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DECO
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd \n",
    "import ipdb\n",
    "import os\n",
    "import pickle as pkl\n",
    "import os.path as osp\n",
    "import numpy as np\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images found: 5459\n",
      "Images after KP filtering: 2386\n",
      "['vcoco_000000051899.jpg', 'vcoco_000000093127.jpg', 'vcoco_000000455665.jpg', 'vcoco_000000248389.jpg', 'vcoco_000000403947.jpg']\n"
     ]
    }
   ],
   "source": [
    "# Load Agniv VITpose-base hot dict\n",
    "filter_path = \"./agniv_pose_filter/hot_dict.pkl\"\n",
    "with open(filter_path, 'rb') as f:\n",
    "    pose_md_dict = pkl.load(f)\n",
    "    \n",
    "vcoco_dict = {}\n",
    "\n",
    "for k, v in pose_md_dict.items():\n",
    "    if 'vcoco' in k:\n",
    "        vcoco_dict[k] = v\n",
    "    \n",
    "print(f'Total images found: {len(vcoco_dict)}')\n",
    "\n",
    "# Filter out images with < 10 visible keypoints \n",
    "kp_thresh = 10\n",
    "\n",
    "filter_img_names = {}\n",
    "\n",
    "for imgname, kp_md in vcoco_dict.items():\n",
    "    if kp_md == 0:\n",
    "        continue\n",
    "    if kp_md[\"num_persons\"] == 1 and kp_md[\"num_kpt\"][0.5][0] > kp_thresh:\n",
    "        filter_img_names[imgname] = kp_md[\"num_kpt\"][0.5][0]\n",
    "        \n",
    "print(f'Images after KP filtering: {len(filter_img_names)}')\n",
    "\n",
    "print(list(filter_img_names.keys())[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=1.30s)\n",
      "creating index...\n",
      "index created!\n",
      "Final number of images 2368\n"
     ]
    }
   ],
   "source": [
    "from pycocotools.coco import COCO\n",
    "version = '1'\n",
    "out_dir = f'./filtered_data/v_{version}/vcoco'\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "coco_dir = '/is/cluster/work/stripathi/pycharm_remote/v-coco/data'\n",
    "coco = COCO(os.path.join(coco_dir, 'instances_vcoco_all_2014.json'))\n",
    "\n",
    "imgwise_object_names = {}\n",
