[44382a]: / colab_01_data_preprocess.ipynb

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{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cwinsor/medical_image_uw_madison/blob/main/colab_01_data_preprocess.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssEm3cxfrqE5"
      },
      "source": [
        "# Data Preprocessing\n",
        "This notebook re-implements data_preprocess.py as a colab notebook\n",
        "\n",
        "On colab I am using...\n",
        "* ?\n",
        "\n",
        "On local host...\n",
        "* WSL on windows (my 2-GPU compute host)\n",
        "* conda env pytorch_j_cv2_u_madison\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "5uaZfw_V2vtG"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import os\n",
        "from shutil import rmtree\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import glob\n",
        "from tqdm.auto import tqdm\n",
        "# import albumentations as A\n",
        "import sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcEAS2lcKTIX",
        "outputId": "1b1f41b6-c7fa-4151-ea30-e7cae688d3ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "running local host\n"
          ]
        }
      ],
      "source": [
        "if 'google.colab' in sys.modules:\n",
        "    print(\"running colab\")\n",
        "    # we persist code, dataset and runs on google drive...\n",
        "    from google.colab import drive\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/gdrive')\n",
        "else:\n",
        "    print(\"running local host\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "XYukEgTD0YWc"
      },
      "outputs": [],
      "source": [
        "if 'google.colab' in sys.modules:\n",
        "    pass\n",
        "    # # warning - do not kill the persist folder - you will lose all the run history!\n",
        "    # # the code below should only executed for initial setup!\n",
        "    # drive_persist_folder = '/content/gdrive/MyDrive/Colab_UW_Madison'\n",
        "    # os.chdir(drive_persist_folder)\n",
        "    # git_url = 'https://github.com/cwinsor/medical_image_uw_madison.git'\n",
        "    # !git clone --quiet $git_url\n",
        "    # os.chdir('medical_image_uw_madison')\n",
        "    # # populate the submodule\n",
        "    # !git submodule update --init"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNqvx7sGzQct",
        "outputId": "9fe0eb25-356a-477c-823f-78e36b15e219"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "checking git status (git fetch --dry-run):\n",
            "/mnt/d/code_medimg_kag_uw_madison/medical_image_uw_madison\n"
          ]
        }
      ],
      "source": [
        "if 'google.colab' in sys.modules:\n",
        "    work_folder = '/content/gdrive/MyDrive/Colab_UW_Madison/medical_image_uw_madison/'\n",
        "else:\n",
        "    work_folder = '/mnt/d/code_medimg_kag_uw_madison/medical_image_uw_madison/'\n",
        "os.chdir(work_folder)\n",
        "print(\"checking git status (git fetch --dry-run):\")\n",
        "!pwd\n",
        "!git fetch --dry-run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xw7oQRW5pMJB",
        "outputId": "54c87a1a-50c9-4532-a302-34b47c9c1e24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/mnt/d/code_medimg_kag_uw_madison/medical_image_uw_madison/project/Kaggle-UWMGIT/data/tract\n"
          ]
        }
      ],
      "source": [
        "os.chdir(work_folder + 'project/Kaggle-UWMGIT/data/tract')\n",
        "!pwd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3oJ35jDVo3o"
      },
      "source": [
        "# Side trip to download the data..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "lunMrPLbREce"
      },
      "outputs": [],
      "source": [
        "# download data\n",
        "# see README.md in the Kaggle-UMWGIT\n",
        "# and\n",
        "# see https://www.kaggle.com/discussions/general/156610"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "bgIQqAmcRH_G"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rwxrwxrwx 1 chris chris 63 Jul 20 08:24 /mnt/c/Users/chris/Downloads/kaggle.json\n"
          ]
        }
      ],
      "source": [
        "# Go to your kaggle account, Scroll to API section and Click Expire API Token to remove previous tokens\n",
        "# Click on Create New API Token - It will download kaggle.json file on your machine.\n",
        "# Now upload the kaggle.json file\n",
        "\n",
        "# kaggle API client expects the token to be in ~/.kaggle so create the folder\n",
        "\n",
        "# upload the token and move it to !/.kaggle folder\n",
        "if 'google.colab' in sys.modules:\n",
        "    !mkdir -p ~/.kaggle\n",
        "    from google.colab import files\n",
        "    files.upload() #this will prompt you to upload the kaggle.json\n",
        "    # make sure kaggle.json file is present\n",
        "    !ls -lha kaggle.json\n",
