.

1 files changed, 399 insertions, 0 deletions

diff --git a/megapixels/notebooks/visualize/pose_mpi_clean_data.ipynb b/megapixels/notebooks/visualize/pose_mpi_clean_data.ipynb
new file mode 100644
index 00000000..d8d7b77d
--- /dev/null
+++ b/megapixels/notebooks/visualize/pose_mpi_clean_data.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clean Human Pose MPI Dataset\n",
+    "\n",
+    "Fix data\n",
+    "\n",
+    "Data structure:\n",
+    "- `data[2]` = 2 x 7 x 100 array\n",
+    "- `data[2][0]` = x locations\n",
+    "- `data[2][0]` = y locations\n",
+    "- ordering is `0 Head, 1 Right wrist, 2 Left wrist, 3 Right elbow, 4 Left elbow, 5 Right shoulder and 6 Left shoulder`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import os\n",
+    "from os.path import join\n",
+    "import math\n",
+    "from glob import glob\n",
+    "from random import randint\n",
+    "\n",
+    "import cv2 as cv\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from PIL import Image, ImageDraw\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.io\n",
+    "from pathlib import Path\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('/work/megapixels_dev/megapixels/')\n",
+    "from app.settings import app_cfg as cfg\n",
+    "from app.utils import file_utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_STORE = '/data_store_nas/'\n",
+    "fp_dataset = join(DATA_STORE, 'datasets/people/youtube_poses')\n",
+    "dir_fp_frames = join(fp_dataset, 'YouTube_Pose_dataset_1.0/GT_frames')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dirs_frames = glob(join(dir_fp_frames, '*'))\n",
+    "fps_frames = {}\n",
+    "for dir_frames in dirs_frames:\n",
+    "  fps_frames[dir_frames] = join(dir_frames, '*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_pose_data = join(fp_dataset, 'YouTube_Pose_dataset_1.0/YouTube_Pose_dataset.mat')\n",
+    "fp_out = join(fp_dataset, 'poses.csv')\n",
+    "pose_data = scipy.io.loadmat(fp_pose_data)['data'][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert data to pandas DF for sanity\n",
+    "poses = []\n",
+    "for i, pose in enumerate(pose_data):\n",
+    "\n",
+    "  video_id = pose[1][0]\n",
+    "  pose_pts = pose[2]\n",
+    "  crop_x1 = int(pose[6][0][0])\n",
+    "  crop_y1 = int(pose[6][0][1])\n",
+    "  crop_x2 = int(pose[6][0][2])\n",
+    "  crop_y2 = int(pose[6][0][3])\n",
+    "  w = pose[7][0][0]\n",
+    "  h = pose[7][0][1]\n",
+    "  scale = pose[5][0][0]\n",
+    "  \n",
+    "  for j in range(pose_pts.shape[2]): # 100 frames\n",
+    "    x = [pose_pts[0][i][j] for i in range(7)]\n",
+    "    y = [pose_pts[1][i][j] for i in range(7)]\n",
+    "    poses.append({\n",
+    "      'video_id': video_id, \n",
+    "      'scale': scale,\n",
+    "      'crop_x1': crop_x1,\n",
+    "      'crop_y1': crop_y1,\n",
+    "      'crop_x2': crop_x2,\n",
+    "      'crop_y2': crop_y2,\n",
+    "      'width': w, \n",
+    "      'height': h,\n",
+    "      'head_x': x[0],\n",
+    "      'head_y': y[0],\n",
+    "      'wrist_right_x': x[1],\n",
+    "      'wrist_right_y': y[1],\n",
+    "      'wrist_left_x': x[2], \n",
+    "      'wrist_left_y': y[2],\n",
+    "      'elbow_right_x': x[3],\n",
+    "      'elbow_right_y': y[3],\n",
+    "      'elbow_left_x': x[4], \n",
+    "      'elbow_left_y': y[4],\n",
+    "      'shoulder_right_x': x[5],\n",
+    "      'shoulder_right_y': y[5],\n",
+    "      'shoulder_left_x': x[6], \n",
+    "      'shoulder_left_y':  y[6],\n",
+    "      })\n",
+    "df_poses = pd.DataFrame.from_dict(poses)\n",
+    "df_poses.to_csv(fp_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>crop_x1</th>\n",
+       "      <th>crop_x2</th>\n",
+       "      <th>crop_y1</th>\n",
+       "      <th>crop_y2</th>\n",
+       "      <th>elbow_left_x</th>\n",
+       "      <th>elbow_left_y</th>\n",
+       "      <th>elbow_right_x</th>\n",
+       "      <th>elbow_right_y</th>\n",
+       "      <th>head_x</th>\n",
+       "      <th>head_y</th>\n",
+       "      <th>...</th>\n",
+       "      <th>shoulder_left_x</th>\n",
+       "      <th>shoulder_left_y</th>\n",
+       "      <th>shoulder_right_x</th>\n",
+       "      <th>shoulder_right_y</th>\n",
+       "      <th>video_id</th>\n",
+       "      <th>width</th>\n",
+       "      <th>wrist_left_x</th>\n",
+       "      <th>wrist_left_y</th>\n",
+       "      <th>wrist_right_x</th>\n",
+       "      <th>wrist_right_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>277.721438</td>\n",
+       "      <td>192.416331</td>\n",
+       "      <td>147.628696</td>\n",
+       "      <td>169.326277</td>\n",
+       "      <td>195.498320</td>\n",
+       "      <td>81.471438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>254.631384</td>\n",
+       "      <td>127.088374</td>\n",
+       "      <td>178.603159</td>\n",
+       "      <td>134.691196</td>\n",
+       "      <td>-osma2n86oA</td>\n",
+       "      <td>720</td>\n",
+       "      <td>278.566196</td>\n",
+       "      <td>235.498992</td>\n",
+       "      <td>158.047379</td>\n",
+       "      <td>122.301411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>273.497648</td>\n",
