{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b347a214",
   "metadata": {},
   "source": [
    "# Recheck and convert to AIHub form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "88213593",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib as plt\n",
    "from PIL import Image\n",
    "import natsort\n",
    "import json\n",
    "from collections import Counter\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import cv2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2ad6434d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "IMAGE_DIR= \"/data/si/darwin/si/construction-equipment/images/\"\n",
    "SAVE_LABEL_DIR = \"/data/si/darwin/hdvehicle/temp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6138ec67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_json_path = \"/data/si/darwin/hdvehicle/annotations/train.json\"\n",
    "with open(train_json_path, 'r') as f:\n",
    "    train_json = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1769378d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class_dict_raw = {'0':'09',\n",
    "                 '1':'10',\n",
    "                 '2':'11',\n",
    "                 '3':'12',\n",
    "                 '4':'13',\n",
    "                 '5':'16',\n",
    "                 '6':'17',\n",
    "                 '7':'18',}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d49f903",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████| 24589/24589 [08:54<00:00, 46.04it/s]\n"
     ]
    }
   ],
   "source": [
    "for image_info in tqdm(train_json['images']):\n",
    "    image_id = image_info['id']\n",
    "    bboxes = []\n",
    "    \n",
    "    # Declare saved json form\n",
    "    save_json = {}\n",
    "    save_json['image'] = []\n",
    "    image_dict = {}\n",
    "    image_dict['filename'] = image_info['file_name']\n",
    "    image_dict['resolution'] = [image_info['width'], image_info['height']]\n",
    "                               \n",
    "    annotation_dict = []\n",
    "    for annotation in train_json['annotations']:\n",
    "        annotation_dict_temp = {}\n",
    "        if annotation['image_id'] == image_id:\n",
    "            temp_bbox = [annotation['bbox'][0],\n",
    "                         annotation['bbox'][1],\n",
    "                         annotation['bbox'][2],\n",
    "                         annotation['bbox'][3]]\n",
    "            bboxes.append(temp_bbox)\n",
    "            class_name_raw = annotation[\"category_id\"]\n",
    "            class_name = class_dict_raw[str(class_name_raw-1)]\n",
    "            # Add\n",
    "            annotation_dict_temp['box'] = temp_bbox\n",
    "            annotation_dict_temp['class'] = class_name\n",
    "            annotation_dict.append(annotation_dict_temp)\n",
    "        \n",
    "    save_json['image'] = image_dict\n",
    "    save_json['annotations'] = annotation_dict\n",
    "    \n",
    "    save_path = os.path.join(SAVE_LABEL_DIR, \n",
    "                             save_json['image']['filename'].split('/')[0] \n",
    "                             + '_' \n",
    "                             + save_json['image']['filename'].split('/')[1][:-4] + '.json')\n",
    "    with open(save_path, 'w', encoding='utf-8') as make_file:\n",
    "        json.dump(save_json, make_file, indent=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "642d8a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save_json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd017df7",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e179a2af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# class_dict_raw_str = {'0':'Excavator',\n",
    "#             '1':'Dodger',\n",
    "#             '2':'Forklift',\n",
    "#             '3':'Dump Truck',\n",
    "#             '4':'Mixer Truck',\n",
    "#             '5':'Cargo Truck',\n",
    "#             '6':'Scissor lift',\n",
    "#             '7':'Crane'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38aa0834",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fontScale = 1\n",
    "# font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "# for image_info in train_json['images']:\n",
    "#     image_id = image_info['id']\n",
    "#     # print(image_id)\n",
    "#     temp_bbox = []\n",
    "#     image_path = os.path.join(IMAGE_DIR, image_info['file_name'])\n",
    "#     image = plt.imread(image_path)\n",
    "#     for annotation in train_json['annotations']:\n",
    "#         if annotation['image_id'] == image_id:\n",
    "\n",
    "#             temp_bbox = [annotation['bbox'][0],\n",