        "    !cp kaggle.json ~/.kaggle/\n",
        "    !chmod 600 /root/.kaggle/kaggle.json\n",
        "else:\n",
        "    import getpass\n",
        "    password = getpass.getpass()\n",
        "    # command = \"sudo -S apt-get update\"\n",
        "    # command = \"sudo -S mkdir -p ~/.kaggle\"\n",
        "    os.system('echo %s | %s' % (password, \"sudo -S mkdir -p ~/.kaggle\"))\n",
        "    os.system('echo %s | %s' % (password, \"ls -lha /mnt/c/Users/chris/Downloads/kaggle.json\"))\n",
        "    os.system('echo %s | %s' % (password, \"cp /mnt/c/Users/chris/Downloads/kaggle.json ~/.kaggle/\"))\n",
        "    os.system('echo %s | %s' % (password, \"chmod 600 ~/.kaggle/kaggle.json\"))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/mnt/d/code_medimg_kag_uw_madison/medical_image_uw_madison/project/Kaggle-UWMGIT/data/tract\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "# now that token is in place, install Kaggle API client\n",
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2idp2W3ZSW7T",
        "outputId": "3d1b8903-136f-4023-ea66-77db90921191"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "you already have the data downloaded\n"
          ]
        }
      ],
      "source": [
        "# download the dataset\n",
        "if os.path.isdir('train'):\n",
        "    print(\"you already have the data downloaded\")\n",
        "else:\n",
        "    # !rm uw-madison-gi-tract-image-segmentation.zip\n",
        "    print(\"download the dataset from kaggle - (it is 2.3GB)\")\n",
        "    !kaggle competitions download -o -c uw-madison-gi-tract-image-segmentation\n",
        "    !unzip -o uw-madison-gi-tract-image-segmentation.zip\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64Mb1rjKVYy4"
      },
      "source": [
        "# Return to \"data_preprocess.py\" ..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgxNmFh9pkYO",
        "outputId": "da3f6271-6334-4340-ed18-08240991500e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'#slices:': 38496, '#empty slices:': 21906, '#patients': 85, '#days': 274}\n"
          ]
        }
      ],
      "source": [
        "df_train = pd.read_csv(\"./train.csv\")\n",
        "df_train = df_train.sort_values([\"id\", \"class\"]).reset_index(drop = True)\n",
        "df_train[\"patient\"] = df_train.id.apply(lambda x: x.split(\"_\")[0])\n",
        "df_train[\"days\"] = df_train.id.apply(lambda x: \"_\".join(x.split(\"_\")[:2]))\n",
        "num_slices = len(np.unique(df_train.id))\n",
        "num_empty_slices = df_train.groupby(\"id\").apply(lambda x: x.segmentation.isna().all()).sum()\n",
        "num_patients = len(np.unique(df_train.patient))\n",
        "num_days = len(np.unique(df_train.days))\n",
        "print({\n",
        "    \"#slices:\": num_slices,\n",
        "    \"#empty slices:\": num_empty_slices,\n",
        "    \"#patients\": num_patients,\n",
        "    \"#days\": num_days\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "S5UOSR32gPC4",
        "outputId": "4d0f1ae7-e672-43dd-fcd6-2d273022488a"
      },
      "outputs": [
        {
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              "                              id        class  \\\n",
              "count                     115488       115488   \n",
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              "top     case101_day20_slice_0001  large_bowel   \n",
              "freq                           3        38496   \n",
              "\n",
              "                                             segmentation patient  \\\n",
              "count                                               33913  115488   \n",
              "unique                                              33899      85   \n",
              "top     26519 8 26784 11 27049 13 27314 16 27565 7 275...  case36   \n",
              "freq                                                    2    2592   \n",
              "\n",
              "                 days  \n",
              "count          115488  \n",
              "unique            274  \n",
              "top     case101_day20  \n",
              "freq              432  "
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          },
          "execution_count": 40,
          "metadata": {},
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      ],
      "source": [
        "df_train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "N7r2S6LehOEU"
      },
      "outputs": [],
      "source": [
        "all_image_files = sorted(glob.glob(\"./train/*/*/scans/*.png\"), key = lambda x: x.split(\"/\")[3] + \"_\" + x.split(\"/\")[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
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              "list"
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          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "type(all_image_files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
