create_new_dataset_and_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4134a244-398b-40f7-ae57-7427540a7a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from enum import Enum\n",
    "\n",
    "class Workspace(Enum):\n",
    "    py=1\n",
    "    note=2\n",
    "    \n",
    "workspace = Workspace.note\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9668ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from pylab import rcParams\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rc\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "import csv\n",
    "import cv2\n",
    "import time\n",
    "from PIL import Image\n",
    "from datetime import datetime\n",
    "import random\n",
    "import json\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import shutil\n",
    "\n",
    "import imghdr\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "register_matplotlib_converters()\n",
    "RANDOM_SEED = 42\n",
    "np.random.seed(RANDOM_SEED)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e01b52c8-f68e-40ab-adc3-647f31974755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if workspace != Workspace.py:\n",
    "    path = '.'\n",
    "else:\n",
    "    parser = optparse.OptionParser()\n",
    "    \n",
    "    parser.add_option('-p', '--path', help='Pass the path image', default='.')\n",
    "    \n",
    "    (opts, args) = parser.parse_args()  # instantiate parser\n",
    "    \n",
    "    path = opts.path\n",
    "    new_dataset_path = f'{path}/new_dataset/'\n",
    "    \n",
    "    print(opts.path)\n",
    "\n",
    "\n",
    "# In[2]:\n",
    "\n",
    "path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca2e6f70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "dataset_sketch_it/images/8ded829b-7f25-4cd7-8d40-c1d3100d755d.png\n",
      "dataset_sketch_it/images/3e6baa71-dc1e-4ec8-8a3f-a4349e6cbf1a.png\n",
      "dataset_sketch_it/images/96822e44-dbbd-4d6c-9a81-5419f519ac22.png\n",
      "dataset_sketch_it/images/30f5f8e2-47c5-4977-933c-e005cc275663.png\n",
      "dataset_sketch_it/images/8bfdbd32-32ad-4b48-8db5-41671fc15d2b.png\n",
      "dataset_sketch_it/images/76f51eb5-afd2-4e83-82c0-a105b10dc6f1.png\n",
      "dataset_sketch_it/images/9e4b1496-e1cf-4bcc-9171-34e1fbbef25f.png\n",
      "dataset_sketch_it/images/5c696efc-7f6e-4bf3-9b7f-8cd952df3bfa.png\n",
      "dataset_sketch_it/images/890c905a-8da1-4a68-b1f9-b57babf1d1bf.png\n",
      "dataset_sketch_it/images/236b436f-ce60-4a25-8e81-3603552536e3.png\n",
      "dataset_sketch_it/images/6c993a8f-a251-415b-af5d-1781980d47f5.png\n",
      "dataset_sketch_it/images/999aa799-f234-4a6e-9c76-b6e98b031c5e.png\n",
      "dataset_sketch_it/images/f66ced22-8f4e-4c35-8277-e49fba7bf192.png\n",
      "dataset_sketch_it/images/feef1db7-6cbe-4d0d-9929-18e42ba09407.png\n",
      "dataset_sketch_it/images/3da7a00f-f912-41df-b9bd-0d469bad53a4.png\n",
      "dataset_sketch_it/images/12ca11b1-9d0b-475b-96c4-ce30aeb496ec.png\n",
      "dataset_sketch_it/images/504bd55e-069d-4a18-b535-da3ca2d45f56.png\n",
      "dataset_sketch_it/images/96b1c403-5923-47cb-8bd8-99b20e12620a.png\n",
      "dataset_sketch_it/images/7273531e-1908-475a-96b3-5c1776d10c5a.png\n",
      "dataset_sketch_it/images/7f288e89-a0fb-4f69-b6e8-458faae5915f.png\n",
      "dataset_sketch_it/images/f7178070-ea83-4a9e-b814-e367468705c3.png\n",
      "dataset_sketch_it/images/1b1f4c8c-92a7-4000-8f07-471d6f8016b1.png\n",
      "dataset_sketch_it/images/9f638ca2-de14-47dd-9f81-c697e849c3c5.png\n",
      "dataset_sketch_it/images/95e8e4eb-51ba-4193-8d2e-10ccda0547a7.png\n",
      "dataset_sketch_it/images/26a69d17-f61d-4edf-a993-fa5610d5bd0b.png\n",
      "dataset_sketch_it/images/1f6dce1a-bbe1-4fff-971a-79ebeaa99c1a.png\n",
      "dataset_sketch_it/images/68bea336-2616-431e-9321-99a8abe47e64.png\n",
      "dataset_sketch_it/images/e4da414a-0ad2-4580-ab2e-039a06532ea9.png\n",
      "dataset_sketch_it/images/653aa448-284d-4664-b993-734097595667.png\n",
      "dataset_sketch_it/images/8c7ccc76-83a9-4dde-8be3-84895b387230.png\n",
      "dataset_sketch_it/images/9d9f477c-0060-48d0-bde2-2f4cbf16d93d.png\n",
      "dataset_sketch_it/images/b04dc998-9b68-4d01-9670-0727d0d43617.png\n",
      "dataset_sketch_it/images/b61a2a6c-c46d-4575-a091-3508bf0b2b88.png\n",
      "dataset_sketch_it/images/ae3de36d-ba93-4a5d-829c-38fa0b865812.png\n",
      "dataset_sketch_it/images/64090cc3-7882-419b-b22b-3b6f51fd5115.png\n",
      "dataset_sketch_it/images/cf81631e-ee08-49b5-9c44-818024734b81.png\n",
      "dataset_sketch_it/images/0f5761f9-a85c-4b7f-949a-189ab7cf853e.png\n",
      "dataset_sketch_it/images/b0d1635f-2f9a-4285-8517-444cae326761.png\n",
      "dataset_sketch_it/images/5679fe0e-d661-4f54-b664-19f87d31a090.png\n",
      "dataset_sketch_it/images/57ace871-49e6-40be-8841-dd460a7ceb1a.png\n",
      "dataset_sketch_it/images/a5d02313-1d8b-4148-9cf9-54e832e827a6.png\n",
      "dataset_sketch_it/images/a2e5bc0f-6e8a-442e-affe-ca4bda44d262.png\n",
      "dataset_sketch_it/images/bde0646d-e0d6-497f-b832-cb2bbf907eb1.png\n",
      "dataset_sketch_it/images/bc18ff8e-a345-40ed-9f3e-ccaf0ca2141f.png\n",
      "dataset_sketch_it/images/13b9f109-f532-471a-8f39-f19eef551919.png\n",
      "dataset_sketch_it/images/fa3aae44-9496-4557-a085-d1407b5b3e8e.png\n",
      "dataset_sketch_it/images/05495118-dcee-4e4e-bee1-b6823096e2b7.png\n",
      "dataset_sketch_it/images/22b40473-2892-4ab9-96be-9aee12ffd17a.png\n",
      "dataset_sketch_it/dataset.json\n",
      "dataset_sketch_it/images/annotations.csv\n",
      "dataset_sketch_it/images/938936cd-9222-4a2a-a46a-cbb9e25d232c.png\n",
      "dataset_sketch_it/.ipynb_checkpoints/sketchit-checkpoint.ipynb\n",
      "dataset_sketch_it/images/431bea08-f917-46c5-97b7-78766ee34ab5.png\n",
      "dataset_sketch_it/images/d20b2bf2-ee77-404f-8de3-9238284d1957.png\n",
      "dataset_sketch_it/images/5bc04e44-1903-40b3-a782-79035dcec16d.png\n",
      "dataset_sketch_it/images/b5a04858-3e4a-4db4-9117-47f7e533798a.png\n",
      "dataset_sketch_it/images/e86d7eae-07d8-49da-86c6-883cea3a87c4.png\n",
      "dataset_sketch_it/images/7f7bf1e5-2f17-4316-ac56-448d99afb35f.png\n",
      "dataset_sketch_it/images/a6c59f33-2285-4644-97aa-52f193d4e5ce.png\n",
      "dataset_sketch_it/images/df16cc89-07db-46d0-9748-ca450e8fbfba.png\n",
      "dataset_sketch_it/images/e61e2973-7712-4b2c-91e9-b3221756a220.png\n",
      "dataset_sketch_it/images/eaa0ceec-7f26-42b2-81a7-b8cd52416fec.png\n",
      "dataset_sketch_it/images/2156be4b-6d9b-461b-98ed-1ca46f07b453.png\n",
      "dataset_sketch_it/images/c0822cd3-0bcb-458b-8dba-eef63fe4e3a3.png\n",
      "dataset_sketch_it/images/2451e8de-9c13-40ab-ba9d-3141d9d2fdec.png\n",
      "dataset_sketch_it/images/6e9ac9b9-b034-49e5-8aac-911b7a482dfd.png\n",
      "dataset_sketch_it/images/782ebd0a-b6bd-4607-a109-4beca493c694.png\n",
      "dataset_sketch_it/images/\n",
      "dataset_sketch_it/images/d960506e-ca2c-4393-911c-6a1e7fbaddf7.png\n",
      "dataset_sketch_it/images/54f95c56-6e1e-42b9-b0ee-2f9de7134c6e.png\n",
      "dataset_sketch_it/images/e8f8f3bf-6241-4b9f-91c4-3422c6dadee7.png\n",
      "dataset_sketch_it/images/c6d6c834-f44b-4227-aec1-5bf40f05b0c7.png\n",
      "dataset_sketch_it/images/48002818-9e4b-486c-96cf-db1052cda8b7.png\n",
      "dataset_sketch_it/images/85c935d2-a69a-4d58-907b-22cf24a53fd5.png\n",
