Image.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1lMUxeStOTjS4LKL-hyNGmOj07qIjEwr6",
      "authorship_tag": "ABX9TyNM/DrvX7r0EeqhQbTDgHSJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kamrulhasan04947/cancer_detection_votingClassifeir/blob/main/Image.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o2bPMTtGBm_Z"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "import matplotlib.pyplot as plt\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "xJKsMLdOUWI7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/00000_56.bmp')"
      ],
      "metadata": {
        "id": "xD6ZSW-xB_iG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mx2lS80DHbP",
        "outputId": "5d3f5b71-79a2-4834-fb37-373e50a94265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[188, 188, 188],\n",
              "        [166, 166, 166],\n",
              "        [ 39,  39,  39],\n",
              "        ...,\n",
              "        [254, 254, 254],\n",
              "        [254, 254, 254],\n",
              "        [251, 251, 251]],\n",
              "\n",
              "       [[239, 239, 239],\n",
              "        [100, 100, 100],\n",
              "        [  0,   0,   0],\n",
              "        ...,\n",
              "        [254, 254, 254],\n",
              "        [254, 254, 254],\n",
              "        [251, 251, 251]],\n",
              "\n",
              "       [[160, 160, 160],\n",
              "        [  0,   0,   0],\n",
              "        [  0,   0,   0],\n",
              "        ...,\n",
              "        [254, 254, 254],\n",
              "        [251, 251, 251],\n",
              "        [254, 254, 254]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[224, 224, 224],\n",
              "        [130, 130, 130],\n",
              "        [ 48,  48,  48],\n",
              "        ...,\n",
              "        [248, 248, 248],\n",
              "        [248, 248, 248],\n",
              "        [254, 254, 254]],\n",
              "\n",
              "       [[251, 251, 251],\n",
              "        [182, 182, 182],\n",
              "        [ 24,  24,  24],\n",
              "        ...,\n",
              "        [236, 236, 236],\n",
              "        [245, 245, 245],\n",
              "        [254, 254, 254]],\n",
              "\n",
              "       [[236, 236, 236],\n",
              "        [239, 239, 239],\n",
              "        [194, 194, 194],\n",
              "        ...,\n",
              "        [230, 230, 230],\n",
              "        [242, 242, 242],\n",
              "        [254, 254, 254]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "id": "GLTelSCDDJkz",
        "outputId": "af92f509-6eb5-4c63-ccf2-2dbff5c5ae0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=160x160>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BGR = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "plt.imshow(BGR)\n",
        "plt.axis(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PBWEPR5zEaHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite('fingerprint.jpg', img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzF3P_xWGC8T",
        "outputId": "28fe4c0c-f0a8-4279-cd3b-71e12af5a415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Starting project**"
      ],
      "metadata": {
        "id": "u45CTspJWu4Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "ojB1WoJ8Jn4M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SIZE = 224\n",
        "train_images = []\n",
        "train_labels = []\n",
        "for directory_path in glob.glob(\"/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/*\"):\n",
        "    label = directory_path.split(\"\\\\\")[-1]\n",
        "    for img_path in glob.glob(os.path.join(directory_path)):\n",
        "        print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
        "        img = cv2.resize(img, (SIZE, SIZE))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "        cv2_imshow(img)\n",
        "        train_images.append(img)\n",
        "        train_labels.append(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yCUjLqXlJL7Q",
        "outputId": "181d6dea-8a18-4706-f2c6-bf80e32e7317"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "WDePxDDbNpwf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqKrveDAxT6t",
        "outputId": "995f82ea-3c13-43d7-89fd-7699ec39082e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " \n",
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              "         ...,\n",
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              "         [150, 150, 150]],\n",
              " \n",
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              "         ...,\n",
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              " \n",
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              "         ...,\n",
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              " \n",
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              "         ...,\n",
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              "         ...,\n",
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              "         ...,\n",
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              "         ...,\n",
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              "         ...,\n",
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              "         [255, 255, 255]],\n",
              " \n",
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              "         [115, 115, 115],\n",
              "         ...,\n",
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              "         [253, 253, 253],\n",
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              "         [216, 216, 216],\n",
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              "         ...,\n",
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              "         ...,\n",
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              "         [244, 244, 244],\n",
              "         [242, 242, 242]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [253, 253, 253],\n",
              "         [252, 252, 252],\n",
              "         ...,\n",
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              "         ...,\n",
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              " \n",
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              "         ...,\n",
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              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
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              "         ...,\n",
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              "        [[  3,   3,   3],\n",
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              "         ...,\n",
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              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[  0,   0,   0],\n",
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              "         ...,\n",
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              "         [255, 255, 255],\n",
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              " \n",
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              " \n",
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              "         [ 77,  77,  77],\n",
              "         [ 15,  15,  15]],\n",
              " \n",
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              "         ...,\n",
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              " array([[[255, 255, 255],\n",
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              "         ...,\n",
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              "         ...,\n",
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              "         ...,\n",
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              "         [186, 186, 186]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
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              "         ...,\n",
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              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
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              "         ...,\n",
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              "         [190, 190, 190]],\n",
              " \n",
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              "         ...,\n",
              "         [223, 223, 223],\n",
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              "         [225, 225, 225]],\n",
              " \n",
              "        [[251, 251, 251],\n",
              "         [251, 251, 251],\n",
              "         [251, 251, 251],\n",
              "         ...,\n",
              "         [162, 162, 162],\n",
              "         [185, 185, 185],\n",
              "         [232, 232, 232]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [242, 242, 242],\n",
              "         ...,\n",
              "         [246, 246, 246],\n",
              "         [238, 238, 238],\n",
              "         [232, 232, 232]],\n",
              " \n",
              "        [[233, 233, 233],\n",
              "         [211, 211, 211],\n",
              "         [183, 183, 183],\n",
              "         ...,\n",
              "         [252, 252, 252],\n",
              "         [246, 246, 246],\n",
              "         [240, 240, 240]],\n",
              " \n",
              "        [[204, 204, 204],\n",
              "         [152, 152, 152],\n",
              "         [110, 110, 110],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[ 89,  89,  89],\n",
              "         [ 38,  38,  38],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 40,  40,  40],\n",
              "         [ 17,  17,  17],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  2,   2,   2],\n",
              "         [  1,   1,   1],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [202, 202, 202],\n",
              "         [190, 190, 190],\n",
              "         [166, 166, 166]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [168, 168, 168],\n",
              "         [159, 159, 159],\n",
              "         [143, 143, 143]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [132, 132, 132],\n",
              "         [117, 117, 117],\n",
              "         [111, 111, 111]]], dtype=uint8),\n",
              " array([[[252, 252, 252],\n",
              "         [252, 252, 252],\n",
              "         [252, 252, 252],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[252, 252, 252],\n",
              "         [252, 252, 252],\n",
              "         [252, 252, 252],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[252, 252, 252],\n",
              "         [252, 252, 252],\n",
              "         [253, 253, 253],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[189, 189, 189],\n",
              "         [159, 159, 159],\n",
              "         [129, 129, 129],\n",
              "         ...,\n",
              "         [  9,   9,   9],\n",
              "         [ 21,  21,  21],\n",
              "         [ 37,  37,  37]],\n",
              " \n",
              "        [[185, 185, 185],\n",
              "         [174, 174, 174],\n",
              "         [164, 164, 164],\n",
              "         ...,\n",
              "         [  1,   1,   1],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[180, 180, 180],\n",
