{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing 3 similar images for label 2, using euclidean_distance_measure for cm_fd feature descriptor...\n"
     ]
    },
    {
     "ename": "ServerSelectionTimeoutError",
     "evalue": "localhost:27017: [Errno 61] Connection refused, Timeout: 30s, Topology Description: <TopologyDescription id: 65275087330d96f89929cd0a, topology_type: Unknown, servers: [<ServerDescription ('localhost', 27017) server_type: Unknown, rtt: None, error=AutoReconnect('localhost:27017: [Errno 61] Connection refused')>]>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mServerSelectionTimeoutError\u001b[0m               Traceback (most recent call last)",
      "\u001b[1;32m/Users/pavanrathnakarshetty/Documents/Multimedia and Web Databases/Phase 2/CSE515_MWDB_Project/Phase 2/task_1.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m selected_feature_model \u001b[39m=\u001b[39m valid_feature_models[\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     \u001b[39mstr\u001b[39m(\u001b[39minput\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mEnter feature model - one of \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_feature_models\u001b[39m.\u001b[39mkeys()))))\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m ]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m selected_distance_measure \u001b[39m=\u001b[39m feature_distance_matches[selected_feature_model]\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m show_similar_images_for_label(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     fd_collection,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m     sample_label,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m     k,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m     selected_feature_model,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     selected_distance_measure,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m     save_plots\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/pavanrathnakarshetty/Documents/Multimedia%20and%20Web%20Databases/Phase%202/CSE515_MWDB_Project/Phase%202/task_1.ipynb#W2sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m )\n",
      "File \u001b[0;32m~/Documents/Multimedia and Web Databases/Phase 2/CSE515_MWDB_Project/Phase 2/utils.py:538\u001b[0m, in \u001b[0;36mshow_similar_images_for_label\u001b[0;34m(fd_collection, target_label, k, feature_model, distance_measure, save_plots)\u001b[0m\n\u001b[1;32m    535\u001b[0m min_dists \u001b[39m=\u001b[39m {}\n\u001b[1;32m    537\u001b[0m \u001b[39m# Calculate representative feature vector for label\u001b[39;00m\n\u001b[0;32m--> 538\u001b[0m label_rep \u001b[39m=\u001b[39m calculate_label_representatives(\n\u001b[1;32m    539\u001b[0m     fd_collection, target_label, feature_model\n\u001b[1;32m    540\u001b[0m )\n\u001b[1;32m    542\u001b[0m \u001b[39mfor\u001b[39;00m cur_img \u001b[39min\u001b[39;00m all_images:\n\u001b[1;32m    543\u001b[0m     cur_img_id \u001b[39m=\u001b[39m cur_img[\u001b[39m\"\u001b[39m\u001b[39mimage_id\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[0;32m~/Documents/Multimedia and Web Databases/Phase 2/CSE515_MWDB_Project/Phase 2/utils.py:497\u001b[0m, in \u001b[0;36mcalculate_label_representatives\u001b[0;34m(fd_collection, label, feature_model)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcalculate_label_representatives\u001b[39m(fd_collection, label, feature_model):\n\u001b[1;32m    495\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate representative feature vector of a label as the mean of all feature vectors under a feature model\"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m     label_fds \u001b[39m=\u001b[39m [\n\u001b[1;32m    498\u001b[0m         img_fds[feature_model]  \u001b[39m# get the specific feature model's feature vector\u001b[39;49;00m\n\u001b[1;32m    499\u001b[0m         \u001b[39mfor\u001b[39;49;00m img_fds \u001b[39min\u001b[39;49;00m fd_collection\u001b[39m.\u001b[39;49mfind(\n\u001b[1;32m    500\u001b[0m             {\u001b[39m\"\u001b[39;49m\u001b[39mtrue_label\u001b[39;49m\u001b[39m\"\u001b[39;49m: label}\n\u001b[1;32m    501\u001b[0m         )  \u001b[39m# repeat for all images\u001b[39;49;00m\n\u001b[1;32m    502\u001b[0m     ]\n\u001b[1;32m    504\u001b[0m     \u001b[39m# Calculate mean across each dimension\u001b[39;00m\n\u001b[1;32m    505\u001b[0m     \u001b[39m# and build a mean vector out of these means\u001b[39;00m\n\u001b[1;32m    506\u001b[0m     label_mean_vector \u001b[39m=\u001b[39m [\u001b[39msum\u001b[39m(col) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(col) \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mlabel_fds)]\n",
