Project_keras.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGr32P5dmBAH"
      },
      "source": [
        "# Импорт"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUdYrbj7q4R8",
        "outputId": "3588a09c-fc67-448d-8372-d712f2beafae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.17.1)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (24.12.23)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.12.1)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.4.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.25.5)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.69.0)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.17.1)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.5.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras>=3.2.0->tensorflow) (13.9.4)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras>=3.2.0->tensorflow) (0.13.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.12.14)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (3.0.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n",
        "import tensorflow as tf\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooLz0o0vrE9L",
        "outputId": "054fe65f-8b85-4ffd-88ca-b928089d868b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.17.1\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)\n",
        "print(tf.executing_eagerly())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wj34WYIq4aqP",
        "outputId": "7167826b-ee91-4927-8ecc-ae69fb16b97c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pickle\n",
        "import shutil\n",
        "from PIL import Image\n",
        "import pandas as pd\n",
        "import zipfile\n",
        "from zipfile import ZipFile\n",
        "from io import BytesIO\n",
        "from PIL import Image\n",
        "from google.colab import drive\n",
        "import glob\n",
        "\n",
        "\n",
        "import os\n",
        "print(os.getcwd())\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NpoSj7KCmHke"
      },
      "source": [
        "# Подключение диска"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15GaBSKkpDUQ",
        "outputId": "fa2f49c2-6813-424d-bec9-ad565e5e3af0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcPN-RHe8AE1",
        "outputId": "9347fe6e-f456-4a2b-c44c-49a30b302463"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/.config/.last_survey_prompt.yaml\n",
            "/content/.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db\n",
            "/content/.config/default_configs.db\n",
            "/content/.config/.last_update_check.json\n",
            "/content/.config/gce\n",
            "/content/.config/config_sentinel\n",
            "/content/.config/.last_opt_in_prompt.yaml\n",
            "/content/.config/active_config\n",
            "/content/.config/logs/2025.01.16/14.28.58.277099.log\n",
            "/content/.config/logs/2025.01.16/14.28.59.271536.log\n",
            "/content/.config/logs/2025.01.16/14.28.49.862607.log\n",
            "/content/.config/logs/2025.01.16/14.28.29.838121.log\n",
            "/content/.config/logs/2025.01.16/14.29.08.514080.log\n",
            "/content/.config/logs/2025.01.16/14.29.07.896294.log\n",
            "/content/.config/configurations/config_default\n",
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            "/content/drive/MyDrive/Конспект 8 Чеботарев  РТ-1.zip\n",
            "/content/drive/MyDrive/Лабараторная работа 28 Чеботарев Рт-1.zip\n",
            "/content/drive/MyDrive/РТ-1 Чеботарев ИДЗ-1.7z\n",
            "/content/drive/MyDrive/РТ-1 Чеботарев ИДЗ-2.7z\n",
            "/content/drive/MyDrive/H5_Game.7z\n",
            "/content/drive/MyDrive/Heroes5_Manual_3.0.pdf\n",
            "/content/drive/MyDrive/sceneTree.zip\n",
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            "/content/drive/MyDrive/Копия Торталитто калькулейшонс (1).gsheet\n",
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            "/content/drive/MyDrive/Копия ростер Дилюк.gsheet\n",
            "/content/drive/MyDrive/Копия Нинка.gsheet\n",
            "/content/drive/MyDrive/Копия Ростер Хэзо.gsheet\n",
            "/content/drive/MyDrive/Копия дендро баффнет.gsheet\n",
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            "/content/drive/MyDrive/Копия «Царство эвтюмии».gsheet\n",
            "/content/drive/MyDrive/Копия Ростер Буба.gsheet\n",
            "/content/drive/MyDrive/Копия Scaramouche, Scaramouche, will you do the Fandango. (1).gsheet\n",
            "/content/drive/MyDrive/Копия Ростер Сяо (1).gsheet\n",
            "/content/drive/MyDrive/Копия Ростер Аяк.gsheet\n",
            "/content/drive/MyDrive/Копия Сян Лин Supremacy.gsheet\n",
            "/content/drive/MyDrive/Копия Кlee или то, как контора пидарасов умеет делать сложных персонажей.gsheet\n",
            "/content/drive/MyDrive/Копия Ростер Тава.gsheet\n",
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            "/content/drive/MyDrive/Копия Ростер Сайно aka I can't stop the Dogs of War.gsheet\n",
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            "/content/drive/MyDrive/Копия Торталитто калькулейшонс.gsheet\n",
            "/content/drive/MyDrive/Копия loliarcon.gsheet\n",
            "/content/drive/MyDrive/Проект без названия.gscript\n",
            "/content/drive/MyDrive/Копия 02. Инженерия данных. Форматы хранения данных.pptx\n",
            "/content/drive/MyDrive/Копия Scaramouche, Scaramouche, will you do the Fandango..gsheet\n",
            "/content/drive/MyDrive/Пояснительная записка Чеботарев 7.DOCX\n",
            "/content/drive/MyDrive/Biznes-plan.docx\n",
            "/content/drive/MyDrive/Копия_блокнота__01_task_ipynb_ (1).ipynb\n",
            "/content/drive/MyDrive/Task_3.ipynb\n",
            "/content/drive/MyDrive/Копия Ушастый и пушыстый притворяется козой и кидает насвай, но считает ростеры и дико лажает (я).gsheet\n",
            "/content/drive/MyDrive/predict_by_model.ipynb\n",
            "/content/drive/MyDrive/Proj_Data.zip\n",
            "/content/drive/MyDrive/train_by_product_data.ipynb\n",
            "/content/drive/MyDrive/Задача (1).ipynb\n",
            "/content/drive/MyDrive/Копия Магистры 2023-2024-копия.gform\n",
            "/content/drive/MyDrive/Копия Магистры 2023-2024-копия.gsheet\n",
            "/content/drive/MyDrive/пайтон глуб.docx\n",
            "/content/drive/MyDrive/Копия diagram.png\n",
            "/content/drive/MyDrive/Запирсь в школу.drawio\n",
            "/content/drive/MyDrive/Отчёт_модели..gdoc\n",
            "/content/drive/MyDrive/Защита (1).pptx\n",
            "/content/drive/MyDrive/Копия аято(?).gsheet\n",
            "/content/drive/MyDrive/C#/Animals.zip\n",
            "/content/drive/MyDrive/C#/CertainSnake.zip\n",
            "/content/drive/MyDrive/C#/UpToDate.zip\n",
            "/content/drive/MyDrive/Хранилище/проект  Чеботарев Костя.docx\n",
            "/content/drive/MyDrive/Хранилище/https:  vk.com .txt\n",
            "/content/drive/MyDrive/Хранилище/THE LANGUAGE OF EXPLANATIONS.docx\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Итог (1).PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Новый документ.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Itog.mp4\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Статья1перевод.docx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Статья1.PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Статья1.mp4\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_3/Статья3.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4_перевод.docx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4 (1).PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4.mp4\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья_5_перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья5 презентация.PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья5_ускоренная.mp4\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_6/Статья_6 видеопрезентация.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_6/Статья_6.pptx\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_7/Статья_7.pptx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_7/Статья_7 - видеопрезентация.mp4\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья_8_перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья_8.pptx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья_8 - видеопрезентация.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_9/Статья_9.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_9/Статья_9_перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_9/Статья_9.pptx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_9/Статья_9_видео.mp4\n",
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            "/content/drive/MyDrive/Отчёты/Otchyot_1.docx\n",
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            "/content/drive/MyDrive/Б24/Таблица с ответами.gdoc\n",
            "/content/drive/MyDrive/Запись в школу - схема/Копия Запирсь в школу.drawio\n",
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            "/content/drive/MyDrive/Управление проектами/Отчёт_проект_3сем.docx\n",
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            "/content/drive/MyDrive/ЦКНД/Текст_ИИ.gdoc\n",
            "/content/drive/MyDrive/ЦКНД/Свой_текст.gdoc\n",
            "/content/drive/MyDrive/ЦКНД/Задание_3_Чеботарев.gdoc\n",
            "/content/drive/MyDrive/ЦКНД/Чеботарев_Задание_5.gdoc\n",
            "/content/drive/MyDrive/NLP/Чеботарев_Общение с гпт.gdoc\n",
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            "/content/drive/MyDrive/Proj_img_2/Cabbage/photo_2024-06-14_12-33-26.jpg\n",
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            "/content/drive/MyDrive/Proj_img_2/Cabbage/photo_2024-06-14_12-34-02.jpg\n",
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            "/content/drive/MyDrive/Proj_img_2/Cabbage/photo_2024-06-14_12-36-51.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Cabbage/photo_2024-06-14_12-36-59.jpg\n",
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            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-09-32.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-10-29.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-10-42.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-10-51.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-11-01.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-11-09.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-11-43.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-00.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-07.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-15.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-23.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-31.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-46.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-12-59.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-13-09.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-13-16.jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-13-24 (2).jpg\n",
            "/content/drive/MyDrive/Proj_img_2/Watercress/photo_2024-06-14_12-13-24.jpg\n",
            "/content/drive/MyDrive/ML NLP/Поиск Аномалий (1).pptx\n",
            "/content/drive/MyDrive/ML NLP/Лекция 04 - Трансформеры и NLP.pptx\n",
            "/content/drive/MyDrive/ML NLP/Лекция 02 - NLP до Нейронок.pptx\n",
            "/content/drive/MyDrive/ML NLP/Блиц-вопросы на Зачет.docx\n",
            "/content/drive/MyDrive/ML NLP/Нейронки и Таблички.pptx\n",
            "/content/drive/MyDrive/ML NLP/Модели.pptx\n",
            "/content/drive/MyDrive/ML NLP/Баланс.pptx\n",
            "/content/drive/MyDrive/ML NLP/AutoML.pptx\n",
            "/content/drive/MyDrive/ML NLP/Поиск Аномалий (2).pptx\n",
            "/content/drive/MyDrive/ML NLP/Интерпретируемость моделей вне sklearn.pptx\n",
            "/content/drive/MyDrive/ML NLP/Анализ моделей.pptx\n",
            "/content/drive/MyDrive/ML NLP/Блиц-вопросы на Экзамен.docx\n",
            "/content/drive/MyDrive/model/proj_v2.h5\n",
            "/content/drive/MyDrive/model/proj_v4.h5\n",
            "/content/drive/MyDrive/model/proj_v6.h5\n",
            "/content/drive/MyDrive/model/xception_cleard_v1.keras\n",
            "/content/drive/MyDrive/model/EN_v1.keras\n",
            "/content/drive/MyDrive/model/EN_v2.keras\n",
            "/content/drive/MyDrive/model/Inception_v2.keras\n",
            "/content/drive/MyDrive/model/Inception_v2_cleard.keras\n",
            "/content/drive/MyDrive/model/Xception_vTrue1_cleard.keras\n",
            "/content/drive/MyDrive/model/ENB3_vTrue1_cleard.keras\n",
            "/content/drive/MyDrive/model/ResNet101_vTrue1_cleard.keras\n",
            "/content/drive/MyDrive/model/base_model_wbg.keras\n",
            "/content/drive/MyDrive/model/base_model.keras\n",
            "/content/drive/MyDrive/model/InceptionV3.keras\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-21-52.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-02.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-49.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-03.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-23.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-21-57.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-53.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-09.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-06.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-13.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-10.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-40.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-32.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-36.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-45.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-28.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-18.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-22.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-38.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-14.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-50.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-25.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-18.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-41.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-34.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-23-29.png\n",
            "/content/drive/MyDrive/Proj_img_3/Amarant/photo_2024-06-18_15-22-58.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-31-20.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-30-13.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-29-59.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-31-32.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-30-25.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-31-06.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-33-26.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-32-41.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-32-06.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-32-33.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-34-12.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-33-52.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-33-08.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-33-18.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-32-25.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-34-24.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-31-54.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-34-02.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-32-15.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-32-51.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-36-40.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-35-07.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-36-10.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-36-32.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-36-51.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-35-58.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-36-18.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-36-59.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-35-43.png\n",
            "/content/drive/MyDrive/Proj_img_3/Watercress/photo_2024-06-14_12-37-08.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-11-09.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-11-01.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-10-42.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-10-29.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-00.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-09-32.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-10-51.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-11-43.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-13-24.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-13-24 (2).png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-31.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-46.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-13-16.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-59.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-13-09.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-07.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-15.png\n",
            "/content/drive/MyDrive/Proj_img_3/Cabbage/photo_2024-06-14_12-12-23.png\n",
            "/content/sample_data/README.md\n",
            "/content/sample_data/anscombe.json\n",
            "/content/sample_data/mnist_train_small.csv\n",
            "/content/sample_data/mnist_test.csv\n",
            "/content/sample_data/california_housing_test.csv\n",
            "/content/sample_data/california_housing_train.csv\n"
          ]
        }
      ],
      "source": [
        "for dirname, _, filenames in os.walk('/content'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v1E_quaM8rkV"
      },
      "outputs": [],
      "source": [
        "#path_to_zip_file = '/content/drive/My Drive/Proj_Data.zip'\n",
        "# Создайте директорию, куда будет распакован архив\n",
        "#directory_to_extract_to = '/content/drive/My Drive/extracted_images'\n",
        "#os.makedirs(directory_to_extract_to, exist_ok=True)\n",
        "# Распакуйте архив\n",
        "#with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:\n",
        "  #zip_ref.extractall(directory_to_extract_to)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfyTWa1nmX1m"
      },
      "source": [
        "# Работа с изображениями"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PgiOKiaa8X2F"
      },
      "outputs": [],
      "source": [
        "classes = [ 'Amarant', 'Cabbage', 'Watercress']\n",
        "directory_to_extract_to = '/content/drive/My Drive/Proj_img'\n",
        "image_df = pd.DataFrame(columns = ['X','y'])\n",
        "images_set = []\n",
        "for e in classes:\n",
        "  dir = directory_to_extract_to  + f'/{e}'\n",
        "  # Получите список всех файлов изображений в директории\n",
        "  images_path = glob.glob(dir + '/*.jpg') # Или другой формат изображения\n",
        "  # Считайте изображения\n",
        "  images = [Image.open(img_path) for img_path in images_path]\n",
        "\n",
        "  images_set.append(images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylZ5rRdmsAyD",
        "outputId": "a4b43cfd-c748-4d4a-cabf-9a51363b749d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2B8790>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E0850>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2B8890>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F8390>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AC410>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AC690>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F247650>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E0B50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F25A990>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F246B50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AC4D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F82D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F83D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F247150>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E3790>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F237950>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F8B10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F8CD0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AC8D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AEB10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F9FD0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8FA150>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AEC10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AF050>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B948B30BA10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AEB50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AF750>], [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8F9950>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AFB90>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AFDD0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2AE250>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C0090>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C0050>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C0710>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C0390>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C1390>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C16D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C1890>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C1790>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C1FD0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C1A10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C2750>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C24D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C2E10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C3510>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2147D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C3C50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C3F10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8C2D90>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D0590>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D0910>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D0B50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D0A90>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D1410>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D0690>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D1AD0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D1250>], [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F2462D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D21D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D2910>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D2790>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D2A50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D3450>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D3210>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D3B90>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D3F10>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8D3C50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E4610>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E47D0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E4750>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E5090>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947F25B990>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E5810>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E5D50>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7B947E8E4F10>]]\n"
