model_with_removing_lim_from_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "model_with_removing_lim_from_dataset.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EklemjEBhWt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten,MinPool2D\n",
        "import tensorflow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8sWoLZBBpIj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_experimented_model=Sequential()\n",
        "my_experimented_model.add(Conv2D(25, kernel_size=(3,3), strides=(1,1), padding='valid', activation='relu', input_shape=(45,45,3)))\n",
        "my_experimented_model.add(MinPool2D(pool_size=(1,1)))\n",
        "my_experimented_model.add(Conv2D(75, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))\n",
        "my_experimented_model.add(MinPool2D(pool_size=(2,2)))\n",
        "my_experimented_model.add(Dropout(0.25))\n",
        "my_experimented_model.add(Conv2D(75, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))\n",
        "my_experimented_model.add(MinPool2D(pool_size=(2,2)))\n",
        "my_experimented_model.add(Dropout(0.2))\n",
        "my_experimented_model.add(Flatten())\n",
        "my_experimented_model.add(Dense(1024))\n",
        "my_experimented_model.add(Dropout(0.2))\n",
        "my_experimented_model.add(Dense(56, activation='softmax'))\n",
        "\n",
        "# compiling the sequential model\n",
        "my_experimented_model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')\n",
        "\n",
        "my_experimented_model.summary()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnvcC5YSBrF4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pathlib\n",
        "import random\n",
        "import shutil\n",
        "from os import path\n",
        "\n",
        "try:\n",
        "    from tqdm import tqdm\n",
        "\n",
        "    tqdm_is_installed = True\n",
        "except ImportError:\n",
        "    tqdm_is_installed = False\n",
        "\n",
        "\n",
        "def list_dirs(directory):\n",
        "    \"\"\"Returns all directories in a given directory\n",
        "    \"\"\"\n",
        "    return [f for f in pathlib.Path(directory).iterdir() if f.is_dir()]\n",
        "\n",
        "\n",
        "def list_files(directory):\n",
        "    \"\"\"Returns all files in a given directory\n",
        "    \"\"\"\n",
        "    return [\n",
        "        f\n",
        "        for f in pathlib.Path(directory).iterdir()\n",
        "        if f.is_file() and not f.name.startswith(\".\")\n",
        "    ]\n",
        "\n",
        "\n",
        "def ratio(input, output=\"output\", seed=1337, ratio=(0.8, 0.1, 0.1)):\n",
        "    # make up for some impression\n",
        "    assert round(sum(ratio), 5) == 1\n",
        "    assert len(ratio) in (2, 3)\n",
        "\n",
        "    if tqdm_is_installed:\n",
        "        prog_bar = tqdm(desc=f\"Copying files\", unit=\" files\")\n",
        "\n",
        "    for class_dir in list_dirs(input):\n",
        "        split_class_dir_ratio(\n",
        "            class_dir, output, ratio, seed, prog_bar if tqdm_is_installed else None\n",
        "        )\n",
        "\n",
        "    if tqdm_is_installed:\n",
        "        prog_bar.close()\n",
        "\n",
        "\n",
        "def fixed(input, output=\"output\", seed=1337, fixed=(100, 100), oversample=False):\n",
        "    # make sure its reproducible\n",
        "    if isinstance(fixed, int):\n",
        "        fixed = fixed\n",
        "\n",
        "    assert len(fixed) in (1, 2)\n",
        "\n",
        "    if tqdm_is_installed:\n",
        "        prog_bar = tqdm(desc=f\"Copying files\", unit=\" files\")\n",
        "\n",
        "    dirs = list_dirs(input)\n",
        "    lens = []\n",
        "    for class_dir in dirs:\n",
        "        lens.append(\n",
        "            split_class_dir_fixed(\n",
        "                class_dir, output, fixed, seed, prog_bar if tqdm_is_installed else None\n",
        "            )\n",
        "        )\n",
        "\n",
        "    if tqdm_is_installed:\n",
        "        prog_bar.close()\n",
        "\n",
        "    if not oversample:\n",
        "        return\n",
        "\n",
        "    max_len = max(lens)\n",
        "\n",
        "    iteration = zip(lens, dirs)\n",
        "\n",
        "    if tqdm_is_installed:\n",
        "        iteration = tqdm(iteration, desc=\"Oversampling\", unit=\" classes\")\n",
        "\n",
        "    for length, class_dir in iteration:\n",
        "        class_name = path.split(class_dir)[1]\n",
        "        full_path = path.join(output, \"train\", class_name)\n",
        "        train_files = list_files(full_path)\n",
        "        for i in range(max_len - length):\n",
        "            f_orig = random.choice(train_files)\n",
        "            new_name = f_orig.stem + \"_\" + str(i) + f_orig.suffix\n",
        "            f_dest = f_orig.with_name(new_name)\n",
        "            shutil.copy2(f_orig, f_dest)\n",
        "\n",
        "\n",
        "def setup_files(class_dir, seed):\n",
        "    \"\"\"Returns shuffled files\n",
        "    \"\"\"\n",
        "    # make sure its reproducible\n",
        "    random.seed(seed)\n",
        "\n",
        "    files = list_files(class_dir)\n",
        "\n",
        "    files.sort()\n",
        "    random.shuffle(files)\n",
        "    return files\n",
        "\n",
        "\n",
        "def split_class_dir_fixed(class_dir, output, fixed, seed, prog_bar):\n",
        "    \"\"\"Splits one very class folder\n",
        "    \"\"\"\n",
        "    files = setup_files(class_dir, seed)\n",
        "\n",
        "    if not len(files) > sum(fixed):\n",
        "        raise ValueError(\n",
        "            f'The number of samples in class \"{class_dir.stem}\" are too few. There are only {len(files)} samples available but your fixed parameter {fixed} requires at least {sum(fixed)} files. You may want to split your classes by ratio.'\n",
        "        )\n",
        "\n",
        "    split_train = len(files) - sum(fixed)\n",
        "    split_val = split_train + fixed[0]\n",
        "\n",
        "    li = split_files(files, split_train, split_val, len(fixed) == 2)\n",
        "    copy_files(li, class_dir, output, prog_bar)\n",
        "    return len(files)\n",
        "\n",
        "\n",
        "def split_class_dir_ratio(class_dir, output, ratio, seed, prog_bar):\n",
        "    \"\"\"Splits one very class folder\n",
        "    \"\"\"\n",
        "    files = setup_files(class_dir, seed)\n",
        "\n",
        "    split_train = int(ratio[0] * len(files))\n",
        "    split_val = split_train + int(ratio[1] * len(files))\n",
        "\n",
        "    li = split_files(files, split_train, split_val, len(ratio) == 3)\n",
        "    copy_files(li, class_dir, output, prog_bar)\n",
        "\n",
        "\n",
        "def split_files(files, split_train, split_val, use_test):\n",
        "    \"\"\"Splits the files along the provided indices\n",
        "    \"\"\"\n",
        "    files_train = files[:split_train]\n",
        "    files_val = files[split_train:split_val] if use_test else files[split_train:]\n",
        "\n",
        "    li = [(files_train, \"train\"), (files_val, \"val\")]\n",
        "\n",
        "    # optional test folder\n",
        "    if use_test:\n",
        "        files_test = files[split_val:]\n",
        "        li.append((files_test, \"test\"))\n",
        "    return li\n",
        "\n",
        "\n",
        "def copy_files(files_type, class_dir, output, prog_bar):\n",
        "    \"\"\"Copies the files from the input folder to the output folder\n",
        "    \"\"\"\n",
        "    # get the last part within the file\n",
        "    class_name = path.split(class_dir)[1]\n",
        "    for (files, folder_type) in files_type:\n",
        "        full_path = path.join(output, folder_type, class_name)\n",
        "\n",
        "        pathlib.Path(full_path).mkdir(parents=True, exist_ok=True)\n",
        "        for f in files:\n",
        "            if not prog_bar is None:\n",
        "                prog_bar.update()\n",
        "            shutil.copy2(f, full_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErfduuUnBtdy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e692cf87-c867-48b1-c5d4-ddf422d2e131"
      },
      "source": [
        "!unzip '/content/drive/My Drive/Internship_Dataset/crohme_dataset2.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: crohme_dataset2/X/exp39780.jpg  \n",
            " extracting: crohme_dataset2/X/exp39805.jpg  \n",
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            " extracting: crohme_dataset2/X/exp47836.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48151.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp48182.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48231.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48540.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp4857.jpg  \n",
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            " extracting: crohme_dataset2/X/exp4858.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp48675.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48721.jpg  \n",
            " extracting: crohme_dataset2/X/exp48789.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48890.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48933.jpg  \n",
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            " extracting: crohme_dataset2/X/exp48935.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49045.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49123.jpg  \n",
            " extracting: crohme_dataset2/X/exp49167.jpg  \n",
            " extracting: crohme_dataset2/X/exp49226.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49251.jpg  \n",
            "  inflating: crohme_dataset2/X/exp49300.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49320.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49327.jpg  \n",
            " extracting: crohme_dataset2/X/exp49385.jpg  \n",
            " extracting: crohme_dataset2/X/exp4943.jpg  \n",
            " extracting: crohme_dataset2/X/exp49534.jpg  \n",
            " extracting: crohme_dataset2/X/exp4956.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49618.jpg  \n",
            "  inflating: crohme_dataset2/X/exp49624.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49630.jpg  \n",
            " extracting: crohme_dataset2/X/exp4966.jpg  \n",
            " extracting: crohme_dataset2/X/exp49689.jpg  \n",
            " extracting: crohme_dataset2/X/exp49691.jpg  \n",
            " extracting: crohme_dataset2/X/exp4971.jpg  \n",
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            " extracting: crohme_dataset2/X/exp49719.jpg  \n",
            "  inflating: crohme_dataset2/X/exp4973.jpg  \n",
            " extracting: crohme_dataset2/X/exp49734.jpg  \n",
            " extracting: crohme_dataset2/X/exp4976.jpg  \n",
            " extracting: crohme_dataset2/X/exp49764.jpg  \n",
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            " extracting: crohme_dataset2/X/exp54737.jpg  \n",
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            " extracting: crohme_dataset2/X/exp5480.jpg  \n",
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            " extracting: crohme_dataset2/X/exp54839.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp55108.jpg  \n",
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            " extracting: crohme_dataset2/X/exp55231.jpg  \n",
            " extracting: crohme_dataset2/X/exp55235.jpg  \n",
            " extracting: crohme_dataset2/X/exp55298.jpg  \n",
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            " extracting: crohme_dataset2/X/exp55328.jpg  \n",
            " extracting: crohme_dataset2/X/exp55334.jpg  \n",
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            " extracting: crohme_dataset2/X/exp5538.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp55403.jpg  \n",
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            " extracting: crohme_dataset2/X/exp5545.jpg  \n",
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            " extracting: crohme_dataset2/X/exp55508.jpg  \n",
            " extracting: crohme_dataset2/X/exp55551.jpg  \n",
            " extracting: crohme_dataset2/X/exp55554.jpg  \n",
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            " extracting: crohme_dataset2/X/exp55584.jpg  \n",
            " extracting: crohme_dataset2/X/exp5561.jpg  \n",
            " extracting: crohme_dataset2/X/exp55655.jpg  \n",
            " extracting: crohme_dataset2/X/exp55704.jpg  \n",
            "  inflating: crohme_dataset2/X/exp55705.jpg  \n",
            " extracting: crohme_dataset2/X/exp55755.jpg  \n",
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            " extracting: crohme_dataset2/X/exp55770.jpg  \n",
            " extracting: crohme_dataset2/X/exp55771.jpg  \n",
            "  inflating: crohme_dataset2/X/exp55787.jpg  \n",
            " extracting: crohme_dataset2/X/exp55790.jpg  \n",
            " extracting: crohme_dataset2/X/exp55791.jpg  \n",
            "  inflating: crohme_dataset2/X/exp55833.jpg  \n",
            " extracting: crohme_dataset2/X/exp5586.jpg  \n",
            " extracting: crohme_dataset2/X/exp55864.jpg  \n",
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            " extracting: crohme_dataset2/X/exp58602.jpg  \n",
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            " extracting: crohme_dataset2/X/exp59531.jpg  \n",
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            " extracting: crohme_dataset2/X/exp59539.jpg  \n",
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            " extracting: crohme_dataset2/X/exp59737.jpg  \n",
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            " extracting: crohme_dataset2/X/exp6294.jpg  \n",
            " extracting: crohme_dataset2/X/exp6299.jpg  \n",
            "  inflating: crohme_dataset2/X/exp63.jpg  \n",
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            " extracting: crohme_dataset2/X/exp6304.jpg  \n",
            " extracting: crohme_dataset2/X/exp6332.jpg  \n",
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            " extracting: crohme_dataset2/X/exp63743.jpg  \n",
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            " extracting: crohme_dataset2/X/exp63977.jpg  \n",
            " extracting: crohme_dataset2/X/exp63997.jpg  \n",
            " extracting: crohme_dataset2/X/exp64003.jpg  \n",
            " extracting: crohme_dataset2/X/exp6407.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64095.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64109.jpg  \n",
            " extracting: crohme_dataset2/X/exp64111.jpg  \n",
            " extracting: crohme_dataset2/X/exp64120.jpg  \n",
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            " extracting: crohme_dataset2/X/exp6414.jpg  \n",
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            " extracting: crohme_dataset2/X/exp6420.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp64308.jpg  \n",
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            " extracting: crohme_dataset2/X/exp6433.jpg  \n",
            " extracting: crohme_dataset2/X/exp64343.jpg  \n",
            " extracting: crohme_dataset2/X/exp64349.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64388.jpg  \n",
            " extracting: crohme_dataset2/X/exp64397.jpg  \n",
            " extracting: crohme_dataset2/X/exp64415.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64422.jpg  \n",
            " extracting: crohme_dataset2/X/exp64425.jpg  \n",
            " extracting: crohme_dataset2/X/exp64426.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64427.jpg  \n",
            " extracting: crohme_dataset2/X/exp6443.jpg  \n",
            " extracting: crohme_dataset2/X/exp64430.jpg  \n",
            " extracting: crohme_dataset2/X/exp64431.jpg  \n",
            " extracting: crohme_dataset2/X/exp64434.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64438.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64442.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64449.jpg  \n",
            " extracting: crohme_dataset2/X/exp64455.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64461.jpg  \n",
            " extracting: crohme_dataset2/X/exp64464.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64466.jpg  \n",
            " extracting: crohme_dataset2/X/exp64469.jpg  \n",
            " extracting: crohme_dataset2/X/exp64495.jpg  \n",
            " extracting: crohme_dataset2/X/exp64529.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64539.jpg  \n",
            " extracting: crohme_dataset2/X/exp64572.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64586.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64598.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64603.jpg  \n",
            " extracting: crohme_dataset2/X/exp64606.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64612.jpg  \n",
            " extracting: crohme_dataset2/X/exp64640.jpg  \n",
            " extracting: crohme_dataset2/X/exp64673.jpg  \n",
            " extracting: crohme_dataset2/X/exp64679.jpg  \n",
            " extracting: crohme_dataset2/X/exp64717.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64723.jpg  \n",
            " extracting: crohme_dataset2/X/exp64729.jpg  \n",
            " extracting: crohme_dataset2/X/exp64730.jpg  \n",
            " extracting: crohme_dataset2/X/exp64731.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64732.jpg  \n",
            " extracting: crohme_dataset2/X/exp64739.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64746.jpg  \n",
            " extracting: crohme_dataset2/X/exp64749.jpg  \n",
            " extracting: crohme_dataset2/X/exp64752.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64772.jpg  \n",
            "  inflating: crohme_dataset2/X/exp64777.jpg  \n",
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            " extracting: crohme_dataset2/X/exp64783.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp65256.jpg  \n",
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            " extracting: crohme_dataset2/X/exp66028.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp66391.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp66516.jpg  \n",
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            " extracting: crohme_dataset2/X/exp66531.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp69747.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp70144.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp70438.jpg  \n",
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            " extracting: crohme_dataset2/X/exp7050.jpg  \n",
            " extracting: crohme_dataset2/X/exp71513.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp74503.jpg  \n",
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            " extracting: crohme_dataset2/X/exp74706.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp74736.jpg  \n",
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            " extracting: crohme_dataset2/X/exp74860.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp76602.jpg  \n",
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            " extracting: crohme_dataset2/X/exp76616.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp77623.jpg  \n",
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            " extracting: crohme_dataset2/X/exp77629.jpg  \n",
            " extracting: crohme_dataset2/X/exp77632.jpg  \n",
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            " extracting: crohme_dataset2/X/exp77653.jpg  \n",
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            "  inflating: crohme_dataset2/X/exp77757.jpg  \n",
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            " extracting: crohme_dataset2/X/exp77791.jpg  \n",
            " extracting: crohme_dataset2/X/exp77818.jpg  \n",
            " extracting: crohme_dataset2/X/exp77831.jpg  \n",
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            " extracting: crohme_dataset2/X/exp77893.jpg  \n",
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            " extracting: crohme_dataset2/X/exp77921.jpg  \n",
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            " extracting: crohme_dataset2/X/exp77931.jpg  \n",
            " extracting: crohme_dataset2/X/exp77934.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78.jpg  \n",
            " extracting: crohme_dataset2/X/exp78005.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78052.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78065.jpg  \n",
            " extracting: crohme_dataset2/X/exp78104.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78169.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78179.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78194.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78210.jpg  \n",
            " extracting: crohme_dataset2/X/exp78214.jpg  \n",
            " extracting: crohme_dataset2/X/exp78219.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78511.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78524.jpg  \n",
            " extracting: crohme_dataset2/X/exp78561.jpg  \n",
            " extracting: crohme_dataset2/X/exp78572.jpg  \n",
            " extracting: crohme_dataset2/X/exp78583.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78599.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78616.jpg  \n",
            " extracting: crohme_dataset2/X/exp78625.jpg  \n",
            " extracting: crohme_dataset2/X/exp78641.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78647.jpg  \n",
            " extracting: crohme_dataset2/X/exp78703.jpg  \n",
            " extracting: crohme_dataset2/X/exp78708.jpg  \n",
            " extracting: crohme_dataset2/X/exp78728.jpg  \n",
            " extracting: crohme_dataset2/X/exp78733.jpg  \n",
            " extracting: crohme_dataset2/X/exp78737.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78742.jpg  \n",
            " extracting: crohme_dataset2/X/exp78748.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78775.jpg  \n",
            " extracting: crohme_dataset2/X/exp78780.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78801.jpg  \n",
            " extracting: crohme_dataset2/X/exp78809.jpg  \n",
            " extracting: crohme_dataset2/X/exp78831.jpg  \n",
            " extracting: crohme_dataset2/X/exp78835.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78838.jpg  \n",
            " extracting: crohme_dataset2/X/exp78843.jpg  \n",
            " extracting: crohme_dataset2/X/exp78846.jpg  \n",
            " extracting: crohme_dataset2/X/exp78880.jpg  \n",
            "  inflating: crohme_dataset2/X/exp78901.jpg  \n",
            " extracting: crohme_dataset2/X/exp78947.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78956.jpg  \n",
            " extracting: crohme_dataset2/X/exp78960.jpg  \n",
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            " extracting: crohme_dataset2/X/exp78994.jpg  \n",
            " extracting: crohme_dataset2/X/exp78999.jpg  \n",
            " extracting: crohme_dataset2/X/exp79032.jpg  \n",
            " extracting: crohme_dataset2/X/exp79042.jpg  \n",
            " extracting: crohme_dataset2/X/exp79043.jpg  \n",
            "  inflating: crohme_dataset2/X/exp79051.jpg  \n",
            " extracting: crohme_dataset2/X/exp79061.jpg  \n",
            " extracting: crohme_dataset2/X/exp79069.jpg  \n",
            " extracting: crohme_dataset2/X/exp79079.jpg  \n",
            " extracting: crohme_dataset2/X/exp79094.jpg  \n",
            " extracting: crohme_dataset2/X/exp79181.jpg  \n",
            " extracting: crohme_dataset2/X/exp82.jpg  \n",
            "  inflating: crohme_dataset2/X/exp85.jpg  \n",
            "  inflating: crohme_dataset2/y/exp1.jpg  \n",
            " extracting: crohme_dataset2/y/exp100.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100027.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100046.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100049.jpg  \n",
            " extracting: crohme_dataset2/y/exp100065.jpg  \n",
            " extracting: crohme_dataset2/y/exp100068.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100072.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100104.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100109.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100113.jpg  \n",
            " extracting: crohme_dataset2/y/exp100134.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100148.jpg  \n",
            " extracting: crohme_dataset2/y/exp100170.jpg  \n",
            " extracting: crohme_dataset2/y/exp100175.jpg  \n",
            " extracting: crohme_dataset2/y/exp100179.jpg  \n",
            " extracting: crohme_dataset2/y/exp100183.jpg  \n",
            " extracting: crohme_dataset2/y/exp100188.jpg  \n",
            "  inflating: crohme_dataset2/y/exp100192.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp101933.jpg  \n",
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            " extracting: crohme_dataset2/y/exp102399.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp102412.jpg  \n",
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            " extracting: crohme_dataset2/y/exp102433.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp102502.jpg  \n",
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            " extracting: crohme_dataset2/y/exp102515.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp102521.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp1032.jpg  \n",
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            " extracting: crohme_dataset2/y/exp103291.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp103323.jpg  \n",
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            " extracting: crohme_dataset2/y/exp103867.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp103996.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10400.jpg  \n",
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            " extracting: crohme_dataset2/y/exp104289.jpg  \n",
            "  inflating: crohme_dataset2/y/exp104296.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10436.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10439.jpg  \n",
            "  inflating: crohme_dataset2/y/exp10440.jpg  \n",
            " extracting: crohme_dataset2/y/exp104414.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10458.jpg  \n",
            "  inflating: crohme_dataset2/y/exp104598.jpg  \n",
            " extracting: crohme_dataset2/y/exp10460.jpg  \n",
            " extracting: crohme_dataset2/y/exp104608.jpg  \n",
            "  inflating: crohme_dataset2/y/exp104629.jpg  \n",
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            " extracting: crohme_dataset2/y/exp104657.jpg  \n",
            "  inflating: crohme_dataset2/y/exp104660.jpg  \n",
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            " extracting: crohme_dataset2/y/exp104708.jpg  \n",
            "  inflating: crohme_dataset2/y/exp104722.jpg  \n",
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            " extracting: crohme_dataset2/y/exp104806.jpg  \n",
            " extracting: crohme_dataset2/y/exp104808.jpg  \n",
            " extracting: crohme_dataset2/y/exp10481.jpg  \n",
            " extracting: crohme_dataset2/y/exp10489.jpg  \n",
            "  inflating: crohme_dataset2/y/exp104898.jpg  \n",
            " extracting: crohme_dataset2/y/exp104905.jpg  \n",
            " extracting: crohme_dataset2/y/exp10495.jpg  \n",
            " extracting: crohme_dataset2/y/exp105.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10509.jpg  \n",
            " extracting: crohme_dataset2/y/exp105211.jpg  \n",
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            " extracting: crohme_dataset2/y/exp105220.jpg  \n",
            " extracting: crohme_dataset2/y/exp105240.jpg  \n",
            " extracting: crohme_dataset2/y/exp105247.jpg  \n",
            "  inflating: crohme_dataset2/y/exp105251.jpg  \n",
            " extracting: crohme_dataset2/y/exp10526.jpg  \n",
            " extracting: crohme_dataset2/y/exp10532.jpg  \n",
            " extracting: crohme_dataset2/y/exp105360.jpg  \n",
            " extracting: crohme_dataset2/y/exp105366.jpg  \n",
            "  inflating: crohme_dataset2/y/exp105380.jpg  \n",
            " extracting: crohme_dataset2/y/exp105385.jpg  \n",
            " extracting: crohme_dataset2/y/exp105392.jpg  \n",
            " extracting: crohme_dataset2/y/exp105400.jpg  \n",
            "  inflating: crohme_dataset2/y/exp105471.jpg  \n",
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            " extracting: crohme_dataset2/y/exp105623.jpg  \n",
            "  inflating: crohme_dataset2/y/exp105775.jpg  \n",
            " extracting: crohme_dataset2/y/exp105789.jpg  \n",
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            " extracting: crohme_dataset2/y/exp105911.jpg  \n",
            "  inflating: crohme_dataset2/y/exp105946.jpg  \n",
            " extracting: crohme_dataset2/y/exp105952.jpg  \n",
            " extracting: crohme_dataset2/y/exp105955.jpg  \n",
            " extracting: crohme_dataset2/y/exp106.jpg  \n",
            " extracting: crohme_dataset2/y/exp106115.jpg  \n",
            " extracting: crohme_dataset2/y/exp106121.jpg  \n",
            " extracting: crohme_dataset2/y/exp106179.jpg  \n",
            "  inflating: crohme_dataset2/y/exp106237.jpg  \n",
            " extracting: crohme_dataset2/y/exp106244.jpg  \n",
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            " extracting: crohme_dataset2/y/exp106258.jpg  \n",
            " extracting: crohme_dataset2/y/exp106265.jpg  \n",
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            " extracting: crohme_dataset2/y/exp106352.jpg  \n",
            " extracting: crohme_dataset2/y/exp106358.jpg  \n",
            "  inflating: crohme_dataset2/y/exp10675.jpg  \n",
            " extracting: crohme_dataset2/y/exp10681.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10713.jpg  \n",
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            " extracting: crohme_dataset2/y/exp10754.jpg  \n",
            " extracting: crohme_dataset2/y/exp10756.jpg  \n",
            " extracting: crohme_dataset2/y/exp10762.jpg  \n",
            " extracting: crohme_dataset2/y/exp10927.jpg  \n",
            " extracting: crohme_dataset2/y/exp10934.jpg  \n",
            " extracting: crohme_dataset2/y/exp10948.jpg  \n",
            " extracting: crohme_dataset2/y/exp10966.jpg  \n",
            " extracting: crohme_dataset2/y/exp10967.jpg  \n",
            "  inflating: crohme_dataset2/y/exp110.jpg  \n",
            " extracting: crohme_dataset2/y/exp11009.jpg  \n",
            " extracting: crohme_dataset2/y/exp11174.jpg  \n",
            "  inflating: crohme_dataset2/y/exp11179.jpg  \n",
            " extracting: crohme_dataset2/y/exp11194.jpg  \n",
            "  inflating: crohme_dataset2/y/exp11217.jpg  \n",
            "  inflating: crohme_dataset2/y/exp11220.jpg  \n",
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            " extracting: crohme_dataset2/y/exp11267.jpg  \n",
            " extracting: crohme_dataset2/y/exp11429.jpg  \n",
            " extracting: crohme_dataset2/y/exp1143.jpg  \n",
            " extracting: crohme_dataset2/y/exp11434.jpg  \n",
            " extracting: crohme_dataset2/y/exp11448.jpg  \n",
            " extracting: crohme_dataset2/y/exp11467.jpg  \n",
            " extracting: crohme_dataset2/y/exp11469.jpg  \n",
            " extracting: crohme_dataset2/y/exp1149.jpg  \n",
            "  inflating: crohme_dataset2/y/exp11508.jpg  \n",
            " extracting: crohme_dataset2/y/exp11514.jpg  \n",
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            " extracting: crohme_dataset2/y/exp11624.jpg  \n",
            " extracting: crohme_dataset2/y/exp11676.jpg  \n",
            " extracting: crohme_dataset2/y/exp11681.jpg  \n",
            " extracting: crohme_dataset2/y/exp11696.jpg  \n",
            " extracting: crohme_dataset2/y/exp11715.jpg  \n",
