Cycle_GAN_Artworks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cycle GAN Artworks.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JjaaQOdL6XU",
        "colab_type": "text"
      },
      "source": [
        "# **Scrap Images from Bing**\n",
        "Here I've tried to scrap 500 images with keyword *Waterfall* and 500 images with keyword *Famous Abstract Art* from **Bing** but got less than 500 images for both of these categories."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rw7tptJBL50Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install icrawler==0.6.2    #Install icrawler\n",
        "%rm -rf ./data    # if directory ./data exists then delete it\n",
        "\n",
        "from icrawler.builtin import BingImageCrawler\n",
        "\n",
        "# Download Images with keyword \"waterfall\"\n",
        "bing_crawler = BingImageCrawler(storage = {'root_dir': './data/waterfall/waterfall/'})\n",
        "bing_crawler.crawl(keyword='waterfall', max_num=500)\n",
        "\n",
        "# Download Images with keyword \"famous abstract art\"\n",
        "bing_crawler = BingImageCrawler(storage = {'root_dir': './data/abstract_art/abstract_art/'})\n",
        "bing_crawler.crawl(keyword='famous abstract art', max_num=500)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecB_IH-rDy8i",
        "colab_type": "code",
        "outputId": "81b6cb6f-3a47-4ef1-deda-d38b09a2e679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Load images from directory\n",
        "BATCH_SIZE = 1\n",
        "IMG_WIDTH = 256\n",
        "IMG_HEIGHT = 256\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "image_generator = ImageDataGenerator(rescale=1./255)\n",
        "abstract_art_data_gen = image_generator.flow_from_directory(directory='./data/abstract_art',\n",
        "                                                     batch_size=BATCH_SIZE,\n",
        "                                                     shuffle=True,\n",
        "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH))\n",
        "\n",
        "waterfall_data_gen = image_generator.flow_from_directory(directory='./data/waterfall',\n",
        "                                                     batch_size=BATCH_SIZE,\n",
        "                                                     shuffle=True,\n",
        "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 20 images belonging to 1 classes.\n",
            "Found 20 images belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_BFWRkVHPvo",
        "colab_type": "code",
        "outputId": "68741c0f-ae1f-4a90-cd3a-21f303127b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "# Visualise Images\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def show_batch(image):\n",
        "  plt.figure(figsize=(10,10))\n",
        "  for n in range(1):\n",
        "      ax = plt.subplot(3,3,n+1)\n",
        "      plt.imshow(image[0])\n",
        "      plt.axis('off')\n",
        "\n",
        "image_batch1, _ = next(waterfall_data_gen)\n",
        "image_batch2, _ = next(abstract_art_data_gen)\n",
        "show_batch(image_batch1)\n",
        "show_batch(image_batch2)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J0pFYVRvTthFaonMwpJyLy8/n9ueDe+HcDTZ2v8x0kqO0zfGTuww7He7f+4h44jNqtdEq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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R5fRkRqgmmqM528sVeWN443Dg80vPv/nkBWoO+Olj4Vtu5PPBY4YV98qKkIVH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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUIo7ktvOIgP",
        "colab_type": "text"
      },
      "source": [
        "# Cycle GAN model Tensorflow\n",
        "Here I'e used the [Cycle GAN model from Tensorflow official website](https://www.tensorflow.org/tutorials/generative/cyclegan) with some changes to make it compatible with **Keras ImageDataGenerator**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1rh-xVYR0AF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9da9f780-d5e0-47d5-f3d5-720dc549c8de"
      },
      "source": [
        "# Install tensorflow_examples\n",
        "!pip install -q git+https://github.com/tensorflow/examples.git\n",
        "# Import necessary packages\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "from tensorflow_examples.models.pix2pix import pix2pix\n",
        "import os\n",
        "import time\n",
        "from IPython.display import clear_output\n",
        "import numpy as np\n",
        "import glob\n",
        "import pathlib\n",
        "\n",
        "tfds.disable_progress_bar()\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "# Some important patrameters\n",
        "BUFFER_SIZE = 1000\n",
        "EPOCHS = 50\n",
        "IMG_WIDTH = 256\n",
        "IMG_HEIGHT = 256\n",
        "num_images = 456    # Number of Waterfall Images\n",
        "OUTPUT_CHANNELS = 3\n",
        "\n",
        "# Generators\n",
        "generator_g = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')\n",
        "generator_f = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')\n",
        "\n",
        "# Discriminators\n",
        "discriminator_x = pix2pix.discriminator(norm_type='instancenorm', target=False)\n",
