EyeDiagnosis_Tool.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNwvzk0YbYmrg06JL1ywl7O",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CosmicFlaw/CosmicFlaw/blob/main/EyeDiagnosis_Tool.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mED3MII5IWXn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "nPuX2b3RdgkK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "653e5491-ad3f-44e9-95dc-b3ee8d6d9ccd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.10/dist-packages (3.1.2)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl) (1.1.0)\n",
            "Requirement already satisfied: keras.utils in /usr/local/lib/python3.10/dist-packages (1.0.13)\n",
            "Requirement already satisfied: Keras>=2.1.5 in /usr/local/lib/python3.10/dist-packages (from keras.utils) (2.13.1)\n",
            "Requirement already satisfied: np_utils in /usr/local/lib/python3.10/dist-packages (0.6.0)\n",
            "Requirement already satisfied: numpy>=1.0 in /usr/local/lib/python3.10/dist-packages (from np_utils) (1.23.5)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!pip install openpyxl\n",
        "!pip install keras.utils\n",
        "!pip install np_utils\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import random\n",
        "import itertools\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "import math\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.preprocessing.image import load_img,img_to_array\n",
        "#from keras.utils import to_categorical\n",
        "from keras.models import Model, Sequential, load_model\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Reshape, Dropout\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "\n",
        "## models to import\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "\n",
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zweQoV2eTatN"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "%cd /content/drive/MyDrive\n",
        "\n",
        "img_dir = \"data.zip\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYi98RNZfKDG",
        "outputId": "97a80442-14d9-42c3-e6ad-28cbd8beb7c1"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/MyDrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_size = 224"
      ],
      "metadata": {
        "id": "RBz1eBo0es0A"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "df_data = pd.read_csv('/full_df.csv')\n",
        "df_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "KCSQpUOIFTap",
        "outputId": "55254d99-c676-419a-d0d1-23be6b688657"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID  Patient Age Patient Sex Left-Fundus Right-Fundus  \\\n",
              "0   0           69      Female  0_left.jpg  0_right.jpg   \n",
              "1   1           57        Male  1_left.jpg  1_right.jpg   \n",
              "2   2           42        Male  2_left.jpg  2_right.jpg   \n",
              "3   4           53        Male  4_left.jpg  4_right.jpg   \n",
              "4   5           50      Female  5_left.jpg  5_right.jpg   \n",
              "\n",
              "                            Left-Diagnostic Keywords  \\\n",
              "0                                           cataract   \n",
              "1                                      normal fundus   \n",
              "2  laser spot，moderate non proliferative retinopathy   \n",
              "3                        macular epiretinal membrane   \n",
              "4             moderate non proliferative retinopathy   \n",
              "\n",
              "                Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \\\n",
              "0                           normal fundus  0  0  0  1  0  0  0  0   \n",
              "1                           normal fundus  1  0  0  0  0  0  0  0   \n",
              "2  moderate non proliferative retinopathy  0  1  0  0  0  0  0  1   \n",
              "3       mild nonproliferative retinopathy  0  1  0  0  0  0  0  1   \n",
              "4  moderate non proliferative retinopathy  0  1  0  0  0  0  0  0   \n",
              "\n",
              "                                            filepath labels  \\\n",
              "0  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
              "1  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
              "2  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "3  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "4  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "\n",
              "                     target     filename  \n",
              "0  [1, 0, 0, 0, 0, 0, 0, 0]  0_right.jpg  \n",
              "1  [1, 0, 0, 0, 0, 0, 0, 0]  1_right.jpg  \n",
              "2  [0, 1, 0, 0, 0, 0, 0, 0]  2_right.jpg  \n",
              "3  [0, 1, 0, 0, 0, 0, 0, 0]  4_right.jpg  \n",
              "4  [0, 1, 0, 0, 0, 0, 0, 0]  5_right.jpg  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f04f209e-5d60-4e7c-b0e0-fbc934d3792a\" class=\"colab-df-container\">\n",
              "    <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>ID</th>\n",
              "      <th>Patient Age</th>\n",
              "      <th>Patient Sex</th>\n",
              "      <th>Left-Fundus</th>\n",
              "      <th>Right-Fundus</th>\n",
              "      <th>Left-Diagnostic Keywords</th>\n",
              "      <th>Right-Diagnostic Keywords</th>\n",
              "      <th>N</th>\n",
              "      <th>D</th>\n",
              "      <th>G</th>\n",
              "      <th>C</th>\n",
              "      <th>A</th>\n",
              "      <th>H</th>\n",
              "      <th>M</th>\n",
              "      <th>O</th>\n",
              "      <th>filepath</th>\n",
              "      <th>labels</th>\n",
              "      <th>target</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>0_left.jpg</td>\n",
              "      <td>0_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['N']</td>\n",
              "      <td>[1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>0_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>Male</td>\n",
              "      <td>1_left.jpg</td>\n",
              "      <td>1_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['N']</td>\n",
              "      <td>[1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>1_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>42</td>\n",
              "      <td>Male</td>\n",
              "      <td>2_left.jpg</td>\n",
              "      <td>2_right.jpg</td>\n",
