2b_trainval_split.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split dataprocessedv0 dataset into train val and store in dataprocessedv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this\n",
    "#!pip install split-folders tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "import os\n",
    "import skimage.draw\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import cv2\n",
    "from glob import glob\n",
    "import warnings\n",
    "import random\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Helpers\n",
    "\n",
    "def create_directory(directory):\n",
    "    '''\n",
    "    Creates a new folder in the specified directory if the folder doesn't exist.\n",
    "    INPUT\n",
    "        directory: Folder to be created, called as \"folder/\".\n",
    "    OUTPUT\n",
    "        New folder in the current directory.\n",
    "    '''\n",
    "    if not os.path.exists(directory):\n",
    "        os.makedirs(directory)\n",
    "        \n",
    "    \n",
    "# Root directory of the project\n",
    "ROOT_DIR = os.path.abspath(\".\")\n",
    "print(ROOT_DIR)\n",
    "\n",
    "# Training file directory\n",
    "IMAGES_FOLDER = os.path.join(ROOT_DIR, \"dataset\", \"data_processedv0\", \"images/\")\n",
    "MASKS_FOLDER = os.path.join(ROOT_DIR, \"dataset\", \"data_processedv0\", \"images/\")\n",
    "print(IMAGES_FOLDER, MASKS_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data_processed0 into train val and store in data_processedv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_FOLDER = os.path.join(ROOT_DIR, \"dataset\", \"data_processedv0\") # raw images\n",
    "OUT_FOLDER = os.path.join(ROOT_DIR, \"dataset\", \"data_processedv2\")\n",
    "IN_FOLDER, OUT_FOLDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import split_folders\n",
    "\n",
    "# Split with a ratio.\n",
    "# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.\n",
    "split_folders.ratio(IN_FOLDER, output=OUT_FOLDER, seed=1337, ratio=(.8, .2)) # default values # .8 .2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOT USING THIS NOW!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data_processedv1(patches) into train val and store in data_processedv3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IN_FOLDER = os.path.join(ROOT_DIR, \"dataset\", \"data_processedv1\") # raw images\n",
    "#OUT_FOLDER = os.path.join(ROOT_DIR, \"dataset\", \"data_processedv3\")\n",
    "#IN_FOLDER, OUT_FOLDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import split_folders\n",
    "\n",
    "# Split with a ratio.\n",
    "# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.\n",
    "#split_folders.ratio(IN_FOLDER, output=OUT_FOLDER, seed=1337, ratio=(.8, .2)) # default values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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