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            " extracting: crohme_dataset2/y/exp26327.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp27592.jpg  \n",
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            " extracting: crohme_dataset2/y/exp27615.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp27703.jpg  \n",
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            " extracting: crohme_dataset2/y/exp27834.jpg  \n",
            " extracting: crohme_dataset2/y/exp27923.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp2868.jpg  \n",
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            " extracting: crohme_dataset2/y/exp29105.jpg  \n",
            " extracting: crohme_dataset2/y/exp2911.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp2915.jpg  \n",
            " extracting: crohme_dataset2/y/exp2925.jpg  \n",
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            " extracting: crohme_dataset2/y/exp2934.jpg  \n",
            " extracting: crohme_dataset2/y/exp2955.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp29765.jpg  \n",
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            " extracting: crohme_dataset2/y/exp29784.jpg  \n",
            " extracting: crohme_dataset2/y/exp29795.jpg  \n",
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            " extracting: crohme_dataset2/y/exp29822.jpg  \n",
            "  inflating: crohme_dataset2/y/exp29831.jpg  \n",
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            " extracting: crohme_dataset2/y/exp29873.jpg  \n",
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            " extracting: crohme_dataset2/y/exp29915.jpg  \n",
            " extracting: crohme_dataset2/y/exp29933.jpg  \n",
            " extracting: crohme_dataset2/y/exp29952.jpg  \n",
            " extracting: crohme_dataset2/y/exp29968.jpg  \n",
            " extracting: crohme_dataset2/y/exp29984.jpg  \n",
            " extracting: crohme_dataset2/y/exp30000.jpg  \n",
            "  inflating: crohme_dataset2/y/exp30017.jpg  \n",
            " extracting: crohme_dataset2/y/exp30034.jpg  \n",
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            " extracting: crohme_dataset2/y/exp30067.jpg  \n",
            " extracting: crohme_dataset2/y/exp30084.jpg  \n",
            "  inflating: crohme_dataset2/y/exp30100.jpg  \n",
            " extracting: crohme_dataset2/y/exp30117.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp32141.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp32189.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp32234.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp32308.jpg  \n",
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            " extracting: crohme_dataset2/y/exp32720.jpg  \n",
            " extracting: crohme_dataset2/y/exp32732.jpg  \n",
            "  inflating: crohme_dataset2/y/exp3274.jpg  \n",
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            " extracting: crohme_dataset2/y/exp32792.jpg  \n",
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            " extracting: crohme_dataset2/y/exp32823.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp34333.jpg  \n",
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            " extracting: crohme_dataset2/y/exp36729.jpg  \n",
            "  inflating: crohme_dataset2/y/exp36730.jpg  \n",
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            " extracting: crohme_dataset2/y/exp38445.jpg  \n",
            " extracting: crohme_dataset2/y/exp38490.jpg  \n",
            " extracting: crohme_dataset2/y/exp38693.jpg  \n",
            "  inflating: crohme_dataset2/y/exp3909.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp39331.jpg  \n",
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            " extracting: crohme_dataset2/y/exp3944.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp39862.jpg  \n",
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            " extracting: crohme_dataset2/y/exp40182.jpg  \n",
            " extracting: crohme_dataset2/y/exp40220.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp40320.jpg  \n",
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            " extracting: crohme_dataset2/y/exp40403.jpg  \n",
            " extracting: crohme_dataset2/y/exp40405.jpg  \n",
            "  inflating: crohme_dataset2/y/exp40416.jpg  \n",
            " extracting: crohme_dataset2/y/exp4046.jpg  \n",
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            " extracting: crohme_dataset2/y/exp40586.jpg  \n",
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            " extracting: crohme_dataset2/y/exp40687.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp4076.jpg  \n",
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            " extracting: crohme_dataset2/y/exp41343.jpg  \n",
            " extracting: crohme_dataset2/y/exp41347.jpg  \n",
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            " extracting: crohme_dataset2/y/exp41428.jpg  \n",
            " extracting: crohme_dataset2/y/exp41429.jpg  \n",
            " extracting: crohme_dataset2/y/exp41586.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp41690.jpg  \n",
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            " extracting: crohme_dataset2/y/exp4178.jpg  \n",
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            " extracting: crohme_dataset2/y/exp4210.jpg  \n",
            " extracting: crohme_dataset2/y/exp4211.jpg  \n",
            " extracting: crohme_dataset2/y/exp4215.jpg  \n",
            "  inflating: crohme_dataset2/y/exp42231.jpg  \n",
            " extracting: crohme_dataset2/y/exp42236.jpg  \n",
            " extracting: crohme_dataset2/y/exp42362.jpg  \n",
            "  inflating: crohme_dataset2/y/exp42365.jpg  \n",
            " extracting: crohme_dataset2/y/exp42369.jpg  \n",
            " extracting: crohme_dataset2/y/exp42447.jpg  \n",
            " extracting: crohme_dataset2/y/exp42540.jpg  \n",
            " extracting: crohme_dataset2/y/exp42802.jpg  \n",
            " extracting: crohme_dataset2/y/exp42880.jpg  \n",
            " extracting: crohme_dataset2/y/exp42884.jpg  \n",
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            " extracting: crohme_dataset2/y/exp42947.jpg  \n",
            " extracting: crohme_dataset2/y/exp42950.jpg  \n",
            "  inflating: crohme_dataset2/y/exp42976.jpg  \n",
            " extracting: crohme_dataset2/y/exp43027.jpg  \n",
            " extracting: crohme_dataset2/y/exp4305.jpg  \n",
            " extracting: crohme_dataset2/y/exp4310.jpg  \n",
            " extracting: crohme_dataset2/y/exp43111.jpg  \n",
            " extracting: crohme_dataset2/y/exp43234.jpg  \n",
            " extracting: crohme_dataset2/y/exp43239.jpg  \n",
            "  inflating: crohme_dataset2/y/exp43241.jpg  \n",
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            " extracting: crohme_dataset2/y/exp4339.jpg  \n",
            " extracting: crohme_dataset2/y/exp4340.jpg  \n",
            " extracting: crohme_dataset2/y/exp4341.jpg  \n",
            " extracting: crohme_dataset2/y/exp43460.jpg  \n",
            "  inflating: crohme_dataset2/y/exp43461.jpg  \n",
            " extracting: crohme_dataset2/y/exp4347.jpg  \n",
            " extracting: crohme_dataset2/y/exp43827.jpg  \n",
            " extracting: crohme_dataset2/y/exp44069.jpg  \n",
            " extracting: crohme_dataset2/y/exp44143.jpg  \n",
            " extracting: crohme_dataset2/y/exp44264.jpg  \n",
            " extracting: crohme_dataset2/y/exp44321.jpg  \n",
            " extracting: crohme_dataset2/y/exp44333.jpg  \n",
            " extracting: crohme_dataset2/y/exp44350.jpg  \n",
            " extracting: crohme_dataset2/y/exp4437.jpg  \n",
            " extracting: crohme_dataset2/y/exp4438.jpg  \n",
            " extracting: crohme_dataset2/y/exp44399.jpg  \n",
            "  inflating: crohme_dataset2/y/exp44401.jpg  \n",
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            " extracting: crohme_dataset2/y/exp4471.jpg  \n",
            " extracting: crohme_dataset2/y/exp4472.jpg  \n",
            " extracting: crohme_dataset2/y/exp4480.jpg  \n",
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            " extracting: crohme_dataset2/y/exp44802.jpg  \n",
            " extracting: crohme_dataset2/y/exp44805.jpg  \n",
            " extracting: crohme_dataset2/y/exp44833.jpg  \n",
            "  inflating: crohme_dataset2/y/exp44864.jpg  \n",
            " extracting: crohme_dataset2/y/exp44871.jpg  \n",
            " extracting: crohme_dataset2/y/exp44909.jpg  \n",
            " extracting: crohme_dataset2/y/exp44910.jpg  \n",
            " extracting: crohme_dataset2/y/exp44981.jpg  \n",
            " extracting: crohme_dataset2/y/exp45021.jpg  \n",
            " extracting: crohme_dataset2/y/exp45051.jpg  \n",
            " extracting: crohme_dataset2/y/exp45180.jpg  \n",
            "  inflating: crohme_dataset2/y/exp45181.jpg  \n",
            " extracting: crohme_dataset2/y/exp45196.jpg  \n",
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            " extracting: crohme_dataset2/y/exp45248.jpg  \n",
            " extracting: crohme_dataset2/y/exp45298.jpg  \n",
            " extracting: crohme_dataset2/y/exp45396.jpg  \n",
            " extracting: crohme_dataset2/y/exp45478.jpg  \n",
            "  inflating: crohme_dataset2/y/exp45527.jpg  \n",
            " extracting: crohme_dataset2/y/exp45630.jpg  \n",
            "  inflating: crohme_dataset2/y/exp45665.jpg  \n",
            " extracting: crohme_dataset2/y/exp45674.jpg  \n",
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            " extracting: crohme_dataset2/y/exp4571.jpg  \n",
            " extracting: crohme_dataset2/y/exp45741.jpg  \n",
            "  inflating: crohme_dataset2/y/exp45744.jpg  \n",
            "  inflating: crohme_dataset2/y/exp45749.jpg  \n",
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            " extracting: crohme_dataset2/y/exp45924.jpg  \n",
            " extracting: crohme_dataset2/y/exp4602.jpg  \n",
            "  inflating: crohme_dataset2/y/exp4603.jpg  \n",
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            " extracting: crohme_dataset2/y/exp46080.jpg  \n",
            " extracting: crohme_dataset2/y/exp4611.jpg  \n",
            " extracting: crohme_dataset2/y/exp46235.jpg  \n",
            " extracting: crohme_dataset2/y/exp46243.jpg  \n",
            " extracting: crohme_dataset2/y/exp46546.jpg  \n",
            "  inflating: crohme_dataset2/y/exp46784.jpg  \n",
            " extracting: crohme_dataset2/y/exp46813.jpg  \n",
            " extracting: crohme_dataset2/y/exp46819.jpg  \n",
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            " extracting: crohme_dataset2/y/exp59277.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp59448.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp60821.jpg  \n",
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            " extracting: crohme_dataset2/y/exp61.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp63336.jpg  \n",
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            " extracting: crohme_dataset2/y/exp64448.jpg  \n",
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            " extracting: crohme_dataset2/y/exp64553.jpg  \n",
            " extracting: crohme_dataset2/y/exp64558.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp64600.jpg  \n",
            " extracting: crohme_dataset2/y/exp64636.jpg  \n",
            " extracting: crohme_dataset2/y/exp64663.jpg  \n",
            " extracting: crohme_dataset2/y/exp64699.jpg  \n",
            " extracting: crohme_dataset2/y/exp64703.jpg  \n",
            " extracting: crohme_dataset2/y/exp64728.jpg  \n",
            " extracting: crohme_dataset2/y/exp64734.jpg  \n",
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            " extracting: crohme_dataset2/y/exp64743.jpg  \n",
            " extracting: crohme_dataset2/y/exp64787.jpg  \n",
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            " extracting: crohme_dataset2/y/exp64848.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp65454.jpg  \n",
            " extracting: crohme_dataset2/y/exp65477.jpg  \n",
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            " extracting: crohme_dataset2/y/exp65703.jpg  \n",
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            " extracting: crohme_dataset2/y/exp84355.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84357.jpg  \n",
            " extracting: crohme_dataset2/y/exp84421.jpg  \n",
            " extracting: crohme_dataset2/y/exp84428.jpg  \n",
            " extracting: crohme_dataset2/y/exp84464.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84469.jpg  \n",
            " extracting: crohme_dataset2/y/exp84475.jpg  \n",
            " extracting: crohme_dataset2/y/exp84476.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84669.jpg  \n",
            " extracting: crohme_dataset2/y/exp84674.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84881.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84887.jpg  \n",
            " extracting: crohme_dataset2/y/exp84890.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84894.jpg  \n",
            " extracting: crohme_dataset2/y/exp84903.jpg  \n",
            " extracting: crohme_dataset2/y/exp84908.jpg  \n",
            " extracting: crohme_dataset2/y/exp84964.jpg  \n",
            "  inflating: crohme_dataset2/y/exp84971.jpg  \n",
            "  inflating: crohme_dataset2/y/exp85007.jpg  \n",
            " extracting: crohme_dataset2/y/exp85012.jpg  \n",
            " extracting: crohme_dataset2/y/exp85018.jpg  \n",
            " extracting: crohme_dataset2/y/exp85019.jpg  \n",
            " extracting: crohme_dataset2/y/exp85063.jpg  \n",
            "  inflating: crohme_dataset2/y/exp85069.jpg  \n",
            " extracting: crohme_dataset2/y/exp85126.jpg  \n",
            " extracting: crohme_dataset2/y/exp85133.jpg  \n",
            "  inflating: crohme_dataset2/y/exp85141.jpg  \n",
            " extracting: crohme_dataset2/y/exp85207.jpg  \n",
            " extracting: crohme_dataset2/y/exp85214.jpg  \n",
            " extracting: crohme_dataset2/y/exp85218.jpg  \n",
            " extracting: crohme_dataset2/y/exp85379.jpg  \n",
            " extracting: crohme_dataset2/y/exp85385.jpg  \n",
            " extracting: crohme_dataset2/y/exp85431.jpg  \n",
            " extracting: crohme_dataset2/y/exp85587.jpg  \n",
            " extracting: crohme_dataset2/y/exp85592.jpg  \n",
            " extracting: crohme_dataset2/y/exp85772.jpg  \n",
            "  inflating: crohme_dataset2/y/exp85778.jpg  \n",
            " extracting: crohme_dataset2/y/exp85781.jpg  \n",
            "  inflating: crohme_dataset2/y/exp85784.jpg  \n",
            " extracting: crohme_dataset2/y/exp85793.jpg  \n",
            " extracting: crohme_dataset2/y/exp85798.jpg  \n",
            " extracting: crohme_dataset2/y/exp85859.jpg  \n",
            "  inflating: crohme_dataset2/y/exp85866.jpg  \n",
            " extracting: crohme_dataset2/y/exp85874.jpg  \n",
            " extracting: crohme_dataset2/y/exp85880.jpg  \n",
            " extracting: crohme_dataset2/y/exp85887.jpg  \n",
            " extracting: crohme_dataset2/y/exp85891.jpg  \n",
            " extracting: crohme_dataset2/y/exp86.jpg  \n",
            " extracting: crohme_dataset2/y/exp86049.jpg  \n",
            " extracting: crohme_dataset2/y/exp86055.jpg  \n",
            " extracting: crohme_dataset2/y/exp86110.jpg  \n",
            " extracting: crohme_dataset2/y/exp86134.jpg  \n",
            " extracting: crohme_dataset2/y/exp86141.jpg  \n",
            " extracting: crohme_dataset2/y/exp86171.jpg  \n",
            " extracting: crohme_dataset2/y/exp86178.jpg  \n",
            " extracting: crohme_dataset2/y/exp86182.jpg  \n",
            "  inflating: crohme_dataset2/y/exp86295.jpg  \n",
            " extracting: crohme_dataset2/y/exp86355.jpg  \n",
            " extracting: crohme_dataset2/y/exp86357.jpg  \n",
            " extracting: crohme_dataset2/y/exp86422.jpg  \n",
            "  inflating: crohme_dataset2/y/exp86429.jpg  \n",
            " extracting: crohme_dataset2/y/exp86465.jpg  \n",
            " extracting: crohme_dataset2/y/exp86470.jpg  \n",
            " extracting: crohme_dataset2/y/exp86476.jpg  \n",
            "  inflating: crohme_dataset2/y/exp86477.jpg  \n",
            " extracting: crohme_dataset2/y/exp86710.jpg  \n",
            " extracting: crohme_dataset2/y/exp86715.jpg  \n",
            " extracting: crohme_dataset2/y/exp86809.jpg  \n",
            " extracting: crohme_dataset2/y/exp86815.jpg  \n",
            " extracting: crohme_dataset2/y/exp86818.jpg  \n",
            "  inflating: crohme_dataset2/y/exp86972.jpg  \n",
            "  inflating: crohme_dataset2/y/exp86978.jpg  \n",
            " extracting: crohme_dataset2/y/exp86981.jpg  \n",
            " extracting: crohme_dataset2/y/exp86985.jpg  \n",
            " extracting: crohme_dataset2/y/exp86994.jpg  \n",
            "  inflating: crohme_dataset2/y/exp86999.jpg  \n",
            " extracting: crohme_dataset2/y/exp87050.jpg  \n",
            " extracting: crohme_dataset2/y/exp87056.jpg  \n",
            " extracting: crohme_dataset2/y/exp87078.jpg  \n",
            " extracting: crohme_dataset2/y/exp87086.jpg  \n",
            " extracting: crohme_dataset2/y/exp87109.jpg  \n",
            " extracting: crohme_dataset2/y/exp87116.jpg  \n",
            " extracting: crohme_dataset2/y/exp87120.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87169.jpg  \n",
            " extracting: crohme_dataset2/y/exp87304.jpg  \n",
            " extracting: crohme_dataset2/y/exp87354.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87385.jpg  \n",
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            "  inflating: crohme_dataset2/y/exp87465.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87508.jpg  \n",
            " extracting: crohme_dataset2/y/exp87510.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87542.jpg  \n",
            " extracting: crohme_dataset2/y/exp87547.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87553.jpg  \n",
            " extracting: crohme_dataset2/y/exp87554.jpg  \n",
            " extracting: crohme_dataset2/y/exp87652.jpg  \n",
            " extracting: crohme_dataset2/y/exp87705.jpg  \n",
            " extracting: crohme_dataset2/y/exp87712.jpg  \n",
            " extracting: crohme_dataset2/y/exp87720.jpg  \n",
            " extracting: crohme_dataset2/y/exp87743.jpg  \n",
            " extracting: crohme_dataset2/y/exp87750.jpg  \n",
            " extracting: crohme_dataset2/y/exp87754.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87773.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87779.jpg  \n",
            " extracting: crohme_dataset2/y/exp87804.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87838.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87840.jpg  \n",
            " extracting: crohme_dataset2/y/exp87885.jpg  \n",
            " extracting: crohme_dataset2/y/exp87890.jpg  \n",
            " extracting: crohme_dataset2/y/exp87896.jpg  \n",
            " extracting: crohme_dataset2/y/exp87985.jpg  \n",
            "  inflating: crohme_dataset2/y/exp87990.jpg  \n",
            "  inflating: crohme_dataset2/y/exp88159.jpg  \n",
            " extracting: crohme_dataset2/y/exp88165.jpg  \n",
            " extracting: crohme_dataset2/y/exp88168.jpg  \n",
            " extracting: crohme_dataset2/y/exp88172.jpg  \n",
            " extracting: crohme_dataset2/y/exp88181.jpg  \n",
            " extracting: crohme_dataset2/y/exp88186.jpg  \n",
            "  inflating: crohme_dataset2/y/exp88197.jpg  \n",
            " extracting: crohme_dataset2/y/exp88205.jpg  \n",
            " extracting: crohme_dataset2/y/exp88210.jpg  \n",
            " extracting: crohme_dataset2/y/exp88246.jpg  \n",
            " extracting: crohme_dataset2/y/exp88252.jpg  \n",
            " extracting: crohme_dataset2/y/exp88348.jpg  \n",
            " extracting: crohme_dataset2/y/exp88369.jpg  \n",
            " extracting: crohme_dataset2/y/exp88376.jpg  \n",
            " extracting: crohme_dataset2/y/exp88412.jpg  \n",
            " extracting: crohme_dataset2/y/exp88417.jpg  \n",
            "  inflating: crohme_dataset2/y/exp88423.jpg  \n",
            " extracting: crohme_dataset2/y/exp88424.jpg  \n",
            " extracting: crohme_dataset2/y/exp88554.jpg  \n",
            "  inflating: crohme_dataset2/y/exp88559.jpg  \n",
            " extracting: crohme_dataset2/y/exp88564.jpg  \n",
            "  inflating: crohme_dataset2/y/exp88573.jpg  \n",
            " extracting: crohme_dataset2/y/exp88578.jpg  \n",
            " extracting: crohme_dataset2/y/exp88622.jpg  \n",
            " extracting: crohme_dataset2/y/exp88631.jpg  \n",
            " extracting: crohme_dataset2/y/exp88657.jpg  \n",
            " extracting: crohme_dataset2/y/exp88659.jpg  \n",
            " extracting: crohme_dataset2/y/exp88840.jpg  \n",
            " extracting: crohme_dataset2/y/exp88916.jpg  \n",
            " extracting: crohme_dataset2/y/exp88925.jpg  \n",
            " extracting: crohme_dataset2/y/exp88930.jpg  \n",
            " extracting: crohme_dataset2/y/exp88985.jpg  \n",
            "  inflating: crohme_dataset2/y/exp88991.jpg  \n",
            " extracting: crohme_dataset2/y/exp89030.jpg  \n",
            " extracting: crohme_dataset2/y/exp89034.jpg  \n",
            " extracting: crohme_dataset2/y/exp89140.jpg  \n",
            " extracting: crohme_dataset2/y/exp89158.jpg  \n",
            "  inflating: crohme_dataset2/y/exp89165.jpg  \n",
            " extracting: crohme_dataset2/y/exp89173.jpg  \n",
            " extracting: crohme_dataset2/y/exp89196.jpg  \n",
            " extracting: crohme_dataset2/y/exp89203.jpg  \n",
            " extracting: crohme_dataset2/y/exp89207.jpg  \n",
            " extracting: crohme_dataset2/y/exp89284.jpg  \n",
            " extracting: crohme_dataset2/y/exp89402.jpg  \n",
            " extracting: crohme_dataset2/y/exp89408.jpg  \n",
            " extracting: crohme_dataset2/y/exp89524.jpg  \n",
            " extracting: crohme_dataset2/y/exp89537.jpg  \n",
            " extracting: crohme_dataset2/y/exp89544.jpg  \n",
            " extracting: crohme_dataset2/y/exp89552.jpg  \n",
            " extracting: crohme_dataset2/y/exp89637.jpg  \n",
            " extracting: crohme_dataset2/y/exp89647.jpg  \n",
            "  inflating: crohme_dataset2/y/exp89692.jpg  \n",
            "  inflating: crohme_dataset2/y/exp89706.jpg  \n",
            "  inflating: crohme_dataset2/y/exp89718.jpg  \n",
            " extracting: crohme_dataset2/y/exp89728.jpg  \n",
            " extracting: crohme_dataset2/y/exp89736.jpg  \n",
            " extracting: crohme_dataset2/y/exp89741.jpg  \n",
            "  inflating: crohme_dataset2/y/exp89745.jpg  \n",
            " extracting: crohme_dataset2/y/exp89786.jpg  \n",
            " extracting: crohme_dataset2/y/exp89789.jpg  \n",
            " extracting: crohme_dataset2/y/exp89793.jpg  \n",
            " extracting: crohme_dataset2/y/exp90153.jpg  \n",
            " extracting: crohme_dataset2/y/exp90160.jpg  \n",
            " extracting: crohme_dataset2/y/exp90164.jpg  \n",
            "  inflating: crohme_dataset2/y/exp90170.jpg  \n",
            " extracting: crohme_dataset2/y/exp90177.jpg  \n",
            " extracting: crohme_dataset2/y/exp90185.jpg  \n",
            " extracting: crohme_dataset2/y/exp90192.jpg  \n",
            " extracting: crohme_dataset2/y/exp90202.jpg  \n",
            " extracting: crohme_dataset2/y/exp90244.jpg  \n",
            " extracting: crohme_dataset2/y/exp90254.jpg  \n",
            " extracting: crohme_dataset2/y/exp90287.jpg  \n",
            "  inflating: crohme_dataset2/y/exp90290.jpg  \n",
            "  inflating: crohme_dataset2/y/exp90294.jpg  \n",
            " extracting: crohme_dataset2/y/exp90478.jpg  \n",
            " extracting: crohme_dataset2/y/exp90486.jpg  \n",
            " extracting: crohme_dataset2/y/exp90494.jpg  \n",
            " extracting: crohme_dataset2/y/exp90560.jpg  \n",
            " extracting: crohme_dataset2/y/exp90567.jpg  \n",
            " extracting: crohme_dataset2/y/exp90575.jpg  \n",
            "  inflating: crohme_dataset2/y/exp90616.jpg  \n",
            "  inflating: crohme_dataset2/y/exp90626.jpg  \n",
            " extracting: crohme_dataset2/y/exp90681.jpg  \n",
            " extracting: crohme_dataset2/y/exp90689.jpg  \n",
            " extracting: crohme_dataset2/y/exp90694.jpg  \n",
            " extracting: crohme_dataset2/y/exp90791.jpg  \n",
            " extracting: crohme_dataset2/y/exp90858.jpg  \n",
            " extracting: crohme_dataset2/y/exp90898.jpg  \n",
            "  inflating: crohme_dataset2/y/exp90909.jpg  \n",
            " extracting: crohme_dataset2/y/exp90922.jpg  \n",
            " extracting: crohme_dataset2/y/exp91022.jpg  \n",
            "  inflating: crohme_dataset2/y/exp91030.jpg  \n",
            " extracting: crohme_dataset2/y/exp91034.jpg  \n",
            " extracting: crohme_dataset2/y/exp91038.jpg  \n",
            " extracting: crohme_dataset2/y/exp91041.jpg  \n",
            " extracting: crohme_dataset2/y/exp91045.jpg  \n",
            " extracting: crohme_dataset2/y/exp91060.jpg  \n",
            " extracting: crohme_dataset2/y/exp91089.jpg  \n",
            " extracting: crohme_dataset2/y/exp91148.jpg  \n",
            " extracting: crohme_dataset2/y/exp91212.jpg  \n",
            " extracting: crohme_dataset2/y/exp91222.jpg  \n",
            " extracting: crohme_dataset2/y/exp91258.jpg  \n",
            " extracting: crohme_dataset2/y/exp91304.jpg  \n",
            " extracting: crohme_dataset2/y/exp91318.jpg  \n",
            " extracting: crohme_dataset2/y/exp91328.jpg  \n",
            " extracting: crohme_dataset2/y/exp91333.jpg  \n",
            "  inflating: crohme_dataset2/y/exp91336.jpg  \n",
            " extracting: crohme_dataset2/y/exp91340.jpg  \n",
            " extracting: crohme_dataset2/y/exp91966.jpg  \n",
            " extracting: crohme_dataset2/y/exp92006.jpg  \n",
            " extracting: crohme_dataset2/y/exp93144.jpg  \n",
            " extracting: crohme_dataset2/y/y_65888.jpg  \n",
            " extracting: crohme_dataset2/y/y_65889.jpg  \n",
            "  inflating: crohme_dataset2/y/y_78260.jpg  \n",
            "  inflating: crohme_dataset2/z/1221.jpg  \n",
            "  inflating: crohme_dataset2/z/exp101028.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10103.jpg  \n",
            " extracting: crohme_dataset2/z/exp101100.jpg  \n",
            " extracting: crohme_dataset2/z/exp10111.jpg  \n",
            " extracting: crohme_dataset2/z/exp10126.jpg  \n",
            " extracting: crohme_dataset2/z/exp10154.jpg  \n",
            " extracting: crohme_dataset2/z/exp10157.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10160.jpg  \n",
            " extracting: crohme_dataset2/z/exp101627.jpg  \n",
            "  inflating: crohme_dataset2/z/exp101641.jpg  \n",
            " extracting: crohme_dataset2/z/exp10165.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10166.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10172.jpg  \n",
            "  inflating: crohme_dataset2/z/exp101760.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10179.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10186.jpg  \n",
            " extracting: crohme_dataset2/z/exp101934.jpg  \n",
            "  inflating: crohme_dataset2/z/exp101952.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10201.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10204.jpg  \n",
            " extracting: crohme_dataset2/z/exp10211.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10214.jpg  \n",
            "  inflating: crohme_dataset2/z/exp102434.jpg  \n",
            "  inflating: crohme_dataset2/z/exp102613.jpg  \n",
            "  inflating: crohme_dataset2/z/exp102884.jpg  \n",
            " extracting: crohme_dataset2/z/exp102889.jpg  \n",
            "  inflating: crohme_dataset2/z/exp103242.jpg  \n",
            "  inflating: crohme_dataset2/z/exp103292.jpg  \n",
            "  inflating: crohme_dataset2/z/exp103579.jpg  \n",
            " extracting: crohme_dataset2/z/exp103597.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10395.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10403.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104154.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104159.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10418.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10424.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10435.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104350.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104359.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104365.jpg  \n",
            " extracting: crohme_dataset2/z/exp104379.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10438.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104415.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10442.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104422.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10446.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10449.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10456.jpg  \n",
            " extracting: crohme_dataset2/z/exp104599.jpg  \n",
            " extracting: crohme_dataset2/z/exp10462.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10465.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10484.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10487.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104936.jpg  \n",
            "  inflating: crohme_dataset2/z/exp104941.jpg  \n",
            " extracting: crohme_dataset2/z/exp105361.jpg  \n",
            "  inflating: crohme_dataset2/z/exp105368.jpg  \n",
            " extracting: crohme_dataset2/z/exp106058.jpg  \n",
            "  inflating: crohme_dataset2/z/exp106067.jpg  \n",
            "  inflating: crohme_dataset2/z/exp106116.jpg  \n",
            " extracting: crohme_dataset2/z/exp106123.jpg  \n",
            " extracting: crohme_dataset2/z/exp106353.jpg  \n",
            "  inflating: crohme_dataset2/z/exp106360.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10677.jpg  \n",
            " extracting: crohme_dataset2/z/exp10684.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10696.jpg  \n",
            " extracting: crohme_dataset2/z/exp10719.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10721.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10929.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10936.jpg  \n",
            " extracting: crohme_dataset2/z/exp10949.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10970.jpg  \n",
            "  inflating: crohme_dataset2/z/exp10973.jpg  \n",
            " extracting: crohme_dataset2/z/exp11176.jpg  \n",
            " extracting: crohme_dataset2/z/exp11182.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11196.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11223.jpg  \n",
            " extracting: crohme_dataset2/z/exp11226.jpg  \n",
            " extracting: crohme_dataset2/z/exp11431.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11437.jpg  \n",
            " extracting: crohme_dataset2/z/exp11449.jpg  \n",
            " extracting: crohme_dataset2/z/exp11471.jpg  \n",
            " extracting: crohme_dataset2/z/exp11473.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11563.jpg  \n",
            "  inflating: crohme_dataset2/z/exp1157.jpg  \n",
            "  inflating: crohme_dataset2/z/exp1165.jpg  \n",
            " extracting: crohme_dataset2/z/exp11677.jpg  \n",
            " extracting: crohme_dataset2/z/exp11684.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11697.jpg  \n",
            " extracting: crohme_dataset2/z/exp11719.jpg  \n",
            " extracting: crohme_dataset2/z/exp11722.jpg  \n",
            " extracting: crohme_dataset2/z/exp1176.jpg  \n",
            "  inflating: crohme_dataset2/z/exp1183.jpg  \n",
            "  inflating: crohme_dataset2/z/exp1190.jpg  \n",
            " extracting: crohme_dataset2/z/exp11934.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11942.jpg  \n",
            "  inflating: crohme_dataset2/z/exp11955.jpg  \n",
            " extracting: crohme_dataset2/z/exp11982.jpg  \n",
