{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a3ffbc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import gc\n",
    "import json\n",
    "import cv2\n",
    "from tqdm.auto import tqdm\n",
    "from HCFA_OCR_XML_to_DataFrame import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8467e426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8c63fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "HCFA_final_keys = pd.read_excel(r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_Keys_list_verification.xlsx\", sheet_name = 'Field_Names from KEY file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f24fa208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_names = HCFA_final_keys['Key_Name'].tolist()\n",
    "len(key_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32b3d5d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_ground_truth(row):\n",
    "    ground_truth = ''\n",
    "    try:\n",
    "        if \"?\" not in str(row[\"OCR_Optimizer\"]):\n",
    "            ground_truth = row[\"OCR_Optimizer\"]\n",
    "        else:\n",
    "            ground_truth = row[\"Data\"]\n",
    "    except:\n",
    "        ground_truth = row[\"Data\"]\n",
    "    return ground_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edc973bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1eec6362",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_path = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\train\\key\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a409aab1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "69a5ae0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7f75da0fab544f8a81ecde645b6a492",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/27919 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 26.0 MiB for an array with shape (1, 3408809) and data type object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m         overal_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([overal_data, df], ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     13\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m---> 14\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m     15\u001b[0m c \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m c \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "Cell \u001b[1;32mIn[14], line 12\u001b[0m\n\u001b[0;32m     10\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImage_Name\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m images \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(df)\n\u001b[0;32m     11\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mXML_File\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m filename\n\u001b[1;32m---> 12\u001b[0m     overal_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([overal_data, df], ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:385\u001b[0m, in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m    370\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    372\u001b[0m op \u001b[38;5;241m=\u001b[39m _Concatenator(\n\u001b[0;32m    373\u001b[0m     objs,\n\u001b[0;32m    374\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    382\u001b[0m     sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m    383\u001b[0m )\n\u001b[1;32m--> 385\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:616\u001b[0m, in \u001b[0;36m_Concatenator.get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    612\u001b[0m             indexers[ax] \u001b[38;5;241m=\u001b[39m obj_labels\u001b[38;5;241m.\u001b[39mget_indexer(new_labels)\n\u001b[0;32m    614\u001b[0m     mgrs_indexers\u001b[38;5;241m.\u001b[39mappend((obj\u001b[38;5;241m.\u001b[39m_mgr, indexers))\n\u001b[1;32m--> 616\u001b[0m new_data \u001b[38;5;241m=\u001b[39m concatenate_managers(\n\u001b[0;32m    617\u001b[0m     mgrs_indexers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnew_axes, concat_axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy\n\u001b[0;32m    618\u001b[0m )\n\u001b[0;32m    619\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m using_copy_on_write():\n\u001b[0;32m    620\u001b[0m     new_data\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\concat.py:232\u001b[0m, in \u001b[0;36mconcatenate_managers\u001b[1;34m(mgrs_indexers, axes, concat_axis, copy)\u001b[0m\n\u001b[0;32m    226\u001b[0m vals \u001b[38;5;241m=\u001b[39m [ju\u001b[38;5;241m.\u001b[39mblock\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;28;01mfor\u001b[39;00m ju \u001b[38;5;129;01min\u001b[39;00m join_units]\n\u001b[0;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mis_extension:\n\u001b[0;32m    229\u001b[0m     \u001b[38;5;66;03m# _is_uniform_join_units ensures a single dtype, so\u001b[39;00m\n\u001b[0;32m    230\u001b[0m     \u001b[38;5;66;03m#  we can use np.concatenate, which is more performant\u001b[39;00m\n\u001b[0;32m    231\u001b[0m     \u001b[38;5;66;03m#  than concat_compat\u001b[39;00m\n\u001b[1;32m--> 232\u001b[0m     values \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(vals, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    233\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    234\u001b[0m     \u001b[38;5;66;03m# TODO(EA2D): special-casing not needed with 2D EAs\u001b[39;00m\n\u001b[0;32m    235\u001b[0m     values \u001b[38;5;241m=\u001b[39m concat_compat(vals, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 26.0 MiB for an array with shape (1, 3408809) and data type object"
     ]
    }
   ],
   "source": [
    "c = 0\n",
    "overal_data = pd.DataFrame()\n",
    "for filename in tqdm(os.listdir(folder_path)):\n",
    "    if filename.endswith(\".KEY\"):\n",
    "        file_path = os.path.join(folder_path, filename)\n",
    "        try:\n",
    "            df, single_fields, grp_field_names, table_field_names, images = OCR_XML_to_DataFrame(file_path, key_names)\n",
    "            df[\"Ground_truth\"] = df.apply(generate_ground_truth, axis=1)\n",
    "            df.replace(to_replace=[None], value='[BLANK]', inplace=True)\n",
    "            df[\"Image_Name\"] = images * len(df)\n",
    "            df[\"XML_File\"] = filename\n",
    "            overal_data = pd.concat([overal_data, df], ignore_index=True)\n",
    "        except Exception as e:\n",
    "            raise e\n",
    "    c += 1\n",
    "    if c % 100 == 0:\n",
    "        gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c72f8d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "overal_data.to_excel(\"HCFA_BSC_Test_GRND_T_from XML.xlsx\", index=False)"
   ]
  }
 ],
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