              "         [183, 183, 183],\n",
              "         [188, 188, 188],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [230, 230, 230],\n",
              "         [229, 229, 229],\n",
              "         [227, 227, 227]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [229, 229, 229],\n",
              "         [233, 233, 233],\n",
              "         [233, 233, 233]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [226, 226, 226],\n",
              "         [235, 235, 235],\n",
              "         [235, 235, 235]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 72,  72,  72],\n",
              "         [ 89,  89,  89],\n",
              "         [127, 127, 127]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 70,  70,  70],\n",
              "         [ 87,  87,  87],\n",
              "         [122, 122, 122]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 78,  78,  78],\n",
              "         [ 96,  96,  96],\n",
              "         [129, 129, 129]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 43,  43,  43],\n",
              "         [114, 114, 114],\n",
              "         [185, 185, 185]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [107, 107, 107],\n",
              "         [ 89,  89,  89],\n",
              "         [ 79,  79,  79]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [115, 115, 115],\n",
              "         [ 50,  50,  50],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[228, 228, 228],\n",
              "         [163, 163, 163],\n",
              "         [ 81,  81,  81],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [253, 253, 253],\n",
              "         [250, 250, 250]],\n",
              " \n",
              "        [[194, 194, 194],\n",
              "         [143, 143, 143],\n",
              "         [ 75,  75,  75],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[152, 152, 152],\n",
              "         [121, 121, 121],\n",
              "         [ 70,  70,  70],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  3,   3,   3],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[246, 246, 246],\n",
              "         [225, 225, 225],\n",
              "         [168, 168, 168],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[209, 209, 209],\n",
              "         [220, 220, 220],\n",
              "         [198, 198, 198],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[164, 164, 164],\n",
              "         [194, 194, 194],\n",
              "         [211, 211, 211],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [205, 205, 205],\n",
              "         [206, 206, 206],\n",
              "         [212, 212, 212]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [210, 210, 210],\n",
              "         [210, 210, 210],\n",
              "         [215, 215, 215]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [225, 225, 225],\n",
              "         [225, 225, 225],\n",
              "         [228, 228, 228]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[210, 210, 210],\n",
              "         [198, 198, 198],\n",
              "         [188, 188, 188],\n",
              "         ...,\n",
              "         [253, 253, 253],\n",
              "         [249, 249, 249],\n",
              "         [244, 244, 244]],\n",
              " \n",
              "        [[221, 221, 221],\n",
              "         [213, 213, 213],\n",
              "         [207, 207, 207],\n",
              "         ...,\n",
              "         [229, 229, 229],\n",
              "         [235, 235, 235],\n",
              "         [240, 240, 240]],\n",
              " \n",
              "        [[230, 230, 230],\n",
              "         [225, 225, 225],\n",
              "         [221, 221, 221],\n",
              "         ...,\n",
              "         [199, 199, 199],\n",
              "         [217, 217, 217],\n",
              "         [234, 234, 234]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 54,  54,  54],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [150, 150, 150],\n",
              "         [ 88,  88,  88],\n",
              "         [ 26,  26,  26]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [230, 230, 230],\n",
              "         [157, 157, 157],\n",
              "         [ 46,  46,  46]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[219, 219, 219],\n",
              "         [221, 221, 221],\n",
              "         [211, 211, 211],\n",
              "         ...,\n",
              "         [193, 193, 193],\n",
              "         [117, 117, 117],\n",
              "         [ 37,  37,  37]],\n",
              " \n",
              "        [[212, 212, 212],\n",
              "         [199, 199, 199],\n",
              "         [183, 183, 183],\n",
              "         ...,\n",
              "         [160, 160, 160],\n",
              "         [ 89,  89,  89],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[202, 202, 202],\n",
              "         [174, 174, 174],\n",
              "         [158, 158, 158],\n",
              "         ...,\n",
              "         [101, 101, 101],\n",
              "         [ 58,  58,  58],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [232, 232, 232],\n",
              "         [230, 230, 230],\n",
              "         [226, 226, 226]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [227, 227, 227],\n",
              "         [220, 220, 220],\n",
              "         [216, 216, 216]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [225, 225, 225],\n",
              "         [212, 212, 212],\n",
              "         [208, 208, 208]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[ 89,  89,  89],\n",
              "         [119, 119, 119],\n",
              "         [164, 164, 164],\n",
              "         ...,\n",
              "         [249, 249, 249],\n",
              "         [253, 253, 253],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 26,  26,  26],\n",
              "         [ 47,  47,  47],\n",
              "         [ 80,  80,  80],\n",
              "         ...,\n",
              "         [252, 252, 252],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [253, 253, 253],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [235, 235, 235],\n",
              "         [219, 219, 219],\n",
              "         [208, 208, 208]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [227, 227, 227],\n",
              "         [218, 218, 218],\n",
              "         [211, 211, 211]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [212, 212, 212],\n",
              "         [212, 212, 212],\n",
              "         [211, 211, 211]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [223, 223, 223],\n",
              "         [237, 237, 237],\n",
              "         [243, 243, 243]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [229, 229, 229],\n",
              "         [234, 234, 234],\n",
              "         [236, 236, 236]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [232, 232, 232],\n",
              "         [231, 231, 231],\n",
              "         [229, 229, 229]]], dtype=uint8),\n",
              " array([[[250, 250, 250],\n",
              "         [226, 226, 226],\n",
              "         [194, 194, 194],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[229, 229, 229],\n",
              "         [152, 152, 152],\n",
              "         [ 86,  86,  86],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[215, 215, 215],\n",
              "         [109, 109, 109],\n",
              "         [ 22,  22,  22],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [140, 140, 140],\n",
              "         [ 77,  77,  77],\n",
              "         [  6,   6,   6]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 87,  87,  87],\n",
              "         [ 39,  39,  39],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 28,  28,  28],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[217, 217, 217],\n",
              "         [217, 217, 217],\n",
              "         [213, 213, 213],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[219, 219, 219],\n",
              "         [219, 219, 219],\n",
              "         [218, 218, 218],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[223, 223, 223],\n",
              "         [222, 222, 222],\n",
              "         [223, 223, 223],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [171, 171, 171],\n",
              "         [156, 156, 156],\n",
              "         [134, 134, 134]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [185, 185, 185],\n",
              "         [177, 177, 177],\n",
              "         [165, 165, 165]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [186, 186, 186],\n",
              "         [188, 188, 188],\n",
              "         [188, 188, 188]]], dtype=uint8),\n",
              " array([[[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [ 30,  30,  30],\n",
              "         [ 57,  57,  57],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [ 53,  53,  53],\n",
              "         [104, 104, 104],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  2,   2,   2],\n",
              "         [ 10,  10,  10],\n",
              "         ...,\n",
              "         [236, 236, 236],\n",
              "         [206, 206, 206],\n",
              "         [171, 171, 171]],\n",
              " \n",
              "        [[ 60,  60,  60],\n",
              "         [ 52,  52,  52],\n",
              "         [ 33,  33,  33],\n",
              "         ...,\n",
              "         [212, 212, 212],\n",
              "         [174, 174, 174],\n",
              "         [126, 126, 126]],\n",
              " \n",
              "        [[139, 139, 139],\n",
              "         [122, 122, 122],\n",
              "         [ 78,  78,  78],\n",
              "         ...,\n",
              "         [181, 181, 181],\n",
              "         [151, 151, 151],\n",
              "         [112, 112, 112]]], dtype=uint8),\n",
              " array([[[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [240, 240, 240],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [244, 244, 244],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [249, 249, 249],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[248, 248, 248],\n",
              "         [237, 237, 237],\n",
              "         [227, 227, 227],\n",
              "         ...,\n",
              "         [233, 233, 233],\n",
              "         [221, 221, 221],\n",
              "         [213, 213, 213]],\n",
              " \n",
              "        [[249, 249, 249],\n",
              "         [242, 242, 242],\n",
              "         [234, 234, 234],\n",
              "         ...,\n",
              "         [219, 219, 219],\n",
              "         [206, 206, 206],\n",
              "         [202, 202, 202]],\n",
              " \n",
              "        [[250, 250, 250],\n",
              "         [250, 250, 250],\n",
              "         [244, 244, 244],\n",
              "         ...,\n",
              "         [209, 209, 209],\n",
              "         [199, 199, 199],\n",
              "         [202, 202, 202]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [247, 247, 247],\n",
              "         [237, 237, 237],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [252, 252, 252],\n",