    "img_dir = '/ps/project/datasets/HOT/Contact_Data/images/training/'\n",
    "\n",
    "bad_object_names = ['bird', 'cat', 'dog', 'horse', 'sheep', 'cow', \n",
    "                    'elephant', 'bear', 'zebra', 'giraffe']\n",
    "\n",
    "# # Get all coco objects\n",
    "# count = 0\n",
    "# for id in range(91):\n",
    "#     try: \n",
    "#         print(coco.loadCats(id))\n",
    "#         count += 1\n",
    "#     except:\n",
    "#         print(f'ID:{id} does not exist')\n",
    "#         continue\n",
    "# print(count)\n",
    "\n",
    "objectwise_img_names = {}\n",
    "for img_name in filter_img_names.keys():\n",
    "    img_id = int(os.path.splitext(img_name.split('_')[-1])[0])\n",
    "    ann_ids = coco.getAnnIds([img_id])\n",
    "    anns = coco.loadAnns(ann_ids)\n",
    "    object_list = []\n",
    "    for ann in anns:\n",
    "        object_cat = coco.loadCats(ann[\"category_id\"])\n",
    "        if len(object_cat) > 1:\n",
    "            ipdb.set_trace()\n",
    "        object_name = object_cat[0]['name']\n",
    "        if object_name in bad_object_names:\n",
    "            continue\n",
    "        if object_name != 'person':\n",
    "            object_list.append(object_name)\n",
    "    if len(object_list) != 0:\n",
    "        imgwise_object_names[img_name] = list(set(object_list))\n",
    "        \n",
    "    for obj_name in set(object_list):\n",
    "        objectwise_img_names.setdefault(obj_name,[]).append(img_name)\n",
    "        \n",
    "#         # Visualize images \n",
    "#         print(img_name)\n",
    "#         print(list(set(object_list)))\n",
    "#         img = Image.open(osp.join(img_dir,img_name))\n",
    "#         display(img)\n",
    "    \n",
    "    \n",
    "print(f'Final number of images {len(imgwise_object_names)}')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./filtered_data/v_1/vcoco/object_per_image_dict.json\n",
      "saved at ./filtered_data/v_1/vcoco/vcoco_imglist_all_170223.txt\n",