          ]
        }
      ],
      "source": [
        "print(images_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bME7kQCM8YmM",
        "outputId": "a6220ed4-e4d3-47fc-9a50-37abf07a4350"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/.config/.last_survey_prompt.yaml\n",
            "/content/.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db\n",
            "/content/.config/default_configs.db\n",
            "/content/.config/.last_update_check.json\n",
            "/content/.config/gce\n",
            "/content/.config/config_sentinel\n",
            "/content/.config/.last_opt_in_prompt.yaml\n",
            "/content/.config/active_config\n",
            "/content/.config/logs/2025.01.16/14.28.58.277099.log\n",
            "/content/.config/logs/2025.01.16/14.28.59.271536.log\n",
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            "/content/.config/logs/2025.01.16/14.29.08.514080.log\n",
            "/content/.config/logs/2025.01.16/14.29.07.896294.log\n",
            "/content/.config/configurations/config_default\n",
            "/content/drive/MyDrive/ВЛР I семестр.zip\n",
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            "/content/drive/MyDrive/Лабараторная работа №9 РТ-1 К. В. Чеботарев.zip\n",
            "/content/drive/MyDrive/Конспект 8 Чеботарев  РТ-1.zip\n",
            "/content/drive/MyDrive/Лабараторная работа 28 Чеботарев Рт-1.zip\n",
            "/content/drive/MyDrive/РТ-1 Чеботарев ИДЗ-1.7z\n",
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            "/content/drive/MyDrive/sceneTree.zip\n",
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            "/content/drive/MyDrive/Копия Дед набухался на новый год и исекайнулся в могилу.gsheet\n",
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            "/content/drive/MyDrive/Копия Отчет Таргем 3 курс.gdoc\n",
            "/content/drive/MyDrive/Отзыв Чеботарев.gdoc\n",
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            "/content/drive/MyDrive/Копия ростер Дилюк.gsheet\n",
            "/content/drive/MyDrive/Копия Нинка.gsheet\n",
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            "/content/drive/MyDrive/Копия дендро баффнет.gsheet\n",
            "/content/drive/MyDrive/repos.7z\n",
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            "/content/drive/MyDrive/Копия Ростер Сяо (1).gsheet\n",
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            "/content/drive/MyDrive/Копия Сян Лин Supremacy.gsheet\n",
            "/content/drive/MyDrive/Копия Кlee или то, как контора пидарасов умеет делать сложных персонажей.gsheet\n",
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            "/content/drive/MyDrive/Копия Ростер Сайно aka I can't stop the Dogs of War.gsheet\n",
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            "/content/drive/MyDrive/Копия Торталитто калькулейшонс.gsheet\n",
            "/content/drive/MyDrive/Копия loliarcon.gsheet\n",
            "/content/drive/MyDrive/Проект без названия.gscript\n",
            "/content/drive/MyDrive/Копия 02. Инженерия данных. Форматы хранения данных.pptx\n",
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            "/content/drive/MyDrive/Пояснительная записка Чеботарев 7.DOCX\n",
            "/content/drive/MyDrive/Biznes-plan.docx\n",
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            "/content/drive/MyDrive/predict_by_model.ipynb\n",
            "/content/drive/MyDrive/Proj_Data.zip\n",
            "/content/drive/MyDrive/train_by_product_data.ipynb\n",
            "/content/drive/MyDrive/Задача (1).ipynb\n",
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            "/content/drive/MyDrive/Запирсь в школу.drawio\n",
            "/content/drive/MyDrive/Отчёт_модели..gdoc\n",
            "/content/drive/MyDrive/Защита (1).pptx\n",
            "/content/drive/MyDrive/Копия аято(?).gsheet\n",
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            "/content/drive/MyDrive/C#/CertainSnake.zip\n",
            "/content/drive/MyDrive/C#/UpToDate.zip\n",
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            "/content/drive/MyDrive/Хранилище/https:  vk.com .txt\n",
            "/content/drive/MyDrive/Хранилище/THE LANGUAGE OF EXPLANATIONS.docx\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Словарь.xlsx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Итог (1).PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Новый документ.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Itog.mp4\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Статья1перевод.docx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Статья1.PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Статья1.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_1/Итог (1).PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_2/Статья2_перевод.docx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_2/Статья2.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_2/Статья2.PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_2/Статья2.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_2/Статья2.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_3/Статья3.pdf\n",
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            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_3/Статья3  (2).PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_3/Статья3.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4_перевод.docx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4 (1).PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_4/Статья4.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья5.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья_5_перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья5 презентация.PPTX\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_5/Статья5_ускоренная.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_6/Статья6.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_6/Статья 6 перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_6/Статья_6 видеопрезентация.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_6/Статья_6.pptx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_7/Статья7.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_7/Стать7_перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_7/Статья_7.pptx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_7/Статья_7 - видеопрезентация.mp4\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья8.pdf\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья_8_перевод.gdoc\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья_8.pptx\n",
            "/content/drive/MyDrive/Английский магистратура/Статьи/Статья_8/Статья_8 - видеопрезентация.mp4\n",
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        }
      ],
      "source": [
        "for dirname, _, filenames in os.walk('/content'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "        # Проходим по всем файлам в архиве\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfQ59oACmkzF"
      },
      "source": [
        "# Полезные методы"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CTeNhB_66AjQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#cv2_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)\n",
        "#image_rgb = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)\n",
        "#image_pillow = Image.fromarray(image_rgb)\n",
        "def crop_img(image_CV2, size_x, size_y):\n",
        "    image = image_CV2\n",
        "\n",
        "    # Получение размеров изображения\n",
        "    height, width = image.shape[:2]\n",
        "\n",
        "    # Определение размера центральной части\n",
        "    center_x, center_y = height // 2, width // 2\n",
        "\n",
        "    crop_size_x = (size_x // 224 - 0) * 224\n",
        "    crop_size_y = (size_y // 224 - 0) * 224\n",
        "\n",
        "    # Определение координат для обрезки\n",
        "    x1, y1 = center_x - crop_size_x // 2, center_y - crop_size_y // 2\n",
        "    x2, y2 = center_x + crop_size_x // 2, center_y + crop_size_y // 2\n",
        "    print(x1, y1, x2, y2)\n",
        "    # Вырезание центральной части изображения\n",
        "    cropped_image = image[y1:y2, x1:x2]\n",
        "    return cropped_image\n",
        "\n",
        "\n",
        "#img_dirs = ['C:/Proj_Data/Amarant', 'C:/Proj_Data/Cabbage', 'C:/Proj_Data/Watercress']\n",
        "\n",
        "def slice_image(image_PIL, chunk_size, tmp_set):\n",
        "    image = image_PIL\n",
        "    width, height = image.size\n",
        "\n",
        "    for x in range(0, width, chunk_size):\n",
        "        for y in range(0, height, chunk_size):\n",
        "            box = (x, y, x + chunk_size, y + chunk_size)\n",
        "            region = image.crop(box)\n",
        "            #region.save(f'chunk_{x}_{y}.png')\n",
        "            tmp_set.append(region)\n",
        "\n",
        "\n",
        "chunk_size = 224\n",
        "#slice_image(image_path, chunk_size)\n",
        "\n",
        "\n",
        "\n",
        "# Задаем путь к директории, в которой будут создаваться папки\n",
        "output_path = \"C:/Proj_Data/data\"\n",
        "\n",
        "\n",
        "\n",
        "# Создание папок с названиями классов\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VkYVNLMmmoBE"
      },
      "source": [
        "# Продолжаем работать с картинками"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxDo8e3dxMxC",
        "outputId": "b2da0730-e28d-4305-9aa7-9a0d90d51eaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
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            "48 64 720 960\n",
            "48 64 720 960\n",
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            "48 64 720 960\n",
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            "48 64 720 960\n",
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            "48 64 720 960\n",
            "48 64 720 960\n",
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            "48 64 720 960\n",
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            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n",
            "48 64 720 960\n"
          ]
        }
      ],
      "source": [
        "images_set_2 = []\n",
        "for img_set in images_set:\n",
        "  tmp_set = []\n",
        "  for img in img_set:\n",
        "    tmp = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\n",
        "    height, width, chans = tmp.shape\n",
        "    image = crop_img(tmp, height, width)\n",
        "    tmp_set.append(image)\n",
        "  images_set_2.append(tmp_set)\n",
        "images_set = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQFr65TVzGHg",
        "outputId": "53c16cd5-061f-461e-8623-fefb8d9561b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[array([[[203, 199, 198],\n",
            "        [203, 200, 196],\n",
            "        [203, 199, 198],\n",
            "        ...,\n",
            "        [152, 150, 149],\n",
            "        [149, 147, 146],\n",
            "        [150, 147, 143]],\n",
            "\n",
            "       [[205, 201, 200],\n",
            "        [206, 201, 198],\n",
            "        [207, 204, 200],\n",
            "        ...,\n",
            "        [150, 146, 145],\n",
            "        [148, 143, 144],\n",
            "        [148, 143, 142]],\n",
            "\n",
            "       [[207, 203, 202],\n",
            "        [209, 205, 204],\n",
            "        [211, 207, 206],\n",
            "        ...,\n",
            "        [157, 156, 152],\n",
            "        [156, 154, 153],\n",
            "        [158, 155, 151]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [208, 216, 215],\n",
            "        [208, 216, 215],\n",
            "        [208, 216, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [205, 216, 214],\n",
            "        [205, 216, 214],\n",
            "        [207, 215, 214]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [204, 215, 213],\n",
            "        [204, 215, 213],\n",
            "        [205, 213, 212]]], dtype=uint8), array([[[207, 208, 204],\n",
            "        [204, 207, 205],\n",
            "        [204, 207, 205],\n",
            "        ...,\n",
            "        [152, 148, 147],\n",
            "        [153, 148, 149],\n",
            "        [157, 153, 152]],\n",
            "\n",
            "       [[209, 208, 204],\n",
            "        [207, 208, 204],\n",
            "        [206, 207, 203],\n",
            "        ...,\n",
            "        [152, 150, 150],\n",
            "        [155, 150, 151],\n",
            "        [157, 155, 155]],\n",
            "\n",
            "       [[210, 210, 204],\n",
            "        [207, 209, 203],\n",
            "        [206, 208, 202],\n",
            "        ...,\n",
            "        [152, 150, 150],\n",
            "        [154, 152, 152],\n",
            "        [157, 155, 155]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [211, 216, 217],\n",
            "        [212, 217, 218],\n",
            "        [212, 217, 218]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [209, 214, 215],\n",
            "        [206, 214, 214],\n",
            "        [206, 214, 214]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [206, 211, 212],\n",
            "        [203, 211, 211],\n",
            "        [202, 210, 210]]], dtype=uint8), array([[[199, 200, 198],\n",
            "        [197, 200, 198],\n",
            "        [197, 200, 198],\n",
            "        ...,\n",
            "        [159, 163, 157],\n",
            "        [158, 162, 157],\n",
            "        [155, 161, 156]],\n",
            "\n",
            "       [[199, 200, 198],\n",
            "        [199, 200, 198],\n",
            "        [197, 200, 198],\n",
            "        ...,\n",
            "        [144, 147, 145],\n",
            "        [145, 146, 144],\n",
            "        [142, 145, 143]],\n",
            "\n",
            "       [[198, 199, 197],\n",
            "        [199, 200, 198],\n",
            "        [197, 200, 198],\n",
            "        ...,\n",
            "        [145, 146, 150],\n",
            "        [147, 146, 150],\n",
            "        [144, 146, 147]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [201, 212, 210],\n",
            "        [199, 212, 210],\n",
            "        [201, 212, 210]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [199, 210, 208],\n",
            "        [196, 210, 206],\n",
            "        [198, 209, 206]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [198, 209, 207],\n",
            "        [194, 208, 204],\n",
            "        [194, 208, 204]]], dtype=uint8), array([[[207, 215, 208],\n",
            "        [206, 217, 209],\n",
            "        [208, 216, 209],\n",
            "        ...,\n",
            "        [153, 155, 156],\n",
            "        [152, 154, 155],\n",
            "        [152, 154, 155]],\n",
            "\n",
            "       [[204, 215, 207],\n",
            "        [205, 216, 206],\n",
            "        [206, 217, 209],\n",
            "        ...,\n",
            "        [153, 155, 155],\n",
            "        [152, 154, 155],\n",
            "        [152, 154, 155]],\n",
            "\n",
            "       [[206, 214, 207],\n",
            "        [207, 215, 208],\n",
            "        [208, 216, 209],\n",
            "        ...,\n",
            "        [153, 156, 154],\n",
            "        [152, 154, 154],\n",
            "        [151, 153, 153]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [209, 223, 221],\n",
            "        [205, 222, 219],\n",
            "        [204, 221, 218]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [204, 221, 218],\n",
            "        [201, 220, 217],\n",
            "        [201, 218, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [200, 219, 216],\n",
            "        [199, 218, 215],\n",
            "        [197, 216, 213]]], dtype=uint8), array([[[212, 206, 211],\n",
            "        [209, 203, 208],\n",
            "        [209, 204, 206],\n",
            "        ...,\n",
            "        [149, 147, 147],\n",
            "        [147, 145, 145],\n",
            "        [144, 143, 145]],\n",
            "\n",
            "       [[214, 208, 213],\n",
            "        [212, 205, 210],\n",
            "        [210, 204, 209],\n",
            "        ...,\n",
            "        [133, 128, 129],\n",
            "        [129, 124, 125],\n",
            "        [126, 121, 122]],\n",
            "\n",
            "       [[209, 206, 208],\n",
            "        [211, 206, 208],\n",
            "        [212, 207, 209],\n",
            "        ...,\n",
            "        [161, 159, 159],\n",
            "        [159, 157, 157],\n",
            "        [159, 157, 157]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [205, 215, 215],\n",
            "        [204, 214, 214],\n",
            "        [204, 214, 214]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [203, 212, 215],\n",
            "        [199, 211, 211],\n",
            "        [200, 210, 210]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [202, 211, 214],\n",
            "        [198, 210, 210],\n",
            "        [196, 208, 208]]], dtype=uint8), array([[[197, 195, 194],\n",
            "        [196, 197, 195],\n",
            "        [197, 198, 194],\n",
            "        ...,\n",
            "        [161, 165, 160],\n",
            "        [160, 164, 159],\n",
            "        [159, 163, 158]],\n",
            "\n",
            "       [[201, 199, 198],\n",
            "        [200, 201, 199],\n",
            "        [200, 201, 199],\n",
            "        ...,\n",
            "        [143, 146, 144],\n",
            "        [140, 144, 139],\n",
            "        [139, 143, 138]],\n",
            "\n",
            "       [[206, 204, 204],\n",
            "        [204, 204, 204],\n",
            "        [205, 206, 204],\n",
            "        ...,\n",
            "        [147, 150, 148],\n",
            "        [145, 149, 144],\n",
            "        [144, 148, 143]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [210, 218, 217],\n",
            "        [209, 217, 216],\n",
            "        [208, 216, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [207, 215, 214],\n",
            "        [206, 214, 213],\n",
            "        [205, 213, 212]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [206, 214, 213],\n",
            "        [204, 212, 211],\n",
            "        [200, 211, 209]]], dtype=uint8), array([[[209, 209, 203],\n",
            "        [209, 211, 205],\n",
            "        [211, 213, 207],\n",
            "        ...,\n",
            "        [151, 154, 152],\n",
            "        [153, 153, 153],\n",
            "        [153, 154, 152]],\n",
            "\n",
            "       [[210, 210, 204],\n",
            "        [211, 211, 205],\n",
            "        [213, 213, 207],\n",
            "        ...,\n",
            "        [152, 153, 151],\n",
            "        [152, 153, 151],\n",
            "        [153, 154, 152]],\n",
            "\n",
            "       [[211, 210, 206],\n",
            "        [212, 211, 207],\n",
            "        [213, 212, 208],\n",
            "        ...,\n",
            "        [153, 151, 150],\n",
            "        [154, 152, 151],\n",