        "discriminator_y = pix2pix.discriminator(norm_type='instancenorm', target=False)\n",
        "\n",
        "LAMBDA = 10\n",
        "loss_obj = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "def discriminator_loss(real, generated):\n",
        "  real_loss = loss_obj(tf.ones_like(real), real)\n",
        "  generated_loss = loss_obj(tf.zeros_like(generated), generated)\n",
        "  total_disc_loss = real_loss + generated_loss\n",
        "  return total_disc_loss * 0.5\n",
        "\n",
        "def generator_loss(generated):\n",
        "  return loss_obj(tf.ones_like(generated), generated)\n",
        "\n",
        "def calc_cycle_loss(real_image, cycled_image):\n",
        "  loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))\n",
        "  return LAMBDA * loss1\n",
        "\n",
        "def identity_loss(real_image, same_image):\n",
        "  loss = tf.reduce_mean(tf.abs(real_image - same_image))\n",
        "  return LAMBDA * 0.5 * loss\n",
        "\n",
        "# Generator Optimizers\n",
        "generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n",
        "generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n",
        "\n",
        "# Discriminator Optimizers\n",
        "discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n",
        "discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n",
        "\n",
        "# Here I've saved checkpoints to my Google drive so that I can restore them later\n",
        "checkpoint_path = './drive/My Drive/checkpoints/train'\n",
        "\n",
        "ckpt = tf.train.Checkpoint(generator_g=generator_g,\n",
        "                           generator_f=generator_f,\n",
        "                           discriminator_x=discriminator_x,\n",
        "                           discriminator_y=discriminator_y,\n",
        "                           generator_g_optimizer=generator_g_optimizer,\n",
        "                           generator_f_optimizer=generator_f_optimizer,\n",
        "                           discriminator_x_optimizer=discriminator_x_optimizer,\n",
        "                           discriminator_y_optimizer=discriminator_y_optimizer)\n",
        "\n",
        "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
        "\n",
        "# If a checkpoint exists, restore the latest checkpoint.\n",
        "if ckpt_manager.latest_checkpoint:\n",
        "  ckpt.restore(ckpt_manager.latest_checkpoint)\n",
        "  print ('Latest checkpoint restored!!')\n",
        "\n",
        "def generate_images(model, test_input):\n",
        "  prediction = model(test_input)\n",
        "    \n",
        "  plt.figure(figsize=(12, 12))\n",
        "\n",
        "  display_list = [test_input[0], prediction[0]]\n",
        "  title = ['Input Image', 'Predicted Image']\n",
        "\n",
        "  for i in range(2):\n",
        "    plt.subplot(1, 2, i+1)\n",
        "    plt.title(title[i])\n",
        "    # getting the pixel values between [0, 1] to plot it.\n",
        "    plt.imshow(display_list[i] * 0.5 + 0.5)\n",
        "    plt.axis('off')\n",
        "  plt.show()\n",
        "\n",
        "@tf.function\n",
        "def train_step(real_x, real_y):\n",
        "  # persistent is set to True because the tape is used more than\n",
        "  # once to calculate the gradients.\n",
        "  with tf.GradientTape(persistent=True) as tape:\n",
        "    # Generator G translates X -> Y\n",
        "    # Generator F translates Y -> X.\n",
        "    \n",
        "    fake_y = generator_g(real_x, training=True)\n",
        "    cycled_x = generator_f(fake_y, training=True)\n",
        "\n",
        "    fake_x = generator_f(real_y, training=True)\n",
        "    cycled_y = generator_g(fake_x, training=True)\n",
        "\n",
        "    # same_x and same_y are used for identity loss.\n",
        "    same_x = generator_f(real_x, training=True)\n",
        "    same_y = generator_g(real_y, training=True)\n",
        "\n",
        "    disc_real_x = discriminator_x(real_x, training=True)\n",
        "    disc_real_y = discriminator_y(real_y, training=True)\n",
        "\n",
        "    disc_fake_x = discriminator_x(fake_x, training=True)\n",
        "    disc_fake_y = discriminator_y(fake_y, training=True)\n",
        "\n",
        "    # calculate the loss\n",
        "    gen_g_loss = generator_loss(disc_fake_y)\n",
        "    gen_f_loss = generator_loss(disc_fake_x)\n",
        "    \n",
        "    total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)\n",
        "    \n",
        "    # Total generator loss = adversarial loss + cycle loss\n",
        "    total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)\n",
        "    total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)\n",
        "\n",
        "    disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)\n",
        "    disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)\n",
        "  \n",
        "  # Calculate the gradients for generator and discriminator\n",
        "  generator_g_gradients = tape.gradient(total_gen_g_loss, \n",
