notebooks/02-AMASS_DNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using AMASS for training DNNs for Human Body and Motion\n",
    "AMASS is a large database of human bodies, ready to enable proper deep learning on human body. Instead of images, as in [ImageNet](http://www.image-net.org/),\n",
    " AMASS consists of human body parameters, controlling the surface mesh of the [SMPL](http://smpl.is.tue.mpg.de/) body model family.\n",
    "\n",
    "Here we provide basic tools to turn AMASS bodies into suitable formats for deep learning frameworks.\n",
    "We produce PyTorch readable *.pt*, as well as *.h5* files.\n",
    "\n",
    "The provided data preparation code has three stages that could be flexibly customized to your own specific needs.\n",
    "\n",
    "**Stage I** goes over the previously downloaded numpy npz files, sub-samples the time-length and dumps the result into one place as PyTorch pt files.\n",
    "\n",
    "**Stage II** uses PyTorch to apply all sorts of data augmentations in parallel on the original data and produces HDF5 files.\n",
    "HDF5 makes it possible to write files in chunks to avoid memory shortage,\n",
    "while PyTorch speeds up data augmentation though batch processing .\n",
    "\n",
    "**Stage III** simply turns the h5 files into pt files again to be readily usable by PyTorch.\n",
    "One can replace this stage's output to any other suitable kind.\n",
    "\n",
    "The progress at all stages is logged and could be inspected at any time during the process or later.\n",
    "We suggest using an experiment ID that would help in referring to a specific, traceable, data preparation run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dependencies to run this notebook:\n",
    "- [Human Body Prior](https://github.com/nghorbani/human_body_prior)\n",
    "- [PyTorch>=1.7.0](https://pytorch.org/)\n",
    "- [PyTables](https://www.pytables.org/usersguide/installation.html)\n",
    "- [tqdm](https://pypi.org/project/tqdm/2.2.3/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you continue with this tutorial, it is recommended to first take a look at the [**AMASS Visualization**](01-AMASS_Visualization.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the environment\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "from os import path as osp\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "support_dir = '../support_data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Choose the device to run the body model on.\n",
    "comp_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from human_body_prior.tools.omni_tools import log2file, makepath\n",
    "from human_body_prior.tools.omni_tools import copy2cpu as c2c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first pick an experiment ID that later helps us to identify the specific run of data preparation and augmentations.\n",
    "We also prepare a message about this specific experiment, that is going to be included in our log file as future notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr_code = 'VXX_SVXX_TXX' #VERSION_SUBVERSION_TRY\n",
    "\n",
    "msg = ''' Initial use of standard AMASS dataset preparation pipeline '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you have to download the body npz files from [AMASS](https://amass.is.tue.mpg.de/).\n",
    "You would need to first register and agree to the license and pick the subsets you like from datasets.\n",
    "Uncompress the body data at your desired location. The final folder structure should be like the following:\n",
    "\n",
    "**amass_dir>sub_dataset>subjects>*_poses.npz**\n",
    "\n",
    "Now specify the directory of the downloaded npz files and the final output folder for the prepared data, e.g. **work_dir**.\n",
    "At this location at each stage of the data processing pipeline, an appropriate folder is created.\n",
    "Moreover, the log file, and a snapshot of the code is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[VXX_SVXX_TXX] AMASS Data Preparation Began.\n",
      " Initial use of standard AMASS dataset preparation pipeline \n"
     ]
    }
   ],
   "source": [
    "amass_dir =  '../support_data/amass_npz' #'PATH_TO_DOWNLOADED_NPZFILES/*/*_poses.npz'\n",
    "\n",
    "work_dir = '../support_data/prepared_data/VXX_SVXX_TXX'\n",
    "\n",
    "logger = log2file(makepath(work_dir, '%s.log' % (expr_code), isfile=True))\n",
    "logger('[%s] AMASS Data Preparation Began.'%expr_code)\n",
    "logger(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to specify data splits for train/validation/test. Below is the recommended data splits by AMASS\n",
    "for train/validation/test that chooses data for each split from non-overlapping datasets. \n",
    "One reason is to avoid similarity of marker layouts used for the original data.\n",
    "Remember, AMASS is growing, and it is a good idea to check the website for new datasets.\n",
    "You can also follow [AMASS twitter](https://twitter.com/mocap_amass) account for news."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# amass_splits = {\n",
    "#     'vald': ['HumanEva', 'MPI_HDM05', 'SFU', 'MPI_mosh'],\n",
    "#     'test': ['Transitions_mocap', 'SSM_synced'],\n",
    "#     'train': ['CMU', 'MPI_Limits', 'TotalCapture', 'Eyes_Japan_Dataset', 'KIT',\n",
    "#               'BML', 'EKUT', 'TCD_handMocap', 'ACCAD']\n",