            "        [155, 153, 152]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [207, 219, 219],\n",
            "        [206, 218, 218],\n",
            "        [207, 217, 217]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [205, 218, 216],\n",
            "        [203, 216, 214],\n",
            "        [204, 215, 213]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [203, 216, 214],\n",
            "        [201, 214, 212],\n",
            "        [202, 213, 211]]], dtype=uint8), array([[[202, 200, 199],\n",
            "        [204, 203, 199],\n",
            "        [207, 206, 202],\n",
            "        ...,\n",
            "        [153, 154, 150],\n",
            "        [151, 152, 148],\n",
            "        [152, 151, 147]],\n",
            "\n",
            "       [[209, 207, 206],\n",
            "        [208, 209, 205],\n",
            "        [211, 212, 208],\n",
            "        ...,\n",
            "        [159, 158, 154],\n",
            "        [161, 158, 154],\n",
            "        [160, 157, 153]],\n",
            "\n",
            "       [[217, 215, 214],\n",
            "        [215, 216, 214],\n",
            "        [215, 216, 214],\n",
            "        ...,\n",
            "        [163, 161, 160],\n",
            "        [164, 162, 161],\n",
            "        [164, 162, 161]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [209, 216, 219],\n",
            "        [210, 217, 220],\n",
            "        [208, 218, 218]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [206, 215, 218],\n",
            "        [205, 214, 217],\n",
            "        [205, 215, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [204, 213, 216],\n",
            "        [203, 212, 215],\n",
            "        [202, 212, 212]]], dtype=uint8), array([[[204, 207, 205],\n",
            "        [204, 207, 205],\n",
            "        [203, 206, 204],\n",
            "        ...,\n",
            "        [147, 148, 146],\n",
            "        [148, 149, 147],\n",
            "        [150, 151, 149]],\n",
            "\n",
            "       [[208, 211, 209],\n",
            "        [207, 210, 208],\n",
            "        [207, 210, 208],\n",
            "        ...,\n",
            "        [145, 148, 146],\n",
            "        [146, 149, 147],\n",
            "        [147, 150, 148]],\n",
            "\n",
            "       [[213, 216, 214],\n",
            "        [212, 216, 211],\n",
            "        [210, 213, 211],\n",
            "        ...,\n",
            "        [143, 148, 146],\n",
            "        [143, 148, 146],\n",
            "        [144, 149, 147]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [199, 210, 208],\n",
            "        [198, 211, 209],\n",
            "        [198, 211, 209]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [197, 207, 207],\n",
            "        [195, 207, 207],\n",
            "        [194, 207, 205]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [195, 205, 205],\n",
            "        [192, 204, 204],\n",
            "        [192, 205, 203]]], dtype=uint8), array([[[200, 200, 200],\n",
            "        [200, 201, 199],\n",
            "        [200, 200, 200],\n",
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            "        [158, 162, 163],\n",
            "        [158, 162, 163]],\n",
            "\n",
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            "        [202, 203, 201],\n",
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            "        [146, 150, 151]],\n",
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            "        [150, 154, 155]],\n",
            "\n",
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            "        [206, 214, 213],\n",
            "        [205, 213, 212]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [200, 211, 209],\n",
            "        [200, 211, 209]],\n",
            "\n",
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            "        [197, 208, 206]]], dtype=uint8), array([[[212, 215, 213],\n",
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            "        [155, 154, 156],\n",
            "        [153, 155, 155]],\n",
            "\n",
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            "        [211, 215, 210],\n",
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            "        [153, 153, 153],\n",
            "        [152, 155, 153]],\n",
            "\n",
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            "        [151, 153, 153],\n",
            "        [152, 155, 153]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [215, 223, 222],\n",
            "        [213, 224, 221]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [211, 222, 220],\n",
            "        [210, 221, 218]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [209, 220, 218],\n",
            "        [206, 220, 216]]], dtype=uint8), array([[[203, 200, 195],\n",
            "        [203, 200, 195],\n",
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            "\n",
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            "        [204, 205, 196],\n",
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            "        [148, 157, 160],\n",
            "        [149, 157, 157]],\n",
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            "        ...,\n",
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            "        [150, 157, 160],\n",
            "        [151, 156, 157]],\n",
            "\n",
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            "        [194, 211, 208],\n",
            "        [195, 209, 207]],\n",
            "\n",
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            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [193, 210, 207],\n",
            "        [194, 208, 206]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [190, 209, 206],\n",
            "        [193, 207, 205]]], dtype=uint8), array([[[209, 208, 204],\n",
            "        [209, 208, 204],\n",
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            "        [147, 153, 152]],\n",
            "\n",
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            "        [208, 206, 205],\n",
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            "        [144, 152, 151]],\n",
            "\n",
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            "        [209, 207, 206],\n",
            "        ...,\n",
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            "        [143, 151, 150],\n",
            "        [143, 151, 150]],\n",
            "\n",
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            "\n",
            "       [[255, 255, 255],\n",
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            "        [255, 255, 255],\n",
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            "        [204, 219, 215],\n",
            "        [204, 218, 214]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [202, 218, 211],\n",
            "        [201, 215, 211]],\n",
            "\n",
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            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [197, 215, 208],\n",
            "        [197, 212, 208]]], dtype=uint8), array([[[195, 201, 196],\n",
            "        [196, 203, 196],\n",
            "        [198, 204, 199],\n",
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            "        [179, 194, 186]],\n",
            "\n",
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            "        [199, 205, 200],\n",
            "        ...,\n",
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            "        [142, 157, 149],\n",
            "        [142, 156, 150]],\n",
            "\n",
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            "        [202, 206, 201],\n",
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            "        [146, 158, 152],\n",
            "        [146, 158, 152]],\n",
            "\n",
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            "        [202, 212, 212],\n",
            "        [203, 211, 210]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [199, 208, 211],\n",
            "        [200, 208, 208]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [197, 206, 209],\n",
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            "        [147, 153, 152]],\n",
            "\n",
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            "        [144, 152, 151]],\n",
            "\n",
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            "        [209, 207, 206],\n",
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            "        [143, 151, 150],\n",
            "        [143, 151, 150]],\n",
            "\n",
            "       ...,\n",
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            "        [204, 219, 215],\n",
            "        [204, 218, 214]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "        [202, 218, 211],\n",
            "        [201, 215, 211]],\n",
            "\n",
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            "        [197, 212, 208]]], dtype=uint8), array([[[204, 205, 203],\n",
            "        [204, 205, 203],\n",
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            "\n",
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            "        [204, 207, 205],\n",
            "        [204, 207, 205],\n",
            "        ...,\n",
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            "        [170, 163, 166],\n",
            "        [166, 161, 162]],\n",
            "\n",
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            "        [205, 210, 208],\n",
            "        ...,\n",
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            "        [164, 159, 160],\n",
            "        [161, 159, 159]],\n",
            "\n",
            "       ...,\n",
            "\n",
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            "        [211, 221, 221],\n",
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            "\n",
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            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [205, 217, 217],\n",
            "        [210, 215, 216]],\n",
            "\n",
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            "        [255, 255, 255],\n",
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            "\n",
            "       [[210, 210, 204],\n",
            "        [209, 212, 203],\n",
            "        [209, 212, 203],\n",
            "        ...,\n",
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            "        [158, 158, 152],\n",
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            "\n",
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            "        [212, 214, 208],\n",
            "        ...,\n",
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            "        [157, 157, 151],\n",
            "        [154, 156, 150]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [206, 216, 216]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [203, 216, 214],\n",
            "        [203, 216, 214]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [202, 216, 214],\n",
            "        [202, 215, 213]]], dtype=uint8), array([[[220, 215, 214],\n",
            "        [218, 213, 212],\n",
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            "        [145, 148, 139]],\n",
            "\n",
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            "        [213, 208, 207],\n",
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            "\n",
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            "        [210, 206, 205],\n",
            "        ...,\n",
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            "        [150, 149, 145],\n",
            "        [148, 149, 145]],\n",
            "\n",
            "       ...,\n",
            "\n",
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            "        [204, 216, 216],\n",
            "        [204, 216, 216]],\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        ...,\n",
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            "        [201, 215, 214],\n",
            "        [201, 213, 213]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
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            "        ...,\n",
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            "        [199, 213, 212],\n",
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            "        [204, 205, 201],\n",
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            "        [153, 156, 160],\n",
            "        [154, 157, 161]],\n",
            "\n",
            "       [[212, 213, 211],\n",
            "        [209, 210, 206],\n",
            "        [207, 208, 204],\n",
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            "        [152, 155, 159],\n",
            "        [153, 156, 160]],\n",
            "\n",
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            "        [211, 212, 208],\n",
            "        [210, 211, 207],\n",
            "        ...,\n",
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            "        [152, 156, 157],\n",
            "        [152, 156, 157]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [208, 218, 218],\n",
            "        [207, 218, 216]],\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [255, 255, 255],\n",
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            "        [205, 215, 215],\n",
            "        [205, 216, 214]],\n",
            "\n",
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            "        [203, 213, 213],\n",
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            "        [200, 204, 199],\n",
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            "        [155, 148, 151],\n",
            "        [156, 150, 151]],\n",
            "\n",
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            "        [197, 203, 198],\n",
            "        [198, 202, 197],\n",
            "        ...,\n",
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            "        [154, 147, 150],\n",
            "        [155, 149, 150]],\n",
            "\n",
            "       [[199, 202, 200],\n",
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            "        [200, 203, 201],\n",
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            "        [153, 148, 150],\n",
            "        [152, 147, 148]],\n",
            "\n",
            "       ...,\n",
            "\n",
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            "        [202, 210, 210],\n",
            "        [204, 209, 210]],\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [200, 208, 208],\n",
            "        [202, 207, 208]],\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [199, 207, 207],\n",
            "        [200, 205, 206]]], dtype=uint8), array([[[196, 203, 200],\n",
            "        [194, 201, 196],\n",
            "        [193, 200, 197],\n",
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            "        [146, 151, 150],\n",
            "        [146, 151, 149]],\n",
            "\n",
            "       [[195, 202, 199],\n",
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            "        [193, 203, 197],\n",
            "        ...,\n",
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            "        [146, 151, 150],\n",
            "        [146, 151, 149]],\n",
            "\n",
            "       [[195, 202, 199],\n",
            "        [194, 204, 198],\n",
            "        [195, 205, 199],\n",
            "        ...,\n",
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            "        [153, 151, 151],\n",
            "        [152, 150, 149]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [200, 212, 212],\n",
            "        [201, 211, 211]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [198, 210, 210],\n",
            "        [199, 209, 209]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
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            "        [196, 208, 208],\n",
            "        [197, 208, 206]]], dtype=uint8), array([[[202, 208, 203],\n",
            "        [203, 207, 201],\n",
            "        [202, 206, 201],\n",
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            "        [177, 184, 177],\n",
            "        [177, 184, 177]],\n",
            "\n",
            "       [[204, 208, 202],\n",
            "        [203, 208, 199],\n",
            "        [202, 206, 200],\n",
            "        ...,\n",
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            "        [158, 166, 159],\n",
            "        [159, 166, 159]],\n",
            "\n",
            "       [[204, 208, 202],\n",
            "        [203, 207, 201],\n",
            "        [202, 206, 200],\n",
            "        ...,\n",
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            "        [143, 150, 143],\n",
            "        [141, 148, 141]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        [255, 255, 255],\n",
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            "        [207, 219, 219],\n",
            "        [209, 219, 219]],\n",
            "\n",
            "       [[255, 255, 255],\n",
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            "        ...,\n",
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            "        [205, 217, 217],\n",
            "        [204, 216, 216]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
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            "        ...,\n",
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            "        [202, 216, 215],\n",
            "        [202, 214, 214]]], dtype=uint8), array([[[210, 211, 209],\n",
            "        [210, 211, 207],\n",
            "        [210, 211, 209],\n",
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            "        [156, 153, 149],\n",
            "        [154, 154, 148]],\n",
            "\n",
            "       [[211, 212, 208],\n",
            "        [211, 212, 208],\n",
            "        [211, 212, 208],\n",
            "        ...,\n",
            "        [154, 153, 149],\n",
            "        [156, 153, 149],\n",
            "        [156, 153, 149]],\n",
            "\n",
            "       [[212, 213, 209],\n",
            "        [211, 212, 208],\n",
            "        [211, 212, 208],\n",
            "        ...,\n",
            "        [154, 152, 151],\n",
            "        [156, 152, 151],\n",
            "        [155, 154, 150]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [214, 219, 217],\n",
            "        [215, 218, 216],\n",
            "        [214, 217, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [214, 217, 215],\n",
            "        [214, 215, 213],\n",
            "        [213, 214, 212]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [213, 216, 214],\n",
            "        [212, 213, 211],\n",
            "        [210, 211, 209]]], dtype=uint8), array([[[207, 205, 205],\n",
            "        [210, 206, 205],\n",
            "        [210, 205, 206],\n",
            "        ...,\n",
            "        [150, 148, 147],\n",
            "        [152, 148, 147],\n",
            "        [152, 149, 145]],\n",
            "\n",
            "       [[209, 207, 207],\n",
            "        [212, 207, 206],\n",
            "        [212, 206, 207],\n",
            "        ...,\n",
            "        [149, 147, 147],\n",
            "        [151, 146, 147],\n",
            "        [149, 147, 146]],\n",
            "\n",
            "       [[210, 208, 207],\n",
            "        [211, 208, 204],\n",
            "        [211, 207, 206],\n",
            "        ...,\n",
            "        [144, 147, 145],\n",
            "        [147, 148, 146],\n",
            "        [148, 149, 147]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [203, 212, 209],\n",
            "        [202, 211, 208],\n",
            "        [203, 210, 207]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [202, 212, 206],\n",
            "        [199, 211, 205],\n",
            "        [200, 209, 206]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [201, 211, 205],\n",
            "        [198, 210, 204],\n",
            "        [198, 207, 204]]], dtype=uint8), array([[[202, 202, 196],\n",
            "        [198, 198, 192],\n",
            "        [192, 192, 186],\n",
            "        ...,\n",
            "        [140, 141, 139],\n",
            "        [140, 140, 140],\n",
            "        [139, 142, 140]],\n",
            "\n",
            "       [[205, 202, 197],\n",
            "        [203, 200, 195],\n",
            "        [200, 197, 192],\n",
            "        ...,\n",
            "        [156, 157, 155],\n",
            "        [156, 156, 156],\n",
            "        [155, 157, 157]],\n",
            "\n",
            "       [[201, 198, 194],\n",
            "        [202, 199, 195],\n",
            "        [203, 200, 196],\n",
            "        ...,\n",
            "        [153, 156, 154],\n",
            "        [155, 155, 155],\n",
            "        [153, 155, 155]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [201, 212, 210],\n",