              "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
              "      <td>moderate non proliferative retinopathy</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['D']</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>2_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>Male</td>\n",
              "      <td>4_left.jpg</td>\n",
              "      <td>4_right.jpg</td>\n",
              "      <td>macular epiretinal membrane</td>\n",
              "      <td>mild nonproliferative retinopathy</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['D']</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>4_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>Female</td>\n",
              "      <td>5_left.jpg</td>\n",
              "      <td>5_right.jpg</td>\n",
              "      <td>moderate non proliferative retinopathy</td>\n",
              "      <td>moderate non proliferative retinopathy</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['D']</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>5_right.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f04f209e-5d60-4e7c-b0e0-fbc934d3792a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f04f209e-5d60-4e7c-b0e0-fbc934d3792a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f04f209e-5d60-4e7c-b0e0-fbc934d3792a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5399253f-2598-4e21-bdf9-03bfd1116b9c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5399253f-2598-4e21-bdf9-03bfd1116b9c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5399253f-2598-4e21-bdf9-03bfd1116b9c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data[df_data.C==1].head()"
      ],
      "metadata": {
        "id": "jLYcCXn_FX5Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "ff839149-3a07-4d6e-ebdf-f12bcc14c942"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ID  Patient Age Patient Sex   Left-Fundus   Right-Fundus  \\\n",
              "0     0           69      Female    0_left.jpg    0_right.jpg   \n",
              "20   24           75      Female   24_left.jpg   24_right.jpg   \n",
              "65   81           66        Male   81_left.jpg   81_right.jpg   \n",
              "86  103           55      Female  103_left.jpg  103_right.jpg   \n",
              "93  112           57      Female  112_left.jpg  112_right.jpg   \n",
              "\n",
              "                           Left-Diagnostic Keywords  \\\n",
              "0                                          cataract   \n",
              "20                                    normal fundus   \n",
              "65  moderate non proliferative retinopathy，cataract   \n",
              "86                                         cataract   \n",
              "93                                    normal fundus   \n",
              "\n",
              "                          Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \\\n",
              "0                                     normal fundus  0  0  0  1  0  0  0  0   \n",
              "20                                         cataract  0  0  0  1  0  0  0  0   \n",
              "65  moderate non proliferative retinopathy，cataract  0  1  0  1  0  0  0  0   \n",
              "86                                    normal fundus  0  0  0  1  0  0  0  0   \n",
              "93                                         cataract  0  0  0  1  0  0  0  0   \n",
              "\n",
              "                                             filepath labels  \\\n",
              "0   ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
              "20  ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "65  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "86  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
              "93  ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "\n",
              "                      target       filename  \n",
              "0   [1, 0, 0, 0, 0, 0, 0, 0]    0_right.jpg  \n",
              "20  [0, 0, 0, 1, 0, 0, 0, 0]   24_right.jpg  \n",
              "65  [0, 1, 0, 0, 0, 0, 0, 0]   81_right.jpg  \n",
              "86  [1, 0, 0, 0, 0, 0, 0, 0]  103_right.jpg  \n",
              "93  [0, 0, 0, 1, 0, 0, 0, 0]  112_right.jpg  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bce84481-9449-475d-b072-3df8214ea2c4\" class=\"colab-df-container\">\n",
              "    <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>ID</th>\n",
              "      <th>Patient Age</th>\n",
              "      <th>Patient Sex</th>\n",
              "      <th>Left-Fundus</th>\n",
              "      <th>Right-Fundus</th>\n",
              "      <th>Left-Diagnostic Keywords</th>\n",
              "      <th>Right-Diagnostic Keywords</th>\n",
              "      <th>N</th>\n",
              "      <th>D</th>\n",
              "      <th>G</th>\n",
              "      <th>C</th>\n",
              "      <th>A</th>\n",
              "      <th>H</th>\n",
              "      <th>M</th>\n",
              "      <th>O</th>\n",
              "      <th>filepath</th>\n",
              "      <th>labels</th>\n",
              "      <th>target</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>0_left.jpg</td>\n",
              "      <td>0_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['N']</td>\n",
              "      <td>[1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>0_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>24</td>\n",
              "      <td>75</td>\n",
              "      <td>Female</td>\n",
              "      <td>24_left.jpg</td>\n",
              "      <td>24_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>24_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>81</td>\n",
              "      <td>66</td>\n",
              "      <td>Male</td>\n",
              "      <td>81_left.jpg</td>\n",
              "      <td>81_right.jpg</td>\n",
              "      <td>moderate non proliferative retinopathy，cataract</td>\n",
              "      <td>moderate non proliferative retinopathy，cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['D']</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>81_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>103</td>\n",
              "      <td>55</td>\n",
              "      <td>Female</td>\n",
              "      <td>103_left.jpg</td>\n",
              "      <td>103_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['N']</td>\n",