        "                                        generator_g.trainable_variables)\n",
        "  generator_f_gradients = tape.gradient(total_gen_f_loss, \n",
        "                                        generator_f.trainable_variables)\n",
        "  \n",
        "  discriminator_x_gradients = tape.gradient(disc_x_loss, \n",
        "                                            discriminator_x.trainable_variables)\n",
        "  discriminator_y_gradients = tape.gradient(disc_y_loss, \n",
        "                                            discriminator_y.trainable_variables)\n",
        "  \n",
        "  # Apply the gradients to the optimizer\n",
        "  generator_g_optimizer.apply_gradients(zip(generator_g_gradients, \n",
        "                                            generator_g.trainable_variables))\n",
        "\n",
        "  generator_f_optimizer.apply_gradients(zip(generator_f_gradients, \n",
        "                                            generator_f.trainable_variables))\n",
        "  \n",
        "  discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,\n",
        "                                                discriminator_x.trainable_variables))\n",
        "  \n",
        "  discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,\n",
        "                                                discriminator_y.trainable_variables))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for tensorflow-examples (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Latest checkpoint restored!!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-5nOTomgMFp",
        "colab_type": "text"
      },
      "source": [
        "As I've previously trained the model so last checkpoint have been restored. So I've skipped the Training Process."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qI6sKeaLWiZS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train model\n",
        "sample_waterfall, _ = next(waterfall_data_gen)\n",
        "for epoch in range(EPOCHS):\n",
        "  start = time.time()\n",
        "\n",
        "  n = 0\n",
        "  for _ in range(num_images):\n",
        "    image_x, _= next(waterfall_data_gen)\n",
        "    image_y, _= next(abstract_art_data_gen)\n",
        "    image_x = tf.convert_to_tensor(image_x)\n",
        "    image_y = tf.convert_to_tensor(image_y)\n",
        "    train_step(image_x, image_y)\n",
        "    if n % 10 == 0:\n",
        "      print ('.', end='')\n",
        "    n+=1\n",
        "\n",
        "  clear_output(wait=True)\n",
        "  # Using a consistent image (sample_horse) so that the progress of the model\n",
        "  # is clearly visible.\n",
        "  generate_images(generator_g, sample_waterfall)\n",
        "\n",
        "  if (epoch + 1) % 5 == 0:\n",
        "    ckpt_save_path = ckpt_manager.save()\n",
        "    print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,\n",
        "                                                         ckpt_save_path))\n",
        "\n",
        "  print ('Time taken for epoch {} is {} sec\\n'.format(epoch + 1,\n",
        "                                                      time.time()-start))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LduL6-46VjMj",
        "colab_type": "text"
      },
      "source": [
        "# Now generate realistic artworks\n",
        "Now scrap some images, pass those images to the model and save the generated artworks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKDKWsPv70MI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "count=0\n",
        "%rm -rf './drive/My Drive/Cycle-GAN Artworks/'\n",
        "%mkdir './drive/My Drive/Cycle-GAN Artworks/'\n",
        "def predict_and_display(model, data_gen, count):\n",
        "  for i in range(count):\n",
        "     pred = model(next(data_gen))\n",
        "     tf.keras.preprocessing.image.save_img('./drive/My Drive/Cycle-GAN Artworks/'+str(count)+'.jpeg', pred[0])\n",
        "     count += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4KuGtAAb1MV",
        "colab_type": "text"
      },
      "source": [
        "Scrap images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vHypNG6q-t1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc65048f-ba18-4285-bbe9-683969965c11"
      },
      "source": [
        "bing_crawler = BingImageCrawler(storage = {'root_dir': './data/forest/forest/'})\n",
        "bing_crawler.crawl(keyword='forest', max_num=20)\n",
        "\n",
        "bing_crawler = BingImageCrawler(storage = {'root_dir': './data/nature_scenes/nature_scenes/'})\n",
        "bing_crawler.crawl(keyword='nature scenes', max_num=100)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-01 16:08:45,902 - INFO - icrawler.crawler - start crawling...\n",
            "2020-04-01 16:08:45,903 - INFO - icrawler.crawler - starting 1 feeder threads...\n",
            "2020-04-01 16:08:45,907 - INFO - feeder - thread feeder-001 exit\n",
            "2020-04-01 16:08:45,907 - INFO - icrawler.crawler - starting 1 parser threads...\n",
            "2020-04-01 16:08:45,914 - INFO - icrawler.crawler - starting 1 downloader threads...\n",