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        "id": "yM7BoKWdHDfZ",
        "outputId": "325b6dc8-14e6-4b8f-c8a0-81c2368fd55d"
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      "outputs": [
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              "      <td>case101</td>\n",
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              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
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              "      <td>small_bowel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>case9</td>\n",
              "      <td>case9_day22</td>\n",
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              "      <td>case9_day22</td>\n",
              "      <td>./train/case9/case9_day22/scans/slice_0143_360...</td>\n",
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              "      <td>360</td>\n",
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              "      <th>115485</th>\n",
              "      <td>case9_day22_slice_0144</td>\n",
              "      <td>large_bowel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>case9</td>\n",
              "      <td>case9_day22</td>\n",
              "      <td>./train/case9/case9_day22/scans/slice_0144_360...</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>360</td>\n",
              "      <td>310</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115486</th>\n",
              "      <td>case9_day22_slice_0144</td>\n",
              "      <td>small_bowel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>case9</td>\n",
              "      <td>case9_day22</td>\n",
              "      <td>./train/case9/case9_day22/scans/slice_0144_360...</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>360</td>\n",
              "      <td>310</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115487</th>\n",
              "      <td>case9_day22_slice_0144</td>\n",
              "      <td>stomach</td>\n",
              "      <td>NaN</td>\n",
              "      <td>case9</td>\n",
              "      <td>case9_day22</td>\n",
              "      <td>./train/case9/case9_day22/scans/slice_0144_360...</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>360</td>\n",
              "      <td>310</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115488 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                              id        class segmentation  patient  \\\n",
              "0       case101_day20_slice_0001  large_bowel          NaN  case101   \n",
              "1       case101_day20_slice_0001  small_bowel          NaN  case101   \n",
              "2       case101_day20_slice_0001      stomach          NaN  case101   \n",
              "3       case101_day20_slice_0002  large_bowel          NaN  case101   \n",
              "4       case101_day20_slice_0002  small_bowel          NaN  case101   \n",
              "...                          ...          ...          ...      ...   \n",
              "115483    case9_day22_slice_0143  small_bowel          NaN    case9   \n",
              "115484    case9_day22_slice_0143      stomach          NaN    case9   \n",
              "115485    case9_day22_slice_0144  large_bowel          NaN    case9   \n",
              "115486    case9_day22_slice_0144  small_bowel          NaN    case9   \n",
              "115487    case9_day22_slice_0144      stomach          NaN    case9   \n",
              "\n",
              "                 days                                        image_files  \\\n",
              "0       case101_day20  ./train/case101/case101_day20/scans/slice_0001...   \n",
              "1       case101_day20  ./train/case101/case101_day20/scans/slice_0001...   \n",
              "2       case101_day20  ./train/case101/case101_day20/scans/slice_0001...   \n",
              "3       case101_day20  ./train/case101/case101_day20/scans/slice_0002...   \n",
              "4       case101_day20  ./train/case101/case101_day20/scans/slice_0002...   \n",
              "...               ...                                                ...   \n",
              "115483    case9_day22  ./train/case9/case9_day22/scans/slice_0143_360...   \n",
              "115484    case9_day22  ./train/case9/case9_day22/scans/slice_0143_360...   \n",
              "115485    case9_day22  ./train/case9/case9_day22/scans/slice_0144_360...   \n",
              "115486    case9_day22  ./train/case9/case9_day22/scans/slice_0144_360...   \n",
              "115487    case9_day22  ./train/case9/case9_day22/scans/slice_0144_360...   \n",
              "\n",
              "        spacing_x  spacing_y  size_x  size_y  slice  \n",
              "0             1.5        1.5     266     266      1  \n",
              "1             1.5        1.5     266     266      1  \n",
