GLCM.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5tnObngG9DL-"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import cv2 \n",
        "import os\n",
        "import re"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "727IPTnP_k3J"
      },
      "outputs": [],
      "source": [
        "def normalize_label(str_):\n",
        "    str_ = str_.replace(\" \", \"\")\n",
        "    str_ = str_.translate(str_.maketrans(\"\",\"\", \"()\"))\n",
        "    str_ = str_.split(\"_\")\n",
        "    return ''.join(str_[:2])\n",
        "\n",
        "def normalize_word(names):\n",
        "    text = '-'.join(names) if len(names) > 1 else names[0]\n",
        "    text = re.sub(r'\\d+', '', text)\n",
        "    text = text.replace(\".\", \"\")\n",
        "    text = text.strip()\n",
        "    return text\n",
        "\n",
        "def print_progress(val, val_len, folder, sub_folder, filename, bar_size=10):\n",
        "    progr = \"#\"*round((val)*bar_size/val_len) + \" \"*round((val_len - (val))*bar_size/val_len)\n",
        "    if val == 0:\n",
        "        print(\"\", end = \"\\n\")\n",
        "    else:\n",
        "        print(\"[%s] folder : %s/%s/ ----> file : %s\" % (progr, folder, sub_folder, filename), end=\"\\r\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mendefinisikan variabel dataset_dir yang berisi nama folder yang berisi dataset yang akan diolah\n",
        "dataset_dir = \"Dataset\"\n",
        "\n",
        "# Mendefinisikan list kosong bernama imgs yang nantinya akan diisi dengan matriks citra yang telah diolah\n",
        "imgs = [] #list image matrix \n",
        "\n",
        "# Mendefinisikan list kosong bernama labels yang nantinya akan diisi dengan label dari citra yang telah diolah\n",
        "labels = []\n",
        "\n",
        "# Mendefinisikan list kosong bernama header_labels yang nantinya akan diisi dengan nama folder yang berisi citra yang telah diolah\n",
        "header_labels = []\n",
        "\n",
        "# Mendefinisikan list kosong bernama descs yang nantinya akan diisi dengan deskripsi citra yang telah diolah\n",
        "descs = []\n",
        "\n",
        "# Melakukan looping terhadap setiap folder yang ada di dalam folder dataset_dir\n",
        "for folder in os.listdir(dataset_dir):\n",
        "  # Melakukan looping terhadap setiap subfolder yang ada di dalam folder yang sedang diiterasi oleh loop sebelumnya\n",
        "    for sub_folder in os.listdir(os.path.join(dataset_dir, folder)):\n",
        "      # Mendapatkan daftar nama file yang ada di dalam subfolder yang sedang diiterasi oleh loop sebelumnya\n",
        "        sub_folder_files = os.listdir(os.path.join(dataset_dir, folder, sub_folder))\n",
        "        # Mendapatkan panjang daftar file yang ada di dalam subfolder yang sedang diiterasi oleh loop sebelumnya\n",
        "        len_sub_folder = len(sub_folder_files) - 1\n",
        "         # Melakukan looping terhadap setiap file yang ada di dalam subfolder yang sedang diiterasi oleh loop sebelumnya\n",
        "        for i, filename in enumerate(sub_folder_files):\n",
        "          # Membaca file citra yang sedang diiterasi oleh loop sebelumnya\n",
        "            img = cv2.imread(os.path.join(dataset_dir, folder, sub_folder, filename))\n",
        "            # Mengubah citra yang telah dibaca menjadi citra grayscale\n",
        "            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "            # Mendapatkan tinggi dan lebar citra grayscale yang telah diubah\n",
        "            h, w = gray.shape\n",
        "            # Mendapatkan koordinat batas citra yang akan dipotong\n",
        "            ymin, ymax, xmin, xmax = h//3, h*2//3, w//3, w*2//3\n",
        "            # Memotong citra grayscale sesuai dengan batas yang telah ditentukan\n",
        "            crop = gray[ymin:ymax, xmin:xmax]\n",
        "            # Mengubah ukuran citra yang telah dipotong menjadi setengah dari ukurannya sebelumnya\n",
        "            resize = cv2.resize(crop, (0,0), fx=0.5, fy=0.5)\n",
        "            \n",
        "            \n",
        "            # Menambahkan citra yang telah diolah ke dalam list imgs\n",
        "            # Menambahkan label citra yang telah diolah ke dalam list labels            \n",
        "            # Menambahkan nama folder yang berisi citra yang telah diolah ke dalam list header_labels            \n",
        "            # Menambahkan deskripsi citra yang telah diolah ke dalam list descs            \n",
        "            # Menampilkan progress bar saat melakukan proses pengolahan citra\n",