      "File \u001b[0;32m~/Documents/Multimedia and Web Databases/Phase 2/CSE515_MWDB_Project/Phase 2/utils.py:497\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcalculate_label_representatives\u001b[39m(fd_collection, label, feature_model):\n\u001b[1;32m    495\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate representative feature vector of a label as the mean of all feature vectors under a feature model\"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m     label_fds \u001b[39m=\u001b[39m [\n\u001b[1;32m    498\u001b[0m         img_fds[feature_model]  \u001b[39m# get the specific feature model's feature vector\u001b[39;00m\n\u001b[1;32m    499\u001b[0m         \u001b[39mfor\u001b[39;00m img_fds \u001b[39min\u001b[39;00m fd_collection\u001b[39m.\u001b[39mfind(\n\u001b[1;32m    500\u001b[0m             {\u001b[39m\"\u001b[39m\u001b[39mtrue_label\u001b[39m\u001b[39m\"\u001b[39m: label}\n\u001b[1;32m    501\u001b[0m         )  \u001b[39m# repeat for all images\u001b[39;00m\n\u001b[1;32m    502\u001b[0m     ]\n\u001b[1;32m    504\u001b[0m     \u001b[39m# Calculate mean across each dimension\u001b[39;00m\n\u001b[1;32m    505\u001b[0m     \u001b[39m# and build a mean vector out of these means\u001b[39;00m\n\u001b[1;32m    506\u001b[0m     label_mean_vector \u001b[39m=\u001b[39m [\u001b[39msum\u001b[39m(col) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(col) \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mlabel_fds)]\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/cursor.py:1262\u001b[0m, in \u001b[0;36mCursor.next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1260\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__empty:\n\u001b[1;32m   1261\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m\n\u001b[0;32m-> 1262\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__data) \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_refresh():\n\u001b[1;32m   1263\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__data\u001b[39m.\u001b[39mpopleft()\n\u001b[1;32m   1264\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/cursor.py:1153\u001b[0m, in \u001b[0;36mCursor._refresh\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1150\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__data)\n\u001b[1;32m   1152\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__session:\n\u001b[0;32m-> 1153\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__session \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__collection\u001b[39m.\u001b[39;49mdatabase\u001b[39m.\u001b[39;49mclient\u001b[39m.\u001b[39;49m_ensure_session()\n\u001b[1;32m   1155\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__id \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:  \u001b[39m# Query\u001b[39;00m\n\u001b[1;32m   1156\u001b[0m     \u001b[39mif\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__min \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__max) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__hint:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/mongo_client.py:1868\u001b[0m, in \u001b[0;36mMongoClient._ensure_session\u001b[0;34m(self, session)\u001b[0m\n\u001b[1;32m   1863\u001b[0m     \u001b[39mreturn\u001b[39;00m session\n\u001b[1;32m   1865\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1866\u001b[0m     \u001b[39m# Don't make implicit sessions causally consistent. Applications\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m     \u001b[39m# should always opt-in.\u001b[39;00m\n\u001b[0;32m-> 1868\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__start_session(\u001b[39mTrue\u001b[39;49;00m, causal_consistency\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m   1869\u001b[0m \u001b[39mexcept\u001b[39;00m (ConfigurationError, InvalidOperation):\n\u001b[1;32m   1870\u001b[0m     \u001b[39m# Sessions not supported.\u001b[39;00m\n\u001b[1;32m   1871\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/mongo_client.py:1811\u001b[0m, in \u001b[0;36mMongoClient.__start_session\u001b[0;34m(self, implicit, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__start_session\u001b[39m(\u001b[39mself\u001b[39m, implicit: \u001b[39mbool\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m ClientSession:\n\u001b[1;32m   1809\u001b[0m     \u001b[39m# Raises ConfigurationError if sessions are not supported.