              "         [246, 246, 246],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [252, 252, 252],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[248, 248, 248],\n",
              "         [246, 246, 246],\n",
              "         [246, 246, 246],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[239, 239, 239],\n",
              "         [240, 240, 240],\n",
              "         [243, 243, 243],\n",
              "         ...,\n",
              "         [250, 250, 250],\n",
              "         [253, 253, 253],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[232, 232, 232],\n",
              "         [236, 236, 236],\n",
              "         [242, 242, 242],\n",
              "         ...,\n",
              "         [244, 244, 244],\n",
              "         [250, 250, 250],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 54,  54,  54],\n",
              "         [ 12,  12,  12],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [139, 139, 139],\n",
              "         [ 91,  91,  91],\n",
              "         [ 61,  61,  61]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [198, 198, 198],\n",
              "         [154, 154, 154],\n",
              "         [118, 118, 118]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[ 50,  50,  50],\n",
              "         [ 21,  21,  21],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [253, 253, 253],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 57,  57,  57],\n",
              "         [ 34,  34,  34],\n",
              "         [ 12,  12,  12],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [250, 250, 250],\n",
              "         [244, 244, 244]],\n",
              " \n",
              "        [[132, 132, 132],\n",
              "         [ 79,  79,  79],\n",
              "         [ 28,  28,  28],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [244, 244, 244],\n",
              "         [229, 229, 229]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [181, 181, 181],\n",
              "         [180, 180, 180],\n",
              "         [180, 180, 180]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [147, 147, 147],\n",
              "         [147, 147, 147],\n",
              "         [147, 147, 147]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [131, 131, 131],\n",
              "         [130, 130, 130],\n",
              "         [126, 126, 126]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[192, 192, 192],\n",
              "         [182, 182, 182],\n",
              "         [186, 186, 186],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[183, 183, 183],\n",
              "         [179, 179, 179],\n",
              "         [184, 184, 184],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[168, 168, 168],\n",
              "         [168, 168, 168],\n",
              "         [171, 171, 171],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [253, 253, 253],\n",
              "         [251, 251, 251]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [241, 241, 241],\n",
              "         [246, 246, 246],\n",
              "         [251, 251, 251]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [199, 199, 199],\n",
              "         [211, 211, 211],\n",
              "         [236, 236, 236]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[212, 212, 212],\n",
              "         [225, 225, 225],\n",
              "         [234, 234, 234],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[199, 199, 199],\n",
              "         [191, 191, 191],\n",
              "         [197, 197, 197],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[179, 179, 179],\n",
              "         [148, 148, 148],\n",
              "         [155, 155, 155],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [228, 228, 228],\n",
              "         [193, 193, 193]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [243, 243, 243],\n",
              "         [228, 228, 228]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [245, 245, 245],\n",
              "         [235, 235, 235],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[213, 213, 213],\n",
              "         [236, 236, 236],\n",
              "         [249, 249, 249],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[158, 158, 158],\n",
              "         [213, 213, 213],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [231, 231, 231],\n",
              "         [207, 207, 207],\n",
              "         ...,\n",
              "         [253, 253, 253],\n",
              "         [206, 206, 206],\n",
              "         [144, 144, 144]],\n",
              " \n",
              "        [[248, 248, 248],\n",
              "         [234, 234, 234],\n",
              "         [219, 219, 219],\n",
              "         ...,\n",
              "         [239, 239, 239],\n",
              "         [226, 226, 226],\n",
              "         [207, 207, 207]],\n",
              " \n",
              "        [[243, 243, 243],\n",
              "         [232, 232, 232],\n",
              "         [218, 218, 218],\n",
              "         ...,\n",
              "         [221, 221, 221],\n",
              "         [236, 236, 236],\n",
              "         [253, 253, 253]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[236, 236, 236],\n",
              "         [235, 235, 235],\n",
              "         [217, 217, 217],\n",
              "         ...,\n",
              "         [189, 189, 189],\n",
              "         [105, 105, 105],\n",
              "         [ 19,  19,  19]],\n",
              " \n",
              "        [[217, 217, 217],\n",
              "         [205, 205, 205],\n",
              "         [170, 170, 170],\n",
              "         ...,\n",
              "         [103, 103, 103],\n",
              "         [ 52,  52,  52],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[185, 185, 185],\n",
              "         [169, 169, 169],\n",
              "         [120, 120, 120],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [217, 217, 217],\n",
              "         [220, 220, 220],\n",
              "         [227, 227, 227]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [206, 206, 206],\n",
              "         [205, 205, 205],\n",
              "         [212, 212, 212]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [198, 198, 198],\n",
              "         [187, 187, 187],\n",
              "         [187, 187, 187]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[240, 240, 240],\n",
              "         [231, 231, 231],\n",
              "         [221, 221, 221],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [252, 252, 252],\n",
              "         [250, 250, 250]],\n",
              " \n",
              "        [[228, 228, 228],\n",
              "         [215, 215, 215],\n",
              "         [205, 205, 205],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [249, 249, 249],\n",
              "         [241, 241, 241]],\n",
              " \n",
              "        [[219, 219, 219],\n",
              "         [206, 206, 206],\n",
              "         [199, 199, 199],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [245, 245, 245],\n",
              "         [232, 232, 232]]], dtype=uint8),\n",
              " array([[[252, 252, 252],\n",
              "         [254, 254, 254],\n",
              "         [248, 248, 248],\n",
              "         ...,\n",
              "         [230, 230, 230],\n",
              "         [209, 209, 209],\n",
              "         [190, 190, 190]],\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [249, 249, 249],\n",
              "         ...,\n",
              "         [208, 208, 208],\n",
              "         [177, 177, 177],\n",
              "         [149, 149, 149]],\n",
              " \n",
              "        [[252, 252, 252],\n",
              "         [252, 252, 252],\n",
              "         [249, 249, 249],\n",
              "         ...,\n",
              "         [186, 186, 186],\n",
              "         [147, 147, 147],\n",
              "         [111, 111, 111]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [201, 201, 201],\n",
              "         [210, 210, 210],\n",
              "         [226, 226, 226]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [252, 252, 252],\n",
              "         [251, 251, 251],\n",
              "         ...,\n",
              "         [204, 204, 204],\n",
              "         [210, 210, 210],\n",
              "         [224, 224, 224]],\n",
              " \n",
              "        [[252, 252, 252],\n",
              "         [247, 247, 247],\n",
              "         [245, 245, 245],\n",
              "         ...,\n",
              "         [211, 211, 211],\n",
              "         [215, 215, 215],\n",
              "         [224, 224, 224]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[216, 216, 216],\n",
              "         [206, 206, 206],\n",
              "         [191, 191, 191],\n",
              "         ...,\n",
              "         [251, 251, 251],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[223, 223, 223],\n",
              "         [210, 210, 210],\n",
              "         [195, 195, 195],\n",
              "         ...,\n",
              "         [245, 245, 245],\n",
              "         [255, 255, 255],\n",
              "         [254, 254, 254]],\n",
              " \n",
              "        [[238, 238, 238],\n",
              "         [220, 220, 220],\n",
              "         [200, 200, 200],\n",
              "         ...,\n",
              "         [238, 238, 238],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254]]], dtype=uint8),\n",
              " array([[[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [222, 222, 222],\n",
              "         [225, 225, 225],\n",
              "         [224, 224, 224]],\n",
              " \n",
              "        [[244, 244, 244],\n",
              "         [249, 249, 249],\n",
              "         [251, 251, 251],\n",
              "         ...,\n",
              "         [220, 220, 220],\n",
              "         [221, 221, 221],\n",
              "         [217, 217, 217]],\n",
              " \n",
              "        [[237, 237, 237],\n",
              "         [242, 242, 242],\n",
              "         [245, 245, 245],\n",
              "         ...,\n",
              "         [216, 216, 216],\n",
              "         [217, 217, 217],\n",
              "         [209, 209, 209]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[ 69,  69,  69],\n",
              "         [ 65,  65,  65],\n",
              "         [ 54,  54,  54],\n",
              "         ...,\n",
              "         [239, 239, 239],\n",
              "         [231, 231, 231],\n",
              "         [226, 226, 226]],\n",
              " \n",
              "        [[ 49,  49,  49],\n",
              "         [ 43,  43,  43],\n",
              "         [ 35,  35,  35],\n",
              "         ...,\n",
              "         [247, 247, 247],\n",
              "         [239, 239, 239],\n",
              "         [234, 234, 234]],\n",
              " \n",
              "        [[ 38,  38,  38],\n",
              "         [ 30,  30,  30],\n",
              "         [ 27,  27,  27],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [249, 249, 249],\n",
              "         [241, 241, 241]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [238, 238, 238],\n",
              "         [208, 208, 208],\n",