            "        [200, 211, 209],\n",
            "        [202, 210, 209]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [199, 210, 208],\n",
            "        [198, 209, 207],\n",
            "        [199, 207, 206]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [197, 208, 206],\n",
            "        [196, 207, 205],\n",
            "        [198, 206, 205]]], dtype=uint8), array([[[198, 202, 197],\n",
            "        [198, 202, 197],\n",
            "        [199, 203, 198],\n",
            "        ...,\n",
            "        [146, 151, 154],\n",
            "        [148, 149, 153],\n",
            "        [148, 150, 151]],\n",
            "\n",
            "       [[200, 204, 199],\n",
            "        [200, 204, 199],\n",
            "        [201, 205, 200],\n",
            "        ...,\n",
            "        [147, 151, 152],\n",
            "        [150, 149, 153],\n",
            "        [151, 148, 150]],\n",
            "\n",
            "       [[203, 207, 202],\n",
            "        [203, 207, 202],\n",
            "        [204, 208, 203],\n",
            "        ...,\n",
            "        [145, 151, 150],\n",
            "        [147, 149, 150],\n",
            "        [149, 149, 149]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [209, 216, 219],\n",
            "        [208, 215, 218],\n",
            "        [207, 215, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [207, 213, 218],\n",
            "        [203, 212, 216],\n",
            "        [203, 211, 211]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [205, 211, 216],\n",
            "        [201, 210, 214],\n",
            "        [199, 209, 209]]], dtype=uint8), array([[[211, 210, 212],\n",
            "        [211, 210, 214],\n",
            "        [210, 209, 211],\n",
            "        ...,\n",
            "        [170, 168, 160],\n",
            "        [163, 162, 152],\n",
            "        [162, 161, 151]],\n",
            "\n",
            "       [[211, 210, 212],\n",
            "        [210, 209, 211],\n",
            "        [210, 209, 211],\n",
            "        ...,\n",
            "        [167, 168, 159],\n",
            "        [165, 166, 156],\n",
            "        [167, 168, 158]],\n",
            "\n",
            "       [[210, 209, 211],\n",
            "        [210, 209, 211],\n",
            "        [210, 209, 211],\n",
            "        ...,\n",
            "        [157, 160, 151],\n",
            "        [159, 162, 153],\n",
            "        [160, 165, 156]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [206, 219, 217],\n",
            "        [205, 218, 216],\n",
            "        [206, 217, 215]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [204, 216, 216],\n",
            "        [203, 215, 215],\n",
            "        [202, 215, 213]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [203, 215, 215],\n",
            "        [200, 214, 213],\n",
            "        [200, 213, 211]]], dtype=uint8)], [array([[[238, 244, 233],\n",
            "        [245, 251, 240],\n",
            "        [247, 251, 240],\n",
            "        ...,\n",
            "        [185, 189, 194],\n",
            "        [184, 188, 193],\n",
            "        [184, 188, 193]],\n",
            "\n",
            "       [[242, 248, 237],\n",
            "        [242, 246, 235],\n",
            "        [239, 240, 230],\n",
            "        ...,\n",
            "        [187, 190, 198],\n",
            "        [187, 190, 198],\n",
            "        [187, 190, 198]],\n",
            "\n",
            "       [[230, 238, 228],\n",
            "        [216, 221, 212],\n",
            "        [206, 209, 200],\n",
            "        ...,\n",
            "        [190, 194, 199],\n",
            "        [191, 195, 200],\n",
            "        [191, 195, 200]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[212, 216, 211],\n",
            "        [212, 216, 210],\n",
            "        [213, 214, 210],\n",
            "        ...,\n",
            "        [220, 225, 216],\n",
            "        [219, 224, 215],\n",
            "        [217, 225, 215]],\n",
            "\n",
            "       [[212, 216, 210],\n",
            "        [210, 218, 208],\n",
            "        [212, 216, 210],\n",
            "        ...,\n",
            "        [217, 225, 215],\n",
            "        [216, 224, 214],\n",
            "        [216, 224, 214]],\n",
            "\n",
            "       [[212, 216, 210],\n",
            "        [210, 218, 208],\n",
            "        [212, 217, 208],\n",
            "        ...,\n",
            "        [216, 224, 214],\n",
            "        [216, 224, 213],\n",
            "        [214, 224, 212]]], dtype=uint8), array([[[194, 208, 207],\n",
            "        [193, 207, 206],\n",
            "        [193, 207, 206],\n",
            "        ...,\n",
            "        [192, 188, 187],\n",
            "        [191, 186, 187],\n",
            "        [190, 186, 185]],\n",
            "\n",
            "       [[195, 208, 210],\n",
            "        [192, 208, 207],\n",
            "        [193, 207, 206],\n",
            "        ...,\n",
            "        [197, 189, 189],\n",
            "        [197, 189, 189],\n",
            "        [196, 188, 188]],\n",
            "\n",
            "       [[194, 209, 211],\n",
            "        [191, 209, 208],\n",
            "        [191, 207, 206],\n",
            "        ...,\n",
            "        [201, 192, 189],\n",
            "        [200, 191, 188],\n",
            "        [198, 191, 188]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [220, 226, 225],\n",
            "        [218, 226, 225],\n",
            "        [218, 226, 225]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [220, 228, 227],\n",
            "        [219, 227, 226],\n",
            "        [221, 227, 226]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [222, 230, 229],\n",
            "        [222, 230, 229],\n",
            "        [223, 229, 228]]], dtype=uint8), array([[[201, 212, 204],\n",
            "        [203, 211, 204],\n",
            "        [200, 211, 203],\n",
            "        ...,\n",
            "        [247, 251, 252],\n",
            "        [247, 252, 253],\n",
            "        [249, 255, 254]],\n",
            "\n",
            "       [[203, 214, 206],\n",
            "        [202, 213, 203],\n",
            "        [201, 212, 202],\n",
            "        ...,\n",
            "        [189, 195, 200],\n",
            "        [194, 200, 205],\n",
            "        [195, 204, 207]],\n",
            "\n",
            "       [[203, 216, 208],\n",
            "        [202, 215, 207],\n",
            "        [201, 214, 206],\n",
            "        ...,\n",
            "        [146, 153, 162],\n",
            "        [147, 154, 163],\n",
            "        [147, 157, 164]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[231, 233, 233],\n",
            "        [230, 232, 232],\n",
            "        [230, 232, 232],\n",
            "        ...,\n",
            "        [205, 210, 211],\n",
            "        [205, 210, 211],\n",
            "        [205, 211, 210]],\n",
            "\n",
            "       [[232, 232, 232],\n",
            "        [233, 231, 231],\n",
            "        [231, 231, 231],\n",
            "        ...,\n",
            "        [218, 226, 225],\n",
            "        [220, 226, 225],\n",
            "        [220, 227, 224]],\n",
            "\n",
            "       [[233, 231, 231],\n",
            "        [235, 230, 231],\n",
            "        [233, 231, 230],\n",
            "        ...,\n",
            "        [226, 235, 232],\n",
            "        [225, 232, 229],\n",
            "        [224, 231, 228]]], dtype=uint8), array([[[192, 203, 200],\n",
            "        [192, 204, 198],\n",
            "        [191, 202, 199],\n",
            "        ...,\n",
            "        [187, 187, 187],\n",
            "        [190, 188, 188],\n",
            "        [190, 188, 187]],\n",
            "\n",
            "       [[189, 204, 200],\n",
            "        [188, 204, 197],\n",
            "        [189, 203, 197],\n",
            "        ...,\n",
            "        [185, 181, 180],\n",
            "        [185, 180, 181],\n",
            "        [186, 181, 180]],\n",
            "\n",
            "       [[186, 203, 199],\n",
            "        [186, 203, 199],\n",
            "        [187, 202, 198],\n",
            "        ...,\n",
            "        [185, 180, 177],\n",
            "        [187, 179, 179],\n",
            "        [188, 181, 178]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [202, 218, 217],\n",
            "        [200, 216, 215],\n",
            "        [199, 216, 213]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [206, 222, 221],\n",
            "        [203, 221, 220],\n",
            "        [202, 221, 218]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [204, 222, 221],\n",
            "        [204, 222, 221],\n",
            "        [203, 222, 219]]], dtype=uint8), array([[[229, 234, 232],\n",
            "        [225, 230, 228],\n",
            "        [225, 230, 228],\n",
            "        ...,\n",
            "        [208, 210, 211],\n",
            "        [209, 208, 210],\n",
            "        [208, 208, 208]],\n",
            "\n",
            "       [[230, 235, 233],\n",
            "        [231, 236, 234],\n",
            "        [235, 240, 238],\n",
            "        ...,\n",
            "        [218, 217, 219],\n",
            "        [216, 215, 217],\n",
            "        [217, 215, 215]],\n",
            "\n",
            "       [[236, 241, 239],\n",
            "        [241, 247, 242],\n",
            "        [245, 252, 247],\n",
            "        ...,\n",
            "        [242, 239, 241],\n",
            "        [241, 239, 239],\n",
            "        [239, 239, 239]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[237, 240, 238],\n",
            "        [235, 239, 234],\n",
            "        [233, 237, 232],\n",
            "        ...,\n",
            "        [178, 191, 193],\n",
            "        [177, 189, 193],\n",
            "        [177, 190, 192]],\n",
            "\n",
            "       [[236, 239, 237],\n",
            "        [235, 239, 234],\n",
            "        [233, 237, 232],\n",
            "        ...,\n",
            "        [175, 187, 191],\n",
            "        [176, 188, 192],\n",
            "        [176, 189, 191]],\n",
            "\n",
            "       [[239, 240, 238],\n",
            "        [236, 240, 235],\n",
            "        [235, 239, 234],\n",
            "        ...,\n",
            "        [170, 182, 186],\n",
            "        [171, 183, 187],\n",
            "        [172, 185, 187]]], dtype=uint8), array([[[192, 209, 205],\n",
            "        [191, 209, 202],\n",
            "        [189, 206, 202],\n",
            "        ...,\n",
            "        [196, 214, 215],\n",
            "        [194, 214, 215],\n",
            "        [196, 214, 215]],\n",
            "\n",
            "       [[192, 213, 204],\n",
            "        [191, 212, 203],\n",
            "        [191, 209, 202],\n",
            "        ...,\n",
            "        [196, 213, 216],\n",
            "        [196, 213, 216],\n",
            "        [197, 214, 217]],\n",
            "\n",
            "       [[198, 216, 209],\n",
            "        [197, 215, 208],\n",
            "        [194, 212, 205],\n",
            "        ...,\n",
            "        [196, 214, 215],\n",
            "        [197, 214, 217],\n",
            "        [197, 214, 217]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[219, 220, 218],\n",
            "        [216, 219, 217],\n",
            "        [216, 220, 215],\n",
            "        ...,\n",
            "        [227, 232, 230],\n",
            "        [226, 231, 229],\n",
            "        [226, 231, 229]],\n",
            "\n",
            "       [[220, 221, 219],\n",
            "        [218, 221, 219],\n",
            "        [218, 222, 217],\n",
            "        ...,\n",
            "        [225, 230, 228],\n",
            "        [225, 230, 228],\n",
            "        [225, 230, 228]],\n",
            "\n",
            "       [[222, 223, 221],\n",
            "        [220, 223, 221],\n",
            "        [220, 224, 219],\n",
            "        ...,\n",
            "        [224, 229, 227],\n",
            "        [224, 229, 227],\n",
            "        [224, 229, 227]]], dtype=uint8), array([[[213, 227, 225],\n",
            "        [212, 226, 224],\n",
            "        [212, 225, 223],\n",
            "        ...,\n",
            "        [185, 189, 183],\n",
            "        [186, 188, 182],\n",
            "        [186, 189, 180]],\n",
            "\n",
            "       [[212, 226, 224],\n",
            "        [213, 227, 225],\n",
            "        [216, 229, 227],\n",
            "        ...,\n",
            "        [174, 178, 173],\n",
            "        [173, 177, 172],\n",
            "        [173, 177, 171]],\n",
            "\n",
            "       [[218, 231, 229],\n",
            "        [219, 232, 230],\n",
            "        [222, 233, 231],\n",
            "        ...,\n",
            "        [158, 162, 157],\n",
            "        [156, 162, 157],\n",
            "        [157, 161, 155]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [198, 209, 207],\n",
            "        [196, 206, 206],\n",
            "        [197, 205, 204]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [212, 221, 218],\n",
            "        [211, 219, 218],\n",
            "        [210, 219, 216]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [213, 220, 217],\n",
            "        [210, 219, 216],\n",
            "        [210, 220, 214]]], dtype=uint8), array([[[214, 221, 224],\n",
            "        [206, 213, 216],\n",
            "        [195, 205, 205],\n",
            "        ...,\n",
            "        [203, 197, 192],\n",
            "        [203, 197, 190],\n",
            "        [201, 198, 190]],\n",
            "\n",
            "       [[198, 205, 208],\n",
            "        [195, 202, 205],\n",
            "        [192, 202, 202],\n",
            "        ...,\n",
            "        [205, 197, 190],\n",
            "        [204, 197, 188],\n",
            "        [204, 197, 188]],\n",
            "\n",
            "       [[195, 203, 203],\n",
            "        [196, 204, 204],\n",
            "        [196, 206, 206],\n",
            "        ...,\n",
            "        [201, 195, 188],\n",
            "        [201, 196, 187],\n",
            "        [201, 196, 187]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [176, 184, 184],\n",
            "        [171, 182, 180],\n",
            "        [171, 180, 177]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [203, 211, 210],\n",
            "        [202, 210, 209],\n",
            "        [203, 210, 207]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [209, 217, 216],\n",
            "        [211, 219, 218],\n",
            "        [215, 222, 219]]], dtype=uint8), array([[[220, 222, 222],\n",
            "        [219, 221, 221],\n",
            "        [217, 219, 219],\n",
            "        ...,\n",
            "        [208, 215, 218],\n",
            "        [208, 215, 218],\n",
            "        [208, 215, 218]],\n",
            "\n",
            "       [[219, 221, 221],\n",
            "        [218, 220, 221],\n",
            "        [217, 219, 219],\n",
            "        ...,\n",
            "        [205, 214, 217],\n",
            "        [207, 214, 217],\n",
            "        [205, 214, 217]],\n",
            "\n",
            "       [[216, 221, 219],\n",
            "        [216, 221, 220],\n",
            "        [217, 219, 219],\n",
            "        ...,\n",
            "        [202, 214, 216],\n",
            "        [204, 213, 216],\n",
            "        [204, 213, 216]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[222, 223, 219],\n",
            "        [222, 223, 219],\n",
            "        [223, 224, 220],\n",
            "        ...,\n",
            "        [193, 209, 202],\n",
            "        [192, 208, 201],\n",
            "        [193, 207, 201]],\n",
            "\n",
            "       [[222, 223, 219],\n",
            "        [223, 225, 219],\n",
            "        [222, 226, 220],\n",
            "        ...,\n",
            "        [193, 209, 202],\n",
            "        [190, 208, 201],\n",
            "        [192, 208, 201]],\n",
            "\n",
            "       [[224, 223, 219],\n",
            "        [223, 225, 219],\n",
            "        [222, 226, 220],\n",
            "        ...,\n",
            "        [206, 224, 217],\n",
            "        [205, 223, 216],\n",
            "        [206, 222, 215]]], dtype=uint8), array([[[211, 217, 216],\n",
            "        [208, 216, 215],\n",
            "        [208, 216, 216],\n",
            "        ...,\n",
            "        [243, 250, 243],\n",
            "        [243, 249, 244],\n",
            "        [242, 249, 242]],\n",
            "\n",
            "       [[213, 218, 217],\n",
            "        [211, 217, 216],\n",
            "        [210, 216, 215],\n",
            "        ...,\n",
            "        [243, 247, 242],\n",
            "        [244, 245, 241],\n",
            "        [243, 245, 239]],\n",
            "\n",
            "       [[213, 220, 217],\n",
            "        [213, 220, 217],\n",
            "        [212, 219, 216],\n",
            "        ...,\n",
            "        [241, 246, 244],\n",
            "        [243, 246, 244],\n",
            "        [243, 245, 245]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[244, 249, 240],\n",
            "        [244, 250, 239],\n",
            "        [246, 250, 239],\n",
            "        ...,\n",
            "        [206, 211, 214],\n",
            "        [203, 207, 212],\n",
            "        [202, 207, 208]],\n",
            "\n",
            "       [[244, 247, 238],\n",
            "        [244, 248, 237],\n",
            "        [245, 249, 238],\n",
            "        ...,\n",
            "        [216, 220, 221],\n",
            "        [214, 217, 221],\n",
            "        [214, 216, 216]],\n",
            "\n",
            "       [[243, 246, 237],\n",
            "        [243, 247, 236],\n",
            "        [244, 248, 237],\n",
            "        ...,\n",
            "        [237, 241, 242],\n",
            "        [238, 240, 241],\n",
            "        [237, 240, 238]]], dtype=uint8), array([[[187, 199, 193],\n",
            "        [185, 199, 193],\n",
            "        [185, 199, 193],\n",
            "        ...,\n",
            "        [179, 174, 171],\n",
            "        [180, 175, 172],\n",
            "        [182, 175, 172]],\n",
            "\n",
            "       [[191, 205, 199],\n",
            "        [190, 207, 198],\n",
            "        [191, 207, 200],\n",
            "        ...,\n",
            "        [185, 180, 177],\n",
            "        [185, 180, 177],\n",
            "        [186, 180, 175]],\n",
            "\n",
            "       [[193, 209, 202],\n",
            "        [194, 211, 202],\n",
            "        [195, 211, 204],\n",
            "        ...,\n",
            "        [192, 188, 183],\n",
            "        [193, 187, 182],\n",
            "        [191, 185, 178]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [180, 184, 185],\n",
            "        [178, 183, 182],\n",
            "        [176, 181, 180]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [171, 175, 176],\n",
            "        [169, 174, 173],\n",
            "        [169, 171, 171]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [188, 192, 193],\n",
            "        [186, 191, 190],\n",
            "        [186, 188, 188]]], dtype=uint8), array([[[201, 203, 203],\n",
            "        [202, 204, 204],\n",
            "        [201, 206, 205],\n",
            "        ...,\n",
            "        [255, 255, 252],\n",
            "        [254, 255, 253],\n",
            "        [254, 255, 251]],\n",
            "\n",
            "       [[206, 206, 206],\n",
            "        [204, 206, 207],\n",
            "        [205, 207, 208],\n",
            "        ...,\n",
            "        [253, 255, 252],\n",
            "        [255, 255, 254],\n",
            "        [255, 255, 252]],\n",
            "\n",
            "       [[206, 208, 208],\n",
            "        [207, 209, 209],\n",
            "        [207, 209, 209],\n",
            "        ...,\n",
            "        [249, 254, 252],\n",
            "        [251, 253, 253],\n",
            "        [251, 254, 252]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[245, 247, 241],\n",
            "        [245, 247, 241],\n",
            "        [246, 248, 242],\n",
            "        ...,\n",
            "        [218, 239, 231],\n",
            "        [218, 239, 231],\n",
            "        [219, 240, 232]],\n",
            "\n",
            "       [[244, 245, 241],\n",
            "        [246, 245, 241],\n",
            "        [246, 245, 241],\n",
            "        ...,\n",