+       "      <td>187.629368</td>\n",
+       "      <td>152.134073</td>\n",
+       "      <td>129.341062</td>\n",
+       "      <td>207.324933</td>\n",
+       "      <td>72.742272</td>\n",
+       "      <td>...</td>\n",
+       "      <td>254.349798</td>\n",
+       "      <td>131.593750</td>\n",
+       "      <td>181.137433</td>\n",
+       "      <td>123.990927</td>\n",
+       "      <td>-osma2n86oA</td>\n",
+       "      <td>720</td>\n",
+       "      <td>274.342406</td>\n",
+       "      <td>235.498992</td>\n",
+       "      <td>135.238911</td>\n",
+       "      <td>91.608535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>258.010417</td>\n",
+       "      <td>159.752352</td>\n",
+       "      <td>160.581653</td>\n",
+       "      <td>143.138777</td>\n",
+       "      <td>229.007056</td>\n",
+       "      <td>76.966062</td>\n",
+       "      <td>...</td>\n",
+       "      <td>250.407594</td>\n",
+       "      <td>125.117272</td>\n",
+       "      <td>190.992944</td>\n",
+       "      <td>117.232863</td>\n",
+       "      <td>-osma2n86oA</td>\n",
+       "      <td>720</td>\n",
+       "      <td>213.801411</td>\n",
+       "      <td>108.785282</td>\n",
+       "      <td>181.982191</td>\n",
+       "      <td>89.074261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>274.342406</td>\n",
+       "      <td>188.192540</td>\n",
+       "      <td>142.841734</td>\n",
+       "      <td>110.193212</td>\n",
+       "      <td>203.101142</td>\n",
+       "      <td>76.402890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>253.786626</td>\n",
+       "      <td>128.777890</td>\n",
+       "      <td>185.361223</td>\n",
+       "      <td>120.611895</td>\n",
+       "      <td>-osma2n86oA</td>\n",
+       "      <td>720</td>\n",
+       "      <td>276.595094</td>\n",
+       "      <td>231.556788</td>\n",
+       "      <td>156.921035</td>\n",
+       "      <td>55.847110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>272.371304</td>\n",
+       "      <td>194.387433</td>\n",
+       "      <td>225.628024</td>\n",
+       "      <td>164.820901</td>\n",
+       "      <td>245.902218</td>\n",
+       "      <td>93.016465</td>\n",
+       "      <td>...</td>\n",
+       "      <td>255.476142</td>\n",
+       "      <td>139.478159</td>\n",
+       "      <td>183.390121</td>\n",
+       "      <td>126.806788</td>\n",
+       "      <td>-osma2n86oA</td>\n",
+       "      <td>720</td>\n",
+       "      <td>305.316868</td>\n",
+       "      <td>172.423723</td>\n",
+       "      <td>278.284610</td>\n",
+       "      <td>165.102487</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   crop_x1  crop_x2  crop_y1  crop_y2  elbow_left_x  elbow_left_y  \\\n",
+       "0        1     1920        1     1080    277.721438    192.416331   \n",
+       "1        1     1920        1     1080    273.497648    187.629368   \n",
+       "2        1     1920        1     1080    258.010417    159.752352   \n",
+       "3        1     1920        1     1080    274.342406    188.192540   \n",
+       "4        1     1920        1     1080    272.371304    194.387433   \n",
+       "\n",
+       "   elbow_right_x  elbow_right_y      head_x     head_y      ...        \\\n",
+       "0     147.628696     169.326277  195.498320  81.471438      ...         \n",
+       "1     152.134073     129.341062  207.324933  72.742272      ...         \n",
+       "2     160.581653     143.138777  229.007056  76.966062      ...         \n",
+       "3     142.841734     110.193212  203.101142  76.402890      ...         \n",
+       "4     225.628024     164.820901  245.902218  93.016465      ...         \n",
+       "\n",
+       "   shoulder_left_x  shoulder_left_y  shoulder_right_x  shoulder_right_y  \\\n",
+       "0       254.631384       127.088374        178.603159        134.691196   \n",
+       "1       254.349798       131.593750        181.137433        123.990927   \n",
+       "2       250.407594       125.117272        190.992944        117.232863   \n",
+       "3       253.786626       128.777890        185.361223        120.611895   \n",
+       "4       255.476142       139.478159        183.390121        126.806788   \n",
+       "\n",
+       "      video_id  width wrist_left_x  wrist_left_y  wrist_right_x  wrist_right_y  \n",
+       "0  -osma2n86oA    720   278.566196    235.498992     158.047379     122.301411  \n",
+       "1  -osma2n86oA    720   274.342406    235.498992     135.238911      91.608535  \n",
+       "2  -osma2n86oA    720   213.801411    108.785282     181.982191      89.074261  \n",
+       "3  -osma2n86oA    720   276.595094    231.556788     156.921035      55.847110  \n",
+       "4  -osma2n86oA    720   305.316868    172.423723     278.284610     165.102487  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_poses.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_poses.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "megapixels",
+   "language": "python",
+   "name": "megapixels"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}