    "#                          annotation['bbox'][1],\n",
    "#                          annotation['bbox'][2],\n",
    "#                          annotation['bbox'][3],\n",
    "#                          annotation['category_id']]\n",
    "#             class_name = annotation[\"category_id\"]\n",
    "#             start_point =(int(temp_bbox[0]), int(temp_bbox[1]))\n",
    "#             end_point = (int(temp_bbox[0])+int(temp_bbox[2]),\n",
    "#                         int(temp_bbox[1])+int(temp_bbox[3]))\n",
    "#             image = cv2.rectangle(image,start_point, end_point, \n",
    "#                                           (255, 0, 0), 2)\n",
    "#             image = cv2.putText(image, class_dict_raw_str[str(class_name-1)], start_point, font, \n",
    "#                fontScale, (255, 0, 0), 3, cv2.LINE_AA)\n",
    "#     plt.figure(figsize=(15,10))\n",
    "#     plt.imshow(image)\n",
    "#     plt.show()\n",
    "    \n",
    "#     break\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5551cf",
   "metadata": {},
   "source": [
    "## Split base on ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7423dfa2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load json file\n",
    "annotations_path = SAVE_LABEL_DIR # Train included test file\n",
    "\n",
    "label_file_names = natsort.natsorted(os.listdir(annotations_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "161a52c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24589"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(label_file_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "765c7e27",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "file_list = label_file_names\n",
    "path = annotations_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a226625a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24589it [00:05, 4417.79it/s]\n"
     ]
    }
   ],
   "source": [
    "category_list=[]\n",
    "for idx,file_name in tqdm(enumerate(file_list)):\n",
    "    with open(os.path.join(annotations_path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "\n",
    "    # categories\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        if instance[\"class\"] in category_list:\n",
    "            pass\n",
    "        else:\n",
    "            category_list.append(instance[\"class\"])\n",
    "    category_list=natsort.natsorted(category_list)\n",
    "    category_dic_count = dict.fromkeys(category_list, 0)\n",
    "    category_dic_filename = dict.fromkeys(category_list)\n",
    "    \n",
    "    for key in category_dic_filename.keys():\n",
    "        category_dic_filename[key]=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2edd2433",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['09', '10', '11', '12', '13', '16', '17', '18']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "612a6c04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'09': 0, '10': 0, '11': 0, '12': 0, '13': 0, '16': 0, '17': 0, '18': 0}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_dic_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "753489cb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# category_dic_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "144a7338",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24589it [00:01, 19028.37it/s]\n"
     ]
    }
   ],
   "source": [
    "file_list = natsort.natsorted(file_list)\n",
    "\n",
    "ban_list = []\n",
    "for idx,file_name in tqdm(enumerate(file_list)):\n",
    "\n",
    "    with open(os.path.join(path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "\n",
    "    class_id_list = []\n",
    "    # categories\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        class_id_list.append(instance[\"class\"])\n",
    "\n",
    "\n",
    "    # print(class_id_list)\n",
    "\n",
    "    count_items = Counter(class_id_list)\n",
    "\n",
    "    # print(count_items)\n",
    "    \n",
    "    try:\n",
    "        class_id = count_items.most_common(n=1)[0][0]\n",
    "        category_dic_count[class_id]+=1\n",
    "        category_dic_filename[class_id].append(file_name)\n",
    "    except:\n",
    "        ban_list.append(file_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "12ab6019",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'09': 10978,\n",
       " '10': 1119,\n",
       " '11': 1072,\n",
       " '12': 3375,\n",
       " '13': 1372,\n",
       " '16': 1204,\n",
       " '17': 1369,\n",
       " '18': 2280}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_dic_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c7dfd56b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train = [] \n",