            "2020-04-01 16:08:46,399 - INFO - parser - parsing result page https://www.bing.com/images/async?q=forest&first=0\n",
            "2020-04-01 16:08:46,439 - INFO - downloader - image #1\thttp://i.ytimg.com/vi/oHLIyot2ArA/maxresdefault.jpg\n",
            "2020-04-01 16:08:46,468 - INFO - downloader - image #2\thttps://i.ytimg.com/vi/OtP8wicpJFk/maxresdefault.jpg\n",
            "2020-04-01 16:08:49,206 - INFO - downloader - image #3\thttps://upload.wikimedia.org/wikipedia/commons/8/8c/Creek_and_old-growth_forest-Larch_Mountain.jpg\n",
            "2020-04-01 16:08:53,514 - INFO - downloader - image #4\thttps://news.ufl.edu/media/newsufledu/images/2018/03/Climate-change-forests-square.jpg\n",
            "2020-04-01 16:08:55,252 - INFO - downloader - image #5\thttps://images.theconversation.com/files/94013/original/image-20150907-22253-rnp0iv.jpg\n",
            "2020-04-01 16:08:55,281 - INFO - downloader - image #6\thttps://i.ytimg.com/vi/6N7ot9LpF8g/maxresdefault.jpg\n",
            "2020-04-01 16:08:55,296 - INFO - downloader - image #7\thttp://1.bp.blogspot.com/-GR4FJ64aBCc/TtzQ6YTdw_I/AAAAAAAAAuE/OaD9RY3Y00k/s1600/forest-background-6-748902.jpg\n",
            "2020-04-01 16:08:55,311 - INFO - downloader - image #8\thttps://i.ytimg.com/vi/_2fzRP7Kt5c/maxresdefault.jpg\n",
            "2020-04-01 16:08:55,317 - INFO - downloader - image #9\thttps://i.ytimg.com/vi/q6F05r2TtMQ/maxresdefault.jpg\n",
            "2020-04-01 16:08:55,325 - INFO - downloader - image #10\thttps://i.ytimg.com/vi/1TrvyTGr_is/maxresdefault.jpg\n",
            "2020-04-01 16:08:56,513 - INFO - downloader - image #11\thttp://assets.worldwildlife.org/photos/2842/images/hero_small/shutterstock_12730534.jpg\n",
            "2020-04-01 16:08:56,833 - ERROR - downloader - Response status code 404, file https://upload.wikimedia.org/wikipedia/commons/thumb/1/17/Tarvasj%C3%B5gi.jpg\n",
            "2020-04-01 16:08:56,845 - INFO - downloader - image #12\thttps://i.ytimg.com/vi/TOHYkccYfUk/maxresdefault.jpg\n",
            "2020-04-01 16:08:56,856 - INFO - downloader - image #13\thttps://i.ytimg.com/vi/3cPH_fKf-Bk/hqdefault.jpg\n",
            "2020-04-01 16:08:57,978 - INFO - downloader - image #14\thttps://cdn.onlyinyourstate.com/wp-content/uploads/2018/03/2-96.jpg\n",
            "2020-04-01 16:08:57,990 - INFO - downloader - image #15\thttps://i.ytimg.com/vi/nqGXYz5Sln4/maxresdefault.jpg\n",
            "2020-04-01 16:08:58,000 - ERROR - downloader - Response status code 404, file http://i.ytimg.com/vi/Vsg7hTRX9iI/maxresdefault.jpg\n",
            "2020-04-01 16:08:58,007 - INFO - downloader - image #16\thttps://i.ytimg.com/vi/U9S6eBZkndc/maxresdefault.jpg\n",
            "2020-04-01 16:08:58,016 - INFO - downloader - image #17\thttps://i.ytimg.com/vi/jfa29pq6NFs/maxresdefault.jpg\n",
            "2020-04-01 16:08:58,379 - INFO - downloader - image #18\thttp://i.telegraph.co.uk/multimedia/archive/01809/The_Wyre_Forest_1809317c.jpg\n",
            "2020-04-01 16:08:58,871 - INFO - downloader - image #19\thttps://i.ytimg.com/vi/A6QLzeOLV40/maxresdefault.jpg\n",
            "2020-04-01 16:08:59,996 - INFO - downloader - image #20\thttps://upload.wikimedia.org/wikipedia/commons/8/8b/Thetford_forest_dtab.jpg\n",
            "2020-04-01 16:09:02,875 - INFO - parser - no more page urls for thread parser-001 to parse\n",
            "2020-04-01 16:09:02,876 - INFO - parser - thread parser-001 exit\n",
            "2020-04-01 16:09:03,818 - INFO - downloader - downloaded images reach max num, thread downloader-001 is ready to exit\n",
            "2020-04-01 16:09:03,819 - INFO - downloader - thread downloader-001 exit\n",
            "2020-04-01 16:09:03,937 - INFO - icrawler.crawler - Crawling task done!\n",
            "2020-04-01 16:09:03,948 - INFO - icrawler.crawler - start crawling...\n",
            "2020-04-01 16:09:03,949 - INFO - icrawler.crawler - starting 1 feeder threads...\n",
            "2020-04-01 16:09:03,950 - INFO - feeder - thread feeder-001 exit\n",
            "2020-04-01 16:09:03,950 - INFO - icrawler.crawler - starting 1 parser threads...\n",
            "2020-04-01 16:09:03,953 - INFO - icrawler.crawler - starting 1 downloader threads...\n",
            "2020-04-01 16:09:04,453 - INFO - parser - parsing result page https://www.bing.com/images/async?q=nature scenes&first=0\n",
            "2020-04-01 16:09:04,497 - INFO - downloader - image #1\thttps://speakzeasy.files.wordpress.com/2013/12/free-wallpaper-nature-scenes.jpg\n",
            "2020-04-01 16:09:04,862 - INFO - downloader - image #2\thttp://2.bp.blogspot.com/-I91kB3xYppQ/TZDPvc-ilzI/AAAAAAAABvw/JQBz0_8E_I4/s1600/Natural+Scenes+%25286%2529.jpg\n",
            "2020-04-01 16:09:05,021 - INFO - downloader - image #3\thttps://i.ytimg.com/vi/0rMEnB8ZYZg/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,031 - INFO - downloader - image #4\thttps://i.ytimg.com/vi/EDir9-UoPjo/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,060 - INFO - downloader - image #5\thttps://freeinfonature.files.wordpress.com/2012/07/plitvice-national-park.jpg\n",
