MEBeauty_tf2_vggface_extraction_train_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "XLRDError",
     "evalue": "Excel xlsx file; not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mXLRDError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-3d349a0d1a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0md1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_and_test_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m      \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-3d349a0d1a55>\u001b[0m in \u001b[0;36mtrain_and_test_set\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLABELS_FILE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0mlabels_dict\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATA_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m                 )\n\u001b[1;32m    295\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m         \u001b[0mio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_or_buffer, engine)\u001b[0m\n\u001b[1;32m    865\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 867\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__fspath__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/io/excel/_xlrd.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Install xlrd >= 1.0.0 for Excel support\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mimport_optional_dependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"xlrd\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m    351\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/io/excel/_xlrd.py\u001b[0m in \u001b[0;36mload_workbook\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/xlrd/__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[0;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows, ignore_workbook_corruption)\u001b[0m\n\u001b[1;32m    168\u001b[0m     \u001b[0;31m# files that xlrd can parse don't start with the expected signature.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfile_format\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfile_format\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'xls'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mXLRDError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFILE_FORMAT_DESCRIPTIONS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile_format\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'; not supported'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m     bk = open_workbook_xls(\n",
      "\u001b[0;31mXLRDError\u001b[0m: Excel xlsx file; not supported"
     ]
    }
   ],
   "source": [
    "#dataset create train, test\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import glob\n",
    "import xlrd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "\n",
    "def train_and_test_set():\n",
    "    \"\"\"\n",
    "    ECUST-FBP dataset creation\n",
    "    \"\"\"\n",
    "    \n",
    "    DATA_DIR = '/home/ubuntu/crop/'\n",
    "    LABELS_FILE = '/home/ubuntu/test5.xlsx'\n",
    "    \n",
    "    \n",
    "    df = pd.read_excel(LABELS_FILE)  \n",
    "    labels_dict= {}\n",
    "    for root, dirs, files in os.walk(DATA_DIR):\n",
    "     \n",
    "      for file in files: \n",
    "        \n",
    "        f = os.path.join(root, file)\n",
    "        df1 = df.loc[df['image']==file]\n",
    "        if (len(df1.index)==1):\n",
    "         \n",
    "           labels_dict[f] = float(df1['mean'])\n",
    "  \n",
    "    keys =  list(labels_dict.keys())\n",
    "    print(len(labels_dict))\n",
    "    random.shuffle(keys)\n",
    "    d1 = dict(list(labels_dict.items())[len(labels_dict)//2:])\n",
    "    d2 = dict(list(labels_dict.items())[:len(labels_dict)//2])\n",
    "    d3 = dict(list(labels_dict.items())[:len(labels_dict)//2])\n",
    "    return d1, d2, d3      \n",
    "\n",
    "train,test,val = train_and_test_set()\n",
    "with open('train.txt', 'w') as file:\n",
    "     for k,v in train.items():\n",
    "            file.write(str(k)+' '+str(v)+'\\n')\n",
    "\n",
    "with open('test.txt', 'w') as file2:\n",
    "     for k,v in test.items():\n",
    "            file2.write(str(k)+' '+str(v)+'\\n')\n",
    "            \n",
    "with open('val.txt', 'w') as file3:\n",
    "     for k,v in val.items():\n",
    "            file3.write(str(k)+' '+str(v)+'\\n')            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openpyxl in /home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages (3.0.6)\r\n",
      "Requirement already satisfied: jdcal in /home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages (from openpyxl) (1.4.1)\r\n",
      "Requirement already satisfied: et-xmlfile in /home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages (from openpyxl) (1.0.1)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'keras_vggface'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-b4e859db7796>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[0;32mimport\u001b[0m  \u001b[0mModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras_vggface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvggface\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mVGGFace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras_vggface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvggface\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mVGGFace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras_vggface'"