    "test = [] \n",
    "\n",
    "for key in category_dic_count.keys():\n",
    "\n",
    "    if (category_dic_count[key]):\n",
    "        temp=category_dic_filename[key]\n",
    "\n",
    "        train_temp, test_temp = train_test_split(temp, test_size=0.2)\n",
    "\n",
    "        train.extend(train_temp)\n",
    "        test.extend(test_temp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773103e9",
   "metadata": {},
   "source": [
    "## Visualize data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d4104b4f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24589it [00:01, 23213.66it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'09': 20740,\n",
       " '10': 2304,\n",
       " '11': 2242,\n",
       " '12': 10949,\n",
       " '13': 3384,\n",
       " '16': 2613,\n",
       " '17': 2922,\n",
       " '18': 6224}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_dic_total_count = dict.fromkeys(category_list, 0)\n",
    "\n",
    "for idx,file_name in tqdm(enumerate(file_list)):\n",
    "    with open(os.path.join(path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "    \n",
    "    # categories\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        category_dic_total_count[instance[\"class\"]]+=1\n",
    "        \n",
    "category_dic_total_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aaec045e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18213it [00:00, 21371.62it/s]\n",
      "4556it [00:00, 21976.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'09': 16635, '10': 1855, '11': 1801, '12': 8786, '13': 2677, '16': 2071, '17': 2338, '18': 4973}\n",
      "{'09': 4105, '10': 449, '11': 441, '12': 2163, '13': 707, '16': 542, '17': 584, '18': 1251}\n",
      "{'09': 20740, '10': 2304, '11': 2242, '12': 10949, '13': 3384, '16': 2613, '17': 2922, '18': 6224}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_dic_count = dict.fromkeys(category_list, 0)\n",
    "# val_dic_count = dict.fromkeys(category_list, 0)\n",
    "test_dic_count = dict.fromkeys(category_list, 0)\n",
    "\n",
    "for idx,file_name in tqdm(enumerate(train)):\n",
    "    with open(os.path.join(path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "    \n",
    "    # categories\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        train_dic_count[instance[\"class\"]]+=1\n",
    "        \n",
    "for idx,file_name in tqdm(enumerate(test)):\n",
    "    with open(os.path.join(path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "    \n",
    "    # categories\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        test_dic_count[instance[\"class\"]]+=1\n",
    "        \n",
    "print(train_dic_count)\n",
    "# print(val_dic_count)\n",
    "print(test_dic_count)\n",
    "print(category_dic_total_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "61b2b42a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0a109a2e",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18213"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "41945ff0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4556"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b967c27c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ind = np.arange(1)\n",
    "# dist = 0.1\n",
    "# width = 0.03\n",
    "# p1 = ax2.bar(ind-dist, train, width, label=\"Train\", edgecolor='black') \n",
    "# p3 = ax2.bar(ind+dist, len(test),width, label=\"Test\", edgecolor='black')          \n",
    "# ax2.legend()\n",
    "# ax2.set_ylabel('Number of images')\n",
    "# plt.yticks([])\n",
    "# plt.xticks([])\n",
    "# ax2.bar_label(p1, label_type='edge')\n",
    "# # ax2.bar_label(p2, label_type='edge')\n",
    "# ax2.bar_label(p3, label_type='edge')\n",
    "# ax2.spines['top'].set_visible(False)\n",
    "# ax2.spines['right'].set_visible(False)\n",
    "# # ax2.spines['bottom'].set_visible(False)\n",
    "# ax2.spines['left'].set_visible(False)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7ee28ce9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/si/darwin'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb950355",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# ind = np.arange(8)\n",
    "# f, (ax1, ax2) = plt.subplots(1, 2, sharey='row', figsize=(15,5),\n",
    "#                              gridspec_kw={'width_ratios': [5, 1]})\n",
    "# # Subplot 1\n",
    "# plt.rcParams.update({'font.size': 13})\n",
    "# dist = 0.25\n",
    "# width = 0.2\n",
    "# p1 = ax1.bar(ind-dist, train_dic_count.values(), width, label=\"Train\", edgecolor='black',color='cornflowerblue')  \n",