      "dataset_sketch_it/images/39f28af1-a6d1-4620-9d9d-840450a1e6a2.png\n",
      "dataset_sketch_it/images/b492b141-a162-4e40-bba2-920a9d1c9555.png\n",
      "dataset_sketch_it/images/8d7dfde9-323b-42b8-ae30-8665233cc870.png\n",
      "dataset_sketch_it/images/552147ca-3b14-44ed-8de7-4638f1598c92.png\n",
      "dataset_sketch_it/images/98e674be-e53f-4fad-880d-a97eca09d16f.png\n",
      "dataset_sketch_it/images/6450fc7c-cd20-4f5d-a41d-c23bba871984.png\n",
      "dataset_sketch_it/images/d8f1369c-5c96-4cdd-b437-a77353c831c6.png\n",
      "dataset_sketch_it/images/da8781f7-e181-455c-9639-2eb2df710ca5.png\n",
      "dataset_sketch_it/images/4ffebf6a-a75b-4361-81f8-95b08fee802b.png\n",
      "dataset_sketch_it/images/1b080c62-85d1-4222-9601-c4eec8b2cff8.png\n",
      "dataset_sketch_it/images/4ffbe90a-7888-4dd3-9e5c-7565fa1b4847.png\n",
      "dataset_sketch_it/images/2c70e243-010a-492c-b55e-6131c7211328.png\n",
      "dataset_sketch_it/images/603eb545-1b3c-4e44-8d46-80c452d4fe3d.png\n",
      "dataset_sketch_it/images/a2d2b1cd-d4b8-4a1c-90fa-e5549e39b91b.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "os.system(f'tar -zxvf {path}/dataset_roboflow.tar.gz -C {path}')\n",
    "os.system(f'tar -zxvf {path}/dataset_sketch_it.tar.gz -C {path}')\n",
    "\n",
    "# !tar -zxvf dataset_roboflow.tar.gz && tar -zxvf dataset_sketch_it.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ffefabd",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dataset_path=f'{path}/new_dataset/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9cbdcea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "THRES_SCORE = 0.4\n",
    "\n",
    "def draw_detections(image, box, scores=0, label=''):\n",
    "#   for box, score, label in zip(boxes[0], scores[0], labels[0]):\n",
    "#     if score < THRES_SCORE:\n",
    "#         continue\n",
    "\n",
    "#     color = label_color(labels)\n",
    "\n",
    "#     box = box.astype(int)\n",
    "    \n",
    "    draw_box(image, box, color=(0,255,0) )\n",
    "\n",
    "    caption = \"{} {:.3f}\".format(label, 0)\n",
    "    draw_caption(image, box, caption)\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "def show_detected_objects(image_row,img_folder=''):\n",
    "    \n",
    "    img_path = img_folder+image_row.filename\n",
    "    true_box = [\n",
    "        image_row.xmin, image_row.ymin, image_row.xmax, image_row.ymax]\n",
    "    image = read_image_bgr(img_path)\n",
    "\n",
    "    draw = image.copy()\n",
    "    draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    image = preprocess_image(image)\n",
    "    image, scale = resize_image(image)\n",
    "\n",
    "#     boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis = 0))\n",
    "#     print('scale',scale)\n",
    "#     print('scores',scores)\n",
    "#     print('labels',labels)\n",
    "#     print('image',image)\n",
    "\n",
    "#     boxes /= scale\n",
    "#     print('boxes',boxes[0])\n",
    "\n",
    "    draw_box(draw, true_box, color=(255, 0, 0))\n",
    "    draw_detections(image, box=true_box, label= labels_to_name)\n",
    "    \n",
    "#     caption = \"{} {:.3f}\".format(image_row.class_name,0)\n",
    "#     draw_caption(image,true_box , image_row.class_name)\n",
    "\n",
    "    plt.axis('off')\n",
    "    plt.imshow(draw)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3691aa06",
   "metadata": {},
   "outputs": [],
   "source": [
    "roboflow = pd.read_csv(f'{path}/dataset_roboflow/new_train/annotations.csv')\n",
    "sketch_it = pd.read_csv(f'{path}/dataset_sketch_it/images/annotations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18ccd88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>class_name</th>\n",
       "      <th>x_min</th>\n",
       "      <th>y_min</th>\n",
       "      <th>x_max</th>\n",
       "      <th>y_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00024_png.rf.097dd8beb3a90a32588e3b7d14105214.jpg</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Select</td>\n",
       "      <td>95</td>\n",
       "      <td>98</td>\n",
       "      <td>345</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00024_png.rf.097dd8beb3a90a32588e3b7d14105214.jpg</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Select</td>\n",
       "      <td>102</td>\n",
       "      <td>244</td>\n",
       "      <td>342</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>00024_png.rf.097dd8beb3a90a32588e3b7d14105214.jpg</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Button</td>\n",
       "      <td>106</td>\n",
       "      <td>542</td>\n",
       "      <td>372</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00027_png.rf.051c242efd7c63dbc96a34fdfc9e8499.jpg</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Image</td>\n",
       "      <td>12</td>\n",
       "      <td>160</td>\n",
       "      <td>139</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00027_png.rf.051c242efd7c63dbc96a34fdfc9e8499.jpg</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Image</td>\n",
       "      <td>191</td>\n",
       "      <td>151</td>\n",
       "      <td>306</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4577</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>44</td>\n",
       "      <td>288</td>\n",
       "      <td>621</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4578</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>27</td>\n",
       "      <td>157</td>\n",
       "      <td>257</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4579</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>277</td>\n",
       "      <td>153</td>\n",
       "      <td>620</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>319</td>\n",
       "      <td>34</td>\n",
       "      <td>607</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>292</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4582 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             image_name  width  height  \\\n",
       "0     00024_png.rf.097dd8beb3a90a32588e3b7d14105214.jpg    640     640   \n",
       "1     00024_png.rf.097dd8beb3a90a32588e3b7d14105214.jpg    640     640   \n",
       "2     00024_png.rf.097dd8beb3a90a32588e3b7d14105214.jpg    640     640   \n",
       "3     00027_png.rf.051c242efd7c63dbc96a34fdfc9e8499.jpg    640     640   \n",
       "4     00027_png.rf.051c242efd7c63dbc96a34fdfc9e8499.jpg    640     640   \n",
       "...                                                 ...    ...     ...   \n",
       "4577  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "4578  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "4579  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "4580  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "4581  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "\n",
       "     class_name  x_min  y_min  x_max  y_max  \n",
       "0        Select     95     98    345    157  \n",
       "1        Select    102    244    342    309  \n",
       "2        Button    106    542    372    614  \n",
       "3         Image     12    160    139    400  \n",
       "4         Image    191    151    306    395  \n",
       "...         ...    ...    ...    ...    ...  \n",
       "4577   Carousel     44    288    621    415  \n",
       "4578   Carousel     27    157    257    275  \n",