            " extracting: crohme_dataset2/z/exp11984.jpg  \n",
            " extracting: crohme_dataset2/z/exp1202.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12187.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12194.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12208.jpg  \n",
            " extracting: crohme_dataset2/z/exp12232.jpg  \n",
            " extracting: crohme_dataset2/z/exp12235.jpg  \n",
            "  inflating: crohme_dataset2/z/exp1226.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12448.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12455.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12470.jpg  \n",
            " extracting: crohme_dataset2/z/exp12495.jpg  \n",
            " extracting: crohme_dataset2/z/exp1250.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12711.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12719.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12732.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12757.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12760.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12973.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12980.jpg  \n",
            "  inflating: crohme_dataset2/z/exp12993.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13015.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13018.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13224.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13229.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13243.jpg  \n",
            " extracting: crohme_dataset2/z/exp13267.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13270.jpg  \n",
            " extracting: crohme_dataset2/z/exp13477.jpg  \n",
            " extracting: crohme_dataset2/z/exp13482.jpg  \n",
            " extracting: crohme_dataset2/z/exp13495.jpg  \n",
            " extracting: crohme_dataset2/z/exp13521.jpg  \n",
            " extracting: crohme_dataset2/z/exp13523.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13725.jpg  \n",
            " extracting: crohme_dataset2/z/exp13732.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13746.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13770.jpg  \n",
            " extracting: crohme_dataset2/z/exp13773.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13943.jpg  \n",
            " extracting: crohme_dataset2/z/exp13951.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13964.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13990.jpg  \n",
            "  inflating: crohme_dataset2/z/exp13991.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14017.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14201.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14208.jpg  \n",
            " extracting: crohme_dataset2/z/exp14222.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14243.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14246.jpg  \n",
            " extracting: crohme_dataset2/z/exp1433.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14449.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14455.jpg  \n",
            " extracting: crohme_dataset2/z/exp14469.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14493.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14496.jpg  \n",
            "  inflating: crohme_dataset2/z/exp14718.jpg  \n",
            " extracting: crohme_dataset2/z/exp14743.jpg  \n",
            " extracting: crohme_dataset2/z/exp14745.jpg  \n",
            " extracting: crohme_dataset2/z/exp15540.jpg  \n",
            " extracting: crohme_dataset2/z/exp15544.jpg  \n",
            "  inflating: crohme_dataset2/z/exp16578.jpg  \n",
            "  inflating: crohme_dataset2/z/exp16616.jpg  \n",
            " extracting: crohme_dataset2/z/exp16671.jpg  \n",
            "  inflating: crohme_dataset2/z/exp16711.jpg  \n",
            " extracting: crohme_dataset2/z/exp16996.jpg  \n",
            " extracting: crohme_dataset2/z/exp17006.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17031.jpg  \n",
            " extracting: crohme_dataset2/z/exp17035.jpg  \n",
            " extracting: crohme_dataset2/z/exp17117.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17126.jpg  \n",
            " extracting: crohme_dataset2/z/exp17150.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17154.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17156.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17239.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17248.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17273.jpg  \n",
            " extracting: crohme_dataset2/z/exp17276.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17278.jpg  \n",
            " extracting: crohme_dataset2/z/exp17357.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17367.jpg  \n",
            " extracting: crohme_dataset2/z/exp17391.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17394.jpg  \n",
            " extracting: crohme_dataset2/z/exp17396.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17476.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17487.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17511.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17515.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17517.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17598.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17609.jpg  \n",
            " extracting: crohme_dataset2/z/exp17636.jpg  \n",
            " extracting: crohme_dataset2/z/exp17640.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17642.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17732.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17757.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17761.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17763.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17845.jpg  \n",
            " extracting: crohme_dataset2/z/exp17854.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17881.jpg  \n",
            " extracting: crohme_dataset2/z/exp17885.jpg  \n",
            " extracting: crohme_dataset2/z/exp17887.jpg  \n",
            "  inflating: crohme_dataset2/z/exp17969.jpg  \n",
            " extracting: crohme_dataset2/z/exp17978.jpg  \n",
            " extracting: crohme_dataset2/z/exp18001.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18005.jpg  \n",
            " extracting: crohme_dataset2/z/exp18007.jpg  \n",
            " extracting: crohme_dataset2/z/exp18026.jpg  \n",
            " extracting: crohme_dataset2/z/exp18033.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18047.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18067.jpg  \n",
            " extracting: crohme_dataset2/z/exp18073.jpg  \n",
            " extracting: crohme_dataset2/z/exp18076.jpg  \n",
            " extracting: crohme_dataset2/z/exp18288.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18294.jpg  \n",
            " extracting: crohme_dataset2/z/exp18308.jpg  \n",
            " extracting: crohme_dataset2/z/exp18333.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18336.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18554.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18561.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18575.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18602.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18604.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18800.jpg  \n",
            " extracting: crohme_dataset2/z/exp18818.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18824.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18838.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18864.jpg  \n",
            "  inflating: crohme_dataset2/z/exp18866.jpg  \n",
            "  inflating: crohme_dataset2/z/exp2929.jpg  \n",
            " extracting: crohme_dataset2/z/exp2930.jpg  \n",
            "  inflating: crohme_dataset2/z/exp2931.jpg  \n",
            "  inflating: crohme_dataset2/z/exp2936.jpg  \n",
            " extracting: crohme_dataset2/z/exp2949.jpg  \n",
            "  inflating: crohme_dataset2/z/exp2954.jpg  \n",
            "  inflating: crohme_dataset2/z/exp2956.jpg  \n",
            " extracting: crohme_dataset2/z/exp2960.jpg  \n",
            " extracting: crohme_dataset2/z/exp3068.jpg  \n",
            " extracting: crohme_dataset2/z/exp3072.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3073.jpg  \n",
            " extracting: crohme_dataset2/z/exp3078.jpg  \n",
            " extracting: crohme_dataset2/z/exp3172.jpg  \n",
            " extracting: crohme_dataset2/z/exp3176.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3178.jpg  \n",
            " extracting: crohme_dataset2/z/exp3181.jpg  \n",
            " extracting: crohme_dataset2/z/exp3269.jpg  \n",
            " extracting: crohme_dataset2/z/exp3270.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3271.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3276.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3290.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3295.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3296.jpg  \n",
            " extracting: crohme_dataset2/z/exp3298.jpg  \n",
            " extracting: crohme_dataset2/z/exp3404.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3405.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3406.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3411.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3424.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3428.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3429.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3433.jpg  \n",
            "  inflating: crohme_dataset2/z/exp34352.jpg  \n",
            " extracting: crohme_dataset2/z/exp34811.jpg  \n",
            " extracting: crohme_dataset2/z/exp34819.jpg  \n",
            " extracting: crohme_dataset2/z/exp35064.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3541.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3542.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3543.jpg  \n",
            "  inflating: crohme_dataset2/z/exp35449.jpg  \n",
            "  inflating: crohme_dataset2/z/exp35451.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3548.jpg  \n",
            " extracting: crohme_dataset2/z/exp35572.jpg  \n",
            " extracting: crohme_dataset2/z/exp3561.jpg  \n",
            " extracting: crohme_dataset2/z/exp3566.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3568.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3572.jpg  \n",
            "  inflating: crohme_dataset2/z/exp36073.jpg  \n",
            "  inflating: crohme_dataset2/z/exp36731.jpg  \n",
            " extracting: crohme_dataset2/z/exp36732.jpg  \n",
            " extracting: crohme_dataset2/z/exp36733.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3680.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3681.jpg  \n",
            " extracting: crohme_dataset2/z/exp3682.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3687.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3700.jpg  \n",
            " extracting: crohme_dataset2/z/exp3705.jpg  \n",
            " extracting: crohme_dataset2/z/exp3707.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3712.jpg  \n",
            " extracting: crohme_dataset2/z/exp37132.jpg  \n",
            " extracting: crohme_dataset2/z/exp37140.jpg  \n",
            " extracting: crohme_dataset2/z/exp37217.jpg  \n",
            " extracting: crohme_dataset2/z/exp37262.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3813.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3814.jpg  \n",
            " extracting: crohme_dataset2/z/exp3815.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3820.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3834.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3839.jpg  \n",
            " extracting: crohme_dataset2/z/exp3841.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3845.jpg  \n",
            "  inflating: crohme_dataset2/z/exp38695.jpg  \n",
            " extracting: crohme_dataset2/z/exp38727.jpg  \n",
            " extracting: crohme_dataset2/z/exp38729.jpg  \n",
            " extracting: crohme_dataset2/z/exp38734.jpg  \n",
            "  inflating: crohme_dataset2/z/exp39334.jpg  \n",
            " extracting: crohme_dataset2/z/exp39367.jpg  \n",
            " extracting: crohme_dataset2/z/exp39431.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3948.jpg  \n",
            "  inflating: crohme_dataset2/z/exp39487.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3949.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3950.jpg  \n",
            "  inflating: crohme_dataset2/z/exp39557.jpg  \n",
            " extracting: crohme_dataset2/z/exp39558.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3956.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3968.jpg  \n",
            " extracting: crohme_dataset2/z/exp39690.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3971.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3973.jpg  \n",
            " extracting: crohme_dataset2/z/exp39743.jpg  \n",
            "  inflating: crohme_dataset2/z/exp3976.jpg  \n",
            " extracting: crohme_dataset2/z/exp40326.jpg  \n",
            "  inflating: crohme_dataset2/z/exp40330.jpg  \n",
            " extracting: crohme_dataset2/z/exp40390.jpg  \n",
            " extracting: crohme_dataset2/z/exp40392.jpg  \n",
            " extracting: crohme_dataset2/z/exp40453.jpg  \n",
            " extracting: crohme_dataset2/z/exp40455.jpg  \n",
            " extracting: crohme_dataset2/z/exp40456.jpg  \n",
            " extracting: crohme_dataset2/z/exp40459.jpg  \n",
            " extracting: crohme_dataset2/z/exp40695.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4078.jpg  \n",
            " extracting: crohme_dataset2/z/exp4079.jpg  \n",
            "  inflating: crohme_dataset2/z/exp40932.jpg  \n",
            "  inflating: crohme_dataset2/z/exp40938.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4097.jpg  \n",
            "  inflating: crohme_dataset2/z/exp40996.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4101.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4103.jpg  \n",
            " extracting: crohme_dataset2/z/exp4108.jpg  \n",
            "  inflating: crohme_dataset2/z/exp41337.jpg  \n",
            " extracting: crohme_dataset2/z/exp41697.jpg  \n",
            " extracting: crohme_dataset2/z/exp41941.jpg  \n",
            " extracting: crohme_dataset2/z/exp41950.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4213.jpg  \n",
            " extracting: crohme_dataset2/z/exp4217.jpg  \n",
            " extracting: crohme_dataset2/z/exp4229.jpg  \n",
            " extracting: crohme_dataset2/z/exp42290.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4231.jpg  \n",
            " extracting: crohme_dataset2/z/exp4236.jpg  \n",
            "  inflating: crohme_dataset2/z/exp42419.jpg  \n",
            "  inflating: crohme_dataset2/z/exp42543.jpg  \n",
            "  inflating: crohme_dataset2/z/exp42586.jpg  \n",
            " extracting: crohme_dataset2/z/exp42961.jpg  \n",
            "  inflating: crohme_dataset2/z/exp42977.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4302.jpg  \n",
            " extracting: crohme_dataset2/z/exp4308.jpg  \n",
            "  inflating: crohme_dataset2/z/exp43144.jpg  \n",
            " extracting: crohme_dataset2/z/exp43146.jpg  \n",
            " extracting: crohme_dataset2/z/exp43150.jpg  \n",
            " extracting: crohme_dataset2/z/exp43153.jpg  \n",
            "  inflating: crohme_dataset2/z/exp43251.jpg  \n",
            " extracting: crohme_dataset2/z/exp43364.jpg  \n",
            " extracting: crohme_dataset2/z/exp4342.jpg  \n",
            " extracting: crohme_dataset2/z/exp4343.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4344.jpg  \n",
            " extracting: crohme_dataset2/z/exp4348.jpg  \n",
            " extracting: crohme_dataset2/z/exp4361.jpg  \n",
            " extracting: crohme_dataset2/z/exp4366.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4368.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4371.jpg  \n",
            "  inflating: crohme_dataset2/z/exp43979.jpg  \n",
            "  inflating: crohme_dataset2/z/exp43981.jpg  \n",
            " extracting: crohme_dataset2/z/exp43985.jpg  \n",
            " extracting: crohme_dataset2/z/exp44137.jpg  \n",
            "  inflating: crohme_dataset2/z/exp44370.jpg  \n",
            " extracting: crohme_dataset2/z/exp4440.jpg  \n",
            "  inflating: crohme_dataset2/z/exp44404.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4441.jpg  \n",
            "  inflating: crohme_dataset2/z/exp44413.jpg  \n",
            " extracting: crohme_dataset2/z/exp44600.jpg  \n",
            " extracting: crohme_dataset2/z/exp44632.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4468.jpg  \n",
            " extracting: crohme_dataset2/z/exp4474.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4475.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4476.jpg  \n",
            " extracting: crohme_dataset2/z/exp44799.jpg  \n",
            " extracting: crohme_dataset2/z/exp44800.jpg  \n",
            " extracting: crohme_dataset2/z/exp4482.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4493.jpg  \n",
            " extracting: crohme_dataset2/z/exp4498.jpg  \n",
            " extracting: crohme_dataset2/z/exp4500.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4501.jpg  \n",
            "  inflating: crohme_dataset2/z/exp45183.jpg  \n",
            "  inflating: crohme_dataset2/z/exp45452.jpg  \n",
            "  inflating: crohme_dataset2/z/exp45453.jpg  \n",
            " extracting: crohme_dataset2/z/exp45456.jpg  \n",
            " extracting: crohme_dataset2/z/exp45633.jpg  \n",
            "  inflating: crohme_dataset2/z/exp45692.jpg  \n",
            " extracting: crohme_dataset2/z/exp45913.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4606.jpg  \n",
            " extracting: crohme_dataset2/z/exp4607.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4608.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4613.jpg  \n",
            " extracting: crohme_dataset2/z/exp4626.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4631.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4632.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4636.jpg  \n",
            "  inflating: crohme_dataset2/z/exp46413.jpg  \n",
            " extracting: crohme_dataset2/z/exp46524.jpg  \n",
            " extracting: crohme_dataset2/z/exp46548.jpg  \n",
            "  inflating: crohme_dataset2/z/exp46555.jpg  \n",
            "  inflating: crohme_dataset2/z/exp46916.jpg  \n",
            "  inflating: crohme_dataset2/z/exp46947.jpg  \n",
            " extracting: crohme_dataset2/z/exp4716.jpg  \n",
            " extracting: crohme_dataset2/z/exp4717.jpg  \n",
            " extracting: crohme_dataset2/z/exp4718.jpg  \n",
            " extracting: crohme_dataset2/z/exp4721.jpg  \n",
            " extracting: crohme_dataset2/z/exp4733.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4737.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4739.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4742.jpg  \n",
            " extracting: crohme_dataset2/z/exp47652.jpg  \n",
            " extracting: crohme_dataset2/z/exp48011.jpg  \n",
            " extracting: crohme_dataset2/z/exp48019.jpg  \n",
            " extracting: crohme_dataset2/z/exp48115.jpg  \n",
            " extracting: crohme_dataset2/z/exp4843.jpg  \n",
            " extracting: crohme_dataset2/z/exp4844.jpg  \n",
            " extracting: crohme_dataset2/z/exp4845.jpg  \n",
            " extracting: crohme_dataset2/z/exp48482.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4849.jpg  \n",
            "  inflating: crohme_dataset2/z/exp48558.jpg  \n",
            " extracting: crohme_dataset2/z/exp4860.jpg  \n",
            " extracting: crohme_dataset2/z/exp4865.jpg  \n",
            " extracting: crohme_dataset2/z/exp48669.jpg  \n",
            " extracting: crohme_dataset2/z/exp48671.jpg  \n",
            " extracting: crohme_dataset2/z/exp4870.jpg  \n",
            "  inflating: crohme_dataset2/z/exp48990.jpg  \n",
            "  inflating: crohme_dataset2/z/exp49061.jpg  \n",
            "  inflating: crohme_dataset2/z/exp49090.jpg  \n",
            " extracting: crohme_dataset2/z/exp4962.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4963.jpg  \n",
            " extracting: crohme_dataset2/z/exp4964.jpg  \n",
            " extracting: crohme_dataset2/z/exp4970.jpg  \n",
            "  inflating: crohme_dataset2/z/exp4982.jpg  \n",
            " extracting: crohme_dataset2/z/exp49822.jpg  \n",
            "  inflating: crohme_dataset2/z/exp49827.jpg  \n",
            " extracting: crohme_dataset2/z/exp4987.jpg  \n",
            " extracting: crohme_dataset2/z/exp4989.jpg  \n",
            " extracting: crohme_dataset2/z/exp4994.jpg  \n",
            "  inflating: crohme_dataset2/z/exp49989.jpg  \n",
            " extracting: crohme_dataset2/z/exp50198.jpg  \n",
            " extracting: crohme_dataset2/z/exp50376.jpg  \n",
            " extracting: crohme_dataset2/z/exp50743.jpg  \n",
            " extracting: crohme_dataset2/z/exp5091.jpg  \n",
            " extracting: crohme_dataset2/z/exp5092.jpg  \n",
            " extracting: crohme_dataset2/z/exp5093.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5099.jpg  \n",
            " extracting: crohme_dataset2/z/exp51028.jpg  \n",
            " extracting: crohme_dataset2/z/exp51030.jpg  \n",
            " extracting: crohme_dataset2/z/exp5112.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5117.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5119.jpg  \n",
            " extracting: crohme_dataset2/z/exp5124.jpg  \n",
            "  inflating: crohme_dataset2/z/exp51606.jpg  \n",
            "  inflating: crohme_dataset2/z/exp51622.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5163.jpg  \n",
            "  inflating: crohme_dataset2/z/exp51818.jpg  \n",
            "  inflating: crohme_dataset2/z/exp51819.jpg  \n",
            "  inflating: crohme_dataset2/z/exp51820.jpg  \n",
            "  inflating: crohme_dataset2/z/exp51933.jpg  \n",
            " extracting: crohme_dataset2/z/exp52058.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5219.jpg  \n",
            " extracting: crohme_dataset2/z/exp5220.jpg  \n",
            " extracting: crohme_dataset2/z/exp5221.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5227.jpg  \n",
            "  inflating: crohme_dataset2/z/exp52277.jpg  \n",
            "  inflating: crohme_dataset2/z/exp52301.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5239.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5244.jpg  \n",
            " extracting: crohme_dataset2/z/exp52474.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5249.jpg  \n",
            "  inflating: crohme_dataset2/z/exp52610.jpg  \n",
            "  inflating: crohme_dataset2/z/exp52612.jpg  \n",
            " extracting: crohme_dataset2/z/exp52730.jpg  \n",
            " extracting: crohme_dataset2/z/exp53231.jpg  \n",
            " extracting: crohme_dataset2/z/exp53233.jpg  \n",
            " extracting: crohme_dataset2/z/exp53401.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5354.jpg  \n",
            " extracting: crohme_dataset2/z/exp5355.jpg  \n",
            " extracting: crohme_dataset2/z/exp5356.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5361.jpg  \n",
            "  inflating: crohme_dataset2/z/exp53623.jpg  \n",
            " extracting: crohme_dataset2/z/exp53629.jpg  \n",
            " extracting: crohme_dataset2/z/exp53661.jpg  \n",
            " extracting: crohme_dataset2/z/exp53690.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5377.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5379.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5383.jpg  \n",
            " extracting: crohme_dataset2/z/exp54276.jpg  \n",
            "  inflating: crohme_dataset2/z/exp54280.jpg  \n",
            " extracting: crohme_dataset2/z/exp54300.jpg  \n",
            " extracting: crohme_dataset2/z/exp5473.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5474.jpg  \n",
            " extracting: crohme_dataset2/z/exp5475.jpg  \n",
            " extracting: crohme_dataset2/z/exp5479.jpg  \n",
            " extracting: crohme_dataset2/z/exp5492.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5494.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5496.jpg  \n",
            " extracting: crohme_dataset2/z/exp5501.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55022.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55405.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55585.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55590.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55595.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55599.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55604.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55608.jpg  \n",
            " extracting: crohme_dataset2/z/exp55612.jpg  \n",
            "  inflating: crohme_dataset2/z/exp55654.jpg  \n",
            " extracting: crohme_dataset2/z/exp5575.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5608.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5609.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5610.jpg  \n",
            "  inflating: crohme_dataset2/z/exp56142.jpg  \n",
            " extracting: crohme_dataset2/z/exp5615.jpg  \n",
            " extracting: crohme_dataset2/z/exp5628.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5632.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5634.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5639.jpg  \n",
            " extracting: crohme_dataset2/z/exp56686.jpg  \n",
            " extracting: crohme_dataset2/z/exp56692.jpg  \n",
            "  inflating: crohme_dataset2/z/exp56751.jpg  \n",
            " extracting: crohme_dataset2/z/exp56753.jpg  \n",
            " extracting: crohme_dataset2/z/exp57096.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5713.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57177.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57180.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57218.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5742.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5743.jpg  \n",
            " extracting: crohme_dataset2/z/exp5744.jpg  \n",
            " extracting: crohme_dataset2/z/exp5748.jpg  \n",
            " extracting: crohme_dataset2/z/exp5761.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5765.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5767.jpg  \n",
            " extracting: crohme_dataset2/z/exp57717.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5772.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57724.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57940.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57951.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57953.jpg  \n",
            "  inflating: crohme_dataset2/z/exp57957.jpg  \n",
            " extracting: crohme_dataset2/z/exp58015.jpg  \n",
            "  inflating: crohme_dataset2/z/exp58019.jpg  \n",
            " extracting: crohme_dataset2/z/exp58026.jpg  \n",
            "  inflating: crohme_dataset2/z/exp58093.jpg  \n",
            " extracting: crohme_dataset2/z/exp58095.jpg  \n",
            "  inflating: crohme_dataset2/z/exp58115.jpg  \n",
            "  inflating: crohme_dataset2/z/exp58274.jpg  \n",
            " extracting: crohme_dataset2/z/exp58406.jpg  \n",
            " extracting: crohme_dataset2/z/exp58526.jpg  \n",
            " extracting: crohme_dataset2/z/exp58530.jpg  \n",
            " extracting: crohme_dataset2/z/exp58585.jpg  \n",
            "  inflating: crohme_dataset2/z/exp58586.jpg  \n",
            "  inflating: crohme_dataset2/z/exp58600.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5877.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5878.jpg  \n",
            " extracting: crohme_dataset2/z/exp58782.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5879.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5883.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5896.jpg  \n",
            " extracting: crohme_dataset2/z/exp5900.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5902.jpg  \n",
            "  inflating: crohme_dataset2/z/exp5907.jpg  \n",
            " extracting: crohme_dataset2/z/exp59209.jpg  \n",
            " extracting: crohme_dataset2/z/exp59295.jpg  \n",
            " extracting: crohme_dataset2/z/exp59296.jpg  \n",
            " extracting: crohme_dataset2/z/exp59297.jpg  \n",
            " extracting: crohme_dataset2/z/exp59373.jpg  \n",
            " extracting: crohme_dataset2/z/exp59758.jpg  \n",
            " extracting: crohme_dataset2/z/exp59764.jpg  \n",
            "  inflating: crohme_dataset2/z/exp59813.jpg  \n",
            " extracting: crohme_dataset2/z/exp59966.jpg  \n",
            " extracting: crohme_dataset2/z/exp59968.jpg  \n",
            " extracting: crohme_dataset2/z/exp59972.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6016.jpg  \n",
            " extracting: crohme_dataset2/z/exp6017.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6018.jpg  \n",
            " extracting: crohme_dataset2/z/exp6024.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6037.jpg  \n",
            " extracting: crohme_dataset2/z/exp6041.jpg  \n",
            " extracting: crohme_dataset2/z/exp6043.jpg  \n",
            " extracting: crohme_dataset2/z/exp6046.jpg  \n",
            " extracting: crohme_dataset2/z/exp60698.jpg  \n",
            " extracting: crohme_dataset2/z/exp60699.jpg  \n",
            " extracting: crohme_dataset2/z/exp60700.jpg  \n",
            "  inflating: crohme_dataset2/z/exp60706.jpg  \n",
            "  inflating: crohme_dataset2/z/exp60823.jpg  \n",
            " extracting: crohme_dataset2/z/exp60824.jpg  \n",
            " extracting: crohme_dataset2/z/exp60825.jpg  \n",
            " extracting: crohme_dataset2/z/exp60830.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6150.jpg  \n",
            " extracting: crohme_dataset2/z/exp6151.jpg  \n",
            " extracting: crohme_dataset2/z/exp6152.jpg  \n",
            " extracting: crohme_dataset2/z/exp6157.jpg  \n",
            " extracting: crohme_dataset2/z/exp6170.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6175.jpg  \n",
            " extracting: crohme_dataset2/z/exp6177.jpg  \n",
            " extracting: crohme_dataset2/z/exp6182.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6290.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6291.jpg  \n",
            " extracting: crohme_dataset2/z/exp6292.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6298.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6310.jpg  \n",
            "  inflating: crohme_dataset2/z/exp63143.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6315.jpg  \n",
            " extracting: crohme_dataset2/z/exp63152.jpg  \n",
            " extracting: crohme_dataset2/z/exp6316.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6321.jpg  \n",
            " extracting: crohme_dataset2/z/exp63229.jpg  \n",
            " extracting: crohme_dataset2/z/exp63286.jpg  \n",
            " extracting: crohme_dataset2/z/exp63292.jpg  \n",
            " extracting: crohme_dataset2/z/exp63403.jpg  \n",
            " extracting: crohme_dataset2/z/exp63407.jpg  \n",
            " extracting: crohme_dataset2/z/exp63410.jpg  \n",
            " extracting: crohme_dataset2/z/exp63447.jpg  \n",
            "  inflating: crohme_dataset2/z/exp63782.jpg  \n",
            "  inflating: crohme_dataset2/z/exp63987.jpg  \n",
            " extracting: crohme_dataset2/z/exp64011.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64014.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64016.jpg  \n",
            " extracting: crohme_dataset2/z/exp64069.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64118.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64141.jpg  \n",
            " extracting: crohme_dataset2/z/exp64145.jpg  \n",
            " extracting: crohme_dataset2/z/exp64148.jpg  \n",
            "  inflating: crohme_dataset2/z/exp6429.jpg  \n",