    "# # p2 = ax1.bar(ind, val_dic_count.values(),width, label=\"Validation\", edgecolor='black')  \n",
    "# p3 = ax1.bar(ind+dist, test_dic_count.values(),width, label=\"Test\", edgecolor='black', color='lightyellow')       \n",
    "# ax1.set_xticks(ind, labels=[\"Excavator\", \"Dodger\", \"Forklift\", \"Dump Truck\",\n",
    "#            \"Mixer Truck\", \"Cargo Truck\", \"Scissor lift\", \"Crane\"],\n",
    "#              rotation=40)\n",
    "# ax1.axhline(0, color='grey', linewidth=0.8)\n",
    "# # ax1.legend()\n",
    "# ax1.set_ylabel('Number of instances')\n",
    "# ax1.bar_label(p1, label_type='edge')\n",
    "# # ax1.bar_label(p2, label_type='edge')\n",
    "# ax1.bar_label(p3, label_type='edge')\n",
    "# ax1.spines['top'].set_visible(False)\n",
    "# ax1.spines['right'].set_visible(False)\n",
    "# # ax1.spines['bottom'].set_visible(False)\n",
    "# ax1.spines['left'].set_visible(False)\n",
    "\n",
    "# # # Subplot 2\n",
    "# ind = np.arange(1)\n",
    "# dist = 0.1\n",
    "# width = 0.03\n",
    "# p1 = ax2.bar(ind-dist, len(train), width, label=\"Train\", edgecolor='black',color='cornflowerblue')  \n",
    "# # p2 = ax2.bar(ind, len(val),width, label=\"Val\", edgecolor='black')   \n",
    "# p3 = ax2.bar(ind+dist, len(test),width, label=\"Test\", edgecolor='black', color='lightyellow')             \n",
    "# ax2.legend()\n",
    "# ax2.set_ylabel('Number of images')\n",
    "# plt.yticks([])\n",
    "# plt.xticks([])\n",
    "# ax2.bar_label(p1, label_type='edge')\n",
    "# # ax2.bar_label(p2, label_type='edge')\n",
    "# ax2.bar_label(p3, label_type='edge')\n",
    "# ax2.spines['top'].set_visible(False)\n",
    "# ax2.spines['right'].set_visible(False)\n",
    "# # ax2.spines['bottom'].set_visible(False)\n",
    "# ax2.spines['left'].set_visible(False)\n",
    "\n",
    "# plt.savefig(\"cctv_v_b_distribution.pdf\",\n",
    "#            bbox_inches='tight')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a36a8645",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2bb7105",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "24186042",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# ind = np.arange(8)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(20,13))\n",
    "# plt.rcParams.update({'font.size': 20})\n",
    "\n",
    "# dist = 0.25\n",
    "# width = 0.2\n",
    "# p1 = ax.bar(ind-dist, train_dic_count.values(), width, label=\"Train\")\n",
    "# p2 = ax.bar(ind, test_dic_count.values(),width, label=\"Test\")\n",
    "\n",
    "# ax.set_xticks(ind, labels=[\"Excavator\", \"Dodger\", \"Forklift\", \"Dump Truck\",\n",
    "#            \"Mixer Truck\", \"Cargo Truck\", \"Scissor lift\", \"Crane\"],\n",
    "#              rotation=40)\n",
    "# ax.axhline(0, color='grey', linewidth=0.8)\n",
    "# ax.legend()\n",
    "\n",
    "# ax.set_title('Classes distribution')\n",
    "# ax.set_ylabel('Number of instances')\n",
    "# ax.bar_label(p1, label_type='edge')\n",
    "# ax.bar_label(p2, label_type='edge')\n",
    "# plt.savefig(\"cctv_balance_class_distribution.pdf\",\n",
    "#            bbox_inches='tight')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d3a4726c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ind = np.arange(1)\n",
    "# fig, ax = plt.subplots(figsize=(10,3))\n",
    "# plt.rcParams.update({'font.size': 12})\n",
    "# dist = 0.1\n",
    "# width = 0.1\n",
    "# p1 = ax.barh(ind-dist, len(train), width, label=\"Train\")\n",
    "# p3 = ax.barh(ind+dist, len(test),width, label=\"Test\")           \n",
    "\n",
    "# ax.legend()\n",
    "# ax.set_xlabel('Number of images')\n",
    "# plt.yticks([])\n",
    "# ax.bar_label(p1, label_type='edge')\n",
    "# ax.bar_label(p3, label_type='edge')\n",
    "# ax.spines['bottom'].set_visible(False)\n",
    "# ax.spines['top'].set_visible(False)\n",
    "# ax.spines['right'].set_visible(False)\n",
    "\n",
    "\n",
    "# plt.savefig(\"cctv_balance_image_distribution.pdf\",\n",
    "#            bbox_inches='tight')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c01d83e",
   "metadata": {},
   "source": [
    "## Convert to json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3772dc6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# categoires part of coco\n",
    "category_dic_list=[]\n",
    "\n",
    "for idx, label in enumerate(category_list):\n",
    "    category_dic={}\n",
    "    category_dic[\"id\"]=idx+1\n",
    "    category_dic[\"name\"]=label\n",
    "    category_dic[\"supercategory\"]='root'\n",