       "4579   Carousel    277    153    620    269  \n",
       "4580   Carousel    319     34    607    133  \n",
       "4581   Carousel     38     26    292    134  \n",
       "\n",
       "[4582 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roboflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "322dfb9e",
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       "      <td>414</td>\n",
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       "      <th>1</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
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       "      <th>2</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>Label</td>\n",
       "      <td>18</td>\n",
       "      <td>146</td>\n",
       "      <td>328</td>\n",
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       "      <th>3</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
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       "      <td>989</td>\n",
       "      <td>220</td>\n",
       "      <td>1292</td>\n",
       "      <td>294</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>RadioButton</td>\n",
       "      <td>804</td>\n",
       "      <td>107</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>1996</th>\n",
       "      <td>39f28af1-a6d1-4620-9d9d-840450a1e6a2.png</td>\n",
       "      <td>1200</td>\n",
       "      <td>900</td>\n",
       "      <td>Heading</td>\n",
       "      <td>453</td>\n",
       "      <td>628</td>\n",
       "      <td>645</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>39f28af1-a6d1-4620-9d9d-840450a1e6a2.png</td>\n",
       "      <td>1200</td>\n",
       "      <td>900</td>\n",
       "      <td>Paragraph</td>\n",
       "      <td>133</td>\n",
       "      <td>734</td>\n",
       "      <td>369</td>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>39f28af1-a6d1-4620-9d9d-840450a1e6a2.png</td>\n",
       "      <td>1200</td>\n",
       "      <td>900</td>\n",
       "      <td>Paragraph</td>\n",
       "      <td>300</td>\n",
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       "      <td>1072</td>\n",
       "      <td>478</td>\n",
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       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>39f28af1-a6d1-4620-9d9d-840450a1e6a2.png</td>\n",
       "      <td>1200</td>\n",
       "      <td>900</td>\n",
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       "      <td>752</td>\n",
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       "      <td>983</td>\n",
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       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>39f28af1-a6d1-4620-9d9d-840450a1e6a2.png</td>\n",
       "      <td>1200</td>\n",
       "      <td>900</td>\n",
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       "      <td>433</td>\n",
       "      <td>736</td>\n",
       "      <td>665</td>\n",
       "      <td>859</td>\n",
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       "<p>2001 rows × 8 columns</p>\n",
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       "                                    image_name  width  height   class_name  \\\n",
       "0     2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564     CheckBox   \n",
       "1     2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564       Button   \n",
       "2     2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564        Label   \n",
       "3     2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564     CheckBox   \n",
       "4     2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564  RadioButton   \n",
       "...                                        ...    ...     ...          ...   \n",
       "1996  39f28af1-a6d1-4620-9d9d-840450a1e6a2.png   1200     900      Heading   \n",
       "1997  39f28af1-a6d1-4620-9d9d-840450a1e6a2.png   1200     900    Paragraph   \n",
       "1998  39f28af1-a6d1-4620-9d9d-840450a1e6a2.png   1200     900    Paragraph   \n",
       "1999  39f28af1-a6d1-4620-9d9d-840450a1e6a2.png   1200     900      Heading   \n",
       "2000  39f28af1-a6d1-4620-9d9d-840450a1e6a2.png   1200     900    Paragraph   \n",
       "\n",
       "      x_min  y_min  x_max  y_max  \n",
       "0       423    332    690    414  \n",
       "1       989    422   1186    554  \n",
       "2        18    146    328    207  \n",
       "3       989    220   1292    294  \n",
       "4       804    107    952    168  \n",
       "...     ...    ...    ...    ...  \n",
       "1996    453    628    645    678  \n",
       "1997    133    734    369    855  \n",
       "1998    300    204   1072    478  \n",
       "1999    752    639    983    679  \n",
       "2000    433    736    665    859  \n",
       "\n",
       "[2001 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   "source": [
    "sketch_it"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d63c2157",
   "metadata": {},
   "outputs": [
    {
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       "      <th>6578</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
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       "      <td>44</td>\n",
       "      <td>288</td>\n",
       "      <td>621</td>\n",
       "      <td>415</td>\n",
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       "    <tr>\n",
       "      <th>6579</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>27</td>\n",
       "      <td>157</td>\n",
       "      <td>257</td>\n",
       "      <td>275</td>\n",
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       "    <tr>\n",
       "      <th>6580</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
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       "      <td>Carousel</td>\n",
       "      <td>277</td>\n",
       "      <td>153</td>\n",
       "      <td>620</td>\n",
       "      <td>269</td>\n",
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       "    <tr>\n",
       "      <th>6581</th>\n",
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       "      <td>34</td>\n",
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       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
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       "      <td>134</td>\n",
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       "<p>6583 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "                                             image_name  width  height  \\\n",
       "0              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "1              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "2              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "3              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
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       "...                                                 ...    ...     ...   \n",
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       "6580  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "6581  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "6582  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "\n",
       "       class_name  x_min  y_min  x_max  y_max  \n",
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       "4     RadioButton    804    107    952    168  \n",