    "# }\n",
    "amass_splits = {\n",
    "    'vald': ['SFU',],\n",
    "    'test': ['SSM_synced'],\n",
    "    'train': ['MPI_Limits']\n",
    "}\n",
    "amass_splits['train'] = list(set(amass_splits['train']).difference(set(amass_splits['test'] + amass_splits['vald'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have everything in place to run the preparation code.\n",
    "Take a look at the code for **prepare_amass** and customize it to your need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Stage I: Fetch data from AMASS npz files\n",
      "Stage II: augment the data and save into h5 files to be used in a cross framework scenario.\n",
      "\n",
      "Stage III: dump every data field for all the splits as final pytorch pt files\n",
      "Dumped final pytorch dataset at ../support_data/prepared_data/VXX_SVXX_TXX/stage_III\n"
     ]
    }
   ],
   "source": [
    "from amass.data.prepare_data import prepare_amass\n",
    "prepare_amass(amass_splits, amass_dir, work_dir, logger=logger)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the prepared data\n",
    "Now we can try to open them with PyTorch which a dataloader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test split has 56 datapoints.\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "import glob \n",
    "\n",
    "class AMASS_DS(Dataset):\n",
    "    \"\"\"AMASS: a pytorch loader for unified human motion capture dataset. http://amass.is.tue.mpg.de/\"\"\"\n",
    "\n",
    "    def __init__(self, dataset_dir, num_betas=16):\n",
    "\n",
    "        self.ds = {}\n",
    "        for data_fname in glob.glob(os.path.join(dataset_dir, '*.pt')):\n",
    "            k = os.path.basename(data_fname).replace('.pt','')\n",
    "            self.ds[k] = torch.load(data_fname)\n",
    "        self.num_betas = num_betas\n",
    "\n",
    "    def __len__(self):\n",
    "       return len(self.ds['trans'])\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        data =  {k: self.ds[k][idx] for k in self.ds.keys()}\n",
    "        data['root_orient'] = data['pose'][:3]\n",
    "        data['pose_body'] = data['pose'][3:66]\n",
    "        data['pose_hand'] = data['pose'][66:]\n",
    "        data['betas'] = data['betas'][:self.num_betas]\n",
    "\n",
    "        return data\n",
    "\n",
    "num_betas = 16 # number of body parameters\n",
    "testsplit_dir = os.path.join(work_dir, 'stage_III', 'test')\n",
    "\n",
    "ds = AMASS_DS(dataset_dir=testsplit_dir, num_betas=num_betas)\n",
    "print('Test split has %d datapoints.'%len(ds))\n",
    "\n",
    "batch_size = 5\n",
    "dataloader = DataLoader(ds, batch_size=batch_size, shuffle=True, num_workers=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the tutorial in [**AMASS Visualization**](01-AMASS_Visualization.ipynb) to visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import trimesh\n",
    "from body_visualizer.tools.vis_tools import colors, imagearray2file\n",
    "from body_visualizer.mesh.mesh_viewer import MeshViewer\n",
    "from body_visualizer.tools.vis_tools import show_image\n",
    "\n",
    "imw, imh = 1600, 1600\n",
    "mv = MeshViewer(width=imw, height=imh, use_offscreen=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from human_body_prior.body_model.body_model import BodyModel\n",
    "\n",
    "bm_fname = osp.join(support_dir, 'body_models/smplh/male/model.npz')\n",
    "\n",
    "num_betas = 16 # number of body parameters\n",
    "num_dmpls = 8 # number of DMPL parameters\n",
    "\n",
    "bm = BodyModel(bm_fname=bm_fname, num_betas=num_betas).to(comp_device)\n",
    "faces = c2c(bm.f)\n",
    "\n",
    "bdata = next(iter(dataloader))\n",
    "body_v = bm.forward(**{k:v.to(comp_device) for k,v in bdata.items() if k in ['pose_body', 'betas']}).v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_angles = [0, 180, 90, -90]\n",
    "images = np.zeros([len(view_angles), batch_size, 1, imw, imh, 3])\n",
    "for cId in range(0, batch_size):\n",
    "\n",
    "    orig_body_mesh = trimesh.Trimesh(vertices=c2c(body_v[cId]), faces=c2c(bm.f), vertex_colors=np.tile(colors['grey'], (6890, 1)))\n",
    "\n",
    "    for rId, angle in enumerate(view_angles):\n",
    "        if angle != 0: orig_body_mesh.apply_transform(trimesh.transformations.rotation_matrix(np.radians(angle), (0, 1, 0)))\n",
    "        mv.set_meshes([orig_body_mesh], group_name='static')\n",
    "        images[rId, cId, 0] = mv.render()\n",
    "\n",
    "        if angle != 0: orig_body_mesh.apply_transform(trimesh.transformations.rotation_matrix(np.radians(-angle), (0, 1, 0)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = imagearray2file(images)\n",
    "show_image(np.array(img)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above image each column is a data point and at each row we rotate the body to visualize it from different angles.\n",
    "AMASS has been recently used to train a human body prior for advanced human body inverse kinematics.\n",
    "Take a look at the paper [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://smpl-x.is.tue.mpg.de/)\n",
    "and the [VPoser](https://github.com/nghorbani/human_body_prior)  GitHub page for further information.\n",
    "VPoser is capable of generating new body poses for SMPL body model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}