            " extracting: crohme_dataset2/z/exp6430.jpg  \n",
            " extracting: crohme_dataset2/z/exp6431.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64317.jpg  \n",
            " extracting: crohme_dataset2/z/exp64330.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64335.jpg  \n",
            " extracting: crohme_dataset2/z/exp64337.jpg  \n",
            " extracting: crohme_dataset2/z/exp64342.jpg  \n",
            " extracting: crohme_dataset2/z/exp64452.jpg  \n",
            " extracting: crohme_dataset2/z/exp64453.jpg  \n",
            " extracting: crohme_dataset2/z/exp64454.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64460.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64472.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64476.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64478.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64482.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64483.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64485.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64488.jpg  \n",
            " extracting: crohme_dataset2/z/exp64497.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64508.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64531.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64553.jpg  \n",
            " extracting: crohme_dataset2/z/exp64594.jpg  \n",
            " extracting: crohme_dataset2/z/exp64595.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64596.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64602.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64608.jpg  \n",
            " extracting: crohme_dataset2/z/exp64616.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64621.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64623.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64628.jpg  \n",
            " extracting: crohme_dataset2/z/exp64637.jpg  \n",
            " extracting: crohme_dataset2/z/exp64671.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64724.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64738.jpg  \n",
            " extracting: crohme_dataset2/z/exp64739.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64740.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64745.jpg  \n",
            " extracting: crohme_dataset2/z/exp64759.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64764.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64766.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64771.jpg  \n",
            " extracting: crohme_dataset2/z/exp64879.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64883.jpg  \n",
            "  inflating: crohme_dataset2/z/exp64886.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65044.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65267.jpg  \n",
            " extracting: crohme_dataset2/z/exp65269.jpg  \n",
            " extracting: crohme_dataset2/z/exp65274.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65305.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65308.jpg  \n",
            " extracting: crohme_dataset2/z/exp65311.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65330.jpg  \n",
            " extracting: crohme_dataset2/z/exp65383.jpg  \n",
            " extracting: crohme_dataset2/z/exp65400.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65407.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65463.jpg  \n",
            " extracting: crohme_dataset2/z/exp65539.jpg  \n",
            " extracting: crohme_dataset2/z/exp65546.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65620.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65700.jpg  \n",
            " extracting: crohme_dataset2/z/exp65876.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65881.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65883.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65920.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65923.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65926.jpg  \n",
            "  inflating: crohme_dataset2/z/exp65938.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66001.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66048.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66054.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66143.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66178.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66182.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66185.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66319.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66544.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66546.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66551.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66556.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66559.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66593.jpg  \n",
            " extracting: crohme_dataset2/z/exp66596.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66599.jpg  \n",
            " extracting: crohme_dataset2/z/exp66672.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66741.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66751.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66754.jpg  \n",
            " extracting: crohme_dataset2/z/exp66779.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66816.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66826.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66916.jpg  \n",
            " extracting: crohme_dataset2/z/exp66920.jpg  \n",
            "  inflating: crohme_dataset2/z/exp66923.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67213.jpg  \n",
            " extracting: crohme_dataset2/z/exp67291.jpg  \n",
            " extracting: crohme_dataset2/z/exp67292.jpg  \n",
            " extracting: crohme_dataset2/z/exp67297.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67343.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67346.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67349.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67391.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67435.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67459.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67615.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67625.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67670.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67776.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67780.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67783.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67852.jpg  \n",
            " extracting: crohme_dataset2/z/exp67947.jpg  \n",
            "  inflating: crohme_dataset2/z/exp67983.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68111.jpg  \n",
            " extracting: crohme_dataset2/z/exp68178.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68276.jpg  \n",
            " extracting: crohme_dataset2/z/exp68298.jpg  \n",
            " extracting: crohme_dataset2/z/exp68334.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68338.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68341.jpg  \n",
            " extracting: crohme_dataset2/z/exp68636.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68638.jpg  \n",
            " extracting: crohme_dataset2/z/exp68643.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68664.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68667.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68670.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68748.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68820.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68835.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68880.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68896.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68922.jpg  \n",
            " extracting: crohme_dataset2/z/exp68928.jpg  \n",
            " extracting: crohme_dataset2/z/exp68959.jpg  \n",
            "  inflating: crohme_dataset2/z/exp68998.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69085.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69099.jpg  \n",
            " extracting: crohme_dataset2/z/exp69194.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69226.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69380.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69382.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69387.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69416.jpg  \n",
            " extracting: crohme_dataset2/z/exp69419.jpg  \n",
            " extracting: crohme_dataset2/z/exp69422.jpg  \n",
            " extracting: crohme_dataset2/z/exp69449.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69472.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69480.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69518.jpg  \n",
            " extracting: crohme_dataset2/z/exp69579.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69599.jpg  \n",
            " extracting: crohme_dataset2/z/exp69623.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69707.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69754.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69839.jpg  \n",
            " extracting: crohme_dataset2/z/exp69843.jpg  \n",
            " extracting: crohme_dataset2/z/exp69846.jpg  \n",
            "  inflating: crohme_dataset2/z/exp69851.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70192.jpg  \n",
            " extracting: crohme_dataset2/z/exp70195.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70199.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70215.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70289.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70298.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70408.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70417.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70520.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70524.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70527.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70550.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70575.jpg  \n",
            " extracting: crohme_dataset2/z/exp70715.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70734.jpg  \n",
            "  inflating: crohme_dataset2/z/exp70751.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71085.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71088.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71092.jpg  \n",
            " extracting: crohme_dataset2/z/exp71135.jpg  \n",
            " extracting: crohme_dataset2/z/exp71138.jpg  \n",
            " extracting: crohme_dataset2/z/exp71140.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71180.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71211.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71240.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71280.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71284.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71308.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71340.jpg  \n",
            " extracting: crohme_dataset2/z/exp71383.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71427.jpg  \n",
            " extracting: crohme_dataset2/z/exp71485.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71489.jpg  \n",
            " extracting: crohme_dataset2/z/exp71492.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71546.jpg  \n",
            " extracting: crohme_dataset2/z/exp71560.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71712.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71744.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71750.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71960.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71963.jpg  \n",
            "  inflating: crohme_dataset2/z/exp71966.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72037.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72196.jpg  \n",
            " extracting: crohme_dataset2/z/exp72213.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72245.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72260.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72292.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72303.jpg  \n",
            " extracting: crohme_dataset2/z/exp72307.jpg  \n",
            " extracting: crohme_dataset2/z/exp72310.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72426.jpg  \n",
            " extracting: crohme_dataset2/z/exp72488.jpg  \n",
            " extracting: crohme_dataset2/z/exp72494.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72573.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72599.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72674.jpg  \n",
            " extracting: crohme_dataset2/z/exp72700.jpg  \n",
            " extracting: crohme_dataset2/z/exp72740.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72743.jpg  \n",
            " extracting: crohme_dataset2/z/exp72746.jpg  \n",
            " extracting: crohme_dataset2/z/exp72793.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72821.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72963.jpg  \n",
            "  inflating: crohme_dataset2/z/exp72967.jpg  \n",
            " extracting: crohme_dataset2/z/exp73017.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73119.jpg  \n",
            " extracting: crohme_dataset2/z/exp73123.jpg  \n",
            " extracting: crohme_dataset2/z/exp73126.jpg  \n",
            " extracting: crohme_dataset2/z/exp73181.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73539.jpg  \n",
            " extracting: crohme_dataset2/z/exp73564.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73566.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73571.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73587.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73593.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73597.jpg  \n",
            " extracting: crohme_dataset2/z/exp73683.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73689.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73758.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73844.jpg  \n",
            " extracting: crohme_dataset2/z/exp73860.jpg  \n",
            " extracting: crohme_dataset2/z/exp73913.jpg  \n",
            "  inflating: crohme_dataset2/z/exp73950.jpg  \n",
            " extracting: crohme_dataset2/z/exp73996.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74000.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74003.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74069.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74091.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74126.jpg  \n",
            " extracting: crohme_dataset2/z/exp74472.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74476.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74480.jpg  \n",
            " extracting: crohme_dataset2/z/exp74511.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74515.jpg  \n",
            " extracting: crohme_dataset2/z/exp74517.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74523.jpg  \n",
            " extracting: crohme_dataset2/z/exp74577.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74722.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74737.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74774.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74784.jpg  \n",
            " extracting: crohme_dataset2/z/exp74867.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74871.jpg  \n",
            " extracting: crohme_dataset2/z/exp74874.jpg  \n",
            " extracting: crohme_dataset2/z/exp74879.jpg  \n",
            " extracting: crohme_dataset2/z/exp74950.jpg  \n",
            "  inflating: crohme_dataset2/z/exp74963.jpg  \n",
            " extracting: crohme_dataset2/z/exp75026.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75202.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75224.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75227.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75229.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75235.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75245.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75293.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75392.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75449.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75460.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75461.jpg  \n",
            " extracting: crohme_dataset2/z/exp75499.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75512.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75516.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75519.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75743.jpg  \n",
            " extracting: crohme_dataset2/z/exp75782.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75787.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75789.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75832.jpg  \n",
            " extracting: crohme_dataset2/z/exp75834.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75838.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75869.jpg  \n",
            "  inflating: crohme_dataset2/z/exp75898.jpg  \n",
            " extracting: crohme_dataset2/z/exp75941.jpg  \n",
            " extracting: crohme_dataset2/z/exp76010.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76023.jpg  \n",
            " extracting: crohme_dataset2/z/exp76032.jpg  \n",
            " extracting: crohme_dataset2/z/exp76110.jpg  \n",
            " extracting: crohme_dataset2/z/exp76240.jpg  \n",
            " extracting: crohme_dataset2/z/exp76262.jpg  \n",
            " extracting: crohme_dataset2/z/exp76396.jpg  \n",
            " extracting: crohme_dataset2/z/exp76398.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76402.jpg  \n",
            " extracting: crohme_dataset2/z/exp76406.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76462.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76486.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76502.jpg  \n",
            " extracting: crohme_dataset2/z/exp76551.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76608.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76689.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76735.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76962.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76968.jpg  \n",
            " extracting: crohme_dataset2/z/exp76976.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76979.jpg  \n",
            "  inflating: crohme_dataset2/z/exp76982.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77074.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77093.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77105.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77251.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77342.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77356.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77365.jpg  \n",
            " extracting: crohme_dataset2/z/exp77369.jpg  \n",
            " extracting: crohme_dataset2/z/exp77372.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77474.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77657.jpg  \n",
            " extracting: crohme_dataset2/z/exp77659.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77664.jpg  \n",
            "  inflating: crohme_dataset2/z/exp77926.jpg  \n",
            "  inflating: crohme_dataset2/z/exp78061.jpg  \n",
            "  inflating: crohme_dataset2/z/exp78070.jpg  \n",
            " extracting: crohme_dataset2/z/exp78643.jpg  \n",
            "  inflating: crohme_dataset2/z/exp78850.jpg  \n",
            "  inflating: crohme_dataset2/z/exp78856.jpg  \n",
            " extracting: crohme_dataset2/z/exp78862.jpg  \n",
            "  inflating: crohme_dataset2/z/exp78868.jpg  \n",
            "  inflating: crohme_dataset2/z/exp79073.jpg  \n",
            "  inflating: crohme_dataset2/z/exp80500.jpg  \n",
            "  inflating: crohme_dataset2/z/exp80505.jpg  \n",
            "  inflating: crohme_dataset2/z/exp80775.jpg  \n",
            "  inflating: crohme_dataset2/z/exp80875.jpg  \n",
            " extracting: crohme_dataset2/z/exp80889.jpg  \n",
            "  inflating: crohme_dataset2/z/exp80946.jpg  \n",
            "  inflating: crohme_dataset2/z/exp80954.jpg  \n",
            " extracting: crohme_dataset2/z/exp81168.jpg  \n",
            "  inflating: crohme_dataset2/z/exp81175.jpg  \n",
            "  inflating: crohme_dataset2/z/exp81558.jpg  \n",
            "  inflating: crohme_dataset2/z/exp81563.jpg  \n",
            "  inflating: crohme_dataset2/z/exp81928.jpg  \n",
            "  inflating: crohme_dataset2/z/exp81942.jpg  \n",
            "  inflating: crohme_dataset2/z/exp81999.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82008.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82229.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82236.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82546.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82551.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82908.jpg  \n",
            "  inflating: crohme_dataset2/z/exp82922.jpg  \n",
            " extracting: crohme_dataset2/z/exp83114.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83121.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83157.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83430.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83435.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83806.jpg  \n",
            " extracting: crohme_dataset2/z/exp83841.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83855.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83923.jpg  \n",
            " extracting: crohme_dataset2/z/exp83932.jpg  \n",
            " extracting: crohme_dataset2/z/exp83934.jpg  \n",
            "  inflating: crohme_dataset2/z/exp83943.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84509.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84511.jpg  \n",
            " extracting: crohme_dataset2/z/exp84596.jpg  \n",
            " extracting: crohme_dataset2/z/exp84601.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84759.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84779.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84781.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84896.jpg  \n",
            "  inflating: crohme_dataset2/z/exp84910.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85089.jpg  \n",
            " extracting: crohme_dataset2/z/exp85098.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85197.jpg  \n",
            " extracting: crohme_dataset2/z/exp85202.jpg  \n",
            " extracting: crohme_dataset2/z/exp85380.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85387.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85786.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85800.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85845.jpg  \n",
            "  inflating: crohme_dataset2/z/exp85854.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86050.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86057.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86120.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86129.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86579.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86584.jpg  \n",
            "  inflating: crohme_dataset2/z/exp86987.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87001.jpg  \n",
            " extracting: crohme_dataset2/z/exp87058.jpg  \n",
            " extracting: crohme_dataset2/z/exp87067.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87305.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87311.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87387.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87460.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87463.jpg  \n",
            " extracting: crohme_dataset2/z/exp87466.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87642.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87647.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87654.jpg  \n",
            " extracting: crohme_dataset2/z/exp87692.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87701.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87774.jpg  \n",
            "  inflating: crohme_dataset2/z/exp87781.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88174.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88188.jpg  \n",
            " extracting: crohme_dataset2/z/exp88199.jpg  \n",
            " extracting: crohme_dataset2/z/exp88212.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88256.jpg  \n",
            " extracting: crohme_dataset2/z/exp88265.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88566.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88580.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88624.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88628.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88633.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88723.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88728.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88807.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88918.jpg  \n",
            " extracting: crohme_dataset2/z/exp88932.jpg  \n",
            "  inflating: crohme_dataset2/z/exp88995.jpg  \n",
            "  inflating: crohme_dataset2/z/exp89004.jpg  \n",
            "  inflating: crohme_dataset2/z/exp89147.jpg  \n",
            "  inflating: crohme_dataset2/z/exp89403.jpg  \n",
            "  inflating: crohme_dataset2/z/exp89410.jpg  \n",
            "  inflating: crohme_dataset2/z/exp89638.jpg  \n",
            "  inflating: crohme_dataset2/z/exp90193.jpg  \n",
            "  inflating: crohme_dataset2/z/exp90245.jpg  \n",
            "  inflating: crohme_dataset2/z/exp90617.jpg  \n",
            "  inflating: crohme_dataset2/z/exp90889.jpg  \n",
            "  inflating: crohme_dataset2/z/exp91213.jpg  \n",
            "  inflating: crohme_dataset2/z/exp91319.jpg  \n",
            "  inflating: crohme_dataset2/[/exp2999.jpg  \n",
            " extracting: crohme_dataset2/[/exp3041.jpg  \n",
            " extracting: crohme_dataset2/[/exp3117.jpg  \n",
            " extracting: crohme_dataset2/[/exp3219.jpg  \n",
            " extracting: crohme_dataset2/[/exp3336.jpg  \n",
            " extracting: crohme_dataset2/[/exp3381.jpg  \n",
            " extracting: crohme_dataset2/[/exp3471.jpg  \n",
            "  inflating: crohme_dataset2/[/exp3516.jpg  \n",
            " extracting: crohme_dataset2/[/exp3613.jpg  \n",
            " extracting: crohme_dataset2/[/exp3657.jpg  \n",
            " extracting: crohme_dataset2/[/exp3751.jpg  \n",
            "  inflating: crohme_dataset2/[/exp3793.jpg  \n",
            "  inflating: crohme_dataset2/[/exp3881.jpg  \n",
            " extracting: crohme_dataset2/[/exp3926.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4016.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4060.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4149.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4193.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4278.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4321.jpg  \n",
            " extracting: crohme_dataset2/[/exp4408.jpg  \n",
            " extracting: crohme_dataset2/[/exp4450.jpg  \n",
            " extracting: crohme_dataset2/[/exp4541.jpg  \n",
            "  inflating: crohme_dataset2/[/exp4586.jpg  \n",
            " extracting: crohme_dataset2/[/exp4696.jpg  \n",
            " extracting: crohme_dataset2/[/exp4780.jpg  \n",
            " extracting: crohme_dataset2/[/exp4822.jpg  \n",
            " extracting: crohme_dataset2/[/exp4923.jpg  \n",
            " extracting: crohme_dataset2/[/exp4941.jpg  \n",
            " extracting: crohme_dataset2/[/exp5031.jpg  \n",
            "  inflating: crohme_dataset2/[/exp5073.jpg  \n",
            "  inflating: crohme_dataset2/[/exp5157.jpg  \n",
            "  inflating: crohme_dataset2/[/exp5201.jpg  \n",
            " extracting: crohme_dataset2/[/exp5285.jpg  \n",
            " extracting: crohme_dataset2/[/exp5332.jpg  \n",
            "  inflating: crohme_dataset2/[/exp5406.jpg  \n",
            " extracting: crohme_dataset2/[/exp5449.jpg  \n",
            " extracting: crohme_dataset2/[/exp5541.jpg  \n",
            " extracting: crohme_dataset2/[/exp5676.jpg  \n",
            " extracting: crohme_dataset2/[/exp5721.jpg  \n",
            " extracting: crohme_dataset2/[/exp5811.jpg  \n",
            " extracting: crohme_dataset2/[/exp5853.jpg  \n",
            " extracting: crohme_dataset2/[/exp5929.jpg  \n",
            " extracting: crohme_dataset2/[/exp5948.jpg  \n",
            " extracting: crohme_dataset2/[/exp5992.jpg  \n",
            " extracting: crohme_dataset2/[/exp6081.jpg  \n",
            "  inflating: crohme_dataset2/[/exp6126.jpg  \n",
            " extracting: crohme_dataset2/[/exp6222.jpg  \n",
            "  inflating: crohme_dataset2/[/exp6265.jpg  \n",
            "  inflating: crohme_dataset2/[/exp63090.jpg  \n",
            "  inflating: crohme_dataset2/[/exp63169.jpg  \n",
            " extracting: crohme_dataset2/[/exp63215.jpg  \n",
            "  inflating: crohme_dataset2/[/exp63249.jpg  \n",
            " extracting: crohme_dataset2/[/exp63288.jpg  \n",
            " extracting: crohme_dataset2/[/exp6360.jpg  \n",
            " extracting: crohme_dataset2/[/exp63869.jpg  \n",
            " extracting: crohme_dataset2/[/exp63884.jpg  \n",
            " extracting: crohme_dataset2/[/exp63900.jpg  \n",
            " extracting: crohme_dataset2/[/exp63957.jpg  \n",
            "  inflating: crohme_dataset2/[/exp6405.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64384.jpg  \n",
            " extracting: crohme_dataset2/[/exp64429.jpg  \n",
            " extracting: crohme_dataset2/[/exp64460.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64525.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64567.jpg  \n",
            " extracting: crohme_dataset2/[/exp64570.jpg  \n",
            " extracting: crohme_dataset2/[/exp64588.jpg  \n",
            " extracting: crohme_dataset2/[/exp64669.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64711.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64715.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64741.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64752.jpg  \n",
            " extracting: crohme_dataset2/[/exp64813.jpg  \n",
            "  inflating: crohme_dataset2/[/exp64854.jpg  \n",
            " extracting: crohme_dataset2/[/exp64860.jpg  \n",
            " extracting: crohme_dataset2/[/exp65357.jpg  \n",
            " extracting: crohme_dataset2/[/exp65372.jpg  \n",
            " extracting: crohme_dataset2/[/exp65429.jpg  \n",
            "  inflating: crohme_dataset2/[/exp65598.jpg  \n",
            "  inflating: crohme_dataset2/[/exp65633.jpg  \n",
            "  inflating: crohme_dataset2/[/exp65944.jpg  \n",
            " extracting: crohme_dataset2/[/exp66057.jpg  \n",
            "  inflating: crohme_dataset2/[/exp66063.jpg  \n",
            " extracting: crohme_dataset2/[/exp66094.jpg  \n",
            "  inflating: crohme_dataset2/[/exp66555.jpg  \n",
            "  inflating: crohme_dataset2/[/exp66613.jpg  \n",
            " extracting: crohme_dataset2/[/exp66648.jpg  \n",
            " extracting: crohme_dataset2/[/exp67190.jpg  \n",
            "  inflating: crohme_dataset2/[/exp67315.jpg  \n",
            " extracting: crohme_dataset2/[/exp67449.jpg  \n",
            " extracting: crohme_dataset2/[/exp67548.jpg  \n",
            " extracting: crohme_dataset2/[/exp67560.jpg  \n",
            " extracting: crohme_dataset2/[/exp67672.jpg  \n",
            " extracting: crohme_dataset2/[/exp67673.jpg  \n",
            " extracting: crohme_dataset2/[/exp67686.jpg  \n",
            "  inflating: crohme_dataset2/[/exp67701.jpg  \n",
            "  inflating: crohme_dataset2/[/exp67768.jpg  \n",
            " extracting: crohme_dataset2/[/exp68233.jpg  \n",
            " extracting: crohme_dataset2/[/exp68715.jpg  \n",