              "      <td>[1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>103_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>112</td>\n",
              "      <td>57</td>\n",
              "      <td>Female</td>\n",
              "      <td>112_left.jpg</td>\n",
              "      <td>112_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>112_right.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bce84481-9449-475d-b072-3df8214ea2c4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bce84481-9449-475d-b072-3df8214ea2c4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bce84481-9449-475d-b072-3df8214ea2c4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4c43939a-db2f-4662-9647-b86e5c64be4a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4c43939a-db2f-4662-9647-b86e5c64be4a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4c43939a-db2f-4662-9647-b86e5c64be4a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data.info()"
      ],
      "metadata": {
        "id": "NAN9s0SPFUu6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30be4365-ba61-41f5-f725-ff0c8476607d"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6392 entries, 0 to 6391\n",
            "Data columns (total 19 columns):\n",
            " #   Column                     Non-Null Count  Dtype \n",
            "---  ------                     --------------  ----- \n",
            " 0   ID                         6392 non-null   int64 \n",
            " 1   Patient Age                6392 non-null   int64 \n",
            " 2   Patient Sex                6392 non-null   object\n",
            " 3   Left-Fundus                6392 non-null   object\n",
            " 4   Right-Fundus               6392 non-null   object\n",
            " 5   Left-Diagnostic Keywords   6392 non-null   object\n",
            " 6   Right-Diagnostic Keywords  6392 non-null   object\n",
            " 7   N                          6392 non-null   int64 \n",
            " 8   D                          6392 non-null   int64 \n",
            " 9   G                          6392 non-null   int64 \n",
            " 10  C                          6392 non-null   int64 \n",
            " 11  A                          6392 non-null   int64 \n",
            " 12  H                          6392 non-null   int64 \n",
            " 13  M                          6392 non-null   int64 \n",
            " 14  O                          6392 non-null   int64 \n",
            " 15  filepath                   6392 non-null   object\n",
            " 16  labels                     6392 non-null   object\n",
            " 17  target                     6392 non-null   object\n",
            " 18  filename                   6392 non-null   object\n",
            "dtypes: int64(10), object(9)\n",
            "memory usage: 948.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data[df_data == 1].sum(axis=0)"
      ],
      "metadata": {
        "id": "oGY6b3rIFVBD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b435c24-7d68-4730-9c94-b42eb7ff1e19"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                              2.0\n",
              "Patient Age                    28.0\n",
              "Patient Sex                       0\n",
              "Left-Fundus                       0\n",
              "Right-Fundus                      0\n",
              "Left-Diagnostic Keywords          0\n",
              "Right-Diagnostic Keywords         0\n",
              "N                            2101.0\n",
              "D                            2123.0\n",
              "G                             397.0\n",
              "C                             402.0\n",
              "A                             319.0\n",
              "H                             203.0\n",
              "M                             306.0\n",
              "O                            1588.0\n",
              "filepath                          0\n",
              "labels                            0\n",
              "target                            0\n",
              "filename                          0\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data2 = df_data.iloc[:, 1:7]\n",
        "#df_data2['filepath'] = pd.Series(df_data['filepath'])\n",
        "df_data2.head()"
      ],
      "metadata": {
        "id": "PrZYYTo3FVHF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "727d1cad-932b-4c84-c85c-9bce30693a49"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Patient Age Patient Sex Left-Fundus Right-Fundus  \\\n",
              "0           69      Female  0_left.jpg  0_right.jpg   \n",
              "1           57        Male  1_left.jpg  1_right.jpg   \n",
              "2           42        Male  2_left.jpg  2_right.jpg   \n",
              "3           53        Male  4_left.jpg  4_right.jpg   \n",
              "4           50      Female  5_left.jpg  5_right.jpg   \n",
              "\n",
              "                            Left-Diagnostic Keywords  \\\n",
              "0                                           cataract   \n",
              "1                                      normal fundus   \n",
              "2  laser spot，moderate non proliferative retinopathy   \n",
              "3                        macular epiretinal membrane   \n",
              "4             moderate non proliferative retinopathy   \n",
              "\n",
              "                Right-Diagnostic Keywords  \n",
              "0                           normal fundus  \n",
              "1                           normal fundus  \n",
              "2  moderate non proliferative retinopathy  \n",
              "3       mild nonproliferative retinopathy  \n",
              "4  moderate non proliferative retinopathy  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4ff2f87-0cc1-4116-9aa2-055a1e12128e\" class=\"colab-df-container\">\n",
              "    <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>Patient Age</th>\n",
              "      <th>Patient Sex</th>\n",
              "      <th>Left-Fundus</th>\n",
              "      <th>Right-Fundus</th>\n",
              "      <th>Left-Diagnostic Keywords</th>\n",
              "      <th>Right-Diagnostic Keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>0_left.jpg</td>\n",
              "      <td>0_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>Male</td>\n",
              "      <td>1_left.jpg</td>\n",
