brain_tumor_dataset_preparation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "brain_tumor_dataset_preparation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXh66ZKmLUA1",
        "colab_type": "text"
      },
      "source": [
        "# Brain Tumor Detection - Dataset Preparation\n",
        "\n",
        "## Include necessary libraries\n",
        "\n",
        "We'll need the following libraries to prepare our dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3XcXuKJDjT0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import PIL\n",
        "import zipfile\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "import h5py\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLifrnvcc7OG",
        "colab_type": "text"
      },
      "source": [
        "Import Google Drive, where we'll be storing our dataset so we can use them later. (Persistent preparation of Dataset)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jH6JT7YRDr-d",
        "colab_type": "code",
        "outputId": "03762fcf-7912-4311-dcd6-7784f229f81d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjbuxQHPLk4e",
        "colab_type": "text"
      },
      "source": [
        "## Extract the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oS26YfYdLqI",
        "colab_type": "text"
      },
      "source": [
        "Dataset taken from [this link](https://figshare.com/articles/brain_tumor_dataset/1512427), which consists of brain tumor images belonging to 3 classes of tumor along with other details present in .mat format.\n",
        "\n",
        "Create a folder in Google Drive under Colab Notebooks folder named dataset, where we'll be extracting our main zip file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raq86BibDuAp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.exists('/content/drive/My Drive/Colab Notebooks/dataset'):\n",
        "  os.mkdir('/content/drive/My Drive/Colab Notebooks/dataset')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c18E9qkwdUos",
        "colab_type": "text"
      },
      "source": [
        "Extract the dataset .zip file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyWcKMdTFY9L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/1512427.zip') as zf:\n",
        "  zip_dir = zf.namelist()[0]\n",
        "  zf.extractall('/content/drive/My Drive/Colab Notebooks/dataset')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2hNITFpzdZnJ",
        "colab_type": "text"
      },
      "source": [
        "List the extracted files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39Id1fqCGVGI",
        "colab_type": "code",
        "outputId": "cce6fd82-ccf1-43e9-bf46-f06243a3512a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!ls '/content/drive/My Drive/Colab Notebooks/dataset/'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "brainTumorDataPublic_1533-2298.zip  brainTumorDataPublic_767-1532.zip\n",
            "brainTumorDataPublic_1-766.zip\t    cvind.mat\n",
            "brainTumorDataPublic_2299-3064.zip  README.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdP3WEIGdcAl",
        "colab_type": "text"
      },
      "source": [
        "Let's go through the README file for the information about the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JosjvwpBG8u2",
        "colab_type": "code",
        "outputId": "bcf5a0ff-0a3e-484b-9e17-f0bfd606ed41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "!cat '/content/drive/My Drive/Colab Notebooks/dataset/README.txt'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This brain tumor dataset containing 3064 T1-weighted contrast-inhanced images\r\n",
            "from 233 patients with three kinds of brain tumor: meningioma (708 slices), \r\n",
            "glioma (1426 slices), and pituitary tumor (930 slices). Due to the file size\r\n",
            "limit of repository, we split the whole dataset into 4 subsets, and achive \r\n",
            "them in 4 .zip files with each .zip file containing 766 slices.The 5-fold\r\n",
            "cross-validation indices are also provided.\r\n",
            "\r\n",
            "-----\r\n",
            "This data is organized in matlab data format (.mat file). Each file stores a struct\r\n",
            "containing the following fields for an image:\r\n",
            "\r\n",
            "cjdata.label: 1 for meningioma, 2 for glioma, 3 for pituitary tumor\r\n",
            "cjdata.PID: patient ID\r\n",
            "cjdata.image: image data\r\n",
            "cjdata.tumorBorder: a vector storing the coordinates of discrete points on tumor border.\r\n",
            "\t\tFor example, [x1, y1, x2, y2,...] in which x1, y1 are planar coordinates on tumor border.\r\n",
            "\t\tIt was generated by manually delineating the tumor border. So we can use it to generate\r\n",