            "2020-04-01 16:09:05,069 - INFO - downloader - image #6\thttps://i.ytimg.com/vi/IwX3bjzbLLI/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,081 - INFO - downloader - image #7\thttp://i.ytimg.com/vi/u3W1lkCv8vM/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,094 - INFO - downloader - image #8\thttps://i.ytimg.com/vi/LVipeo8wtTY/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,103 - INFO - downloader - image #9\thttp://i.ytimg.com/vi/VW0TdN5r4ug/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,115 - INFO - downloader - image #10\thttps://i.ytimg.com/vi/VkvcpHuEDKc/maxresdefault.jpg\n",
            "2020-04-01 16:09:05,395 - INFO - downloader - image #11\thttp://2.bp.blogspot.com/-90RlMBH9dg0/TZDP3bTyGZI/AAAAAAAABwE/Gq5LZMmsQqg/s1600/Natural+Scenes+%252811%2529.jpg\n",
            "2020-04-01 16:09:05,424 - INFO - downloader - image #12\thttp://beautifulcoolwallpapers.files.wordpress.com/2011/07/3dnaturewallpapers2-3.jpg\n",
            "2020-04-01 16:09:05,434 - INFO - downloader - image #13\thttps://i.ytimg.com/vi/neU08oGBUks/hqdefault.jpg\n",
            "2020-04-01 16:09:08,086 - INFO - downloader - image #14\thttp://www.fotolip.com/wp-content/uploads/2016/05/Nature-Scenes-21.jpg\n",
            "2020-04-01 16:09:08,149 - INFO - downloader - image #15\thttps://yunusadhyprasetyo.files.wordpress.com/2013/01/img_8190trip3-copy.jpg\n",
            "2020-04-01 16:09:08,522 - INFO - downloader - image #16\thttp://4.bp.blogspot.com/-oPDJM2wN8Ws/VTy7xDk53FI/AAAAAAAAEPs/r0j9fF0zN0U/s1600/Nature-Scenes-Wallpaper-5.jpg\n",
            "2020-04-01 16:09:08,786 - INFO - downloader - image #17\thttps://i.pinimg.com/originals/a6/0c/2f/a60c2f6162972d3059b8ce681a43463e.jpg\n",
            "2020-04-01 16:09:08,799 - INFO - downloader - image #18\thttps://i.ytimg.com/vi/84DHahdDW6c/maxresdefault.jpg\n",
            "2020-04-01 16:09:08,809 - INFO - downloader - image #19\thttps://i.ytimg.com/vi/6NVCKeiqvtY/maxresdefault.jpg\n",
            "2020-04-01 16:09:08,819 - INFO - downloader - image #20\thttps://i.ytimg.com/vi/8RAkmxbmv2M/hqdefault.jpg\n",
            "2020-04-01 16:09:08,844 - INFO - downloader - image #21\thttps://fashionfreeway.files.wordpress.com/2012/03/591991220.jpg\n",
            "2020-04-01 16:09:09,483 - INFO - downloader - image #22\thttp://3.bp.blogspot.com/_-mY2ck9YCeE/TP4wO46tgPI/AAAAAAAAA1M/xU71ee6c50o/s1600/nature6.jpg\n",
            "2020-04-01 16:09:12,646 - INFO - downloader - image #23\thttp://getwallpapers.com/wallpaper/full/d/1/e/111197.jpg\n",
            "2020-04-01 16:09:12,841 - INFO - downloader - image #24\thttp://2.bp.blogspot.com/-wrmwUHfAKM0/TZDP1HzNEtI/AAAAAAAABwA/NA8IChGpqK4/s1600/Natural+Scenes+%252810%2529.jpg\n",
            "2020-04-01 16:09:13,027 - INFO - downloader - image #25\thttps://i.ytimg.com/vi/yp9N9bi5Q_A/maxresdefault.jpg\n",
            "2020-04-01 16:09:13,034 - INFO - downloader - image #26\thttps://i.ytimg.com/vi/58edc4IL5MA/maxresdefault.jpg\n",
            "2020-04-01 16:09:13,403 - INFO - downloader - image #27\thttp://3.bp.blogspot.com/-NCM_HWq7d1w/TdU2BZ_cKaI/AAAAAAAAAR8/RQzmuFVdbOA/s1600/1280_1024_nature_scene_wallpaper_1600FG100_2_083.jpg\n",
            "2020-04-01 16:09:13,719 - INFO - parser - parsing result page https://www.bing.com/images/async?q=nature scenes&first=20\n",
            "2020-04-01 16:09:16,164 - INFO - downloader - image #28\thttp://www.fotolip.com/wp-content/uploads/2016/05/Nature-Scenes-11.jpg\n",
            "2020-04-01 16:09:16,172 - INFO - downloader - image #29\thttps://i.ytimg.com/vi/x30YOmfeVTE/maxresdefault.jpg\n",
            "2020-04-01 16:09:16,184 - INFO - downloader - image #30\thttps://i.ytimg.com/vi/vm4qB8KzMtU/maxresdefault.jpg\n",
            "2020-04-01 16:09:16,585 - INFO - downloader - image #31\thttp://4.bp.blogspot.com/-RDoG20tA884/ToOQrTTq57I/AAAAAAAAAGk/-zxtaBIxkz4/s1600/nature+scenes+wallpaper-3.jpg\n",
            "2020-04-01 16:09:16,597 - INFO - downloader - image #32\thttps://i.ytimg.com/vi/KYuOU1uIX_c/maxresdefault.jpg\n",
            "2020-04-01 16:09:17,002 - INFO - downloader - image #33\thttps://i.ytimg.com/vi/oJDnWmGgz5s/maxresdefault.jpg\n",
            "2020-04-01 16:09:17,873 - INFO - downloader - image #34\thttp://3.bp.blogspot.com/-xkbGm7TDh_g/UATxGPRuAII/AAAAAAAAAhk/eM4LB-6nkgk/s1600/1280_1024_nature_scene_wallpaper_1600FG100_069.jpg\n",
            "2020-04-01 16:09:17,882 - INFO - downloader - image #35\thttps://i.ytimg.com/vi/OvXJ5XnYdZE/maxresdefault.jpg\n",
            "2020-04-01 16:09:17,892 - INFO - downloader - image #36\thttps://i.ytimg.com/vi/rCwnT-LwQDg/hqdefault.jpg\n",
            "2020-04-01 16:09:19,715 - INFO - downloader - image #37\thttps://media.treehugger.com/assets/images/2011/10/miniature-mountain-scene.jpg\n",
            "2020-04-01 16:09:21,873 - INFO - downloader - image #38\thttps://georgiacremation.com/welisten/wp-content/uploads/2017/10/nature-scene.jpg\n",
            "2020-04-01 16:09:22,469 - INFO - downloader - image #39\thttps://i.ytimg.com/vi/E2eIfrv6JU8/maxresdefault.jpg\n",
            "2020-04-01 16:09:22,826 - INFO - downloader - image #40\thttp://3.bp.blogspot.com/-NXJABFHD6CE/UAPoeYnhDzI/AAAAAAAALpU/lptIBkTFS60/s1600/natural+scene+image+%282%29.jpg\n",