              "         [161, 161, 161]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [181, 181, 181],\n",
              "         [135, 135, 135],\n",
              "         [ 69,  69,  69]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 99,  99,  99],\n",
              "         [ 57,  57,  57],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [ 53,  53,  53],\n",
              "         [ 75,  75,  75],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 90,  90,  90],\n",
              "         [131, 131, 131],\n",
              "         [140, 140, 140],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[211, 211, 211],\n",
              "         [207, 207, 207],\n",
              "         [186, 186, 186],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[202, 202, 202],\n",
              "         [209, 209, 209],\n",
              "         [214, 214, 214],\n",
              "         ...,\n",
              "         [252, 252, 252],\n",
              "         [235, 235, 235],\n",
              "         [212, 212, 212]],\n",
              " \n",
              "        [[194, 194, 194],\n",
              "         [202, 202, 202],\n",
              "         [210, 210, 210],\n",
              "         ...,\n",
              "         [242, 242, 242],\n",
              "         [230, 230, 230],\n",
              "         [221, 221, 221]],\n",
              " \n",
              "        [[188, 188, 188],\n",
              "         [196, 196, 196],\n",
              "         [209, 209, 209],\n",
              "         ...,\n",
              "         [239, 239, 239],\n",
              "         [233, 233, 233],\n",
              "         [234, 234, 234]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [223, 223, 223],\n",
              "         [231, 231, 231],\n",
              "         [233, 233, 233]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [234, 234, 234],\n",
              "         [243, 243, 243],\n",
              "         [244, 244, 244]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [238, 238, 238],\n",
              "         [249, 249, 249],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[153, 153, 153],\n",
              "         [193, 193, 193],\n",
              "         [232, 232, 232],\n",
              "         ...,\n",
              "         [140, 140, 140],\n",
              "         [170, 170, 170],\n",
              "         [194, 194, 194]],\n",
              " \n",
              "        [[104, 104, 104],\n",
              "         [149, 149, 149],\n",
              "         [201, 201, 201],\n",
              "         ...,\n",
              "         [ 90,  90,  90],\n",
              "         [119, 119, 119],\n",
              "         [145, 145, 145]],\n",
              " \n",
              "        [[ 48,  48,  48],\n",
              "         [ 85,  85,  85],\n",
              "         [144, 144, 144],\n",
              "         ...,\n",
              "         [ 38,  38,  38],\n",
              "         [ 58,  58,  58],\n",
              "         [ 77,  77,  77]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[ 76,  76,  76],\n",
              "         [136, 136, 136],\n",
              "         [197, 197, 197],\n",
              "         ...,\n",
              "         [246, 246, 246],\n",
              "         [250, 250, 250],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 48,  48,  48],\n",
              "         [117, 117, 117],\n",
              "         [184, 184, 184],\n",
              "         ...,\n",
              "         [244, 244, 244],\n",
              "         [249, 249, 249],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 47,  47,  47],\n",
              "         [118, 118, 118],\n",
              "         [184, 184, 184],\n",
              "         ...,\n",
              "         [243, 243, 243],\n",
              "         [248, 248, 248],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[  0,   0,   0],\n",
              "         [ 78,  78,  78],\n",
              "         [164, 164, 164],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 74,  74,  74],\n",
              "         [123, 123, 123],\n",
              "         [173, 173, 173],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[145, 145, 145],\n",
              "         [164, 164, 164],\n",
              "         [177, 177, 177],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [212, 212, 212],\n",
              "         [228, 228, 228],\n",
              "         [243, 243, 243]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [171, 171, 171],\n",
              "         [197, 197, 197],\n",
              "         [220, 220, 220]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [104, 104, 104],\n",
              "         [138, 138, 138],\n",
              "         [177, 177, 177]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [199, 199, 199],\n",
              "         [227, 227, 227],\n",
              "         [243, 243, 243]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [204, 204, 204],\n",
              "         [220, 220, 220],\n",
              "         [228, 228, 228]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [210, 210, 210],\n",
              "         [213, 213, 213],\n",
              "         [214, 214, 214]]], dtype=uint8),\n",
              " array([[[218, 218, 218],\n",
              "         [226, 226, 226],\n",
              "         [229, 229, 229],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[214, 214, 214],\n",
              "         [223, 223, 223],\n",
              "         [227, 227, 227],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[215, 215, 215],\n",
              "         [226, 226, 226],\n",
              "         [229, 229, 229],\n",
              "         ...,\n",
              "         [252, 252, 252],\n",
              "         [253, 253, 253],\n",
              "         [254, 254, 254]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[244, 244, 244],\n",
              "         [244, 244, 244],\n",
              "         [246, 246, 246],\n",
              "         ...,\n",
              "         [250, 250, 250],\n",
              "         [245, 245, 245],\n",
              "         [237, 237, 237]],\n",
              " \n",
              "        [[245, 245, 245],\n",
              "         [245, 245, 245],\n",
              "         [247, 247, 247],\n",
              "         ...,\n",
              "         [253, 253, 253],\n",
              "         [246, 246, 246],\n",
              "         [237, 237, 237]],\n",
              " \n",
              "        [[246, 246, 246],\n",
              "         [246, 246, 246],\n",
              "         [248, 248, 248],\n",
              "         ...,\n",
              "         [251, 251, 251],\n",
              "         [246, 246, 246],\n",
              "         [239, 239, 239]]], dtype=uint8),\n",
              " array([[[ 54,  54,  54],\n",
              "         [ 85,  85,  85],\n",
              "         [124, 124, 124],\n",
              "         ...,\n",
              "         [ 69,  69,  69],\n",
              "         [144, 144, 144],\n",
              "         [230, 230, 230]],\n",
              " \n",
              "        [[140, 140, 140],\n",
              "         [150, 150, 150],\n",
              "         [164, 164, 164],\n",
              "         ...,\n",
              "         [ 30,  30,  30],\n",
              "         [ 85,  85,  85],\n",
              "         [154, 154, 154]],\n",
              " \n",
              "        [[192, 192, 192],\n",
              "         [194, 194, 194],\n",
              "         [198, 198, 198],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [ 38,  38,  38],\n",
              "         [ 89,  89,  89]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [244, 244, 244],\n",
              "         [236, 236, 236],\n",
              "         [229, 229, 229]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [231, 231, 231],\n",
              "         [214, 214, 214],\n",
              "         [201, 201, 201]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [247, 247, 247],\n",
              "         [240, 240, 240],\n",
              "         [228, 228, 228]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [253, 253, 253],\n",
              "         [232, 232, 232],\n",
              "         [203, 203, 203]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [223, 223, 223],\n",
              "         [180, 180, 180]]], dtype=uint8),\n",
              " array([[[122, 122, 122],\n",
              "         [192, 192, 192],\n",
              "         [246, 246, 246],\n",
              "         ...,\n",
              "         [ 67,  67,  67],\n",
              "         [ 66,  66,  66],\n",
              "         [ 68,  68,  68]],\n",
              " \n",
              "        [[152, 152, 152],\n",
              "         [201, 201, 201],\n",
              "         [241, 241, 241],\n",
              "         ...,\n",
              "         [117, 117, 117],\n",
              "         [109, 109, 109],\n",
              "         [100, 100, 100]],\n",
              " \n",
              "        [[161, 161, 161],\n",
              "         [202, 202, 202],\n",
              "         [238, 238, 238],\n",
              "         ...,\n",
              "         [168, 168, 168],\n",
              "         [162, 162, 162],\n",
              "         [162, 162, 162]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [  5,   5,   5],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 12,  12,  12],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [243, 243, 243],\n",
              "         [235, 235, 235],\n",
              "         ...,\n",
              "         [188, 188, 188],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[238, 238, 238],\n",
              "         [217, 217, 217],\n",
              "         [203, 203, 203],\n",
              "         ...,\n",
              "         [151, 151, 151],\n",
              "         [224, 224, 224],\n",
              "         [245, 245, 245]],\n",
              " \n",
              "        [[233, 233, 233],\n",
              "         [210, 210, 210],\n",
              "         [190, 190, 190],\n",
              "         ...,\n",
              "         [ 93,  93,  93],\n",
              "         [156, 156, 156],\n",
              "         [191, 191, 191]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[104, 104, 104],\n",
              "         [190, 190, 190],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[ 45,  45,  45],\n",
              "         [ 82,  82,  82],\n",
              "         [136, 136, 136],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [ 34,  34,  34],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[237, 237, 237],\n",
              "         [232, 232, 232],\n",
              "         [226, 226, 226],\n",
              "         ...,\n",
              "         [238, 238, 238],\n",
              "         [227, 227, 227],\n",
              "         [215, 215, 215]],\n",
              " \n",
              "        [[243, 243, 243],\n",
              "         [231, 231, 231],\n",
              "         [217, 217, 217],\n",
              "         ...,\n",
              "         [191, 191, 191],\n",
              "         [157, 157, 157],\n",
              "         [115, 115, 115]],\n",
              " \n",
              "        [[251, 251, 251],\n",
              "         [237, 237, 237],\n",
              "         [220, 220, 220],\n",
              "         ...,\n",
              "         [136, 136, 136],\n",
