mapping/measure/measurements_S_overall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\yanpe\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\core\\__init__.py:29: UserWarning: loaded more than 1 DLL from .libs:\n",
      "c:\\users\\yanpe\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\.libs\\libopenblas.TXA6YQSD3GCQQC22GEQ54J2UDCXDXHWN.gfortran-win_amd64.dll\n",
      "c:\\users\\yanpe\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA is available. Device:  NVIDIA GeForce RTX 3070\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import gc\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pickle\n",
    "import scipy.io as sio\n",
    "from scipy.io import loadmat\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import torchvision.transforms as transforms\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "# Pytorch 1.9\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils\n",
    "import torch.distributions\n",
    "\n",
    "# Pytorch3d\n",
    "from pytorch3d.structures import Meshes\n",
    "from pytorch3d.renderer import (\n",
    "    look_at_view_transform,\n",
    "    FoVPerspectiveCameras,\n",
    "    PointLights,\n",
    "    RasterizationSettings,\n",
    "    MeshRenderer,\n",
    "    MeshRasterizer,\n",
    "    SoftPhongShader,\n",
    "    TexturesVertex,\n",
    "    blending\n",
    ")\n",
    "\n",
    "import open3d as o3d\n",
    "\n",
    "# facenet-pytorch 2.5.2\n",
    "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
    "\n",
    "# face-alignment 1.3.4\n",
    "import face_alignment\n",
    "\n",
    "#######################################\n",
    "## Setup PyTorch\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    torch.cuda.set_device(device)\n",
    "    print('CUDA is available. Device: ', torch.cuda.get_device_name(0))\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "    print('CUDA is NOT available. Use CPU instead.')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape_path = '../../datasets/FFHQ/raw_bfm_shape/'\n",
    "file_indices = []\n",
    "for fname in os.listdir(shape_path):\n",
    "    if fname.endswith('.npy'):\n",
    "        file_indices.append(fname.split('.')[0])\n",
    "shape_path += '{}.npy'\n",
    "\n",
    "ffhq_meanshape = np.load('../ffhq_meanshape.npy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5, 6}\n",
      "BFM model loaded\n",
      "\n",
      "S_overall  n_vert:  35709\n",
      "S_eyebrows  n_vert:  444\n",
      "S_eyes  n_vert:  586\n",
      "S_llip  n_vert:  309\n",
      "S_nose  n_vert:  1711\n",
      "S_ulip  n_vert:  576\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    " 1: face skin\n",
    " 2: eye brows\n",
    " 3: eyes\n",
    " 4: nose\n",
    " 5: upper lip\n",
    " 6: lower lip\n",
    "\"\"\"\n",
    "label_map = {\n",
    "    'skin': 1,\n",
    "    'eye_brow': 2,\n",
    "    'eye': 3,\n",
    "    'nose': 4,\n",
    "    'u_lip': 5,\n",
    "    'l_lip': 6\n",
    "}\n",
    "\n",
    "## Load the face parsing labels (per-vertex)\n",
    "vert_labels = np.load('../../BFM/bfm_vertex_labels.npy')\n",
    "print(set(vert_labels))\n",
    "\n",
    "\n",
    "## Load the BFM model\n",
    "import pickle\n",
    "with open('../../BFM/bfm09.pkl', 'rb') as f:\n",
    "    bfm = pickle.load(f)\n",
    "print('BFM model loaded\\n')\n",
    "\n",
    "## Triangal Facets\n",
    "Faces = bfm['tri'] - 1 ## -1 is critical !!!\n",
    "\n",
    "# find the vertices of part\n",
    "part_vertices = {\n",
    "    'S_overall':[],\n",
    "    #'S_rest':[],\n",
    "    'S_eyebrows':[],\n",
    "    'S_eyes':[],\n",
    "    'S_llip':[],\n",
    "    'S_nose':[],\n",
    "    'S_ulip':[]\n",
    "}\n",
    "for idx in range(len(vert_labels)):\n",
    "    part_vertices['S_overall'].append(idx)\n",
    "    if vert_labels[idx] in [label_map['eye_brow']]:\n",
    "        part_vertices['S_eyebrows'].append(idx)\n",
    "    if vert_labels[idx] in [label_map['eye']]:\n",
    "        part_vertices['S_eyes'].append(idx)\n",
    "    if vert_labels[idx] in [label_map['l_lip']]:\n",
    "        part_vertices['S_llip'].append(idx)\n",
    "    if vert_labels[idx] in [label_map['u_lip']]:\n",
    "        part_vertices['S_ulip'].append(idx)\n",
