -0.41607569,\n",
        "             -0.26874970, +0.40249335, +0.21223768, +0.03365140, -0.05243080, +0.16074013,\n",
        "             +0.13433811, +0.10414972, -0.98688595, -0.17270103, +0.29374368, +0.61868383,\n",
        "             +0.00458329, -0.15357027, +0.09531648, -0.10624117, +0.94679869, -0.26851003,\n",
        "             +0.58547889, -0.13735695, -0.39952280, -0.16598853, -0.14982575, -0.27937399,\n",
        "             +0.12354536, -0.55101035, -0.41938681, +0.52238684, -0.23376718, -0.29814804,\n",
        "             -0.42671473, -0.85829819, -0.50662164, +1.97374622, -0.84298473, -1.29958491])\n",
        "\n",
        "np.testing.assert_allclose(smplh_f(pose, betas), verts[\"female\"])\n",
        "np.testing.assert_allclose(smplh_m(pose, betas), verts[\"male\"])\n",
        "\n",
        "fig = plt.figure()\n",
        "\n",
        "ax = fig.add_subplot(projection='3d', proj_type=\"ortho\")\n",
        "vs = smplh_f(pose,betas)\n",
        "ax.plot_trisurf(vs[:,0],vs[:,1],vs[:,2], triangles=smplh_f.f)\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": 330
        },
        "id": "Tvv_Dc7k1oWX",
        "outputId": "94cfd676-d51c-43ab-d97d-251dcb3e153d"
      },
      "execution_count": null,
      "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": 318,
              "height": 313
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QsVf19JP165U"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}