      "saved at ./filtered_data/v_1/vcoco/imgnames_per_object_dict.json\n"
     ]
    }
   ],
   "source": [
    "# Save the imagewise_object dict\n",
    "out_path = osp.join(out_dir, 'object_per_image_dict.json')\n",
    "with open(out_path, 'w') as fp:\n",
    "    json.dump(imgwise_object_names, fp)\n",
    "    print(f'saved at {out_path}')\n",
    "    \n",
    "# save image_list \n",
    "out_path = osp.join(out_dir, 'vcoco_imglist_all_170223.txt')\n",
    "with open(out_path, 'w') as f:\n",
    "    f.write('\\n'.join(imgwise_object_names.keys()))\n",
    "    print(f'saved at {out_path}')\n",
    "\n",
    "\n",
    "# Save the object_wise dict\n",
    "out_path = osp.join(out_dir, 'imgnames_per_object_dict.json')\n",
    "with open(out_path, 'w') as fp:\n",
    "    json.dump(objectwise_img_names, fp)\n",
    "    print(f'saved at {out_path}')\n",
    "\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5072\n"
     ]
    },
    {
     "data": {
      "image/png": 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y/EhgWWG65TlOREQ6UGe50Gw1xnnNCc3OMrOFZrZw5cqVDU6WiMjmpaODwnNmNgIg/6/I8cuB0YXpRgF/rbUAd7/Q3Se4+4ShQ4c2NLEiIpubjg4Kc4Az8vMZwNWF8SebWU8zGwuMAxZ0cNpERDZ7Dbv7yMwuBQ4BhpjZcuBrwDeAy83sTOAp4AQAd19sZpcDDwFvA2frziMRkY7XsKDg7qe08NVhLUx/HnBeo9IjIiJt02suZL1989LKZxc+f4qeXRDZVCgoSLv4j19XBoqvnKRAIdIVKShIw3zpN5W/APd/TtAvwIl0dp3lOQUREekEFBRERKRMQUFERMp0TUE61GeuqLzO8N3jdJ1BpDNRS0FERMoUFEREpEzdR9J0p1xV2aV06XR1KYk0i4KCdEpHXV35lpTrpl3apJSIbF7UfSQiImUKCiIiUqbuI+kyjrrqnIrh66af36SUiGy6FBSkSzv6qnMrhudN/3qTUiKyaVD3kYiIlKmlIJuco6/83xXD82Z8tUkpEel6FBRks3D0lf9ZMTxvxhealBKRzk1BQTZbU67474rha4/7pyalRKTzUFAQKZhyxffKn6897mymzL6g4vtrZ36yo5Mk0qEUFETW0zGzf1IxPHfmmU1KiUj7U1AQaQfHzL64YnjuzDOalBKRjaOgINIgx8z6VcXw3ONPbVJKROqnoCDSgY6Z9euK4bnHn9SklIjUpqAg0mTHzppdMXzN8TOblBIRBQWRTmnqrKsrhuccP42ps66tGjelI5MkmwkFBZEubNqsGyuGrz7+iCalRDYVeveRiIiUqaUgsomZMft3FcNXzvwgx83+Y8W4K2ZOYubsuyrGzZ65b8PTJp2fgoKIlJ0w+4Hy59/M3IOTZj9W8f2vZ+7Y0UmSDqagICLr5RNXPFUx/KPjtuMrVz5dMe4/ZozsyCRJO1JQEJGG+NaVz1YM//OM4U1KiawPBQUR6TAXXrGiYvis47ZpUkqkJQoKItJUl8xeWTH84ZlDuXLW8xXjZhw/hOt