    "    if vert_labels[idx] in [label_map['nose']]:\n",
    "        part_vertices['S_nose'].append(idx)\n",
    "    \n",
    "for key in part_vertices:\n",
    "    part_vertices[key] = np.array(part_vertices[key])\n",
    "    print(key, ' n_vert: ', len(part_vertices[key]))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def standardize_part_shape(part_shapes):\n",
    "    # part_shapes: [n, n_vert, 3]  n -- batch size\n",
    "    y_max, _ = np.max(part_shapes[...,1], dim=1) # [n, 1]\n",
    "    y_min, _ = np.min(part_shapes[...,1], dim=1) # [n, 1]\n",
    "    y_center = (y_max + y_min) / 2 # [n, 1]\n",
    "    z_max, _ = np.max(part_shapes[...,2], dim=1) # [n, 1]\n",
    "    z_min, _ = np.min(part_shapes[...,2], dim=1) # [n, 1]\n",
    "    z_center = (z_max + z_min) / 2 # [n, 1]\n",
    "    batch_size = part_shapes.shape[0]\n",
    "    for i in range(batch_size):\n",
    "        part_shapes[i, :, 1] -= y_center[i]\n",
    "        part_shapes[i, :, 2] -= z_center[i]\n",
    "    return part_shapes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "bfm_68_landmarks = bfm['keypoints'][0] # mapping between 68 landmarks and their bfm vertices\n",
    "\n",
    "def landmark_2_vertex(landmark_idx):\n",
    "    \"\"\"convert landmark (1~68) to corresponding bfm vertex index\"\"\"\n",
    "    return bfm_68_landmarks[landmark_idx-1]\n",
    "\n",
    "def bounding_box_height(shape, vertices):\n",
    "    y_max = np.max(shape[0, vertices, 1])\n",
    "    y_min = np.min(shape[0, vertices, 1])\n",
    "    height = y_max - y_min\n",
    "    return height\n",
    "\n",
    "def bounding_box_width(shape, vertices):\n",
    "    x_max = np.max(shape[0, vertices, 0])\n",
    "    x_min = np.min(shape[0, vertices, 0])\n",
    "    width = x_max - x_min\n",
    "    return width\n",
    "\n",
    "\n",
    "def euclidean_distance(shape, v1, v2):\n",
    "    p1 = shape[0, v1, :]\n",
    "    p2 = shape[0, v2, :]\n",
    "    return l2_distance(p1, p2)\n",
    "\n",
    "def l2_distance(p1, p2):\n",
    "    pp = np.square(p1 - p2)    \n",
    "    return math.sqrt(pp[0] + pp[1] + pp[2])\n",
    "    \n",
    "\n",
    "def y_axis_distance(shape, v1, v2):\n",
    "    p1_y = shape[0, v1, 1]\n",
    "    p2_y = shape[0, v2, 1]\n",
    "    return abs(p1_y - p2_y)\n",
    "\n",
    "def x_axis_distance(shape, v1, v2):\n",
    "    p1_x = shape[0, v1, 0]\n",
    "    p2_x = shape[0, v2, 0]\n",
    "    return abs(p1_x - p2_x)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def maximum_facial_width(ffhq_meanshape, shape):\n",
    "    distance_mean = bounding_box_width(ffhq_meanshape, part_vertices['S_overall'])\n",
    "    distance = bounding_box_width(shape, part_vertices['S_overall'])\n",
    "    return distance - distance_mean \n",
    "    \n",
    "def madibular_width(ffhq_meanshape, shape):\n",
    "    v1 = 256\n",
    "    v2 = 22238\n",
    "    distance_mean = x_axis_distance(ffhq_meanshape, v1, v2)\n",
    "    distance = x_axis_distance(shape, v1, v2)\n",
    "    return distance - distance_mean \n",
    "\n",
    "def upper_facial_depth(ffhq_meanshape, shape):\n",
    "    v1 = 8132\n",
    "    distance_mean = ffhq_meanshape[0, v1, 2]\n",
    "    distance = shape[0, v1, 2]\n",
    "    return distance - distance_mean \n",
    "\n",
    "def middle_facial_depth(ffhq_meanshape, shape):\n",
    "    v1 = 8205\n",
    "    distance_mean = ffhq_meanshape[0, v1, 2]\n",
    "    distance = shape[0, v1, 2]\n",
    "    return distance - distance_mean \n",
    "\n",
    "def lower_facial_depth(ffhq_meanshape, shape):\n",
    "    v1 = 33838\n",
    "    distance_mean = ffhq_meanshape[0, v1, 2]\n",
    "    distance = shape[0, v1, 2]\n",
    "    return distance - distance_mean \n",
    "\n",
    "def facial_height(ffhq_meanshape, shape):\n",
    "    v1 = 8132\n",
    "    v2 = 33838\n",
    "    distance_mean = y_axis_distance(ffhq_meanshape, v1, v2)\n",
    "    distance = y_axis_distance(shape, v1, v2)\n",
    "    return distance - distance_mean \n",
    "\n",
    "def upper_facial_height(ffhq_meanshape, shape):\n",
    "    v1 = 8132\n",
    "    v2 = 8229\n",
    "    distance_mean = y_axis_distance(ffhq_meanshape, v1, v2)\n",
    "    distance = y_axis_distance(shape, v1, v2)\n",