              "      <td>1_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>normal fundus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>42</td>\n",
              "      <td>Male</td>\n",
              "      <td>2_left.jpg</td>\n",
              "      <td>2_right.jpg</td>\n",
              "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
              "      <td>moderate non proliferative retinopathy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Male</td>\n",
              "      <td>4_left.jpg</td>\n",
              "      <td>4_right.jpg</td>\n",
              "      <td>macular epiretinal membrane</td>\n",
              "      <td>mild nonproliferative retinopathy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50</td>\n",
              "      <td>Female</td>\n",
              "      <td>5_left.jpg</td>\n",
              "      <td>5_right.jpg</td>\n",
              "      <td>moderate non proliferative retinopathy</td>\n",
              "      <td>moderate non proliferative retinopathy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4ff2f87-0cc1-4116-9aa2-055a1e12128e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a4ff2f87-0cc1-4116-9aa2-055a1e12128e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a4ff2f87-0cc1-4116-9aa2-055a1e12128e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-248b958e-34ad-4157-94e9-f254f27341f8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-248b958e-34ad-4157-94e9-f254f27341f8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-248b958e-34ad-4157-94e9-f254f27341f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#img_dir = \"https://drive.google.com/file/d/1yHtwXF14Gpd90I2j6GLwPs3Sh3TuiZVU/view?usp=drive_link\""
      ],
      "metadata": {
        "id": "mdV3wS2SUGBi"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Left diagnosis with 'cataract' keyword\n",
        "\n",
        "df_data2[df_data2['Left-Diagnostic Keywords'].str.match('cataract')].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "48_X4L62UGK_",
        "outputId": "916272b2-6a18-430d-c61b-32d5d7081b4b"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Patient Age Patient Sex   Left-Fundus   Right-Fundus  \\\n",
              "0             69      Female    0_left.jpg    0_right.jpg   \n",
              "86            55      Female  103_left.jpg  103_right.jpg   \n",
              "100           59        Male  119_left.jpg  119_right.jpg   \n",
              "218           60      Female  254_left.jpg  254_right.jpg   \n",
              "256           69      Female  294_left.jpg  294_right.jpg   \n",
              "\n",
              "    Left-Diagnostic Keywords    Right-Diagnostic Keywords  \n",
              "0                   cataract                normal fundus  \n",
              "86                  cataract                normal fundus  \n",
              "100                 cataract                       drusen  \n",
              "218                 cataract  macular epiretinal membrane  \n",
              "256                 cataract                normal fundus  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31cd82d5-4a82-446f-a02d-2d299d7e9912\" class=\"colab-df-container\">\n",
              "    <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>Patient Age</th>\n",
              "      <th>Patient Sex</th>\n",
              "      <th>Left-Fundus</th>\n",
              "      <th>Right-Fundus</th>\n",
              "      <th>Left-Diagnostic Keywords</th>\n",
              "      <th>Right-Diagnostic Keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>0_left.jpg</td>\n",
              "      <td>0_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>55</td>\n",
              "      <td>Female</td>\n",
              "      <td>103_left.jpg</td>\n",
              "      <td>103_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>59</td>\n",
              "      <td>Male</td>\n",
              "      <td>119_left.jpg</td>\n",
              "      <td>119_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>drusen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>60</td>\n",
              "      <td>Female</td>\n",
              "      <td>254_left.jpg</td>\n",
              "      <td>254_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>macular epiretinal membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>294_left.jpg</td>\n",
              "      <td>294_right.jpg</td>\n",
              "      <td>cataract</td>\n",
              "      <td>normal fundus</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31cd82d5-4a82-446f-a02d-2d299d7e9912')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-31cd82d5-4a82-446f-a02d-2d299d7e9912 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-31cd82d5-4a82-446f-a02d-2d299d7e9912');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-df8f7991-6395-4630-b689-f5b86fb2fc81\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df8f7991-6395-4630-b689-f5b86fb2fc81')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df8f7991-6395-4630-b689-f5b86fb2fc81 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## let's place the left cataract data into its own dataframe and print the number of rows\n",
        "\n",
        "df_left_cat = df_data2[df_data2['Left-Diagnostic Keywords'].str.match('cataract')]\n",
        "print(len(df_left_cat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zicMj8H8UGNx",
        "outputId": "9fe5aa24-ac9e-4300-80e0-053824cb7dc4"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "294\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Right diagnosis with 'cataract' keyword\n",
        "\n",
        "df_data[df_data['Right-Diagnostic Keywords'].str.match('cataract')].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "k9U6wHoHUGQG",
        "outputId": "003339bf-f3e6-4192-daec-133ea99dfd7e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ID  Patient Age Patient Sex   Left-Fundus   Right-Fundus  \\\n",
              "20    24           75      Female   24_left.jpg   24_right.jpg   \n",
              "93   112           57      Female  112_left.jpg  112_right.jpg   \n",
              "160  188           55      Female  188_left.jpg  188_right.jpg   \n",
              "186  218           36      Female  218_left.jpg  218_right.jpg   \n",
              "309  354           65        Male  354_left.jpg  354_right.jpg   \n",
              "\n",
              "                              Left-Diagnostic Keywords  \\\n",
              "20                                       normal fundus   \n",