            "        [213, 235, 230],\n",
            "        [214, 236, 231],\n",
            "        [214, 236, 231]],\n",
            "\n",
            "       [[245, 244, 240],\n",
            "        [245, 244, 240],\n",
            "        [245, 244, 240],\n",
            "        ...,\n",
            "        [209, 230, 227],\n",
            "        [209, 232, 227],\n",
            "        [209, 232, 227]]], dtype=uint8), array([[[215, 222, 217],\n",
            "        [207, 218, 210],\n",
            "        [201, 211, 205],\n",
            "        ...,\n",
            "        [226, 237, 235],\n",
            "        [224, 235, 233],\n",
            "        [226, 234, 233]],\n",
            "\n",
            "       [[188, 198, 192],\n",
            "        [183, 196, 188],\n",
            "        [180, 192, 186],\n",
            "        ...,\n",
            "        [226, 237, 235],\n",
            "        [227, 235, 234],\n",
            "        [228, 234, 233]],\n",
            "\n",
            "       [[199, 213, 207],\n",
            "        [198, 215, 206],\n",
            "        [198, 214, 207],\n",
            "        ...,\n",
            "        [233, 241, 240],\n",
            "        [234, 240, 239],\n",
            "        [234, 240, 239]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[183, 187, 192],\n",
            "        [184, 189, 192],\n",
            "        [185, 190, 193],\n",
            "        ...,\n",
            "        [210, 224, 220],\n",
            "        [196, 210, 206],\n",
            "        [191, 202, 199]],\n",
            "\n",
            "       [[189, 192, 200],\n",
            "        [188, 194, 199],\n",
            "        [190, 196, 201],\n",
            "        ...,\n",
            "        [217, 231, 227],\n",
            "        [208, 222, 218],\n",
            "        [204, 215, 212]],\n",
            "\n",
            "       [[200, 203, 211],\n",
            "        [199, 205, 212],\n",
            "        [200, 206, 211],\n",
            "        ...,\n",
            "        [217, 230, 228],\n",
            "        [214, 228, 224],\n",
            "        [214, 225, 222]]], dtype=uint8), array([[[207, 214, 209],\n",
            "        [205, 212, 207],\n",
            "        [202, 212, 206],\n",
            "        ...,\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255]],\n",
            "\n",
            "       [[206, 212, 207],\n",
            "        [204, 211, 206],\n",
            "        [202, 212, 206],\n",
            "        ...,\n",
            "        [254, 254, 254],\n",
            "        [253, 253, 253],\n",
            "        [251, 251, 251]],\n",
            "\n",
            "       [[204, 211, 206],\n",
            "        [204, 211, 206],\n",
            "        [203, 213, 207],\n",
            "        ...,\n",
            "        [212, 214, 214],\n",
            "        [209, 211, 212],\n",
            "        [204, 209, 208]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[205, 206, 210],\n",
            "        [205, 207, 208],\n",
            "        [206, 208, 209],\n",
            "        ...,\n",
            "        [219, 230, 227],\n",
            "        [217, 229, 223],\n",
            "        [215, 227, 221]],\n",
            "\n",
            "       [[204, 205, 209],\n",
            "        [206, 205, 207],\n",
            "        [204, 206, 207],\n",
            "        ...,\n",
            "        [225, 235, 229],\n",
            "        [221, 233, 227],\n",
            "        [219, 231, 225]],\n",
            "\n",
            "       [[205, 206, 210],\n",
            "        [206, 205, 209],\n",
            "        [204, 206, 207],\n",
            "        ...,\n",
            "        [225, 235, 229],\n",
            "        [221, 233, 227],\n",
            "        [219, 232, 224]]], dtype=uint8), array([[[199, 205, 200],\n",
            "        [197, 203, 198],\n",
            "        [197, 203, 198],\n",
            "        ...,\n",
            "        [205, 210, 211],\n",
            "        [205, 211, 210],\n",
            "        [205, 210, 211]],\n",
            "\n",
            "       [[198, 205, 200],\n",
            "        [197, 204, 199],\n",
            "        [199, 205, 200],\n",
            "        ...,\n",
            "        [206, 210, 211],\n",
            "        [206, 211, 210],\n",
            "        [205, 210, 211]],\n",
            "\n",
            "       [[199, 206, 201],\n",
            "        [200, 207, 202],\n",
            "        [202, 208, 203],\n",
            "        ...,\n",
            "        [205, 210, 211],\n",
            "        [206, 211, 210],\n",
            "        [205, 210, 211]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[234, 241, 234],\n",
            "        [233, 241, 234],\n",
            "        [234, 241, 234],\n",
            "        ...,\n",
            "        [222, 230, 230],\n",
            "        [221, 228, 231],\n",
            "        [220, 227, 230]],\n",
            "\n",
            "       [[236, 243, 236],\n",
            "        [235, 243, 236],\n",
            "        [236, 243, 236],\n",
            "        ...,\n",
            "        [216, 225, 228],\n",
            "        [216, 225, 229],\n",
            "        [217, 224, 227]],\n",
            "\n",
            "       [[236, 243, 236],\n",
            "        [235, 243, 236],\n",
            "        [236, 243, 236],\n",
            "        ...,\n",
            "        [220, 229, 232],\n",
            "        [221, 230, 234],\n",
            "        [224, 231, 234]]], dtype=uint8), array([[[190, 204, 198],\n",
            "        [192, 204, 198],\n",
            "        [193, 203, 197],\n",
            "        ...,\n",
            "        [212, 222, 216],\n",
            "        [210, 220, 214],\n",
            "        [209, 219, 213]],\n",
            "\n",
            "       [[192, 204, 198],\n",
            "        [191, 204, 196],\n",
            "        [193, 203, 197],\n",
            "        ...,\n",
            "        [221, 231, 225],\n",
            "        [219, 229, 223],\n",
            "        [216, 228, 222]],\n",
            "\n",
            "       [[191, 203, 197],\n",
            "        [191, 203, 197],\n",
            "        [193, 203, 197],\n",
            "        ...,\n",
            "        [244, 251, 246],\n",
            "        [240, 247, 242],\n",
            "        [238, 245, 240]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[209, 210, 208],\n",
            "        [208, 209, 205],\n",
            "        [207, 208, 204],\n",
            "        ...,\n",
            "        [190, 202, 196],\n",
            "        [190, 203, 195],\n",
            "        [190, 203, 195]],\n",
            "\n",
            "       [[210, 211, 209],\n",
            "        [209, 210, 206],\n",
            "        [208, 209, 205],\n",
            "        ...,\n",
            "        [207, 219, 213],\n",
            "        [209, 222, 214],\n",
            "        [209, 222, 214]],\n",
            "\n",
            "       [[210, 211, 209],\n",
            "        [210, 211, 207],\n",
            "        [209, 210, 206],\n",
            "        ...,\n",
            "        [211, 223, 217],\n",
            "        [212, 225, 217],\n",
            "        [212, 225, 217]]], dtype=uint8), array([[[203, 212, 209],\n",
            "        [202, 211, 208],\n",
            "        [202, 211, 208],\n",
            "        ...,\n",
            "        [253, 255, 255],\n",
            "        [253, 255, 254],\n",
            "        [253, 255, 254]],\n",
            "\n",
            "       [[203, 212, 209],\n",
            "        [203, 212, 209],\n",
            "        [202, 211, 208],\n",
            "        ...,\n",
            "        [223, 225, 225],\n",
            "        [224, 227, 225],\n",
            "        [223, 226, 224]],\n",
            "\n",
            "       [[202, 213, 210],\n",
            "        [202, 213, 210],\n",
            "        [201, 212, 209],\n",
            "        ...,\n",
            "        [181, 186, 185],\n",
            "        [183, 188, 186],\n",
            "        [183, 188, 186]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[210, 210, 204],\n",
            "        [209, 209, 203],\n",
            "        [209, 208, 204],\n",
            "        ...,\n",
            "        [210, 224, 223],\n",
            "        [212, 224, 224],\n",
            "        [212, 226, 224]],\n",
            "\n",
            "       [[211, 208, 203],\n",
            "        [210, 211, 202],\n",
            "        [210, 210, 204],\n",
            "        ...,\n",
            "        [210, 225, 227],\n",
            "        [215, 228, 230],\n",
            "        [215, 231, 230]],\n",
            "\n",
            "       [[211, 209, 201],\n",
            "        [210, 211, 202],\n",
            "        [211, 212, 203],\n",
            "        ...,\n",
            "        [203, 218, 220],\n",
            "        [207, 222, 225],\n",
            "        [210, 225, 227]]], dtype=uint8), array([[[198, 205, 208],\n",
            "        [196, 203, 206],\n",
            "        [191, 200, 203],\n",
            "        ...,\n",
            "        [227, 225, 224],\n",
            "        [228, 226, 225],\n",
            "        [226, 227, 225]],\n",
            "\n",
            "       [[201, 206, 209],\n",
            "        [199, 204, 207],\n",
            "        [194, 202, 202],\n",
            "        ...,\n",
            "        [229, 225, 224],\n",
            "        [229, 225, 224],\n",
            "        [228, 226, 225]],\n",
            "\n",
            "       [[202, 207, 210],\n",
            "        [200, 205, 208],\n",
            "        [195, 203, 203],\n",
            "        ...,\n",
            "        [227, 225, 224],\n",
            "        [229, 227, 226],\n",
            "        [228, 229, 227]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[197, 198, 194],\n",
            "        [197, 199, 193],\n",
            "        [199, 198, 194],\n",
            "        ...,\n",
            "        [237, 241, 235],\n",
            "        [239, 240, 236],\n",
            "        [236, 240, 235]],\n",
            "\n",
            "       [[196, 197, 193],\n",
            "        [198, 197, 193],\n",
            "        [198, 197, 193],\n",
            "        ...,\n",
            "        [230, 238, 228],\n",
            "        [233, 237, 231],\n",
            "        [232, 239, 232]],\n",
            "\n",
            "       [[200, 199, 195],\n",
            "        [199, 198, 194],\n",
            "        [198, 197, 193],\n",
            "        ...,\n",
            "        [228, 236, 226],\n",
            "        [230, 238, 228],\n",
            "        [232, 239, 232]]], dtype=uint8), array([[[237, 245, 235],\n",
            "        [233, 242, 232],\n",
            "        [232, 241, 231],\n",
            "        ...,\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 254]],\n",
            "\n",
            "       [[239, 244, 235],\n",
            "        [234, 242, 231],\n",
            "        [236, 241, 232],\n",
            "        ...,\n",
            "        [254, 254, 254],\n",
            "        [254, 254, 254],\n",
            "        [255, 255, 255]],\n",
            "\n",
            "       [[243, 246, 237],\n",
            "        [239, 244, 235],\n",
            "        [241, 244, 235],\n",
            "        ...,\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[174, 175, 166],\n",
            "        [172, 174, 168],\n",
            "        [172, 174, 168],\n",
            "        ...,\n",
            "        [210, 221, 218],\n",
            "        [209, 220, 217],\n",
            "        [206, 220, 216]],\n",
            "\n",
            "       [[199, 197, 189],\n",
            "        [197, 198, 189],\n",
            "        [196, 197, 188],\n",
            "        ...,\n",
            "        [208, 219, 216],\n",
            "        [207, 218, 215],\n",
            "        [204, 218, 214]],\n",
            "\n",
            "       [[213, 210, 202],\n",
            "        [212, 210, 202],\n",
            "        [209, 210, 201],\n",
            "        ...,\n",
            "        [207, 216, 213],\n",
            "        [205, 216, 213],\n",
            "        [202, 216, 212]]], dtype=uint8), array([[[238, 236, 235],\n",
            "        [220, 220, 220],\n",
            "        [204, 204, 204],\n",
            "        ...,\n",
            "        [255, 255, 254],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255]],\n",
            "\n",
            "       [[227, 226, 222],\n",
            "        [214, 215, 213],\n",
            "        [203, 204, 202],\n",
            "        ...,\n",
            "        [255, 255, 254],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255]],\n",
            "\n",
            "       [[209, 211, 205],\n",
            "        [202, 206, 201],\n",
            "        [199, 203, 198],\n",
            "        ...,\n",
            "        [253, 254, 252],\n",
            "        [251, 251, 251],\n",
            "        [250, 250, 250]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[207, 207, 207],\n",
            "        [208, 208, 208],\n",
            "        [208, 209, 207],\n",
            "        ...,\n",
            "        [201, 221, 222],\n",
            "        [201, 221, 222],\n",
            "        [200, 221, 219]],\n",
            "\n",
            "       [[206, 206, 206],\n",
            "        [207, 207, 207],\n",
            "        [208, 209, 207],\n",
            "        ...,\n",
            "        [194, 209, 212],\n",
            "        [195, 209, 215],\n",
            "        [196, 211, 214]],\n",
            "\n",
            "       [[205, 205, 205],\n",
            "        [207, 207, 207],\n",
            "        [208, 209, 207],\n",
            "        ...,\n",
            "        [194, 204, 211],\n",
            "        [196, 207, 215],\n",
            "        [198, 210, 216]]], dtype=uint8), array([[[189, 199, 199],\n",
            "        [188, 198, 198],\n",
            "        [188, 198, 198],\n",
            "        ...,\n",
            "        [153, 146, 149],\n",
            "        [154, 144, 150],\n",
            "        [155, 144, 147]],\n",
            "\n",
            "       [[192, 200, 200],\n",
            "        [191, 199, 199],\n",
            "        [191, 199, 199],\n",
            "        ...,\n",
            "        [120, 112, 119],\n",
            "        [121, 107, 118],\n",
            "        [119, 106, 114]],\n",
            "\n",
            "       [[193, 201, 201],\n",
            "        [192, 200, 200],\n",
            "        [192, 200, 200],\n",
            "        ...,\n",
            "        [163, 154, 157],\n",
            "        [166, 152, 158],\n",
            "        [165, 151, 157]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [187, 196, 199],\n",
            "        [189, 197, 197],\n",
            "        [187, 197, 197]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [181, 188, 191],\n",
            "        [184, 189, 190],\n",
            "        [182, 190, 190]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [180, 185, 188],\n",
            "        [180, 185, 186],\n",
            "        [179, 187, 187]]], dtype=uint8), array([[[201, 216, 212],\n",
            "        [200, 215, 211],\n",
            "        [199, 214, 210],\n",
            "        ...,\n",
            "        [239, 245, 244],\n",
            "        [240, 245, 244],\n",
            "        [238, 243, 244]],\n",
            "\n",
            "       [[201, 215, 213],\n",
            "        [201, 216, 212],\n",
            "        [200, 215, 211],\n",
            "        ...,\n",
            "        [233, 238, 237],\n",
            "        [237, 237, 237],\n",
            "        [234, 236, 236]],\n",
            "\n",
            "       [[201, 215, 213],\n",
            "        [201, 215, 213],\n",
            "        [201, 215, 213],\n",
            "        ...,\n",
            "        [237, 242, 241],\n",
            "        [239, 239, 239],\n",
            "        [237, 237, 237]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[249, 250, 248],\n",
            "        [249, 250, 248],\n",
            "        [249, 250, 246],\n",
            "        ...,\n",
            "        [231, 248, 237],\n",
            "        [231, 248, 237],\n",
            "        [231, 248, 237]],\n",
            "\n",
            "       [[250, 248, 247],\n",
            "        [251, 249, 248],\n",
            "        [251, 249, 248],\n",
            "        ...,\n",
            "        [229, 251, 239],\n",
            "        [228, 250, 238],\n",
            "        [229, 249, 237]],\n",
            "\n",
            "       [[249, 247, 246],\n",
            "        [249, 247, 246],\n",
            "        [250, 248, 247],\n",
            "        ...,\n",
            "        [217, 241, 229],\n",
            "        [216, 241, 227],\n",
            "        [217, 240, 226]]], dtype=uint8), array([[[185, 206, 204],\n",
            "        [186, 205, 202],\n",
            "        [184, 202, 201],\n",
            "        ...,\n",
            "        [206, 194, 188],\n",
            "        [207, 193, 187],\n",
            "        [207, 193, 187]],\n",
            "\n",
            "       [[185, 206, 204],\n",
            "        [184, 205, 202],\n",
            "        [183, 204, 202],\n",
            "        ...,\n",
            "        [205, 193, 189],\n",
            "        [207, 192, 189],\n",
            "        [204, 192, 188]],\n",
            "\n",
            "       [[184, 206, 204],\n",
            "        [182, 206, 204],\n",
            "        [182, 206, 204],\n",
            "        ...,\n",
            "        [203, 191, 189],\n",
            "        [204, 191, 189],\n",
            "        [203, 190, 188]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [220, 228, 227],\n",
            "        [221, 229, 228],\n",
            "        [221, 229, 228]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [217, 228, 226],\n",
            "        [219, 227, 226],\n",
            "        [219, 227, 226]],\n",
            "\n",
            "       [[255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        [255, 255, 255],\n",
            "        ...,\n",
            "        [213, 224, 222],\n",
            "        [216, 224, 223],\n",
            "        [216, 224, 223]]], dtype=uint8), array([[[196, 211, 203],\n",
            "        [195, 211, 200],\n",
            "        [194, 210, 199],\n",
            "        ...,\n",
            "        [200, 210, 210],\n",
            "        [197, 207, 207],\n",
            "        [196, 206, 206]],\n",
            "\n",
            "       [[195, 212, 201],\n",
            "        [194, 212, 199],\n",
            "        [194, 211, 200],\n",
            "        ...,\n",
            "        [198, 210, 210],\n",
            "        [196, 210, 209],\n",
            "        [196, 208, 208]],\n",
            "\n",
            "       [[195, 212, 203],\n",
            "        [195, 212, 201],\n",
            "        [192, 212, 200],\n",
            "        ...,\n",
            "        [198, 212, 211],\n",
            "        [198, 212, 211],\n",
            "        [198, 210, 210]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[202, 200, 199],\n",
            "        [203, 201, 200],\n",
            "        [203, 201, 200],\n",
            "        ...,\n",
            "        [218, 232, 231],\n",
            "        [218, 232, 231],\n",
            "        [218, 232, 230]],\n",
            "\n",
            "       [[217, 214, 210],\n",
            "        [219, 214, 211],\n",
            "        [219, 214, 211],\n",
            "        ...,\n",
            "        [209, 224, 226],\n",
            "        [210, 225, 227],\n",
            "        [208, 226, 225]],\n",
            "\n",
            "       [[225, 221, 216],\n",
            "        [227, 220, 217],\n",
            "        [226, 221, 218],\n",
            "        ...,\n",
            "        [199, 214, 217],\n",
            "        [198, 215, 218],\n",
            "        [199, 217, 218]]], dtype=uint8), array([[[184, 200, 199],\n",
            "        [184, 201, 198],\n",
            "        [184, 201, 198],\n",
            "        ...,\n",
            "        [222, 228, 227],\n",
            "        [222, 228, 227],\n",
            "        [222, 227, 228]],\n",
            "\n",
            "       [[183, 199, 198],\n",
            "        [185, 199, 197],\n",
            "        [186, 200, 199],\n",
            "        ...,\n",
            "        [223, 228, 227],\n",
            "        [223, 228, 227],\n",
            "        [222, 228, 227]],\n",
            "\n",
            "       [[183, 199, 198],\n",
            "        [183, 200, 197],\n",
            "        [183, 199, 198],\n",
            "        ...,\n",
            "        [225, 230, 229],\n",
            "        [225, 230, 229],\n",
            "        [224, 230, 229]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[221, 221, 221],\n",
            "        [221, 221, 221],\n",
            "        [221, 221, 221],\n",
            "        ...,\n",