\u001b[39;00m\n\u001b[1;32m   1810\u001b[0m     \u001b[39mif\u001b[39;00m implicit:\n\u001b[0;32m-> 1811\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_topology\u001b[39m.\u001b[39;49m_check_implicit_session_support()\n\u001b[1;32m   1812\u001b[0m         server_session: Union[_EmptyServerSession, _ServerSession] \u001b[39m=\u001b[39m _EmptyServerSession()\n\u001b[1;32m   1813\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/topology.py:583\u001b[0m, in \u001b[0;36mTopology._check_implicit_session_support\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    581\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_implicit_session_support\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    582\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m--> 583\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_check_session_support()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/topology.py:599\u001b[0m, in \u001b[0;36mTopology._check_session_support\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    595\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_servers_loop(\n\u001b[1;32m    596\u001b[0m             any_server_selector, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_server_selection_timeout(), \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    597\u001b[0m         )\n\u001b[1;32m    598\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_description\u001b[39m.\u001b[39mreadable_servers:\n\u001b[0;32m--> 599\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_select_servers_loop(\n\u001b[1;32m    600\u001b[0m         readable_server_selector, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_server_selection_timeout(), \u001b[39mNone\u001b[39;49;00m\n\u001b[1;32m    601\u001b[0m     )\n\u001b[1;32m    603\u001b[0m session_timeout \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_description\u001b[39m.\u001b[39mlogical_session_timeout_minutes\n\u001b[1;32m    604\u001b[0m \u001b[39mif\u001b[39;00m session_timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pymongo/topology.py:269\u001b[0m, in \u001b[0;36mTopology._select_servers_loop\u001b[0;34m(self, selector, timeout, address)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mnot\u001b[39;00m server_descriptions:\n\u001b[1;32m    267\u001b[0m     \u001b[39m# No suitable servers.\u001b[39;00m\n\u001b[1;32m    268\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m now \u001b[39m>\u001b[39m end_time:\n\u001b[0;32m--> 269\u001b[0m         \u001b[39mraise\u001b[39;00m ServerSelectionTimeoutError(\n\u001b[1;32m    270\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_error_message(selector)\u001b[39m}\u001b[39;00m\u001b[39m, Timeout: \u001b[39m\u001b[39m{\u001b[39;00mtimeout\u001b[39m}\u001b[39;00m\u001b[39ms, Topology Description: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdescription\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m    271\u001b[0m         )\n\u001b[1;32m    273\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ensure_opened()\n\u001b[1;32m    274\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_request_check_all()\n",
      "\u001b[0;31mServerSelectionTimeoutError\u001b[0m: localhost:27017: [Errno 61] Connection refused, Timeout: 30s, Topology Description: <TopologyDescription id: 65275087330d96f89929cd0a, topology_type: Unknown, servers: [<ServerDescription ('localhost', 27017) server_type: Unknown, rtt: None, error=AutoReconnect('localhost:27017: [Errno 61] Connection refused')>]>"
     ]
    }
   ],
   "source": [
    "sample_label = int(input(\"Enter query label: (0 to 100)\"))\n",
    "if sample_label < 0 or sample_label > 100:\n",
    "    raise ValueError(\"Query label should be between [0, 100]\")\n",
    "\n",
    "k = int(input(\"Enter value of k: \"))\n",
    "if k < 1:\n",
    "    raise ValueError(\"k should be a positive integer\")\n",
    "\n",
    "selected_feature_model = valid_feature_models[\n",
    "    str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
    "]\n",
    "selected_distance_measure = feature_distance_matches[selected_feature_model]\n",
    "\n",
    "show_similar_images_for_label(\n",
    "    fd_collection,\n",
    "    sample_label,\n",
    "    k,\n",
    "    selected_feature_model,\n",
    "    selected_distance_measure,\n",
    "    save_plots=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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