Ekstraksi_citra_LBP.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "lFsifWPLgqZn"
      },
      "outputs": [],
      "source": [
        "from skimage.feature import local_binary_pattern\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import traceback\n",
        "\n",
        "try:\n",
        "    def calculate_uniformity(lbp_image):\n",
        "        # Ubah tipe data citra LBP menjadi integer dengan menghitung nilai integer terdekat\n",
        "        # lbp_image = np.rint(lbp_image)\n",
        "        lbp_image = np.rint(lbp_image).astype(int)\n",
        "\n",
        "\n",
        "        # Hitung jumlah kemunculan setiap nilai LBP\n",
        "        histogram = np.bincount(lbp_image.flatten())\n",
        "\n",
        "        # Hitung uniformity dengan menjumlahkan setiap elemen pada histogram yang bernilai 1\n",
        "        uniformity = sum([histogram[i] for i in range(len(histogram)) if histogram[i] == 1])\n",
        "\n",
        "        return uniformity\n",
        "\n",
        "    def calculate_energy(lbp_image):\n",
        "\n",
        "        lbp_image = np.rint(lbp_image).astype(int)\n",
        "\n",
        "        # Hitung histogram LBP\n",
        "        histogram = np.bincount(lbp_image.flatten())\n",
        "\n",
        "        # Hitung energy dengan menghitung sum-of-squares dari histogram\n",
        "        energy = np.sum(histogram ** 2)\n",
        "\n",
        "        return energy\n",
        "\n",
        "    def calculate_entropy(lbp_image):\n",
        "\n",
        "        lbp_image = np.rint(lbp_image).astype(int)\n",
        "\n",
        "        # Hitung histogram LBP\n",
        "        histogram = np.bincount(lbp_image.flatten())\n",
        "\n",
        "        # Hitung probabilitas setiap nilai LBP\n",
        "        prob = histogram / np.sum(histogram)\n",
        "\n",
        "        # Hitung entropy dengan menghitung -sum(p * log(p)) untuk setiap nilai LBP\n",
        "        entropy = -np.sum(prob * np.log2(prob))\n",
        "\n",
        "        return entropy\n",
        "except Exception as e:\n",
        "    print(traceback.format_exc())"
      ],
      "metadata": {
        "id": "8CsY9YrpijTh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import traceback\n",
        "\n",
        "# Dictionary yang menyimpan label untuk setiap nama folder gambar\n",
        "labels = {'SAPI': 'SAPI', 'BABI': 'BABI'}\n",
        "\n",
        "# Direktori yang berisi sekumpulan folder gambar\n",
        "image_dir = '/content/Dataset2'\n",
        "\n",
        "# Ambil nama-nama folder di dalam direktori\n",
        "folder_names = os.listdir(image_dir)\n",
        "\n",
        "# Buat list kosong untuk menyimpan atribut LBP\n",
        "uniformities = []\n",
        "energies = []\n",
        "entropies = []\n",
        "\n",
        "# Buat list kosong untuk menyimpan label\n",
        "image_labels = []\n",
        "\n",
        "for folder_name in folder_names:\n",
        "  # Ambil nama-nama file gambar di dalam folder\n",
        "  image_filenames = os.listdir(os.path.join(image_dir, folder_name))\n",
        "\n",
        "  for filename in image_filenames:\n",
        "    # Baca gambar\n",
        "    image = cv2.imread(os.path.join(image_dir, folder_name, filename))\n",
        "\n",
        "    # Ubah citra ke skala abu-abu\n",
        "    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    # Buat objek LBP\n",
        "    lbp_image = local_binary_pattern(gray_image, 8, 1, method='uniform')\n",
        "\n",
        "    # Hitung atribut LBP\n",
        "    uniformity = calculate_uniformity(lbp_image)\n",
        "    energy = calculate_energy(lbp_image)\n",
        "    entropy = calculate_entropy(lbp_image)\n",
        "\n",
        "    # Tambahkan atribut LBP ke dalam list\n",
        "    uniformities.append(uniformity)\n",
        "    energies.append(energy)\n",
        "    entropies.append(entropy)\n",
        "    \n",
        "\n",
        "    # Tambahkan label ke dalam list\n",
        "    # image_labels.append(labels[folder_name])\n",
        "    # Tambahkan label ke dalam list\n",
        "    if folder_name in labels:\n",
        "      image_labels.append(labels[folder_name])\n",
        "    else:\n",
        "      image_labels.append('unknown')\n",
        "\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# Buat objek scaler\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "# Transformasikan nilai atribut menggunakan scaler\n",
        "uniformities_scaled = scaler.fit_transform(np.array(uniformities).reshape(-1, 1))\n",
        "energies_scaled = scaler.fit_transform(np.array(energies).reshape(-1, 1))\n",
        "entropies_scaled = scaler.fit_transform(np.array(entropies).reshape(-1, 1))\n",
        "\n",
        "\n",
        "# Buat data frame dengan atribut LBP dan label\n",
        "# df = pd.DataFrame({'Uniformity': uniformities_scaled, 'Energy': energies_scaled, 'Entropy': entropies_scaled, 'Label': image_labels})\n",
        "df = pd.DataFrame({'Uniformity': uniformities_scaled.flatten(), 'Energy': energies_scaled.flatten(), 'Entropy': entropies_scaled.flatten(), 'Label': image_labels})\n",
        "\n",
        "# Simpan file CSV\n",
        "df.to_csv('Dataset_LBP.csv', index=False)"
      ],
      "metadata": {
        "id": "vj4KIv0iiVzR"
      },
      "execution_count": 4,
      "outputs": []
    }
  ]
}