            "2020-04-01 16:09:23,053 - INFO - parser - parsing result page https://www.bing.com/images/async?q=nature scenes&first=40\n",
            "2020-04-01 16:09:24,860 - INFO - downloader - image #41\thttp://www.lotuspointwellness.com/wp-content/uploads/2016/03/nature-scenes-wallpaper-1.jpg\n",
            "2020-04-01 16:09:24,874 - INFO - downloader - image #42\thttp://i.ytimg.com/vi/dTp72sci8CA/maxresdefault.jpg\n",
            "2020-04-01 16:09:27,022 - INFO - downloader - image #43\thttp://getwallpapers.com/wallpaper/full/d/1/b/362327.jpg\n",
            "2020-04-01 16:09:27,183 - INFO - downloader - image #44\thttp://img.xcitefun.net/users/2011/08/261118,xcitefun-relaxing-nature-scene.jpg\n",
            "2020-04-01 16:09:27,680 - INFO - downloader - image #45\thttps://2.bp.blogspot.com/-AVUSC1Ttyug/UsEuvS7oN9I/AAAAAAAADWI/CdACKvdxfwc/s1600/natural+scene.jpg\n",
            "2020-04-01 16:09:29,493 - INFO - downloader - image #46\thttp://imgs.abduzeedo.com/files/articles/beautiful-3d-nature-scenes/1232045_large.jpg\n",
            "2020-04-01 16:09:33,946 - INFO - downloader - image #47\thttps://cdn.wallpapersafari.com/46/66/8egEQy.jpg\n",
            "2020-04-01 16:09:34,969 - INFO - downloader - image #48\thttps://1.bp.blogspot.com/_ioD6UOQqcyA/Sw7r2joWxXI/AAAAAAAABbI/CiKFNlR1P4Q/s1600/Coquille%2BRiver%2Bin%2BSpring,%2BSiskiyou%2BNational%2BForest,%2BOregon.jpg\n",
            "2020-04-01 16:09:35,150 - INFO - downloader - image #49\thttp://4.bp.blogspot.com/-MSGufwugCQA/Tl8Ff3cAbVI/AAAAAAAAAvk/33Wf72CHSgo/s1600/5.jpg\n",
            "2020-04-01 16:09:36,191 - INFO - parser - parsing result page https://www.bing.com/images/async?q=nature scenes&first=60\n",
            "2020-04-01 16:09:40,168 - ERROR - downloader - Exception caught when downloading file http://img.xcitefun.net/users/2011/08/261109,xcitefun-relaxing-nature-scene-9.jpg, error: HTTPSConnectionPool(host='img.xcitefun.net', port=443): Read timed out. (read timeout=5), remaining retry times: 2\n",
            "2020-04-01 16:09:42,033 - INFO - downloader - image #50\thttp://img.xcitefun.net/users/2011/08/261109,xcitefun-relaxing-nature-scene-9.jpg\n",
            "2020-04-01 16:09:42,276 - INFO - downloader - image #51\thttps://i.ytimg.com/vi/4lpsdKuhJ-g/maxresdefault.jpg\n",
            "2020-04-01 16:09:42,652 - INFO - downloader - image #52\thttp://upload.wikimedia.org/wikipedia/commons/c/c5/Nature_Scene.jpg\n",
            "2020-04-01 16:09:43,831 - INFO - downloader - image #53\thttps://s-media-cache-ak0.pinimg.com/originals/8b/75/f5/8b75f5760662bc9207b03da1cce5c5cf.jpg\n",
            "2020-04-01 16:09:44,841 - INFO - downloader - image #54\thttp://1.bp.blogspot.com/-KOy0rj-HZQM/T-soIxYmS9I/AAAAAAAAAxk/m1W4FHU6a2w/s1600/Natural+scenes+%281%29359.jpg\n",
            "2020-04-01 16:09:45,634 - INFO - downloader - image #55\thttp://1.bp.blogspot.com/-fLNR9IFjZEE/UAPoIEEznmI/AAAAAAAALpM/iKf3HdpMm8E/s1600/natural+scene+image+(1).jpg\n",
            "2020-04-01 16:09:46,910 - INFO - downloader - image #56\thttps://www.curearthritis.org/wp-content/uploads/arthritis-poem.jpg\n",
            "2020-04-01 16:09:50,001 - INFO - downloader - image #57\thttp://wallpapercave.com/wp/2DxhqZ6.jpg\n",
            "2020-04-01 16:09:50,240 - INFO - parser - parsing result page https://www.bing.com/images/async?q=nature scenes&first=80\n",
            "2020-04-01 16:09:50,641 - INFO - downloader - image #58\thttp://2.bp.blogspot.com/-x1kTluVwm7w/UUMn89GYdMI/AAAAAAAAUjI/rYf1pdikUws/s1600/Nature+Scenery+Wallpapers+1.jpg\n",
            "2020-04-01 16:09:51,762 - INFO - downloader - image #59\thttps://opimedia.azureedge.net/-/media/images/men/editorial/articles/magazine-articles/2015/12-01/seeing-green-the-importance-of-nature-for-our-health/nature-scene-jpg.jpg\n",
            "2020-04-01 16:09:54,013 - INFO - downloader - image #60\thttps://cdn.wallpapersafari.com/5/7/yqe3im.jpg\n",
            "2020-04-01 16:09:54,075 - INFO - downloader - image #61\thttp://2.bp.blogspot.com/-qKDLV1wxrMA/UMIAfeT19LI/AAAAAAAAOsE/VhSoAvOwBtU/s1600/Beautiful+Autumn+Scenery+Wallpapers+05.jpg\n",
            "2020-04-01 16:09:55,099 - INFO - downloader - image #62\thttp://2.bp.blogspot.com/-nYAomHLlNrc/TZDP0LIIfaI/AAAAAAAABv8/Ecrp37OVGsM/s1600/Natural+Scenes+%25289%2529.jpg\n",
            "2020-04-01 16:09:55,358 - INFO - downloader - image #63\thttp://3.bp.blogspot.com/-3McFIkwgQH0/Tm_TJzkAXlI/AAAAAAAAFFs/oclgHN5Yu7c/s1600/nature+scenes+wallpaper-3.jpg\n",
            "2020-04-01 16:09:56,406 - INFO - downloader - image #64\thttp://4.bp.blogspot.com/-YLSpC8M6utg/UqQ8MjJn31I/AAAAAAAAI9A/uuvAQ7smeH4/s1600/natural+screensaver+Wallpapers+(1).jpg\n",
            "2020-04-01 16:09:56,417 - INFO - downloader - image #65\thttps://i.ytimg.com/vi/Tl_DqWDk6w0/maxresdefault.jpg\n",
            "2020-04-01 16:09:57,635 - INFO - downloader - image #66\thttps://static.boredpanda.com/blog/wp-content/uploads/2015/12/stock-photo-131945715__880.jpg\n",
            "2020-04-01 16:09:58,597 - INFO - downloader - image #67\thttp://1.bp.blogspot.com/-mvjtCRd2q_Y/UNGWe8YD0tI/AAAAAAAAAJg/l4tESKimOsA/s1600/02785_aspenwetland_1280x1024.jpg\n",