       "...           ...    ...    ...    ...    ...  \n",
       "6578     Carousel     44    288    621    415  \n",
       "6579     Carousel     27    157    257    275  \n",
       "6580     Carousel    277    153    620    269  \n",
       "6581     Carousel    319     34    607    133  \n",
       "6582     Carousel     38     26    292    134  \n",
       "\n",
       "[6583 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "new_dataset = pd.concat([sketch_it,roboflow],ignore_index=True)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f877c9f7",
   "metadata": {},
   "outputs": [
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       "      <td>621</td>\n",
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       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
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       "      <td>640</td>\n",
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       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>277</td>\n",
       "      <td>153</td>\n",
       "      <td>620</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6581</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>319</td>\n",
       "      <td>34</td>\n",
       "      <td>607</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6582</th>\n",
       "      <td>images_14_png.rf.2e1996b917db3d4b700281e422338...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>Carousel</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>292</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6583 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               filename  width  height  \\\n",
       "0              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "1              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "2              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "3              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "4              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "...                                                 ...    ...     ...   \n",
       "6578  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "6579  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "6580  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "6581  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "6582  images_14_png.rf.2e1996b917db3d4b700281e422338...    640     640   \n",
       "\n",
       "            label  xmin  ymin  xmax  ymax  \n",
       "0        CheckBox   423   332   690   414  \n",
       "1          Button   989   422  1186   554  \n",
       "2           Label    18   146   328   207  \n",
       "3        CheckBox   989   220  1292   294  \n",
       "4     RadioButton   804   107   952   168  \n",
       "...           ...   ...   ...   ...   ...  \n",
       "6578     Carousel    44   288   621   415  \n",
       "6579     Carousel    27   157   257   275  \n",
       "6580     Carousel   277   153   620   269  \n",
       "6581     Carousel   319    34   607   133  \n",
       "6582     Carousel    38    26   292   134  \n",
       "\n",
       "[6583 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset.rename(columns={'image_name': 'filename'}, inplace=True)\n",
    "new_dataset.rename(columns={'x_min': 'xmin'}, inplace=True)\n",
    "new_dataset.rename(columns={'y_min': 'ymin'}, inplace=True)\n",
    "new_dataset.rename(columns={'x_max': 'xmax'}, inplace=True)\n",
    "new_dataset.rename(columns={'y_max': 'ymax'}, inplace=True)\n",
    "new_dataset.rename(columns={'class_name': 'label'}, inplace=True)\n",
    "\n",
    "\n",
    "roboflow.rename(columns={'image_name': 'filename'}, inplace=True)\n",
    "roboflow.rename(columns={'x_min': 'xmin'}, inplace=True)\n",
    "roboflow.rename(columns={'y_min': 'ymin'}, inplace=True)\n",
    "roboflow.rename(columns={'x_max': 'xmax'}, inplace=True)\n",
    "roboflow.rename(columns={'y_max': 'ymax'}, inplace=True)\n",
    "roboflow.rename(columns={'class_name': 'label'}, inplace=True)\n",
    "\n",
    "sketch_it.rename(columns={'image_name': 'filename'}, inplace=True)\n",
    "sketch_it.rename(columns={'x_min': 'xmin'}, inplace=True)\n",
    "sketch_it.rename(columns={'y_min': 'ymin'}, inplace=True)\n",
    "sketch_it.rename(columns={'x_max': 'xmax'}, inplace=True)\n",
    "sketch_it.rename(columns={'y_max': 'ymax'}, inplace=True)\n",
    "sketch_it.rename(columns={'class_name': 'label'}, inplace=True)\n",
    "\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdd7ab85",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def copy2dri(df,destination,fromDir=[]):\n",
    "\n",
    "    print(destination)\n",
    "\n",
    "    for i in df.filename: \n",
    "\n",
    "\n",
    "    #     create dir\n",
    "        os.makedirs(destination,exist_ok=True)\n",
    "\n",
    "        for Dir in fromDir:\n",
    "#             print(f'{Dir+i} -')\n",
    "            \n",
    "            if os.path.exists(Dir+i):\n",
    "                pli_img = Image.open(Dir+i)  \n",
    "                cv_img = cv2.imread(Dir+i)\n",
    "\n",
    "#                 print(f'{Dir+i} - {pli_img.format}')\n",
    "        #         cv2.imwrite(destination+'/'+i, cv_img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])  \n",
    "                shutil.copy2(Dir+i,destination)\n",
    "\n",
    "\n",
    "    df.to_csv(destination+'/annotations.csv',index=False)      \n",
    "\n",
    "    return 'done'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc588619",
   "metadata": {},
   "source": [
    "# filter by accpted classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07dd4cee",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Button</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Carousel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CheckBox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ComboBox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Heading</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Link</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pagination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Paragraph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RadioButton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Select</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TextBox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Textarea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0\n",
       "1        Button\n",
       "12     Carousel\n",
       "0      CheckBox\n",
       "6      ComboBox\n",
       "5       Heading\n",
       "8         Image\n",
       "2         Label\n",
       "7          Link\n",
       "14   Pagination\n",
       "9     Paragraph\n",
       "3   RadioButton\n",
       "10       Select\n",
       "11        Table\n",
       "4       TextBox\n",
       "13     Textarea"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_to_name=pd.DataFrame( new_dataset['label'].unique()).sort_values(by=0)\n",
    "labels_to_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2cfecf0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "Label          1075\n",
       "TextBox         719\n",
       "CheckBox        695\n",
       "Button          643\n",
       "Image           592\n",
       "Link            491\n",
       "Heading         490\n",
       "RadioButton     416\n",