     ]
    }
   ],
   "source": [
    "# get SCUT5500 train test \n",
    "\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import glob\n",
    "from xlrd import open_workbook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "from sklearn import linear_model\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import dask.dataframe as dd\n",
    "import dask.array as da\n",
    "from util.calc_util import split_train_and_test_data\n",
    "from util.cfg import config\n",
    "from util.file_util import mkdirs_if_not_exist, out_result, prepare_scutfbp5500\n",
    "from util.vgg_face_feature import extract_feature\n",
    "\n",
    "from keras.engine import  Model\n",
    "from keras.layers import Input\n",
    "from keras_vggface.vggface import VGGFace\n",
    "from keras.preprocessing import image\n",
    "from keras_vggface.vggface import VGGFace\n",
    "from keras_vggface import utils\n",
    "vgg_model = VGGFace() # pooling: None, avg or max\n",
    "\n",
    "def scut5500_train_and_test_set():\n",
    "    \"\"\"\n",
    "    SCUT-FBP5500 dataset creation\n",
    "    \"\"\" \n",
    "\n",
    "    df = pd.read_csv(\"train2.txt\", sep=\" \", header=None)\n",
    "    df.columns = ['image', 'score']\n",
    "    train1 = {}\n",
    "    \n",
    "    for index, row in df.iterrows():\n",
    "        img= row['image']\n",
    "        train1[img] =  float(row['score'] ) \n",
    "   \n",
    "    df = pd.read_csv(\"/home/ubuntu/test2.txt\", sep=\" \", header=None)\n",
    "    df.columns = ['image', 'score']\n",
    "    test1 = {}\n",
    "    \n",
    "    for index, row in df.iterrows():\n",
    "        img= row['image']\n",
    "        test1[img] =  float(row['score'] ) \n",
    "    return train1, test1\n",
    "    \n",
    "train, test = scut5500_train_and_test_set()\n",
    "train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mtw83.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1704.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1611.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mtw9.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty890.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty956.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1641.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw587.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw669.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1729.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw673.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw726.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1274.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw592.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1967.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1699.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1197.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw650.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1474.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty636.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1660.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1726.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw668.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1640.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw707.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw719.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1238.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty855.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1453.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1256.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty217.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw628.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty882.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty325.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty898.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw343.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1437.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1161.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty959.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw7.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1222.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1977.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1690.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mtw647.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1745.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty438.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1134.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1639.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1439.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw548.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1750.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1889.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1698.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty933.