              "93                                       normal fundus   \n",
              "160  laser spot，severe proliferative diabetic retin...   \n",
              "186                           refractive media opacity   \n",
              "309                                      normal fundus   \n",
              "\n",
              "    Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \\\n",
              "20                   cataract  0  0  0  1  0  0  0  0   \n",
              "93                   cataract  0  0  0  1  0  0  0  0   \n",
              "160                  cataract  0  1  0  1  0  0  0  1   \n",
              "186                  cataract  0  0  0  1  0  0  0  1   \n",
              "309                  cataract  0  0  0  1  0  0  0  0   \n",
              "\n",
              "                                              filepath labels  \\\n",
              "20   ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "93   ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "160  ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "186  ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "309  ../input/ocular-disease-recognition-odir5k/ODI...  ['C']   \n",
              "\n",
              "                       target       filename  \n",
              "20   [0, 0, 0, 1, 0, 0, 0, 0]   24_right.jpg  \n",
              "93   [0, 0, 0, 1, 0, 0, 0, 0]  112_right.jpg  \n",
              "160  [0, 0, 0, 1, 0, 0, 0, 0]  188_right.jpg  \n",
              "186  [0, 0, 0, 1, 0, 0, 0, 0]  218_right.jpg  \n",
              "309  [0, 0, 0, 1, 0, 0, 0, 0]  354_right.jpg  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9cacd9a1-62b6-4cfa-8da9-c67bc8037e5c\" class=\"colab-df-container\">\n",
              "    <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>ID</th>\n",
              "      <th>Patient Age</th>\n",
              "      <th>Patient Sex</th>\n",
              "      <th>Left-Fundus</th>\n",
              "      <th>Right-Fundus</th>\n",
              "      <th>Left-Diagnostic Keywords</th>\n",
              "      <th>Right-Diagnostic Keywords</th>\n",
              "      <th>N</th>\n",
              "      <th>D</th>\n",
              "      <th>G</th>\n",
              "      <th>C</th>\n",
              "      <th>A</th>\n",
              "      <th>H</th>\n",
              "      <th>M</th>\n",
              "      <th>O</th>\n",
              "      <th>filepath</th>\n",
              "      <th>labels</th>\n",
              "      <th>target</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>24</td>\n",
              "      <td>75</td>\n",
              "      <td>Female</td>\n",
              "      <td>24_left.jpg</td>\n",
              "      <td>24_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>24_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>112</td>\n",
              "      <td>57</td>\n",
              "      <td>Female</td>\n",
              "      <td>112_left.jpg</td>\n",
              "      <td>112_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>112_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>188</td>\n",
              "      <td>55</td>\n",
              "      <td>Female</td>\n",
              "      <td>188_left.jpg</td>\n",
              "      <td>188_right.jpg</td>\n",
              "      <td>laser spot，severe proliferative diabetic retin...</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>188_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>218</td>\n",
              "      <td>36</td>\n",
              "      <td>Female</td>\n",
              "      <td>218_left.jpg</td>\n",
              "      <td>218_right.jpg</td>\n",
              "      <td>refractive media opacity</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>218_right.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>354</td>\n",
              "      <td>65</td>\n",
              "      <td>Male</td>\n",
              "      <td>354_left.jpg</td>\n",
              "      <td>354_right.jpg</td>\n",
              "      <td>normal fundus</td>\n",
              "      <td>cataract</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>../input/ocular-disease-recognition-odir5k/ODI...</td>\n",
              "      <td>['C']</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
              "      <td>354_right.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cacd9a1-62b6-4cfa-8da9-c67bc8037e5c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9cacd9a1-62b6-4cfa-8da9-c67bc8037e5c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9cacd9a1-62b6-4cfa-8da9-c67bc8037e5c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-201b7554-e549-4cb1-beef-7c5310216d44\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-201b7554-e549-4cb1-beef-7c5310216d44')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-201b7554-e549-4cb1-beef-7c5310216d44 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## let's place the right cataract data into its own dataframe and print the number of rows\n",
        "\n",
        "df_rt_cat = df_data2[df_data2['Right-Diagnostic Keywords'].str.match('cataract')]\n",
        "print(len(df_rt_cat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXqs23MaUGTK",
        "outputId": "754090db-0d6c-4523-e7e7-f9640446f2c0"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat_filenames = df_left_cat['Left-Fundus'].append(df_rt_cat['Right-Fundus'], ignore_index=True)\n",
        "df_cat_filenames.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbkBpEjwUGVM",
        "outputId": "ea66cee2-d062-4733-db00-825639e421d4"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-90-e401f10684d0>:1: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df_cat_filenames = df_left_cat['Left-Fundus'].append(df_rt_cat['Right-Fundus'], ignore_index=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0_left.jpg\n",
              "1    103_left.jpg\n",
              "2    119_left.jpg\n",
              "3    254_left.jpg\n",
              "4    294_left.jpg\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat_filenames.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMIW43gHUGZq",
        "outputId": "e04945ce-6324-4670-e259-91fb9b4caacf"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "567    2243_right.jpg\n",
              "568    2246_right.jpg\n",
              "569    2247_right.jpg\n",
              "570    2248_right.jpg\n",