            "2020-04-01 16:09:59,654 - INFO - downloader - image #68\thttp://3.bp.blogspot.com/-OKU6457xLTk/U7m5_TCm7LI/AAAAAAAAGnc/siXdVBDetNE/s1600/IMG_1017.jpg\n",
            "2020-04-01 16:10:00,473 - INFO - downloader - image #69\thttp://4.bp.blogspot.com/-AeLGXSbZ8G0/UAPo91pl8OI/AAAAAAAALp8/vtN_bexrkSY/s1600/natural+scene+image+%287%29.jpg\n",
            "2020-04-01 16:10:00,479 - INFO - downloader - image #70\thttps://i.ytimg.com/vi/pEw4m_X8ceE/hqdefault.jpg\n",
            "2020-04-01 16:10:00,639 - INFO - downloader - image #71\thttp://i.ytimg.com/vi/XDR9MT2C3No/maxresdefault.jpg\n",
            "2020-04-01 16:10:02,603 - INFO - parser - no more page urls for thread parser-001 to parse\n",
            "2020-04-01 16:10:02,604 - INFO - parser - thread parser-001 exit\n",
            "2020-04-01 16:10:03,127 - INFO - downloader - image #72\thttp://getwallpapers.com/wallpaper/full/8/c/f/623244.jpg\n",
            "2020-04-01 16:10:03,174 - INFO - downloader - image #73\thttps://s-media-cache-ak0.pinimg.com/originals/34/c9/c5/34c9c58025c05396d5e8dbe8f225c2a5.jpg\n",
            "2020-04-01 16:10:03,183 - INFO - downloader - image #74\thttps://i.ytimg.com/vi/rTN8frhWOlA/maxresdefault.jpg\n",
            "2020-04-01 16:10:03,483 - INFO - downloader - image #75\thttp://4.bp.blogspot.com/_HeMkQFoSCkY/TUWQC4bzCMI/AAAAAAAADts/RAq_4lnd0Sc/s1600/A+Gift+from+Afar.jpg\n",
            "2020-04-01 16:10:04,226 - INFO - downloader - image #76\thttps://cloud.netlifyusercontent.com/assets/344dbf88-fdf9-42bb-adb4-46f01eedd629/833be8b5-9db0-4e10-b89d-737d6394ade9/nature-wallpaper20.jpg\n",
            "2020-04-01 16:10:09,228 - INFO - downloader - no more download task for thread downloader-001\n",
            "2020-04-01 16:10:09,229 - INFO - downloader - thread downloader-001 exit\n",
            "2020-04-01 16:10:10,030 - INFO - icrawler.crawler - Crawling task done!\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfO_Vkl5cBS7",
        "colab_type": "text"
      },
      "source": [
        "Load images from directory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZSomNPnrLhJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "57c2548d-79e6-4f72-ddb4-03b9fe63bf00"
      },
      "source": [
        "forest_data_gen = image_generator.flow_from_directory(directory='./data/forest',\n",
        "                                                     batch_size=BATCH_SIZE,\n",
        "                                                     shuffle=True,\n",
        "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH))\n",
        "\n",
        "nature_data_gen = image_generator.flow_from_directory(directory='./data/nature_scenes',\n",
        "                                                     batch_size=BATCH_SIZE,\n",
        "                                                     shuffle=True,\n",
        "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 20 images belonging to 1 classes.\n",
            "Found 76 images belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lmm7Cpl_cF_W",
        "colab_type": "text"
      },
      "source": [
        "Generate Artworks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lh2XoELo6jIE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict_and_display(generator_g, forest_data_gen, 20)\n",
        "predict_and_display(generator_g, waterfall_data_gen, 20)\n",
        "predict_and_display(generator_g, nature_data_gen, 76)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGr8aQ8WjO-W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5a435a0-3b1d-4c28-82c6-17431f9fcf20"
      },
      "source": [
        "!ls -l './drive/My Drive/Cycle-GAN Artworks/'"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1515\n",
            "-rw------- 1 root root 14095 Apr  1 16:16 100.jpeg\n",
            "-rw------- 1 root root 12485 Apr  1 16:16 101.jpeg\n",
            "-rw------- 1 root root 14060 Apr  1 16:16 102.jpeg\n",
            "-rw------- 1 root root 10282 Apr  1 16:16 103.jpeg\n",
            "-rw------- 1 root root 16903 Apr  1 16:16 104.jpeg\n",
            "-rw------- 1 root root 17221 Apr  1 16:16 105.jpeg\n",
            "-rw------- 1 root root 13043 Apr  1 16:16 106.jpeg\n",
            "-rw------- 1 root root 19958 Apr  1 16:16 107.jpeg\n",
            "-rw------- 1 root root 17824 Apr  1 16:16 108.jpeg\n",
            "-rw------- 1 root root 13075 Apr  1 16:16 109.jpeg\n",
            "-rw------- 1 root root 14370 Apr  1 16:16 110.jpeg\n",
            "-rw------- 1 root root 18855 Apr  1 16:16 111.jpeg\n",
            "-rw------- 1 root root 15191 Apr  1 16:16 112.jpeg\n",
            "-rw------- 1 root root 19437 Apr  1 16:16 113.jpeg\n",
            "-rw------- 1 root root 14477 Apr  1 16:16 114.jpeg\n",
            "-rw------- 1 root root 19210 Apr  1 16:16 115.jpeg\n",
            "-rw------- 1 root root 13377 Apr  1 16:16 116.jpeg\n",
            "-rw------- 1 root root 13271 Apr  1 16:16 117.jpeg\n",
            "-rw------- 1 root root 10989 Apr  1 16:16 118.jpeg\n",
            "-rw------- 1 root root 13715 Apr  1 16:16 119.jpeg\n",