       "Paragraph       360\n",
       "Select          351\n",
       "Table           214\n",
       "Carousel        178\n",
       "Textarea        151\n",
       "Pagination      150\n",
       "ComboBox         58\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6455024b",
   "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>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Button</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CheckBox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heading</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Link</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Paragraph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RadioButton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Select</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TextBox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_label\n",
       "0       Button\n",
       "1     CheckBox\n",
       "2      Heading\n",
       "3        Image\n",
       "4        Label\n",
       "5         Link\n",
       "6    Paragraph\n",
       "7  RadioButton\n",
       "8       Select\n",
       "9      TextBox"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accpted_classes = pd.read_csv(f'{path}/accpted_classes.csv')\n",
    "accpted_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "093ee44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "accpted_classes_list = accpted_classes.values.reshape(1,-1)[0]\n",
    "\n",
    "classes = []\n",
    "\n",
    "for i,item in enumerate(accpted_classes_list):\n",
    "        classes.append({'name':item, 'id':i+1})\n",
    "\n",
    "\n",
    "with open(f'{path}/label_map.pbtxt', 'w') as f:\n",
    "    for item in classes:\n",
    "        f.write('item { \\n')\n",
    "        f.write('\\tname:\\'{}\\'\\n'.format(item['name']))\n",
    "        f.write('\\tid:{}\\n'.format(item['id']))\n",
    "        f.write('}\\n') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90c69c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>filename</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>label</th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
       "      <th>xmax</th>\n",
       "      <th>ymax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>423</td>\n",
       "      <td>332</td>\n",
       "      <td>690</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>Button</td>\n",
       "      <td>989</td>\n",
       "      <td>422</td>\n",
       "      <td>1186</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>Label</td>\n",
       "      <td>18</td>\n",
       "      <td>146</td>\n",
       "      <td>328</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>989</td>\n",
       "      <td>220</td>\n",
       "      <td>1292</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
       "      <td>1600</td>\n",
       "      <td>564</td>\n",
       "      <td>RadioButton</td>\n",
       "      <td>804</td>\n",
       "      <td>107</td>\n",
       "      <td>952</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>393</td>\n",
       "      <td>125</td>\n",
       "      <td>563</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6570</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>401</td>\n",
       "      <td>161</td>\n",
       "      <td>565</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6571</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>395</td>\n",
       "      <td>194</td>\n",
       "      <td>573</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6572</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>201</td>\n",
       "      <td>122</td>\n",
       "      <td>340</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6573</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>211</td>\n",
       "      <td>10</td>\n",
       "      <td>356</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5832 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               filename  width  height  \\\n",
       "0              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "1              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "2              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "3              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "4              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "...                                                 ...    ...     ...   \n",
       "6569  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "6570  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "6571  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "6572  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "6573  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "\n",
       "            label  xmin  ymin  xmax  ymax  \n",
       "0        CheckBox   423   332   690   414  \n",
       "1          Button   989   422  1186   554  \n",
       "2           Label    18   146   328   207  \n",
       "3        CheckBox   989   220  1292   294  \n",
       "4     RadioButton   804   107   952   168  \n",
       "...           ...   ...   ...   ...   ...  \n",
       "6569     CheckBox   393   125   563   149  \n",
       "6570     CheckBox   401   161   565   182  \n",
       "6571     CheckBox   395   194   573   220  \n",
       "6572     CheckBox   201   122   340   140  \n",
       "6573     CheckBox   211    10   356    32  \n",
       "\n",
       "[5832 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = new_dataset[new_dataset.label.isin(list(accpted_classes.values.reshape(1,-1)[0]))]\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "225cb06e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./new_dataset/\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'done'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "copy2dri(destination=new_dataset_path,df= new_dataset,fromDir=[f'{path}/dataset_roboflow/new_train/',f'{path}/dataset_sketch_it/images/'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f0fb9126",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>332</td>\n",
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       "      <td>1186</td>\n",
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       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
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       "      <td>328</td>\n",
       "      <td>207</td>\n",
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       "      <th>3</th>\n",
       "      <td>2469da39-65f0-428b-a1de-cedbba7db32e.png</td>\n",
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       "      <th>5827</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>393</td>\n",
       "      <td>125</td>\n",
       "      <td>563</td>\n",
       "      <td>149</td>\n",
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       "    <tr>\n",
       "      <th>5828</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>401</td>\n",
       "      <td>161</td>\n",
       "      <td>565</td>\n",
       "      <td>182</td>\n",
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       "    <tr>\n",
       "      <th>5829</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>395</td>\n",
       "      <td>194</td>\n",
       "      <td>573</td>\n",
       "      <td>220</td>\n",