              "2             1.5        1.5     266     266      1  \n",
              "3             1.5        1.5     266     266      2  \n",
              "4             1.5        1.5     266     266      2  \n",
              "...           ...        ...     ...     ...    ...  \n",
              "115483        1.5        1.5     360     310    143  \n",
              "115484        1.5        1.5     360     310    143  \n",
              "115485        1.5        1.5     360     310    144  \n",
              "115486        1.5        1.5     360     310    144  \n",
              "115487        1.5        1.5     360     310    144  \n",
              "\n",
              "[115488 rows x 11 columns]"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_image_files = sorted(glob.glob(\"./train/*/*/scans/*.png\"), key = lambda x: x.split(\"/\")[3] + \"_\" + x.split(\"/\")[5])\n",
        "size_x = [int(os.path.basename(_)[:-4].split(\"_\")[-4]) for _ in all_image_files]\n",
        "size_y = [int(os.path.basename(_)[:-4].split(\"_\")[-3]) for _ in all_image_files]\n",
        "spacing_x = [float(os.path.basename(_)[:-4].split(\"_\")[-2]) for _ in all_image_files]\n",
        "spacing_y = [float(os.path.basename(_)[:-4].split(\"_\")[-1]) for _ in all_image_files]\n",
        "df_train[\"image_files\"] = np.repeat(all_image_files, 3)\n",
        "df_train[\"spacing_x\"] = np.repeat(spacing_x, 3)\n",
        "df_train[\"spacing_y\"] = np.repeat(spacing_y, 3)\n",
        "df_train[\"size_x\"] = np.repeat(size_x, 3)\n",
        "df_train[\"size_y\"] = np.repeat(size_y, 3)\n",
        "df_train[\"slice\"] = np.repeat([int(os.path.basename(_)[:-4].split(\"_\")[-5]) for _ in all_image_files], 3)\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0INZq22sh83Q",
        "outputId": "7d6a2d88-0bb8-41bf-e24c-c519584ca117"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 115488 entries, 0 to 115487\n",
            "Data columns (total 11 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   id            115488 non-null  object \n",
            " 1   class         115488 non-null  object \n",
            " 2   segmentation  33913 non-null   object \n",
            " 3   patient       115488 non-null  object \n",
            " 4   days          115488 non-null  object \n",
            " 5   image_files   115488 non-null  object \n",
            " 6   spacing_x     115488 non-null  float64\n",
            " 7   spacing_y     115488 non-null  float64\n",
            " 8   size_x        115488 non-null  int64  \n",
            " 9   size_y        115488 non-null  int64  \n",
            " 10  slice         115488 non-null  int64  \n",
            "dtypes: float64(2), int64(3), object(6)\n",
            "memory usage: 9.7+ MB\n"
          ]
        }
      ],
      "source": [
        "df_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [],
      "source": [
        "def rle_decode(mask_rle, shape):\n",
        "    s = np.array(mask_rle.split(), dtype=int)\n",
        "    starts, lengths = s[0::2] - 1, s[1::2]\n",
        "    ends = starts + lengths\n",
        "    h, w = shape\n",
        "    img = np.zeros((h * w,), dtype = np.uint8)\n",
        "    for lo, hi in zip(starts, ends):\n",
        "        img[lo : hi] = 1\n",
        "    return img.reshape(shape)\n",
        "\n",
        "def rle_encode(img):\n",
        "    pixels = img.flatten()\n",
        "    pixels = np.concatenate([[0], pixels, [0]])\n",
        "    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n",
        "    runs[1::2] -= runs[::2]\n",
        "    return ' '.join(str(x) for x in runs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        "id": "yTWUUTlfWVpp",
        "outputId": "6d2c5aa6-b4ee-42e1-a660-19097241d6b9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/274 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 274/274 [17:25<00:00,  3.81s/it]\n"
          ]
        }
      ],
      "source": [
        "os.system(\"mkdir -p ./mmseg_train/images ./mmseg_train/labels\")\n",
        "for day, group in tqdm(df_train.groupby(\"days\")):\n",
        "    patient = group.patient.iloc[0]\n",
        "    imgs = []\n",
        "    msks = []\n",
        "    for file_name in tqdm(group.image_files.unique(), leave = False):\n",
        "        img = cv2.imread(file_name, cv2.IMREAD_ANYDEPTH)\n",
        "        segms = group.loc[group.image_files == file_name]\n",
        "        masks = {}\n",
        "        for segm, label in zip(segms.segmentation, segms[\"class\"]):\n",
        "            if not pd.isna(segm):\n",
        "                mask = rle_decode(segm, img.shape[:2])\n",
        "                masks[label] = mask\n",
        "            else:\n",
        "                masks[label] = np.zeros(img.shape[:2], dtype = np.uint8)\n",
        "        masks = np.stack([masks[k] for k in sorted(masks)], -1)\n",