            "        [168, 185, 182],\n",
            "        [167, 184, 180],\n",
            "        [164, 184, 179]],\n",
            "\n",
            "       [[216, 215, 217],\n",
            "        [219, 215, 220],\n",
            "        [219, 215, 220],\n",
            "        ...,\n",
            "        [160, 168, 167],\n",
            "        [156, 167, 164],\n",
            "        [152, 167, 163]],\n",
            "\n",
            "       [[217, 213, 218],\n",
            "        [217, 213, 219],\n",
            "        [218, 214, 219],\n",
            "        ...,\n",
            "        [190, 196, 195],\n",
            "        [188, 195, 192],\n",
            "        [180, 194, 190]]], dtype=uint8), array([[[195, 206, 204],\n",
            "        [193, 207, 203],\n",
            "        [192, 206, 202],\n",
            "        ...,\n",
            "        [240, 246, 241],\n",
            "        [238, 244, 239],\n",
            "        [235, 242, 237]],\n",
            "\n",
            "       [[195, 206, 204],\n",
            "        [195, 206, 203],\n",
            "        [195, 206, 203],\n",
            "        ...,\n",
            "        [249, 250, 241],\n",
            "        [245, 248, 239],\n",
            "        [244, 247, 238]],\n",
            "\n",
            "       [[194, 207, 205],\n",
            "        [193, 206, 204],\n",
            "        [193, 206, 204],\n",
            "        ...,\n",
            "        [247, 248, 239],\n",
            "        [247, 248, 239],\n",
            "        [245, 248, 239]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[228, 231, 229],\n",
            "        [228, 231, 229],\n",
            "        [228, 231, 229],\n",
            "        ...,\n",
            "        [208, 216, 215],\n",
            "        [208, 216, 215],\n",
            "        [208, 217, 214]],\n",
            "\n",
            "       [[227, 229, 229],\n",
            "        [227, 229, 229],\n",
            "        [228, 230, 230],\n",
            "        ...,\n",
            "        [218, 224, 223],\n",
            "        [217, 223, 222],\n",
            "        [216, 223, 220]],\n",
            "\n",
            "       [[227, 229, 229],\n",
            "        [227, 229, 229],\n",
            "        [227, 229, 229],\n",
            "        ...,\n",
            "        [222, 227, 226],\n",
            "        [219, 224, 223],\n",
            "        [218, 223, 221]]], dtype=uint8), array([[[201, 207, 202],\n",
            "        [201, 208, 201],\n",
            "        [202, 209, 202],\n",
            "        ...,\n",
            "        [232, 238, 237],\n",
            "        [233, 238, 237],\n",
            "        [231, 237, 236]],\n",
            "\n",
            "       [[203, 208, 206],\n",
            "        [203, 209, 204],\n",
            "        [203, 210, 205],\n",
            "        ...,\n",
            "        [230, 237, 234],\n",
            "        [231, 236, 234],\n",
            "        [229, 236, 233]],\n",
            "\n",
            "       [[205, 210, 208],\n",
            "        [205, 211, 206],\n",
            "        [205, 212, 207],\n",
            "        ...,\n",
            "        [232, 237, 235],\n",
            "        [234, 237, 235],\n",
            "        [232, 237, 235]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[216, 217, 215],\n",
            "        [212, 212, 212],\n",
            "        [209, 209, 209],\n",
            "        ...,\n",
            "        [213, 221, 214],\n",
            "        [214, 221, 214],\n",
            "        [214, 222, 215]],\n",
            "\n",
            "       [[221, 224, 222],\n",
            "        [217, 220, 218],\n",
            "        [216, 217, 215],\n",
            "        ...,\n",
            "        [210, 221, 213],\n",
            "        [212, 220, 213],\n",
            "        [211, 222, 214]],\n",
            "\n",
            "       [[228, 231, 229],\n",
            "        [225, 228, 226],\n",
            "        [224, 225, 221],\n",
            "        ...,\n",
            "        [209, 220, 212],\n",
            "        [210, 221, 213],\n",
            "        [211, 222, 214]]], dtype=uint8), array([[[197, 212, 204],\n",
            "        [198, 212, 206],\n",
            "        [198, 212, 208],\n",
            "        ...,\n",
            "        [223, 226, 230],\n",
            "        [223, 226, 230],\n",
            "        [223, 227, 228]],\n",
            "\n",
            "       [[196, 213, 204],\n",
            "        [197, 213, 206],\n",
            "        [197, 213, 206],\n",
            "        ...,\n",
            "        [228, 233, 236],\n",
            "        [227, 232, 235],\n",
            "        [227, 232, 233]],\n",
            "\n",
            "       [[196, 213, 204],\n",
            "        [197, 214, 205],\n",
            "        [197, 214, 205],\n",
            "        ...,\n",
            "        [236, 240, 241],\n",
            "        [236, 240, 241],\n",
            "        [235, 241, 240]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[210, 210, 210],\n",
            "        [211, 211, 211],\n",
            "        [214, 212, 212],\n",
            "        ...,\n",
            "        [212, 229, 225],\n",
            "        [212, 229, 225],\n",
            "        [211, 229, 222]],\n",
            "\n",
            "       [[212, 209, 211],\n",
            "        [212, 209, 211],\n",
            "        [213, 210, 212],\n",
            "        ...,\n",
            "        [208, 229, 226],\n",
            "        [208, 229, 226],\n",
            "        [209, 229, 224]],\n",
            "\n",
            "       [[210, 206, 211],\n",
            "        [210, 206, 211],\n",
            "        [210, 207, 209],\n",
            "        ...,\n",
            "        [203, 224, 222],\n",
            "        [201, 224, 220],\n",
            "        [202, 223, 220]]], dtype=uint8), array([[[255, 252, 245],\n",
            "        [255, 251, 244],\n",
            "        [255, 250, 243],\n",
            "        ...,\n",
            "        [213, 213, 213],\n",
            "        [213, 212, 214],\n",
            "        [213, 213, 213]],\n",
            "\n",
            "       [[190, 184, 177],\n",
            "        [191, 184, 175],\n",
            "        [188, 182, 175],\n",
            "        ...,\n",
            "        [211, 213, 214],\n",
            "        [211, 213, 214],\n",
            "        [210, 212, 213]],\n",
            "\n",
            "       [[144, 142, 134],\n",
            "        [143, 141, 133],\n",
            "        [143, 140, 135],\n",
            "        ...,\n",
            "        [213, 215, 216],\n",
            "        [213, 215, 215],\n",
            "        [212, 214, 214]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[205, 204, 200],\n",
            "        [206, 203, 198],\n",
            "        [205, 202, 197],\n",
            "        ...,\n",
            "        [212, 221, 218],\n",
            "        [210, 219, 216],\n",
            "        [208, 217, 214]],\n",
            "\n",
            "       [[223, 219, 214],\n",
            "        [222, 219, 211],\n",
            "        [221, 218, 210],\n",
            "        ...,\n",
            "        [213, 224, 221],\n",
            "        [213, 225, 219],\n",
            "        [216, 226, 220]],\n",
            "\n",
            "       [[230, 227, 219],\n",
            "        [231, 225, 218],\n",
            "        [228, 226, 216],\n",
            "        ...,\n",
            "        [214, 225, 222],\n",
            "        [213, 225, 219],\n",
            "        [214, 224, 218]]], dtype=uint8), array([[[199, 210, 207],\n",
            "        [199, 210, 208],\n",
            "        [199, 210, 208],\n",
            "        ...,\n",
            "        [194, 214, 215],\n",
            "        [194, 214, 215],\n",
            "        [196, 214, 213]],\n",
            "\n",
            "       [[199, 210, 207],\n",
            "        [199, 210, 208],\n",
            "        [199, 210, 208],\n",
            "        ...,\n",
            "        [192, 212, 213],\n",
            "        [191, 212, 213],\n",
            "        [192, 213, 211]],\n",
            "\n",
            "       [[199, 210, 207],\n",
            "        [199, 210, 207],\n",
            "        [200, 211, 208],\n",
            "        ...,\n",
            "        [193, 211, 212],\n",
            "        [193, 211, 212],\n",
            "        [193, 211, 210]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[228, 234, 229],\n",
            "        [228, 234, 229],\n",
            "        [229, 235, 230],\n",
            "        ...,\n",
            "        [218, 228, 228],\n",
            "        [216, 228, 230],\n",
            "        [214, 227, 229]],\n",
            "\n",
            "       [[229, 235, 230],\n",
            "        [229, 235, 230],\n",
            "        [229, 235, 230],\n",
            "        ...,\n",
            "        [209, 221, 223],\n",
            "        [208, 219, 223],\n",
            "        [207, 220, 222]],\n",
            "\n",
            "       [[229, 235, 230],\n",
            "        [229, 235, 230],\n",
            "        [229, 235, 230],\n",
            "        ...,\n",
            "        [199, 211, 213],\n",
            "        [199, 210, 214],\n",
            "        [198, 210, 214]]], dtype=uint8)], [array([[[242, 245, 243],\n",
            "        [228, 231, 229],\n",
            "        [216, 219, 217],\n",
            "        ...,\n",
            "        [196, 203, 206],\n",
            "        [196, 203, 206],\n",
            "        [199, 204, 207]],\n",
            "\n",
            "       [[222, 225, 223],\n",
            "        [212, 217, 215],\n",
            "        [208, 213, 211],\n",
            "        ...,\n",
            "        [199, 204, 207],\n",
            "        [198, 202, 207],\n",
            "        [198, 203, 206]],\n",
            "\n",
            "       [[206, 209, 207],\n",
            "        [205, 208, 206],\n",
            "        [206, 211, 209],\n",
            "        ...,\n",
            "        [201, 205, 206],\n",
            "        [199, 202, 206],\n",
            "        [200, 201, 205]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[223, 222, 218],\n",
            "        [223, 222, 218],\n",
            "        [224, 224, 218],\n",
            "        ...,\n",
            "        [255, 254, 255],\n",
            "        [255, 254, 255],\n",
            "        [255, 254, 255]],\n",
            "\n",
            "       [[224, 223, 219],\n",
            "        [225, 224, 220],\n",
            "        [225, 225, 219],\n",
            "        ...,\n",
            "        [255, 254, 255],\n",
            "        [255, 254, 255],\n",
            "        [255, 254, 255]],\n",
            "\n",
            "       [[225, 224, 220],\n",
            "        [226, 225, 221],\n",
            "        [226, 226, 220],\n",
            "        ...,\n",
            "        [255, 254, 255],\n",
            "        [255, 254, 255],\n",
            "        [255, 254, 255]]], dtype=uint8), array([[[220, 224, 218],\n",
            "        [220, 225, 216],\n",
            "        [220, 224, 218],\n",
            "        ...,\n",
            "        [210, 222, 226],\n",
            "        [209, 221, 225],\n",
            "        [207, 222, 225]],\n",
            "\n",
            "       [[219, 223, 217],\n",
            "        [219, 224, 215],\n",
            "        [219, 223, 217],\n",
            "        ...,\n",
            "        [211, 221, 228],\n",
            "        [209, 221, 227],\n",
            "        [208, 220, 226]],\n",
            "\n",
            "       [[218, 222, 216],\n",
            "        [220, 222, 216],\n",
            "        [220, 222, 216],\n",
            "        ...,\n",
            "        [211, 221, 228],\n",
            "        [209, 221, 227],\n",
            "        [208, 220, 226]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[226, 223, 215],\n",
            "        [228, 222, 215],\n",
            "        [228, 222, 215],\n",
            "        ...,\n",
            "        [221, 227, 226],\n",
            "        [221, 227, 226],\n",
            "        [221, 228, 225]],\n",
            "\n",
            "       [[229, 223, 216],\n",
            "        [228, 222, 215],\n",
            "        [227, 221, 214],\n",
            "        ...,\n",
            "        [225, 231, 230],\n",
            "        [226, 232, 231],\n",
            "        [227, 234, 231]],\n",
            "\n",
            "       [[229, 223, 216],\n",
            "        [230, 222, 215],\n",
            "        [227, 221, 214],\n",
            "        ...,\n",
            "        [228, 234, 233],\n",
            "        [230, 236, 235],\n",
            "        [231, 238, 235]]], dtype=uint8), array([[[233, 237, 231],\n",
            "        [238, 243, 234],\n",
            "        [244, 247, 238],\n",
            "        ...,\n",
            "        [193, 206, 204],\n",
            "        [193, 206, 204],\n",
            "        [193, 206, 204]],\n",
            "\n",
            "       [[234, 239, 230],\n",
            "        [238, 244, 233],\n",
            "        [241, 246, 237],\n",
            "        ...,\n",
            "        [194, 205, 203],\n",
            "        [194, 205, 203],\n",
            "        [192, 205, 203]],\n",
            "\n",
            "       [[236, 244, 234],\n",
            "        [238, 246, 236],\n",
            "        [240, 248, 238],\n",
            "        ...,\n",
            "        [191, 204, 202],\n",
            "        [193, 204, 202],\n",
            "        [191, 204, 202]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[224, 218, 213],\n",
            "        [227, 218, 214],\n",
            "        [227, 218, 214],\n",
            "        ...,\n",
            "        [218, 223, 221],\n",
            "        [218, 223, 221],\n",
            "        [220, 223, 221]],\n",
            "\n",
            "       [[227, 218, 214],\n",
            "        [230, 219, 215],\n",
            "        [228, 219, 215],\n",
            "        ...,\n",
            "        [219, 224, 222],\n",
            "        [219, 224, 222],\n",
            "        [220, 223, 221]],\n",
            "\n",
            "       [[229, 220, 216],\n",
            "        [231, 220, 216],\n",
            "        [229, 220, 216],\n",
            "        ...,\n",
            "        [220, 225, 223],\n",
            "        [220, 225, 223],\n",
            "        [222, 225, 223]]], dtype=uint8), array([[[214, 221, 216],\n",
            "        [219, 227, 220],\n",
            "        [225, 233, 226],\n",
            "        ...,\n",
            "        [198, 215, 218],\n",
            "        [197, 214, 217],\n",
            "        [197, 214, 217]],\n",
            "\n",
            "       [[222, 229, 224],\n",
            "        [225, 233, 226],\n",
            "        [230, 238, 231],\n",
            "        ...,\n",
            "        [200, 214, 220],\n",
            "        [199, 213, 219],\n",
            "        [199, 214, 217]],\n",
            "\n",
            "       [[230, 237, 232],\n",
            "        [233, 240, 233],\n",
            "        [237, 244, 237],\n",
            "        ...,\n",
            "        [202, 214, 218],\n",
            "        [201, 213, 219],\n",
            "        [201, 213, 217]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[226, 223, 219],\n",
            "        [224, 224, 218],\n",
            "        [225, 225, 219],\n",
            "        ...,\n",
            "        [225, 228, 226],\n",
            "        [225, 228, 226],\n",
            "        [226, 229, 227]],\n",
            "\n",
            "       [[226, 223, 219],\n",
            "        [225, 224, 220],\n",
            "        [225, 225, 219],\n",
            "        ...,\n",
            "        [222, 227, 226],\n",
            "        [222, 227, 226],\n",
            "        [223, 228, 226]],\n",
            "\n",
            "       [[226, 222, 221],\n",
            "        [225, 224, 220],\n",
            "        [226, 226, 220],\n",
            "        ...,\n",
            "        [221, 226, 225],\n",
            "        [222, 227, 226],\n",
            "        [222, 227, 225]]], dtype=uint8), array([[[207, 214, 209],\n",
            "        [208, 215, 208],\n",
            "        [209, 215, 210],\n",
            "        ...,\n",
            "        [202, 219, 215],\n",
            "        [203, 220, 216],\n",
            "        [203, 220, 216]],\n",
            "\n",
            "       [[207, 214, 209],\n",
            "        [207, 214, 209],\n",
            "        [209, 215, 210],\n",
            "        ...,\n",
            "        [203, 220, 216],\n",
            "        [203, 220, 216],\n",
            "        [203, 220, 216]],\n",
            "\n",
            "       [[207, 214, 209],\n",
            "        [207, 214, 209],\n",
            "        [208, 214, 209],\n",
            "        ...,\n",
            "        [202, 222, 217],\n",
            "        [202, 222, 217],\n",
            "        [202, 222, 217]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[213, 214, 210],\n",
            "        [213, 215, 209],\n",
            "        [216, 215, 211],\n",
            "        ...,\n",
            "        [202, 209, 204],\n",
            "        [201, 208, 203],\n",
            "        [200, 207, 202]],\n",
            "\n",
            "       [[214, 214, 208],\n",
            "        [213, 216, 207],\n",
            "        [215, 215, 209],\n",
            "        ...,\n",
            "        [200, 207, 200],\n",
            "        [199, 206, 199],\n",
            "        [198, 205, 198]],\n",
            "\n",
            "       [[214, 215, 206],\n",
            "        [212, 215, 206],\n",
            "        [215, 216, 207],\n",
            "        ...,\n",
            "        [202, 206, 200],\n",
            "        [202, 206, 200],\n",
            "        [200, 207, 200]]], dtype=uint8), array([[[226, 236, 230],\n",
            "        [226, 236, 230],\n",
            "        [228, 238, 232],\n",
            "        ...,\n",
            "        [212, 217, 220],\n",
            "        [210, 215, 218],\n",
            "        [206, 213, 216]],\n",
            "\n",
            "       [[227, 239, 233],\n",
            "        [229, 241, 235],\n",
            "        [234, 244, 238],\n",
            "        ...,\n",
            "        [197, 203, 208],\n",
            "        [196, 202, 207],\n",
            "        [192, 201, 205]],\n",
            "\n",
            "       [[233, 243, 237],\n",
            "        [235, 246, 238],\n",
            "        [241, 249, 242],\n",
            "        ...,\n",
            "        [185, 194, 197],\n",
            "        [184, 193, 196],\n",
            "        [182, 194, 196]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[216, 223, 216],\n",
            "        [216, 225, 215],\n",
            "        [216, 225, 215],\n",
            "        ...,\n",
            "        [253, 254, 250],\n",
            "        [252, 253, 249],\n",
            "        [252, 253, 249]],\n",
            "\n",
            "       [[217, 224, 217],\n",
            "        [214, 225, 217],\n",
            "        [214, 225, 215],\n",
            "        ...,\n",
            "        [253, 254, 252],\n",
            "        [253, 254, 252],\n",
            "        [252, 253, 251]],\n",
            "\n",
            "       [[218, 224, 219],\n",
            "        [215, 226, 218],\n",
            "        [215, 226, 216],\n",
            "        ...,\n",
            "        [254, 255, 253],\n",
            "        [253, 254, 252],\n",
            "        [253, 254, 252]]], dtype=uint8), array([[[209, 208, 204],\n",
            "        [211, 208, 204],\n",
            "        [212, 209, 205],\n",
            "        ...,\n",
            "        [195, 210, 206],\n",
            "        [196, 211, 207],\n",
            "        [195, 212, 208]],\n",
            "\n",
            "       [[209, 208, 204],\n",
            "        [210, 209, 205],\n",
            "        [212, 209, 205],\n",
            "        ...,\n",
            "        [194, 209, 205],\n",
            "        [195, 210, 206],\n",
            "        [194, 211, 207]],\n",
            "\n",
            "       [[209, 210, 208],\n",
            "        [209, 210, 208],\n",
            "        [210, 209, 205],\n",
            "        ...,\n",
            "        [194, 209, 205],\n",
            "        [195, 210, 206],\n",
            "        [193, 210, 206]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[211, 212, 210],\n",
            "        [212, 213, 209],\n",
            "        [215, 213, 212],\n",
            "        ...,\n",
            "        [200, 205, 204],\n",
            "        [203, 208, 207],\n",
            "        [205, 211, 210]],\n",
            "\n",
            "       [[214, 213, 209],\n",
            "        [215, 215, 209],\n",
            "        [218, 215, 211],\n",
            "        ...,\n",
            "        [198, 204, 203],\n",
            "        [201, 207, 206],\n",
