data_prep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "#### Подготовка данных\n",
    "\n",
    "Преобразование изображений в массив\n",
    "Создание пар изображений для обучения"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import os\n",
    "from random import randint\n",
    "import numpy as np\n",
    "\n",
    "from numpy import asarray\n",
    "from IPython import display\n",
    "from PIL import Image\n",
    "\n",
    "from config import DATA_DIR, IMAGES_DIR, IMAGE_SIZE"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Создание списка пар директорий и имен изображений"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "path_file_pairs = []\n",
    "\n",
    "for dirpath, dirnames, filenames in os.walk(IMAGES_DIR):\n",
    "    for filename in filenames:\n",
    "        path_file_pairs.append([dirpath, filename])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "400"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(path_file_pairs)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display.Image(f'{path_file_pairs[0][0]}\\\\{path_file_pairs[0][1]}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Функция преобразования изображения в массив"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def read_image(filename):\n",
    "    img = Image.open(filename)\n",
    "    img = img.resize((IMAGE_SIZE, IMAGE_SIZE), resample=0)\n",
    "    img = asarray(img)\n",
    "    img = img / 255.\n",
    "    return img"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Генерируем массивы пар изображений\n",
    "\n",
    "1800 одинаковых пар монет и 1800 разных"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "pair_list = []\n",
    "labels = []\n",
    "\n",
    "folders = 40\n",
    "files = 10\n",
    "\n",
    "for i in range(len(path_file_pairs)):\n",
    "    for j in range(i + 1, len(path_file_pairs)):\n",
    "        img_1_path, img_2_path = path_file_pairs[i][0], path_file_pairs[j][0]\n",
    "        img_1_name, img_2_name = path_file_pairs[i][1], path_file_pairs[j][1]\n",
    "\n",
    "        if img_1_path == img_2_path:\n",
    "            img_1 = f'{img_1_path}\\\\{img_1_name}'\n",
    "            img_2 = f'{img_2_path}\\\\{img_2_name}'\n",
    "            new_row = [read_image(img_1), read_image(img_2)]\n",
    "            pair_list.append(new_row)\n",
    "            labels.append(1)\n",
    "\n",
    "for i in range(len(pair_list)):\n",
    "    k = randint(0, len(path_file_pairs) - 1)\n",
    "    m = k\n",
    "    while k == m:\n",
    "        m = randint(0, len(path_file_pairs) - 1)\n",
    "    img_1_path, img_2_path = path_file_pairs[k][0], path_file_pairs[m][0]\n",
    "    img_1_name, img_2_name = path_file_pairs[k][1], path_file_pairs[m][1]\n",
    "    img_1 = f'{img_1_path}\\\\{img_1_name}'\n",
    "    img_2 = f'{img_2_path}\\\\{img_2_name}'\n",
    "    new_row = [read_image(img_1), read_image(img_2)]\n",
    "    pair_list.append(new_row)\n",
    "    labels.append(0)\n",
    "\n",
    "pairs = np.array(pair_list)\n",
    "labels = np.array(labels)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "(3600, 2, 128, 128)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(3600,)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Сохраняем массивы для дальнейшего использования"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "if not os.path.exists(DATA_DIR):\n",
    "    os.makedirs(DATA_DIR)\n",
    "\n",
    "np.save(f'{DATA_DIR}/pairs', pairs)\n",
    "np.save(f'{DATA_DIR}/labels', labels)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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