              "571    2282_right.jpg\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_cat_filenames)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTCzWWQ-UGdh",
        "outputId": "e71f4829-3709-4669-a3ee-01d17943fde6"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "572"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = df_cat_filenames[342]\n",
        "image = cv2.imread(os.path.join(img_dir, img))\n",
        "print(image)\n",
        "plt.imshow(float(image))\n",
        "print(image.shape)s\n",
        "print(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "KgWq0mXDUGjv",
        "outputId": "3da0a062-9267-41f0-84f5-76881462ced7"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-96-0c318d7690f1>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a real number, not 'NoneType'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "for i in range(9):\n",
        "    img = df_cat_filenames[i]\n",
        "    image = cv2.imread(os.path.join(img_dir, img))\n",
        "\n",
        "    ## convert image to RGB\n",
        "    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "    ## subplot variables - (# of rows, # of columns, iterate through locations on grid)\n",
        "    plt.subplot(3,3,i+1)\n",
        "    plt.imshow(image_rgb)\n",
        "\n",
        "    ## label with filename and diagnosis\n",
        "    plt.xlabel('Filename: {}\\n''Cataract'.format(df_cat_filenames[i]))\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "odsyIsYlUGmS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_data[df_data == 1].sum(axis=0)"
      ],
      "metadata": {
        "id": "E2LR-S2hrt8z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_data2.head()"
      ],
      "metadata": {
        "id": "fJc28phdruN5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## let's place the Normal data into its own dataframe and print the number of rows\n",
        "\n",
        "df_left_norm = df_data2[df_data2['Left-Diagnostic Keywords'].str.match('normal')]\n",
        "print(len(df_left_norm))"
      ],
      "metadata": {
        "id": "IAVv2iYoruV3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_norm_filenames = df_left_norm['Left-Fundus'].append(df_rt_norm['Right-Fundus'], ignore_index=True)\n",
        "df_norm_filenames"
      ],
      "metadata": {
        "id": "hKOlmgnIruXy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## remember we're choosing 572 to equal the number of cataract images we pulled\n",
        "\n",
        "df_norm_filenames_random = df_norm_filenames.sample(n = 572)\n",
        "df_norm_filenames_random.head()\n",
        "\n",
        "## looks like the index is out of order now that we got all the randos"
      ],
      "metadata": {
        "id": "PMXaO3H4rudM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## we will reset the index as well\n",
        "df_norm_filenames_random = df_norm_filenames_random.reset_index(drop=True)\n",
        "df_norm_filenames_random"
      ],
      "metadata": {
        "id": "amYsHhYSUGo-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "for i in range(9):\n",
        "    img = df_norm_filenames_random[i]\n",
        "    image = cv2.imread(os.path.join(img_dir, img))\n",
        "\n",
        "    ## convert image to RGB\n",
        "    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "    ## subplot variables - (# of rows, # of columns, iterate through locations on grid)\n",
        "    plt.subplot(3,3,i+1)\n",
        "    plt.imshow(image_rgb)\n",
        "\n",
        "    ## label with filename and diagnosis\n",
        "    plt.xlabel('Filename: {}\\n''Normal'.format(df_norm_filenames_random[i]))\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "QGWDoUkxUGrL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat_filenames = pd.DataFrame(df_cat_filenames, columns = [\"filename\"])\n",
        "#df_cat_filenames.set_index(\"filename\", inplace = True)\n",
        "\n",
        "# add a new column of '1' to the dataframe\n",
        "df_cat_filenames[\"label\"] = \"cataract\"\n",
        "\n",
        "df_cat_filenames.head()"
      ],
      "metadata": {
        "id": "helT50YuFVKS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_norm_filenames_random = pd.DataFrame(df_norm_filenames_random, columns = [\"filename\"])\n",
        "#df_cat_filenames.set_index(\"filename\", inplace = True)\n",
        "\n",
        "# add a new column of '1' to the dataframe\n",
        "df_norm_filenames_random[\"label\"] = \"normal\"\n",
        "\n",
        "df_norm_filenames_random.head()"
      ],
      "metadata": {
        "id": "9gTxe6bZFVNG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_combined = df_cat_filenames.append(df_norm_filenames_random, ignore_index=True)\n",
        "df_combined"
      ],
      "metadata": {
        "id": "Ejrw95ZEFVPZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_combined_random = df_combined.sample(frac=1).reset_index(drop=True)\n",
        "df_combined_random"
      ],
      "metadata": {
        "id": "Yr3OepEYtac3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pull 80% of the combined dataset and reserve it for the training data\n",
        "# the data generator will automatically create a validation set for us later\n",
        "\n",
        "df_train = df_combined_random.sample(frac=0.8,random_state=42)\n",
        "df_train.reset_index(drop=True)\n",
        "\n",
        "# exclude the 80% that was already chosen, the remaining 20% will go into testing\n",
        "df_test = df_combined_random.drop(df_train.index)\n",
        "df_test.reset_index(drop=True)\n",
        "\n",
        "print(len(df_combined_random))\n",
        "print(len(df_train))\n",
        "print(len(df_test))"
      ],
      "metadata": {
        "id": "rbacjgPgtami"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen=tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "            rescale=1./255.,\n",
        "            validation_split=0.20,\n",
        "            rotation_range=90,\n",
        "#            width_shift_range=0.2,\n",
        "#            height_shift_range=0.2,\n",
        "            horizontal_flip=True,\n",
        "            vertical_flip=True,\n",
        "            shear_range=0.2,\n",
        "            brightness_range=[0.3,1]\n",
        "#            zoom_range=0.2\n",
        "            )\n",
        "\n",
        "## for testing we don't want to do too much augmentation, we'll just scale it.\n",
        "\n",
        "test_datagen=ImageDataGenerator(rescale=1./255.)"