    "    category_dic_list.append(category_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "82f562dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 1, 'name': '09', 'supercategory': 'root'},\n",
       " {'id': 2, 'name': '10', 'supercategory': 'root'},\n",
       " {'id': 3, 'name': '11', 'supercategory': 'root'},\n",
       " {'id': 4, 'name': '12', 'supercategory': 'root'},\n",
       " {'id': 5, 'name': '13', 'supercategory': 'root'},\n",
       " {'id': 6, 'name': '16', 'supercategory': 'root'},\n",
       " {'id': 7, 'name': '17', 'supercategory': 'root'},\n",
       " {'id': 8, 'name': '18', 'supercategory': 'root'}]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_dic_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e840e5c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_path = \"/data/si/darwin/hdvehicle/\"\n",
    "train_name = \"train_balance.json\"\n",
    "test_name = \"test_balance.json\"\n",
    "if not os.path.exists(save_path): \n",
    "    os.makedirs(save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28a2b45-ca5f-4406-bae5-502e325a61e7",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "27ea0a18",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18213it [00:01, 13146.45it/s]\n"
     ]
    }
   ],
   "source": [
    "coco_format={}\n",
    "coco_format['images']=[]\n",
    "coco_format['annotations']=[]\n",
    "\n",
    "category=[]\n",
    "\n",
    "total_n=0\n",
    "for idx,file_name in tqdm(enumerate(train)):\n",
    "    total_n+=1\n",
    "    with open(os.path.join(path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "\n",
    "    image_dic={}\n",
    "    image_dic['id']=idx\n",
    "    image_dic['width']=json_data[\"image\"][\"resolution\"][0]\n",
    "    image_dic['height']=json_data[\"image\"][\"resolution\"][1]\n",
    "    image_dic['file_name']=json_data[\"image\"][\"filename\"]\n",
    "    coco_format['images'].append(image_dic)\n",
    "\n",
    "\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        total_n+=1\n",
    "        if \"box\" in instance.keys(): # only bbox\n",
    "            annoation_dic={} \n",
    "            annoation_dic['id']=total_n\n",
    "            annoation_dic['image_id']=idx\n",
    "            annoation_dic['category_id']=category_list.index(instance[\"class\"])+1\n",
    "            \n",
    "            # x,y,w,h\n",
    "            temp=[]\n",
    "            temp.append(instance[\"box\"][0])\n",
    "            temp.append(instance[\"box\"][1])\n",
    "            temp.append((instance[\"box\"][2]))\n",
    "            temp.append((instance[\"box\"][3]))\n",
    "            annoation_dic['bbox']=temp\n",
    "            \n",
    "            annoation_dic['segmentation']=[[temp[0], temp[1], temp[0]+temp[2], temp[1],\n",
    "                                               temp[0]+temp[2], temp[1]+temp[3], temp[0], temp[1]+temp[3]]]\n",
    "            \n",
    "            annoation_dic['area']=temp[2]*temp[3]\n",
    "            annoation_dic['iscrowd']=0\n",
    "            coco_format['annotations'].append(annoation_dic)\n",
    "    \n",
    "# categories\n",
    "coco_format['categories']=category_dic_list\n",
    "\n",
    "#save json\n",
    "with open(os.path.join(save_path, train_name), 'w', encoding='utf-8') as make_file:\n",
    "\n",
    "    json.dump(coco_format, make_file, indent=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea47885-049f-460f-907f-894b4c83496a",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6edab0d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4556it [00:00, 18203.93it/s]\n"
     ]
    }
   ],
   "source": [
    "coco_format={}\n",
    "coco_format['images']=[]\n",
    "coco_format['annotations']=[]\n",
    "\n",
    "category=[]\n",
    "\n",
    "total_n=0\n",
    "for idx,file_name in tqdm(enumerate(test)):\n",
    "    total_n+=1\n",
    "    with open(os.path.join(path, file_name), 'r') as f:\n",
    "        json_data = json.load(f)\n",
    "\n",
    "    image_dic={}\n",
    "    image_dic['id']=idx\n",
    "    image_dic['width']=json_data[\"image\"][\"resolution\"][0]\n",
    "    image_dic['height']=json_data[\"image\"][\"resolution\"][1]\n",
    "    image_dic['file_name']=json_data[\"image\"][\"filename\"]\n",
    "    coco_format['images'].append(image_dic)\n",
    "\n",
    "\n",
    "    for idy,instance in enumerate(json_data[\"annotations\"]):\n",
    "        total_n+=1\n",
    "        if \"box\" in instance.keys(): # only bbox\n",
    "            annoation_dic={} \n",
    "            annoation_dic['id']=total_n\n",