+XTnuqJOG8NtfVc576KlDG5PIzYiCgohsUv7488pAMen0oSy8qLKFMuFjaqG0REFBRDZLiy6oDBS7f1KBAhQURETKlpz/XPnzuHOG8dS3Ky+Wb/e5Tf9ieacLCmY2GfgO0B34sbt/o8lJEhEpe+b/Lq8YHvG/RvHsN5dWjBv++ffy7LcXV4773Hie++97K8YN+6e9G5PIjdCpgoKZdQe+BxwOLAfuMrM57v5Qc1MmItIYz33nzorhYZ99f5NSEjpVUAAmAkvd/c8AZnYZMA1QUBCRzcZz//OHiuFh//ABVnx3fsW4bT5zGCvOv75y3DmTWfG9OZXjzp66XuvubEFhJLCsMLwcaG7YFBHp4lZ8f1bd05q7NzAp68fMTgCOdPeP5/BHgInu/pnCNGcBZ+XgTsCj+XkIUHkj84aPa89ldcVxnSUdzRrXWdLRrHGdJR2daVxnSUd7jdve3Ws/1OHuneYP2B+4oTB8LnBunfMubK9x7bmsrjius6RD26/t7yzjOks6GjGu+q+z/Z7CXcA4MxtrZlsCJwNz2phHRETaSae6puDub5vZOcANxC2pF7n74jZmExGRdtKpggKAu88D5m3ArBe247j2XFZXHNdZ0tGscZ0lHc0a11nS0ZnGdZZ0NGJchU51oVlERJqrs11TEJHNjJlZs9Mg63TJoGBm/c1sWGG4ZqYysy3NbJSZtbmdZjbMzAYUhseZ2VYtrLt7G8vqVjVsrQ2vLzPrs77La4d1trSP616umfWsGt7bzIZsTLrWV97E0D0/N6Uwqne9rU3X3sfczHqbWUO6k82sm5ltYWZbVI3f1swGu7u3kr96mdlgM+vfyvLfNe96bvuW1WnrCGbWz8z61RjfavlSx3I3Kl93yaAAbAnsUSrEPfvAiid8GgTsC4wojTCzgdWBwsx6AzOAozMTDgUmABPNbOucZiczGwfMzHVbjh+VhdtYS8ChVYXdljlt96r07lw8gGY21Mz2qlF47mVmvQqj3mdme5QGCsvrX5inv5mNMbM9i9PUmK5vpn3b2ru6rFSQDsv/lifSITXS27d65pxvqpltn8MDgAOAw4rTt3CSDK0aHlBaTkty+wfWCOwjgRlm1sNb6TttYRsmmtmI/Fw8bluU/pvZIWb2ntLxqjWOdfuyVmHWszC+NN2+Vfm1HzCp6lhsXaMwGWZmB1pUcIrprT5e2wInAoNzeOvSeVI1XWmbisvaoVaBXbWO/sBkYF/LCk0elxOBz5rZkKr82TP/9yfO35nAB/I8fZdC/j+0sA+6m9lWZjawsNwBeU58oGoRuwFH5vldmnaQmY2vtb6q7exXtT/65j4fUTpmZjY691MxLSOBTwFHWJYxOX4IsEdheFT1ccjxPQqft7IIsLtW7Y/xtc6ntnSZoFB1Au0E7APsmSfEMDPbnyhkiid6L2AMkaG650l0OpFBS4X9cOJhuNXAk+7+BvA28BrQGygFkLeBHXKZawqZ+DDiIPYFSgVNL+DDZjYop9nXzKYBMwsZ/v3AacCOOdwr0z8ZKL/D18xOyW14s7BdfYnCeOfMhAeZ2enAtEJh9hbxNPgky8CQyxsAfNLMPpij3sl1zjCzvXKa0Wb2fjObbGZ9stA72Mw+AZxpZsM9vJXb+v4szAaY2SQiuO5bWOdWwH7AcKBbZvLDgRXAI+6+2qKm+vHcR4MK8+4PHJfLKBVgRwIn54lV3k9mtnshn/QBjsu0jCltFzAW2BnYq1QA50m8W2FZw4ETzezgHB5qZqcSr1zZ1syscOIdDhxuZt0L++N0oHSCvkXko/I44AAzOym3q1S52NbMjgE+TwT9AZmGScDf5XZ0y+0/hngIqVRodSMKzsPNbDDrTM7xu5I3lZjZEbnvSoGsL/CBnKZUuPcknhmaamY75XTDgI+a2bDCtk8GJgHTrTJoHQIcX8iLB2eaxwGDcr4PA68TbzA4Nretb+7ngy1uSR9HnOergbfc/fVc/lQz+3RhO8l0vg/YO+edAvwjUdj3ynEjgGFEpW5IztcLOATYncJ5B4wGdqrKxzOLgcLMtgE+