      ],
      "metadata": {
        "id": "vHlxEvCataqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['label'] = df_train['label'].astype(str)\n",
        "df_test['label'] = df_test['label'].astype(str)"
      ],
      "metadata": {
        "id": "_O4McQEHtav6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator=train_datagen.flow_from_dataframe(\n",
        "dataframe=df_train,\n",
        "directory=img_dir,\n",
        "x_col=\"filename\",\n",
        "y_col=\"label\",\n",
        "subset=\"training\",\n",
        "batch_size=32,\n",
        "seed=42,\n",
        "shuffle=True,\n",
        "class_mode=\"categorical\",\n",
        "target_size=(img_size,img_size))\n",
        "\n",
        "## validation set is created from the training set,\n",
        "## we set it at 20% of the training data in the previous code\n",
        "\n",
        "valid_generator=train_datagen.flow_from_dataframe(\n",
        "dataframe=df_train,\n",
        "directory=img_dir,\n",
        "x_col=\"filename\",\n",
        "y_col=\"label\",\n",
        "subset=\"validation\",\n",
        "batch_size=32,\n",
        "seed=42,\n",
        "shuffle=True,\n",
        "class_mode=\"categorical\",\n",
        "target_size=(img_size,img_size))\n",
        "\n",
        "\n",
        "\n",
        "test_generator=test_datagen.flow_from_dataframe(\n",
        "dataframe=df_test,\n",
        "directory=img_dir,\n",
        "x_col=\"filename\",\n",
        "y_col=\"label\",\n",
        "batch_size=32,\n",
        "#seed=42,\n",
        "shuffle=False,\n",
        "class_mode=\"categorical\",\n",
        "target_size=(img_size,img_size))"
      ],
      "metadata": {
        "id": "f_4uQMLzta7n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_image_data, train_labels = train_generator.next()\n",
        "#train_image_data[0]\n",
        "train_image_data.shape"
      ],
      "metadata": {
        "id": "Dyk1KTBVta9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels[0]"
      ],
      "metadata": {
        "id": "FhzA3sLvta_W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(train_image_data[0], interpolation='nearest')\n",
        "plt.show()\n",
        "#images = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))"
      ],
      "metadata": {
        "id": "Cgoj60KRtbBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "\n",
        "for i in range(9):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(train_image_data[i], interpolation='nearest')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TOjxxFUWtbDz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get VGG16 base model\n",
        "vgg16 = keras.applications.vgg16.VGG16(input_shape=(224, 224, 3),\n",
        "                                       weights='imagenet',\n",
        "                                       include_top=False)\n",
        "\n",
        "# add new dense layers at the top\n",
        "x = keras.layers.Flatten()(vgg16.output)\n",
        "x = keras.layers.Dense(1024, activation='relu')(x)\n",
        "x = keras.layers.Dropout(0.5)(x)\n",
        "x = keras.layers.Dense(128, activation='relu')(x)\n",
        "\n",
        "## remember we are using 2 outputs only\n",
        "predictions = keras.layers.Dense(2, activation='softmax')(x)\n",
        "\n",
        "# define and compile model\n",
        "model = keras.Model(inputs=vgg16.inputs, outputs=predictions)\n",
        "for layer in vgg16.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "w3JQIzmdtbIn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = ModelCheckpoint(\"vgg16_1.h5\",\n",
        "                             monitor='val_accuracy',\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             save_weights_only=False,\n",
        "                             mode='auto',\n",
        "                             period=1)\n",
        "\n",
        "early = EarlyStopping(monitor='val_accuracy',\n",
        "                      min_delta=0,\n",
        "                      patience=3,\n",
        "                      verbose=1,\n",
        "                      mode='auto')"
      ],
      "metadata": {
        "id": "OKQ77eP_tbKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "n_spe = train_generator.samples // batch_size\n",
        "n_val_steps = valid_generator.samples // batch_size\n",
        "n_epochs = 30\n",
        "\n",
        "print(n_spe,n_val_steps)"
      ],
      "metadata": {
        "id": "2gl4L8MVtbNB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(train_generator,\n",
        "                        steps_per_epoch=n_spe,\n",
        "                        validation_data=valid_generator,\n",
        "                        validation_steps=n_val_steps,\n",
        "                        epochs=n_epochs,\n",
        "                        shuffle=True,\n",
        "                        workers=5,\n",
        "                        use_multiprocessing=True,\n",
        "                        callbacks=[checkpoint,early])"
      ],
      "metadata": {
        "id": "1S1QglectbQC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "model = load_model('./vgg16_1.h5')"
      ],
      "metadata": {
        "id": "Ao5UeBEStbR-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(hist.history[\"accuracy\"])\n",
        "plt.plot(hist.history['val_accuracy'])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Accuracy\",\"Validation Accuracy\"])\n",
        "plt.show()\n",
        "\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title(\"Model Loss\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"loss\",\"Validation Loss\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "iWVEGBcrtbXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_generator.reset()"
      ],
      "metadata": {
        "id": "JzzwLhcOtbcJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict_generator(test_generator,verbose=1,steps=test_generator.samples/batch_size)"
      ],
      "metadata": {
        "id": "Nh32BojCtbex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## let's get the first 10 rows\n",
        "print(pred[0:10])"
      ],
      "metadata": {
        "id": "v2qV8QD3tbgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_class_idx=np.argmax(pred,axis=1)\n",
        "\n",
        "## print the same 10 rows\n",
        "\n",
        "print(predicted_class_idx[0:10])"
      ],
      "metadata": {
        "id": "nC5Aly5avD_C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(predicted_class_idx))"
      ],
      "metadata": {
        "id": "f3McqVUzvELz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_generator,use_multiprocessing=True,workers=10)"
      ],
      "metadata": {
        "id": "hJCCYQEwvEQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_generator.class_indices.items()"
      ],
      "metadata": {
        "id": "_2mOhUiSvETP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_labels = dict((value,key) for key,value in valid_generator.class_indices.items())\n",
        "pred_labels = [valid_labels[key] for key in predicted_class_idx]"
      ],
      "metadata": {
        "id": "MqI-qW6PvEVy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_labels[1:10]"
      ],
      "metadata": {