            "  inflating: crohme_dataset2/[/exp68786.jpg  \n",
            " extracting: crohme_dataset2/[/exp68834.jpg  \n",
            " extracting: crohme_dataset2/[/exp68840.jpg  \n",
            " extracting: crohme_dataset2/[/exp68873.jpg  \n",
            "  inflating: crohme_dataset2/[/exp68902.jpg  \n",
            " extracting: crohme_dataset2/[/exp68978.jpg  \n",
            "  inflating: crohme_dataset2/[/exp69020.jpg  \n",
            "  inflating: crohme_dataset2/[/exp69068.jpg  \n",
            " extracting: crohme_dataset2/[/exp69094.jpg  \n",
            " extracting: crohme_dataset2/[/exp69429.jpg  \n",
            " extracting: crohme_dataset2/[/exp69452.jpg  \n",
            "  inflating: crohme_dataset2/[/exp69678.jpg  \n",
            " extracting: crohme_dataset2/[/exp69698.jpg  \n",
            " extracting: crohme_dataset2/[/exp69774.jpg  \n",
            " extracting: crohme_dataset2/[/exp69793.jpg  \n",
            " extracting: crohme_dataset2/[/exp69826.jpg  \n",
            " extracting: crohme_dataset2/[/exp69884.jpg  \n",
            " extracting: crohme_dataset2/[/exp70279.jpg  \n",
            " extracting: crohme_dataset2/[/exp70453.jpg  \n",
            " extracting: crohme_dataset2/[/exp70459.jpg  \n",
            " extracting: crohme_dataset2/[/exp70546.jpg  \n",
            " extracting: crohme_dataset2/[/exp70609.jpg  \n",
            " extracting: crohme_dataset2/[/exp70662.jpg  \n",
            " extracting: crohme_dataset2/[/exp70686.jpg  \n",
            " extracting: crohme_dataset2/[/exp70689.jpg  \n",
            "  inflating: crohme_dataset2/[/exp71200.jpg  \n",
            " extracting: crohme_dataset2/[/exp71224.jpg  \n",
            " extracting: crohme_dataset2/[/exp71313.jpg  \n",
            " extracting: crohme_dataset2/[/exp71349.jpg  \n",
            "  inflating: crohme_dataset2/[/exp71503.jpg  \n",
            " extracting: crohme_dataset2/[/exp71508.jpg  \n",
            "  inflating: crohme_dataset2/[/exp71528.jpg  \n",
            " extracting: crohme_dataset2/[/exp71621.jpg  \n",
            "  inflating: crohme_dataset2/[/exp71849.jpg  \n",
            "  inflating: crohme_dataset2/[/exp71968.jpg  \n",
            " extracting: crohme_dataset2/[/exp71991.jpg  \n",
            " extracting: crohme_dataset2/[/exp72042.jpg  \n",
            " extracting: crohme_dataset2/[/exp72086.jpg  \n",
            " extracting: crohme_dataset2/[/exp72148.jpg  \n",
            " extracting: crohme_dataset2/[/exp72354.jpg  \n",
            "  inflating: crohme_dataset2/[/exp72459.jpg  \n",
            " extracting: crohme_dataset2/[/exp72525.jpg  \n",
            " extracting: crohme_dataset2/[/exp72749.jpg  \n",
            " extracting: crohme_dataset2/[/exp72754.jpg  \n",
            " extracting: crohme_dataset2/[/exp72760.jpg  \n",
            " extracting: crohme_dataset2/[/exp72878.jpg  \n",
            " extracting: crohme_dataset2/[/exp72922.jpg  \n",
            " extracting: crohme_dataset2/[/exp72946.jpg  \n",
            " extracting: crohme_dataset2/[/exp72950.jpg  \n",
            "  inflating: crohme_dataset2/[/exp73037.jpg  \n",
            " extracting: crohme_dataset2/[/exp73038.jpg  \n",
            "  inflating: crohme_dataset2/[/exp73065.jpg  \n",
            " extracting: crohme_dataset2/[/exp73092.jpg  \n",
            " extracting: crohme_dataset2/[/exp73133.jpg  \n",
            " extracting: crohme_dataset2/[/exp73271.jpg  \n",
            " extracting: crohme_dataset2/[/exp73534.jpg  \n",
            "  inflating: crohme_dataset2/[/exp73576.jpg  \n",
            " extracting: crohme_dataset2/[/exp73599.jpg  \n",
            "  inflating: crohme_dataset2/[/exp73637.jpg  \n",
            " extracting: crohme_dataset2/[/exp73738.jpg  \n",
            " extracting: crohme_dataset2/[/exp73756.jpg  \n",
            "  inflating: crohme_dataset2/[/exp73757.jpg  \n",
            " extracting: crohme_dataset2/[/exp73761.jpg  \n",
            " extracting: crohme_dataset2/[/exp73772.jpg  \n",
            " extracting: crohme_dataset2/[/exp73903.jpg  \n",
            " extracting: crohme_dataset2/[/exp73965.jpg  \n",
            " extracting: crohme_dataset2/[/exp74105.jpg  \n",
            " extracting: crohme_dataset2/[/exp74153.jpg  \n",
            " extracting: crohme_dataset2/[/exp74522.jpg  \n",
            " extracting: crohme_dataset2/[/exp74530.jpg  \n",
            " extracting: crohme_dataset2/[/exp74607.jpg  \n",
            " extracting: crohme_dataset2/[/exp74639.jpg  \n",
            " extracting: crohme_dataset2/[/exp74650.jpg  \n",
            " extracting: crohme_dataset2/[/exp74711.jpg  \n",
            " extracting: crohme_dataset2/[/exp74811.jpg  \n",
            " extracting: crohme_dataset2/[/exp74824.jpg  \n",
            " extracting: crohme_dataset2/[/exp75057.jpg  \n",
            " extracting: crohme_dataset2/[/exp75234.jpg  \n",
            "  inflating: crohme_dataset2/[/exp75259.jpg  \n",
            "  inflating: crohme_dataset2/[/exp75301.jpg  \n",
            " extracting: crohme_dataset2/[/exp75302.jpg  \n",
            "  inflating: crohme_dataset2/[/exp75316.jpg  \n",
            " extracting: crohme_dataset2/[/exp75330.jpg  \n",
            " extracting: crohme_dataset2/[/exp75340.jpg  \n",
            "  inflating: crohme_dataset2/[/exp75363.jpg  \n",
            " extracting: crohme_dataset2/[/exp75373.jpg  \n",
            " extracting: crohme_dataset2/[/exp75383.jpg  \n",
            "  inflating: crohme_dataset2/[/exp75388.jpg  \n",
            "  inflating: crohme_dataset2/[/exp75484.jpg  \n",
            " extracting: crohme_dataset2/[/exp75649.jpg  \n",
            " extracting: crohme_dataset2/[/exp75682.jpg  \n",
            " extracting: crohme_dataset2/[/exp75854.jpg  \n",
            " extracting: crohme_dataset2/[/exp76017.jpg  \n",
            " extracting: crohme_dataset2/[/exp76022.jpg  \n",
            "  inflating: crohme_dataset2/[/exp76055.jpg  \n",
            "  inflating: crohme_dataset2/[/exp76451.jpg  \n",
            " extracting: crohme_dataset2/[/exp76467.jpg  \n",
            "  inflating: crohme_dataset2/[/exp76511.jpg  \n",
            "  inflating: crohme_dataset2/[/exp76872.jpg  \n",
            " extracting: crohme_dataset2/[/exp77036.jpg  \n",
            " extracting: crohme_dataset2/[/exp77064.jpg  \n",
            "  inflating: crohme_dataset2/[/exp77565.jpg  \n",
            " extracting: crohme_dataset2/]/exp3005.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3054.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3124.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3226.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3344.jpg  \n",
            " extracting: crohme_dataset2/]/exp3393.jpg  \n",
            " extracting: crohme_dataset2/]/exp3478.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3529.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3619.jpg  \n",
            " extracting: crohme_dataset2/]/exp3670.jpg  \n",
            " extracting: crohme_dataset2/]/exp3756.jpg  \n",
            " extracting: crohme_dataset2/]/exp3805.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3888.jpg  \n",
            "  inflating: crohme_dataset2/]/exp3937.jpg  \n",
            "  inflating: crohme_dataset2/]/exp4024.jpg  \n",
            " extracting: crohme_dataset2/]/exp4069.jpg  \n",
            " extracting: crohme_dataset2/]/exp4156.jpg  \n",
            "  inflating: crohme_dataset2/]/exp4206.jpg  \n",
            " extracting: crohme_dataset2/]/exp4284.jpg  \n",
            " extracting: crohme_dataset2/]/exp4334.jpg  \n",
            " extracting: crohme_dataset2/]/exp4413.jpg  \n",
            " extracting: crohme_dataset2/]/exp4462.jpg  \n",
            " extracting: crohme_dataset2/]/exp4549.jpg  \n",
            " extracting: crohme_dataset2/]/exp4598.jpg  \n",
            "  inflating: crohme_dataset2/]/exp4663.jpg  \n",
            "  inflating: crohme_dataset2/]/exp4708.jpg  \n",
            " extracting: crohme_dataset2/]/exp4787.jpg  \n",
            " extracting: crohme_dataset2/]/exp4832.jpg  \n",
            " extracting: crohme_dataset2/]/exp4953.jpg  \n",
            " extracting: crohme_dataset2/]/exp5038.jpg  \n",
            " extracting: crohme_dataset2/]/exp5085.jpg  \n",
            "  inflating: crohme_dataset2/]/exp5164.jpg  \n",
            " extracting: crohme_dataset2/]/exp5213.jpg  \n",
            " extracting: crohme_dataset2/]/exp5293.jpg  \n",
            " extracting: crohme_dataset2/]/exp5345.jpg  \n",
            " extracting: crohme_dataset2/]/exp5414.jpg  \n",
            "  inflating: crohme_dataset2/]/exp5462.jpg  \n",
            "  inflating: crohme_dataset2/]/exp5549.jpg  \n",
            " extracting: crohme_dataset2/]/exp5597.jpg  \n",
            "  inflating: crohme_dataset2/]/exp5684.jpg  \n",
            " extracting: crohme_dataset2/]/exp5732.jpg  \n",
            " extracting: crohme_dataset2/]/exp5816.jpg  \n",
            " extracting: crohme_dataset2/]/exp5865.jpg  \n",
            "  inflating: crohme_dataset2/]/exp5955.jpg  \n",
            " extracting: crohme_dataset2/]/exp6004.jpg  \n",
            "  inflating: crohme_dataset2/]/exp6088.jpg  \n",
            "  inflating: crohme_dataset2/]/exp6138.jpg  \n",
            "  inflating: crohme_dataset2/]/exp6230.jpg  \n",
            " extracting: crohme_dataset2/]/exp6278.jpg  \n",
            " extracting: crohme_dataset2/]/exp63092.jpg  \n",
            " extracting: crohme_dataset2/]/exp63165.jpg  \n",
            "  inflating: crohme_dataset2/]/exp63214.jpg  \n",
            " extracting: crohme_dataset2/]/exp63248.jpg  \n",
            "  inflating: crohme_dataset2/]/exp63291.jpg  \n",
            "  inflating: crohme_dataset2/]/exp6368.jpg  \n",
            " extracting: crohme_dataset2/]/exp63871.jpg  \n",
            " extracting: crohme_dataset2/]/exp63886.jpg  \n",
            " extracting: crohme_dataset2/]/exp63902.jpg  \n",
            " extracting: crohme_dataset2/]/exp63960.jpg  \n",
            " extracting: crohme_dataset2/]/exp6417.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64391.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64442.jpg  \n",
            " extracting: crohme_dataset2/]/exp64462.jpg  \n",
            " extracting: crohme_dataset2/]/exp64533.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64569.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64582.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64591.jpg  \n",
            " extracting: crohme_dataset2/]/exp64677.jpg  \n",
            " extracting: crohme_dataset2/]/exp64719.jpg  \n",
            " extracting: crohme_dataset2/]/exp64726.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64743.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64758.jpg  \n",
            " extracting: crohme_dataset2/]/exp64821.jpg  \n",
            "  inflating: crohme_dataset2/]/exp64852.jpg  \n",
            " extracting: crohme_dataset2/]/exp64873.jpg  \n",
            " extracting: crohme_dataset2/]/exp65362.jpg  \n",
            "  inflating: crohme_dataset2/]/exp65373.jpg  \n",
            " extracting: crohme_dataset2/]/exp65433.jpg  \n",
            "  inflating: crohme_dataset2/]/exp65600.jpg  \n",
            " extracting: crohme_dataset2/]/exp65635.jpg  \n",
            "  inflating: crohme_dataset2/]/exp65946.jpg  \n",
            "  inflating: crohme_dataset2/]/exp66059.jpg  \n",
            " extracting: crohme_dataset2/]/exp66065.jpg  \n",
            " extracting: crohme_dataset2/]/exp66097.jpg  \n",
            "  inflating: crohme_dataset2/]/exp66558.jpg  \n",
            " extracting: crohme_dataset2/]/exp66615.jpg  \n",
            " extracting: crohme_dataset2/]/exp66649.jpg  \n",
            "  inflating: crohme_dataset2/]/exp67192.jpg  \n",
            "  inflating: crohme_dataset2/]/exp67316.jpg  \n",
            "  inflating: crohme_dataset2/]/exp67451.jpg  \n",
            " extracting: crohme_dataset2/]/exp67543.jpg  \n",
            " extracting: crohme_dataset2/]/exp67562.jpg  \n",
            "  inflating: crohme_dataset2/]/exp67675.jpg  \n",
            "  inflating: crohme_dataset2/]/exp67676.jpg  \n",
            " extracting: crohme_dataset2/]/exp67690.jpg  \n",
            "  inflating: crohme_dataset2/]/exp67707.jpg  \n",
            " extracting: crohme_dataset2/]/exp67771.jpg  \n",
            "  inflating: crohme_dataset2/]/exp68234.jpg  \n",
            " extracting: crohme_dataset2/]/exp68717.jpg  \n",
            " extracting: crohme_dataset2/]/exp68781.jpg  \n",
            "  inflating: crohme_dataset2/]/exp68839.jpg  \n",
            "  inflating: crohme_dataset2/]/exp68842.jpg  \n",
            " extracting: crohme_dataset2/]/exp68875.jpg  \n",
            "  inflating: crohme_dataset2/]/exp68904.jpg  \n",
            " extracting: crohme_dataset2/]/exp68981.jpg  \n",
            " extracting: crohme_dataset2/]/exp69013.jpg  \n",
            "  inflating: crohme_dataset2/]/exp69070.jpg  \n",
            " extracting: crohme_dataset2/]/exp69093.jpg  \n",
            "  inflating: crohme_dataset2/]/exp69431.jpg  \n",
            "  inflating: crohme_dataset2/]/exp69455.jpg  \n",
            " extracting: crohme_dataset2/]/exp69679.jpg  \n",
            " extracting: crohme_dataset2/]/exp69699.jpg  \n",
            "  inflating: crohme_dataset2/]/exp69775.jpg  \n",
            " extracting: crohme_dataset2/]/exp69794.jpg  \n",
            " extracting: crohme_dataset2/]/exp69825.jpg  \n",
            " extracting: crohme_dataset2/]/exp69887.jpg  \n",
            " extracting: crohme_dataset2/]/exp70278.jpg  \n",
            " extracting: crohme_dataset2/]/exp70452.jpg  \n",
            " extracting: crohme_dataset2/]/exp70460.jpg  \n",
            " extracting: crohme_dataset2/]/exp70545.jpg  \n",
            " extracting: crohme_dataset2/]/exp70608.jpg  \n",
            " extracting: crohme_dataset2/]/exp70661.jpg  \n",
            " extracting: crohme_dataset2/]/exp70685.jpg  \n",
            " extracting: crohme_dataset2/]/exp70687.jpg  \n",
            "  inflating: crohme_dataset2/]/exp71202.jpg  \n",
            " extracting: crohme_dataset2/]/exp71226.jpg  \n",
            "  inflating: crohme_dataset2/]/exp71316.jpg  \n",
            " extracting: crohme_dataset2/]/exp71351.jpg  \n",
            "  inflating: crohme_dataset2/]/exp71505.jpg  \n",
            " extracting: crohme_dataset2/]/exp71507.jpg  \n",
            " extracting: crohme_dataset2/]/exp71533.jpg  \n",
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            " extracting: crohme_dataset2/]/exp72762.jpg  \n",
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            " extracting: crohme_dataset2/]/exp73135.jpg  \n",
            "  inflating: crohme_dataset2/]/exp73275.jpg  \n",
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            " extracting: crohme_dataset2/]/exp73578.jpg  \n",
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            " extracting: crohme_dataset2/]/exp73905.jpg  \n",
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            " extracting: crohme_dataset2/]/exp74524.jpg  \n",
            "  inflating: crohme_dataset2/]/exp74535.jpg  \n",
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            " extracting: crohme_dataset2/]/exp74654.jpg  \n",
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            " extracting: crohme_dataset2/]/exp74805.jpg  \n",
            " extracting: crohme_dataset2/]/exp74826.jpg  \n",
            "  inflating: crohme_dataset2/]/exp75059.jpg  \n",
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            " extracting: crohme_dataset2/]/exp75261.jpg  \n",
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            " extracting: crohme_dataset2/]/exp75319.jpg  \n",
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            " extracting: crohme_dataset2/]/exp75368.jpg  \n",
            " extracting: crohme_dataset2/]/exp75385.jpg  \n",
            " extracting: crohme_dataset2/]/exp75390.jpg  \n",
            " extracting: crohme_dataset2/]/exp75486.jpg  \n",
            "  inflating: crohme_dataset2/]/exp75652.jpg  \n",
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            " extracting: crohme_dataset2/]/exp75856.jpg  \n",
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            "  inflating: crohme_dataset2/]/exp76024.jpg  \n",
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            " extracting: crohme_dataset2/]/exp76454.jpg  \n",
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            "  inflating: crohme_dataset2/]/exp77569.jpg  \n",
            " extracting: crohme_dataset2/{/exp3022.jpg  \n",
            " extracting: crohme_dataset2/{/exp3044.jpg  \n",
            " extracting: crohme_dataset2/{/exp3140.jpg  \n",
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            " extracting: crohme_dataset2/{/exp3906.jpg  \n",
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            " extracting: crohme_dataset2/{/exp4589.jpg  \n",
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            " extracting: crohme_dataset2/{/exp5204.jpg  \n",
            " extracting: crohme_dataset2/{/exp5224.jpg  \n",
            "  inflating: crohme_dataset2/{/exp5312.jpg  \n",
            " extracting: crohme_dataset2/{/exp5335.jpg  \n",
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            " extracting: crohme_dataset2/{/exp5587.jpg  \n",
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            " extracting: crohme_dataset2/{/exp5855.jpg  \n",
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            " extracting: crohme_dataset2/{/exp5995.jpg  \n",
            " extracting: crohme_dataset2/{/exp6107.jpg  \n",
            " extracting: crohme_dataset2/{/exp6129.jpg  \n",
            " extracting: crohme_dataset2/{/exp6247.jpg  \n",
            " extracting: crohme_dataset2/{/exp6268.jpg  \n",
            " extracting: crohme_dataset2/{/exp6385.jpg  \n",
            " extracting: crohme_dataset2/{/exp6408.jpg  \n",
            " extracting: crohme_dataset2/{/exp64409.jpg  \n",
            "  inflating: crohme_dataset2/{/exp64432.jpg  \n",
            " extracting: crohme_dataset2/{/exp64550.jpg  \n",
            " extracting: crohme_dataset2/{/exp64573.jpg  \n",
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            " extracting: crohme_dataset2/}/exp3027.jpg  \n",
            " extracting: crohme_dataset2/}/exp3053.jpg  \n",
            " extracting: crohme_dataset2/}/exp3145.jpg  \n",
            " extracting: crohme_dataset2/}/exp3248.jpg  \n",
            " extracting: crohme_dataset2/}/exp3367.jpg  \n",
            " extracting: crohme_dataset2/}/exp3392.jpg  \n",
            " extracting: crohme_dataset2/}/exp34269.jpg  \n",
            "  inflating: crohme_dataset2/}/exp34272.jpg  \n",
            " extracting: crohme_dataset2/}/exp34276.jpg  \n",
            " extracting: crohme_dataset2/}/exp34280.jpg  \n",
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            " extracting: crohme_dataset2/}/exp34297.jpg  \n",
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            " extracting: crohme_dataset2/}/exp3501.jpg  \n",
            " extracting: crohme_dataset2/}/exp3528.jpg  \n",
            " extracting: crohme_dataset2/}/exp3643.jpg  \n",
            " extracting: crohme_dataset2/}/exp3669.jpg  \n",
            " extracting: crohme_dataset2/}/exp3780.jpg  \n",
            " extracting: crohme_dataset2/}/exp3804.jpg  \n",
            " extracting: crohme_dataset2/}/exp3911.jpg  \n",
            " extracting: crohme_dataset2/}/exp3936.jpg  \n",
            " extracting: crohme_dataset2/}/exp4048.jpg  \n",
            " extracting: crohme_dataset2/}/exp4068.jpg  \n",
            "  inflating: crohme_dataset2/}/exp4180.jpg  \n",
            " extracting: crohme_dataset2/}/exp4205.jpg  \n",
            " extracting: crohme_dataset2/}/exp4307.jpg  \n",
            " extracting: crohme_dataset2/}/exp4333.jpg  \n",
            " extracting: crohme_dataset2/}/exp4436.jpg  \n",
            "  inflating: crohme_dataset2/}/exp4573.jpg  \n",
            " extracting: crohme_dataset2/}/exp4597.jpg  \n",
            " extracting: crohme_dataset2/}/exp4684.jpg  \n",
            " extracting: crohme_dataset2/}/exp4707.jpg  \n",
            " extracting: crohme_dataset2/}/exp4714.jpg  \n",
            " extracting: crohme_dataset2/}/exp4809.jpg  \n",
            " extracting: crohme_dataset2/}/exp4831.jpg  \n",
            " extracting: crohme_dataset2/}/exp4927.jpg  \n",
            " extracting: crohme_dataset2/}/exp4952.jpg  \n",
            " extracting: crohme_dataset2/}/exp5061.jpg  \n",
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            " extracting: crohme_dataset2/}/exp5344.jpg  \n",
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            " extracting: crohme_dataset2/}/exp5572.jpg  \n",
            " extracting: crohme_dataset2/}/exp5596.jpg  \n",
            "  inflating: crohme_dataset2/}/exp5708.jpg  \n",
            " extracting: crohme_dataset2/}/exp5731.jpg  \n",
            " extracting: crohme_dataset2/}/exp5840.jpg  \n",
            " extracting: crohme_dataset2/}/exp5864.jpg  \n",
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            " extracting: crohme_dataset2/}/exp6003.jpg  \n",
            " extracting: crohme_dataset2/}/exp6112.jpg  \n",
            "  inflating: crohme_dataset2/}/exp6137.jpg  \n",
            " extracting: crohme_dataset2/}/exp6252.jpg  \n",
            " extracting: crohme_dataset2/}/exp6277.jpg  \n",
            " extracting: crohme_dataset2/}/exp6390.jpg  \n",
            " extracting: crohme_dataset2/}/exp6416.jpg  \n",
            " extracting: crohme_dataset2/}/exp64414.jpg  \n",
            " extracting: crohme_dataset2/}/exp64441.jpg  \n",
            "  inflating: crohme_dataset2/}/exp64555.jpg  \n",
            " extracting: crohme_dataset2/}/exp64581.jpg  \n",
            " extracting: crohme_dataset2/}/exp64700.jpg  \n",
            " extracting: crohme_dataset2/}/exp64725.jpg  \n",
            " extracting: crohme_dataset2/}/exp64845.jpg  \n",
            " extracting: crohme_dataset2/}/exp64872.jpg  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1y7RoGdCzxb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir='/content/crohme_dataset2/'\n",
        "output_dir='/content/drive/My Drive/Internship_Dataset/2_gray_scale'\n",
        "import os\n",
        "#os.remove(\"/content/Dataset/extracted_images/.DS_Store\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQRqhJn8DX5u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0bb3b6da-4b14-45d6-da48-45d5573d67c8"
      },
      "source": [
        "#for filename in os.listdir(\"/content/Dataset/extracted_images\"):\n",
        "  #print(filename)\n",
        "len(os.listdir(data_dir))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 420
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2KZy9O0DZzV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "d6cd5137-7032-4693-e48a-c4a7b8ea58ac"
      },
      "source": [
        "# Infos about our Dataset\n",
        "nbr_of_pictures = []\n",
        "\n",
        "lab = os.listdir(\"/content/crohme_dataset2\")\n",
        "for _, _, files in os.walk(r'/content/crohme_dataset2'):\n",
        "    nbr_of_pictures.append(len(files))\n",
        "\n",
        "nbr_of_pictures=nbr_of_pictures[1:]\n",
        "#print nbr of pictures in every class\n",
        "print(\"Number of samples in every class ...\")\n",
        "print(len(lab))\n",
        "#sum=0\n",
        "for i in range(56):  # 82 : Nbr of classes\n",
        "    print(lab[i],\" : \",nbr_of_pictures[i])\n",
        "    #sum=sum+nbr_of_pictures[i]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of samples in every class ...\n",
            "56\n",
            "gt  :  65\n",
            "!  :  224\n",
            "pi  :  498\n",
            ",  :  596\n",
            "}  :  69\n",
            "times  :  600\n",
            "3  :  2469\n",
            "neq  :  111\n",
            "gamma  :  90\n",
            "infty  :  386\n",
            "leq  :  220\n",
            "geq  :  147\n",
            "lt  :  106\n",
            "div  :  157\n",
            "forward_slash  :  196\n",
            ")  :  3978\n",
            "beta  :  295\n",
            "0  :  1810\n",
            "(  :  3986\n",
            "sqrt  :  1649\n",
            "mu  :  46\n",
            "theta  :  543\n",
            "{  :  69\n",
            "=  :  3640\n",
            "+  :  5443\n",
            "phi  :  83\n",
            "prime  :  76\n",
            "w  :  139\n",
            "rightarrow  :  299\n",
            "Delta  :  35\n",
            "forall  :  9\n",
            "tan  :  281\n",
            "1  :  6327\n",
            "4  :  1641\n",
            "z  :  1075\n",
            "8  :  731\n",
            "exists  :  4\n",
            "y  :  1895\n",
            "cos  :  574\n",
            "lambda  :  27\n",
            "X  :  2818\n",
            "7  :  753\n",
            "ldots  :  139\n",
            "alpha  :  383\n",
            "]  :  201\n",
            "sum  :  642\n",
            "9  :  742\n",
            "sin  :  783\n",
            "sigma  :  52\n",
            "in  :  15\n",
            "6  :  812\n",
            "integration  :  606\n",
            "-  :  6022\n",
            "2  :  6210\n",
            "[  :  201\n",
            "5  :  1008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i68V18V9lJLa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "a64c50eb-c393-4cee-b54d-b2a9c6612495"
      },
      "source": [
        "img=plt.imread('/content/drive/My Drive/Internship_Dataset/2_gray_scale/test/theta/exp10692.jpg')\n",
        "print(img.shape)\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(45, 45)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9c81c1c550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 423
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R10IdtzSDvhL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SEED=999\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "el9Kaq6XD1lu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "3979f6f9-5b8d-4303-b8a9-b4f71e0bff61"
      },
      "source": [
        "labels1 = lab\n",
        "print(labels1)\n",
        "print(len(lab),\" Classes : \",lab)\n",
        "lab = np.array(lab)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty', 'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0', '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w', 'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8', 'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']', 'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2', '[', '5']\n",
            "56  Classes :  ['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty', 'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0', '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w', 'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8', 'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']', 'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2', '[', '5']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYR1LkGoD664",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "ece12ed8-4a96-425e-da17-eaafd3987776"
      },
      "source": [
        "lab.shape\n",
        "lab = lab.reshape((1,lab.shape[0]))\n",
        "lab"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty',\n",
              "        'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0',\n",
              "        '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w',\n",
              "        'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8',\n",
              "        'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']',\n",
              "        'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2',\n",
              "        '[', '5']], dtype='<U13')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVPOo5u3D9Iz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "#import PY2\n",
        "#from tensorflow.keras.utils.vis_utils import plot_model\n",
        "import sklearn\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from tensorflow.keras import backend as K\n",
        "#from myModel.model import VGGNet\n",
        "import matplotlib.pyplot as plt\n",
        "#from imutils import paths#import pydot\n",
        "import numpy as np\n",
        "import argparse\n",
        "import random\n",
        "import pickle\n",
        "import cv2\n",
        "import os\n",
        "# mlb = MultiLabelBinarizer()\n",
        "# #labels = mlb.fit_transform(labels)\n",
        "# #l.reshape((1,82))\n",
        "# l=mlb.fit(lab)\n",
        "# mlb.fit_transform(lab)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyxvqroND-r7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path='/content/drive/My Drive/Internship_Dataset/'\n",
        "f = open(path+ \"/56_mlb.pickle\", \"wb\")\n",
        "f.write(pickle.dumps(mlb))\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CA1nquHfECYJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image  \n",
        "import PIL  \n",
        "path='/content/crohme_dataset2'\n",
        "for files in os.listdir(path):\n",
        "  for f in os.listdir(os.path.join(path,files)):\n",
        "      img=cv2.imread(os.path.join(os.path.join(path,files),f))\n",
        "      img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "      #print(os.path.join(os.path.join(path,files),f))\n",
        "      _,bin_img = cv2.threshold(img,0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
        "      kernel = np.ones((2,2)) \n",
        "      img = cv2.erode(bin_img, kernel,iterations=1)\n",
        "\n",
        "      # #print(img.shape)\n",
        "      cv2.imwrite(os.path.join(os.path.join(path,files),f),img)\n",
        "       "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIzT9g4LEEwC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d85e85f9-b3b6-42c6-b4d2-f131faabb810"
      },
      "source": [
        "ratio(data_dir, output=output_dir, seed=999, ratio=(.8, .1, .1)) # default val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGVZMKZYEPf8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        "import numpy as np\n",
        "import argparse\n",
        "import imutils\n",
        "import pickle\n",
        "import cv2\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmNOGeB5Oirh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "\n",
        "train_dir='/content/drive/My Drive/Internship_Dataset/56_data/train'\n",
        "val_dir='/content/drive/My Drive/Internship_Dataset/56_data/val'\n",
        "test_dir='/content/drive/My Drive/Internship_Dataset/56_data/test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWeYJKD2Op-c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "3494d62f-1c3d-49cd-aae0-3573f65310a9"
      },
      "source": [
        "cnt=0\n",
        "for files in os.listdir(train_dir):\n",
        "    cnt=cnt+1\n",
        "print(cnt)\n",
        "print(len(os.listdir(train_dir)))\n",
        "print(len(os.listdir(val_dir)))\n",
        "print(os.listdir(train_dir))\n",
        "print(os.listdir(val_dir))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "56\n",
            "56\n",
            "56\n",
            "['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty', 'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0', '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w', 'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8', 'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']', 'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2', '[', '5']\n",
            "['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty', 'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0', '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w', 'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8', 'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']', 'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2', '[', '5']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPC1OZdsOrbW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS = 15\n",
        "BS = 128 #Batch size\n",
        "LR = 1e-3 #Learning rate 0.001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WC4teKE1Otin",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "early_stopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200)\n",