            "-rw------- 1 root root 11502 Apr  1 16:16 120.jpeg\n",
            "-rw------- 1 root root 13726 Apr  1 16:16 121.jpeg\n",
            "-rw------- 1 root root 15782 Apr  1 16:16 122.jpeg\n",
            "-rw------- 1 root root 15095 Apr  1 16:16 123.jpeg\n",
            "-rw------- 1 root root 14151 Apr  1 16:16 124.jpeg\n",
            "-rw------- 1 root root 16179 Apr  1 16:16 125.jpeg\n",
            "-rw------- 1 root root 20978 Apr  1 16:16 126.jpeg\n",
            "-rw------- 1 root root 14943 Apr  1 16:16 127.jpeg\n",
            "-rw------- 1 root root 16262 Apr  1 16:16 128.jpeg\n",
            "-rw------- 1 root root 12377 Apr  1 16:16 129.jpeg\n",
            "-rw------- 1 root root 21779 Apr  1 16:16 130.jpeg\n",
            "-rw------- 1 root root 17188 Apr  1 16:16 131.jpeg\n",
            "-rw------- 1 root root 18536 Apr  1 16:16 132.jpeg\n",
            "-rw------- 1 root root 15138 Apr  1 16:16 133.jpeg\n",
            "-rw------- 1 root root 14223 Apr  1 16:16 134.jpeg\n",
            "-rw------- 1 root root 14936 Apr  1 16:16 135.jpeg\n",
            "-rw------- 1 root root 15457 Apr  1 16:16 136.jpeg\n",
            "-rw------- 1 root root 13396 Apr  1 16:16 137.jpeg\n",
            "-rw------- 1 root root 16210 Apr  1 16:16 138.jpeg\n",
            "-rw------- 1 root root 15708 Apr  1 16:16 139.jpeg\n",
            "-rw------- 1 root root 18447 Apr  1 16:16 140.jpeg\n",
            "-rw------- 1 root root 16213 Apr  1 16:16 141.jpeg\n",
            "-rw------- 1 root root 19423 Apr  1 16:16 142.jpeg\n",
            "-rw------- 1 root root 16169 Apr  1 16:16 143.jpeg\n",
            "-rw------- 1 root root 18341 Apr  1 16:16 144.jpeg\n",
            "-rw------- 1 root root 15012 Apr  1 16:16 145.jpeg\n",
            "-rw------- 1 root root 14768 Apr  1 16:16 146.jpeg\n",
            "-rw------- 1 root root 12450 Apr  1 16:16 147.jpeg\n",
            "-rw------- 1 root root 23628 Apr  1 16:16 148.jpeg\n",
            "-rw------- 1 root root 16462 Apr  1 16:16 149.jpeg\n",
            "-rw------- 1 root root 11191 Apr  1 16:16 150.jpeg\n",
            "-rw------- 1 root root 20010 Apr  1 16:16 151.jpeg\n",
            "-rw------- 1 root root 18864 Apr  1 16:16 20.jpeg\n",
            "-rw------- 1 root root 16578 Apr  1 16:16 21.jpeg\n",
            "-rw------- 1 root root 16360 Apr  1 16:16 22.jpeg\n",
            "-rw------- 1 root root 18313 Apr  1 16:16 23.jpeg\n",
            "-rw------- 1 root root 14784 Apr  1 16:16 24.jpeg\n",
            "-rw------- 1 root root 14086 Apr  1 16:16 25.jpeg\n",
            "-rw------- 1 root root 15244 Apr  1 16:16 26.jpeg\n",
            "-rw------- 1 root root 14164 Apr  1 16:16 27.jpeg\n",
            "-rw------- 1 root root 13705 Apr  1 16:16 28.jpeg\n",
            "-rw------- 1 root root 15409 Apr  1 16:16 29.jpeg\n",
            "-rw------- 1 root root 22613 Apr  1 16:16 30.jpeg\n",
            "-rw------- 1 root root 12151 Apr  1 16:16 31.jpeg\n",
            "-rw------- 1 root root 17698 Apr  1 16:16 32.jpeg\n",
            "-rw------- 1 root root 20841 Apr  1 16:16 33.jpeg\n",
            "-rw------- 1 root root 18399 Apr  1 16:16 34.jpeg\n",
            "-rw------- 1 root root 16270 Apr  1 16:16 35.jpeg\n",
            "-rw------- 1 root root 15882 Apr  1 16:16 36.jpeg\n",
            "-rw------- 1 root root 15882 Apr  1 16:16 37.jpeg\n",
            "-rw------- 1 root root 18864 Apr  1 16:16 38.jpeg\n",
            "-rw------- 1 root root 17952 Apr  1 16:16 39.jpeg\n",
            "-rw------- 1 root root 13813 Apr  1 16:16 76.jpeg\n",
            "-rw------- 1 root root 18595 Apr  1 16:16 77.jpeg\n",
            "-rw------- 1 root root 14260 Apr  1 16:16 78.jpeg\n",
            "-rw------- 1 root root 20591 Apr  1 16:16 79.jpeg\n",
            "-rw------- 1 root root 16107 Apr  1 16:16 80.jpeg\n",
            "-rw------- 1 root root 21832 Apr  1 16:16 81.jpeg\n",
            "-rw------- 1 root root 11695 Apr  1 16:16 82.jpeg\n",
            "-rw------- 1 root root 14743 Apr  1 16:16 83.jpeg\n",
            "-rw------- 1 root root 19318 Apr  1 16:16 84.jpeg\n",
            "-rw------- 1 root root 12526 Apr  1 16:16 85.jpeg\n",
            "-rw------- 1 root root 10527 Apr  1 16:16 86.jpeg\n",
            "-rw------- 1 root root 13809 Apr  1 16:16 87.jpeg\n",
            "-rw------- 1 root root 13108 Apr  1 16:16 88.jpeg\n",
            "-rw------- 1 root root 17421 Apr  1 16:16 89.jpeg\n",
            "-rw------- 1 root root 12912 Apr  1 16:16 90.jpeg\n",
            "-rw------- 1 root root 19431 Apr  1 16:16 91.jpeg\n",
            "-rw------- 1 root root 12363 Apr  1 16:16 92.jpeg\n",
            "-rw------- 1 root root 14287 Apr  1 16:16 93.jpeg\n",
            "-rw------- 1 root root 15753 Apr  1 16:16 94.jpeg\n",
            "-rw------- 1 root root 19935 Apr  1 16:16 95.jpeg\n",
            "-rw------- 1 root root 18871 Apr  1 16:16 96.jpeg\n",
            "-rw------- 1 root root 15780 Apr  1 16:16 97.jpeg\n",
            "-rw------- 1 root root 17731 Apr  1 16:16 98.jpeg\n",
            "-rw------- 1 root root 12284 Apr  1 16:16 99.jpeg\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}