        "        imgs.append(img)\n",
        "        msks.append(masks)\n",
        "\n",
        "    imgs = np.stack(imgs, 0)\n",
        "    msks = np.stack(msks, 0)\n",
        "    for i in range(msks.shape[0]):\n",
        "        img = imgs[[\n",
        "            max(0, i - 2),\n",
        "            i,\n",
        "            min(imgs.shape[0] - 1, i + 2)\n",
        "        ]].transpose(1, 2, 0)\n",
        "        msk = msks[i]\n",
        "        new_image_name = f\"{day}_{i}.png\"\n",
        "        cv2.imwrite(f\"./mmseg_train/images/{new_image_name}\", img)\n",
        "        cv2.imwrite(f\"./mmseg_train/labels/{new_image_name}\", msk)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "HmOfkVftZRZg"
      },
      "outputs": [],
      "source": [
        "os.system(\"mkdir -p ./mmseg_train/splits\")\n",
        "all_image_files = glob.glob(\"./mmseg_train/images/*\")\n",
        "patients = [os.path.basename(_).split(\"_\")[0] for _ in all_image_files]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "zDPtXqPHcl3R"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GroupKFold\n",
        "split = list(GroupKFold(5).split(patients, groups = patients))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "DEfrLQnmdMVq"
      },
      "outputs": [],
      "source": [
        "for fold, (train_idx, valid_idx) in enumerate(split):\n",
        "    with open(f\"./mmseg_train/splits/fold_{fold}.txt\", \"w\") as f:\n",
        "        for idx in train_idx:\n",
        "            f.write(os.path.basename(all_image_files[idx])[:-4] + \"\\n\")\n",
        "    with open(f\"./mmseg_train/splits/holdout_{fold}.txt\", \"w\") as f:\n",
        "        for idx in valid_idx:\n",
        "            f.write(os.path.basename(all_image_files[idx])[:-4] + \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "T37YSqaMdakZ"
      },
      "outputs": [],
      "source": [
        "os.system(\"mkdir -p ./mmseg_train/splits_notail ./mmseg_train/splits_noanno ./mmseg_train/splits_case\")\n",
        "tails = set()\n",
        "noannos = set()\n",
        "faults = set()\n",
        "for day, group in df_train.groupby(\"days\"):\n",
        "    end_slice = group.slice.iloc[np.where(~group.segmentation.isna())[0][-1]]\n",
        "    tail_slice = np.arange(end_slice + 1, end_slice + 6)\n",
        "    tail_group = group[group.slice.isin(tail_slice)].drop_duplicates([\"days\", \"image_files\"])\n",
        "    noanno_group = group[group.segmentation.isna()].drop_duplicates([\"days\", \"image_files\"])\n",
        "    tails.update([os.path.basename(row.image_files)[:-4].replace(\"slice\", row.days) for i, row in tail_group.iterrows()])\n",
        "    noannos.update([os.path.basename(row.image_files)[:-4].replace(\"slice\", row.days) for i, row in noanno_group.iterrows()])\n",
        "    if day in ['case7_day0', 'case81_day30']:\n",
        "        faults.update([os.path.basename(row.image_files)[:-4].replace(\"slice\", row.days) for i, row in group.iterrows()])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "1xOhRDozeFvJ"
      },
      "outputs": [],
      "source": [
        "for f in range(5):\n",
        "    split = pd.read_csv(f\"./mmseg_train/splits/fold_{f}.txt\", header = None)\n",
        "    x = list(set(split.iloc[:,0].tolist()) - tails)\n",
        "    pd.DataFrame(x).to_csv(f\"./mmseg_train/splits_notail/fold_{f}.txt\", index = False, header = False)\n",
        "    x = list(set(split.iloc[:,0].tolist()) - noannos)\n",
        "    pd.DataFrame(x).to_csv(f\"./mmseg_train/splits_noanno/fold_{f}.txt\", index = False, header = False)\n",
        "    x = list(set(split.iloc[:,0].tolist()) - faults)\n",
        "    pd.DataFrame(x).to_csv(f\"./mmseg_train/splits_case/fold_{f}.txt\", index = False, header = False)\n",
        "\n",
        "\n",
        "    split = pd.read_csv(f\"./mmseg_train/splits/holdout_{f}.txt\", header = None)\n",
        "    x = list(set(split.iloc[:,0].tolist()) - tails)\n",
        "    pd.DataFrame(x).to_csv(f\"./mmseg_train/splits_notail/holdout_{f}.txt\", index = False, header = False)\n",
        "    x = list(set(split.iloc[:,0].tolist()) - noannos)\n",
        "    pd.DataFrame(x).to_csv(f\"./mmseg_train/splits_noanno/holdout_{f}.txt\", index = False, header = False)\n",
        "    x = list(set(split.iloc[:,0].tolist()) - faults)\n",
        "    pd.DataFrame(x).to_csv(f\"./mmseg_train/splits_case/holdout_{f}.txt\", index = False, header = False)\n",
        "\n",
        "    for d in [\"\", \"_notail\", \"_noanno\", \"_case\"]:\n",
        "        os.system(f\"cat ./mmseg_train/splits{d}/holdout_{f}.txt > ./mmseg_train/splits{d}/fold_all.txt\")\n",
        "        os.system(f\"cat ./mmseg_train/splits{d}/fold_{f}.txt >> ./mmseg_train/splits{d}/fold_all.txt\")"
      ]
    }
  ],
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