              "         [ 78,  78,  78],\n",
              "         [  0,   0,   0]]], dtype=uint8),\n",
              " array([[[196, 196, 196],\n",
              "         [199, 199, 199],\n",
              "         [198, 198, 198],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[183, 183, 183],\n",
              "         [183, 183, 183],\n",
              "         [179, 179, 179],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[167, 167, 167],\n",
              "         [165, 165, 165],\n",
              "         [158, 158, 158],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[250, 250, 250],\n",
              "         [249, 249, 249],\n",
              "         [240, 240, 240],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[250, 250, 250],\n",
              "         [251, 251, 251],\n",
              "         [248, 248, 248],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[246, 246, 246],\n",
              "         [247, 247, 247],\n",
              "         [246, 246, 246],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [190, 190, 190],\n",
              "         [174, 174, 174],\n",
              "         [152, 152, 152]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [174, 174, 174],\n",
              "         [161, 161, 161],\n",
              "         [149, 149, 149]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [147, 147, 147],\n",
              "         [137, 137, 137],\n",
              "         [138, 138, 138]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [252, 252, 252],\n",
              "         [250, 250, 250],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[237, 237, 237],\n",
              "         [246, 246, 246],\n",
              "         [250, 250, 250],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[214, 214, 214],\n",
              "         [234, 234, 234],\n",
              "         [249, 249, 249],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]]], dtype=uint8),\n",
              " array([[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [249, 249, 249],\n",
              "         [253, 253, 253],\n",
              "         [251, 251, 251]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [252, 252, 252],\n",
              "         [254, 254, 254],\n",
              "         [253, 253, 253]],\n",
              " \n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [238, 238, 238],\n",
              "         [247, 247, 247],\n",
              "         [253, 253, 253]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[241, 241, 241],\n",
              "         [235, 235, 235],\n",
              "         [228, 228, 228],\n",
              "         ...,\n",
              "         [184, 184, 184],\n",
              "         [193, 193, 193],\n",
              "         [199, 199, 199]],\n",
              " \n",
              "        [[221, 221, 221],\n",
              "         [220, 220, 220],\n",
              "         [220, 220, 220],\n",
              "         ...,\n",
              "         [187, 187, 187],\n",
              "         [200, 200, 200],\n",
              "         [215, 215, 215]],\n",
              " \n",
              "        [[202, 202, 202],\n",
              "         [207, 207, 207],\n",
              "         [217, 217, 217],\n",
              "         ...,\n",
              "         [186, 186, 186],\n",
              "         [195, 195, 195],\n",
              "         [212, 212, 212]]], dtype=uint8),\n",
              " array([[[234, 234, 234],\n",
              "         [205, 205, 205],\n",
              "         [184, 184, 184],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[232, 232, 232],\n",
              "         [220, 220, 220],\n",
              "         [199, 199, 199],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[231, 231, 231],\n",
              "         [221, 221, 221],\n",
              "         [196, 196, 196],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [247, 247, 247],\n",
              "         [235, 235, 235],\n",
              "         ...,\n",
              "         [222, 222, 222],\n",
              "         [206, 206, 206],\n",
              "         [185, 185, 185]],\n",
              " \n",
              "        [[235, 235, 235],\n",
              "         [239, 239, 239],\n",
              "         [239, 239, 239],\n",
              "         ...,\n",
              "         [208, 208, 208],\n",
              "         [206, 206, 206],\n",
              "         [201, 201, 201]],\n",
              " \n",
              "        [[210, 210, 210],\n",
              "         [225, 225, 225],\n",
              "         [236, 236, 236],\n",
              "         ...,\n",
              "         [187, 187, 187],\n",
              "         [202, 202, 202],\n",
              "         [222, 222, 222]]], dtype=uint8),\n",
              " array([[[245, 245, 245],\n",
              "         [244, 244, 244],\n",
              "         [241, 241, 241],\n",
              "         ...,\n",
              "         [247, 247, 247],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[250, 250, 250],\n",
              "         [248, 248, 248],\n",
              "         [235, 235, 235],\n",
              "         ...,\n",
              "         [248, 248, 248],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[254, 254, 254],\n",
              "         [244, 244, 244],\n",
              "         [218, 218, 218],\n",
              "         ...,\n",
              "         [250, 250, 250],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[204, 204, 204],\n",
              "         [218, 218, 218],\n",
              "         [236, 236, 236],\n",
              "         ...,\n",
              "         [226, 226, 226],\n",
              "         [222, 222, 222],\n",
              "         [218, 218, 218]],\n",
              " \n",
              "        [[229, 229, 229],\n",
              "         [238, 238, 238],\n",
              "         [247, 247, 247],\n",
              "         ...,\n",
              "         [209, 209, 209],\n",
              "         [210, 210, 210],\n",
              "         [211, 211, 211]],\n",
              " \n",
              "        [[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [193, 193, 193],\n",
              "         [202, 202, 202],\n",
              "         [207, 207, 207]]], dtype=uint8)]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_arr = np.array(train_images)\n",
        "train_labels = np.array(train_labels)\n"
      ],
      "metadata": {
        "id": "u_b3H7MiNuzm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "train_img_arr\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wztrPGZ9QO5k",
        "outputId": "0170b5e5-696f-4ee4-d006-fc73371d265a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "         [222, 222, 222],\n",
              "         [210, 210, 210],\n",
              "         ...,\n",
              "         [161, 161, 161],\n",
              "         [139, 139, 139],\n",
              "         [128, 128, 128]],\n",
              "\n",
              "        [[229, 229, 229],\n",
              "         [197, 197, 197],\n",
              "         [178, 178, 178],\n",
              "         ...,\n",
              "         [199, 199, 199],\n",
              "         [171, 171, 171],\n",
              "         [150, 150, 150]],\n",
              "\n",
              "        [[201, 201, 201],\n",
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              "         [147, 147, 147],\n",
              "         ...,\n",
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              "         [207, 207, 207],\n",
              "         [181, 181, 181]],\n",
              "\n",
              "        ...,\n",
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              "        [[255, 255, 255],\n",
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              "         [255, 255, 255],\n",
              "         ...,\n",
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              "\n",
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              "         [255, 255, 255],\n",
              "         ...,\n",
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              "         [255, 255, 255],\n",
              "         ...,\n",
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              "         [255, 255, 255]]],\n",
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              "         [255, 255, 255],\n",
              "         ...,\n",
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              "         [254, 254, 254],\n",
              "         [252, 252, 252]],\n",
              "\n",
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              "         ...,\n",
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              "         [254, 254, 254],\n",
              "         [252, 252, 252]],\n",
              "\n",
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              "         [255, 255, 255],\n",
              "         ...,\n",
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              "\n",
              "        ...,\n",
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              "\n",
              "        [[  0,   0,   0],\n",
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              "         [ 61,  61,  61],\n",
              "         ...,\n",
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              "         [222, 222, 222]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
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              "         [ 87,  87,  87],\n",
              "         ...,\n",
              "         [158, 158, 158],\n",
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              "         [244, 244, 244]]],\n",
              "\n",
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              "       [[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [153, 153, 153],\n",
              "         [179, 179, 179],\n",
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              "         [248, 248, 248],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [106, 106, 106],\n",
              "         [159, 159, 159],\n",
              "         [207, 207, 207]],\n",
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              "        [[226, 226, 226],\n",
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              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [ 71,  71,  71],\n",
              "         [145, 145, 145],\n",
              "         [207, 207, 207]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
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              "         ...,\n",
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              "         [ 36,  36,  36]],\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [148, 148, 148],\n",
              "         [ 98,  98,  98],\n",
              "         [ 69,  69,  69]],\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [194, 194, 194],\n",
              "         [149, 149, 149],\n",
              "         [119, 119, 119]]],\n",
              "\n",
              "\n",
              "       ...,\n",
              "\n",
              "\n",
              "       [[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [249, 249, 249],\n",
              "         [253, 253, 253],\n",
              "         [251, 251, 251]],\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [252, 252, 252],\n",
              "         [254, 254, 254],\n",
