make_figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_PATH = 'data_process/dsb_data_with/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_paths = []\n",
    "\n",
    "for path in ['train', 'test', 'valid']:\n",
    "    label_paths += glob.glob(os.path.join(DATASET_PATH, 'labels', path, '*.txt'))\n",
    "\n",
    "label_paths = list(sorted(label_paths))\n",
    "\n",
    "classes = open(os.path.join(DATASET_PATH, 'classes.txt'), 'r').read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Received folder path: data_process/dsb_data_without/\n",
      "Number of labels: 2110\n",
      "Classes: ['1_AND_2', '1_AND_4', '1_AND_3', '2_AND_4', 'OTHER']\n"
     ]
    }
   ],
   "source": [
    "print('Received folder path:',DATASET_PATH)\n",
    "print('Number of labels:', len(label_paths))\n",
    "print('Classes:', classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_AND_2</td>\n",
       "      <td>0.465885</td>\n",
       "      <td>0.519444</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.022222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_AND_2</td>\n",
       "      <td>0.465862</td>\n",
       "      <td>0.520118</td>\n",
       "      <td>0.011458</td>\n",
       "      <td>0.022407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_AND_2</td>\n",
       "      <td>0.465388</td>\n",
       "      <td>0.520623</td>\n",
       "      <td>0.011458</td>\n",
       "      <td>0.022407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_AND_2</td>\n",
       "      <td>0.454390</td>\n",
       "      <td>0.534788</td>\n",
       "      <td>0.011458</td>\n",
       "      <td>0.022989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1_AND_2</td>\n",
       "      <td>0.453497</td>\n",
       "      <td>0.536376</td>\n",
       "      <td>0.011458</td>\n",
       "      <td>0.024735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      type         x         y         w         h\n",
       "0  1_AND_2  0.465885  0.519444  0.011198  0.022222\n",
       "1  1_AND_2  0.465862  0.520118  0.011458  0.022407\n",
       "2  1_AND_2  0.465388  0.520623  0.011458  0.022407\n",
       "3  1_AND_2  0.454390  0.534788  0.011458  0.022989\n",
       "4  1_AND_2  0.453497  0.536376  0.011458  0.024735"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = []\n",
    "\n",
    "for label_path in label_paths:\n",
    "    lines = open(label_path, 'r').read().splitlines()\n",
    "    \n",
    "    for line in lines:\n",
    "        type, x, y, w, h = line.split(' ')\n",
    "        x, y, w, h = float(x), float(y), float(w), float(h)\n",
    "        \n",
    "        type = classes[int(type)]\n",
    "        labels.append([type, x, y, w, h])\n",
    "        \n",
    "labels = pd.DataFrame(labels, columns=['type', 'x', 'y', 'w', 'h'])\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3410.000000</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>3410.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.496276</td>\n",
       "      <td>0.491072</td>\n",
       "      <td>0.025245</td>\n",
       "      <td>0.056196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.186668</td>\n",
       "      <td>0.152256</td>\n",
       "      <td>0.015082</td>\n",
       "      <td>0.035101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.016146</td>\n",
       "      <td>0.065319</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.384154</td>\n",
       "      <td>0.387533</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>0.031481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.479687</td>\n",
       "      <td>0.527168</td>\n",
       "      <td>0.021012</td>\n",
       "      <td>0.045833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.604883</td>\n",
       "      <td>0.592614</td>\n",
       "      <td>0.030469</td>\n",
       "      <td>0.068981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.992448</td>\n",
       "      <td>0.976389</td>\n",
       "      <td>0.122591</td>\n",
       "      <td>0.267141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x            y            w            h\n",
       "count  3410.000000  3410.000000  3410.000000  3410.000000\n",
       "mean      0.496276     0.491072     0.025245     0.056196\n",
       "std       0.186668     0.152256     0.015082     0.035101\n",
       "min       0.016146     0.065319     0.006250     0.011111\n",
       "25%       0.384154     0.387533     0.014844     0.031481\n",
       "50%       0.479687     0.527168     0.021012     0.045833\n",
       "75%       0.604883     0.592614     0.030469     0.068981\n",
       "max       0.992448     0.976389     0.122591     0.267141"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels.groupby('type')['type'].describe()['count'].plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('machine-learning')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "cd9e4fcb2220c3e20a32e629766165bb66c6b69635fb3a79566f0e2602efa810"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}