dataset_generator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>image_id</th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>frame1278</td>\n",
       "      <td>1370</td>\n",
       "      <td>518</td>\n",
       "      <td>1384</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>frame1298</td>\n",
       "      <td>1163</td>\n",
       "      <td>605</td>\n",
       "      <td>1177</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>frame1299</td>\n",
       "      <td>1151</td>\n",
       "      <td>610</td>\n",
       "      <td>1165</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>frame1300</td>\n",
       "      <td>1140</td>\n",
       "      <td>616</td>\n",
       "      <td>1154</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>frame1301</td>\n",
       "      <td>1130</td>\n",
       "      <td>623</td>\n",
       "      <td>1144</td>\n",
       "      <td>637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>frame63007</td>\n",
       "      <td>1042</td>\n",
       "      <td>663</td>\n",
       "      <td>1056</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>frame63008</td>\n",
       "      <td>1043</td>\n",
       "      <td>660</td>\n",
       "      <td>1057</td>\n",
       "      <td>674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>frame63009</td>\n",
       "      <td>1043</td>\n",
       "      <td>657</td>\n",
       "      <td>1057</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6970</th>\n",
       "      <td>frame63010</td>\n",
       "      <td>1044</td>\n",
       "      <td>655</td>\n",
       "      <td>1058</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6971</th>\n",
       "      <td>frame63011</td>\n",
       "      <td>1045</td>\n",
       "      <td>653</td>\n",
       "      <td>1059</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6972 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        image_id    x1   y1    x2   y2\n",
       "0      frame1278  1370  518  1384  532\n",
       "1      frame1298  1163  605  1177  619\n",
       "2      frame1299  1151  610  1165  624\n",
       "3      frame1300  1140  616  1154  630\n",
       "4      frame1301  1130  623  1144  637\n",
       "...          ...   ...  ...   ...  ...\n",
       "6967  frame63007  1042  663  1056  677\n",
       "6968  frame63008  1043  660  1057  674\n",
       "6969  frame63009  1043  657  1057  671\n",
       "6970  frame63010  1044  655  1058  669\n",
       "6971  frame63011  1045  653  1059  667\n",
       "\n",
       "[6972 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#name of the train folder\n",
    "data_name = \"game4\"\n",
    "image_width = 1920\n",
    "image_height = 1080\n",
    "\n",
    "train = pd.read_json(\"train/\" + data_name + \"/data/ball_markup.json\")\n",
    "train = train.transpose()\n",
    "\n",
    "image_names = np.array([])\n",
    "\n",
    "for e in train.transpose().loc[:,:]:\n",
    "    # print(x, train.get(x)[\"x\"])\n",
    "    image_names = np.append(image_names, \"frame\" + str(e))\n",
    "\n",
    "\n",
    "x_data = train.iloc[:,:].get(\"x\")\n",
    "y_data = train.iloc[:,:].get(\"y\")\n",
    "\n",
    "#remove the fractured indexes\n",
    "x_data = np.full(x_data.size, x_data)\n",
    "y_data = np.full(y_data.size, y_data)\n",
    "image_names = np.full(image_names.size, image_names)\n",
    "\n",
    "\n",
    "#take out the data with -1 coordinates i.e. when theres no ball\n",
    "invalid_indicies = np.where(x_data == -1)\n",
    "\n",
    "x_data_pruned = np.delete(x_data, invalid_indicies)\n",
    "y_data_pruned = np.delete(y_data, invalid_indicies)\n",
    "image_names_pruned = np.delete(image_names, invalid_indicies)\n",
    "\n",
    "\n",
    "#create the coordinates based on the x and y data\n",
    "data_size = x_data_pruned.size\n",
    "\n",
    "x1_data = np.full(data_size, x_data_pruned - 7)\n",
    "x2_data = np.full(data_size, x_data_pruned + 7)\n",
    "y1_data = np.full(data_size, y_data_pruned - 7)\n",
    "y2_data = np.full(data_size, y_data_pruned + 7)\n",
    "\n",
    "#make arrays for the width and height columns\n",
    "width = np.full(data_size, image_width)\n",
    "height = np.full(data_size, image_height)\n",
    "\n",
    "\n",
    "#make the data array\n",
    "\n",
    "columns = [\"image_id\", \"x1\", \"y1\", \"x2\", \"y2\"]\n",
    "data = np.array([image_names_pruned, x1_data, y1_data, x2_data, y2_data])\n",
    "data = data.transpose()\n",
    "\n",
    "\n",
    "\n",
    "data_frame = pd.DataFrame(data = data, columns=columns)\n",
    "\n",
    "\n",
    "\n",
    "data_frame.to_csv(\"train/\" + data_name + \"/data/formatted_data.csv\", index=False)\n",
    "\n",
    "\n",
    "valid_idx = (train.iloc[:,:].index)\n",
    "DF = pd.DataFrame(valid_idx)\n",
    "DF.to_csv(\"train/\" + data_name + \"/data/valid_idx.csv\", index=False)\n",
    "\n",
    "data_frame\n"
   ]
  }
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