231123imageAugmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 버스만 있는 이미지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "valid_total_df = pd.read_csv('valid_total_txt_label.csv')\n",
    "\n",
    "condition1 = valid_total_df['car_cnt'].isnull()\n",
    "condition2 = valid_total_df['truck_cnt'].isnull()\n",
    "\n",
    "busonlyList = list(valid_total_df[(condition1) & (condition2)]['txt_in_valid_folder'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Suwon_CH05_20200720_1830_MON_9m_NH_highway_OW5_sunny_FHD_066.txt',\n",
       " 'Suwon_CH05_20200720_1830_MON_9m_NH_highway_OW5_sunny_FHD_085.txt',\n",
       " 'Suwon_CH06_20200722_1900_WED_9m_NH_highway_OW5_sunny_FHD_033.txt',\n",
       " 'Suwon_CH10_20201213_0752_SUN_9m_NH_highway_OW5_snow_FHD_041.txt',\n",
       " 'Suwon_CH10_20201213_0752_SUN_9m_NH_highway_OW5_snow_FHD_082.txt']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "valid_filename = []\n",
    "for i in busonlyList:\n",
    "    basename = os.path.basename(i)\n",
    "    valid_filename.append(basename)\n",
    "valid_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def glob_files(path, file_type=\"*\"):\n",
    "    search_string = os.path.join(path, file_type)\n",
    "    files = glob(search_string)\n",
    "\n",
    "    # print('searching ', path)\n",
    "    paths = []\n",
    "    for f in files:\n",
    "      if os.path.isdir(f):\n",
    "        sub_paths = glob_files(f + '/')\n",
    "        paths += sub_paths\n",
    "      else:\n",
    "        paths.append(f)\n",
    "\n",
    "    # We sort the images in alphabetical order to match them\n",
    "    #  to the annotation files\n",
    "    paths.sort()\n",
    "\n",
    "    return paths\n",
    "\n",
    "\n",
    "# IMAGE_SIZE = 600\n",
    "\n",
    "def load_images(path):\n",
    "  files = glob_files(path, \"*.png\")\n",
    "\n",
    "  # print(files)\n",
    "  X_data = []\n",
    "  for file in files:\n",
    "    image = cv2.imread(file)\n",
    "    # print(image.shape)\n",
    "    # x = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)\n",
    "\n",
    "    X_data.append(image)\n",
    "    \n",
    "  return np.array(X_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 1920, 1080, 3)\n"
     ]
    }
   ],
   "source": [
    "basePath_image = 'highway/valid/image/'\n",
    "\n",
    "X_test = load_images(basePath_image)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "WIDTH = 1080\n",
    "HEIGHT = 1920\n",
    "\n",
    "def load_labels(path):\n",
    "  files = glob_files(path, \"*.txt\")\n",
    "\n",
    "  Y_data = []\n",
    "  for file in files:\n",
    "    with open(file) as f:\n",
    "      lines = f.readlines()\n",
    "\n",
    "      boxes = []\n",
    "      for line in lines:\n",
    "        tokens = line.split()\n",
    "\n",
    "        class_id = int(tokens[0])\n",
    "        xc = float(tokens[1]) * WIDTH\n",
    "        yc = float(tokens[2]) * HEIGHT\n",
    "        width = float(tokens[3]) * WIDTH\n",
    "        height = float(tokens[4]) * HEIGHT\n",
    "\n",
    "        boxes.append(np.array([class_id, xc, yc, width, height]))\n",
    "        # print(class_id, xc, yc, width, height)\n",
    "\n",
    "      Y_data.append(np.array(boxes))\n",
    "      # print(lines)\n",
    "  return Y_data  ## 이미지마다 탐지 대상 개수가 달라서(행 개수가 달라서) 정해진 shape의 넘파이 어레이로 변환할 수 없다! 변환하려면 패딩 등이 필요.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## car, truck, bus 가 모두 포함된 이미지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>txt_in_valid_folder</th>\n",
       "      <th>car_cnt</th>\n",
       "      <th>truck_cnt</th>\n",
       "      <th>bus_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9290</th>\n",
       "      <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9311</th>\n",
       "      <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>9312</th>\n",
       "      <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9315</th>\n",
       "      <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9324</th>\n",
       "      <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1175 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    txt_in_valid_folder  car_cnt  truck_cnt  \\\n",
       "2     highway/valid/label/Suwon_CH01_20200720_1830_M...      5.0        9.0   \n",
       "3     highway/valid/label/Suwon_CH01_20200720_1830_M...      8.0        7.0   \n",
       "5     highway/valid/label/Suwon_CH01_20200720_1830_M...      9.0        4.0   \n",
       "6     highway/valid/label/Suwon_CH01_20200720_1830_M...      9.0        4.0   \n",
       "7     highway/valid/label/Suwon_CH01_20200720_1830_M...      7.0        4.0   \n",
       "...                                                 ...      ...        ...   \n",
       "9290  highway/valid/label/Suwon_CH10_20201213_0752_S...      2.0        1.0   \n",
       "9311  highway/valid/label/Suwon_CH10_20201213_0752_S...      2.0        2.0   \n",
       "9312  highway/valid/label/Suwon_CH10_20201213_0752_S...      1.0        1.0   \n",
       "9315  highway/valid/label/Suwon_CH10_20201213_0752_S...      1.0        1.0   \n",
       "9324  highway/valid/label/Suwon_CH10_20201213_0752_S...      4.0        1.0   \n",
       "\n",
       "      bus_cnt  \n",
       "2         1.0  \n",
       "3         1.0  \n",
       "5         1.0  \n",
       "6         1.0  \n",
       "7         3.0  \n",
       "...       ...  \n",
       "9290      1.0  \n",
       "9311      1.0  \n",
       "9312      1.0  \n",
       "9315      1.0  \n",
       "9324      1.0  \n",
       "\n",
       "[1175 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition3 = valid_total_df['car_cnt'].notnull()\n",
    "condition4 = valid_total_df['truck_cnt'].notnull()\n",
    "condition5 = valid_total_df['bus_cnt'].notnull()\n",
    "\n",
    "valid_notnull_df = valid_total_df[(condition3) & (condition4) & (condition5)]\n",
    "valid_notnull_df[valid_notnull_df['car_cnt'] <= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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