C+xT2n53X53zfgjY0SIIHgjsCYw0sx657gOBVcD97v5SLm8Qcax3sahM9GZdwOqVZdgWZnZUHpti6+ag3G/jc1kfJs659S7jO93dRy0pZMSJRMF/K/BwNj2nAFsBzwBbAGssno5+C7gJeBYonUz9gGfd/cU8CLsCLxAPdTySGfkAoqC/3t0Xm9lxufzewB+AR8zsQzkf+f9Rd38rM/Uw4CVgbX6/FVFDfZGowZyS2/BT4FmL2ueOOf1d7r4st/VY4qS+BOhjZqtZV4t4Obe3P1FAvA1Y/gcYBQwAHgOW5PKGA8cDTxKtoFWZpkU5z5L8/zaRsYcSJ+1yoPRO4WW5bsxsKlF4PEY+JUkUxg8CT2em3ZoodN4Bnsr1zcjt+KG7P5rH93Uzu5nIyG/k8o8kCvHFeUy7AR8kguKt7v50TlcKIqOAJ7Og3YM4yR8DnreoMe2X6Zvn7vdY1Nw+ncfnt7n8/kSgLx1Xcl9sCTycy+th0VLrx7qn6s3MdicC3QLgiUzb7kT+WwA8kYWEE62AVbncNTnNC7nt77j7qtyOkcD5wF+Jwnrv/Puxu5eO13uIgnM18GZux2653IeBP2TePCWPx+NEcB5K5IengBvdfWkG3wNzH68BXsr9NDHT2h94LgumHYCrgBfc/Z3c3iOIc+1xYGczex+wkjhfryPy1vZEXn3E3W/JQDYSeG+mrxeRpyfmsbmulE8y4LxAPM/Uz91fNbOdieB0C5H31gKPEPn/KXd/IysEuxDn5Y3u/nwG2PGZvuvc/flcxylE3r8L+HPuzx1zPw82syWZzsOIc2FtYfv3Iypw3Yieij0zj8zPfDKaCF7DiXNhTc63F5E3lxHnY0/gzMwjz+Q29Mp5hxD5rDtRxu1DBNA7iPNkpzxOf8nv10unDwpVtbIZwNHAzcBsYAczK2WkN4iDvSaj5SQig1xHHJxS03AB0M+iqbUbMBC4HViZhf82xEG9zt0X5oHYncg4i9z9wUzLO0Rt4E9EQHnLzCYQ+3QhUZC9kge7N1EregGYSmT6N4Fl7v5mnpxHAPcAj5tZT3dfk2kdBPwul3EmceJcQpwAfXJ4DHESzs/tP43IlH/L9PXIgvO9xAn7aO6/gUSBfR8R1N7MAmBSTnc38TLCCUQme4Io/N/I7epJFFbXE7XSnYkM/SRx0u9OZP6dgMuIwuAYYClxspZeUQKAuz9pZsvcfW0WJu8lMv6jRIG/d27TwkIhcXSudwlxUvQCPkKc/H8kTuw+wAm5T/7k7vfkKlflfpgE3E+cbAfldIvc/UGLlsqemYYFWRMstfSeB57OfbwLUZguJvJnqevuAOA2Im++ZdH62J0oiG/L47U1EVyXAddm5eSwPKbbEvnmwPzcJ5dfemnkiUSB9QpRAeoFfJQIptcAV2XAHUO0fp8nAtuAPC7PA0vc/bEs/I7O7f0Ncf7slPv9LeD2DBx75L5aBfwtW9dk/h+e6Vzq7neb2T65jAsyT0BU3Ba7+y2Fw38cEWTuJgrK3YhzeiURGErHeiSR97+f23V0rm9rIt/1IPLiCuBmd3868/6k/JsPvJVlxHtKx7YQEN5HnHPP5rHsCXySOFeeJG6XH0CcE0Nyv6/KecflsVhFlDu75LY8CNxLnMOjc/sXZFpfzJbDh3L5pYrBQUSQv5Z1lZODiHJiLpGP38hKx3uIAHBPHr/XiHP6gVLran10+qBQCAgfICLrfCLzdyO6IPoRB+/erP2XmsKzc3xvoqm6jKgxPZg1iwOIKP8nd1+U67gNOJU4aRZlJu9LHJyn3f0POd0eOe4Bcsdn4XFIruc+IjOdQ5ycc4lMMhj4GFG4XuXub+Y2PmlmX3f3V6u2/Tkz+6W7r831Xkk85f0UEVT2JDLYIqJQW52ZfUV+f3fWpA7LdQ8jHghcatFVMZA4ya7OgDCI6JoZkst8DPgMcVI/TAS7nkQTt1ToXOvur5nZfKLAmpv74z3ESVia7y+5f/Yhuunm5zaVg35u89osTHcjCoR5mfYDiJPs94WAsDdRkLxAFACl7oXZRNfZPUTtdiJRe7yvEBBKeWuZmf0mj/O43NYbskIwk8hfTxGBqBQQjiFqiRcRFYqTiJbQm0QQ9Vz/c0SBcH8GhKOIc24JcEd2mw3O4zieqCQ8kgX9/sRDnHOIgDwgl/d0FraHmdkbRD6clfOW0ndVpum+PBYH5fAi4MeZtmFE0J3r7o/l/njHzO7I4/94prXU7Tfb3R/KY/PhPPb3Ffb5JCJv3k4UeCMsul5+lPuwP7AXUfD9uBRIcr0v5DGbQRSC3YjKyj25fc/m+XU0cDnwhMeDrvsQlbhXcvyWud/GEq2Xpy26vT5GtKovz2UbEUjuJ1qNb+c2nEoEyR8CVhg/j6hU3JDH9hQizy0gzocVmb6euY+X5fR9icrEn4iguiNxjqx09zty2YPzOKzMeUuV0N2JwPm8RRfuzEzbI0SZ80a2Snrk9lxBVLp2JQLrfVmxXG+d+jmF3GE7EgdiEnFyXlOombyXqBVcSNQW9yOidG+iEN4/h48gCqIfWfTVnkwcwBuJgq8vcaCvJ2o+r2TTfThx8t9Zaqpnl8ZU4JdE99WqzBBjidrp7939lZx2O6IveS5RsG1J1GhucvdS9K/e5opCssb3PYjC/GSiQLwO+D1x0n2YKNh+QRRiay26w04iuoBecvc7Lfp0pxKB844s1IcQ/bB9iBrx3bm+UTntpe7+YmG7TgJ+4u5/q0rbyFxX6frII0TtfkUei12IFs3qFrbvcLIbiAj0q7Lp/3GihnwHcWLvQBSkLxNdbquqlrNDLmcH4qS5290faGGd2xK16/uILo3HM5DuQ5zQdxVrXBZ9yUcTwfTFrIXvQQSTUnfATkS++ylRq3w198mLRC28O+u6qhYT+fxNIsh3y314P1EoTsttXeLul+fyjyWCyT1EfqroJrC4kF4q1IYTeeOW7DY7kMir/d399hr7owfRfXIg0d3zOhHY9yJqvmNz218qzDMYOIrIj3sSQWw1UavfgmghvgCscPf7a6yzd+6T3XO7uxfOo9Ny3/QlgtPLFt1Uh+T2X5/78kiiBfU6cQ3ivszXHycCx68yLTOI8/GGUiUh9+cZRIv2ilJ3UNU+GU0U4LvlPi/1GuxLBLGlucw1eY7sT1Rq+mVa7wP+XMgjI4iL7YuJQLIqp1uT05Xzq5mNzX3zOyJoH0yUed8izq8PZrpW5bF5sXof16tTBoVSwWjRfziR2Kl3EjX9l6um3SJrYbsThRrAD9z9pSxMTiOuA2xDRO3HiKbqWnefZXGNolSLft7d52YGP4c4oZcVWisfJCL+1kTXxPas62t8mKitVhR2WVM5mThB7iCa1RsUwauWux2RoS4qFcw57mSiJva3HB5P1H7vcvc/5nQjiJP0CuJkGkYUoKOBx939mqp1lQuYNsZtTwTvpcSJPZFoig8jTuhriQy/vNT6qZr/RKIr7CGiAHu18F0pfc8Qmd+IAvYed3+txrL6EwX3NkRN/fet7MsRxEXJ+4g+8plERWQJWSurMU/34jZUD+e4Ut6cAhwKXE10I6wFjiUqJsOIgmNVbt9kooC4hMhnfYmujP2Igra0D0cQheBV9RQA2TV0TG7XQHf/eeG7ioqIRRfioUQF4U6PrqWJRIG4BHioRj7vS3R77E30e0Psy1Jg2Z5old9VI23b5TxriBbXktzm14lgOyHH3ZMt39L1jAeJfPYW0f3Sh6g43g+8WWqJW1zH6ZPL35fY/w8WK2Z5nk4G5tTanxY3NfyaOGcWEMHodSKojyS7b4rlk8WdUIOJ83Qb4hrl7fldb6Ic6kX0fvQgAvdaoqL22xppKN2M8hJxbvXMNB1OVIRXEmXQy9Xzro9OGRRKsnk0kjg5rirWSqum+wBxcK4janrFGszArM1NIWoJ9xEFy35EpppCnJB3ZnfNKCKD7kHU7C/y6N7pw7prE3/M7papZFOVaMGU11uVvm2JKP+HWgXYhmqtsLZ1d2BsQ2TWu6qm654tiW2Igrw7cX2gB9EH7q21WFpIz3Bi3/UkCvY3iNrlK8DbrRXMOf8oovZzW0sFMevuHnqeaCLXmm5rIhAeQQSEa6qnqTHPYKIAXk4UOLsRXQtvtjpj7WUVr4NNJfLRWCIvvZDjRxO3JF7r7rdn10UvsoVEBL39iTx3KVGYe1uBqIX0bE8EkV2Jc6A3cc1kfo1phwPTidrn7e6+PGvqA4gupVu8Rj+1RZfqTkQF6QHiGL6cx/QwovX5cI35BhIF3VAiv9yTQWgo61q+C4FZGRA+QLTgtyRaCCOIlslLRGH6rha4xQ0PxxL78jWioJ7t7n+tmq7F/Zl56u+By9z9iUzfKURQX0i0HIqVmG5EkN+PCKZ/ISpmj2YAGkhUdtcQefmg3Mf3Az3d/dYawbofcWyGEGXOtVn5257oEryu1LraGJ02KGRzaQxx10Lpdr/Sd8WTbjKxM98iouS7CmaLOyrOIQ7Mox79xX2IbpGxwJUefbnbEk3Lp1l3PeIlomCbSNwF8iwRqEp356wiuiZqdocU0tCtuknaCFljOyrT+A5xYpe6gqozWV+iJjee7FrYiPX2I2pr44ng/BbRxB0M/MLX/ZpeW91jLe6nDOwDiRbCzdUtrtyeKUTQ+D1RsG3t7n9oa7011lVXgdvGMkrXJO4kWkevVn0/KE/q/Yla4y1EF9lWRN5cTRTeG/VSyCzAphHH4g8ZhLYsBrxC98huRCH7iLvPyu/eQ9Swry0WOjWC31hin8+pqpj1aakylMHxKKJ1tMTdlxa+247om7+IdV1LuxD76WHivJ9KXB+41Wt0hZXSSQSRycR5/RLwevXxaIuZbV21XdsT+/WiGsd2WyJAbkN0Bd2bwWkLIkhuTxTij2eQOJWowMyvlVezQjSeaNG/TrS6il2aG51fSzrlhWaLC3kDiKhuXtVfWsiIuxE76U0iI9bqRhjIugs+T2VAGEw0pZcQB+yRzJw7E7XcZ3zdNYS9iP20lGiSusUdJKOB39JC90K1jggIuZ7VZnY7kVkfLAWE/K6030qBY1ei4Lnb1/Wt1l145sk2mGjeb0t0E8wnAufOuex7SgGhmIZW0t/aflpEFE53VAeEnHe1md1JdKv0JyoUPepZb41lbWxA+DCxjy8lugzfrp6m0PJdQtQQbyNqxqU+/3u96g6tDUjHQKIW2ouorZcKzmIlaxRR6B5AXPtYAAzPQLE2C64nq/dJIT8dRhz70gXO6hsmWgoIfYkAtE9u69Kq+Z4ys+9ky/cYooB9kWjNDSVq4auJWvrCFtYxmOhGvJVozW/weVhd4XT3v5jZD6rLp1QKYgvd/d4cN564ZvAykS8Pzv36nJldWCtIFc7HA4nz9R6iolDRWmuvgACdtKWQNYR9gd95y11GJxCZ+FHgN7WajTldqe/1Go+7HIYRUfpDRA3uCaJWPZIIRIt93V1GHyIy7d1EE3FbovVQuo5w/vrWNjqKmfX2dXeG1Kp5jAQ+R1wYu3Ej1jOUqM31ILrmHiS6bZ4n7pR5uKU0bOD62mxxlWrgG7uujZEF7aFUXZBtZfrSheePEBea57r74xux/h5EV94uRGt2UTE45zSla3dHEMHoZeIW3keJBzHbvMfd4m6304lAcm09FaRM2wSiBb4EWFWsvNSY/kSiFfJHomttIHGjw2qiS+ZdLeHsHehPtCTeILqzltRYfMOUzsHs9fgb0cUznmg5P295zamNZfQhniXpTRyfud4OXUStrrMzBgVo++TPptsU4BKvuvOkxrTlZl8GnClEV9JjRBfLkUTNbEGptlLoqlhO3IHyDnHC7kzcjndPZw0I9SoWnhtSaGdA2IU4YRcR12r2J1pRd7j7gnZO8nppr0C0Eetfrya9xS3BnycKsA15fXz18kYR/fK/8xoXeHOabYk7Z94gum8eqmO5fYkujEOIStOLrOcdL1kpOZXoQindEl7zeOV27EJ0fb2RaZ5GdCG+q6DPgnQ6cS2rP9EKedf1jI5icYPKGPLOqPU9L2zdNYN5HVHmdNqgUA+rcaG1zvl6+brbWocSD4UtIE4ezz7eccSdDAtKzd8MKNOISL+qnTaj6da3yyj3UT/iVr+3Wdc3OprYl/OaHRC6qvZu5Vg+9dvK92OJu2Mudfen6lzmUOLa2zvErca3+Abc8WJm/eut9VZXElu7TpHfjyJ6AxZ63jraDBlAP0Z0S99Z6oXYgOW02zWDNtfVlYNCeyllzqxhnEB0I91JtAberJp2gwLRpsTiTozSw29L3P2GwnejgNHufkeza+pdWUfuu/UpnAvzjCXywM8a3Z2xodoKHB2w/qHE7aJ9iOeXHmtWWtaHgkJBXjgdRVyruLardw+1t6z1HAGUHux7wN1vqzFdh9VqpHnq6RPfXOW1zP2J8+RBb+N27M6ky7wltSN4WEbeE93s9HQ2Hrfd3k08oLSgFBAymBanU0DYDCggtKoHeZNKVwoIoJaCbACLH0apebeXiISu2pWqoCAbrCtldJFm6IpdqQoKIiJSpmsKIiJSpqAgIiJlCgoirbD44fSrzWyJmT1uZt8xsy3N7O/M7PwW5qn5YrY61jXd8sfXRZpFQUGkBXmr7RXEa9tLP7XYDzivtfnc/YANXOV04qVnIk2joCDSskOBN9z9p1B+/uKfiNcW9AFGm9n1ZvaomX2tNJOZFd+r/y9mdpeZPWBm/1YYf3qOu9/MfmFmBxAvb/svM7vP4nXVIh2uU746W6STGE88rFfm8cMxTxHnzkTi9wdeA+4ys2u98ArnwttHJxJPgc+x+L3kF4AvA5PybZml31WYQ7wFc1ZHbJxILQoKIi0z4hfPWhpf/q1tM7uCeOd98b3+R+Rf6X36/YggsSfx1PzzUPG7CiJNp+4jkZYtJt77X5bv6R9N/JZudcCoHjbg6+6+V/69191/QsvBRqTpFBREWjYf6GNmp0P5h3C+BfyM6DI63MwGWfwI+3TiR2CKbgA+lq8Zx8xGWvwm9nzgRItfBSv9jgKse/+/SNMoKIi0IF/hMQM4wcyWED/K9AbwpZzkNuAXxC/OzS5cT/Cc/0bgEuAOM1sEzAL6e/zm8nnArWZ2P/DtnO8y4F/M7F5daJZm0WsuRNpR1v7vcfftm50WkQ2hloJIO8mfibwD+Gaz0yKyodRSEBGRMrUURESkTEFBRETKFBRERKRMQUFERMoUFEREpExBQUREyv4/+QaYIWylDGsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# sort the dictionary \n",
    "objectwise_img_names = dict(sorted(objectwise_img_names.items(), key=lambda x: len(x[1]), reverse=True))\n",
    "\n",
    "# Extract object names and image counts\n",
    "obj_names = list(objectwise_img_names.keys())\n",
    "img_counts = [len(objectwise_img_names[obj]) for obj in objectwise_img_names]\n",
    "print(sum(img_counts))\n",
    "\n",
    "# Create bar plot\n",
    "sns.barplot(x=obj_names, y=img_counts)\n",
    "\n",
    "# Add x-axis and y-axis labels\n",
    "plt.xlabel('Object')\n",
    "plt.ylabel('Number of Images')\n",
    "\n",
    "plt.xticks(rotation=45, ha='right', fontsize=3)\n",
    "\n",
    "# Save the plot as a high-resolution image file\n",
    "out_path = osp.join(out_dir, 'image_per_object_category.png')\n",
    "plt.savefig(out_path, dpi=300)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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