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1588.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty31.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty856.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1466.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty1611.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1584.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1168.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty274.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1792.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1425.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1044.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1295.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty840.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1651.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1897.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1212.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mtw152.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1443.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1714.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty908.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1861.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1203.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1868.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw589.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1348.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1871.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1159.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1681.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1919.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty818.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1713.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1817.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1312.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw427.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty162.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/mty85.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1319.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw740.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1536.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/ftw342.jpg\n",
      "no file/home/ubuntu/mtcnn_aligned_images_SCUT/fty1075.jpg\n",
      "===============The Mean Absolute Error of Model is 0.2626====================\n",
      "===============The Root Mean Square Error of Model is 0.337====================\n",
      "===============The Pearson Correlation of Model is 0.8664====================\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Shape of passed values is (3, 1), indices imply (3, 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_blocks\u001b[0;34m(blocks, axes)\u001b[0m\n\u001b[1;32m   1653\u001b[0m                 blocks = [\n\u001b[0;32m-> 1654\u001b[0;31m                     \u001b[0mmake_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1655\u001b[0m                 ]\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mmake_block\u001b[0;34m(values, placement, klass, ndim, dtype)\u001b[0m\n\u001b[1;32m   3040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3041\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, values, placement, ndim)\u001b[0m\n\u001b[1;32m   2588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2589\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, values, placement, ndim)\u001b[0m\n\u001b[1;32m    124\u001b[0m             raise ValueError(\n\u001b[0;32m--> 125\u001b[0;31m                 \u001b[0;34mf\"Wrong number of items passed {len(self.values)}, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m                 \u001b[0;34mf\"placement implies {len(self.mgr_locs)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Wrong number of items passed 1, placement implies 3",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-13d8ce00ed70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m out_result(list(test.keys()), predicted_label.flatten().tolist(), test_label, None,\n\u001b[0;32m---> 68\u001b[0;31m            path='./result/detail_%s.csv' % csv_tag)\n\u001b[0m",
      "\u001b[0;32m~/TransFBP/util/file_util.py\u001b[0m in \u001b[0;36mout_result\u001b[0;34m(test_set_filenames, predicted_list, gt_lst, attribute_list, path)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'filename'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'predicted'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'groundtruth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_set_filenames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_lst\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m     \u001b[0mmkdirs_if_not_exist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./result/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'UTF-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    462\u001b[0m                 \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m                 \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m         \u001b[0;31m# For data is list-like, or Iterable (will consume into list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_ndarray\u001b[0;34m(values, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0mblock_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_blocks\u001b[0;34m(blocks, axes)\u001b[0m\n\u001b[1;32m   1662\u001b[0m         \u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"values\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \u001b[0mtot_items\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1664\u001b[0;31m         \u001b[0mconstruction_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtot_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[0;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[1;32m   1692\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mblock_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Empty data passed with indices specified.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1694\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Shape of passed values is {passed}, indices imply {implied}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Shape of passed values is (3, 1), indices imply (3, 3)"
     ]
    }
   ],
   "source": [
    "    #  Train, validation and VGGface feature extraction\n",
    "    \n",
    "    def extract_feature_tf2(file, layer_name):\n",
    "   \n",
    "        out = vgg_model.get_layer(layer_name).output\n",
    "        vgg_model_new = Model(vgg_model.input, out)\n",
    "        img = image.load_img(file, target_size=(224, 224))\n",
    "        x = image.img_to_array(img)\n",
    "        x = np.expand_dims(x, axis=0)\n",
    "        x = utils.preprocess_input(x, version=1) # or version=2\n",
    "        features = vgg_model_new.predict(x)\n",
    "    \n",
    "        return features.ravel()\n",
    "    \n",
    "    \n",
    "    train_vec = list()\n",
    "    train_label = list()\n",
    "    test_vec = list()\n",
    "    test_label = list()\n",
    "\n",
    "    for k, v in train.items():\n",
    "\n",
    "        try:\n",
    "            feature = np.concatenate((extract_feature_tf2(k, layer_name=\"fc6\"), extract_feature_tf2(k, layer_name=\"fc7\")),\n",
    "                                 axis=0)\n",
    "      \n",
    "            train_vec.append(feature)\n",
    "            train_label.append(v)\n",
    "        except:\n",
    "            print(\"no file\"+k)\n",
    "    for k, v in test.items():\n",
    "  \n",
    "        try:\n",
    "            feature = np.concatenate((extract_feature_tf2(k, layer_name=\"fc6\"), extract_feature_tf2(k, layer_name=\"fc7\")),\n",
    "                                 axis=0)\n",
    "            test_vec.append(feature)\n",
    "            test_label.append(v)\n",
    "        except:\n",
    "            print(\"no file\"+k)\n",
    "  \n",
    "    #train the model and validate\n",
    "    \n",
    "    \n",
    "    #train_vec = pca.fit_transform(train_vec)\n",
    "    #test_vec = pca.fit_transform(test_vec)    \n",
    "    reg = linear_model.BayesianRidge()\n",
    "    #print(train_label)\n",
    "    train_vec = np.array(train_vec)\n",
    "    train_label = np.array(train_label)\n",
    "    reg.fit(train_vec, train_label)\n",
    "\n",
    "    predicted_label = reg.predict(np.array(test_vec))\n",
    "    mae_lr = round(mean_absolute_error(np.array(test_label), predicted_label), 4)\n",
    "    rmse_lr = round(math.sqrt(mean_squared_error(np.array(test_label), predicted_label)), 4)\n",
    "    pc = round(np.corrcoef(test_label, predicted_label)[0, 1], 4)\n",
    "\n",
    "    print('===============The Mean Absolute Error of Model is {0}===================='.format(mae_lr))\n",
    "    print('===============The Root Mean Square Error of Model is {0}===================='.format(rmse_lr))\n",
    "    print('===============The Pearson Correlation of Model is {0}===================='.format(pc))\n",
    "\n",
    "    csv_tag = time.time()\n",
    "\n",
    "    mkdirs_if_not_exist('./result')\n",
    "    df = pd.DataFrame([mae_lr, rmse_lr, pc])\n",
    "    df.to_csv('./result/performance_%s.csv' % csv_tag, index=False)\n",
    "\n",
    "    out_result(list(test.keys()), predicted_label.flatten().tolist(), test_label, None,\n",
    "               path='./result/detail_%s.csv' % csv_tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the model\n",
    "import pickle\n",
    "filename = 'scut55_trans_all_crop.sav'\n",
    "pickle.dump(reg, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0887281726881683 2.8\n",
      "2.5783428063928544 2.3666669999999996\n",
      "2.355161193655504 2.6166669999999996\n",
      "2.5371307592391625 2.816667\n",
      "2.694794877098517 2.4\n",
      "2.8657352162347998 2.5333330000000003\n",
      "3.220483191301966 2.333333\n",
      "2.7268656599121597 2.8\n",
      "3.7866580520582813 4.5\n",
      "3.3301923831508726 2.75\n",
      "2.960550960408881 2.9\n",
      "2.7670634227014705 2.433333\n",
      "2.912454222489985 2.666667\n",
      "3.0129349446764504 2.733333\n",
      "3.2741671941458472 2.9666669999999997\n",
      "3.7162459708494935 3.583333\n",
      "2.357379569685593 2.266667\n",
      "2.1382603162534184 2.2\n",