              "         [253, 253, 253]],\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [238, 238, 238],\n",
              "         [247, 247, 247],\n",
              "         [253, 253, 253]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[241, 241, 241],\n",
              "         [235, 235, 235],\n",
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              "         ...,\n",
              "         [184, 184, 184],\n",
              "         [193, 193, 193],\n",
              "         [199, 199, 199]],\n",
              "\n",
              "        [[221, 221, 221],\n",
              "         [220, 220, 220],\n",
              "         [220, 220, 220],\n",
              "         ...,\n",
              "         [187, 187, 187],\n",
              "         [200, 200, 200],\n",
              "         [215, 215, 215]],\n",
              "\n",
              "        [[202, 202, 202],\n",
              "         [207, 207, 207],\n",
              "         [217, 217, 217],\n",
              "         ...,\n",
              "         [186, 186, 186],\n",
              "         [195, 195, 195],\n",
              "         [212, 212, 212]]],\n",
              "\n",
              "\n",
              "       [[[234, 234, 234],\n",
              "         [205, 205, 205],\n",
              "         [184, 184, 184],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        [[232, 232, 232],\n",
              "         [220, 220, 220],\n",
              "         [199, 199, 199],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        [[231, 231, 231],\n",
              "         [221, 221, 221],\n",
              "         [196, 196, 196],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[253, 253, 253],\n",
              "         [247, 247, 247],\n",
              "         [235, 235, 235],\n",
              "         ...,\n",
              "         [222, 222, 222],\n",
              "         [206, 206, 206],\n",
              "         [185, 185, 185]],\n",
              "\n",
              "        [[235, 235, 235],\n",
              "         [239, 239, 239],\n",
              "         [239, 239, 239],\n",
              "         ...,\n",
              "         [208, 208, 208],\n",
              "         [206, 206, 206],\n",
              "         [201, 201, 201]],\n",
              "\n",
              "        [[210, 210, 210],\n",
              "         [225, 225, 225],\n",
              "         [236, 236, 236],\n",
              "         ...,\n",
              "         [187, 187, 187],\n",
              "         [202, 202, 202],\n",
              "         [222, 222, 222]]],\n",
              "\n",
              "\n",
              "       [[[245, 245, 245],\n",
              "         [244, 244, 244],\n",
              "         [241, 241, 241],\n",
              "         ...,\n",
              "         [247, 247, 247],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        [[250, 250, 250],\n",
              "         [248, 248, 248],\n",
              "         [235, 235, 235],\n",
              "         ...,\n",
              "         [248, 248, 248],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        [[254, 254, 254],\n",
              "         [244, 244, 244],\n",
              "         [218, 218, 218],\n",
              "         ...,\n",
              "         [250, 250, 250],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[204, 204, 204],\n",
              "         [218, 218, 218],\n",
              "         [236, 236, 236],\n",
              "         ...,\n",
              "         [226, 226, 226],\n",
              "         [222, 222, 222],\n",
              "         [218, 218, 218]],\n",
              "\n",
              "        [[229, 229, 229],\n",
              "         [238, 238, 238],\n",
              "         [247, 247, 247],\n",
              "         ...,\n",
              "         [209, 209, 209],\n",
              "         [210, 210, 210],\n",
              "         [211, 211, 211]],\n",
              "\n",
              "        [[253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [193, 193, 193],\n",
              "         [202, 202, 202],\n",
              "         [207, 207, 207]]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hQ3BlIPQRsg",
        "outputId": "bee46149-7eac-4e96-dd7e-ef6c6cb6dea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_12.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_19.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_29.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_37.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_23.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_17.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_26.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_43.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_39.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_38.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_08.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_44.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_06.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_47.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_18.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_10.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_27.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_28.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_22.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_15.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_46.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_50.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_24.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_42.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_16.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_03.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_20.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_09.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_01.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_49.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_04.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_34.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_41.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_21.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_33.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_31.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_48.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_02.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_36.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_45.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_35.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_40.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_14.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_00.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_05.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_25.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_07.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_13.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_32.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_11.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_51.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/first/00000_30.bmp'],\n",
              "      dtype='<U99')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SIZE = 224\n",
        "test_images = []\n",
        "test_labels = []\n",
        "for directory_path in glob.glob(\"/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/*\"):\n",
        "    test_label = directory_path.split(\"\\\\\")[-1]\n",
        "    for img_path in glob.glob(os.path.join(directory_path)):\n",
        "        print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
        "        img = cv2.resize(img, (SIZE, SIZE))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "        cv2_imshow(img)\n",
        "        test_images.append(img)\n",
        "        test_labels.append(test_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "id": "3QpJ2PZXOjap",
        "outputId": "d39a112c-86d7-4aeb-8aa6-70660de0e4be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_52.bmp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n",
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_54.bmp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n",
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_55.bmp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n",
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_53.bmp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n",
            "image/jpeg": 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0+Vmxgrxite41WLU7OCB7APHOAAFyMZ9Ks31ppfiW3DjDCI9R0q3ZNp9hbQwPJGvlgKpfr7UAS2mj6dZt5kaCLcBlAeBV/Ys3MRAArKuI01S1ka2ugCehU1SsItY06GOMqWAbLMTnigDU1Xw/bavEq3SBwP4T0rNTw/p+hwDyFVXH3T0wPStubUJI4RtXc5GOPWvPvFMuv6hqEMUcTpFuLAjuPSgDsLl70WSSQrkEc454rB8Q6bd7ILuyheWcAEqTwa6XTrWay0RYmkZ32ggk5IqC48RWWmQBLp/mB6Y6UAYFhe6rc2whv7do8DcoHRR6Vgazda6t0Gs42EGQpJ7Ed662PxjptzbyyIFJ2k4A5zWTa+MrPUrn7HFErEP3A6CgDY0OK7TSEuLmVxu5KP1raj1G3WFELJlh27092tZ7Dy5CAuzcQp6VylzocVxItxa3rDacBS3U0AazeGdHuJprkJHuJz1zg1HptuNKtLm4hKsOdqAZz+VY2pQarpumTvE+9jwB64rg/D+ta5bakVl80gk4Dk4AJ9KAOv0HW7+XVbmW5gdUDNtJBHANdNpPipL27eCWMxnAG9iMCksL2CayWK5VFmYfeKgZqhrmlLc2bDTGQyovz7ffuaAM/wAUeCrq+uGutOuDlj820jAz3rR8JW9/o9hcx38jyugwGOMEZrI8O3GuXWo+RI7x+WcOc4BI9q7poDcQusu1JCMEZxQBlwapbajby2dwEAJK4zj86xtO0hdCW6uUkZjksqg8Y/GuK8WW2qaNrQniZ2jByVQnbj616b4Zkh13Q42mwxKg+30oAydF1BfEbvBfWoYg/K/YVDqEP9la3BbW8LmDd8zEHAz6UniWa98KOtzaWm63z6AAc1seD9aPiKxe7u4MOrHYzKKALDz6Vqc6Wc3+tHG5zjOKr+KLUWunW8UDmONeFINYWraPe3viH7RFKI4lII4A/GumkWDVdOW0uJ0MycZAGc0AP0/VLB7RdPkn3sVAO7BIrivFsMelarAssZ8jOQ+RVFbtPDPiF2vQwXOBuAJIrrriKz8daRIFwJV5jO0ZYUAF14X0u6mjuy0caqBhsfoa0LnQNO1C1jEbCSMDHyng1xuq3Os/2SLWKybD9wCBk9DVHSZ9aSyZZhOsiZU5GOaAO8h0m7toDbWB2AD6VxWueH9auWkhErbwwCspzz2q1Za/qNq+Ll2DAYwO/pmuig1tmsJb+4UKFOAG4JNAHn9hYa5oLJNOZOMsQQcD1roR461W+tfs9paPvHylsd89vwrptF1Y6uu66sQyDnfjikOueHrDV2Q7FfoWI7UAVNI1PU4tOkmvMeaGPyHjFY6+NdQS9kaazzDGvL4yAa61tQ0e8lwjqACcn1q8bDTNRtHgSONgyYzigDD8P+NLXXr420SsAvXjrxVXVrzQJ9TeC8yp5OF43dvxq5ZeCLLTrhprNlDbslQcYNS33hKzuY5rqWENcFSAeeDQBU0/w/4eMDm0mXa6jBbnHvSaJ8PtO0/UjfxyLKSxK46AEV5+fDOu6XetdxqxtlYdSQMdq7Twhe3smoOLh5fLPYnAB+lAHTXOkMRdGCbazIVVF55riNG8P6taXc8t3LIkakfMRgt/hXWX/wBqs5ri/gbcAPusaTRr4T2zaldB1QkjaRQBzh8XXFjcNaoxlO8gM5rqrPToZlhutQgQzfw5HrWfctpl7erKtoPNJyGA6eprXmg87T2RJcyKMqA1AGJ4o8NXN9cJcWj7YlTAQDAB965nT9J1mz1eMC9kKuwDAMQCO1dPp+pahKJYZQY3j+UEjit7Snml+a8gUsD8jgdKAFkuLOwCSXRVZiAmf5Vmm2vG1B5pmYW5OeOnTjA71neK7K7fWoJGiaS2IGdoOF57mtqO8IhgtYELIQNxI6+1AEVykOp2/wBknTMSnIJHU9jVGXPh1FW0iPlZyuF4B9K6HUINuiy+VHtk25Xb2NcR4Q8SR3l3c2OpNvkR8LHjI+uaANu3vk8URSWF1EDggjcuaLnTZ7S2FnZRmNVbhxxVOXS7oa+08JEVumT0wMYzU1pqkuqGeGKYb48gN7+g9aANFdGlvbAo82J9vzMB0NYb6ENDuheJKZZN3zAnr/8AXq74d+1abdTNfMzKeNxB59MVvTvbW4a9dS0R5xjOKAOA+Inh9dZ0iPVLONg6rlgI/m981l/CbXGgnk0txiQ8rhec+ma6+Dxdouqz3GlpkswICFTjOfXtXBXGiva+ImSyUJglgwBANAHcw+LDayvDPCJ8tgNt+UfSrV54lskMaNbJIsn3go5zirmrWWj6fbHzxhpPu5PQ1zNlodtcXLzR3qNLIMhCc8ewoA03vNCXa12qRqoH3q0tTsdK8Q2CpBNGqbuqnGa4PxL4N1i7niCfNGBnCselbX9hXnh/w+mwSTS7umcgfjQB1Wk2MFhp32O0cS4XGc1weq/DS7vL2e5FwVYncvznp6Cuv8H5ls/PKyoewcYzWP4ifXbjX44LaR0gL7s9AOKAOVl8P+I9OtktUgP3iFlJzux2/GvUdBt5ofDy+ajpOyFvmxnOK851bxpqeh38FmrieRMhu+016Vo99eXelfa7tVDEZ20AcBYajrlt4kujKJXg3k449K7fT7m+vkeZ8xhVO1T1Irnbjx7p9lqQt3tEDlyrkDqcetWX+I+jWu6N4mRvQ9CO9AGTH4zuoNeksruISRMchWAwPY10kup3BtEuLKwRsnaSijg1l6XD4X8U3d1PGwYhjtA+Xb711GnrbaXa7C+VHbtQBiza9PbXEMFxZBhKcMGBrVhvNPvHFj5YRSc7Rx+NF5Iuo3UIgaNmTqG6Cq134clm1WK8jl2DGCqDAP1oA1m0y3WNUiQYUY4rJ1ixk02zkvbeVt6jODk59qn1DXYNEAM+7gY4GapSeN9OnXypkIjkX+HrQBS8PeIDfW3m6jaJCm/az88e5rY1fX0j0S4uNNdJGTIAXr+ArPu4Y00OaXTow8EnZ1+YH6V57oF9daZ4iaK7jeNZG2MhXJ57UAdn4f8AGRvtOmGpqqOr42nOa6BbwNbLLYxK5PpyRSXPhnTL/TSbeMRl/myqjNc5pYuNAvvLWUtAWwQw5oA6karHdobOZT5r8FQcY9eaxZdC07wvFNfqqu3JVh1+hrZubCOUSXaxYLDIwOn1qq9nFrOkG1ckOCc5HAJ70AGka22pWcjSRcEYBx2x0rARIk1dF05ijsxLjH3fcV0FrBb6VHHaISxPGTTrrT4dILX9ureY4wcnk5oAszzEIscse5mGCQKWWSF0+wlcpKuAM96p6XcrNKkUzgyOOgrK1o6jp+sRXMCGUBuUU449zQBxF/4Xl0zxJ5lpIRIGLBVzg12F94bv73SEvLWVY7lQME5JFbOpX1tY28epahCqsRkHrjjpVXw14vj1m7dYU2W+Plye9AB4x02fUkijhyUPBKjn865zS/D8+m63G6+YFwCee3SuntvEdpNdx26Z3DqDWV4i8cR6HeiNrRXXH3iDxQB1mqGVNMP2diZMZDZ6H3rmtI125aOaK+2kDoMfKtaWh+JbXX9OJSFipGSVBAH51k302hKzRyXJiLEk+vvQBPoni9bi4lhMQITIAHr/AEFadlqEd/M/mKqvzt55qn4f8N2MUzXlvKHRwMe59aux+HXTVHvRP8pPTjpQBVl8M6RcTPfXofeg6mrsd3YXg+zQ6mgONvpir19A11bPCpwCuGx3rz+506bT9VYwQSFs8jt7GgDU/wCEBt5b8Xsl2si5O/n8v6VTvPhra3t1IxmQuRmMA4x+HvVW88W3lnZSW62cyyEH5mXr9KoaDr2rxXe+5WYyOBtBHTjigDf8P+BLzRoZUSUoWcgc547Vt63a6jBp8KWcQYg4Yk9K5h/EOpNqGxpigfO0D3rudH1CRkEF0pJ7M1AHL6DYXq38896jKOAcdPrS+KfGM2k3drb2oOxshyOTnjtVrxFrV7Y6zHb2sJkRsBwMciotU1PQraeIanYlbiReGIyM8c0AXTrejX9kBqKKJCg3DIJOao6p4cgnsVn0lF8vqSeopL/wnaajEtzZz53YYE8HHpV2e6XR9MghiiYrEPmYng80AU9Lu/7PULdOyFANwfqfwrTt7LRtdkkvIYkMyN98Dqans59K1m38xhGsjYGSeTx71SiSDQWm8iErk7ic8UAWEuZtBspZpgNm7HTt9Kgj09deiW7hAjUncR/eFTy3sesaJMY2DFQdy8E1l+FtQuUaW1MPlxoeOuaAN+C5mWY2hT9yqgDiuQ8SeNv+Eev0t0hK5JyxU4xXYJdYvijKAOpOKyPEXg6y8XQpNuRZFJBfn8KAL1vq9pqGnwahbJ5jOAN23HPpirpu2uke16u6ZTHY1yfgpBpVzc6DMyNGrbwxyWzjoPSte88S6Vpmrx20eGnfIwDz1oAyNN0vUNM10399LiJmK+XuwMeprrr+4WNFmSMSA9wa5Pxc+o395brbsUgBySMciun0+dYNLRpv3hRcEnFAEvkW2sWmy4hxxjbnrTNO0C1snl8iBU3HJqtp+qwT3UrJJynVR2pT4qjF2IRGRk45BoAp2NjpIEd1MyLKSQPU1S8UeGLPxHGY4mHmoODuIqnpfgyUOLlZHyM53HGT9acr31rraEhjECAGI460ARaLoetaFpk9naxblb7r+meaxn8I3dys8t1HJ9qlYnn+H1r1efU4LW2WWRuD1NVk1RWU3BliaBQTkd/pQBleH7F9H0Q2t2CHHKsWOaxdX1O5t4ZFgnddoJBHc1ty+KbCW4KTovlg8M1QXV/4d1CBlmOUbIyDx9KAM3wTrt1qUT/aJVLKQu0dfrmrmta9eaZeqsdvHMRycKCcdqsaLpeg290F0+4Vd6jEQPWtO60OG5vEmBO4cHJ7UAZen6pbanaG8vbFRJB13L1+n50++1zRltllNshAIBCqAy8cdK3/ALFaQo0eFXcuDXOp4OtHe4aRxtYkqvUGgC1o9ro+qxi6htcMwOd3JXNb8lrAV+6oIHWuS0PSNR0kyLCykHIXdxuHasG5vPFzay8bo7IoAIQkjGetAHYpYWtzq4mMql07A9RXPeMfCN1q15Fc28qB4T8m9uPpjpWdpkl9pmsteXsMnluDtLr1+lXbvWLvWb1EiheFTwWxxnsRQBUOk+KEljtVG9IgAdh6jsfpWhZajcx3C2Gr2+Y34X5QcfWm3et61aosKHe/JO3gn8aLu6u2ktnvYgijBYmgCjrWiz6bexmyLeUzZDdhnnFddaeZNpG+5CySOvzDaM5qxcrbXmjeZCm4BBj5e+O1YPhu0mubt3aUlQQGDc5FAFrwtBbR/aSE8uQsdwNWNQuLW3u4YYCqSM43cdak1DUdM0m6WGR0jdiAxY44J4qHXG0yK3F35mZwu6Mg9aAJfEMotLAXUO0cEMO5HtXH+E/FEk9/f2pJIKkgKemM9BXR6Kjazosyz3LSEngE5IHtWV4d8Kx6Tf30mdy7W5YZwCO9AHOeD5LrUPHd0Sz+Wznec56Djn8q6K98H7PFI1KVvlDblJUdfTFJ4LsbePWLxXiQS7jgnqR14Fb2sXNzDrdvD5Ze3ON2T0oAl1K7sorYedhEK/K2OauxwQ3GkAKQUkTgrxTNX0SHU4Yo1+UJyPT8qfFYGOyaJJ2AXvnpQBl6Xo0OmTs+4eZJxjPBH0rN8XI2mGK7giBX+PBOasWunah/bAeOUNAoyT1PXtUHiXVpmvRYpGzxN944xg0AT6fJrNx4cQZbz8cK5Gc9jWKuu6lBfxwahbruD4wneu6069sbosbV1chckD0qo9ha3btM6DzI84wBmgCOW6stYtvIDEZHKg8iss3Wj6cn2W4ucKOCueme9TRzWOlzyXXlMoIwQe9YHirwkPEEiXlvc+XG5G4jGMfhQBoax4Yi1YQvYTqqSY43jBrOuvBF/arawrOHUuS3bHWtDS9EuLfS44/tySyQHqjcsPUUviU6tMls9t5i4ADEHjr1oAt6V4Yk07VkvHcLgDIU56dq2Lm5lGogQqxXGc4xXDazrGo6EkMrTNI5IYoen41r2PiXVLrTvtv2QNEq/eC80Acv4r1LX7XVmRTL5bDChDnPNZtjqXiGymSWXf5BfLKQTg16nHfWmo6Ob77MjyJ/DgZ4rJ0/xOss5t59KUK7fe2jAoA4fxF4m1WW6R7FZghADcEAeh9q6XwXqOtarcA3q7E5ySp4OK7C8srOSWOMWUZXbt4HBpl5qkGiwKvkIC/8KjpQBDrXiOHS3S3aHcGGN5XODWBZ+LPtWpLZx2xKk/LlfzFWby4Gr3kCwwh+QeldJa6TbPsnEKrMpBOABg0APWSGByT5SqBk5ri/EmrQalqC2FucsrAg/wB4V19/ocVyzSSTmMc5Oawbc6DBqeIhumQ8hxnBx/KgDqNE0xbHTEhLFtygkZ7nrV63s4bbPlIFz1xVFdRYyRKsZ8sgYIFWRfo4cKQCvrQByPjPQjeXdvLGju28ZAPbNP1jTIBoQkvHZBHHjkjdXO3Hi+5uPEslsjhwWyoXsKveKb6CCW0tL9mMcijq2ASecUAbHgxZIdDO8ARbsRjH3h61pxWSR380sU8bqUO5A2cZrNmuI7DwzFFbSHIGUHGcVz/gqS+tLm+udR80xhMjPIbrxQBVt9fGleKgiKTvk2uAeh9T+FdT4o8UnTETyIUmbILZ5wCKo2KaPreoySCERy5+UlevesjxXpWpQX4mRRJEoACgE0AddZ+Jo08OjUboHITcB1Lfh2qppniaLXIboQoQozjg4xj1rLv7ZbXwnFPPHlcYlC5AX86s+CXtPsc80EK7MHeCR6UAW9IeWK72JIG3Hnd1A9qXxVPDpNk8xjV3cgHvWHp2qxz+LpIoiyKpx24puvTy3t/Lab0YZIoAv6bNp+h6gYnujGZMKxJwoq/aa7pq6jKouM5656Go/Efh9NZe3lxtkLYOGwPrXOat4KubidTZSBSg+baOp6UAdRqzW+sJ5EEkaY4IAANXLDTFjsFtFmVo2Hylea8kHhbxTb6sEjaV4xIAWVvbvXpdkkOh2kE13JIGAG4Mc4PegDlW0XW9G8StLDLKLViNxxwRnpiuzu9Zf5baGHzWK/KcfzrVsru11iDzdmY/9oVXl1SytLhvLsy+zq69qAOVgurfV7s2epQbGVjtU+o9a2LvxBpugxrppw0W3bwegNY2s33hq91FZWlEDjO5gcZPv6VnzeF4vEUnnWN60yxEcE8H1oA7bR/7Kmt5PsrKAxyw7GnzW+n2tozHaEBzkD3rjY/AutLdI0F0ixqeOenH1rrrLRnFv5F84kbjPoD3oAgHi7Tl/wBHgy8qgjOMAYqF4rfU5/3j7pG52A1fTwjpsV6LlYAevHbBpq+H7gaut4riOEY+RTg/j60AQWtvaW14Ikk8uTpjPWpbnxBBp98bZUYt1Lf0p+rWA/tSK881Y0B+YlunbirC/wBlNefdRpn6k0Ac9B4mn1a4uIIYyQoxnpkfSrum6NZQB5ZkCzyevJPvT7y7tdJaU2ln++X+I8/hXL6Amta9r7XcjEQxdAwI4z6UAd9PI9jaxiJdwPG484rP8S6nb6XpDu2VuGHTbnNQarrtjoRefULkGQcrCpzkDjFYEHiOHxjdiCKLdEhO1fvEigDlPAehXWpeIZdSmLGNHBRivDc9D6VqeKhBrXi+2tZmjOXA68DH/wCqvQY9Pgs9Ka3hZYWUZAUc4rzzTdLmttdvNSkb93ErMkkp5HPagC745Gz7BaWchSJBscxt9O1bMd7Ho/hqCKaQGSRgjMT0H1rmvD2qW2v6/LHdDzA3yrjuPYU/4i2dzaR20NqAqsc7s8j8KAO/sLW0h8i4trcZkAYnH4VW8T6zNaXUFtHGx8xhzjj6VneE5r+y0UG5cGZgAA/Jb3roLa0e4cS30CyODuVv7tAGL4z1a0tvD5guzvd1OUxWV4aOmReGrie0bGUOT0H0Jqz458MDUhEiyuisefm/pVi402Hw/wCF1twpUOCGbrjigDgPBsjXviyZWA+8ehwCM/rW5rvh6+j8URCOWRIpWzlWJOa6PwdpWkabbfb4pAXYHezDnrW/Hf2t9eiSMIWVu/WgDzG41HWtY1GK1/eRqjEliCARXZ2ui3dtKixXjlSMt+VYniLUr5NQil0+1UgsBwO+a3h4nFhYxTXkIRuA5PYUAWxKNAhe6ujuV2xye9NutbtZ9P8AtlxZefEBlQimqc2qaL4vTyI5idpJyDwccVo6To1vBELOO6aWILghif0oAh0LXdLvxJFBmNum0jFWb7Srt7d7e1lVBL1Y9RVS98FwQzfabAtG+7JAY81dtk1HyQrjY69MnJNAHFan8NbmaIpGRJI/LOT0NaXhrQrvQzLAGYyLxu9u1aZ1Se319UmkYgjlQTiqOt+NzaagIUiIAbHQZZT3oAd4o1+/0LT4tkJeV2OVXriodF8QXV+sKxwybz18zsfet+yvItZjSS5tY3ZRlcjJ9KrXPiPStG1H7LJbIrtgAqo9KALviG4ntdE85CwIGGwDmuH13x1JDpEa2Tv9qGNw9Ofeuzh8TWGrXDWL/wAa8q1Zlrpfh+41Qwm0BZGBBc5yc8UAeT3Gq+J9TaKZkuPLJO1mB+YZ5Fem+HdLvpLOC5mkWNwB8znGR+NWfFfiK08PyWtlBaxMfvRggYUk84H4ms3XL3V57CC7hgcrnOxeAB6UAdu1xb26KL7y90gwTjOK43xT4vg8P2wg0pQ7vz8o561iD/hINavkLho4Aq7mbgAetdLZeHNHsLWS9vZkuZkHzAnKg54xn8KAMLRNBPjC3e91JjESSVJ+vTmuhsbbSfCUMqafbiSbJbcMk49PWuXufGEl076fpsPlxlyvyDAU57Gup0TTXt7mK8uZ1Mbgbi/XJ7CgDBN/quu68VtlkhhU7iMcsvcVF471SOy09NLtN3numQMc7s9676eCGzP2m0iwD83ua4+6sLO918alckqUGV3jIJ9MUAY3gXQX0m1bVbzzRNvBVNmMdc9q7m/toPFFtbzRH5lPKZH60+28Q6fqf+gcIWBGcdDTrMaToMrRG5G98Ernp70AUNX8mxntsyZkQbSqV0ujh3iMrZAIHB9K56wl07XNcMijcEJzkda1dX8T2ejOlsql5AQNoHAGKALesCOTy4WX5mYc1ma/FZXGlHTpJ0LlcgM3T3q3az/2u8VyhBXA4PQVyOu21rZ37NeSsAxwoBPFAE1ho9to2jS+ddFhN91g2cVa8MaVtIlS6LA9D/Kp20+xv9IS1MuCRuUZxir3h/Sf7PtyNzMOvLE0AY2n30ek6e1zqcWJd3AP86oh4PGE8yRwsoC/8tOn4Vatrc+JYPNuUIxgg4IGK2I7P+ztOZLGMNNjadpGSKAOJ0vwHqGk3rXFtKHAJ2jHA5q/e+KNT0OzcfZCZ8Ebh6npXS/aLq10GSYK3mDqqqTj2qyt1bah4eW6lt0LKuGV8frQBS8JeIbvUdNkvL7EYzgqTyDWpc3935AubOPzMnpjrXLeGtaj1G8nsHiWNUzkKwx+HrVuXxlpWkXDaeDj0BGSf8OaAJZdes/tinUIB9qyBsAzgVNceG9N1VxfMoj2/M6hc8elc/d+H7e+v49SLkKxDAA/zrpbe6KaO0bMgQ5VSe1AFi0udJsrGWKGZYggwWbvmsW28M6frF0L1r5LvZJnA7DnINY7+FLzUJceYWhY7sg4GM1NdeF9U0e08rSrt+STgjOaANS/8L2X2zzrC9jtioHQ8sT0BNXotItLK7S7lukEyckseoxzXKeFvDl6940mp3Lq/G0Hr9Kf4v8ACOpzNG9tcuckkqMk0AdBrGk+GdbvPtN3NGzRAMFGM5yOa2lvtKjsd0SjyosgACuM0jwHJHGkt3csAQMqW4Ix0/Otl7Ww0OEw3d4DGemBnA7UAYl3PqHiLW/sljMFtRnGOBj0rTufBeLQRXN6VjbhgzY461XsvE2nWt1LFpVo0uxiGkH6VV1+/wBS1mNoreKZWJ4wPagCaKw0zRbdoLVFvJTycDgjsfrWdp417WfEMU0ysLZCgYA4GK6Lwl4fn0iya71SUsQoZVYjgYya53XPiLDDqyQaYuUJGXC8++BQB3+s3sGk6Wu6IuQQFTOM81lLo1xrFpuuoxBnlAo6g80x/N12xtbxyI41UM4k6kA5zT08bWJ1FdNsw0rJgZx147UAZGtT2PgzTxIURrhmAAbk9DzXF6Hb3XiK7kurzKkdD2Kn0r1nxBp2n6vYI14pAHzYyBmufttS0+1tzBbWrBFG0FV7e5oAx11K50W5itrGDcxyruOpFOtdein19Y7y3dnb5eVz/kV12km1mtN80ce9mOwDk9Kp2VhZjXJJLizA4wGzQBu2Sw2K4B2BsfKeMVl61pEOrarHNcA+VEuevB96p6vrkL6sLe0kDtyuM9CK01vLmLSHkmhYsRtC45oA529s59Rv/wDQHAWADHHJro/7XTQdFD6i4DBSwyMZqj4Vtp7aeV7nKlz8q46g81L4itDrV5/Z5BEe3gjg0AaBiXTbUrGAFI4HoK84uPEWuaJc3TiJ5ImBwMA/Q1qzXGszSO91uWNQSiliTXRaVZo2jSS3sJYqDkDkkUAcHoPxJnMotdShCK7Y5B5rrr7XtNaB4bZfkYEMwHFc7Zx6FqesSwi2AnVvl4yB6V12n6RpEKyWsq/O2Rg9celAHLaHLo8V280UpWQ8Hg5qGfwnpt9PPem+Ec3mblVm5NdSdM0DTbvZhC7NhgegNY/jPw6pjgudNTDg5OGx+QoA1dIl2WhgfY8SDAGe3rWfr19LJYva2Vu24A5I/nXAXMfiS3Uy25mMe0hlU/KK0tC8VXccjf2mCpQc5X0oA6Lwh4k1OG8/s6+tpWycA4HFbvjLULmw08z2juzMMELjisfSfF8N01zJFbRu6A7XAwTx1rR0PWf+EghuI57dSYuoKDAoA5TT7jxNfWT34bZsbcQRySP8abpXjTxBHdyC9iYrChByOo5rr9Y1RdA0sNHBsMgILgdPSsGPV4fIDTaeAXPzsf4vwoAl0XxRdazfSxywy4LYVegHHaull0221Zxb3dqwKjk7sZ+vNVGKx6SL+whijkxkDOMdh2rm9E8Taneay1teHG4gEhsDNAHXWnh7TvDayXrBSvQqDkY9ayJPiJYQXPyWL7QTkhRj2Oa22hvZpjZMI5LcqTg8kimS6V4dgto7ScxRsy5IXqef8aAOa1HX9X8WuLXTLd0hYgHHABx3NT2Xg/TPDlkl3qRV7rHmLH1ww6jNd7pdja2lsFtIwiEAZA5OO5rP8ReHv7ZSOPc455YHigDy+51LxFql68enxsttuOwAcAY5FdH4P8Lf2OZdV1Fi03LJnp06VvX2o6T4VtBCxVZyD0HRsUyzvH1TRpJwwkZvurQBjP4tj1vWTpTwsqHGCvQGuqtdOhtLWV2gVxGN4A7msO38Nw6fbSX0tuPtLkONo6VZ0jUdUlaS3uIRhjjkcUALouo2epatKqwsjK2NoGADWhrt7Zwy/ZpVCmYYDDjB/CodU1DT/DcXmLbqk8p5YKBg4qhHbReKIFl3HcozvxyKAOXt/CNxF4j+0C4HkZLF9xx+NeizalYRWbLG4meMHIHPIFcJr2sS6bF/Z9szMwyspNFhpWozr59uWD4yUJxu4oA63Sdbi1hJGVDF5fy8+tT6Ukzaq8rzqw6YPU1hadG+maRczXACyOcsxGPwHrVDQtUMmvqUcmNh1PQGgD//2Q==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_arr = np.array(test_images)\n",
        "test_labels = np.array(test_labels)"
      ],
      "metadata": {
        "id": "QALiQltsQZip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3mc11INQg6x",
        "outputId": "d05fe3d9-40c8-471f-deb2-9f3f5e631947"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[210, 210, 210],\n",
              "         [233, 233, 233],\n",
              "         [224, 224, 224],\n",
              "         ...,\n",
              "         [191, 191, 191],\n",
              "         [225, 225, 225],\n",
              "         [250, 250, 250]],\n",
              "\n",
              "        [[217, 217, 217],\n",
              "         [218, 218, 218],\n",
              "         [201, 201, 201],\n",
              "         ...,\n",
              "         [194, 194, 194],\n",
              "         [227, 227, 227],\n",
              "         [249, 249, 249]],\n",
              "\n",
              "        [[225, 225, 225],\n",
              "         [208, 208, 208],\n",
              "         [190, 190, 190],\n",
              "         ...,\n",
              "         [182, 182, 182],\n",
              "         [214, 214, 214],\n",
              "         [238, 238, 238]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [155, 155, 155],\n",
              "         [137, 137, 137],\n",
              "         [129, 129, 129]],\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [115, 115, 115],\n",
              "         [107, 107, 107],\n",
              "         [108, 108, 108]],\n",
              "\n",
              "        [[255, 255, 255],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [ 91,  91,  91],\n",
              "         [ 87,  87,  87],\n",
              "         [ 92,  92,  92]]],\n",
              "\n",
              "\n",
              "       [[[197, 197, 197],\n",
              "         [ 84,  84,  84],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254]],\n",
              "\n",
              "        [[187, 187, 187],\n",
              "         [ 80,  80,  80],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254]],\n",
              "\n",
              "        [[176, 176, 176],\n",
              "         [ 94,  94,  94],\n",
              "         [ 29,  29,  29],\n",
              "         ...,\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[182, 182, 182],\n",
              "         [189, 189, 189],\n",
              "         [194, 194, 194],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              "\n",
              "        [[226, 226, 226],\n",
              "         [223, 223, 223],\n",
              "         [223, 223, 223],\n",
              "         ...,\n",
              "         [  3,   3,   3],\n",
              "         [ 48,  48,  48],\n",
              "         [106, 106, 106]],\n",
              "\n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [  6,   6,   6],\n",
              "         [111, 111, 111],\n",
              "         [248, 248, 248]]],\n",
              "\n",
              "\n",