        "            imgs.append(resize)\n",
        "            labels.append(normalize_label(os.path.splitext(sub_folder)[0]))\n",
        "            header_labels.append(normalize_word([folder]))\n",
        "            descs.append(normalize_word([folder, sub_folder]))\n",
        "            \n",
        "            # Menampilkan progress bar saat melakukan proses pengolahan citra\n",
        "            print_progress(i, len_sub_folder, folder, sub_folder, filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hfn5EHaAyRYM",
        "outputId": "6ede9205-20d4-4fbb-9b4f-8084fa3d8187"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[##########] folder : DagingSapi/SAPI/ ----> file : IMG_20220107_081811.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(imgs))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hjyyswyv4Ro",
        "outputId": "7d08e0a2-de9a-4b7f-8f74-eafa30584336"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "230\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in set(zip (header_labels, labels)):\n",
        "    print(i[1], \"\\t:\", i[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7ajWUpSyV-O",
        "outputId": "8aef1d1c-f8b0-4747-84f1-4800b9a0852d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SAPI \t: DagingSapi\n",
            "BABI \t: DagingBabi\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imgs[8].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfnAj-C8zCg6",
        "outputId": "bc7c2159-1406-4c82-8087-c96b5feef341"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(230, 230)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCTqzSUTzJkT",
        "outputId": "98bef94a-14f7-4132-b27a-342a494d65bc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "230"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from skimage.feature import greycomatrix, greycoprops\n",
        "import pandas as pd "
      ],
      "metadata": {
        "id": "3YaFDJi5zQ_Z"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glcm = greycomatrix(imgs[100], distances=[5], angles=[0], levels=256,\n",
        "                    symmetric=True, normed=True)"
      ],
      "metadata": {
        "id": "WN76OmbozSwh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('dissimilarity \\t: %.04f' % greycoprops(glcm, 'dissimilarity')[0, 0])\n",
        "print('correlation \\t: %.04f' % greycoprops(glcm, 'correlation')[0, 0])\n",
        "print('homogeneity \\t: %.04f' % greycoprops(glcm, 'homogeneity')[0, 0])\n",
        "print('contrast \\t: %.04f' % greycoprops(glcm, 'contrast')[0, 0])\n",
        "print('ASM \\t\\t: %.04f' % greycoprops(glcm, 'ASM')[0, 0])\n",
        "print('energy \\t\\t: %.04f' % greycoprops(glcm, 'energy')[0, 0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUL4UOdyzh0O",
        "outputId": "9a374d56-3086-4d2d-d17e-f41cbe9667b4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dissimilarity \t: 14.7419\n",
            "correlation \t: 0.5846\n",
            "homogeneity \t: 0.1023\n",
            "contrast \t: 521.7919\n",
            "ASM \t\t: 0.0003\n",
            "energy \t\t: 0.0170\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_glcm_feature(img, label, dists=[5], agls=[0], lvl=256, sym=True, norm=True):\n",
        "    \n",
        "    glcm = greycomatrix(img, \n",
        "                        distances=dists, \n",
        "                        angles=agls, \n",
        "                        levels=lvl,\n",
        "                        symmetric=sym, \n",
        "                        normed=norm)\n",
        "    feature = []\n",
        "    feature.append(greycoprops(glcm, 'dissimilarity')[0, 0])\n",
        "    feature.append(greycoprops(glcm, 'correlation')[0, 0])\n",
        "    feature.append(greycoprops(glcm, 'homogeneity')[0, 0])\n",
        "    feature.append(greycoprops(glcm, 'contrast')[0, 0])\n",
        "    feature.append(greycoprops(glcm, 'ASM')[0, 0])\n",
        "    feature.append(greycoprops(glcm, 'energy')[0, 0])\n",
        "    feature.append(label)\n",
        "    \n",
        "    return feature"
      ],
      "metadata": {
        "id": "8o2Ngtk5zm5L"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glcm_features = []\n",
        "for img, label in zip(imgs, labels): \n",
        "    glcm_features.append(calc_glcm_feature(img, label))"
      ],
      "metadata": {
        "id": "7s88e8x6zovb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the pandas DataFrame for GLCM features data\n",
        "glcm_features_df = pd.DataFrame(glcm_features, \n",