        "model_checking = ModelCheckpoint('/content/drive/My Drive/Internship_Dataset/model_with_56_classes_with_gray_scale.h5', monitor='val_loss', mode='min', save_best_only=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLGEKoPeOyAa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "ebdadd09-1988-44fc-9f2c-185a222c697b"
      },
      "source": [
        "# Infos about our Dataset\n",
        "nbr_of_pictures = []\n",
        "\n",
        "lab = os.listdir(\"/content/crohme_dataset2\")\n",
        "for _, _, files in os.walk(r'/content/crohme_dataset2'):\n",
        "    nbr_of_pictures.append(len(files))\n",
        "\n",
        "nbr_of_pictures=nbr_of_pictures[1:]\n",
        "#print nbr of pictures in every class\n",
        "print(\"Number of samples in every class ...\")\n",
        "print(len(lab))\n",
        "for i in range(56):  # 82 : Nbr of classes\n",
        "    print(lab[i],\" : \",nbr_of_pictures[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of samples in every class ...\n",
            "56\n",
            "gt  :  65\n",
            "!  :  224\n",
            "pi  :  498\n",
            ",  :  596\n",
            "}  :  69\n",
            "times  :  600\n",
            "3  :  2469\n",
            "neq  :  111\n",
            "gamma  :  90\n",
            "infty  :  386\n",
            "leq  :  220\n",
            "geq  :  147\n",
            "lt  :  106\n",
            "div  :  157\n",
            "forward_slash  :  196\n",
            ")  :  3978\n",
            "beta  :  295\n",
            "0  :  1810\n",
            "(  :  3986\n",
            "sqrt  :  1649\n",
            "mu  :  46\n",
            "theta  :  543\n",
            "{  :  69\n",
            "=  :  3640\n",
            "+  :  5443\n",
            "phi  :  83\n",
            "prime  :  76\n",
            "w  :  139\n",
            "rightarrow  :  299\n",
            "Delta  :  35\n",
            "forall  :  9\n",
            "tan  :  281\n",
            "1  :  6327\n",
            "4  :  1641\n",
            "z  :  1075\n",
            "8  :  731\n",
            "exists  :  4\n",
            "y  :  1895\n",
            "cos  :  574\n",
            "lambda  :  27\n",
            "X  :  2818\n",
            "7  :  753\n",
            "ldots  :  139\n",
            "alpha  :  383\n",
            "]  :  201\n",
            "sum  :  642\n",
            "9  :  742\n",
            "sin  :  783\n",
            "sigma  :  52\n",
            "in  :  15\n",
            "6  :  812\n",
            "integration  :  606\n",
            "-  :  6022\n",
            "2  :  6210\n",
            "[  :  201\n",
            "5  :  1008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DiNmp67O5_c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels = []\n",
        "#Nbr of training images\n",
        "train_samples_nbr  = sum(len(files) for _, _, files in os.walk(r'/content/drive/My Drive/Internship_Dataset/2_gray_scale/train'))\n",
        "#Nbr of testing images\n",
        "val_samples_nbr  = sum(len(files) for _, _, files in os.walk(r'/content/drive/My Drive/Internship_Dataset/2_gray_scale/val'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHeuTNo5PCLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "598c84f3-48e0-4e20-a275-27c619f3a1e1"
      },
      "source": [
        "print(train_samples_nbr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49558\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOmrrzLtPEMO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "10e8564d-e58d-4a12-d216-b31c5d379c1c"
      },
      "source": [
        "labels1 = lab\n",
        "print(labels1)\n",
        "print(len(lab),\" Classes : \",lab)\n",
        "lab = np.array(lab)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty', 'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0', '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w', 'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8', 'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']', 'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2', '[', '5']\n",
            "56  Classes :  ['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty', 'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0', '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w', 'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8', 'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']', 'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2', '[', '5']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79qgPSoxPF5Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "afd4920f-c56d-47ee-f045-ea6726071c41"
      },
      "source": [
        "lab.shape\n",
        "lab = lab.reshape((1,lab.shape[0]))\n",
        "lab"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['gt', '!', 'pi', ',', '}', 'times', '3', 'neq', 'gamma', 'infty',\n",
              "        'leq', 'geq', 'lt', 'div', 'forward_slash', ')', 'beta', '0',\n",
              "        '(', 'sqrt', 'mu', 'theta', '{', '=', '+', 'phi', 'prime', 'w',\n",
              "        'rightarrow', 'Delta', 'forall', 'tan', '1', '4', 'z', '8',\n",
              "        'exists', 'y', 'cos', 'lambda', 'X', '7', 'ldots', 'alpha', ']',\n",
              "        'sum', '9', 'sin', 'sigma', 'in', '6', 'integration', '-', '2',\n",
              "        '[', '5']], dtype='<U13')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elLvOzjlPHgt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "#import PY2\n",
        "#from tensorflow.keras.utils.vis_utils import plot_model\n",
        "import sklearn\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from tensorflow.keras import backend as K\n",
        "#from myModel.model import VGGNet\n",
        "import matplotlib.pyplot as plt\n",
        "#from imutils import paths#import pydot\n",
        "import numpy as np\n",
        "import argparse\n",
        "import random\n",
        "import pickle\n",
        "import cv2\n",
        "import os\n",
        "# mlb = MultiLabelBinarizer()\n",
        "# #labels = mlb.fit_transform(labels)\n",
        "# #l.reshape((1,82))\n",
        "# l=mlb.fit(lab)\n",
        "# mlb.fit_transform(lab)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wms3oBnTPJUp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1. / 255,\n",
        "    shear_range=0.0,\n",
        "    zoom_range=0.0,\n",
        "    featurewise_center=False,# set input mean to 0 over the dataset\n",
        "    samplewise_center=False,  # set each sample mean to 0\n",
        "    featurewise_std_normalization=False,  # divide inputs by std of dataset\n",
        "    samplewise_std_normalization=False,  # divide each input by its std\n",
        "    zca_whitening=False,  # apply ZCA whitening\n",
        "    rotation_range=0.0,  # randomly rotate images in the range (deg 0 to 180)\n",
        "    width_shift_range=0.0,  # randomly shift images horizontally\n",
        "    height_shift_range=0.0,  # randomly shift images vertically\n",
        "    horizontal_flip=False,  # randomly flip images\n",
        "    vertical_flip=False\n",
        "    )\n",
        "\n",
        "# data augmentation for testing\n",
        "test_datagen = ImageDataGenerator(rescale=1. / 255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNYKf8yZPM6P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4080756a-abc7-400a-b819-2046b8dff08f"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(45,45),\n",
        "    batch_size=256,\n",
        "    class_mode='categorical')\n",
        "\n",
        "validation_generator = train_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=(45, 45),\n",
        "    batch_size=256,\n",
        "    class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 49558 images belonging to 56 classes.\n",
            "Found 6172 images belonging to 56 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwfGHooKPOMj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "08c1a7a0-ec56-4adf-cc9e-76c3091466b0"
      },
      "source": [
        "history = my_experimented_model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_samples_nbr // 256,\n",
        "    epochs=15,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UnimplementedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnimplementedError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-429-f7547f8b0cda>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtrain_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_samples_nbr\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     epochs=1)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnimplementedError\u001b[0m:  Fused conv implementation does not support grouped convolutions for now.\n\t [[node sequential_7/conv2d_17/Relu (defined at <ipython-input-413-f7547f8b0cda>:4) ]] [Op:__inference_train_function_11840]\n\nFunction call stack:\ntrain_function\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1wjTpYrjgXU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "22061d7a-201e-4ddc-ba2b-867b00051bdd"
      },
      "source": [
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=(45, 45),\n",
        "    batch_size=BS,\n",
        "    class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 6246 images belonging to 56 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygoxaMO9PVzw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "4430605e-7280-4ab2-adfc-437bfd960269"
      },
      "source": [
        "my_experimented_model.evaluate_generator( test_generator, 10, max_queue_size = 10,\n",
        "  workers = 1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-37-4f70cf217d9f>:2: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.19006407260894775, 0.9515625238418579]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSv2nPLUds7h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_experimented_model=tensorflow.keras.models.load_model('/content/drive/My Drive/Internship_Dataset/model_with_56_classes.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OULooXoeDdY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "mlb = pickle.loads(open('/content/drive/My Drive/Internship_Dataset/56_mlb.pickle', \"rb\").read())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4IBjXNGjdJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import sys\n",
        "\n",
        "\n",
        "OUTPUT_DIR = '/content/drive/My Drive/Internship_Dataset/segmented'\n",
        "\n",
        "#------------------Functions------------------#\n",
        "def line_array(array):\n",
        "        list_x_upper = []\n",
        "        list_x_lower = []\n",
        "        for y in range(5, len(array) - 5):\n",
        "            s_a, s_p = strtline(y, array)\n",
        "            e_a, e_p = endline(y, array)\n",
        "            if s_a >= 7 and s_p >= 5:\n",
        "                list_x_upper.append(y)\n",
        "            if e_a >= 5 and e_p >= 7:\n",
        "                list_x_lower.append(y)\n",
        "\n",
        "        return list_x_upper, list_x_lower\n",
        "def strtline(y, array):\n",
        "        count_ahead = 0\n",
        "        count_prev = 0\n",
        "        for i in array[y:y+10]:\n",
        "            if i > 3:\n",
        "                count_ahead += 1\n",
        "\n",
        "        for i in array[y-10:y]:\n",
        "            if i==0:\n",
        "                count_prev += 1 \n",
        "\n",
        "        return count_ahead, count_prev\n",
        "def endline(y, array):\n",
        "        count_ahead = 0\n",
        "        count_prev = 0\n",
        "        for i in array[y:y+10]:\n",
        "            if i==0:\n",
        "                count_ahead += 1\n",
        "\n",
        "        for i in array[y-10:y]:\n",
        "            if i > 3:\n",
        "                count_prev += 1\n",
        "\n",
        "        return count_ahead, count_prev\n",
        "\n",
        "def endline_word(y, array, a):\n",
        "        count_ahead = 0\n",
        "        count_prev = 0\n",
        "        for i in array[y:y+2*a]:\n",
        "            if i < 2:\n",
        "                count_ahead+= 1  \n",
        "        for i in array[y-a:y]:\n",
        "            if i > 2:\n",
        "                count_prev += 1  \n",
        "        return count_prev ,count_ahead\n",
        "def end_line_array(array, a):\n",
        "        list_endlines = []\n",
        "        for y in range(len(array)):\n",
        "            e_p, e_a = endline_word(y, array, a)\n",
        "            # print(e_p, e_a)\n",
        "            if e_a >= int(1.5*a) and e_p >= int(0.7*a):\n",
        "                list_endlines.append(y)\n",
        "        return list_endlines\n",
        "def refine_endword(array):\n",
        "        refine_list = []\n",
        "        for y in range(len(array)-1):\n",
        "            if array[y]+1 < array[y+1]:\n",
        "                refine_list.append(array[y])\n",
        "\n",
        "        if len(array) != 0:\n",
        "            refine_list.append(array[-1])\n",
        "        return refine_list\n",
        "def refine_array(array_upper, array_lower):\n",
        "        upperlines = []\n",
        "        lowerlines = []\n",
        "        for y in range(len(array_upper)-1):\n",
        "            if array_upper[y] + 5 < array_upper[y+1]:\n",
        "                upperlines.append(array_upper[y]-10)\n",
        "        for y in range(len(array_lower)-1):\n",
        "            if array_lower[y] + 5 < array_lower[y+1]:\n",
        "                lowerlines.append(array_lower[y]+10)\n",
        "\n",
        "        upperlines.append(array_upper[-1]-10)\n",
        "        lowerlines.append(array_lower[-1]+10)\n",
        "\n",
        "        return upperlines, lowerlines\n",
        "def letter_width(contours):\n",
        "        letter_width_sum = 0\n",
        "        count = 0\n",
        "        for cnt in contours:\n",
        "            if cv2.contourArea(cnt) > 10:\n",
        "                x,y,w,h = cv2.boundingRect(cnt)\n",
        "                letter_width_sum += w\n",
        "                count += 1\n",
        "\n",
        "        return letter_width_sum/count\n",
        "\n",
        "\n",
        "def end_wrd_dtct(lines, i, bin_img, mean_lttr_width, total_width,final_thr):\n",
        "        count_y = np.zeros(shape = total_width)\n",
        "        for x in range(total_width):\n",
        "            for y in range(lines[i][0],lines[i][1]):\n",
        "                if bin_img[y][x] == 255:\n",
        "                    count_y[x] += 1\n",
        "\n",
        "        end_lines = end_line_array(count_y, int(mean_lttr_width))\n",
        "        endlines = refine_endword(end_lines)\n",
        "        for x in endlines:\n",
        "            print(\"in end_wrd_dtct\")\n",
        "            final_thr[lines[i][0]:lines[i][1], x] = 255\n",
        "            #plt.imshow(final_thr)\n",
        "        return endlines\n",
        "\n",
        "def get_letter_rect(k, contours):\n",
        "        \"Helper function for properly identifying '=' symbol. OpenCV\"\n",
        "        \"will treat 2 dashes of 'sign' as separate contours, thus this\"\n",
        "        \"will help to identify and merge them into a single '=' contour\"\n",
        "        valid = True\n",
        "        x,y,w,h = cv2.boundingRect(contours[k])\n",
        "        for i in range(len(contours)):\n",
        "            cnt = contours[i]\n",
        "            if i == k:\n",
        "                continue\n",
        "            elif cv2.contourArea(cnt) < 50:\n",
        "                continue\n",
        "\n",
        "            x1,y1,w1,h1 = cv2.boundingRect(cnt)\n",
        "\n",
        "            if abs(x1 + w1/2 - (x + w/2)) < 50:\n",
        "                if y1 > y:\n",
        "                    h = abs(y - (y1 + h1))\n",
        "                    w = abs(x - (x1 + w1))\n",
        "                else:\n",
        "                    valid = False\n",
        "                break\n",
        "\n",
        "        return (valid,x,y,w,h)\n",
        "def letter_seg(lines_img, x_lines, i):\n",
        "        copy_img = lines_img[i].copy()\n",
        "        x_linescopy = x_lines[i].copy()\n",
        "\n",
        "        letter_img = []\n",
        "        letter_k = []\n",
        "\n",
        "        contours = cv2.findContours(copy_img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]\n",
        "        for k in range(len(contours)):\n",
        "            cnt = contours[k]\n",
        "            if cv2.contourArea(cnt) < 50:\n",
        "                continue\n",
        "\n",
        "            valid,x,y,w,h = get_letter_rect(k, contours)\n",
        "            if valid:\n",
        "                letter_k.append((x,y,w,h))\n",
        "\n",
        "        letter = sorted(letter_k, key=lambda student: student[0])\n",
        "        #print(letter)\n",
        "\n",
        "        word = 1\n",
        "        letter_index = 0\n",
        "        print(letter)\n",
        "        for e in range(len(letter)):\n",
        "            if(letter[e][0]<x_linescopy[0]):\n",
        "                letter_index += 1\n",
        "                letter_img_tmp = lines_img[i][letter[e][1]-5:letter[e][1]+letter[e][3]+5,letter[e][0]-5:letter[e][0]+letter[e][2]+5]\n",
        "                letter_img = letter_img_tmp\n",
        "                print(letter_img.shape)\n",
        "                cv2.resize(letter_img_tmp, dsize =(45, 45), interpolation = cv2.INTER_AREA)\n",
        "                cv2.imwrite(OUTPUT_DIR+str(i+1)+'_'+str(word)+'_'+str(letter_index)+'.jpg', 255-letter_img)\n",
        "                image.append(255-letter_img)\n",
        "                X.append(letter[e][0])\n",
        "                Y.append(letter[e][1])\n",
        "                X1.append(letter[e][2])\n",
        "                Y1.append(letter[e][3])\n",
        "                CX.append((letter[e][0]+letter[e][2])/2)\n",
        "                CY.append((letter[e][1]+letter[e][3])/2)\n",
        "                #print(letter[e][0],letter[e][1],letter[e][2],letter[e][3])\n",
        "            else:\n",
        "                x_linescopy.pop(0)\n",
        "                word += 1\n",
        "                letter_index = 1\n",
        "                letter_img_tmp = lines_img[i][letter[e][1]-5:letter[e][1]+letter[e][3]+5,letter[e][0]-5:letter[e][0]+letter[e][2]+5]\n",
        "                letter_img = cv2.resize(letter_img_tmp, dsize =(45, 45), interpolation = cv2.INTER_AREA)\n",
        "                cv2.imwrite(OUTPUT_DIR+str(i+1)+'_'+str(word)+'_'+str(letter_index)+'.jpg', 255-letter_img_tmp)\n",
        "                image.append(255-letter_img)\n",
        "                X.append(letter[e][0])\n",
        "                Y.append(letter[e][1])\n",
        "                X1.append(letter[e][2])\n",
        "                Y1.append(letter[e][3])\n",
        "                CX.append((letter[e][0]+letter[e][2])/2)\n",
        "                CY.append((letter[e][1]+letter[e][3])/2)\n",
        "                #print(letter[e][0],x_linescopy[0], word)\n",
        "\n",
        "def adjust_gamma(image,gamma):\n",
        "\n",
        "    invGamma = 1.0 / gamma\n",
        "    table = np.array([((i / 255.0) ** invGamma) * 255\n",
        "      for i in np.arange(0, 256)]).astype(\"uint8\")\n",
        "\n",
        "    return cv2.LUT(image, table)                \n",
        "def image_segmentation(filepath):\n",
        "        # -------------Thresholding Image--------------#\n",
        "        print(\"\\n........Program Initiated.......\\n\")\n",
        "        src_img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\n",
        "        orig_height, orig_width = src_img.shape\n",
        "\n",
        "        print(\"\\n Resizing Image........\")\n",
        "        width = 2000\n",
        "        height = int(width * orig_height / orig_width)\n",
        "        src_img = cv2.resize(src_img, dsize=(width, height), interpolation=cv2.INTER_AREA)\n",
        "\n",
        "        print(\"#---------Image Info:--------#\")\n",
        "        print(\"\\tHeight =\", height, \"\\n\\tWidth =\", width)\n",
        "        print(\"#----------------------------#\")\n",
        "        print(\"Applying Adaptive Threshold with kernel :- 21 X 21\")\n",
        "\n",
        "        PIXEL_SET = 255\n",
        "        kernel_size = 21\n",
        "        normalized_mean = 20\n",
        "#         bin_img = cv2.adaptiveThreshold(src_img, PIXEL_SET, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, kernel_size,\n",
        "#                                         normalized_mean)\n",
        "        print(src_img.shape)\n",
        "        equ = adjust_gamma(src_img,3.5)\n",
        "        #plt.imshow(equ,cmap=\"gray\")\n",
        "\n",
        "        _,bin_img = cv2.threshold(equ,0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
        "        plt.imshow(bin_img,cmap=\"gray\")\n",
        "#         kernel = np.array([[-1,-1,-1], \n",
        "#                    [-1, 9,-1],\n",
        "#                    [-1,-1,-1]])\n",
        "#         sharpened = cv2.filter2D(image, -1, kernel)\n",
        "\n",
        "        print(\"Noise Removal From Image.........\")\n",
        "#         kernel = np.array([[-1,-1,-1], \n",
        "#                    [-1, -9,-1],\n",
        "#                    [-1,-1,-1]])\n",
        "        final_thr = cv2.erode(bin_img,(3,3),cv2.MORPH_ELLIPSE)\n",
        "        #plt.imshow(final_thr,cmap=\"gray\")\n",
        "        contr_retrival = final_thr.copy()\n",
        "        #print(\"coming here\")\n",
        "\n",
        "        # -------------/Thresholding Image-------------#\n",
        "\n",
        "        # -------------Line Detection------------------#\n",
        "        print(\"Beginning Character Semenation..............\")\n",
        "        count_x = np.zeros(shape=(height))\n",
        "        for y in range(height):\n",
        "            for x in range(width):\n",
        "                if bin_img[y][x] == PIXEL_SET:\n",
        "                    count_x[y] += 1\n",
        "        \n",
        "        print(count_x)\n",
        "        upper_lines, lower_lines = line_array(count_x)\n",
        "        upperlines, lowerlines = refine_array(upper_lines, lower_lines)\n",
        "        print(upperlines)\n",
        "        print(lowerlines)\n",
        "        if len(upperlines) == len(lowerlines):\n",
        "            lines = []\n",
        "            for y in upperlines:\n",
        "                final_thr[y][:] = PIXEL_SET\n",
        "            for y in lowerlines:\n",
        "                final_thr[y][:] = PIXEL_SET\n",
        "            for y in range(len(upperlines)):\n",
        "                lines.append((upperlines[y], lowerlines[y]))\n",
        "        else:\n",
        "            print(\"Too much noise in image, unable to process.\\nPlease try with another image. Ctrl-C to exit:- \")\n",
        "            #showimages()\n",
        "            #k = cv2.waitKey(0)\n",
        "            #while 1:\n",
        "                #k = cv2.waitKey(0)\n",
        "                #if k & 0xFF == ord('q'):\n",
        "                    #cv2.destroyAllWindows()\n",
        "            exit()\n",
        "\n",
        "        lines = np.array(lines)\n",
        "        no_of_lines = len(lines)\n",
        "        print(\"\\nGiven Text has   # \", no_of_lines, \" #   no. of lines\")\n",
        "\n",
        "        lines_img = []\n",
        "        for i in range(no_of_lines):\n",
        "            lines_img.append(bin_img[lines[i][0]:lines[i][1], :])\n",
        "\n",
        "        # -------------/Line Detection-----------------#\n",
        "\n",
        "        # -------------Letter Width Calculation--------#\n",
        "\n",
        "        contours= cv2.findContours(contr_retrival, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]\n",
        "        cv2.drawContours(src_img, contours, -1, (0, 255, 0), 1)\n",
        "\n",
        "        mean_lttr_width = letter_width(contours)\n",
        "        print(\"\\nAverage Width of Each Letter:- \", mean_lttr_width)\n",
        "\n",
        "        # -------------/Letter Width Calculation-------#\n",
        "\n",
        "        # --------------Word Detection-----------------#\n",
        "        x_lines = []\n",
        "        for i in range(len(lines_img)):\n",
        "            x_lines.append(end_wrd_dtct(lines, i, bin_img, mean_lttr_width, width,final_thr))\n",
        "\n",
        "        for i in range(len(x_lines)):\n",
        "            x_lines[i].append(width)\n",
        "\n",
        "        # -------------/Word Detection-----------------#\n",
        "\n",
        "        # -------------Letter Segmentation-------------#\n",
        "\n",
        "        for i in range(len(lines)):\n",
        "            letter_seg(lines_img, x_lines, i)\n",
        "\n",
        "        # ------------\\Letter Segmentation-------------#\n",
        "\n",
        "        # -------------Character segmenting------------#\n",
        "\n",
        "        contours = cv2.findContours(final_thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]\n",
        "        for cnt in contours:\n",
        "            if cv2.contourArea(cnt) > 20:\n",
        "                x, y, w, h = cv2.boundingRect(cnt)\n",
        "                cv2.rectangle(src_img, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
        "                "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOz9uxTCjxVh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "is_digit=False\n",
        "is_var=False\n",
        "is_constant=False\n",
        "prev_digit=False\n",
        "prev_var=False\n",
        "prev_constant=False\n",
        "other=False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RWciTE9jzxQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','a','b'\n",
        "    ,'c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','COS','cos','sin'\n",
        "    ,'SIN','tan','TAN']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laQl5XVWj1Wx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "digit=['0','1','2','3','4','5','6','7','8','9']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyhXxqLVj2ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "constant=['alpha','beta','lamba','theta','Delta','mu']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYexqDP6j3vy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "85736ceb-f0ef-480f-ca05-ffbe0b43c9f8"
      },
      "source": [
        "X=[]\n",
        "Y=[]\n",
        "X1=[]\n",
        "Y1=[]\n",
        "CX=[]\n",
        "CY=[]\n",
        "image=[]\n",
        "is_digit=False\n",
        "is_var=False\n",
        "is_constant=False\n",
        "prev_digit=False\n",
        "prev_var=False\n",
        "prev_constant=False?\n",
        "other=False\n",
        "image_segmentation('/content/WhatsApp Image 2020-07-26 at 10.19.48 PM.jpeg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "........Program Initiated.......\n",
            "\n",
            "\n",
            " Resizing Image........\n",
            "#---------Image Info:--------#\n",
            "\tHeight = 426 \n",
            "\tWidth = 2000\n",
            "#----------------------------#\n",
            "Applying Adaptive Threshold with kernel :- 21 X 21\n",
            "(426, 2000)\n",
            "Noise Removal From Image.........\n",
            "Beginning Character Semenation..............\n",
            "[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  12.  17.  22.\n",
            "  28.  30.  34.  29.  27.  22.  19.  19.  17.  17.  15.  13.  14.  14.\n",
            "  13.  13.   9.  10.  10.   9.   8.   7.   7.   6.   6.   8.   8.  22.\n",
            "  25.  27.  30.  33.  34.  53.  50.  53.  53.  53.  54.  38.  33.  23.\n",
            "  16.  21.  33.  40.  43.  45.  42.  33.  32.  32.  32.  34.  34.  33.\n",
            "  33.  34.  36.  37.  37.  54.  61.  66.  65.  64.  58.  55.  60.  71.\n",
            "  67.  67.  67.  64.  60.  50.  47.  44.  37.  36.  35.  43.  46.  67.\n",
            "  81.  97. 140. 149. 158. 167. 171. 173. 168. 157. 150. 140. 131. 120.\n",
            " 115. 111.  99. 103. 100. 108. 107. 111. 125. 127. 142. 137. 133. 114.\n",
            " 111. 111.  92.  93. 104. 121. 129. 134. 142. 147. 140. 121. 109. 114.\n",
            " 105.  99.  97.  98.  97.  93.  84.  86.  85.  91.  91.  92.  88.  92.\n",
            "  93.  98.  92.  92.  87.  91.  90.  99. 104. 104. 105. 103. 103.  87.\n",
            "  85.  69.  75.  80.  90.  90.  94.  91.  95.  92.  85.  80.  86.  91.\n",
            "  90.  95.  92.  95. 115. 137. 143. 163. 167. 175. 167. 177. 163. 144.\n",
            " 138. 106. 108. 112. 114. 109. 113. 111. 106. 112. 103.  97.  89.  88.\n",
            "  91. 111. 117. 113. 110. 108. 106. 114. 109.  91.  92. 100. 108. 111.\n",
            " 114. 109. 100.  97.  84.  84.  89.  85.  83.  86.  81.  77.  71.  61.\n",
            "  57.  47.  39.  33.  37.  37.  39.  39.  42.  43.  42.  42.  43.  57.\n",
            "  65.  80.  85.  99.  96.  94.  89.  77.  74.  61.  41.  42.  41.  38.\n",
            "  39.  39.  39.  42.  46.  46.  60.  58.  56.  51.  56.  56.  48.  48.\n",
            "  49.  42.  34.  31.  32.  33.  35.  37.  41.  48.  53.  61.  56.  52.\n",
            "  48.  34.  25.  21.  12.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.]\n",
            "[48]\n",
            "[354]\n",
            "\n",
            "Given Text has   #  1  #   no. of lines\n",
            "\n",
            "Average Width of Each Letter:-  68.44444444444444\n",
            "[(136, 181, 89, 112), (261, 180, 62, 97), (375, 175, 109, 97), (489, 69, 52, 67), (595, 165, 80, 86), (752, 141, 90, 94), (864, 125, 59, 97), (937, 119, 91, 83), (1008, 5, 65, 70), (1123, 128, 89, 71), (1294, 91, 62, 109), (1381, 91, 67, 90), (1549, 91, 76, 41), (1708, 58, 48, 92), (1786, 50, 47, 90), (1867, 41, 89, 81)]\n",
            "(122, 99)\n",
            "(107, 72)\n",
            "(107, 119)\n",
            "(77, 62)\n",
            "(96, 90)\n",
            "(104, 100)\n",
            "(107, 69)\n",
            "(93, 101)\n",
            "(80, 75)\n",
            "(81, 99)\n",
            "(119, 72)\n",
            "(100, 77)\n",
            "(51, 86)\n",
            "(102, 58)\n",
            "(100, 57)\n",
            "(91, 99)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMlhRpyLj5Ca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from keras.models import load_model\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from PIL import Image,ImageOps\n",
        "#import CharacterSegmentation as cs\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "import os\n",
        "dictionary = dict(zip(X, image))\n",
        "import collections\n",
        "od = collections.OrderedDict(sorted(dictionary.items()))\n",
        "ima=[]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2upCqdpZkDZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for image in od.values(): \n",
        "    ima.append(image) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIhin9UWkEED",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ima=np.array(ima)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fWXfyEwkFcw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "cf43ebc9-a8c0-4c5a-81d1-e4ca67176f05"
      },
      "source": [
        "fig = plt.figure()\n",
        "figure(figsize=(18,18))\n",
        "cnt=1\n",
        "for i in range(len(ima)):\n",
        "    ax1 = plt.subplot(2,len(ima),cnt)\n",
        "    ax1.imshow(ima[i],cmap=\"gray\")\n",