            "        [203, 209, 208]],\n",
            "\n",
            "       [[214, 213, 209],\n",
            "        [215, 215, 209],\n",
            "        [219, 216, 211],\n",
            "        ...,\n",
            "        [198, 204, 203],\n",
            "        [199, 205, 204],\n",
            "        [201, 207, 206]]], dtype=uint8), array([[[215, 222, 217],\n",
            "        [217, 225, 218],\n",
            "        [218, 225, 220],\n",
            "        ...,\n",
            "        [198, 207, 210],\n",
            "        [199, 207, 207],\n",
            "        [200, 206, 205]],\n",
            "\n",
            "       [[215, 225, 219],\n",
            "        [217, 227, 221],\n",
            "        [218, 228, 222],\n",
            "        ...,\n",
            "        [198, 208, 208],\n",
            "        [199, 207, 207],\n",
            "        [201, 207, 206]],\n",
            "\n",
            "       [[219, 227, 220],\n",
            "        [221, 229, 222],\n",
            "        [224, 232, 225],\n",
            "        ...,\n",
            "        [199, 209, 209],\n",
            "        [200, 208, 208],\n",
            "        [200, 208, 207]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[209, 210, 208],\n",
            "        [210, 211, 209],\n",
            "        [210, 211, 207],\n",
            "        ...,\n",
            "        [230, 244, 233],\n",
            "        [229, 242, 234],\n",
            "        [230, 244, 233]],\n",
            "\n",
            "       [[211, 209, 208],\n",
            "        [212, 210, 209],\n",
            "        [212, 210, 209],\n",
            "        ...,\n",
            "        [232, 245, 237],\n",
            "        [232, 244, 238],\n",
            "        [233, 246, 238]],\n",
            "\n",
            "       [[211, 209, 208],\n",
            "        [212, 210, 209],\n",
            "        [212, 210, 209],\n",
            "        ...,\n",
            "        [234, 246, 240],\n",
            "        [232, 246, 240],\n",
            "        [235, 248, 240]]], dtype=uint8), array([[[221, 228, 221],\n",
            "        [224, 232, 225],\n",
            "        [227, 236, 226],\n",
            "        ...,\n",
            "        [202, 207, 208],\n",
            "        [201, 206, 207],\n",
            "        [200, 205, 206]],\n",
            "\n",
            "       [[221, 228, 221],\n",
            "        [222, 233, 225],\n",
            "        [225, 236, 226],\n",
            "        ...,\n",
            "        [202, 206, 207],\n",
            "        [201, 205, 206],\n",
            "        [200, 205, 204]],\n",
            "\n",
            "       [[226, 233, 226],\n",
            "        [228, 236, 229],\n",
            "        [230, 238, 231],\n",
            "        ...,\n",
            "        [201, 205, 206],\n",
            "        [200, 204, 205],\n",
            "        [199, 204, 203]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[222, 214, 214],\n",
            "        [220, 215, 214],\n",
            "        [219, 214, 211],\n",
            "        ...,\n",
            "        [215, 222, 215],\n",
            "        [214, 220, 215],\n",
            "        [214, 221, 214]],\n",
            "\n",
            "       [[222, 214, 214],\n",
            "        [220, 215, 214],\n",
            "        [220, 215, 212],\n",
            "        ...,\n",
            "        [214, 222, 212],\n",
            "        [213, 220, 213],\n",
            "        [212, 219, 212]],\n",
            "\n",
            "       [[222, 214, 214],\n",
            "        [220, 215, 214],\n",
            "        [220, 215, 212],\n",
            "        ...,\n",
            "        [213, 221, 211],\n",
            "        [212, 220, 210],\n",
            "        [211, 219, 209]]], dtype=uint8), array([[[203, 214, 211],\n",
            "        [202, 216, 210],\n",
            "        [203, 217, 213],\n",
            "        ...,\n",
            "        [203, 220, 223],\n",
            "        [205, 220, 223],\n",
            "        [205, 220, 222]],\n",
            "\n",
            "       [[204, 215, 212],\n",
            "        [203, 217, 211],\n",
            "        [204, 218, 214],\n",
            "        ...,\n",
            "        [204, 221, 224],\n",
            "        [204, 221, 224],\n",
            "        [206, 221, 224]],\n",
            "\n",
            "       [[206, 217, 214],\n",
            "        [204, 218, 214],\n",
            "        [205, 218, 216],\n",
            "        ...,\n",
            "        [207, 222, 225],\n",
            "        [207, 222, 224],\n",
            "        [209, 222, 224]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[222, 220, 219],\n",
            "        [220, 221, 219],\n",
            "        [222, 223, 221],\n",
            "        ...,\n",
            "        [217, 224, 219],\n",
            "        [215, 222, 217],\n",
            "        [221, 228, 223]],\n",
            "\n",
            "       [[222, 221, 217],\n",
            "        [221, 222, 218],\n",
            "        [222, 223, 219],\n",
            "        ...,\n",
            "        [228, 235, 228],\n",
            "        [223, 230, 223],\n",
            "        [228, 235, 228]],\n",
            "\n",
            "       [[224, 221, 217],\n",
            "        [221, 222, 218],\n",
            "        [223, 224, 220],\n",
            "        ...,\n",
            "        [232, 237, 228],\n",
            "        [224, 232, 222],\n",
            "        [227, 235, 225]]], dtype=uint8), array([[[214, 221, 214],\n",
            "        [216, 224, 214],\n",
            "        [220, 228, 218],\n",
            "        ...,\n",
            "        [186, 200, 198],\n",
            "        [184, 198, 196],\n",
            "        [184, 198, 196]],\n",
            "\n",
            "       [[215, 223, 216],\n",
            "        [220, 228, 218],\n",
            "        [225, 233, 223],\n",
            "        ...,\n",
            "        [185, 199, 198],\n",
            "        [183, 197, 195],\n",
            "        [182, 196, 194]],\n",
            "\n",
            "       [[218, 226, 219],\n",
            "        [222, 230, 223],\n",
            "        [226, 234, 227],\n",
            "        ...,\n",
            "        [184, 198, 197],\n",
            "        [181, 195, 193],\n",
            "        [180, 194, 192]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[214, 208, 209],\n",
            "        [214, 208, 209],\n",
            "        [214, 209, 208],\n",
            "        ...,\n",
            "        [216, 220, 215],\n",
            "        [216, 220, 215],\n",
            "        [213, 220, 213]],\n",
            "\n",
            "       [[215, 209, 210],\n",
            "        [215, 209, 210],\n",
            "        [215, 210, 209],\n",
            "        ...,\n",
            "        [216, 220, 215],\n",
            "        [215, 219, 214],\n",
            "        [213, 220, 213]],\n",
            "\n",
            "       [[216, 210, 211],\n",
            "        [216, 210, 211],\n",
            "        [216, 211, 210],\n",
            "        ...,\n",
            "        [215, 219, 214],\n",
            "        [215, 219, 214],\n",
            "        [213, 220, 213]]], dtype=uint8), array([[[224, 229, 227],\n",
            "        [225, 230, 228],\n",
            "        [226, 233, 230],\n",
            "        ...,\n",
            "        [206, 227, 225],\n",
            "        [207, 225, 224],\n",
            "        [207, 225, 224]],\n",
            "\n",
            "       [[225, 230, 228],\n",
            "        [226, 231, 229],\n",
            "        [228, 234, 229],\n",
            "        ...,\n",
            "        [205, 226, 224],\n",
            "        [205, 226, 224],\n",
            "        [207, 225, 224]],\n",
            "\n",
            "       [[228, 233, 231],\n",
            "        [228, 233, 231],\n",
            "        [229, 235, 230],\n",
            "        ...,\n",
            "        [207, 225, 224],\n",
            "        [207, 225, 224],\n",
            "        [207, 225, 224]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[227, 224, 216],\n",
            "        [225, 222, 217],\n",
            "        [225, 222, 217],\n",
            "        ...,\n",
            "        [213, 224, 221],\n",
            "        [212, 223, 220],\n",
            "        [211, 222, 219]],\n",
            "\n",
            "       [[226, 224, 216],\n",
            "        [225, 223, 215],\n",
            "        [224, 221, 216],\n",
            "        ...,\n",
            "        [213, 224, 221],\n",
            "        [211, 222, 220],\n",
            "        [210, 221, 219]],\n",
            "\n",
            "       [[226, 224, 216],\n",
            "        [225, 223, 215],\n",
            "        [222, 222, 216],\n",
            "        ...,\n",
            "        [213, 224, 221],\n",
            "        [209, 222, 220],\n",
            "        [207, 220, 218]]], dtype=uint8), array([[[222, 226, 220],\n",
            "        [222, 226, 221],\n",
            "        [223, 227, 222],\n",
            "        ...,\n",
            "        [210, 226, 225],\n",
            "        [211, 225, 224],\n",
            "        [210, 224, 223]],\n",
            "\n",
            "       [[224, 225, 221],\n",
            "        [224, 225, 221],\n",
            "        [222, 226, 221],\n",
            "        ...,\n",
            "        [210, 226, 225],\n",
            "        [211, 225, 224],\n",
            "        [211, 225, 224]],\n",
            "\n",
            "       [[223, 227, 222],\n",
            "        [223, 227, 222],\n",
            "        [222, 226, 221],\n",
            "        ...,\n",
            "        [211, 227, 226],\n",
            "        [212, 226, 225],\n",
            "        [212, 226, 225]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[221, 216, 215],\n",
            "        [221, 216, 215],\n",
            "        [222, 217, 216],\n",
            "        ...,\n",
            "        [215, 223, 222],\n",
            "        [214, 222, 221],\n",
            "        [212, 223, 221]],\n",
            "\n",
            "       [[222, 217, 216],\n",
            "        [223, 217, 218],\n",
            "        [223, 218, 217],\n",
            "        ...,\n",
            "        [210, 216, 215],\n",
            "        [209, 215, 214],\n",
            "        [206, 214, 213]],\n",
            "\n",
            "       [[224, 219, 218],\n",
            "        [224, 218, 219],\n",
            "        [225, 220, 219],\n",
            "        ...,\n",
            "        [208, 213, 212],\n",
            "        [207, 212, 211],\n",
            "        [203, 211, 210]]], dtype=uint8), array([[[211, 218, 213],\n",
            "        [210, 218, 211],\n",
            "        [210, 218, 211],\n",
            "        ...,\n",
            "        [200, 214, 210],\n",
            "        [201, 212, 209],\n",
            "        [199, 210, 207]],\n",
            "\n",
            "       [[211, 218, 213],\n",
            "        [211, 218, 211],\n",
            "        [210, 218, 211],\n",
            "        ...,\n",
            "        [197, 212, 208],\n",
            "        [196, 212, 205],\n",
            "        [195, 211, 204]],\n",
            "\n",
            "       [[211, 218, 213],\n",
            "        [210, 217, 212],\n",
            "        [210, 217, 212],\n",
            "        ...,\n",
            "        [197, 211, 207],\n",
            "        [195, 211, 204],\n",
            "        [195, 211, 204]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[213, 213, 207],\n",
            "        [216, 214, 206],\n",
            "        [216, 214, 206],\n",
            "        ...,\n",
            "        [210, 223, 221],\n",
            "        [210, 224, 220],\n",
            "        [209, 223, 219]],\n",
            "\n",
            "       [[213, 213, 207],\n",
            "        [216, 213, 208],\n",
            "        [217, 215, 207],\n",
            "        ...,\n",
            "        [208, 221, 219],\n",
            "        [207, 222, 218],\n",
            "        [209, 223, 219]],\n",
            "\n",
            "       [[214, 213, 209],\n",
            "        [217, 214, 209],\n",
            "        [217, 215, 207],\n",
            "        ...,\n",
            "        [201, 214, 212],\n",
            "        [201, 216, 212],\n",
            "        [202, 217, 213]]], dtype=uint8), array([[[218, 230, 224],\n",
            "        [219, 230, 227],\n",
            "        [220, 231, 228],\n",
            "        ...,\n",
            "        [206, 216, 226],\n",
            "        [204, 214, 224],\n",
            "        [203, 214, 222]],\n",
            "\n",
            "       [[218, 230, 224],\n",
            "        [219, 231, 225],\n",
            "        [222, 231, 228],\n",
            "        ...,\n",
            "        [205, 217, 227],\n",
            "        [204, 216, 226],\n",
            "        [203, 216, 224]],\n",
            "\n",
            "       [[218, 230, 224],\n",
            "        [219, 231, 225],\n",
            "        [222, 231, 228],\n",
            "        ...,\n",
            "        [208, 220, 230],\n",
            "        [209, 219, 229],\n",
            "        [208, 220, 226]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[226, 226, 220],\n",
            "        [224, 226, 220],\n",
            "        [224, 226, 220],\n",
            "        ...,\n",
            "        [227, 241, 240],\n",
            "        [226, 238, 238],\n",
            "        [225, 237, 237]],\n",
            "\n",
            "       [[226, 226, 220],\n",
            "        [223, 225, 219],\n",
            "        [223, 225, 219],\n",
            "        ...,\n",
            "        [225, 241, 240],\n",
            "        [225, 238, 240],\n",
            "        [224, 237, 239]],\n",
            "\n",
            "       [[226, 226, 220],\n",
            "        [223, 225, 219],\n",
            "        [223, 225, 219],\n",
            "        ...,\n",
            "        [221, 239, 238],\n",
            "        [222, 237, 239],\n",
            "        [221, 236, 238]]], dtype=uint8), array([[[229, 236, 233],\n",
            "        [221, 230, 227],\n",
            "        [213, 222, 219],\n",
            "        ...,\n",
            "        [160, 173, 175],\n",
            "        [162, 174, 176],\n",
            "        [162, 174, 176]],\n",
            "\n",
            "       [[210, 222, 216],\n",
            "        [207, 218, 215],\n",
            "        [201, 215, 211],\n",
            "        ...,\n",
            "        [168, 186, 187],\n",
            "        [169, 184, 187],\n",
            "        [168, 183, 186]],\n",
            "\n",
            "       [[197, 209, 203],\n",
            "        [197, 209, 203],\n",
            "        [196, 210, 204],\n",
            "        ...,\n",
            "        [176, 194, 195],\n",
            "        [176, 191, 194],\n",
            "        [175, 190, 193]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[205, 206, 204],\n",
            "        [205, 206, 204],\n",
            "        [205, 206, 202],\n",
            "        ...,\n",
            "        [246, 253, 248],\n",
            "        [245, 252, 247],\n",
            "        [244, 251, 246]],\n",
            "\n",
            "       [[207, 205, 204],\n",
            "        [205, 206, 204],\n",
            "        [205, 206, 204],\n",
            "        ...,\n",
            "        [245, 252, 247],\n",
            "        [244, 251, 246],\n",
            "        [243, 250, 245]],\n",
            "\n",
            "       [[208, 206, 205],\n",
            "        [206, 207, 205],\n",
            "        [206, 207, 205],\n",
            "        ...,\n",
            "        [245, 252, 247],\n",
            "        [244, 251, 246],\n",
            "        [243, 250, 245]]], dtype=uint8), array([[[218, 230, 224],\n",
            "        [219, 230, 227],\n",
            "        [220, 231, 228],\n",
            "        ...,\n",
            "        [206, 216, 226],\n",
            "        [204, 214, 224],\n",
            "        [203, 214, 222]],\n",
            "\n",
            "       [[218, 230, 224],\n",
            "        [219, 231, 225],\n",
            "        [222, 231, 228],\n",
            "        ...,\n",
            "        [205, 217, 227],\n",
            "        [204, 216, 226],\n",
            "        [203, 216, 224]],\n",
            "\n",
            "       [[218, 230, 224],\n",
            "        [219, 231, 225],\n",
            "        [222, 231, 228],\n",
            "        ...,\n",
            "        [208, 220, 230],\n",
            "        [209, 219, 229],\n",
            "        [208, 220, 226]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[226, 226, 220],\n",
            "        [224, 226, 220],\n",
            "        [224, 226, 220],\n",
            "        ...,\n",
            "        [227, 241, 240],\n",
            "        [226, 238, 238],\n",
            "        [225, 237, 237]],\n",
            "\n",
            "       [[226, 226, 220],\n",
            "        [223, 225, 219],\n",
            "        [223, 225, 219],\n",
            "        ...,\n",
            "        [225, 241, 240],\n",
            "        [225, 238, 240],\n",
            "        [224, 237, 239]],\n",
            "\n",
            "       [[226, 226, 220],\n",
            "        [223, 225, 219],\n",
            "        [223, 225, 219],\n",
            "        ...,\n",
            "        [221, 239, 238],\n",
            "        [222, 237, 239],\n",
            "        [221, 236, 238]]], dtype=uint8), array([[[240, 237, 233],\n",
            "        [244, 241, 237],\n",
            "        [240, 239, 235],\n",
            "        ...,\n",
            "        [202, 214, 214],\n",
            "        [200, 213, 211],\n",
            "        [198, 211, 209]],\n",
            "\n",
            "       [[247, 244, 240],\n",
            "        [242, 239, 235],\n",
            "        [230, 228, 227],\n",
            "        ...,\n",
            "        [200, 214, 212],\n",
            "        [200, 213, 211],\n",
            "        [197, 211, 209]],\n",
            "\n",
            "       [[231, 228, 223],\n",
            "        [222, 219, 214],\n",
            "        [209, 208, 204],\n",
            "        ...,\n",
            "        [200, 214, 212],\n",
            "        [199, 213, 211],\n",
            "        [197, 211, 209]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[209, 210, 206],\n",
            "        [209, 211, 205],\n",
            "        [211, 211, 205],\n",
            "        ...,\n",
            "        [252, 255, 246],\n",
            "        [250, 255, 246],\n",
            "        [250, 255, 246]],\n",
            "\n",
            "       [[213, 212, 208],\n",
            "        [212, 211, 207],\n",
            "        [214, 211, 207],\n",
            "        ...,\n",
            "        [253, 255, 247],\n",
            "        [251, 255, 247],\n",
            "        [251, 255, 247]],\n",
            "\n",
            "       [[215, 214, 210],\n",
            "        [214, 213, 209],\n",
            "        [215, 211, 210],\n",
            "        ...,\n",
            "        [253, 255, 247],\n",
            "        [251, 255, 247],\n",
            "        [251, 255, 247]]], dtype=uint8)]]\n"
          ]
        }
      ],
      "source": [
        "print(images_set_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mWI0qJT1ok9",
        "outputId": "b32e5753-28a8-4a19-928f-f6156eaf7412"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "324\n",
            "360\n",
            "216\n"
          ]
        }
      ],
      "source": [
        "images_set_3 = []\n",
        "for img_set in images_set_2:\n",
        "  tmp_set = []\n",
        "  for img in img_set:\n",
        "    tmp = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "    image_pillow = Image.fromarray(tmp)\n",
        "    slice_image(image_pillow, 224, tmp_set)\n",
        "  print(len(tmp_set))\n",
        "  while(len(tmp_set) < 360):\n",
        "    tmp_set = tmp_set + tmp_set\n",
        "  images_set_3.append(tmp_set[0: 360])\n",
        "images_set_2 = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrVDlJMK5J7v",
        "outputId": "329a576e-ce15-4c38-964e-1a6688845343"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "360\n",
            "360\n",
            "360\n"
          ]
        }
      ],
      "source": [
        "for akd in images_set_3:\n",
        "  print(len(akd))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyU8yEh060Ht",