    "            annoation_dic['image_id']=idx\n",
    "            annoation_dic['category_id']=category_list.index(instance[\"class\"])+1\n",
    "            annoation_dic['segmentation']=[[]]\n",
    "            \n",
    "            # x,y,w,h\n",
    "            temp=[]\n",
    "            temp.append(instance[\"box\"][0])\n",
    "            temp.append(instance[\"box\"][1])\n",
    "            temp.append(abs(instance[\"box\"][2]))\n",
    "            temp.append(abs(instance[\"box\"][3]))\n",
    "            annoation_dic['bbox']=temp\n",
    "            \n",
    "            annoation_dic['area']=temp[2]*temp[3]\n",
    "            annoation_dic['iscrowd']=0\n",
    "            coco_format['annotations'].append(annoation_dic)\n",
    "    \n",
    "# categories\n",
    "coco_format['categories']=category_dic_list\n",
    "\n",
    "#save json\n",
    "with open(os.path.join(save_path, test_name), 'w', encoding='utf-8') as make_file:\n",
    "\n",
    "    json.dump(coco_format, make_file, indent=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efaa9d6e-0c70-4ba8-a340-b04a96df8c5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb142a3a-ddc9-4ea3-99f9-88cc3247cb26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff542ac-ae12-4fb2-9235-0fd29ccbf11d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b28adb8f",
   "metadata": {},
   "source": [
    "# Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "349b0c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/workspace/dairesearch/Hyundai_2022/src/vehicle_detection/prepare_data/merge_data_AIHub_CCTV/v7_preprocessed_labels/v7_test_balance.json\", 'r') as f:\n",
    "    v7_test_balance = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "36274374",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/workspace/dairesearch/Hyundai_2022/preprocessed_labels/v7/13july/train.json\", 'r') as f:\n",
    "    train_json = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c38bb224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ORG\n",
      "{'id': 8640, 'width': 1920, 'height': 1080, 'file_name': '20220525_162508_ch01/0000031.png'}\n",
      "Balance\n",
      "{'id': 5, 'width': 1920, 'height': 1080, 'file_name': '20220525_162508_ch01/0000031.png'}\n"
     ]
    }
   ],
   "source": [
    "for image_info in v7_test_balance['images'][5:]:\n",
    "    image_name = image_info['file_name']\n",
    "    for check_image_info in train_json['images']:\n",
    "        if image_name == check_image_info['file_name']:\n",
    "            org_info = check_image_info\n",
    "            balance_info = image_info\n",
    "            print(\"ORG\")\n",
    "            print(check_image_info)\n",
    "            print(\"Balance\")\n",
    "            print(image_info)\n",
    "            \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "171e3dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 19616, 'image_id': 8640, 'category_id': 1, 'segmentation': [[]], 'bbox': [1692.67, 500.31, 226.54, 288.33], 'area': 65318.27819999999, 'iscrowd': 0}\n",
      "{'id': 19617, 'image_id': 8640, 'category_id': 1, 'segmentation': [[]], 'bbox': [1133.79, 1.17, 66.43, 41.59], 'area': 2762.8237000000004, 'iscrowd': 0}\n"
     ]
    }
   ],
   "source": [
    "for check_annotation in train_json['annotations']:\n",
    "    if org_info['id'] == check_annotation['image_id']:\n",
    "        print(check_annotation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b1802c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 16, 'image_id': 5, 'category_id': 1, 'segmentation': [[]], 'bbox': [1692.67, 500.31, 226.54, 288.33], 'area': 65318.27819999999, 'iscrowd': 0}\n",
      "{'id': 17, 'image_id': 5, 'category_id': 1, 'segmentation': [[]], 'bbox': [1133.79, 1.17, 66.43, 41.59], 'area': 2762.8237000000004, 'iscrowd': 0}\n"
     ]
    }
   ],
   "source": [
    "for annotation in v7_test_balance['annotations']:\n",
    "    if balance_info['id'] == annotation['image_id']:\n",
    "        print(annotation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16d51a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_image_path = os.path.join(IMAGE_DIR, balance_info['file_name'])\n",
    "test_image = plt.imread(test_image_path)\n",
    "plt.imshow(test_image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dfbe9b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "742fbe26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2430b354",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "yt38",
   "language": "python",
   "name": "yt38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}