        "    cnt+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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16+KHH36Avb097t69ixEjRiA3N1fsGC6XC0KI2L6yEEKwbNky1KlTB9OnTweXW2OmBdUWDoejteOY6s6yZcuwefNmuf6+tbyvr5Ia8eUghCA8PFzEJNbZ2RkbN25Eo0aNWB+xFStWYNGiRTh37hx73I4dO9CzZ0/0799f7XJL4sSJE9i+fbtY+aNHj/Dq1Ss0atRIA1JJh/Bla9myJZ48edIW9GXTGDweD3///bfYpEwSw4YNU/h6mop6DQjigwwePBi3bt2ClZUVjh07JnOkcEXRtEKlPHl5eTh16hT4fD5sbW3h4OCgKVHkokGDBtDT00NxcbGmRamSWh4/RGvixvz7779i1ollYRgG/v7+YjGIKOrH2dkZnp6eYib56uDBgweIi4tD27Zt1X5tabh8+TI8PDyQn58vUt62bVt8/fXXAICgoCDY2dkBADIyMvD+/f93NW5ubjA2NkZwcDCaN2+OgoICTJw4ETweDykpKbhy5YrYNYuLizF79mx888036Nu3rwpbR9EUfD4fOTk5ImX16tWDrq6u2LFv376Fr68vUlNTwTAMxo0bhylTpiA4OBhbt25Fw4YNAaAtwzA/frbKrTbs27cPy5cvl0uhUMv7+iqpEQqFt2/fikzCnZ2dsX//frHJd7du3bBp0yZ069YNycnJAAQZFXbt2qU1CoXS0lLweDyxck9PT3Tu3FkDEkmP8GXLzMwEfdnUR1paGubNmydSVlxcjLCwMIXqXbx4Mb744gupjtVE1GtAYDrr6+uLS5cuAQCeP3+Ou3fvql2hoEmFiiRu3LiB4cOHg8fjwcLCAvfv39farDaSmDNnDo4cOSKVQkzTSBM/pKa6uzg4OCAnJwcJCQkvALyGGuPGFBUVoaSkBAAwdepU3LhxA3FxcZWec/ToUejq6mLNmjVo3769wjJQizz5qF+/Pho3bqzy68TGxopNzLWZy5cvw9fXV8R1gcPhYNWqVXB2dmYVCkJKSkqwbt06AAKL3MGDB2PTpk0i7rF6enqspUNKSgoiIyMxdepUZGVlse8PIJhwVgcFLkV2CCG4du0aevToIVJ+6NAhDBo0SOx4LpeLlStXwt7eHh8/foSDgwN69+4NAAgMDMSMGTPAMEystioTEhMTERgYqPR6a3usMKDymEHVXqFQVFSEYcOG4d27dwAqViYIad68OXr27Ik9e/awZVlZWfj06RNduVAQ4ctWJsdsrXvZAOX5klfE7t27RfwqCwoKEB0drdRrrFy5EhMnToS+vr7U56gr6nVZrly5IvRp0yiaUqhI4tKlS/D19WUVkxkZGdVyoDhnzhx4e3uLuN1ERUXhzp076NJF7LOi1dQCd5dH6njvX758iadPBa/Vxo0bERUVBUAwsZLGZ/z69esABPFkQkND0blzZ7kVbdQiT/sJCQkpbzmmtURFRWHAgAHIy8tjy0xMTLB+/XoMGzaswuDh+vr6cHV1RUhICJo0aVJpn92kSRMMGTIE/fv3B5/Pxy+//IJDhw6x+8eNG4c+ffrI1O9TtJ+8vDwxZUJlNG3alP0uGhkZoU2bNiJWMNpOfn6+RoM41+T+vjLX+2qvUCgtLUViYiIAge/tvn37qnQLGDZsGM6cOYMPHz4AEASmefbsWbUzC66uaMPLlpycjMmTJyM1NRUzZszAgAEDlFa3snzJAUGk5rKpPgHBx7LsoENehBkeAEEKUeFAHRCsINGoz7KhCYVKeeLj47Fw4UKkpaVpSgSl0a9fP+jo6IgoFJ4/fw53d3fExMRIndJU1Wibu0t5YmJi8PXXX1ebbEYVkZqaiuHDh4vEQZKXjIwMeHh4wM3NDUePHpUrXSG1yFMehBAMHz4cx44dQ7169ZRS59WrV7Fr1y6J+3Jzc0EIkZgyW1MEBweL9Os6OjpYu3YtRowYUeE5urq6iIiIkPlaQguGRYsWiSgUPn36VC0CVlLUx6tXr/Dw4UN06dIFN27cwIYNGxAeHg4ANgzD1CeEZJU/p+yiYXUJei8Nyurro6OjZQ5K2717d61Pq1ntFQoMw6BOnTrQ19fH+PHjpTKjc3Nzw9ChQ7Fhwwa27OTJk1ShoCDaPrAWkpmZCR8fH1y9ehWAoOPu378/OByOhiUTJzc3V6lZFhiGYdPzzJw5UxhgBgCq/YSjtvPp0yeMHDlSKRMubUBHRwddu3Zl31MhaWlpuHz5MoYO1ZhHiQja5u5SliNHjmDChAmwt7fHiRMnJPrLajNv375FVFQU/vjjD5SUlCAlJUWq8xo3bszGThKSlJSE7OxskbIzZ85gxIgRIpMqadFGi7y8vDykp6dDX18fTZs2VcuEWV6LPAcHB9StW5d1Sbh48SLmzp2L1atXK0Wu3NxcdtGoPGPHjsWAAQNQv359pVxLEYqLi7FkyRKx7/by5csrVSYog+bNm2PWrFlYunSpSq9DqZ7k5eXBy8sLa9asgbGxMcaPH4+5c+eCYRhwOJwSACsBjCl/XvlFQ3XKnJSUpDILRkX7+qNHj2LVqlWIi4sr229IRfv27REYGIj+/ftrbVDZaq9QMDQ0xLRp03D+/HkEBARIfd7333+PrVu3oqioCABw8+ZNVYlYaxC+bHXr1oW2DazLcvjwYVy7do393apVK61aqVAm5ubmaNeuHfu7Tp06OH1aK93e5EKoTBS+x7WZGTNmVKhMKB+MqTqgp6eHxYsXS8wXP23aNK1RKGiTu0t5QkJCkJaWhsjISBw/frzSjC/aRkxMDHx8fKqMi1AWDw8PtGzZEu7u7ujWrZvIvoMHD2L06NH49OmTSPmDBw9w//59lWVRUqdFXlJSEh4/fgwzMzO1xVyS1yJv7NixWLp0KWthCgisRZ88ecIGHFQV2rQKn5SUJJaysXXr1ujVq5fKlfxcLlds1bOmjoWqC4aGhvjzzz81LQZKSkrg5eWFYcOGYeDAgQBQfsE2HYDWBXbj8XgoKChQSd2K9PW3b9+Gn5+fmFJbWh4/fozRo0fD3t4eU6dOxfDhw7XuXa32CgUAGDFiBBo2bCjTx7dHjx4iE5H3798jOTm5WgUu0zaEL9vnj49WDayFJCQkYP369eyAwtTUFBMnTqxxq/NcLhf+/v7o2LEj/Pz8NC2OynBzc0PXrl0RGRnJlkmb6rKmUFRUhHv37lUYMb24uBgrVqzAjh07tNIKpzI4HA64XK7EQLXahDa4u1RGUVGRUi2dVElRURH8/f1x69YtxMfHSzyGy+XC0NAQLVu2xMqVK9nyr7/+WswyQYi3tzcsLS0RHh6OrVu3st+JxMREXL16VWaFgrZZ5AmzXc2aNQsmJibYtm0bvLy8NCWOVOzfv19E8fH06VM8ePAA7dq107rBsqoo/15aWVkhIiICtra2Kr/269evxdKma5Oypabz119/4cmTJyJl+vr6GDJkiIYkEkAIgZ+fH9q0aYNp06ax5eXmSKYAlLISK3S1UeSdz8vLQ35+PqytrZUhUoXI29enpKRIVCYYGRlVaSmVlJTEjoEePHiAgIAAREZGYuHChbC0tJRFDJVSI2ZR9erVkxipVBZiY2Px7NkzJUlUe/nxxx9hZ2cHQshXhJDFmpanLAUFBRgxYgQbK4DL5WLy5MkiK/g1hXXr1mHDhg01WpkACFZTFi5cKFK2ZMkSlWmotY2SkhLs3r0bffr0Yc3BDQwMULduXZHjDh8+LDTVq1Y4OTmJZTChyE7jxo3h6uqqaTGq5MOHD/D19cXOnTsrVCbo6upi0aJFyMzMxJ07d9CjRw92q0iZIKRr167YtGkTOnTooLCsQou8oqKishZ5JxSuWE54PB7rupGTk4OFCxdq/eRQUhyU0aNH48iRIwrVy+PxcPnyZYXqUBfjxo0T+b1u3Tq1KBMAxRSNn4Mh2zEMk8AwTJBSBaslPHjwoEK3HE1y48YN7Nq1C5cvX0aHDh3QoUMHnD59Gr///ju+/vprYYYcYwBKSaXQs2dPJCQkyH1+SkoKTExMtCaukiS4XC7MzMxgbGwMHR0dWFlZYcmSJTh69Cji4+Mr3OLi4jB16lSYmZmByxXYAHz69AlhYWH43//+p5R4asqiRlgo1Aby8vJQWFgoV/Co6kpOTg7OnDnDWpHo6enB1dUVpqamcq20JiQk4Pnz5+zvn376CXPnzmVf0urEvHnz2DQ+kujatasapdEs5U02s7OztX4grQwKCgrYoJ1CM26GYRASEoI2bdqge/fuKCwsBCCwUtD2VX5JMAyD7t27w8bGBq9evWLL8/PzcfToUdYUk1I5devWhY2NjabFqBQ+n49JkyaJBaEVsnTpUpibm0NfX18hc8+ywWiFHDx4EMOGDZMplaG2WeTp6uri119/xahRozQlgsw0aNAA/v7+IlmL+Hw+674q70LRvHnzsGbNGqXIqEp27NiBly9fsr/79u0r0cVL2xBmOAEQD8ARNMNJjaJbt24Sx1CfV+cBAAzDJBBCkmWt28jICBYWFmJBCYXKWVmskxISElBYWIhOnTppvWVqnz598PLlS6SkpOD48ePw9/eXOobLkiVL8Mcff2Dfvn1Yu3YtO4/ZunUreDweNm/erErRpab6zaRqONbW1mjVqpXIxBcAa77o5uamIcnUR1JSErZv344DBw4gNjaW/VDo6OigadOmcHV1RYsWLdC2bVu4u7tL5a5QWFiItWvXIitLEJDWxsYGw4YNq3ZByoS0atVKzEeYIiA/Px/nzp2Dp6enpkVRGQUFBZg8eTLCwsJEOtJRo0bBy8sLenp68PHxwY4dOzQnpJLo0aMHWrZsKaJQyMvLw969e6lCoRISEhKqldXdlClTsH//fpEyhmHQpk0bTJo0CePGjVOZy86dO3eQk5Mjk0IB+H+LvPv373+lEsFkpLq57tWpUwdr1qyBjo4OQkJCUFpaCkAQOHnUqFFgGAZ2dnZo1KiRVIPvgoICBAcHY/Xq1VqvVM7KykJERAQblFJfXx8DBw6s0spGmchrxSHMcJKYmFhMM5zIx/z58ytUniobIyMjxMbGom3btiLlw4YNg6+vL2JjY9WmcHZ2dsaWLVswefJkkT69X79+AARjdWnSlj569AgDBgzAmzdvVCWqUtHV1YWpqSlMTU0RFCSbQY/w3PHjx4MQgnXr1uH58+coKirC48ePcenSJbi4uKhIcumptQoFDocDY2Nj5ObmaloUERwcHODs7CymUABqh29bSUkJ1q9fj2XLlont4/P5eP/+PcLCwgAIXF38/PzYXOCVcePGDfY8AAgICNB6/9LK+PDhA3g8XrW0rlA1BQUFuHbtWo1VKHz69AmTJk3Czp07RZQJo0ePxrp161CnTh0AgLGxsaZEVAu5ubnIysrSikjtqiQ3N7fC1Zc6depUmN71/fv35X38tZbY2Fhcv35dpI/r0KEDa0WmasWvs7MzzMzMVHoNdfPhwwe8fPkSLVq00LQolWJgYICNGzfi6NGjIub3+fn5rIWCp6cntm7dWuVkOzo6GsuXL1epvMri7du3OH78OPu7Xr16GD16tNquz+PxxDJqzJgxQ6p00cIMJ2UQy3CizuwmmkDe7CZCSkpKWAWaqvn48aOYMgEQWC4C6p9b9O/fH9HR0QgODhbbd+jQIQwfPrzCcx88eIAXL14gODi42igTlMmECRPw3XffwcnJCYWFhbh58yaCgoJw7tw5jfdhtXY2YmZmhrFjx0p8oLWVmh6kqKSkBIsWLcJff/0FQGBS6ujoCC6XC0IIHj58iIKCAvbjl5eXh7Vr12LPnj1YvXo1nJycKhw8vX79mh2U29jYYOTIkepplIqYPHkyunfvjm+++UbTomglmZmZKC4ulmpwVF3Izc3FqVOn8Ndff+Hhw4cigwChMqFsDvdRo0YhNDQUnz59Ap/PR0hICNatW6cJ0VXChQsXsHv3bkyePFnToiidkpIS5OXlISwsDOvWrcPHjx8lHtenTx9MmjSpWrs4JScnw8vLSySbA8MwcHZ2FouPoiq0ORWXvLx79w63b9/WeoUCILjf4eHhOHz4MLZu3Sq2/9ixY8jKyoKRkREmTpzIZkBgGAZ8Ph+EEPB4PInxVhYvXow//vhDHc1QCEdHR7VZmGRnZ2PChAlicXWUKYM6s5vIS9k+VNbxtbzZTarii5pWPsQAACAASURBVC++UEW1WoeTkxPs7OzEglKOGDEC6enpFd6PI0eO4Pr165XWbW5ujuHDhyMuLk4Y66NG0aZNG/z2229YunQpeDwe7t+/j9jYWI1bLddahUJ15NSpU+jVq5dCqzUlJSVshOthw4Zp1ere3r17sWTJEvB4PDRr1gyTJk3C1KlToaurC0IIHj9+jIKCAixatAgXL15EcXExCCHIyMjAiBEjYGVlhUOHDonloM3OzhaZSPn7+2t18JayuLi44PTp00hPT9e0KFoLh8OBrq4uSkpK2LJdu3Zh1qxZqG6rIi9fvsSHDx9gaWkpknHmyZMnWLhwIRt0TUiTJk3g6emJ5cuXiygTAKBdu3YYPHgwa5lTG7X51Ym0tDQcP34chBCcO3cOt2/fRmpqaqW+ofv27UNkZCRmzZoFe3t7dO3aVeKEwMHBQWvN4b29vcVSQw4ePBgrVqzQkEQ1hzdv3igcPV1duLq6okePHuBwOAgJCRF77qOiogAAFy9ehJ6eHvr374/AwEAMHDiQTYsrKT2uJGUbIQQvXrxQW2pNSaxfv17k96xZs9T2jj5//hz79u0TKfv++++FwfaqRNsynMjC/fv3kZmZienTp4ukm962bRu+//57tciQnp6O5GTx8ANcLldsgl1T6dOnDxiGQWBgIGJjRT1lymaWkBVjY2MsX74co0ePxt69eyUqFJYsWYLVq1dL5Vqhjejp6WH69OnYvHkzMjIyAADh4eEaVyiAEKLxzcHBgaibzMxMYmVlRQCw26VLl5RWP4D7RM72BwQEiMgl3LhcLsnNzZVJDj6fT0pLS8m///5L3N3diZubG2EYhgAgzs7OxN3dnbi7u5PU1FRSWlqqeMMJIZ/bI1Pb09PTib29PQFAmjRpQqKjoyusPzk5mcTFxZFhw4YRAwMDkb+RjY0NuXPnjsjxjx49IhwOh607KSlJKe2sCGnvvbTPvYODg8Tn4dGjR6ppgAIo8tzLC4/HI7/88ovI34ZhGPLs2TOl1C8Litz7wsJC4ubmRgAQBwcHcu/ePfL06VNy4MABYmpqKnb/raysyO3btyuVJzg4mD2+adOmJD4+Xskt/n9Uee99fHzY71bZ7c8//yQlJSVKbYe8KHLv09LSiIuLi8T3vPwm6e8AgJiamhIfHx+SnZ1NCCEkKiqK3bdz506taLuk9nft2pVtF8MwxMvLi3z48EFlskZERJAGDRqI/O1Wrlwpd33y9HeqIjw8XKw/LCoqUkrdCQkJZPXq1eTVq1ci5cru70pLS8n8+fNJt27dpHofKtq++uorEhQURF6+fEnc3d3F9nfo0EHhv4kiz72NjY2IPFFRUQrLIw18Pp+0bdtW5Nq6urpk2bJlUtdRUlJCvvzySwLgMQA9ADEA2hEtGuPn5+eLbG/evCFjx44lRkZGEp+XM2fOyFS/Ivd+8+bNFY7xVUFubm6l70piYqLMdSrrvY+MjCRfffWVQu+6cNPX1yeHDx9m6/7vv//IkCFDJB4r61xKnrZL0355KS4uJqNHj2bb4+joSAoKClRyrbJU1t9p55KFGigtLZWozdYGmjVrpjRT7UOHDqFXr14YP3483r17h6SkJBBCAACRkZE4ceIETpw4gRYtWsDMzAxr167FgwcP8ODBA7WmI/n1118RExMDGxsbLF++HPb29hUe26RJE7Rq1QqhoaGIiIgQOfbVq1fw9vYWSTl27do1drXD1NQURkZGqmuICggNDa12MqsTDodTI7KfxMXF4dy5cwAEvsBdunRB+/btMWTIELH8xcOHD8eJEyfErHEqIzk5WStTVEnD3r17xSwwAGDu3LnVKvCgJNLT0zF06FBcunRJ4n6GYdCjRw/069cP/fr1w+HDhzF9+nRYWFiIHJednY39+/fDyckJcXFx2LBhgzrEV4j169fj3r178PDwQFJSEpKSkrBjxw6VWc4VFBTgzJkzyMzMZMucnJzEUvdRBGOk5ORk7N27F2PGjIGjoyOmTZuGQYMGsdljVIGOjg6Cg4MRHh6OU6dOwdDQUOY6dHV1YWdnh6VLl6J58+YYOnSo1lroqJtjx46JWasFBgbi999/l7oOYYYTALYQZDg5SDSY4aQ858+fh42NDSwsLNitXbt2CA0NrdB9rDazc+dONp6CuunRowf27t2rsKvH2bNncfr0aZHYaDY2NnBwcFBURK1EV1dXxKIoOjqateLSFNTlQQuZNWsWQkNDkZiYKFLO5/Nx4MABjB07VuycuLg4kQA/Hh4eaN26Nby9veHt7c2Wh4aGwt/fX8ycUPiRnTp1KlsWEBCA9evXqyXw3+LFi/HTTz/h66+/hp2dnVTnGBgYwNXVFcbGxpg9ezYiIyMBCOIlTJw4Edu3b0fjxo1x8eJFVokyaNAgiRMTbaZDhw4S74GLiwv+++8/qmyAIGjXtm3b2PSJqubjx48wNDRU6rthYWGBrl27sv6BkkzdW7RogQkTJqBnz55yxc8QRhSvbjAMg6CgIJX5QmdlZWH27Nns77Zt26otNsORI0dw8eJFsXIul4sZM2bA3d0dHTt2FFGaDRw4EOPGjUNoaCj2798vYn4cFxcHHx8fsf5DG8nPz0dJSQkMDQ3V4ob27Nkz/P333yJl+vr61a5PqAgulwsdHR25UqiVXUBITU3F2rVrERYWhqKiIhF3spiYGFy5cgV9+vRRiswV8eWXX+LLL7/E+fPnMWvWLABARkaGmHuMEDs7OzaF8Lp169CuXTt236BBg3Dq1Cns2rVLpTJLS2hoqEgASnVx/fp1jBkzRuRem5mZYciQITK7xXxOIfiEEOKoXCmlp7i4WOTvePz4cZw8eRKPHz+Wyk1UV1cXVlZWWLFiBTp16qRKUbWaBQsWIDAwUGMxpzp37owLFy6ga9euYosn0nDz5k18++23KpCs+kAI0XjqTKpQ+EydOnXY6OjagKQ4CXw+H1u3bsXo0aPB4XBw69YtdoCdlpaGp0//X0HcrFkzfPnllwCAOXPmIDo6GoBAiyXtQ7dlyxY8e/YMOjo6WLdundQTfXmwsbFh5ZUVJycnLF26FK6urmzWjkuXLmHRokX4+eefcf78eQCClWx5r6GNZGZmIjk5WW6FQm5uLjuwKL/aWd3o1KkT9PX1WYUCIQQ3btxQegyFixcv4sWLF1i+fDk8PT3h6+uLDh06KKXuhg0b4uDBg/Dx8cHt27fZFYO6detCR0cH3t7e+PXXX6X2cwUEvnYMw7AKtb/++gs9e/ZUirzqZuDAgSpRKHz69Amurq4oGzTMxcUF/v7+Kh1gZWZm4uTJk5g5cyZbZmBgADs7OxgZGWHmzJno2bNnhTFzbG1tsXz5cgwdOhSTJ0/Gmzdv2JXHmJgYkTo1Hf25Inx9fbFnzx61XIsQgunTp4uUmZiY1KhYDR4eHrC1ta1w0l0WHo8HQJAC8P79+1i/fj3bf/J4vAqtmXR1deHs7IwPHz5g8ODBAGDHMMwFAN6EkCwlNYWlW7duuHbtGgBBNpAjR44gKyuLzVAQFBSEOnXqYNiwYWjevLnEOjgcjlZlRBo7diwWL14skjZPHeTn54tY5pqZmWHPnj1K68PUxadPn7Bp0ya8fv0amzZtYsuFptfSMn36dCxevJhar0AQxPnIkSMa+1u0adMGT58+RWlpKaytraW+j9HR0ejYsaOKpaseVGuFAsMwrwB8BFAKgEcIcWQYxgzAAQA2AF5BRZ2MsunatatM5sOq5tSpUxKjM9+7dw8BAQH4+eef4enpWeGK7KhRo+Dn5wdAoMWV50EjhODKlSsAgB9++AFRUVH4+uuvZa5HGnbt2gVfX1+5z+/SpQtCQ0Pxyy+/ICcnB4QQhIaG4uDBgygoKAAgUBq5uroqS2StoEOHDjKtypeWlrIr4Fu3bmUH8xcuXKhw4mJsbFwtP9j79+9n3wFFSExMxMGDB3HgwAG8evWK1aCvXr0ax44dQ3R0tNImbE2bNsU///yD06dP4+bNm9DR0cG4cePQoEED1K9fX+aArGPGjMHKlStZE29Vmiqrmrp166J169Zik6U7d+4o9F0aPnw4ykcgv3TpEgICArB69WqYmJjIXXdl/PPPP2yaOBsbGwQEBMDBwQEuLi4yrRZ26NAB165dw9q1a0UszIQ0b94cbm5uSpNbmVhYWKB///54/VolAdPFKO8eExISUqNWJg0MDEQmzp8+fWLHEmXdAN++fYuNGzcCECi2srKqHqJZWFjg22+/xa+//go9PT0sW7YMLi4uuHjx4hMAlwAEAZhZRTUK0bZtW7Rt2xbFxcUYPHgwGIaBvb29XMoCPp+vVZmAZs6ciRs3boDD4Si97sLCQsyePRunT58WKTc3N0ffvn2Vfr3KyM7OBiGkUremkpISkUxGN2/eREhICLufx+Ph5cuXVU4669SpI9Y3tG3bFr/99hsAgRUMVSYIEAYE1iTCha2KrBRWrVqFBQsWIDw8HAMGDAAAGBkZVYugs+pg3bp16Nevn8b+HspQ2ToTQjLK/A4CcIkQsoxhmCCooZORh6tXr4pEeGUYRqseyoo6OeFEOTQ0tNLzS0pKRMwUAcGqW48ePQAIFBa3b9+WWp4PHz5g4MCB2L17t0oUL7a2tgrXMWjQIDx+/BiLFy9mzX/kMZ+qqezduxfJycmYMWOG2L7evXtXeJ6NjQ22b98OZ2dnVYqnEAYGBvDx8cHmzZvZsuLiYrkGjLm5uSguLkZ0dDT27NmDW7duISEhQeKxKSkpYu+ZohgZGWHw4MHC1T+FqFOnjsiAKTk5GcnJySIZJKoLVlZWmDp1KgICAkTKp0yZwlptyUNFg6iwsDCMGzcOTk5OctVbFfXq1WOzk4SFhbHfZnlxdXXF6NGj2aweZdGmvq08CxYswN27d1V6jejoaKxdu1Zkdfb7779X2b3VFKmpqSLm7GlpafD29oa5uTlSUlJkqsvAwABdunQBwzB4+PAhjh8/LqJ8iYiIQFRUlNAdYSeAKKhprKenp6fwOOTx48cIDg7GkiVLlCSVYjx58gRRUVFwcXFRet0RERGsRUdZ1K1MAIDLly9j69atsLGxgY2NDaZMmSKyPzY2FmvXrsXu3btlWghjGEYkan+3bt3g7++PQYMGKU326kq9evVw7do1dO/eXdOiSIWxsbHE8nnz5mHOnDngcDha3adpCk24UZVFFTZgAwD0+Px/tXYyslBeoVATqVevHgYPHgx/f38AglQ/Qg3gyJEjRQYYPj4+SEpKqnQFMyEhAd7e3jh37pzSTcmVMbBjGAbTpk3D1atXcfXqVbH9ffv21VrTX3kpLi7G/PnzsWDBgkqP+/vvvzFr1iy5FCyvXr3ChAkTsGnTJq1VKnC5XDg6irpyXr9+HWfPnoW7u7tUdfD5fOzYsQPr1q3DixcvwOPxNBaoSFXExcXh6dOn1VKhUBGFhYWYPHmyiOmrshg/fjzu3r2rUKreinB3d0eXLl3w4sULpQQVbdOmDaZNm4bTp0+LDCy0PT6Anp6eStNd8fl8REVFifjPOzk54cSJEyqzPlEHBQUFePHiBY4ePcoqZN6/fy9mRs/j8aRWJhgbG8PGxgZjxoxBx44d0b17dzAMgy+//BL+/v5gGAb+/v4YN24cUlNTy35HUgA0Ll8fwzDjAIwDBG6Y2kZpaanGrj1//nzWQgkQuCOMHDkSjo6OCA0NZWN7KDpxyszMxF9//SVWPmrUKCxevFihumWFx+Nh5syZrIKey+Vi+fLlIseUlJTIFDiRw+Ggf//+sLCwwMKFC1kFuoGBgVa5MWsShmFgZWVV5XGNGzdm0xBqIzo6OtSipBLUEYeoMhRVKBAA5z+nsdpCCAkB0JgQIkywKrGTAbS/o5EGGxsbGBkZsf559+/fV5tfYUUYGhqiZ8+eYBgG4eHhMDExkfgCWllZiXxg4uPjcebMGTZg1cuXLyX6Yb558wa9e/cun4NYa6hfvz4CAwNx/fp1Ee02h8PB7Nmzq20HExERITFHcmlpKfbv3y+mUHB1dWVdPQCB1l8Ra424uDiMHTsWFhYWOHr0KBo2bCh3XeqCz+dXOmCMi4tjB9qhoaGIjo7Gy5cvpbI4cHJygoGBAfT19bXGZFZaXr58iV69esl1rjZ+8/h8vkj8GGXy5MkTxMbGyhUAsyp0dXVx7tw58Pl8uSLZS6Jdu3YYNGgQa84OCJQitXUQdvv2bZw4cUJs0rJhwwatUibw+XwkJiZi5cqVyM/Ph62tLesCmJ2djTVr1rAxD4RkZGTg7Nmzcpkp6+npoUmTJjA2NkZgYCCrMLO0tESPHj3EJrHXr1/HF198gbS0NPTu3VtsQYEQIkxnWr48BEAIADg6OmrUnnrhwoViyrbt27dj4MCBGnF3dXZ2Rtu2bREbG8uWvX//Hu/fv8eZM2fA4XCwfv16dO/eXSELzpEjR4q5dNWvXx8DBgxQu7KRw+FgzJgxbADcymJ1SDq3cWPR6YSNjQ3mzZtXaawZivQIY6hQqieTJ0/WqOWGogqFboSQ9wzDNAJwgWEYkRloRZ3M531a09EoQmRkJMzNzdnfmvArFOLj48OavcqKUMvbv39/AAITUR8fH4mm3llZWdi9ezeGDx+usMyqoE+fPvj2229x48YNtkzbgjLJSpcuXcDhcCROkPl8Pvh8PjtpKC0txc2bNyuM6M/hcOSKCJuYmIjExES1ZVJQBllZWcjLywOXy2X9hO/evYs9e/bg3r17MgXFatiwIRo0aID58+ejf//+Wr/yCwhcHqZNm4b58+ez1hZr1qyBt7e33Kn5tOmbp2p4PB78/PzEBuTKQtkKToZh8MMPP4goFDZu3AhfX99ap1Tg8/m4cOECli5dKrYvOzu7ypW4evXqqSwdbUlJCWJjY7Fnzx58//33OH/+PMLCwlh3BR0dHSxatAiAwCVHUUspfX19Vmk1evRodOzYEd7e3mAYRiqFqDClW6NGjeDp6Ym7d++icePGSE4WrB0xDNMUQJpCQqoYS0tLsUlnRkaGxjLfWFtbY//+/XB3dxfrh4T3e+zYsbC3t0ebNm3g7u6OgQMHyrRKe+nSJTx48ECkjMPhYOPGjfDw8FBKO2SBYRipg3s7ODiIuIGZm5tj+vTpIhMmhmFUEnNC1RgaGqJOnTrVaixV00hISKiWscEqgsvlirj8aARhVFRFNwDBAGYAeA6g6eeypgCeV3Wug4MDUSdFRUVk2LBhBAILCwKA7N27V+Z6rK2tSXp6ukiZra0tSUpKIgDuK9L+kpISsnjxYhEZK9rMzMyIt7c3ycrKkrkNlfHixQvSuHFjidc0NjYmu3btIqWlpWLnfW6PVM+Nqu790aNHCYfDYeV1cnIi+fn5KrlWeQDcJypoe3x8fIXPQN++fUlubi758OEDad26dYXH1atXj7x584asW7dOqmdL0sblcgmPx1Oo7eXbz+fzJT5L0sLn88nWrVslytqnTx/i6+tL9PX1ib6+PuFyuVK3VUdHh7i5uZGgoCDy5s0bUlRUVKkcqrr3ilBYWEisra1F2nX06FG56lLlN08a/v77b4n36fvvv5erPkIIuXfvHjExManwGTAxMSGhoaFV1qMt9/7evXukQYMGrPwWFhbk+fPnKr2mvO+9KtmyZYtwQUNsYxiG6OjoVLr5+fmRkydPVnkdefq7kydPEiMjIwKAGBoayv0tlmarV68e2bdvH8nMzCSZmZkVfrsrIi8vj+Tm5rL///bbb8mZM2fIjBkzyNKlS4XvfRCA5dK0XZNYWlqK/X0uXbokd33KeO4TEhJIixYtqryPBgYGxNTUlAwZMoRcvXqV8Pn8KuXbtGmT2Fhx3759Up2rzPaXbXtubi65evUq6dy5M3FwcBDbRo8eTR4/fkySkpKUIqMiWFtbEzs7O/LNN98I33OSmZlJevXqRQAUArgAoD6R497PmjVL4nhFVbx//5589dVXlT5jurq6UtenLf1dVezdu5dYWFhIbG9JSYlcdWpLf7dixQq2LU5OTnK3RxYq6+/kXrJlGKYuAB1CyMfP/3cFsBDACQAjASz7/G+EvNdQFe/fv8ehQ4fY33Xr1pUrSjjDMHB1dZXZr/DzuZW6fHC5XHz33Xdo1qwZmwqsIlSVfaFFixa4fPkyfv75ZxGzPEBgGjVixAj06tVL4347knBzc0OrVq1YuR89eoTExESVpr5UNZWtSpw9exYTJ07E27dvK00ZlpeXxz5vNjY2sLa2BiCIKVK3bl1YWlpWmXKstLQUt27dUprfc3FxMebMmYOjR48iICBArnt08OBB/PPPP2LlPB4P586dk6kuR0dHmJubo3PnzvD19UXDhg0rDBJUHZBkAidNVPeK6lLVN08ajI2NUa9ePZHAc4ri6OiIL7/8Eo8ePZK4PycnR2UuFarAwcEBVlZWbGaPpKQk3L59WymBb6sL4eHhmD59OgghEvcLB0CVsW3bNhw5cgRbtmzBoEGDlGrh0aRJE9YyoKxrWkXY29ujVatWUtd/4cIF1gLjt99+YzMiyENqaio8PT0BCL6nQ4cORd++fdGpUyd4e3sDgB2AbADecl1Ajezbt08sMN2YMWPw/PnzClf3VO3m9dVXX+HQoUM4c+YM5syZU6HlYGFhIQoLC7Fv3z4cPXoUv/zyC3R1dbF06VKJsufk5IiMcwFBemUfHx95xFQaRkZG6N69O+7cuaNROaRFnRZ5fD4fkZGRKolVZWFhgT179kgdr6yy5/6zRU1LhmHqy/vcq4shQ4bgwYMHEuOIVGc+fvwoEoTcyclJ81bYFWkaqtoANAcQ83l7CuCPz+UNIHjJXgC4CMCsqrrUrcHatGkT0dHRYTU7gwYNkktj++7dO0IIIampqaR9+/bkypUrxMTEhBDy/xosAFlEgfYPGTKkSs31v//+K7PssiBJkyrcAgMDxY7XBgsFHo9HXF1dRVaklixZopJrlQcq0tymp6eT/v37K7xi1aBBAzJx4kRy7tw5tu4pU6aQ//3vf+TOnTtk4sSJZOLEiewKmqRNR0eH2NjYKEVrn5ycTAwMDFS6SlfVZm5uTmxsbMiOHTtIWlqa1t17RSgtLSXjx48Xaa+dnZ1cFiHq+OZVhY+Pj9j9U8RCgRBC3rx5U+nzERgYWOlq1ecVxhxZnntVMnjwYBH5u3btSoqLi1V2PWmfe6Li9gstlZT5PTE0NCTNmzcnt27dIpmZmWLXlLe/O336NOndu3el24gRI0h0dDRJTU2V6e/w3XffsfKvXbtWvj+mlGjjN68iUlNTiZOTk8j91dfXJ58+farwHElWWb/99lt564z/EQXbX1paSt68eUMCAgJIhw4diJmZmVTPp4eHB3n48CGJj48XaWePHj1EjmMYhvj7+8v3h6uA6nTv5UGVFnlHjhwhzZs3F7ufpqamKmvP7du3K32WylooVPbcE0IIgHfKeO7VwYwZMyS2tzpbKNy/f58YGxsTAKRx48YkNjZWJdcpT2X9nVR/EFVv6n7gyrs7eHt7K1zn/PnzyYoVK5Ru/jt58mQR5UfZzcDAgCxfvlylA0VCCMnOziZ2dnYSZeByuWTu3Lkix2uDQoEQQkJCQkRk9fPzU9m1yqLKTvbUqVMKDY4NDAzIoUOHpLrWrl27RNxGym/dunVjj1VkgKVuhQLDMITL5RJPT0+yYMECsmDBAvLvv/8qxVxMWwdYe/fuFfmOmJubk+joaIXqVNU3ryquXr1K6tevL3JPGzduTI4dOyZ3nVUpFNq2bUuaNm1abQZY5b8TzZs3r9JdRxG0YYBFiOCbpaenp7Jvx/79+8WuqS39XVm8vb1ZmW1tbUlYWJjKrqWt37yK2Lx5s8g9lUehoI5vXkREBAkODiZdu3aV6tm0tLQk8+fPJ/PnzycuLi5i+wcPHkwKCwtlkqEqqtu9lxUbGxvSsWNHYm9vT7Zs2UIIISIKdAAMgGwioc0QWOPdB3C/WbNmEuuXtGBoYGBA1qxZo5L2vH37VqJCXrhVpVAQPvef2x+j6r5eWVSkUJgzZ06F56jS3UVRioqKyKhRo9h29O/fX+nXqIjK+rvaFaUJAlPfly9fipTJYwqYn5/PprbJz8/H+fPnYWdnB3d3d+zcuVN42Ego6PKxdu1aiRH1uVwuFi9ejN9++03l0W1NTEwwcOBAiX8nWaL0apoXL14o1VS6urFnzx5ERkZKnZd5+PDhOHHihFTHRkREYOTIkcKfOwGoP+JTFXz33Xfw8PDA4sWLkZiYiPDwcMybNw/z5s2DnZ2d5s3FVMiAAQPQsmVL9ndGRga2bNkiU9o0dX3zqqJ79+6oW7euSFlqamqFLgvK4L///pNoilzuuc+EFj73tYGEhAQ4Ojpi0qRJEgMY6uvro1GjRuy2f/9+REdHi2wnT55k91eUAWLOnDkypbTTFJMmTWL/Hx8fLzbmoUiP0M3LwcEBISEhACCTmxfDMPcZhrmfnp4u03Xd3d0xf/58HD58GE5OTjA1Na30+Hfv3mHBggVYsGABLl26JLbfw8ND80HbqhknT57EqFGjEBgYiI0bN4qlIyeECCd1YhBCQgghjoQQx4qyYo0fP17MlamwsBDr169XTgPKYWlpiZ9//lmqY6V47kuggudenSxZsqTS/ZGRkXj06BEblFno7gKgrLuL2snIyMDBgwfZ33FxcXj9+rUmRBGh5o6gK+Dt27civlscDkek85UWdfkVMgyD+fPnY8KECWzZoEGDMGTIEPb66mDOnDkoKirC//73P7VdU9k8fPgQ2dnZ1SI6f0UwDAMdHR2ZMzQAAj/c8um+qqJz584V7nv48CFCQkIU9qM3MzNDREQERo0axUYNr4qGDRuCz+eLZSooKioSS2nasGFDrF+/Hr1794aZmZlU9dc0DA0N0aVLFzx//pwtO3fuHHg8ntRRsrXdlzouLg4ZGRki/q7KIiAgAMeOHasqfkQJAGtJ59eENMlCCgsLwePxtOY7+XogvgAAIABJREFU+ujRI3h4eFQ4oNLV1cWyZcswZcoUtkySctze3p5NI/vw4UMMHjxYLMtRYmIizp8/Dy8vLyW2QPno6upCV1dXqhS4tZ3S0lL8888/FU605E2Z+XmfwtnMmjZtilu3buH8+fM4ffo0AME7uHXrVpnGAWfOnIGBgYFGsjtUN/h8PmJjYxEUFIRTp07B2dkZ3bp1U3p2k+7du6Nx48Yi/TIgiHszZcoUrF27VqF2KEJVz/1nVPbcayMRERGIiorCrFmzAMHCWRQ0kNEqJydHaAUDQBA/QRgPTZPUOoVCeZo0aQIbGxuZz2vevDliYmLEyhs0aIBLly6BYZgnhBD5kr2XY9y4cWAYBhs2bEDbtm2xY8cOpacbqwpdXV107dpV4r4PHz7g48ePMDIyUqtMVeHk5IT69euzAegKCgoQFRWltekupcHNzQ2LFi1i8zhLi5mZmdItWdq1a4eNGzcqPMDS09ND7969sWDBAqmDBfbs2RM8Hk9M4ZGUlAQPDw9ER0ezZePGjWNTpNVWGIbBpEmTcPLkSfZvXLZDkgZ1fvPk4cCBA/D395croFVlAZssLS3h6emJ6dOn0wEWgMePH+P58+cYMWKEpkXBixcvMGzYMInKBAsLC6xatQq6urrw8PCQ6v0XHmNvb4+9e/fixx9/FEkvyefzMWrUKHh5ebEByp48eQJFA/Mpm06dOsHZ2Rnnz58HIEiVm5eXpzVKIG2Cx+Nhw4YNGDRokMRnRFtSZrq6usLV1ZWV+YsvvsCaNWsACCzGioqKKj0/PDwcJ0+exLp16xASEgIzMzOYmprWunSylVFSUoK//voL79+/x5YtW8Dj8QAIVqpv3LiBo0ePqsUir6CgAFu3bgWHw8GqVauUXb1UVPbcf1ai60LLU8UqgqYDUFfG+vXr2WC+DMNg2rRpSr+GPFQ7hUJBQQHu3LmDtWvXigyI7ezsMG7cuCpv3q5du0TOc3d3h6WlpcrkVQYcDgcBAQEICAjQtCgS2bdvH3x8fODu7q5pUURo27YtvLy8EBoaCkDQCdcE009ra2s0atQIaWlVf8vNzc3Rrl07LFq0CF999ZVS5dDT04Obm5tSBlgMw+CXX35RWCZLS0vY2dmJKBQ2btyICRMmwMLCQuH6qzOdOnWCra0ta6GVkpKCQ4cOVUsFmyymu6WlpRLN4IUEBQVh06ZNFe5v1qyZSGT42jrAEpKXl4fly5dj0KBBMDQ01Jgc8fHxcHFxwbt370TKTUxM0KZNG+zYsUOmzAjl6dSpEyIiIvDdd9+JlHfo0AHA/5u/ZmVlITo6WqnR3hWFw+GIWB7duHEDHz9+pAoFCPpEExMT5OTksGU3b97E6tWrxQbm+fn54PP5MDIyYt285s2bp3Y3r/JwuVzMmTNHuFKKsLAw/PrrrygsLKz0vKysLERGRqJ169ZgGAZLlixBr1690LFjR3WIrXV8/PgRV65cASD428yaNQspKSkSXQENDAzQp08fODk5KdUi78SJEygpKUHLli2RnZ3NlhcUFGDjxo3YtWsXli5dirFjxypyGZmo6rkPCgoCBAH496pNKDWjbsuk+Ph4RESIfkYcHR3FFkjS09Px8OFD9ne7du3KKjk0SrVSTS5YsACDBw+Gi4sLjh8/joiICHZbvHgxvvvuOwQHB1eYhunFixc4cOAA+5thGK1bVacoDw6Ho/IXrbCwELt27RKm0VELQ4cOFfpxVUmPHj0QFRWltBSPZSktLdWYH70sCDtHCkQGJcXFxVizZg0+ffqkQYnko7wvq5B///1X5F7fuXMHS5YsgbGxcYXb+vXr2ZWo8ujq6uLcuXPSxo9oAC167svyedKrUB02Njb4+uuv4enpiSdPnuDkyZP48OEDevfuDXxOn8cwTP2q6lEGT548gZeXl5gyYdiwYdi0aRNu3bqlkDJBSIsWLdCrl6jRzevXr3Ht2jWtjxtTdgD86dMnbNmyRYPSaA+DBg1Cz549Rcp4PJ7EyXhqaiq6deuGb775Bp07d0a/fv3Qt29fBAUF4cKFC4BgUtkLgjTpaoVhGHC5XHC5XPj5+VUY90MSpaWl4PF4+P333+Hj44MxY8agoKCgxveTBQUFyMzMxJs3bzB06FAMHDgQP/30E3766Sf4+vri/fv3EpUJ9evXx+zZs8HhcFiLPABPCCG9CCEKBREzMTGBubm5RAvS4uJiZGRkICAgAMePH1fkMgAEz7k097iq5/5zPCZjaOC5VyZ8Ph9WVlYS96nLMunZs2fYsWMHvLy88Pvvv4tsP//8MzZs2CCi/Jw1axZu374NQJBKfsyYMWjSpImiYiiFaqFQKCgowIoVK7Bp0yacPHmywhfi3bt3+PPPPxEYGIicnByxVamUlBQR/2pDQ0O54idQKELOnj0Lf39/hIeHq/W6fn5+lVrW6Ovr4++//8a4ceMUuk69evUwc6bkRbeYmBitGmAJ8fDwUHmg0upK+/btRVYp//33X7Fc5dWBiuI+BAYGsgqSw4cPo1evXpg3bx54PF6FW1WuH7q6utV+gJWVlYW///5bYZ/6yMhI1s2ouLhYY0Gq/vvvPxFrM3Nzc4wePRqbNm3C0KFDlXadRo0a4YcffhApe/v2LW7duqWWwHyKMGrUKNaEnxAilUVbbUFS/yDpWyB084qJicHTp0/xxx9/AIDSJ5WqolmzZujdu3el7j7x8fHYsWMHGjVqhBUrVtQopUJGRgY2b97MbkOHDoWlpSVsbW2xb98+XLx4sdLzPT09MWnSJDx//hy//fabSt0m09LSJAZgBwQKIE9PT1y+fFlqt9DyPH78GLq6ulIFZazquX/x4gUAxGvrc1+eyu6bpKDy6gpAzePxsHHjRowePRpPnjwR25+ZmYnJkyezlkjZ2dmIi4tj97ds2RLDhg1TRASlovUuDzdv3sTmzZuxd+9esQ+djo4OOByOyCCJz+cjNDQUR48eRZ8+fbB582Z2AH3r1i2R8zt27CiTVpdS/bl27RoKCwthYGCgUD3W1tYghCApKQmlpaWwtbVlfWrxebUOKvSpdXFxQYMGDcRW6IScPn1abBVGHoqKinD27FmJ++zt7cU6Gk370QNAt27dRAKS8fl83L17V+tdm9RB586d0aVLFzYKeHFxMR48eIDhw4dLHZxRG2AYBgzDiE0A+Hw++vfvDx0dHTx9+lRpWV2qih/xWSatHmBFRERg0qRJ6NSpk1Lq09HR0ViQqp9++gnr16/HjRs3AADBwcFqDXaZmJgo8lvVgfnkQV9fH8bGxuzq1oULF8q659RqduzYgfPnz4uYmC9atAg//fRTtTT/L/8dZBgGQUFB+PHHH9GlSxeEh4ejqKgIv/32GwoKCsSOJ4QgLy8Pc+fOhZGREcaPH8/Wo+0QQhAfHy9mWXD58mVs2bJF4kStMlq0aAFTU1OsXLkS9vb2anUTSk5Ohq2trdj3RYjQMjUmJgZ169aV2o01JiaGddWqjSxfvhzv3r3Dvn37pDpeHQGoP3z4AC8vL1y/fr3KYw8ePIiYmBjk5eXh8ePHbLmbmxsaNWokrwhKR6sVClFRUfDx8UFqaioAoE+fPiKpc3788Ud06tQJq1atQmxsLG7evAlAoM1LT0/H3r17QQjB5MmTYWVlhe3bt7Pn6uvrIyAgAMbGxuptFEWj3LhxA+Hh4fjll1/k7iwJISguLkZaWhr4fD66dOmCbt26sat1Fy9eLLtap7LBtdBPtrS0lM3+AAg60rI+34pQUlIicSKlzZiamsLf3x+rV68GIPge7N69GwMHDtSwZNrBgAEDRNKKbdmyBfb29mXNt7Wehg0bIjw8XGJgQKFPLEWUnJwcXLx4UW6FgjBIVUJCAho2bIgff/wREyZM0FiQKj8/P/j5+SmlLll59+6dRgLzyUKrVq0wePBgNuXbq1evkJ+fr2GptAMDAwOMHj2a7SMAQV9XXVfng4KCILR+adGiBa5du4YGDRqwlhjC98TLywt8Ph9+fn6IiYlBUlKSSD0lJSWYPn06/vzzT7Rv3x7h4eFo3FjiK60SKktjHBoaiv/++0+svLi4GP/X3pnHRVW9f/xzZgZkk01TEVJUxCUyvmqKSYjyJaHE5WupJCRqueQWlvzUVDQr10wLsywVXBM1AQl3LTXLLU3TXAgVN1BU0FSEmTm/P2bmxjAzMNudewfO+/W6L5g79577nLn7c57n8yxbtqxa/YiqkEgkGDJkCFq3bo133nlHp3qUrZBKpfjjjz/QvXt3rlShPl544QU4Ojpy93FPT08EBQXpLHfkyBE8ffpUJ8qqNuLm5gaZTGYwvbEithCgvn79Oo4cOQK5XA4PDw8u9aJRo0YYP348cnJysHXrVhQWFuLu3bvc+60GiUQiKmcCIGKHwv79+xEbG4vCwkI8++yzaNOmDb766is0b95cZ9lvv/0WxcXFGDJkCLKysrj5SqUS69atw/bt2+Hl5aUVIvnCCy+wF4xaQOWLR2lpKd577z0UFRUhLCwMXbt2NcmxcObMGXzxxRcoKiqCUqlEp06dkJ6eDj8/P5uP1h07dgylpaUIDAzEK6+8wolP1nbVZplMZjAvjqHKIf76669x7tw5AKoolF9++QXx8fFGHTsPHz6EQqHgbtBCQAhBUFAQgoKCTB6Bqkx0dDSaN2+OixcvatJ27J5ly5bpnb906VLcv38fs2fPNrkm/aFDh3Dw4EG8/fbbePjwIU6dOqX1vRhH6a1B5dRJNzc3jBw5EgcOHBCtboyGiRMnYtWqVax8ZCUIIXjrrbe0HAoAMHbsWPzyyy92dQ+9cOECdu/ezTlDqsqp1jgHcnJy8PPPP2Pbtm347LPPtJYpLS1FQUEBCgoKEBcXh969e2PcuHG82X/u3Dlcv34dhYWFXMSjPm7fvl1tJQtjkclk6NatGzcos3DhQjRv3tziyFVr4ObmhuzsbMTFxVWZklFWVsYNHLVr104jlKjFqFGj8ODBA95stSeWL1+O3Nxc7N+/X2hTUFhYiNjYWDx58gSenp5IS0tDTEwM9z0hBDExMWjbti0SExP1Otp8fX0xcuRI3m0tKCiAl5eXcc8LlFLBpw4dOtCKPHr0iEZGRlIAdMiQIfTChQvUGO7evUtjYmIoVKW7qpyWLFliVJvmAuA4NbP/YiUzM1PvbxkSEkIvXbrELafuj+B9P3bsGPX19TV4DHh7e9Pk5GS6YMECumbNGnr79m1aVFRElUqlVjtyuZyeOXOGzpkzh/r7+3PrSyQS2qZNG/rNN99QSin18PCglKr2PQACoFiovluLO3fuGPz9QkNDdZYXy3G/e/du6unpydnar18/3rZVEWP7L/S+HzBggNa+rFu3Lr1y5UqV68jlcnrq1CnaunVrWq9ePTpjxgwql8u574XY9+PGjTPqeq9vatq0Kd20aRO9ceMGpZTSdevW6Szj4OBAS0tLjbJFLPs+LS2Nurm5Gey3RCKhEyZMoBcvXjSp3ZMnT9KgoCAKgLq4uNDExEQaGBhIb968qbnm+QC4QEV+7JtKYmKiznFDKaVFRUW0R48etE6dOhTAHgDeVGR9v3z5ssY+KpPJtO7T1kIsx72pnDx5UufccHV1pcePHze6DaHvd0qlkn733Xec/fXr16eXL182ev2ysjJ6/vx5+uuvv9JGjRrpvV7UqVOHfvTRR/Tp06c661tj3w8fPtzsa3h1k1QqpZ6enjQiIoKuX7+emzZv3qx17zIHvvf9lStXaJ8+fXj7bfRNDg4OVu+/mM777t276/TZxcXF5HYs2fdyuZwuXLiQSiQSWqdOHbp161aD23nw4AH18fHRsdnd3Z3u37/fZLtN5ZdffqF+fn508uTJ3Lyq3u9EGaGQnJyMn376CYmJiXj77bcRGBho1Hre3t5ITU1FXFwctm/fbnA5mUzGUh2sSIcOHRAQECC0GVoolUosWbIEN27c4OY1bdoUDx8+5ERY7t27h1mzZgH495hwdnZG7969tUSbSkpKkJGRoaW06uTkhC+++AIxMTHo2bOn0SVl+K5Pa21SU1OFNsEsIiIi0KhRIy5Hdt++fTh69CgnKFfbee+997B582ZuVKu0tNRguK9CocDp06fxzTffYPPmzbh79y4AYM6cOXjxxRfRq1cvm9ldGU9PTy29DEN4eXnB1dUVADB69GgMHz4cMpkM9erVs4WZNkOhUGDOnDla2hFOTk7w9/fHhQsXQCnlro2bN2/GqFGjMG3atGrbTUtLw7hx4/Dw4UM4OzvDyckJkZGRkEqloh+lt4TDhw9j3bp1WvM00U+a8NeOHTvi+PHjgurGGMLX1xcDBw60uWiwvfLo0SN89NFHOuXbxEpZWRkSExO5z5MmTYK/v7/R6zs4OHDVUDIzM7FlyxZcuHBBq/9Pnz7FjBkzQCnFjBkzrGa7hpCQEKxevdriKJq6detiwIABWhGnfn5+mDRpEmQyGRwdHS011aY0bdoUn332GaKiorBx40b89NNPQpvEsJC8vDykpKRg6dKlcHZ2xrhx43SqCFVEKpXqjaB2dXVF+/bt+TQVcrkcI0eOxPXr140+9kTnUDh16hQ2btyIpk2bYu7cuSZfBLy9vbFkyRKcOHHCoKpxq1atEBsbaw1zGSLl/v37WiKc3t7eyMnJwcGDBzFjxgydY0Mul3OOBkPhwhr8/f0xdepUvP322yCEmFRShtpR6C+lFFOnTtX7nbu7OyZNmmRji8ynpKSEU+1lqF6KmjRpwpU7VSqV+PHHH3XyZUtKSrB48WIUFhaiqKhI67vy8nLBS05+9NFHePz4MRYtWgRKdU+nfv36wcnJCcOHDze61Ko98/PPP2s5UQFVmanExES8++67WLduHfc73bhxA19//TXOnDkDAIiPj0dgYCBkMhmaNWvGiV5u2rQJY8aMwaNHj0AIgYODAxITExEdHY1OnTpZtSa72BgwYIBdV0dwcHDgdKcUCgW++eYbLFiwQGCrxIFUKoWzs7PBMuP2RlBQEOLi4sxev1OnTujUqRPu3buH0aNHY9OmTVrX1LS0NF4cCsOGDUNJSQm+/vprvd8PHToUYWFh1bbj7OyMDh06WNs8QWnRogVatGiB0NBQXLlyBYmJicjNzTW7vcDAQPTv3x9z5syxopUMYxk9ejR27doFQPVO8uGHH1Yp+nn27Fm9z1i3b9/GqlWrMGHCBN5spZSafG0UlUNBoVBg3LhxuHbtGgICAszOY2vRogUOHDiA9u3b690ZEydONDl/tLbz5MkTTtxJ7FBKkZiYyGlmEELw5ptvom3btmjbti169OiBI0eOYOPGjThw4ACePHlSbfk4mUwGPz8/vPPOOxgwYAAaNmwIQghXUmbGjBlWLykjNG+88YZBARtNJAfDPvHz80N4eDgXgaK59ppC3bp1RVH/eO7cuWjTpo3eCIu4uDg4Ozsb1Y5GXLWi6rJCocC8efN4eZC2NsXFxViyZImW46xdu3aIj49H3bp1OUdpVlYWl1d748YNpKenAwC2bNkCQgjc3NzQr18/LvJjy5YtUCqV8PPzw5QpUzBy5EiuIoiYqrtYm6+++oqLxtEgkUiqzPMWI8888wwkEgmUSiXOnDljlSpHNYHnn38e69atw8iRI1GxnOfNmzeRl5enV69LbFR8ZnFzc0Pjxo0tbtPb25tzPFYsKZyfn48jR46gZcuWGDhwoMYZ3ZIQ4kUtqGYlkUjw/vvv4/3337fY9pqKRi+oZcuWePz4Mbp27WqWI8zLywv/93//h/LycixcuJAHS+2P8vJyLFmyhNeXcwDYu3cvDh48aPTyx44dw+uvv65ViUaDQqHAokWL0KVLF1FF3YrKobB7924cOXIE9erVwxtvvGG2Q0EikaBu3bp6v/P29kbbtm0tMbNWcuvWLb3lAzt27Ijk5GQBLNKPUqnE2rVr8cMPP3Dz2rVrh08//ZT73LJlS65+a15eHuRyObZt28aN1urjtddeQ3BwMHx8fJCXl4fQ0FAA/JWUEQpNSPTgwYOxdetWvU4WQoiOIJs9cOnSpVoxSm2Ie/fuYfz48bh27RoAlZiXOXh5eWHx4sUICgriPezOGGQymVXU/lu0aIHWrVtrORSUSiWysrLswqGwdu1abNu2jfssk8mwcOFCNGvWDIDqhWPVqlU4dOgQEhIScO3aNS0njEb4qbi4GKtWrdJqOyAgADt27ECzZs3sSrDOEk6dOqWjHN+2bVvu2m8vjBgxAgsWLMCDBw+wa9cunDt3ThTnrRjo168fvvzySy2htuPHjyMnJwdjx44V0DLjWLRoES9RYjKZDMuWLUN2djb34iqXy7Fp0yZIJBJERERg8uTJIIQ8BM/VrBj/oklPycvLM6siiaOjIzw8PJCcnIwHDx7YzSChtXjvvfdw8eJFrSi+8vJyzJkzh3eHwsOHD412Aj19+hSLFi1Cfn4+N09TyeP8+fMoKipCfn4+Xn/9dSxduhQRERFwcXHhy3ScPXsWhw4dqvbeJyqHQmlpKcrLyxEdHa31AmhN/Pz80LlzZ17arskMGDBAr9JoVlYWnnnmGQEs0oVSinXr1mHs2LFceSwPDw8kJibqdTARQrg6vpoLtTHYoqSMEJSXl2PZsmVITEys8mbl4+Mjmn1uCkuWLMHIkSPtora2NaGU4uzZs0hKSqpSW8YQUqkUbdq0wUsvvYSYmBg0bNjQ7NKDDH7Yt28fcnJyOAegVCrF1KlT0b17d63lZDIZwsPDcfLkSaxYsQJHjx7VGoU0hJubG1xdXWuNM+HcuXM4cuSIzvxp06bZtC69NXBzc0NYWBiys7M58SyG/XPt2jVs27atylKLllCvXj29VXw01azU3AXQF8yhYFMsjQzUXM/1QSmtsc9IvXv3xvTp03XSAouLizFhwgQsWbJEIMv+5erVq0hJSeEiBwFVivF3332H/v37Y+zYsVi6dCkA1TWgd+/eCAsLw8yZM3Xu99bi4cOHOtF6+hCVQ0GDvtriprJy5Uq9dWlzc3Oxa9cu9OzZ0+Jt1BY2b96st/7vyJEjufxMa5Obm4tnn33W6NSUBw8eYNOmTUhMTORCfuvVq4eZM2di8ODBvNhYkzh48CD++uuvKr20AQEB8PX1xebNm7mQZ4b4KCsrQ35+Piil2L9/P/bu3YucnBwtob6qcHZ2hq+vLwCVQy4pKQl9+/a1O1Erc+jfvz8yMjK09CKuX7+O7OxsQcUnq6KgoADx8fFaNeUjIiIwbdo0g2U9vby88MEHH+D06dPV1pmXSCSYMGGCKNJbbEF+fj5ee+01rYg1TZ36V199VTjDzMTFxQWdO3dGdna20KaIkh49euDAgQNaL+bHjh3Dw4cPDUa6ioGff/4Zv/32m823W1hYCB8fH83HcgBN9S1XUYAawD+EkLsAivQtyzP1edqu3n7bM+Xl5WjQoIFWClBt4OnTp5y2AV84OzvD0dFRpwyxhtLSUnz66adITU3lokgJIfDz88Pnn3+O/v37AwA++eQT3Lx5ExkZGZxz+MCBA4iNjUXPnj0xaNAgREdHW2QrpRSZmZkm6weJyqEQEhKCbdu2WRyO9/vvv2PFihV6R1kfP36MefPm4T//+Q8aNGhg9jaUSiUuXrwIV1fXGl3zvrS0FPv27eMECzW4ubkhKirK6PxkUxkxYgSCg4PxySefwMnJqUqPqUKhwOrVqzF+/HjuBHNwcMDq1asRHR1dY72t1mLt2rUYOnSoQb0EDePHj+e1HjXDPB4/fozi4mLk5eVh3bp1uH//PpeuolAojAqNdHV1RWhoKPr27QsfHx/uxYkQYvCltCYSFRUFb29vLYdCYWEh9u3bJ0qHwo4dOzB27FgUFBRw8/z9/TFv3jytSjWGaNeuHb788ks+TbQrzpw5gwEDBuikv8XFxWHFihXsXlIDmTRpEj799FOtcOTVq1fjww8/FLVDoTIPHz7EtWvXrPY8WlxcjK5du2qlul6/fl3fonpDXioKUAMAIeQ4pbSjVYwzAaG2a6+wCCZ+iIyMxJQpU7jKckqlEsXFxSgrK8PZs2fxxRdfYMuWLVq/v6enJw4ePKhVEc7DwwNffPEFjh49ivv373MpT4WFhVi9ejWys7MxevRojBkzBnXq1AEhBO7u7kYPAj5+/Bg5OTkYPnw4NzhrbJWUap8UCSErAfQCcJtSGqSe5w1gIwB/AFcADKCU3iequ+0SAK8CeAwggVL6u1G9gCqMJzIy0qID+s6dO0hOTq4yH37//v3YuHGjRS9HhYWF6NatG+rWrYsffvgB7dq1M7stsTJ06FBs3LhRJ+9HIpHA1dUVS5cuRbdu3eDl5QVKKSZMmICcnBzcvHkThJD2puz7ynzwwQcYNmwYdu7ciSFDhuDdd9/Vu9zhw4cxdepUriQaIQS+vr5YtmwZevTowR4AqyAjIwM7duzAhg0bqnQm+Pv7Y/LkyejatasNrWNURi6Xcxf4PXv24PffVafXX3/9hd27d0OpVOqNyqqMVCpFs2bNMGzYMO78aNeuHSIiIphYLYDnnnsOFy9eFNqMatmxYwfi4uK4UMQWLVqgXbt2mD59OoKDgwW2zr6Qy+W4efMmYmNjcf78ea3vpFIpxo0bZ9f3kvj4eGRmZrKS2SbQv39/rgKKPXD27Fls2LABSUlJVmnvxIkTOrpZt27d4qpZqaMUHKCnmhWDwdBGIpEgPj4e69atQ25uLu7cuYPnn38egCqqtKIWioODA8LDwzFmzBg0adJE597j6+uLP//8E7/++ivi4uK0Bnzv3buHTz/9FCkpKSCEgBCChIQEBAcHazTeDFJSUoK33noLBw4c4J4lJRIJOnfuXGV5Sw3GDD2lAkgBULGQ8WQAeymlcwkhk/GvKEs0gJbqqTOAZeq/RlFeXo5p06YhLCwMMTExxq7GIZfLMXjwYOzdu7faZffs2YMRI0aY/QC9YsUK3L59G7dv30ZSUhKys7Nr3EieQqHQ+4J9ErEHAAAgAElEQVTi5uaGgoICzJ07F3PnzsW8efOwfft2XLp0CZcuXUKbNm1w4cIFk/Z9ZV599VV89913SEhIwPTp05GSkqJ3uZKSEk6x3MXFBb1798aCBQvg5+dn7qZrNP/88w8XDnXlyhVDIw4cHh4eyMjIwAsvvGAL8xhQXQcPHDiAEydOaM2/ceMG1q5dC0BVL/3p06dGt+nk5ASJRII33ngDwcHBGDp0KDw8PKxqd01h1apVWuGEgCoE+u+//+Y0V2zFxo0b4e/vr6X78/vvv+Ps2bNITEzknAn+/v54//33MXr0aJvaVxO4f/8+xowZg4yMDB3nuZubGz777DO7FzFs2rQptm7dCm9vb17Fu2oSxuQMC4mnpyfc3d255x9rcuXKFYwYMUJrHiEEgYGB6NSpE9LS0jB58mQAqAdgvdUNYPDO888/j8aNG2ulygGql9s9e/YY9QJZk7hw4QJeeeUVXlMfWrRogc2bN6Nv3764cuWK3goOERERmDBhAsLDww1GSBFC4OnpiejoaKxduxbp6enYvn07CgsLAaiiTEpKSrjlP//8czg6OmLatGlV2ieXy3Hr1i2teWFhYdi4caNREY+cSE9VE1SRCH9W+HwBgI/6fx8AF9T/fwMgVt9yVU0dOnSglFK6bds2KpPJaEBAAD1//jw1BYVCQWfPnk1lMhmFKgSLuri40NGjR9PExETasGFDbj4A6urqSufPn08fPXpk0nY0zJ49m2vLwcGBfvrpp1ShUHDfAzhuzG9bsf9iorCwkHbq1EnrNwNAZTIZ3bdvH6WU0ps3b9LAwEBKKaUjRoyg69evp5RS2qFDB2rqvteHUqmkBw4coLNmzaLPPfccbdSokY49AKiTkxONjo6me/bsoaWlpfz9KEZi7L63xX5XKpVULpdzU5MmTfT+hhUniURCpVIplclk9MaNGyZtTyzHvVKppK1bt9bqV+vWralSqeRtm5Sav+/lcjktLy+nd+/epX379qVubm7V7qfqJplMRl1cXOjQoUPp2bNn6bVr12hZWZngfdfXfzFRXFxMCSE6v2eHDh3ogwcPDK7Hx3mfm5tLv/rqK3ry5El6+PBhumbNGurp6alll4eHBz1z5owFPbYce933CoWCxsfHGzyHvvzyy2rbUPfH7vpuLcR0vzMHhUJBP/nkE5197+PjU+26Qh/3a9asoY6OjpzN8+bNs0q7cXFxOr+Hl5cXpZTSoqIi2qNHDxoQEEABPADgTY3oP4ARxixn7Umo7VacxHrsv/7663qve61atap2XXs975VKJfX19dXb77CwMKPasPS8nz9/PvX396cAKCGERkRE0JiYGDpu3LgqnzGqYt++fTQmJobGxMRwbVsyaez69ddftbZT1f3O3CH1hpRSjRujAIBG1ckXwLUKy11Xz9N2eUBbsEWTHyKXyyGXy5Gbm4u0tDR8/PHHRqlKU0oxf/58zJ49mwvddnFxwZdffomEhARIJBL06tULgwYN4sRGHj16hFmzZiEuLs4sj/2IESOwb98+7N+/H+Xl5Zg1axZefPHFGuHVu337NhISEnD06FGd7xwcHDgl0UaNGnEesRs3blTO3TNp3+uDEIKXX34ZoaGhmDp1Ks6fP4+kpCSuggOgGkEfP348wsPDa1yEiCXcunULCoUC27dvx6hRo7j51eXTOzs7Y9GiRdzohD2rutevX19oE4xi165dWLhwIS5fvgy5XF5lulZVNGrUCN26dQOgEt0bP348HB0d4e/vz0Q0rcCJEydQXl5u0222aNECo0aNwuLFizF16lStiLEmTZpgyJAh6Nq1K1q3bm1Tu8RG37598ddff+n9zsXFBenp6XBwcMClS5cwduxYzJ49GzNmzIBCoUBeXp7W8oQQDBw4ELNmzULz5s1tYT5DQCQSCXr27IklS5aYLEImNK+//jreffddg0JvpkIpRXp6On788Ued7wIDAwH8W80KAAghFyml93QW1t+2IDUKhdquPVNWVqaVNpeXl4ePPvoIxcXF+PbbbzVVvtoSQl6llOYIZqgZEEIEr9QzadIk9O/fHzdv3oRUKkX79u0tTjft3r079252+fJlrF27lou2OHXqlNGi3A4ODujatSumTJmCsLAwODk5GW2DxW9glFLNaI6p63GCLR07dtRZf9GiRahbty6SkpKqfBhWKpVYsGABZs6cyV1UKzsTAJWSb0pKCgYOHMit++TJE0RHR2Pbtm0mC9k0aNAA69evx4svvojr16/j6dOnWi+69ohSqURZWRnefPNNvWkjoaGhOH36NPdZk59jKtXt+8pohOGCgoKQk2NX1y7eUSqVOHjwoM78qKgoo/LpNYSGhkIqlaJnz55aDgh7hRCCqVOnil6V/ebNmxg9erTOS40xaEoAOjg4ICgoCCNHjrR5SD6DfwgheO+999C6dWutEMkuXbrA399fOMNERLt27ZCZmWnwe83LkIaKzwGV24mMjMTcuXN5cVCfOHHiH0LIBas3rB++1O0rY/dq9x06dMDw4cMxZ84coU0xiT179mg5Ew4fPozi4mKzq29t2bIF8fHxOo7Tzp07Y+vWrRbZyhAfEydOxOnTp3U0gxwdHXHq1CkAqtRnX19f9OvXD6tWrUJiYiI++OADEELO2ZszQcP58+fh7u7OaVIJQfPmzXlzWDdr1gzTp0/H9OnTAQCHDh3Sm16hjzp16qBHjx5mDUKZe8csJIT4UEpvEUJ88K8oyw0AFd/M/dTzjMLLywuurq5cfvDMmTNBCMEHH3ygc3PPy8tDfn4+5s6di/3793MX1VatWnGCfpUJDAyEh4cHl1uiVCpx+vRpfPzxx5g4cSJatWplwk+gWl8V/QK88sordi9at3LlSowfP14njxQAunbtil27diE4OJgT5Ll16xZXKcPX15crdaLGpH3PMI6rV69i27Zt3Ge5XI7ExESz2goJCUGHDh0AAIsXL65RER4XLlzAxIkTteZ16NDBaAfYsGHDkJ2djQYNGuDPP/8EoBK7GThwIK5cuQJ/f3+kp6frCJJC5bU3WpD04MGDRjsT3N3d0bNnT04gTiaToWPHjjVqvzH0QwixuBRUTebDDz9Ely5dUF5ejvj4eJPyyiUSCUaMGIHevXvD398fbdq04dFSXKA2Upxn6vaWce/ePSxYsACTJk0S2hSDpKWlaWnpZGZm4vLly/jPf/5jUjuUUmzZsgWjRo3ScSYEBATg+++/r1gqklFD6NKlC7y9vXXmX716FWPHjkVKSgr27t2LFi1aoGlTu/cb1lpCQ0Ntsh1zn0SzAAwBMFf9N7PC/LGEkO+hEuQrqZAaUS3dunVDeHg4F25VVlaG6dOng1LK1eAEVCV91qxZg/z8fK31mzRpglWrVqFLly562w8ODsZXX32FuLg4zhFAKcXy5cuRkZGB1NRUvPzyy9WGwygUCvz1118YPXo0bty4ARcXF8yePdtuQqz1cf/+ffzwww96nQmurq7YtGkTnJ2d0bt3b06QJy0tDX369AEA9O7dGykpKRg0aJAmtMaofW/jERtrUd3Ij9WvvBMnTsTjx4+Rn5+P7du3W9SWn58fpk2bhk6dOpn84GEPKBQKpKamaqm1DxgwwKCwpz4SEhIwduxYvPXWW9y8uXPnIiIiApMnTzYoSCqRSK7CBDHasLAwhIaG4tChQwBU4nqa0SVHR0dMnDiRE0/09PRESEiI0X1gWJ979+7pfQBjCEudOnU4h8vOnTuRnJyMx48fc+dVZUJCQrhqB8899xzmz5/PHHMMLZ4+fYp9+/Zh6NChon2269KlCzIyMrSqNPXt2xdZWVkmCSlv2bIFsbGxeqs99erVy6JIKEJIFFTV36QAvqOUzjW7MeO2dwXAQwAKAHJKaUdDlen4tMOeKS0txdmzZwEA33//PWJjY7nvUlJSsHr1agDwJ4R46fsdjU1rZtQsjCkbuQFAOID6hJDrAJKhciSkE0KGA7gKQFOLIgeqkpG5UJWNHGqqQV9//TUiIyO5lwG5XI4PP/wQM2bM4JZRKBScQwBQvfBOnjwZb7/9Nho2bKjTZkV69uyJHj166IT03759GzExMYiNjcX8+fN1RjJXrlzJjcA/efIE6enp3Mu3t7e32SFmYuDjjz/Gli1buBAnDRKJBJ6ennjw4AFefPFFzJo1C5MnT8aAAQOwYsUKNG3aFOnp6QBUVRlycnIQEBCgUY3VX+dRF5uN2FgLPkd+FAoFwsPDdeb/9ttvVZZ2NBZXV1ds27atRpeVKy8vx7Vr1+Dq6gpPT08kJSVh2LBhJuXNhYWF6WgZZGZm4qeffgIADBkyBOHh4Zg3bx4yMzPx1ltvaa4ZjwB4aiK4qtuOj48P0tPTubSVkJAQrfQrey5VZ684ODjgpZdewi+//KLzXWRkJC5fvmxR+3bqRK0O08L7eCQkJAQ7d+5ESUkJ0tLS9C4zaNAgLrqOwQBU6uqpqalaKuc7duzA9u3bER8fL6Blhnn33Xcxc+ZMrdDt/Px8DBo0CFu3bq1WW+XWrVt47733sHfvXp3ni+DgYEyZMsWsimsaCCFSAEsBREKlq3WMEJJFKT1ndqPG0Z1SWnHQx1BlulrP7t27ERQUhKtXr+p8V1ZWhqysLC4VaPTo0Zg+fToIIZBKpeUAPgOgEw5ualqzEAwcOBArVqzQepcsLCzEwYMH8fLLL1ttOzUwzc3wgGlV6pS2miqrYC5cuFCrWoOhydHRkUZERNAffvjBJPX2wsJC+tJLLxlUtnRzc9OZJBKJQTvatm1Lr1y5orUN8Kj+e+nSJZqTk0OPHj1q0nqVOXDgAM3JyaGRkZE6fWrevDnt3bs3ffr0qcntmqJ6bcrvJJbJWjZX3u+9e/emjRs3plKplEql0iqPueomTRt9+vShBQUF3FRYWGji3jQNPo97U3jy5AktKiqi9+/fN7uNy5cv0+eee4777OHhwf2vVCq5z6+99ho9ePAgpVTVfwB7AXSkusfNCPX3x5s0aWK2XWJFLPveUp48eUL79Omj97zy9/c3uJ6x/bfHa541+yTmfW8OJt7vbKY4b8ttGTPZw35XV6jSmgIDA2lRUZHe5YU+7svKymjXrl31XqueffZZumPHDi3bi4qK6M6dO2lhYSHduXMnXbFihd51g4ODjXpWqK7/ALoA2Fnh8xQAU6pax9IJqgiE+pXm6a1MZ6tJ7Me+vqoA4eHhNCMjg0ZGRupdB8BpVKj+Z2gSc9