            "\t\tbinary image of tumor mask.\r\n",
            "cjdata.tumorMask: a binary image with 1s indicating tumor region\r\n",
            "\r\n",
            "-----\r\n",
            "This data was used in the following paper:\r\n",
            "1. Cheng, Jun, et al. \"Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation\r\n",
            "and Partition.\" PloS one 10.10 (2015).\r\n",
            "2. Cheng, Jun, et al. \"Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector \r\n",
            "Representation.\" PloS one 11.6 (2016). Matlab source codes are available on github \r\n",
            "https://github.com/chengjun583/brainTumorRetrieval\r\n",
            "\r\n",
            "-----\r\n",
            "Jun Cheng\r\n",
            "School of Biomedical Engineering\r\n",
            "Southern Medical University, Guangzhou, China\r\n",
            "Email: chengjun583@qq.com\r\n",
            "\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7i2xmtdzdhsZ",
        "colab_type": "text"
      },
      "source": [
        "Extract all the 4 zip files of brain tumor, consisting .mat files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zFst3tAGee7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/Colab Notebooks/dataset/brainTumorDataPublic_1-766.zip') as zf:\n",
        "  os.mkdir('/content/drive/My Drive/Colab Notebooks/dataset/bt_set1')\n",
        "  zip_dir = zf.namelist()[0]\n",
        "  zf.extractall('/content/drive/My Drive/Colab Notebooks/dataset/bt_set1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTHUf8u9HW_p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/Colab Notebooks/dataset/brainTumorDataPublic_767-1532.zip') as zf:\n",
        "  os.mkdir('/content/drive/My Drive/Colab Notebooks/dataset/bt_set2')\n",
        "  zip_dir = zf.namelist()[0]\n",
        "  zf.extractall('/content/drive/My Drive/Colab Notebooks/dataset/bt_set2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRrlwqZDHkHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/Colab Notebooks/dataset/brainTumorDataPublic_1533-2298.zip') as zf:\n",
        "  os.mkdir('/content/drive/My Drive/Colab Notebooks/dataset/bt_set3')\n",
        "  zip_dir = zf.namelist()[0]\n",
        "  zf.extractall('/content/drive/My Drive/Colab Notebooks/dataset/bt_set3')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZ-1TF46IEQM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/Colab Notebooks/dataset/brainTumorDataPublic_2299-3064.zip') as zf:\n",
        "  os.mkdir('/content/drive/My Drive/Colab Notebooks/dataset/bt_set4')\n",
        "  zip_dir = zf.namelist()[0]\n",
        "  zf.extractall('/content/drive/My Drive/Colab Notebooks/dataset/bt_set4')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvrMSXPWdrRD",
        "colab_type": "text"
      },
      "source": [
        "Move the .mat files to a new folder named imageData"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeaEY6MgITdz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.mkdir('/content/drive/My Drive/Colab Notebooks/dataset/imageData')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIkcEF5CI5AV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv /content/drive/'My Drive'/'Colab Notebooks'/dataset/bt_set1/*.mat '/content/drive/My Drive/Colab Notebooks/dataset/imageData/'\n",
        "!mv /content/drive/'My Drive'/'Colab Notebooks'/dataset/bt_set2/*.mat '/content/drive/My Drive/Colab Notebooks/dataset/imageData/'\n",
        "!mv /content/drive/'My Drive'/'Colab Notebooks'/dataset/bt_set3/*.mat '/content/drive/My Drive/Colab Notebooks/dataset/imageData/'\n",
        "!mv /content/drive/'My Drive'/'Colab Notebooks'/dataset/bt_set4/*.mat '/content/drive/My Drive/Colab Notebooks/dataset/imageData/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ih-0xfeoagQ_",
        "colab_type": "text"
      },
      "source": [
        "## Prepare the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0860tP8dz6h",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the things that we have in a single .mat file. Display the images & contents present inside it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzE6qQ8RPCPC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "arrays = {}\n",
        "img = None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjebDTJlMF_x",
        "colab_type": "code",
        "outputId": "5ebc712f-9bc8-452f-eda4-9a7acd22b5e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        }
      },
      "source": [
        "with h5py.File('/content/drive/My Drive/Colab Notebooks/dataset/imageData/1.mat', 'r') as f:\n",
        "    for key in f.keys():\n",
        "      print(key)\n",
        "    for item in f.items():\n",
