generate_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "from glob import glob\n",
    "import json\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "如果只输出这一句话， info dataframe 是正确的\n"
     ]
    }
   ],
   "source": [
    "items = []\n",
    "for img in imgs:\n",
    "    # img_name = '_'.join(img.split('_')[:-1]) # MIT1003\n",
    "    img_name = img.split('/')[-1]\n",
    "    info = img.split('/')[-2]\n",
    "    \n",
    "    cur_img_path = os.path.join(img_path, info, img_name)\n",
    "    label = int(info.split('-')[0])\n",
    "    ## 检查拼接的路径\n",
    "    if not has_path(cur_img_path):\n",
    "        print(cur_img_path)\n",
    "        continue\n",
    "    \n",
    "    change = lambda x: '/'.join(x.split('/')[len(data_root_dir.split('/'))-1:])\n",
    "    \n",
    "    cur_img_path = change(cur_img_path)\n",
    "    \n",
    "    items.append(\n",
    "        [cur_img_path, label, info]\n",
    "    )\n",
    "print('如果只输出这一句话， info dataframe 是正确的')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 检查生成的路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_1.jpg', 0, '00-工艺品-仿唐三彩']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "如果只输出这一句话， info dataframe 是正确的\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(items)):\n",
    "    for j in range(1):\n",
    "        if not has_path(os.path.join(\n",
    "            data_root_dir, items[i][j]\n",
    "        )):\n",
    "            print(items[i][j])\n",
    "print('如果只输出这一句话， info dataframe 是正确的')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(items, columns=['img', 'label', 'info'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>img</th>\n",
       "      <th>label</th>\n",
       "      <th>info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_1.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>00-工艺品-仿唐三彩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_11.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>00-工艺品-仿唐三彩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_12.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>00-工艺品-仿唐三彩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_13.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>00-工艺品-仿唐三彩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_14.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>00-工艺品-仿唐三彩</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           img  label         info\n",
       "0   DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_1.jpg      0  00-工艺品-仿唐三彩\n",
       "1  DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_11.jpg      0  00-工艺品-仿唐三彩\n",
       "2  DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_12.jpg      0  00-工艺品-仿唐三彩\n",
       "3  DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_13.jpg      0  00-工艺品-仿唐三彩\n",
       "4  DataSets/huawei/imgs/00-工艺品-仿唐三彩/img_14.jpg      0  00-工艺品-仿唐三彩"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(info_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 生成  train valid test csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(info_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nums = len(df)\n",
    "shuffle_index = random.sample(range(nums), nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_num = int(nums*0.8)\n",
    "valid_num = int(nums*0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df.iloc[:train_num, :]\n",
    "df_valid = df.iloc[train_num:train_num+valid_num, :]\n",
    "df_test = df.iloc[train_num+valid_num:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df_train), len(df_valid), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_csv_dir = os.path.join(data_root_dir,\n",
    "                           'CSVs', data_name)\n",
    "if not os.path.exists(data_csv_dir):\n",
    "    os.makedirs(data_csv_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_csv = lambda df, phase: df.to_csv(\n",
    "            data_root_dir + 'CSVs/'+data_name+'/'+phase+'.csv', index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not has_path('CSVs/'+data_name):\n",
    "    os.makedirs('CSVs/'+data_name)\n",
    "save_csv(df_train, 'train')\n",
    "save_csv(df_valid, 'valid')\n",
    "save_csv(df_test, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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