    "    return distance - distance_mean \n",
    "\n",
    "def lower_facial_height(ffhq_meanshape, shape):\n",
    "    v1 = 8205\n",
    "    v2 = 33838\n",
    "    distance_mean = y_axis_distance(ffhq_meanshape, v1, v2)\n",
    "    distance = y_axis_distance(shape, v1, v2)\n",
    "    return distance - distance_mean \n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 294.12it/s]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Measure the shapes and record in a matrix\n",
    "\"\"\"\n",
    "measurements = np.zeros([len(file_indices), 8])\n",
    "\n",
    "for i in tqdm(range(len(file_indices))):\n",
    "    file_idx = file_indices[i]\n",
    "    shape = np.load(shape_path.format(file_idx))\n",
    "    \n",
    "    measurements[i, 0] = maximum_facial_width(ffhq_meanshape, shape)\n",
    "    measurements[i, 1] = madibular_width(ffhq_meanshape, shape)\n",
    "    measurements[i, 2] = upper_facial_depth(ffhq_meanshape, shape)\n",
    "    measurements[i, 3] = middle_facial_depth(ffhq_meanshape, shape)\n",
    "    measurements[i, 4] = lower_facial_depth(ffhq_meanshape, shape)\n",
    "    measurements[i, 5] = facial_height(ffhq_meanshape, shape)\n",
    "    measurements[i, 6] = upper_facial_height(ffhq_meanshape, shape)\n",
    "    measurements[i, 7] = lower_facial_height(ffhq_meanshape, shape)\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   maximum_facial_width  madibular_width  upper_facial_depth  \\\n",
      "0              0.011478        -0.036301           -0.024576   \n",
      "1              0.015947         0.007813            0.014310   \n",
      "2              0.036301         0.032041           -0.026374   \n",
      "3             -0.044098        -0.045500           -0.027022   \n",
      "4             -0.032800        -0.066198            0.013080   \n",
      "\n",
      "   middle_facial_depth  lower_facial_depth  facial_height  \\\n",
      "0            -0.053882           -0.075040      -0.203673   \n",
      "1             0.020969            0.031634      -0.014400   \n",
      "2            -0.015508           -0.013807      -0.067644   \n",
      "3            -0.067560           -0.103204      -0.114380   \n",
      "4             0.023407           -0.026652       0.024795   \n",
      "\n",
      "   upper_facial_height  lower_facial_height  \n",
      "0            -0.116306            -0.093901  \n",
      "1            -0.021252            -0.022102  \n",
      "2            -0.020302            -0.039513  \n",
      "3            -0.055742            -0.045530  \n",
      "4             0.041018            -0.018960  \n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Visualize the data\n",
    "\"\"\"\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "measurement_names = [\n",
    "    'maximum_facial_width',\n",
    "    'madibular_width',\n",
    "    'upper_facial_depth',\n",
    "    'middle_facial_depth',\n",
    "    'lower_facial_depth',\n",
    "    'facial_height',\n",
    "    'upper_facial_height',\n",
    "    'lower_facial_height'\n",
    "]\n",
    "    \n",
    "\n",
    "\n",
    "d = {\n",
    "    measurement_names[0]: measurements[:, 0],\n",
    "    measurement_names[1]: measurements[:, 1],\n",
    "    measurement_names[2]: measurements[:, 2],\n",
    "    measurement_names[3]: measurements[:, 3],\n",
    "    measurement_names[4]: measurements[:, 4],\n",
    "    measurement_names[5]: measurements[:, 5],\n",
    "    measurement_names[6]: measurements[:, 6],\n",
    "    measurement_names[7]: measurements[:, 7],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data=d)\n",
    "print(df.head())\n",
    "\n",
    "_ = sns.pairplot(df, \n",
    "                 plot_kws=dict(marker=\".\", linewidth=0.01), \n",
    "                 diag_kind=\"kde\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Save the data matrix \"\"\"\n",
    "try:\n",
    "    os.mkdir('./cache/measurements/')\n",
    "except:\n",
    "    print('folder exists, please ignore')\n",
    "    \n",
    "#np.save('./cache/measurements/{}.npy'.format('S_overall'), measurements)"
   ]
  }
 ],
 "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": "3.6.7rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}