        "      print(item)\n",
        "    for key, val in f['cjdata'].items():\n",
        "      print(key, val)\n",
        "    img = f['cjdata']['image']\n",
        "    label = f['cjdata']['label'][0][0]\n",
        "    tumorBorder = f['cjdata']['tumorBorder'][0]\n",
        "    mask = f['cjdata']['tumorMask']\n",
        "    fig = plt.figure(2)\n",
        "    img = np.array(img, dtype=np.float32)\n",
        "    img = img/127.5 - 1\n",
        "    mask = np.array(mask, dtype=np.float32)\n",
        "    mask = mask/127.5 - 1\n",
        "    plt.axis('off')\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    print(\"Image shape: \", img.shape)\n",
        "    print(\"Label\", label)\n",
        "    print(\"Coords: \", tumorBorder)\n",
        "    print(\"Mask shape: \", mask.shape)\n",
        "    fig = plt.figure(3)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(mask, cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cjdata\n",
            "('cjdata', <HDF5 group \"/cjdata\" (5 members)>)\n",
            "PID <HDF5 dataset \"PID\": shape (6, 1), type \"<u2\">\n",
            "image <HDF5 dataset \"image\": shape (512, 512), type \"<i2\">\n",
            "label <HDF5 dataset \"label\": shape (1, 1), type \"<f8\">\n",
            "tumorBorder <HDF5 dataset \"tumorBorder\": shape (1, 38), type \"<f8\">\n",
            "tumorMask <HDF5 dataset \"tumorMask\": shape (512, 512), type \"|u1\">\n",
            "Image shape:  (512, 512)\n",
            "Label 1.0\n",
            "Coords:  [267.61524501 231.37568058 277.83666062 248.10163339 289.91651543\n",
            " 250.8892922  305.71324864 253.676951   318.72232305 249.9600726\n",
            " 321.50998185 237.88021779 354.96188748 234.16333938 367.97096189\n",
            " 227.65880218 380.9800363  210.93284936 372.61705989 195.13611615\n",
            " 363.32486388 177.48094374 350.31578947 176.55172414 336.37749546\n",
            " 183.98548094 317.79310345 196.06533575 305.71324864 207.21597096\n",
            " 295.49183303 217.43738657 288.98729583 222.08348457 273.19056261\n",
            " 223.94192377 268.54446461 228.58802178]\n",
            "Mask shape:  (512, 512)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZzGa717x2lPGtGfPFF1/EH//xH2NychJbW1sAgN3dXbzzzjtYWFjAF77wBdy9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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhChxQtRfgS5uvHRLkD0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzAhqm61eApj",
        "colab_type": "text"
      },
      "source": [
        "A single .mat file consists of:\n",
        "\n",
        "* **PID:** Patient ID for that particular tumor image. This is not a necessary feature.\n",
        "* **image:** Image of the brain tumor\n",
        "* **label:** Label between 1-3 denoting what kind of tumor is present\n",
        "* **tumorBorder:** Coordinates of the tumor present in the image\n",
        "* **tumorMask:** Masked version of the tumor.\n",
        "\n",
        "We'll need the image, label, tumorBorder, tumorMask which are the necessary features that'll help us to detect brain tumors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewh0lu2Ue1XN",
        "colab_type": "text"
      },
      "source": [
        "Create new folders named the following:\n",
        "\n",
        "* **new_dataset:** We'll be using this folder to store our images, labels, mask and borders\n",
        "* **bt_images:** We'll store the full brain tumor image in this folder\n",
        "* **bt_mask:** We'll store the mask images of the brain tumor in this folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HaGLuvS5h_m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.mkdir('/content/drive/My Drive/Colab Notebooks/new_dataset')\n",
        "os.mkdir('/content/drive/My Drive/Colab Notebooks/new_dataset/bt_images') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VNjICqWfSok",
        "colab_type": "text"
      },
      "source": [
        "Create empty lists for storing labels\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07LqMUXA5BCx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels = []\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_bJP9dOfY6q",
        "colab_type": "text"
      },
      "source": [
        "Save images of brain tumor, masks and store labels and borders in their respective lists iteratively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYFvHS3Xew7Z",
        "colab_type": "code",
        "outputId": "192f4640-0b14-45dc-ee35-5db44fe24b7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "filename = None\n",
        "\n",
        "for filename in range(1, 3065):\n",