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       "    <tr>\n",
       "      <th>5830</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>201</td>\n",
       "      <td>122</td>\n",
       "      <td>340</td>\n",
       "      <td>140</td>\n",
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       "    <tr>\n",
       "      <th>5831</th>\n",
       "      <td>image_65_png.rf.e3218cec368b8116517298850b15d9...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>CheckBox</td>\n",
       "      <td>211</td>\n",
       "      <td>10</td>\n",
       "      <td>356</td>\n",
       "      <td>32</td>\n",
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       "<p>5832 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               filename  width  height  \\\n",
       "0              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "1              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "2              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "3              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "4              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "...                                                 ...    ...     ...   \n",
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       "5829  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "5830  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "5831  image_65_png.rf.e3218cec368b8116517298850b15d9...    640     640   \n",
       "\n",
       "            label  xmin  ymin  xmax  ymax  \n",
       "0        CheckBox   423   332   690   414  \n",
       "1          Button   989   422  1186   554  \n",
       "2           Label    18   146   328   207  \n",
       "3        CheckBox   989   220  1292   294  \n",
       "4     RadioButton   804   107   952   168  \n",
       "...           ...   ...   ...   ...   ...  \n",
       "5827     CheckBox   393   125   563   149  \n",
       "5828     CheckBox   401   161   565   182  \n",
       "5829     CheckBox   395   194   573   220  \n",
       "5830     CheckBox   201   122   340   140  \n",
       "5831     CheckBox   211    10   356    32  \n",
       "\n",
       "[5832 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = pd.read_csv(f'{path}/new_dataset/annotations.csv')\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8847e93e",
   "metadata": {},
   "outputs": [
    {
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       "      <td>9</td>\n",
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       "      <td>84</td>\n",
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       "      <th>622</th>\n",
       "      <td>8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png</td>\n",
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       "      <td>292</td>\n",
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       "      <td>8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png</td>\n",
       "      <td>900</td>\n",
       "      <td>1248</td>\n",
       "      <td>Label</td>\n",
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       "      <td>569</td>\n",
       "      <td>476</td>\n",
       "      <td>675</td>\n",
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       "    <tr>\n",
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       "      <td>8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png</td>\n",
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       "      <td>TextBox</td>\n",
       "      <td>441</td>\n",
       "      <td>419</td>\n",
       "      <td>762</td>\n",
       "      <td>520</td>\n",
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       "</table>\n",
       "</div>"
      ],
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       "                                     filename  width  height        label  \\\n",
       "615  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248  RadioButton   \n",
       "616  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "617  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "618  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "619  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248  RadioButton   \n",
       "620  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "621  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248      Heading   \n",
       "622  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "623  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248      TextBox   \n",
       "624  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248  RadioButton   \n",
       "625  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248      TextBox   \n",
       "626  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "627  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "628  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248        Label   \n",
       "629  8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png    900    1248      TextBox   \n",
       "\n",
       "     xmin  ymin  xmax  ymax  \n",
       "615   154  1047   384  1140  \n",
       "616   207   756   579   872  \n",
       "617   126   113   518   204  \n",
       "618   207   946   549  1044  \n",
       "619   154   870   453   958  \n",
       "620   167   439   374   522  \n",
       "621   134     9   379    84  \n",
       "622   162   320   420   385  \n",
       "623   446   209   760   305  \n",
       "624   152   695   415   776  \n",
       "625   446   315   762   406  \n",
       "626   212  1125   574  1223  \n",
       "627   167   224   433   292  \n",
       "628   119   569   476   675  \n",
       "629   441   419   762   520  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset[new_dataset['filename']=='8ae99b35-2354-4f8b-ade0-9fd98f09f4fe.png']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88411e17",
   "metadata": {},
   "source": [
    "# spilt into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7da450bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "Label          1075\n",
       "TextBox         719\n",
       "CheckBox        695\n",
       "Button          643\n",
       "Image           592\n",
       "Link            491\n",
       "Heading         490\n",
       "RadioButton     416\n",
       "Paragraph       360\n",
       "Select          351\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "70802296",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "if os.path.exists('train'):\n",
    "    shutil.rmtree('train')\n",
    "    \n",
    "if os.path.exists('test'):\n",
    "    shutil.rmtree('test')\n",
    "\n",
    "train_set = pd.DataFrame([])\n",
    "test_set = pd.DataFrame([])\n",
    "lbls = new_dataset.label.unique().tolist()\n",
    "\n",
    "for lbl in lbls:\n",
    "\n",
    "    train, test = train_test_split(new_dataset[new_dataset['label']==lbl], test_size = 0.2,shuffle=False)\n",
    "    train_set=pd.concat([train_set,train],ignore_index=True)\n",
    "    test_set=pd.concat([test_set,test],ignore_index=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "565c5400",
   "metadata": {},
   "outputs": [
    {
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       "      <td>209</td>\n",
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       "    <tr>\n",