/1vWf279+/2vVMOe9teb8X+tlClDF+EyZM4DQUKKVQKpVa6vQymQxubm747rvv0LNnT5MVOxs0aICRI0fi5MmTOiH+lFKj1TClUikCAgKwefNmm+YXrV+/HsnJyWjatClSU1P1jmgbw4gRI7TCwjX4+voiLS3NZnk3DBUZGRmaiwIAYPny5Xj//ffx+PHjKtdr3LgxN5JNKQUhBOfPn4eLi4vZwpn2jpOTk0nqtKZizu9K7cBrz1CFOmdlZQltBqMGQm2oOG/qtgghKwH0AnCbUhqknqc3VJyoLn5LoIpIfQwggRqpGyNm2rdvjxMnTmjNu3jxos2ruxiLg4MD1q9fj+zsbEyePFkrUuHatWuIiorCa6+9xoWd5+fn48cff0RUVBR27Nih056TkxPmz5+PqKgoa0Xw6Kv8ZlQ6oAVQALvUYvHfqM8DQ5XpGFWwYcMGrXQHjX6aGk8Ah4Wwi0/++OMPbN26Ff369RPaFLtDlA4FmUyGSZMm4Y033gClFIcPH0Z2djYAVT7OqFGj4ODgAH9/f7NflgYPHoyOHTti3rx5+OGHH4x2ImgIDg7GihUr4OPjY1OxmnPnzmHVqlUAVMIpv//+u0kOhVu3biEqKgoKhUIndNfJyQnu7u7Izs62ZUi8PZb04cXmyi+po0aNwv3797Fz506D69SpU6fK7xnWo2HDhlYTJD1x4kQRIeQRbKPCztTercTTp09x/vz5Wl+mkVEjSQWQAmB1hXmGQsWjAbRUT51hgm6MmFmwYAFWrlwJhUIhtClG06RJE4wePRpZWVn4448/UFBQoPW9vhKQlZ0JMpkMwcHBSE5ORq9evXi11waEUkpvEEIaANhNCNEaNaPUvMp0tQ2FQoHdu3fjm2++4eYlJSXh1KlTmudUdwDmKYKLhNWrV+PNN9/Umpebm4t9+/Yxh4IZiNKhAKg8ry1btgSgqs6QkJBg1falUinatm2LtLQ0/Pe//8XKlSvx8OFDHe+0hvbt28Pd3R316tXD+++/j2effRZ+fn5WtckY2rZti4SEBMycOdPkdf/880/0799fp0SMhiFDhmDZsmU2HdG25YiNtbClzVOmTMGUKVNstTmbIlAuuSkv144AWhJCzqo/+zVu3FgO1QhHIwAyta6Mx/Llyxu8+eablwC0AHCJVqOfQCl9xlYq7Ezt3XrcunULc+fORWpqqiXN2N01zwiM7pPINCSs4WyrEY40SukBQoh/pdl9oNLQAoA0AD9B5VDoA2C1Ovz3N0KI0boxYoYQAldXV50qIW+//TY3qCVGCCHYsWMH9u/fj6ysLCxevNio9erXr4/ExES4urpi/PjxfDz7WVT5zRwopTfUf28TQrYC6ATDlekYUGlGXb9+XUtHo6SkBD///DMnCg0Aa9as4f4nhOTa+/k+cOBAHYcCD9jyfi/os4VoHQq2JD4+HvHx8SgpKcGvv/6qd5mQkBDRCC/GxMRgzZo1+Pvvv6tc7vr165g1axb3+fjx43qdCQkJCejatStat25dK8PjGYJhc0FOY1+uK4jRyqByHiQDyACQDqAJgPNQhf/eU4f/pgCIgiqk01hBUoYIcXNzw/z585GUlKSVgqRBUy7Y3GulPTpRq8PEPolGiNeWzjY+1e55VLY3FCquL5TdF4DWC4a9qb27u7sjLS1NZ3Ty5MmTOH36NNq1ayeQZcbRvXt3dO3aFV5eXtzL382bN7m0SRcXFzRu3BiA6rl38ODBaNGiBZ8mHYPKKd8MKkfCIAC8vcERQlwBSCilD9X/vwLgIxiuTMeAqvxoZmYmSkpKuHmnT5/GggULDArb1mQ2bNiA4OBgDB8+3OK2xJzmZm2YQ6ECHh4eiIqKEtqMamnfvj0aNWqEv//+Gx9//DFWr16NzMxMEEJQUFCAAQNURTfKy8s1FRf0IpFI4Ofnh1dffRVvvPGGrcxnMEQPpTTWwFcRepalAMbwaxHDVkilUiQmJqKgoACfffaZzvcbNmxAZGSkaJXfGeLDRmr3vCrbmxMqbo+6MRKJRGfezZs3sXz5cpNKDwuFo6MjZsyYwVVG+/bbb7mBpMDAQLzzzjs2s4VSKieEjAWwEypH2kpK6dlqVrOEhgC2qp29MgDrKaU7CCHHoL8yHaMWI5FIcOnSJS4aXsPdu3dx584dgayyX3SvnAwthg0bhgYNGiAoKIibd+/ePURGRqJly5aIjIzE/fsqpz+lFOPHj0dAQAAAtCWEtOfDplOnTqGwsBAAcP/+ffzxxx/w9/dH06ZN0blzZ1y9ehVXr1416Ezw8/PD0KFDMW7cOFy9elUwZwIhJIoQcoEQkqt+4BEcQshKQshtQsifFeZ5E0J2E0Iuqf96qecTQsgXavtP87W/GTUWW3mTa9yION9IpVKEh4ejUaNGOt/J5XJs375dJyyawaiCTgByKaV5lNIyAN9DlTbAJ32gSlOA+m9fM9ooVIeIo1KouM1D2W1Fjx49MHjwYJ3569atw759+wSwyDLeeecdLFiwAAsWLLCpM0EDpTSHUhpIKW1BKf2E523lUUpfUE/PabZHKb1LKY2glLaklP6XUnqPTzvskWXLluk40w4dOoQtW7YIZJFt0ETsMCxHFBEKIq/T6QZAeefOnWYVc6kByAEU5ObmNvL29pZCdTP1ANAAgCaXmhehIplMpteLbgzOzs5IS0tDjx49rGyVaQhYn7g6UmEjYSobC/NZirHnjSn5xEK86Irm5dpW4WlM7d08evXqBX9/fx2RM0AVpfDxxx/D3d3dpDb5DHvnCx6OB9Gcg7CdLXyr3fOlbG8oVDwLwFhCyPdQ9aPE3vOpNbi5uelNby0uLjZZvJvBsBdiY2N1ou7y8vJw8uRJ9O/fXyCr7Be+7/U8prmZjSgcCrBhTqU5OZNqoaJszXpq50d4BYGXn9Q78xv1/xs0y/EhVBQUFISffvoJr7zyCuRyOeRyOXJzc/Uu6+3tzanRDx8+HAkJCahfv741zTEXbsQGANQPJn0ACOpQsKUwlS2F+SyFDzuFyPcSOsfMTkhFLVd719CwofWqi4nYiVodqbDi8SCmc1BMtliIxcr2FXRj6qvFZpOhciToCxXPgcpplAuV42ioNTsjNHFxcdi8eTMXCcpgMBjGYsN7Pa9pbqYiFoeCvWGRUBFguViRj48Pzpw5AwD4559/MG3aNOzZswf5+fkYNmwYt1z37t3Rpw/fkZVmIUR9YnOxeH8zGPYCU3v/ly1btuB///sfsrKytOaHhITAzc3N1OZE6UStDnY8WAVeUwSsoWzPdGP+JSQkBDt37rRq+ewaWN0EqCEVThgqrl27xlIA1Dx48AClpaVwcnIyZ3Wh7vWG7ss2gTkULMTcmrbWFCtyc3PD4sWLcfr0ady5cwcRETr3f4aVYDWMrYOtQr/FGBZWG9Xe1bbZleI7oNJSmDFjhpZD4aWXXkJqaioX+WUC9uRErQ6TjwcxpHsIeD3gTe2eKdvzgxnnd3XUyuomDPtBX/TynDlz0KxZM6tUPLAn5syZAzc3N4wfP96cwQNb3Ov5SnMzG7GIMtpbnU5RChW1a9fOnpwJ9iTqxOf+tpeQW6vZWSEcLBpAWwCxhJC21mpfD90ppcEVHqA0YWEtAexVf7YJNuo7r/1Vj06a5USllHaklHZ85plnLDHBpgQFBeHSpUvctHXrVh1V6NqMMceDAOd8Vdj8ekAplQPQqN3/BSDdimr3DQEcIoT8AeAogB8ppTugciREEkIuAfiv+jPDBAgro82o5SiVSigUCqHNEIQPP/wQBw8eFNoMQ4RSSttDdU8dQwgJq/iluc9pliAKh4It8xittC2N5x/QFSp6S63+H4IaJFTEA9yIDSHEEaoRm6xq1hEK3va3veTwWtlOIRTPK2IN9XNzYWrvdkadOnUQEBDATRaMXNak38rU40Hoc74qbHI9oDyp3VOmbM8LjRo1wp07d7gy3AwGg2EkvN/rK6a5AdBKcwN07ss2QRQOBTGjFir6FUArQsh1tTiRIc9/DoA8qISKvgXwrgAm2wU8j9iYDdvfNsFQWDQfaMLCTqhD7gFhw8L47jtf/WVOVMuxJydqdZh6PNjynK8KsV0PGCKFEIJ69eohJiYGfn5+SExMRPPmzS1pUkyDB2KyhcEQDJlMhqFDra4py+u9nhDiSgipq/kfqjS3P2H4vmwTmIZCNdhCqEhkYj0aLBHtMUqoh1KaA9VLuWhgwlQ1DovVz+0MpvYuUiilckKIxokqBbBSDE7U6qhhx0Ntux4wLCQuLg4tW7ZE586WpUCLKRpRTLYwGELi6OiIlJQUeHp64vPPP7dKmza41zcEsFWdkiUDsJ5SuoMQcgz678s2QXCHAt9CTWIUZdODaMR6NDDRHn4Rg0BZVfB83tgs9Nsa6udWhqm9Q7ROVIBnR6oYnajVYaXjQRTpHiK8HjDsAEudCQwGQ7y4uLggOTkZderUwdy5qkfx/v3747XXXsPzzz9vVpt83uvV1SNe0DP/LvTcl22FoA4FUktrdTJqNzY87i2Fr/OGN8XziohU/ZypvasQnRMVYI5UHrHJOV8VdnZ+MBgMG0AIWQmgF4DblNIg9Ty9gydENSS8BKoorMcAEiilvwthNx9IpVIsW7ZMZ/7LL78sgDW2xcPDA0lJSQgLU2kbtmrVytIUp1qH0BEKtbJWJ6PWY5f16GGl88aGod+iCwvjue+i6y+DAYgm3YOdHwxBEDIi0U6idIUkFUAKgNUV5hkaPIkG0FI9dQawDPZb+lcHiUSCUaNGCW2GYHh5eSE6OlpoM+wWoR0KtbJWpx7EmM8mRptqCvZQj57X88YWod9iDQvjq+9i7S+DAQif7sHOD2E5ceJEESHkEcxPKeILXtOcRBKRyKJ0DUApPUAI8a8029DgSR8Aq9WpXb8RQjw16VK2sZbBEC9COxRsgehFmMQokCNGmxg2RfTnDcOuEev1Rax2MRh2DaX0GTGmFNnAJjFGJLIo3aoxNHhiqFqNjkNBXUVmBAA0adKEP0sZDJEgdNnIWlmrk1HrEYVAWVWw84bBJ2J1WIrVLgaDYbcIXTKVlUq1AHU0gsmDJ5TS5ZTSjpTSjs888wwPljEY4kLoCAVehZqYCBNDpAguUFYV7LxhMBgMBqNGwKINTcdQ5RezBoNEXNWoOqpKBzKqPLyIU52MwVD/jep7bUNQh0JtrdWpQSylA5loj20RiUBZVYj6vGEwGAyGXSLGCCC+bRI0IpGVSjULQ4MnWQDGqtNWOgMoMVI/QZRVjarDGulAYk11MgZ7tVsoiCqah2Fr1EI9F1FBqAdArBClA9UOhY4VRXsIIfMB3Ksg2uNFKWU5dgwGwyLE4khV23IFzJnKYDB4ghAig+pZLwIqR8IxAG/aYhBBT7ThbqiiDSMA3K3wfOdNKU3i2x4xQgjZAJWeRH0AhQCSAWQASAfQBOrBE0rpPXXZyBQAUVCVjRxKKT1uxDbs8sXUWnbX9v7XFoROeajNiFGopyJMtIfBYFgVkSieV4YpoDMYDF4QOCKRRRtWA6U01sBXOpVf1HoKY/i1iMGwT5hDQTjEVDrQHkprMhgM+0fsjlSAOVMZDIYVEapkKiuVKhrEmOpjDNayu7b3v1YgdJUHhjgIpZS2BxANYAwhJKzil+aq3DIYDEYlhFY8rwxTQGcweIYQEkUIuUAIyVVH/QhlxxVCyBlCyClCyHH1PG9CyG5CyCX1Xy+h7GPUTOy1epC17M8LzwkAAAKNSURBVK7t/a8tMIeCcIimdCArEchgMGopzJnKYPBIhTSnaABtAcQSQtoKaFJ3SmlwhdxoTYpTSwB71Z8ZDAaDYQLMoSAcXOlAQogjVKUDs2xtBCHElRBSV/M/VCUC/8S/KrcAKxHIYDCsg2gcqQBzpjIYNoBLc6KUlgHQpDmJhT5QpTZB/bevgLYwGAyGXcIcCgJBKZUD0Aj1/AUgXaDSgQ0BHCKE/AHgKIAfKaU7oCqXE0kIuQTgv+rPDAaDYQmicKQCzJnKYNgIMaU5sRQnhk0RS7pPdfCRDmQvfdfAUqIsg4kyCohQQj2VbGCiPQwGwyYIrHheGaaAzmDULkIppTcIIQ0A7CaEnK/4JaWUqsWpGQyLEWlVo6qwWsUjO+y7Blb1yUyYQ4HBYDAYNkMMjlS1HcyZymDwj2jSnCqmOBFCtFKcKKW3WIoTw8rYQ1WjqrCk4pG9910Dq/pkJCzlgcFgMBgMBoPBB6JIc2IpTgwBEFO6T3VYOx3InvqugaVEWQCLUGAwGAwGg8FgWB0RpTmxFCcGwzAsHYj9BhbBHAoMBoPBYDAYDF4QQ5oTS3FiCIBo0n2qg4d0ILvpuwaWEmUZLOWBwWAwGAwGg8FgMKyHKNJ9qoOndCC76LsGlhJlOSxCgcFgMBgMBoPBYDCshIjSfarD6ulAdtR3DSwlykIIpSwdhMFgMBgMBoPBYDAYDIZpsJQHBoPBYDAYDAaDwWAwGCbDHAoMBoPBYDAYDAaDwWAwTIY5FBgMBoPBYDAYDAaDwWCYDHMoMBgMBoPBYDAYDAaDwTAZ5lBgMBgMBoPBYDAYDAaDYTLMocBgMBgMBoPBYDAYDAbDZJhDgcFgMBgMBoPBYDAYDIbJ/D8MC2yAzgvDfwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x1296 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLlkKlZLkGpo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#fig = plt.figure()\n",
        "#figure(figsize=(18,18))\n",
        "cnt=1\n",
        "for i in range(len(ima)):\n",
        "    #ax1 = plt.subplot(2,len(ima),cnt)\n",
        "    ima[i]=cv2.resize(ima[i],(45,45),interpolation = cv2.INTER_LINEAR)\n",
        "    # ima[i]=ima[i].reshape((-1,28,28,1))\n",
        "    #ax1.imshow(ima[i])\n",
        "    #print(ima[i].shape)\n",
        "    cnt+=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FA9Zdk0NkbnE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        "import numpy as np\n",
        "import argparse\n",
        "import imutils\n",
        "import pickle\n",
        "import cv2\n",
        "import os\n",
        "import tensorflow\n",
        "for i in range(len(ima)):\n",
        "    #ax1 = plt.subplot(2,len(ima),cnt)\n",
        "    ima[i]=tensorflow.image.grayscale_to_rgb(tensorflow.expand_dims(ima[i], axis=2))\n",
        "    ima[i]=np.array(ima[i])\n",
        "    ima[i] = ima[i].astype(\"float\") / 255.0\n",
        "    ima[i] = img_to_array(ima[i])\n",
        "    ima[i] = np.expand_dims(ima[i], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VXCs2KFlFCk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result=\"\"\n",
        "is_digit=False\n",
        "is_var=False\n",
        "is_constant=False\n",
        "prev_digit=False\n",
        "prev_var=False\n",
        "prev_constant=False\n",
        "other=False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBpsC8MLlGZw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "outputId": "cbffc443-f024-4259-ae15-1c103a8c9853"
      },
      "source": [
        "for im in ima:\n",
        "  proba = my_experimented_model.predict(im)[0]\n",
        "  idxs = np.argsort(proba)[::-1][:1]\n",
        "  for (i, j) in enumerate(idxs):\n",
        "\t# build the label and draw the label on the image\n",
        "    #print(i)\n",
        "    temp=mlb.classes_[j]\n",
        "    print(mlb.classes_[j])\n",
        "    label = \"{}: {:.2f}%\".format(mlb.classes_[j], proba[j] * 100)\n",
        "    print(label)\n",
        "    print()\n",
        "    other=True\n",
        "    for k in var:\n",
        "      if k==temp:\n",
        "        is_var=True\n",
        "        break\n",
        "    \n",
        "    if is_var==False:\n",
        "      for k in digit:\n",
        "        if k==temp:\n",
        "          is_digit=True\n",
        "          break\n",
        "\n",
        "    elif is_digit==False:\n",
        "      for k in constant:\n",
        "        if k==temp:\n",
        "          is_constant=True\n",
        "          break\n",
        "\n",
        "    if prev_digit==True:\n",
        "      if is_var==True:\n",
        "        result+=str(temp)\n",
        "      elif is_constant==True:\n",
        "        result+=str(temp)\n",
        "      elif is_digit==True:\n",
        "        result+=str(temp)\n",
        "      else:\n",
        "        result+=str(temp)\n",
        "\n",
        "    elif prev_constant==True:\n",
        "      if is_var==True:\n",
        "        result+=str(temp)\n",
        "      elif is_constant==True:\n",
        "        result+=str(temp)\n",
        "      elif is_digit==True:\n",
        "        result+=('^'+str(temp))\n",
        "      else:\n",
        "        result+=str(temp)\n",
        "    elif prev_var==True:\n",
        "      if is_var==True:\n",
        "        result+=str(temp)\n",
        "      elif is_constant==True:\n",
        "        result+=str(temp)\n",
        "      elif is_digit==True:\n",
        "        result+=('^'+str(temp))\n",
        "      else:\n",
        "        result+=str(temp)\n",
        "    else:\n",
        "      result+=str(temp)\n",
        "    prev_var=is_var\n",
        "    prev_digit=is_digit\n",
        "    prev_constant=is_constant\n",
        "    is_constant=False\n",
        "    is_digit=False\n",
        "    is_var=False\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "2: 98.31%\n",
            "\n",
            "5\n",
            "5: 100.00%\n",
            "\n",
            "X\n",
            "X: 96.35%\n",
            "\n",
            "5\n",
            "5: 99.83%\n",
            "\n",
            "+\n",
            "+: 99.98%\n",
            "\n",
            "1\n",
            "1: 99.52%\n",
            "\n",
            "4\n",
            "4: 99.90%\n",
            "\n",
            "X\n",
            "X: 91.98%\n",
            "\n",
            "3\n",
            "3: 99.89%\n",
            "\n",
            "+\n",
            "+: 99.99%\n",
            "\n",
            "9\n",
            "9: 67.00%\n",
            "\n",
            "8\n",
            "8: 96.05%\n",
            "\n",
            "=\n",
            "=: 80.64%\n",
            "\n",
            "4\n",
            "4: 100.00%\n",
            "\n",
            "9\n",
            "9: 94.95%\n",
            "\n",
            "2\n",
            "2: 99.56%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSxWwFfLlHxs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e39fdcd-eba4-413e-865d-ff9d88317576"
      },
      "source": [
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25X^5+14X^3+98=492\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pWqLbtJurzz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "acda5c91-0340-44df-fb96-683a31627ad8"
      },
      "source": [
        "#imag=cv2.imread('/content/WhatsApp Image 2020-07-26 at 10.42.07 AM.jpeg')\n",
        "src_img = cv2.imread('/content/WhatsApp Image 2020-07-26 at 11.19.51 AM.jpeg', cv2.IMREAD_GRAYSCALE)\n",
        "orig_height, orig_width = src_img.shape\n",
        "\n",
        "        #print(\"\\n Resizing Image........\")\n",
        "width = 2000\n",
        "# height = int(width * orig_height / orig_width)\n",
        "# src_img = cv2.resize(src_img, dsize=(width, height), interpolation=cv2.INTER_AREA)\n",
        "# plt.imshow(src_img)\n",
        "\n",
        "PIXEL_SET = 255\n",
        "kernel_size = 21\n",
        "normalized_mean = 20\n",
        "#         bin_img = cv2.adaptiveThreshold(src_img, PIXEL_SET, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, kernel_size,\n",
        "#                                         normalized_mean)\n",
        "        #print(src_img.shape)\n",
        "equ = adjust_gamma(src_img,1\n",
        "                   )\n",
        "plt.imshow(equ,cmap=\"gray\")\n",
        "equ = cv2.resize(equ, (45, 45),interpolation=cv2.INTER_LINEAR)\n",
        "plt.imshow(equ)\n",
        "_,bin_img = cv2.threshold(equ,0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
        "\n",
        "#plt.imshow(bin_img)\n",
        "# plt.imshow(bin_img,cmap=\"gray\")\n",
        "#         kernel = np.array([[-1,-1,-1], \n",
        "#                    [-1, 9,-1],\n",
        "#                    [-1,-1,-1]])\n",
        "#         sharpened = cv2.filter2D(image, -1, kernel)\n",
        "\n",
        "        #print(\"Noise Removal From Image.........\")\n",
        "#         kernel = np.array([[-1,-1,-1], \n",
        "#                    [-1, -9,-1],\n",
        "#                    [-1,-1,-1]])\n",
        "final_thr = cv2.dilate(bin_img,(3,3),cv2.MORPH_ELLIPSE)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#print(image.shape)\n",
        "kernel = np.ones((3,3)) \n",
        "final_thr = cv2.dilate(final_thr, kernel,iterations=1) \n",
        "final_thr = ~final_thr\n",
        "\n",
        "final_thr = final_thr.astype(\"float\") / 255.0\n",
        "\n",
        "final_thr = img_to_array(final_thr)\n",
        "\n",
        "final_thr = cv2.cvtColor(final_thr,cv2.COLOR_GRAY2RGB)\n",
        "plt.imshow(final_thr)\n",
        "final_thr = np.expand_dims(final_thr, axis=0)\n",
        "print(final_thr.shape)\n",
        "\n",
        "proba = my_experimented_model.predict(final_thr)[0]\n",
        "\n",
        "print(proba)\n",
        "idxs = np.argsort(proba)[::-1][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 45, 45, 3)\n",
            "[6.3529633e-11 2.5981034e-10 8.0160428e-10 2.4508061e-06 3.1788534e-09\n",
            " 8.4397914e-12 3.8583163e-09 1.2820111e-09 1.4461899e-09 6.6760583e-11\n",
            " 8.4307867e-05 9.8955752e-08 4.4041240e-06 1.0965652e-11 3.1011211e-08\n",
            " 1.4872770e-14 5.1067845e-15 5.7242040e-08 1.4738706e-06 7.0206951e-08\n",
            " 7.2731231e-13 3.9914937e-07 2.1615264e-07 5.4199761e-04 2.3182492e-13\n",
            " 6.1458205e-10 2.0175945e-05 2.5196045e-10 6.1037710e-08 1.3669032e-12\n",
            " 1.8816542e-11 1.7476159e-08 1.9071867e-06 3.1743938e-13 6.6286309e-05\n",
            " 2.4170885e-10 2.2158718e-10 6.9670827e-12 5.4989238e-07 1.5003087e-07\n",
            " 6.7389267e-03 4.6351845e-03 3.2627463e-09 1.8014669e-10 4.7567191e-06\n",
            " 7.6710203e-05 1.4323139e-08 3.5548560e-08 9.8734015e-01 6.3053045e-07\n",
            " 7.2778357e-07 4.7784572e-04 9.4654857e-12 7.6136430e-08 2.2640334e-09\n",
            " 3.1749815e-07]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXViTysUkZs9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "d67d3208-4622-46cf-8a72-f1bc08030715"
      },
      "source": [
        "for (i, j) in enumerate(idxs):\n",
        "\t# build the label and draw the label on the image\n",
        "  print(i)\n",
        "  print(mlb.classes_[j])\n",
        "  label = \"{}: {:.2f}%\".format(mlb.classes_[j], proba[j] * 100)\n",
        "  print(label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "tan\n",
            "tan: 98.73%\n",
            "1\n",
            "phi\n",
            "phi: 0.67%\n",
            "2\n",
            "pi\n",
            "pi: 0.46%\n",
            "3\n",
            "cos\n",
            "cos: 0.05%\n",
            "4\n",
            "w\n",
            "w: 0.05%\n",
            "5\n",
            "4\n",
            "4: 0.01%\n",
            "6\n",
            "sin\n",
            "sin: 0.01%\n",
            "7\n",
            "lambda\n",
            "lambda: 0.01%\n",
            "8\n",
            "forall\n",
            "forall: 0.00%\n",
            "9\n",
            "sigma\n",
            "sigma: 0.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LN2yW6qkkgk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        },
        "outputId": "d162c83d-5db6-4ece-b6ea-3edd506c7e46"
      },
      "source": [
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        "import numpy as np\n",
        "import argparse\n",
        "import imutils\n",
        "import pickle\n",
        "import cv2\n",
        "import os\n",
        "image = cv2.imread('/content/WhatsApp Image 2020-07-26 at 7.48.32 PM.jpeg', cv2.IMREAD_GRAYSCALE)\n",
        "\n",
        "\n",
        "# output = imutils.resize(image, width=400)\n",
        "# plt.imshow(output)\n",
        "# # pre-process the image for classification\n",
        "image = cv2.resize(image, (45, 45),interpolation=cv2.INTER_LINEAR)\n",
        "\n",
        "image=cv2.GaussianBlur(image,(3,3),0)\n",
        "# plt.imshow(image)\n",
        "_,image = cv2.threshold(image,0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
        "\n",
        "kernel = np.ones((3,3)) \n",
        "image = cv2.dilate(image, kernel,iterations=1)\n",
        "# #plt.imshow(bin_img)\n",
        "# # plt.imshow(bin_img,cmap=\"gray\")\n",
        "# kernel = np.array([[-1,-1,-1], \n",
        "#                    [-1, 9,-1],\n",
        "#                    [-1,-1,-1]])\n",
        "# sharpened = cv2.filter2D(bin_img, -1, kernel)\n",
        "\n",
        "#         #print(\"Noise Removal From Image.........\")\n",
        "# kernel = np.array([[-1,-1,-1], \n",
        "#                     [-1, -9,-1],\n",
        "#                     [-1,-1,-1]])\n",
        "# final_thr = cv2.erode(bin_img,kernel,cv2.MORPH_ELLIPSE)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# #print(image.shape)\n",
        "# kernel = np.ones((5,5)) \n",
        "# final_thr = cv2.dilate(sharpened, kernel,iterations=2) \n",
        "# final_thr =255-final_thr\n",
        "\n",
        "\n",
        "# final_thr = img_to_array(final_thr)\n",
        "image=255-image\n",
        "plt.imshow(image,cmap='gray')\n",
        "#image = cv2.cvtColor(image,cv2.COLOR_GRAY2RGB)\n",
        "image = image.astype(\"float\") / 255.0\n",
        "#plt.imshow(final_thr)\n",
        "image = np.expand_dims(image, axis=0)\n",
        "#print(final_thr.shape)\n",
        "\n",
        "proba = my_experimented_model.predict(image)[0]\n",
        "\n",
        "print(proba)\n",
        "idxs = np.argsort(proba)[::-1][:10]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-341-1e6af3c24b84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;31m#print(final_thr.shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_experimented_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproba\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m       raise ValueError('{} is not supported in multi-worker mode.'.format(\n\u001b[1;32m     87\u001b[0m           method.__name__))\n\u001b[0;32m---> 88\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m   return tf_decorator.make_decorator(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1266\u001b[0m           \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_predict_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1268\u001b[0;31m             \u001b[0mtmp_batch_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1269\u001b[0m             \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1270\u001b[0m             \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    616\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2417\u001b[0m     \u001b[0;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2419\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2420\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2772\u001b[0m           \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_signature\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2773\u001b[0m           and call_context_key in self._function_cache.missed):\n\u001b[0;32m-> 2774\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_define_function_with_shape_relaxation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2776\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_define_function_with_shape_relaxation\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2704\u001b[0m         relaxed_arg_shapes)\n\u001b[1;32m   2705\u001b[0m     graph_function = self._create_graph_function(\n\u001b[0;32m-> 2706\u001b[0;31m         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)\n\u001b[0m\u001b[1;32m   2707\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marg_relaxed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrank_only_cache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   2665\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2666\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2667\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   2668\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2669\u001b[0m         \u001b[0;31m# Tell the ConcreteFunction to clean up its graph once it goes out of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    439\u001b[0m         \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    966\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1147 predict_function  *\n        outputs = self.distribute_strategy.run(\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1122 predict_step  **\n        return self(x, training=False)\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__\n        self.name)\n    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility\n        str(x.shape.as_list()))\n\n    ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 45, 45]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jaER6uQ0Q9J9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# print(\"[INFO] classifying image...\")\n",
        "# #proba = my_experimented_model.predict(image)[0]\n",
        "# idxs = np.argsort(proba)[::-1][:2]\n",
        "# loop over the indexes of the high confidence class labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfULEnxoRhjj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "8261b194-bb5b-463a-c129-8d4bfac4d018"
      },
      "source": [
        "for (i, j) in enumerate(idxs):\n",
        "    print(i)\n",
        "    print(mlb.classes_[j])\n",
        "    # build the label and draw the label on the image\n",
        "    label = \"{}: {:.2f}%\".format(mlb.classes_[j], proba[j] * 100)\n",
        "    print(label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "theta\n",
            "theta: 90.08%\n",
            "1\n",
            "4\n",
            "4: 9.12%\n",
            "2\n",
            "beta\n",
            "beta: 0.37%\n",
            "3\n",
            "Delta\n",
            "Delta: 0.31%\n",
            "4\n",
            "8\n",
            "8: 0.03%\n",
            "5\n",
            "mu\n",
            "mu: 0.02%\n",
            "6\n",
            "tan\n",
            "tan: 0.02%\n",
            "7\n",
            "infty\n",
            "infty: 0.01%\n",
            "8\n",
            "phi\n",
            "phi: 0.01%\n",
            "9\n",
            "z\n",
            "z: 0.01%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hkxc2M-qRjFY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=0):\n",
        "\t\"\"\"Scale space technique for word segmentation proposed by R. Manmatha: http://ciir.cs.umass.edu/pubfiles/mm-27.pdf\n",
        "\t\n",
        "\tArgs:\n",
        "\t\timg: grayscale uint8 image of the text-line to be segmented.\n",
        "\t\tkernelSize: size of filter kernel, must be an odd integer.\n",
        "\t\tsigma: standard deviation of Gaussian function used for filter kernel.\n",
        "\t\ttheta: approximated width/height ratio of words, filter function is distorted by this factor.\n",
        "\t\tminArea: ignore word candidates smaller than specified area.\n",
        "\t\t\n",
        "\tReturns:\n",
        "\t\tList of tuples. Each tuple contains the bounding box and the image of the segmented word.\n",
        "\t\"\"\"\n",
        "\n",
        "\t# apply filter kernel\n",
        "\tkernel = createKernel(kernelSize, sigma, theta)\n",
        "\timgFiltered = cv2.filter2D(img, -1, kernel, borderType=cv2.BORDER_REPLICATE).astype(np.uint8)\n",
        "\t(_, imgThres) = cv2.threshold(imgFiltered, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
        "\timgThres = 255 - imgThres\n",
        "\n",
        "\t# find connected components. OpenCV: return type differs between OpenCV2 and 3\n",
        "\tif cv2.__version__.startswith('3.'):\n",
        "\t\t(_, components, _) = cv2.findContours(imgThres, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "\telse:\n",
        "\t\t(components, _) = cv2.findContours(imgThres, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
        "\n",
        "\t# append components to result\n",
        "\tres = []\n",
        "\tfor c in components:\n",
        "\t\t# skip small word candidates\n",
        "\t\tif cv2.contourArea(c) < minArea:\n",
        "\t\t\tcontinue\n",
        "\t\t# append bounding box and image of word to result list\n",
        "\t\tcurrBox = cv2.boundingRect(c) # returns (x, y, w, h)\n",
        "\t\t(x, y, w, h) = currBox\n",
        "\t\tcurrImg = img[y:y+h, x:x+w]\n",
        "\t\tres.append((currBox, currImg))\n",
        "\n",
        "\t# return list of words, sorted by x-coordinate\n",
        "\treturn sorted(res, key=lambda entry:entry[0][0])\n",
        "\n",
        "\n",
        "def prepareImg(img, height):\n",
        "\t\"\"\"convert given image to grayscale image (if needed) and resize to desired height\"\"\"\n",
        "\tassert img.ndim in (2, 3)\n",
        "\tif img.ndim == 3:\n",
        "\t\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "\th = img.shape[0]\n",
        "\tfactor = height / h\n",
        "\treturn cv2.resize(img, dsize=None, fx=factor, fy=factor)\n",
        "\n",
        "\n",
        "def createKernel(kernelSize, sigma, theta):\n",
        "\t\"\"\"create anisotropic filter kernel according to given parameters\"\"\"\n",
        "\tassert kernelSize % 2 # must be odd size\n",
        "\thalfSize = kernelSize // 2\n",
        "\t\n",
        "\tkernel = np.zeros([kernelSize, kernelSize])\n",
        "\tsigmaX = sigma\n",
        "\tsigmaY = sigma * theta\n",
        "\t\n",
        "\tfor i in range(kernelSize):\n",
        "\t\tfor j in range(kernelSize):\n",
        "\t\t\tx = i - halfSize\n",
        "\t\t\ty = j - halfSize\n",
        "\t\t\t\n",
        "\t\t\texpTerm = np.exp(-x**2 / (2 * sigmaX) - y**2 / (2 * sigmaY))\n",
        "\t\t\txTerm = (x**2 - sigmaX**2) / (2 * math.pi * sigmaX**5 * sigmaY)\n",
        "\t\t\tyTerm = (y**2 - sigmaY**2) / (2 * math.pi * sigmaY**5 * sigmaX)\n",
        "\t\t\t\n",
        "\t\t\tkernel[i, j] = (xTerm + yTerm) * expTerm\n",
        "\n",
        "\tkernel = kernel / np.sum(kernel)\n",
        "\treturn kernel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0LWwDlSWwoE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "1988d577-292a-40d8-d34a-92acff74c620"
      },
      "source": [
        "img = prepareImg(cv2.imread('/content/WhatsApp Image 2020-07-26 at 4.49.09 PM.jpeg'), 50)\n",
        "\t\t\n",
        "\t\t# execute segmentation with given parameters\n",
        "\t\t# -kernelSize: size of filter kernel (odd integer)\n",
        "\t\t# -sigma: standard deviation of Gaussian function used for filter kernel\n",
        "\t\t# -theta: approximated width/height ratio of words, filter function is distorted by this factor\n",
        "\t\t# - minArea: ignore word candidates smaller than specified area\n",
        "res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100)\n",
        "\t\t\n",
        "\t\t# write output to 'out/inputFileName' directory\n",
        "\t\t\n",
        "\t\t# iterate over all segmented words\n",
        "print('Segmented into %d words'%len(res))\n",
        "for (j, w) in enumerate(res):\n",
        "\t(wordBox, wordImg) = w\n",
        "\t(x, y, w, h) = wordBox\n",
        "\t#cv2.rectangle(img,(x,y),(x+w,y+h),0,1) # draw bounding box in summary image\n",
        "\tplt.imshow(img,cmap='gray')\n",
        "plt.savefig('/content/img.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Segmented into 1 words\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjJSNImUXWmH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "e53a7c37-4aff-4bb7-873d-ee686747258d"
      },
      "source": [
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        "import numpy as np\n",
        "import argparse\n",
        "import imutils\n",
        "import pickle\n",
        "import cv2\n",
        "import os\n",
        "image = cv2.imread('/content/img.jpg', cv2.IMREAD_GRAYSCALE)\n",
        "output = imutils.resize(image, width=400)\n",
        "plt.imshow(output)\n",
        "# pre-process the image for classification\n",
        "image = cv2.resize(image, (45, 45),interpolation=cv2.INTER_CUBIC)\n",
        "plt.imshow(image)\n",
        "_,bin_img = cv2.threshold(image,0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
        "\n",
        "#plt.imshow(bin_img)\n",
        "# plt.imshow(bin_img,cmap=\"gray\")\n",
        "kernel = np.array([[-1,-1,-1], \n",
        "                   [-1, 9,-1],\n",
        "                   [-1,-1,-1]])\n",
        "sharpened = cv2.filter2D(bin_img, -1, kernel)\n",
        "\n",
        "        #print(\"Noise Removal From Image.........\")\n",
        "kernel = np.array([[-1,-1,-1], \n",
        "                    [-1, -9,-1],\n",
        "                    [-1,-1,-1]])\n",
        "final_thr = cv2.dilate(bin_img,kernel,cv2.MORPH_ELLIPSE)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#print(image.shape)\n",
        "kernel = np.ones((5,5)) \n",
        "final_thr = cv2.dilate(sharpened, kernel,iterations=2) \n",
        "final_thr =255-final_thr\n",
        "\n",
        "final_thr = final_thr.astype(\"float\") / 255.0\n",
        "\n",
        "final_thr = img_to_array(final_thr)\n",
        "\n",
        "final_thr = cv2.cvtColor(final_thr,cv2.COLOR_GRAY2RGB)\n",
        "#plt.imshow(final_thr)\n",
        "final_thr = np.expand_dims(final_thr, axis=0)\n",
        "print(final_thr.shape)\n",
        "\n",
        "proba = my_experimented_model.predict(final_thr)[0]\n",
        "\n",
        "print(proba)\n",
        "idxs = np.argsort(proba)[::-1][:10]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 45, 45, 3)\n",
            "[1.9872827e-26 8.9094338e-20 1.6927012e-19 1.5222043e-10 3.8620274e-20\n",
            " 4.1198651e-21 3.5473454e-15 2.7635173e-16 1.3258087e-12 4.8556520e-19\n",
            " 9.7883275e-09 1.6870301e-17 8.8922169e-13 4.8036522e-15 1.3571773e-15\n",
            " 1.5360369e-22 4.5956515e-23 6.6009828e-12 5.0183980e-06 2.2200646e-17\n",
            " 9.4762950e-22 7.4024120e-09 3.0972601e-16 8.7813650e-09 5.9577249e-24\n",
            " 2.7102315e-16 1.2407783e-11 1.7621597e-19 8.8976025e-15 2.6046931e-17\n",
            " 6.8891624e-22 7.5860889e-15 1.5115686e-10 4.5671595e-25 7.0129791e-10\n",
            " 3.8079613e-26 3.2275283e-16 9.3292548e-18 1.4421604e-12 2.8268695e-11\n",
            " 1.3106423e-12 1.8673058e-08 9.5129675e-18 5.2797605e-17 6.4802870e-16\n",
            " 2.3200191e-05 2.9024645e-18 2.6227723e-15 9.9997175e-01 4.6212023e-12\n",
            " 2.5452319e-11 8.1644178e-11 1.0026633e-18 2.2153359e-09 2.6373147e-19\n",
            " 1.6239968e-19]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAQN-SDYYH8b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppwkqjQb9P_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}