        "  with h5py.File('/content/drive/My Drive/Colab Notebooks/dataset/imageData/{}.mat'.format(filename), 'r') as f:\n",
        "      img = f['cjdata']['image']\n",
        "      label = f['cjdata']['label'][0][0]\n",
        "     \n",
        "     \n",
        "      labels.append(int(label))\n",
        "      \n",
        "      border.append(coord)\n",
        "      img = np.array(img, dtype=np.float32)\n",
        "      \n",
        "      plt.axis('off')\n",
        "      plt.imsave(\"/content/drive/My Drive/Colab Notebooks/new_dataset/bt_images/{}.jpg\".format(filename), img, cmap='gray')\n",
        "      \n",
        "      \n",
        "print(\"{} files successfully saved\".format(filename))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3064 files successfully saved\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAADKUlEQVR4nO3UMQEAIAzAMMC/5+GiHCQKenXPzAKg\ncV4HAPzEdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZ\nLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDI\ndAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFC\npgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQ\nMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2A\nkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkC\nhEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwX\nIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6\nACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHT\nBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZ\nLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDI\ndAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFC\npgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQ\nMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIHQBcjcEy3+fc28AAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WQf_HBUfiQP",
        "colab_type": "text"
      },
      "source": [
        "Convert the Python lists to a Numpy arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zb8znQzg655F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_names = np.array(labels, dtype=np.int64)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm6Hemi0fnkR",
        "colab_type": "text"
      },
      "source": [
        "Check if the array has the right shape & length."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5LnQrPE7cqs",
        "colab_type": "code",
        "outputId": "77b56eb5-a946-4c0b-d4ce-1f56a7b5ea74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label_names.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3064,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzBZl4FifsaC",
        "colab_type": "text"
      },
      "source": [
        "Store the labels and tumor border (coordinates) as a pickle file, which can be loaded whenever we want to use it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UbCgJIZ7DSQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle_out = open(\"/content/drive/My Drive/Colab Notebooks/new_dataset/labels.pickle\",\"wb\")                    \n",
        "pickle.dump(label_names, pickle_out)\n",
        "pickle_out.close() "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsKkbR5TdLbl",
        "colab_type": "text"
      },
      "source": [
        "Create an empty list named 'training_data' in which we'll store our images and their respective labels as arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8WjocXu9cB9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_data = []\n",
        "img = None\n",
        "label = None\n",
        "i = None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3TtXcNpdNVV",
        "colab_type": "text"
      },
      "source": [
        "Read the images from bt_images folder from Google Drive and convert it to RGB images and store it along with their respective labels in the training_data list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SICvCO6rdBLQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(1, 3065):\n",
        "  img = cv2.imread(\"/content/drive/My Drive/Colab Notebooks/new_dataset/bt_images/{}.jpg\".format(i), cv2.IMREAD_GRAYSCALE)\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n",
        "  img = cv2.resize(img, (512, 512))\n",
        "  label = label_names[i-1]\n",
        "  training_data.append([img, label])\n",
        "\n",
        "print(\"shape: {} label: {} | {} samples successfully preprocessed\".format(img.shape, label, i))\n",
        "\n",
        "pickle_out = open(\"/content/drive/My Drive/Colab Notebooks/new_dataset/training_data.pickle\",\"wb\")                    \n",
        "pickle.dump(training_data, pickle_out)\n",
        "pickle_out.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0vHOhwWdR_V",
        "colab_type": "text"
      },
      "source": [
        "Store the training_data list as a pickle file"
      ]
    }
  ]
}