              "       [[[233, 233, 233],\n",
              "         [238, 238, 238],\n",
              "         [227, 227, 227],\n",
              "         ...,\n",
              "         [112, 112, 112],\n",
              "         [121, 121, 121],\n",
              "         [133, 133, 133]],\n",
              "\n",
              "        [[199, 199, 199],\n",
              "         [227, 227, 227],\n",
              "         [203, 203, 203],\n",
              "         ...,\n",
              "         [ 66,  66,  66],\n",
              "         [ 78,  78,  78],\n",
              "         [ 90,  90,  90]],\n",
              "\n",
              "        [[173, 173, 173],\n",
              "         [178, 178, 178],\n",
              "         [131, 131, 131],\n",
              "         ...,\n",
              "         [ 29,  29,  29],\n",
              "         [ 33,  33,  33],\n",
              "         [ 41,  41,  41]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[251, 251, 251],\n",
              "         [253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [226, 226, 226],\n",
              "         [204, 204, 204],\n",
              "         [176, 176, 176]],\n",
              "\n",
              "        [[252, 252, 252],\n",
              "         [253, 253, 253],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [234, 234, 234],\n",
              "         [216, 216, 216],\n",
              "         [194, 194, 194]],\n",
              "\n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [247, 247, 247],\n",
              "         [238, 238, 238],\n",
              "         [218, 218, 218]]],\n",
              "\n",
              "\n",
              "       [[[255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [220, 220, 220],\n",
              "         [230, 230, 230],\n",
              "         [249, 249, 249]],\n",
              "\n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [235, 235, 235],\n",
              "         [241, 241, 241],\n",
              "         [252, 252, 252]],\n",
              "\n",
              "        [[254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         [254, 254, 254],\n",
              "         ...,\n",
              "         [247, 247, 247],\n",
              "         [251, 251, 251],\n",
              "         [255, 255, 255]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[148, 148, 148],\n",
              "         [150, 150, 150],\n",
              "         [152, 152, 152],\n",
              "         ...,\n",
              "         [166, 166, 166],\n",
              "         [202, 202, 202],\n",
              "         [234, 234, 234]],\n",
              "\n",
              "        [[175, 175, 175],\n",
              "         [182, 182, 182],\n",
              "         [185, 185, 185],\n",
              "         ...,\n",
              "         [164, 164, 164],\n",
              "         [195, 195, 195],\n",
              "         [225, 225, 225]],\n",
              "\n",
              "        [[210, 210, 210],\n",
              "         [212, 212, 212],\n",
              "         [208, 208, 208],\n",
              "         ...,\n",
              "         [161, 161, 161],\n",
              "         [186, 186, 186],\n",
              "         [213, 213, 213]]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKmGBQmlQjyW",
        "outputId": "a15c174b-9d65-4bed-ccec-e76413d79ba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_52.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_54.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_55.bmp',\n",
              "       '/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/second/00000_53.bmp'],\n",
              "      dtype='<U100')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n"
      ],
      "metadata": {
        "id": "WBN5OtJxQzZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "encoded_train_label= le.fit_transform(train_labels)\n",
        "encoded_test_label = le.fit_transform(test_labels)\n",
        "\n"
      ],
      "metadata": {
        "id": "PmSDGwMOSAxk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_train_label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tBDhXN9XNVp",
        "outputId": "17a672ff-4bd7-4abe-e313-ee24065e4a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 19, 29, 37, 23, 17, 26, 43, 39, 38,  8, 44,  6, 47, 18, 10, 27,\n",
              "       28, 22, 15, 46, 50, 24, 42, 16,  3, 20,  9,  1, 49,  4, 34, 41, 21,\n",
              "       33, 31, 48,  2, 36, 45, 35, 40, 14,  0,  5, 25,  7, 13, 32, 11, 51,\n",
              "       30])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_test_label"
      ],
      "metadata": {
        "id": "J489H6dWTUTJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "783bcf2c-49c8-49f5-c8e8-eec0f1e2a6b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 3, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "VGG_model = VGG16(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))"
      ],
      "metadata": {
        "id": "ltfFcyQ0anyA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import optimizers\n",
        "\n",
        "\n",
        "adam = optimizers.Adam()\n",
        "\n",
        "VGG_model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "WO2G8GvmhUVe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_images, encoded_train_label, test_images, encoded_test_label"
      ],
      "metadata": {
        "id": "Y3n5y3lgd0GV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# (x_train, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
        "# train_images, test_images = train_images / 255.0, test_images / 255.0"
      ],
      "metadata": {
        "id": "KOnPXDE8R5Ma"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "PE5CmVN1en42",
        "outputId": "1ae77de5-d8e0-4d36-ffe9-fb5ebba5c330"
      },
      "execution_count": null,
      "outputs": [
        {
          "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-133-ff04511ae750>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'float'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train_one_hot = to_categorical(train_images)\n",
        "y_test_one_hot = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "6OYpT5aYdbrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "VGG_model.input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6YQ-KZQcl5R",
        "outputId": "872f1261-e1f3-4dbd-8a6b-44478855e04c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 224, 224, 3) dtype=float32 (created by layer 'input_6')>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dzs31oI3dNbb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QGJ7Jtucnq7",
        "outputId": "e9f1ce2b-22e9-4f3d-da84-458eaf9f2848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 50178     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,764,866\n",
            "Trainable params: 14,764,866\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    preprocessing_function=preprocess_input,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')"
      ],
      "metadata": {
        "id": "FbmtrAyYhfks"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_datagen = ImageDataGenerator(\n",
        "    preprocessing_function=preprocess_input,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')"
      ],
      "metadata": {
        "id": "vwwflv2ojlGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_path =\"/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/\"\n",
        "test_path =\"/content/drive/MyDrive/Data sets/dataset-20230810T102546Z-001/dataset/train_data/\""
      ],
      "metadata": {
        "id": "QMEo__1xiNIe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = train_datagen.flow_from_directory(train_path,\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YykVDpSuh6iQ",
        "outputId": "069b4f7b-ac7c-430e-a9ed-00ffe449ee37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 56 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set = train_datagen.flow_from_directory(test_path,\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qv81BoFvjNlg",
        "outputId": "79edb14d-b74e-413e-f977-973edf992420"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 56 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor=VGG_model.predict(train_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Io9vh9U1fgtW",
        "outputId": "b81754d6-dc76-48ae-af9b-ea748d7c5a2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 56s 19s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "VGG_model.add(tf.keras.layers.Dense(10, activation = 'softmax'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_wTroyM5ofac",
        "outputId": "05a66a57-14fb-4e0c-9905-448dce03dda1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-100-8b39f50012d5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVGG_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'softmax'\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[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'add'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r = VGG_model.fit(\n",
        "  train_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=5,\n",
        "  steps_per_epoch=len(train_set),\n",
        "  validation_steps=len(test_set)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "gTqq5mZykv3V",
        "outputId": "727a3c6f-6825-40af-ea02-234596ee78c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\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-101-da790769bfc7>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m r = VGG_model.fit(\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mtrain_set\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[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_set\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[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\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     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1055, in train_step\n        return self.compute_metrics(x, y, y_pred, sample_weight)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1149, in compute_metrics\n        self.compiled_metrics.update_state(y, y_pred, sample_weight)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/compile_utils.py\", line 605, in update_state\n        metric_obj.update_state(y_t, y_p, sample_weight=mask)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/utils/metrics_utils.py\", line 77, in decorated\n        update_op = update_state_fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/metrics/base_metric.py\", line 140, in update_state_fn\n        return ag_update_state(*args, **kwargs)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/metrics/base_metric.py\", line 691, in update_state  **\n        matches = ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/metrics/accuracy_metrics.py\", line 462, in sparse_categorical_accuracy\n        if matches.shape.ndims > 1 and matches.shape[-1] == 1:\n\n    TypeError: '>' not supported between instances of 'NoneType' and 'int'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model = models.Sequential([\n",
        "#     layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "#     layers.MaxPooling2D((2, 2)),\n",
        "#     layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "#     layers.MaxPooling2D((2, 2)),\n",
        "#     layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "#     layers.Flatten(),\n",
        "#     layers.Dense(64, activation='relu'),\n",
        "#     layers.Dense(10)  # 10 classes for CIFAR-10\n",
        "# ])"
      ],
      "metadata": {
        "id": "-MY7Em7rTpew"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}