        "                                columns = ['dissimilarity', \n",
        "                                           'correlation',\n",
        "                                           'homogeneity',\n",
        "                                           'contrast',\n",
        "                                           'ASM',\n",
        "                                           'energy',\n",
        "                                           'label'])"
      ],
      "metadata": {
        "id": "CtcmoCmmzvHx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glcm_features_df[80:110]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "RURRCyFJzy_Z",
        "outputId": "ada13f2a-f221-48e3-c090-3c4766cde423"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     dissimilarity  correlation  homogeneity     contrast       ASM    energy  \\\n",
              "80       12.064726     0.407804     0.103602   329.257697  0.000591  0.024301   \n",
              "81       18.483436     0.587661     0.078411   714.752920  0.000211  0.014517   \n",
              "82       18.545606     0.616139     0.079710   786.792592  0.000210  0.014503   \n",
              "83        6.809153     0.656345     0.154018    84.838329  0.001056  0.032503   \n",
              "84       14.000229     0.733373     0.088934   402.431375  0.000216  0.014714   \n",
              "85       14.396089     0.628820     0.094796   473.930870  0.000313  0.017687   \n",
              "86       12.653185     0.685293     0.100967   357.057372  0.000340  0.018437   \n",
              "87       11.962369     0.632508     0.122811   379.960329  0.000652  0.025538   \n",
              "88       18.908080     0.503046     0.080695   810.855494  0.000293  0.017109   \n",
              "89       17.567609     0.609985     0.097766   753.249809  0.000283  0.016819   \n",
              "90       12.464186     0.719642     0.095233   328.635913  0.000268  0.016377   \n",
              "91       16.904334     0.668423     0.088069   672.372529  0.000270  0.016423   \n",
              "92       15.259790     0.542486     0.097325   548.023817  0.000397  0.019930   \n",
              "93       22.548314     0.423037     0.057605  1015.127043  0.000162  0.012735   \n",
              "94       16.559012     0.574207     0.079878   567.231325  0.000215  0.014649   \n",
              "95       12.673778     0.607395     0.110449   349.704657  0.000365  0.019094   \n",
              "96       16.290267     0.720868     0.095029   636.073857  0.000239  0.015446   \n",
              "97       16.623556     0.558691     0.084060   565.748155  0.000225  0.015013   \n",
              "98       13.995990     0.692984     0.107720   469.904418  0.000362  0.019018   \n",
              "99       15.387001     0.580052     0.096968   526.229961  0.000324  0.018006   \n",
              "100      14.741914     0.584630     0.102306   521.791890  0.000289  0.016994   \n",
              "101      14.939167     0.637323     0.098419   492.991613  0.000295  0.017190   \n",
              "102      15.283127     0.667370     0.098666   598.249233  0.000300  0.017315   \n",
              "103      14.567947     0.595050     0.089723   435.946026  0.000269  0.016412   \n",
              "104      20.437902     0.601707     0.074552   927.543292  0.000189  0.013742   \n",
              "105      15.622411     0.641689     0.087443   509.422053  0.000227  0.015077   \n",
              "106      13.748290     0.552281     0.096968   407.645101  0.000352  0.018768   \n",
              "107      16.013113     0.518879     0.088305   543.148953  0.000329  0.018137   \n",
              "108      15.243749     0.667889     0.089215   498.294353  0.000244  0.015632   \n",
              "109      11.094629     0.615594     0.107867   254.491967  0.000528  0.022979   \n",
              "\n",
              "    label  \n",
              "80   SAPI  \n",
              "81   SAPI  \n",
              "82   SAPI  \n",
              "83   SAPI  \n",
              "84   SAPI  \n",
              "85   SAPI  \n",
              "86   SAPI  \n",
              "87   SAPI  \n",
              "88   SAPI  \n",
              "89   SAPI  \n",
              "90   SAPI  \n",
              "91   SAPI  \n",
              "92   SAPI  \n",
              "93   SAPI  \n",
              "94   SAPI  \n",
              "95   SAPI  \n",
              "96   SAPI  \n",
              "97   SAPI  \n",
              "98   SAPI  \n",
              "99   SAPI  \n",
              "100  SAPI  \n",
              "101  SAPI  \n",
              "102  SAPI  \n",