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       "      <td>00019_png.rf.30acb091fbd4f5ec416af77dc9445b91.jpg</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
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       "                                               filename  width  height  \\\n",
       "0              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "1              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "2              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
       "3              2469da39-65f0-428b-a1de-cedbba7db32e.png   1600     564   \n",
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       "...                                                 ...    ...     ...   \n",
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       "4661  00036_png.rf.2b34e2144c9bcae70ffe42d5ea58dad5.jpg    640     640   \n",
       "\n",
       "         label  xmin  ymin  xmax  ymax  \n",
       "0     CheckBox   423   332   690   414  \n",
       "1     CheckBox   989   220  1292   294  \n",
       "2     CheckBox   419   243   658   304  \n",
       "3     CheckBox  1329   297  1580   372  \n",
       "4     CheckBox   698   227   948   302  \n",
       "...        ...   ...   ...   ...   ...  \n",
       "4657    Select    75   432   209   500  \n",
       "4658    Select    74   529   211   598  \n",
       "4659    Select   105   480   205   517  \n",
       "4660    Select    35    86   570   143  \n",
       "4661    Select    36   236   569   293  \n",
       "\n",
       "[4662 rows x 8 columns]"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_set"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fbf973a5",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>image_92_png.rf.5826277764057ab0eb4c61c1fd848b...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
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       "      <td>499</td>\n",
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       "    <tr>\n",
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       "      <td>image_92_png.rf.5826277764057ab0eb4c61c1fd848b...</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
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       "                                               filename  width  height  \\\n",
       "0     image_199_png.rf.4ad9745c6688cf1585a112f4e263d...    640     640   \n",
       "1     image_74_png.rf.52beed189736dbe45515e7820607e6...    640     640   \n",
       "2     image_74_png.rf.52beed189736dbe45515e7820607e6...    640     640   \n",
       "3     image_74_png.rf.52beed189736dbe45515e7820607e6...    640     640   \n",
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       "...                                                 ...    ...     ...   \n",
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       "1166  image_92_png.rf.5826277764057ab0eb4c61c1fd848b...    640     640   \n",
       "1167  image_92_png.rf.5826277764057ab0eb4c61c1fd848b...    640     640   \n",
       "1168  image_92_png.rf.5826277764057ab0eb4c61c1fd848b...    640     640   \n",
       "1169  image_92_png.rf.5826277764057ab0eb4c61c1fd848b...    640     640   \n",
       "\n",
       "         label  xmin  ymin  xmax  ymax  \n",
       "0     CheckBox   485   560   620   585  \n",
       "1     CheckBox   473   226   558   246  \n",
       "2     CheckBox   472   262   560   283  \n",
       "3     CheckBox   473   297   559   314  \n",
       "4     CheckBox   473   334   560   352  \n",
       "...        ...   ...   ...   ...   ...  \n",
       "1165    Select   310   574   465   616  \n",
       "1166    Select    41   565   189   608  \n",
       "1167    Select    61   462   218   499  \n",
       "1168    Select    82   370   236   414  \n",
       "1169    Select   102   269   269   314  \n",
       "\n",
       "[1170 rows x 8 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6d8a3bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./train/images\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'done'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy2dri(destination=f'{path}/train/images',df= train_set,fromDir=[new_dataset_path])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0b792fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./test/images\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'done'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy2dri(destination=f'{path}/test/images',df= test_set,fromDir=[new_dataset_path])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "145323d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = pd.read_csv(f'{path}/test/images/annotations.csv')\n",
    "train_set = pd.read_csv(f'{path}/train/images/annotations.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e6b64c",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# CSV to coco format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "60eece38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#coco\n",
    "\n",
    "\n",
    "def csv2coco( data ,save_json_path):\n",
    "\n",
    "#     data = new_dataset.copy()\n",
    "\n",
    "    images = []\n",
    "    categories = []\n",
    "    annotations = []\n",
    "\n",
    "    category = {}\n",
    "    category[\"supercategory\"] = 'none'\n",
    "    category[\"id\"] = 0\n",
    "    category[\"name\"] = 'None'\n",
    "    categories.append(category)\n",
    "\n",
    "    data['fileid'] = data['filename'].astype('category').cat.codes\n",
    "    data['categoryid']= pd.Categorical(data['label'],ordered= True).codes\n",
    "    data['categoryid'] = data['categoryid']+1\n",
    "    data['annid'] = data.index\n",
    "\n",
    "    def image(row):\n",
    "        image = {}\n",
    "        image[\"height\"] = row.height\n",
    "        image[\"width\"] = row.width\n",
    "        image[\"id\"] = row.fileid\n",
    "        image[\"file_name\"] = row.filename\n",
    "        return image\n",
    "\n",
    "    def category(row):\n",
    "        category = {}\n",
    "        category[\"supercategory\"] = 'None'\n",
    "        category[\"id\"] = row.categoryid\n",
    "        category[\"name\"] = row.label\n",
    "        return category\n",
    "\n",
    "    def annotation(row):\n",
    "        annotation = {}\n",
    "        area = (row.xmax -row.xmin)*(row.ymax - row.ymin)\n",
    "        annotation[\"segmentation\"] = []\n",
    "        annotation[\"iscrowd\"] = 0\n",
    "        annotation[\"area\"] = area\n",
    "        annotation[\"image_id\"] = row.fileid\n",
    "\n",
    "        annotation[\"bbox\"] = [row.xmin, row.ymin, row.xmax -row.xmin,row.ymax-row.ymin ]\n",
    "\n",
    "        annotation[\"category_id\"] = row.categoryid\n",
    "        annotation[\"id\"] = row.annid\n",
    "        return annotation\n",
    "\n",
    "    for row in data.itertuples():\n",
    "        annotations.append(annotation(row))\n",
    "\n",
    "    imagedf = data.drop_duplicates(subset=['fileid']).sort_values(by='fileid')\n",
    "    for row in imagedf.itertuples():\n",
    "        images.append(image(row))\n",
    "\n",
    "    catdf = data.drop_duplicates(subset=['categoryid']).sort_values(by='categoryid')\n",
    "    for row in catdf.itertuples():\n",
    "        categories.append(category(row))\n",
    "\n",
    "    data_coco = {}\n",