        "id": "r5z9nv1tvEYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filenames = test_generator.filenames\n",
        "prediction_df = pd.DataFrame({'Filename': filenames,'Prediction': pred_labels})\n",
        "prediction_df.head()"
      ],
      "metadata": {
        "id": "SdSVbW-nvEbF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_df.iloc[35]"
      ],
      "metadata": {
        "id": "HTGq-gGZvZaY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_generator.filenames[35])\n",
        "print(test_generator.labels[35])"
      ],
      "metadata": {
        "id": "qN7qGa8_vZjt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_file_names=test_generator.filenames  # sequential list of name of test files of each sample\n",
        "test_labels=test_generator.labels # is a sequential list  of test labels for each image sample\n",
        "class_dict= test_generator.class_indices # a dictionary where key is the class name and value is the label for the class\n",
        "\n",
        "print (class_dict) # have a look at the dictionary\n",
        "\n",
        "new_dict={}\n",
        "\n",
        "for key in class_dict: # set key in new_dict to value in class_dict and value in new_dict to key in class_dict\n",
        "    value = class_dict[key]\n",
        "    new_dict[value] = key\n",
        "\n",
        "print('  RESULT  PREDICT      TRUE CLASS       FILENAME ' ) # adjust spacing based on your class names\n",
        "for i, p in enumerate(pred):\n",
        "    pred_index=np.argmax(p) # get the index that has the highest probability\n",
        "    pred_class=new_dict[pred_index]  # find the predicted class based on the index\n",
        "    true_class=new_dict[test_labels[i]] # use the test label to get the true class of the test file\n",
        "    file=test_file_names[i]\n",
        "\n",
        "    if true_class == pred_class:\n",
        "        result = \"Correct\"\n",
        "    else:\n",
        "        result = \"Wrong  \"\n",
        "\n",
        "\n",
        "    print(f' {result}   {pred_class}    {true_class}      {file}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "jVBF0dNIvZpg",
        "outputId": "809eaed9-e7e4-4fec-ea65-cf8e0f884bdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-b8260839d183>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_file_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilenames\u001b[0m  \u001b[0;31m# sequential list of name of test files of each sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;31m# is a sequential list  of test labels for each image sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mclass_dict\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtest_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_indices\u001b[0m \u001b[0;31m# a dictionary where key is the class name and value is the label for the class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclass_dict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# have a look at the dictionary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'test_generator' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_image_data, test_labels = test_generator.next()"
      ],
      "metadata": {
        "id": "e3CcxMgdvZth"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_image_data.shape)\n",
        "print(test_labels.shape)"
      ],
      "metadata": {
        "id": "sda8pam0zUuZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z = 0\n",
        "#test filename\n",
        "\n",
        "test_file_names=test_generator.filenames[z]\n",
        "print(test_file_names)\n",
        "\n",
        "test_labels_example=test_generator.labels[z]\n",
        "print(test_labels_example)\n",
        "\n",
        "pred_labels[z]"
      ],
      "metadata": {
        "id": "qWZJYq9bzU2u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[5]"
      ],
      "metadata": {
        "id": "dwmi3PQezU9m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_class_idx=np.argmax(test_labels,axis=1)\n",
        "#test_class_idx[4]\n",
        "test_class_idx"
      ],
      "metadata": {
        "id": "__xbhA-XzVCZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "for i in range(18):\n",
        "    sample = random.choice(range(test_generator.samples))\n",
        "#    print(str(sample))\n",
        "    img = test_generator.filenames[sample]\n",
        "    image = cv2.imread(os.path.join(img_dir, img))\n",
        "    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "    category = test_generator.labels[sample]\n",
        "    pred_category = pred_labels[sample]\n",
        "\n",
        "#    sample = random.choice(range(0,test_generator.samples))\n",
        "#    image = test_image_data[sample]\n",
        "#    category = test_class_idx[sample]\n",
        "#    pred_category = pred_labels[sample]\n",
        "\n",
        "\n",
        "    if category== 1:\n",
        "        label = \"Normal\"\n",
        "    else:\n",
        "        label = \"Cataract\"\n",
        "\n",
        "    if pred_category== \"normal\":\n",
        "        pred_label = \"Normal\"\n",
        "      else:\n",
        "        pred_label = \"Cataract\"\n",
        "\n",
        "    if label == pred_label:\n",
        "        result = \"Correct\"\n",
        "    else:\n",
        "        result = \"Wrong\"\n",
        "\n",
        "\n",
        "    plt.subplot(3,6,i+1)\n",
        "    plt.imshow(image_rgb, interpolation='nearest')\n",
        "    plt.xlabel(\"Actual:{}\\nPrediction:{}\\nResult:{}\\nF:{}\\nRow:{}\".format(label,\n",
        "                                                                          pred_label,\n",
        "                                                                          result,\n",
        "                                                                          test_generator.filenames[sample],\n",
        "                                                                          sample))\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "ukJnV4klzVEq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Classification REPORT:')\n",
        "target_names = ['Cataract', 'Normal']\n",
        "print(classification_report(test_generator.classes, predicted_class_idx, target_names=target_names))"
      ],
      "metadata": {
        "id": "agEB6C2i0BtI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(test_generator.labels, predicted_class_idx)\n",
        "print('Confusion Matrix')\n",
        "cm"
      ],
      "metadata": {
        "id": "3vVAhlp60Bzw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                        normalize=False,\n",
        "                        title='Confusion matrix',\n",
        "                        cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "            horizontalalignment=\"center\",\n",
        "            color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "eacHIEVy0B8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_plot_labels = ['cataract','normal']"
      ],
      "metadata": {
        "id": "XyQDC50ezVHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')"
      ],
      "metadata": {
        "id": "pGs5wujg6Tvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ooQIIlay6Utv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}