              "103  SAPI  \n",
              "104  SAPI  \n",
              "105  SAPI  \n",
              "106  SAPI  \n",
              "107  SAPI  \n",
              "108  SAPI  \n",
              "109  SAPI  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-86b19b6e-873f-47e7-9c79-5e7982469995\">\n",
              "    <div 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>dissimilarity</th>\n",
              "      <th>correlation</th>\n",
              "      <th>homogeneity</th>\n",
              "      <th>contrast</th>\n",
              "      <th>ASM</th>\n",
              "      <th>energy</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>12.064726</td>\n",
              "      <td>0.407804</td>\n",
              "      <td>0.103602</td>\n",
              "      <td>329.257697</td>\n",
              "      <td>0.000591</td>\n",
              "      <td>0.024301</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>18.483436</td>\n",
              "      <td>0.587661</td>\n",
              "      <td>0.078411</td>\n",
              "      <td>714.752920</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.014517</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>18.545606</td>\n",
              "      <td>0.616139</td>\n",
              "      <td>0.079710</td>\n",
              "      <td>786.792592</td>\n",
              "      <td>0.000210</td>\n",
              "      <td>0.014503</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>6.809153</td>\n",
              "      <td>0.656345</td>\n",
              "      <td>0.154018</td>\n",
              "      <td>84.838329</td>\n",
              "      <td>0.001056</td>\n",
              "      <td>0.032503</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>14.000229</td>\n",
              "      <td>0.733373</td>\n",
              "      <td>0.088934</td>\n",
              "      <td>402.431375</td>\n",
              "      <td>0.000216</td>\n",
              "      <td>0.014714</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>14.396089</td>\n",
              "      <td>0.628820</td>\n",
              "      <td>0.094796</td>\n",
              "      <td>473.930870</td>\n",
              "      <td>0.000313</td>\n",
              "      <td>0.017687</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>12.653185</td>\n",
              "      <td>0.685293</td>\n",
              "      <td>0.100967</td>\n",
              "      <td>357.057372</td>\n",
              "      <td>0.000340</td>\n",
              "      <td>0.018437</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>11.962369</td>\n",
              "      <td>0.632508</td>\n",
              "      <td>0.122811</td>\n",
              "      <td>379.960329</td>\n",
              "      <td>0.000652</td>\n",
              "      <td>0.025538</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>18.908080</td>\n",
              "      <td>0.503046</td>\n",
              "      <td>0.080695</td>\n",
              "      <td>810.855494</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>0.017109</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>17.567609</td>\n",
              "      <td>0.609985</td>\n",
              "      <td>0.097766</td>\n",
              "      <td>753.249809</td>\n",
              "      <td>0.000283</td>\n",
              "      <td>0.016819</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>12.464186</td>\n",
              "      <td>0.719642</td>\n",
              "      <td>0.095233</td>\n",
              "      <td>328.635913</td>\n",
              "      <td>0.000268</td>\n",
              "      <td>0.016377</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>16.904334</td>\n",
              "      <td>0.668423</td>\n",
              "      <td>0.088069</td>\n",
              "      <td>672.372529</td>\n",
              "      <td>0.000270</td>\n",
              "      <td>0.016423</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>15.259790</td>\n",
              "      <td>0.542486</td>\n",
              "      <td>0.097325</td>\n",
              "      <td>548.023817</td>\n",
              "      <td>0.000397</td>\n",
              "      <td>0.019930</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>22.548314</td>\n",
              "      <td>0.423037</td>\n",
              "      <td>0.057605</td>\n",
              "      <td>1015.127043</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.012735</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>16.559012</td>\n",
              "      <td>0.574207</td>\n",
              "      <td>0.079878</td>\n",