        "outputId": "2ddda66c-ed69-4929-d602-cc8e8dc00926"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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0x7B947E93FB10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93FBD0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93FC90>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93FD50>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93FE10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93FED0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93FF90>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940090>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940150>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E93F990>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940250>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940410>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940490>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940550>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940610>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9406D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940790>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940850>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940910>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9409D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940A90>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E8C0BD0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940390>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940B50>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940D90>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940E50>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940F10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940FD0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941090>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941150>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941210>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9412D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9413D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E940C50>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941510>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9417D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941850>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941910>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941A10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941B10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941C10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941D10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941E10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941F10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942010>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E941690>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942150>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9423D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942450>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942510>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942610>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942710>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942810>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942910>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942A10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942B10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942C10>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E9422D0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942D50>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E942FD0>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943050>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943110>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943210>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943310>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943410>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943510>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943610>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943710>, <PIL.Image.Image image mode=RGB size=224x224 at 0x7B947E943810>]]\n"
          ]
        }
      ],
      "source": [
        "print(images_set_3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vSIG-RTx8boS"
      },
      "outputs": [],
      "source": [
        "images_set_4 = []\n",
        "for img_set in images_set_3:\n",
        "  tmp_set = []\n",
        "  for img in img_set:\n",
        "    tmp = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\n",
        "    tmp_set.append(tmp)\n",
        "  images_set_4.append(tmp_set)\n",
        "images_set_3 = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7ihWeUj5X9t",
        "outputId": "83bf82eb-37a0-45fe-da39-8a0441726f6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n"
          ]
        }
      ],
      "source": [
        "#img_df = pd.DataFrame(data = images_set_3, columns = classes)\n",
        "list_x = []\n",
        "list_y = []\n",
        "for i in range(3):\n",
        "   list_x = list_x + images_set_4[i]\n",
        "   list_y = list_y + [i for j in range(360)]\n",
        "print(list_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JLxxbqnpPd1l"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5N-bN14j-liN",
        "outputId": "a699b21e-e342-4e33-adf3-8e2fc8f92c7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [X, y]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "print(image_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "86iEGaz3_sPj"
      },
      "outputs": [],
      "source": [
        "final_df = pd.DataFrame({'X': list_x, 'y': list_y})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "36aI1IvPPrjZ",
        "outputId": "1fc0057f-bb4b-4021-b0cc-bf876cf959d6"
      },
      "outputs": [
        {
          "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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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "cv2_imshow(final_df['X'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viVnENmGVOZc",
        "outputId": "4f51fe09-07b4-4fbc-de73-92bdfed3f226"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       [[[203, 199, 198], [203, 200, 196], [203, 199,...\n",
            "1       [[[120, 190, 203], [120, 189, 204], [119, 188,...\n",
            "2       [[[136, 200, 211], [130, 199, 209], [128, 197,...\n",
            "3       [[[255, 255, 255], [255, 255, 255], [255, 255,...\n",
            "4       [[[195, 199, 194], [198, 199, 195], [198, 199,...\n",
            "                              ...                        \n",
            "1075    [[[206, 208, 202], [207, 209, 203], [207, 209,...\n",
            "1076    [[[199, 211, 211], [194, 206, 208], [193, 203,...\n",
            "1077    [[[47, 80, 100], [48, 84, 102], [40, 84, 101],...\n",
            "1078    [[[74, 130, 135], [72, 128, 133], [73, 125, 13...\n",
            "1079    [[[178, 179, 175], [175, 174, 170], [176, 176,...\n",
            "Name: X, Length: 1080, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(final_df['X'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0VJsDA5m1zU"
      },
      "source": [
        "# ..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Ikdwl89WZrJ",
        "outputId": "4c492c68-4a3e-4bf7-b1ac-57933ad31765"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1080\n"
          ]
        }
      ],
      "source": [
        "def map_f(x):\n",
        "  return np.array(x)\n",
        "\n",
        "x = list(map(map_f,  final_df['X'].to_list()))\n",
        "#final_df['X'].map(map_f, )\n",
        "y = final_df['y']\n",
        "print(len(y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "oACIMXVbmrU3",
        "outputId": "b9a5f1f2-b267-45f7-e3cb-385b47c868f1"
      },
      "outputs": [
        {
          "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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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cv2_imshow(x[13])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zIVUQmArk2CZ"
      },
      "outputs": [],
      "source": [
        "for dirname, _, filenames in os.walk('/content/images_2'):\n",
        "    for filename in filenames:\n",
        "        os.remove(f'{dirname}/{filename}' )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CPNF1katXPQZ"
      },
      "outputs": [],
      "source": [
        "\n",
        "imgs_dir = '/content/images_2'\n",
        "os.makedirs(imgs_dir, exist_ok=True)\n",
        "for dir in classes:\n",
        "  os.makedirs(f'{imgs_dir}/{dir}', exist_ok=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HPYfbNnIh9Iy"
      },
      "outputs": [],
      "source": [
        "for i in range(3):\n",
        "  for j in range(len(images_set_4[i])):\n",
        "    cv2.imwrite(f'/content/images_2/{classes[i]}/{j}.jpg', images_set_4[i][j])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RD7BsRIcl7-x",
        "outputId": "359a91a8-281d-4bce-c042-290e223b9815"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "/content/images_2/Watercress/1.jpg\n",
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            "/content/images_2/Watercress/8.jpg\n",
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            "/content/images_2/Watercress/345.jpg\n",
            "/content/images_2/Watercress/115.jpg\n",
            "/content/images_2/Watercress/108.jpg\n",
            "/content/images_2/Watercress/13.jpg\n",
            "/content/images_2/Watercress/238.jpg\n",
            "/content/images_2/Watercress/215.jpg\n",
            "/content/images_2/Watercress/346.jpg\n",
            "/content/images_2/Watercress/173.jpg\n",
            "/content/images_2/Watercress/291.jpg\n",
            "/content/images_2/Watercress/123.jpg\n",
            "/content/images_2/Watercress/150.jpg\n",
            "/content/images_2/Watercress/358.jpg\n",
            "/content/images_2/Watercress/119.jpg\n",
            "/content/images_2/Watercress/104.jpg\n",
            "/content/images_2/Watercress/193.jpg\n",
            "/content/images_2/Watercress/97.jpg\n",
            "/content/images_2/Watercress/271.jpg\n",
            "/content/images_2/Watercress/262.jpg\n",
            "/content/images_2/Watercress/356.jpg\n",
            "/content/images_2/Watercress/48.jpg\n",
            "/content/images_2/Watercress/334.jpg\n",
            "/content/images_2/Watercress/282.jpg\n",
            "/content/images_2/Watercress/199.jpg\n",
            "/content/images_2/Watercress/168.jpg\n",
            "/content/images_2/Watercress/299.jpg\n",
            "/content/images_2/Watercress/95.jpg\n",
            "/content/images_2/Watercress/359.jpg\n",
            "/content/images_2/Watercress/200.jpg\n",
            "/content/images_2/Watercress/230.jpg\n",
            "/content/images_2/Watercress/111.jpg\n",
            "/content/images_2/Watercress/154.jpg\n",
            "/content/images_2/Watercress/265.jpg\n",
            "/content/images_2/Watercress/211.jpg\n",
            "/content/images_2/Watercress/167.jpg\n",
            "/content/images_2/Watercress/174.jpg\n",
            "/content/images_2/Watercress/69.jpg\n",
            "/content/images_2/Watercress/66.jpg\n",
            "/content/images_2/Watercress/51.jpg\n",
            "/content/images_2/Watercress/187.jpg\n",
            "/content/images_2/Watercress/320.jpg\n",
            "/content/images_2/Watercress/24.jpg\n",
            "/content/images_2/Watercress/85.jpg\n",
            "/content/images_2/Watercress/2.jpg\n",
            "/content/images_2/Watercress/309.jpg\n",
            "/content/images_2/Watercress/181.jpg\n",
            "/content/images_2/Watercress/0.jpg\n",
            "/content/images_2/Watercress/221.jpg\n",
            "/content/images_2/Watercress/140.jpg\n",
            "/content/images_2/Watercress/338.jpg\n",
            "/content/images_2/Watercress/21.jpg\n",
            "/content/images_2/Watercress/44.jpg\n",
            "/content/images_2/Watercress/306.jpg\n",
            "/content/images_2/Watercress/78.jpg\n",
            "/content/images_2/Watercress/118.jpg\n",
            "/content/images_2/Watercress/289.jpg\n",
            "/content/images_2/Watercress/206.jpg\n",
            "/content/images_2/Watercress/324.jpg\n",
            "/content/images_2/Watercress/110.jpg\n",
            "/content/images_2/Watercress/165.jpg\n",
            "/content/images_2/Watercress/244.jpg\n",
            "/content/images_2/Watercress/322.jpg\n",
            "/content/images_2/Watercress/47.jpg\n",
            "/content/images_2/Watercress/63.jpg\n",
            "/content/images_2/Watercress/202.jpg\n",
            "/content/images_2/Watercress/46.jpg\n",
            "/content/images_2/Watercress/226.jpg\n",
            "/content/images_2/Watercress/276.jpg\n",
            "/content/images_2/Watercress/229.jpg\n",
            "/content/images_2/Watercress/228.jpg\n",
            "/content/images_2/Watercress/143.jpg\n",
            "/content/images_2/Watercress/227.jpg\n",
            "/content/images_2/Watercress/204.jpg\n",
            "/content/images_2/Watercress/99.jpg\n",
            "/content/images_2/Watercress/25.jpg\n"
          ]
        }
      ],
      "source": [
        "for dirname, _, filenames in os.walk('/content/images_2'):\n",
        "  for filename in filenames:\n",
        "    print(f'{dirname}/{filename}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAkslV2Lm4xt"
      },
      "source": [
        "# Учим модель"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQ8UxQ1EuijO",
        "outputId": "54d00589-86fb-410c-93e2-d785073ca78b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "print(tf.executing_eagerly())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "5X9-XLzf4j-E",
        "outputId": "72ec8cd5-c5cf-409c-b52d-a6445a890bf8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 864 images belonging to 3 classes.\n",
            "Found 216 images belonging to 3 classes.\n",
            "{'Amarant': 0, 'Cabbage': 1, 'Watercress': 2}\n",
            "{'Amarant': 0, 'Cabbage': 1, 'Watercress': 2}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ mobilenet_1.00_224 (\u001b[38;5;33mFunctional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │       \u001b[38;5;34m3,228,864\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_average_pooling2d_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)             │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │       \u001b[38;5;34m1,049,600\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │           \u001b[38;5;34m3,075\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ mobilenet_1.00_224 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │       <span style=\"color: #00af00; text-decoration-color: #00af00\">3,228,864</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_average_pooling2d_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)             │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,600</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,075</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,281,539\u001b[0m (16.33 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,281,539</span> (16.33 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,052,675\u001b[0m (4.02 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,052,675</span> (4.02 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,228,864\u001b[0m (12.32 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,228,864</span> (12.32 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 2s/step - accuracy: 0.7532 - loss: 0.5543 - val_accuracy: 0.9167 - val_loss: 0.1391\n",
            "Epoch 2/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - accuracy: 0.9511 - loss: 0.1234 - val_accuracy: 0.9398 - val_loss: 0.1061\n",
            "Epoch 3/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - accuracy: 0.9523 - loss: 0.1054 - val_accuracy: 0.9769 - val_loss: 0.0778\n",
            "Epoch 4/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.9731 - loss: 0.0887 - val_accuracy: 0.9491 - val_loss: 0.1241\n",
            "Epoch 5/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.9721 - loss: 0.0763 - val_accuracy: 0.9676 - val_loss: 0.0884\n",
            "Epoch 6/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.9781 - loss: 0.0607 - val_accuracy: 0.9722 - val_loss: 0.0697\n",
            "Epoch 7/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.9907 - loss: 0.0391 - val_accuracy: 0.9769 - val_loss: 0.0593\n",
            "Epoch 8/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 2s/step - accuracy: 0.9910 - loss: 0.0349 - val_accuracy: 0.9722 - val_loss: 0.0625\n",
            "Epoch 9/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.9837 - loss: 0.0468 - val_accuracy: 0.9722 - val_loss: 0.0653\n",
            "Epoch 10/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.9913 - loss: 0.0410 - val_accuracy: 0.9676 - val_loss: 0.0673\n",
            "Epoch 11/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.9892 - loss: 0.0439 - val_accuracy: 0.9769 - val_loss: 0.0621\n",
            "Epoch 12/25\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 2s/step - accuracy: 0.9900 - loss: 0.0335 - val_accuracy: 0.9583 - val_loss: 0.1098\n",
            "Epoch 12: early stopping\n",
            "Restoring model weights from the end of the best epoch: 7.\n"
          ]
        }
      ],
      "source": [
        "from re import X\n",
        "import pickle\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, GlobalAveragePooling2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "\n",
        "# Каталог с набором данных\n",
        "data_dir = '/content/images_2'\n",
        "\n",
        "#tf.compat.v1.disable_eager_execution()\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor='val_loss',  # Метрика для отслеживания\n",
        "    patience=5,  # Количество эпох для ожидания улучшения\n",
        "    verbose=1,  # Вывод информации в консоль\n",
        "    mode='min',  # Режим (min для минимизации метрики)\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "callbacks_list = [EarlyStopping(monitor='vasl_loss', patience=10,verbose=1,  restore_best_weights=True)]\n",
        "\n",
        "classes = [ 'Amarant', 'Cabbage', 'Watercress']\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    validation_split=0.2 )\n",
        "\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    data_dir,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical',  # Режим классификации\n",
        "    classes = classes,\n",
        "    subset='training')\n",
        "\n",
        "test_data_generator = datagen.flow_from_directory(\n",
        "    data_dir,\n",
        "    target_size=(224, 224),  # Размер изображений\n",
        "    batch_size=32,  # Размер пакета\n",
        "    class_mode='categorical',  # Режим классификации\n",
        "    classes = classes,\n",
        "    subset='validation'  # Определение тестовой выборки\n",
        ")\n",
        "\n",
        "class_indices = train_generator.class_indices\n",
        "\n",
        "print(class_indices)\n",
        "\n",
        "class_indices = test_data_generator.class_indices\n",
        "\n",
        "print(class_indices)\n",
        "\n",
        "file_paths = train_generator.filepaths\n",
        "for path in file_paths:\n",
        "    if '.ipynb_checkpoints' in path:\n",
        "       print(path)\n",
        "# Загрузка модели\n",
        "base_model = tf.keras.applications.MobileNet(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Замораживаем слои базовой модели\n",
        "base_model.trainable = False\n",
        "model_2 = Sequential([\n",
        "    base_model,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "\n",
        "model_2.summary()\n",
        "\n",
        "model_2.compile(optimizer='adam',\n",
        "          loss='categorical_crossentropy',\n",
        "          metrics=['accuracy'])\n",
        "\n",
        "#sess = tf.compat.v1.Session()\n",
        "#sess.run(tf.compat.v1.global_variables_initializer())\n",
        "\n",
        "#model.fit(X_train, Y_train, epochs=10, batch_size=32, validation_data=(X_test, Y_test))\n",
        "history = model_2.fit(\n",
        "    train_generator,\n",
        "    #steps_per_epoch=nb_train_samples // batch_size,\n",
        "    epochs=25,\n",
        "    #validation_split = 0.2,\n",
        "    validation_data=test_data_generator,\n",
        "    #validation_steps=nb_validation_samples // batch_size,\n",
        "    callbacks=[early_stop]\n",
        "    )\n",
        "#model.save('proj.h5')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MHl2k258SjLb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd9ccfa1-66e4-4096-f349-e55236cafd69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - accuracy: 0.9691 - loss: 0.0829\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05928313359618187, 0.9768518805503845]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "model_2.evaluate(test_data_generator, verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "2WokMY19AmIT",
        "outputId": "3c1559bb-6464-4787-84fa-da0eb77d2a64"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'],\n",
        "         label='Доля правильных ответов на обучающем наборе')\n",
        "plt.plot(history.history['val_accuracy'],\n",
        "         label='Доля правильных ответов на проверочном наборе')\n",
        "plt.xlabel('Эпоха обучения')\n",
        "plt.ylabel('Доля правильных ответов')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0w9md6I-Andt"
      },
      "outputs": [],
      "source": [
        "#tf.compat.v1.saved_model.save(model, '/content/drive/My Drive/saved_model_6')\n",
        "model_2.save('/content/MobileNet_bg_2.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lhdfdTDo7p8G"
      },
      "outputs": [],
      "source": [
        "!mv \"/content/MobileNet_bg_2.keras\" \"/content/drive/My Drive/model/MobileNet_bg_2.keras\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wmHXuxeMA-QH"
      },
      "outputs": [],
      "source": [
        "# from google.colab import files\n",
        "# files.download('proj_v2.h5')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}