      "3.7063184280771315 3.833333\n",
      "2.9521583357186305 2.8833330000000004\n",
      "2.236983638355687 2.2\n",
      "2.5577946276254457 2.4666669999999997\n",
      "2.967433328734728 2.733333\n",
      "2.9815107886349876 2.75\n",
      "1.9700073358517933 1.9333330000000002\n",
      "3.4920970937719518 3.9\n",
      "3.630418686980744 4.05\n",
      "2.857047726343687 2.5333330000000003\n",
      "2.951463680756069 2.983333\n",
      "2.8700501262098035 2.6\n",
      "3.383948624347564 4.0\n",
      "3.1059365212735317 2.666667\n",
      "2.624748885698361 2.766667\n",
      "2.6486704349582677 2.6333330000000004\n",
      "2.725471295033729 2.983333\n",
      "3.2283701540328904 3.166667\n",
      "3.7709259448507706 3.8833330000000004\n",
      "3.206768171517209 3.266667\n",
      "2.5806205489232017 2.8666669999999996\n",
      "2.957546669993098 2.7833330000000003\n",
      "2.306967814621729 2.433333\n",
      "2.6450250463299128 2.683333\n",
      "3.579596871956232 3.933333\n",
      "3.015404775391317 3.15\n",
      "3.544350147580926 4.2\n",
      "3.7265420095232367 3.983333\n",
      "3.628110438193747 3.0333330000000003\n",
      "3.2506853605742876 2.683333\n",
      "3.5669926842158937 3.066667\n",
      "3.1785384809371924 3.266667\n",
      "2.7810903175572146 2.7\n",
      "3.5335422181495577 3.9666669999999997\n",
      "2.527550525600486 1.7166669999999997\n",
      "1.9762492067289414 1.833333\n",
      "3.0085831647148793 3.15\n",
      "2.2464977525161443 2.3833330000000004\n",
      "2.480575641999996 2.5333330000000003\n",
      "2.976722541871397 2.566667\n",
      "2.613073557678832 2.583333\n",
      "2.5525049959352617 2.6333330000000004\n",
      "3.1537194701791553 2.433333\n",
      "3.0570096499287236 3.05\n",
      "2.2668148568323225 2.083333\n",
      "3.6398362718838255 3.683333\n",
      "3.701572185709799 3.75\n",
      "3.468498414047164 3.483333\n",
      "2.46741964250203 2.5\n",
      "3.269546540574412 2.6166669999999996\n",
      "2.780777662319068 2.8666669999999996\n",
      "3.5986145330071566 3.816667\n",
      "3.94675400134086 4.3\n",
      "2.9367917244878834 2.8833330000000004\n",
      "2.4079391522240954 2.2833330000000003\n",
      "3.6805800215095075 4.066667\n",
      "2.590200909093956 2.683333\n",
      "3.484788848919596 3.816667\n",
      "3.161215257686697 3.266667\n",
      "3.0858553764949317 2.9666669999999997\n",
      "2.736183054087937 2.316667\n",
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      "2.749527662447877 2.316667\n",
      "2.373915335294284 2.7166669999999997\n",
      "2.0445135720445933 1.6833330000000002\n",
      "3.0647993843806876 3.166667\n",
      "2.366102480606311 2.416667\n",
      "3.30388184784069 3.2833330000000003\n",
      "2.186008766680784 2.4\n",
      "1.9644981283194685 2.1333330000000004\n",
      "2.6425586289963894 2.5\n",
      "3.088716120577684 3.266667\n",
      "3.5119814332294292 4.016667\n",
      "2.6302468265964736 2.3\n",
      "3.787691457946358 3.95\n",
      "3.456355193359375 4.383333\n",
      "2.5973746027551767 2.316667\n",
      "2.1126376847076522 2.516667\n",
      "4.04007389590335 3.933333\n",
      "3.419669889114349 3.566667\n",
      "3.930858615013438 3.933333\n",
      "2.313865242207142 2.566667\n",
      "3.5486906817717365 4.2\n",
      "2.563776621114418 2.8666669999999996\n",
      "3.115881643597084 3.233333\n",
      "2.267854220423818 2.266667\n",
      "2.6145392519082185 2.75\n",
      "3.16673580519854 3.0\n",
      "3.538766830431458 3.95\n",
      "3.348635164464924 3.233333\n",
      "1.979323447816597 2.4666669999999997\n",
      "4.646845169880439 4.4\n",
      "3.3011523362531863 3.683333\n",
      "3.855776865834629 3.7\n",
      "2.8365333446379135 2.6333330000000004\n",
      "3.114210768838909 3.0\n",
      "3.254409397369418 3.55\n",
      "2.6502035927985057 2.3666669999999996\n",
      "2.561247122169214 2.666667\n",
      "2.9627652440689665 2.9\n",
      "4.085524991505848 4.183333\n",
      "2.6497743955735826 2.666667\n",
      "2.832481861390099 2.6333330000000004\n",
      "2.406728010303203 2.233333\n",
      "3.1413049062810465 3.6166669999999996\n",
      "1.5887153885484109 1.116667\n",
      "3.5043649154122614 3.4\n",
      "2.9950829217424886 3.066667\n",
      "2.7730344068914983 2.9\n",
      "2.603003385917928 2.9666669999999997\n",
      "2.7489472643408797 2.1333330000000004\n",
      "2.721434693608592 2.4666669999999997\n",
      "3.7918247931106874 3.166667\n",
      "2.3280060572653287 2.016667\n",
      "2.5912928155783974 2.666667\n",
      "2.3770912569262137 2.483333\n",
      "2.50063144275898 2.3\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "while i < len(predicted_label):\n",
    "    print(predicted_label[i],test_label[i])\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5405\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "list = os.listdir('/home/ubuntu/mtcnn_aligned_images_SCUT/') # dir is your directory path\n",
    "number_files = len(list)\n",
    "print (number_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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