              "      <td>567.231325</td>\n",
              "      <td>0.000215</td>\n",
              "      <td>0.014649</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>12.673778</td>\n",
              "      <td>0.607395</td>\n",
              "      <td>0.110449</td>\n",
              "      <td>349.704657</td>\n",
              "      <td>0.000365</td>\n",
              "      <td>0.019094</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>16.290267</td>\n",
              "      <td>0.720868</td>\n",
              "      <td>0.095029</td>\n",
              "      <td>636.073857</td>\n",
              "      <td>0.000239</td>\n",
              "      <td>0.015446</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>16.623556</td>\n",
              "      <td>0.558691</td>\n",
              "      <td>0.084060</td>\n",
              "      <td>565.748155</td>\n",
              "      <td>0.000225</td>\n",
              "      <td>0.015013</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>13.995990</td>\n",
              "      <td>0.692984</td>\n",
              "      <td>0.107720</td>\n",
              "      <td>469.904418</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.019018</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>15.387001</td>\n",
              "      <td>0.580052</td>\n",
              "      <td>0.096968</td>\n",
              "      <td>526.229961</td>\n",
              "      <td>0.000324</td>\n",
              "      <td>0.018006</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>14.741914</td>\n",
              "      <td>0.584630</td>\n",
              "      <td>0.102306</td>\n",
              "      <td>521.791890</td>\n",
              "      <td>0.000289</td>\n",
              "      <td>0.016994</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>14.939167</td>\n",
              "      <td>0.637323</td>\n",
              "      <td>0.098419</td>\n",
              "      <td>492.991613</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>0.017190</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>15.283127</td>\n",
              "      <td>0.667370</td>\n",
              "      <td>0.098666</td>\n",
              "      <td>598.249233</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>0.017315</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>14.567947</td>\n",
              "      <td>0.595050</td>\n",
              "      <td>0.089723</td>\n",
              "      <td>435.946026</td>\n",
              "      <td>0.000269</td>\n",
              "      <td>0.016412</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>20.437902</td>\n",
              "      <td>0.601707</td>\n",
              "      <td>0.074552</td>\n",
              "      <td>927.543292</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>0.013742</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>15.622411</td>\n",
              "      <td>0.641689</td>\n",
              "      <td>0.087443</td>\n",
              "      <td>509.422053</td>\n",
              "      <td>0.000227</td>\n",
              "      <td>0.015077</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>13.748290</td>\n",
              "      <td>0.552281</td>\n",
              "      <td>0.096968</td>\n",
              "      <td>407.645101</td>\n",
              "      <td>0.000352</td>\n",
              "      <td>0.018768</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>16.013113</td>\n",
              "      <td>0.518879</td>\n",
              "      <td>0.088305</td>\n",
              "      <td>543.148953</td>\n",
              "      <td>0.000329</td>\n",
              "      <td>0.018137</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>15.243749</td>\n",
              "      <td>0.667889</td>\n",
              "      <td>0.089215</td>\n",
              "      <td>498.294353</td>\n",
              "      <td>0.000244</td>\n",
              "      <td>0.015632</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>11.094629</td>\n",
              "      <td>0.615594</td>\n",
              "      <td>0.107867</td>\n",
              "      <td>254.491967</td>\n",
              "      <td>0.000528</td>\n",
              "      <td>0.022979</td>\n",
              "      <td>SAPI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "glcm_features_df.to_csv(\"Dataset_glcm.csv\")"
      ],
      "metadata": {
        "id": "PGN3GpWtz40c"
      },
      "execution_count": 17,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "nbformat_minor": 0
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