    "    data_coco[\"images\"] = images\n",
    "    data_coco[\"categories\"] = categories\n",
    "    data_coco[\"annotations\"] = annotations\n",
    "\n",
    "\n",
    "    json.dump(data_coco, open(save_json_path, \"w\"), indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e057abd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_SET_NAME = f'{path}/new_dataset/'\n",
    "ANNOTATIONS_FILE_NAME = \"annotations.coco.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d900f9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv2coco(new_dataset.copy(),f'{path}/new_dataset/{ANNOTATIONS_FILE_NAME}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "98f30c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv2coco(train_set.copy(),f'{path}/train/images/{ANNOTATIONS_FILE_NAME}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e5bbac03",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv2coco(test_set.copy(),f'{path}/test/images/{ANNOTATIONS_FILE_NAME}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276e87f5",
   "metadata": {},
   "source": [
    "# CSV to yolo format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94c24b4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "labels = {\n",
    "    \"train\":os.path.join(f'{path}/train',\"labels\"),\n",
    "    \"test\":os.path.join(f'{path}/test',\"labels\"),\n",
    "    \n",
    "}\n",
    "for label in labels.values():\n",
    "    os.makedirs(label,exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b5cc022d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv_2_yolo(df,out_path,cocoAnnPath):\n",
    "    f = open(cocoAnnPath)\n",
    "    data = json.load(f)\n",
    "    f.close()\n",
    "    print(data['categories'])\n",
    "\n",
    "    def get_img_ann(image_id):\n",
    "        img_ann = []\n",
    "        isFound = False\n",
    "        for ann in data['annotations']:\n",
    "            if ann['image_id'] == image_id:\n",
    "                img_ann.append(ann)\n",
    "                isFound = True\n",
    "        if isFound:\n",
    "            return img_ann\n",
    "        else:\n",
    "            return None\n",
    "\n",
    "\n",
    "    def get_img(filename):\n",
    "        for img in data['images']:\n",
    "            if img['file_name'] == filename:\n",
    "                return img\n",
    "        \n",
    "    \n",
    "    for filename in df['filename']:\n",
    "        # Extracting image \n",
    "        img = get_img(filename)\n",
    "        img_id = img['id']\n",
    "        img_w = img['width']\n",
    "        img_h = img['height']\n",
    "\n",
    "        # Get Annotations for this image\n",
    "        img_ann = get_img_ann(img_id)\n",
    "\n",
    "        if img_ann:\n",
    "        # Opening file for current image\n",
    "            file_object = open(f\"{out_path}/{filename[:-4]}.txt\", \"a\")\n",
    "\n",
    "        for ann in img_ann:\n",
    "            current_category = ann['category_id'] - 1 # As yolo format labels start from 0 \n",
    "            current_bbox = ann['bbox']\n",
    "            x = current_bbox[0]\n",
    "            y = current_bbox[1]\n",
    "            w = current_bbox[2]\n",
    "            h = current_bbox[3]\n",
    "\n",
    "            # Finding midpoints\n",
    "            x_centre = (x + (x+w))/2\n",
    "            y_centre = (y + (y+h))/2\n",
    "\n",
    "            # Normalization\n",
    "            x_centre = x_centre / img_w\n",
    "            y_centre = y_centre / img_h\n",
    "            w = w / img_w\n",
    "            h = h / img_h\n",
    "\n",
    "            # Limiting upto fix number of decimal places\n",
    "            x_centre = format(x_centre, '.6f')\n",
    "            y_centre = format(y_centre, '.6f')\n",
    "            w = format(w, '.6f')\n",
    "            h = format(h, '.6f')\n",
    "\n",
    "            # Writing current object \n",
    "            file_object.write(f\"{current_category} {x_centre} {y_centre} {w} {h}\\n\")\n",
    "\n",
    "        file_object.close()\n",
    "    print(\"done\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "380ce842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'supercategory': 'none', 'id': 0, 'name': 'None'}, {'supercategory': 'None', 'id': 1, 'name': 'Button'}, {'supercategory': 'None', 'id': 2, 'name': 'CheckBox'}, {'supercategory': 'None', 'id': 3, 'name': 'Heading'}, {'supercategory': 'None', 'id': 4, 'name': 'Image'}, {'supercategory': 'None', 'id': 5, 'name': 'Label'}, {'supercategory': 'None', 'id': 6, 'name': 'Link'}, {'supercategory': 'None', 'id': 7, 'name': 'Paragraph'}, {'supercategory': 'None', 'id': 8, 'name': 'RadioButton'}, {'supercategory': 'None', 'id': 9, 'name': 'Select'}, {'supercategory': 'None', 'id': 10, 'name': 'TextBox'}]\n",
      "done\n",
      "[{'supercategory': 'none', 'id': 0, 'name': 'None'}, {'supercategory': 'None', 'id': 1, 'name': 'Button'}, {'supercategory': 'None', 'id': 2, 'name': 'CheckBox'}, {'supercategory': 'None', 'id': 3, 'name': 'Heading'}, {'supercategory': 'None', 'id': 4, 'name': 'Image'}, {'supercategory': 'None', 'id': 5, 'name': 'Label'}, {'supercategory': 'None', 'id': 6, 'name': 'Link'}, {'supercategory': 'None', 'id': 7, 'name': 'Paragraph'}, {'supercategory': 'None', 'id': 8, 'name': 'RadioButton'}, {'supercategory': 'None', 'id': 9, 'name': 'Select'}, {'supercategory': 'None', 'id': 10, 'name': 'TextBox'}]\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv(f'{path}/train/images/annotations.csv')\n",
    "test = pd.read_csv(f'{path}/test/images/annotations.csv')\n",
    "\n",
    "csv_2_yolo(df=train,cocoAnnPath=f'{path}/train/images/annotations.coco.json',out_path=labels['train'])\n",
    "csv_2_yolo(df=test,cocoAnnPath=f'{path}/test/images/annotations.coco.json',out_path=labels['test'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "61fd7cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./all_yolo/images\n",
      "[{'supercategory': 'none', 'id': 0, 'name': 'None'}, {'supercategory': 'None', 'id': 1, 'name': 'Button'}, {'supercategory': 'None', 'id': 2, 'name': 'CheckBox'}, {'supercategory': 'None', 'id': 3, 'name': 'Heading'}, {'supercategory': 'None', 'id': 4, 'name': 'Image'}, {'supercategory': 'None', 'id': 5, 'name': 'Label'}, {'supercategory': 'None', 'id': 6, 'name': 'Link'}, {'supercategory': 'None', 'id': 7, 'name': 'Paragraph'}, {'supercategory': 'None', 'id': 8, 'name': 'RadioButton'}, {'supercategory': 'None', 'id': 9, 'name': 'Select'}, {'supercategory': 'None', 'id': 10, 'name': 'TextBox'}]\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#all img in yolo formate\n",
    "\n",
    "all_yolo_path = {\n",
    "    \"all_img\":os.path.join(f'{path}/all_yolo',\"images\"),\n",
    "    \"all_label\":os.path.join(f'{path}/all_yolo',\"labels\"),\n",
    "    \n",
    "}\n",
    "\n",
    "for _path in all_yolo_path.values():\n",
    "    os.makedirs(_path,exist_ok=True)\n",
    "\n",
    "all_yolo = pd.read_csv(f'{path}/new_dataset/annotations.csv')\n",
    "\n",
    "copy2dri(destination=all_yolo_path[\"all_img\"],df=all_yolo,fromDir=[new_dataset_path])\n",
    "csv_2_yolo(df=all_yolo,cocoAnnPath=f'{path}/new_dataset/annotations.coco.json',out_path=all_yolo_path['all_label'])\n"
   ]
  }
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