C4_W1_Lab_2_Fast_NST.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "C4_W1_Lab_2_Fast_NST.ipynb",
      "private_outputs": true,
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4Y03ZD2-imD"
      },
      "source": [
        "# Ungraded Lab: Fast Neural Style Transfer\n",
        "\n",
        "This lab will demonstrate [Fast Neural Style Transfer](https://arxiv.org/abs/1705.06830). Instead of implementing it yourself, you will download an available [model from Tensorflow Hub](https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2) and apply it to the images you used in the previous lab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXWF1akL_Q74"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0F5oBr-O8RH"
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "  \n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svuuU-Iu_Ske"
      },
      "source": [
        "## Utilities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMcuZs-UPS-D"
      },
      "source": [
        "def tensor_to_image(tensor):\n",
        "  '''converts a tensor to an image'''\n",
        "  tensor_shape = tf.shape(tensor)\n",
        "  number_elem_shape = tf.shape(tensor_shape)\n",
        "  if number_elem_shape > 3:\n",
        "    assert tensor_shape[0] == 1\n",
        "    tensor = tensor[0]\n",
        "  return tf.keras.preprocessing.image.array_to_img(tensor) \n",
        "\n",
        "\n",
        "def load_img(path_to_img):\n",
        "  '''loads an image as a tensor and scales it to 512 pixels'''\n",
        "  max_dim = 512\n",
        "  image = tf.io.read_file(path_to_img)\n",
        "  image = tf.image.decode_jpeg(image)\n",
        "  image = tf.image.convert_image_dtype(image, tf.float32)\n",
        "\n",
        "  shape = tf.shape(image)[:-1]\n",
        "  shape = tf.cast(tf.shape(image)[:-1], tf.float32)\n",
        "  long_dim = max(shape)\n",
        "  scale = max_dim / long_dim\n",
        "\n",
        "  new_shape = tf.cast(shape * scale, tf.int32)\n",
        "\n",
        "  image = tf.image.resize(image, new_shape)\n",
        "  image = image[tf.newaxis, :]\n",
        "  image = tf.image.convert_image_dtype(image, tf.uint8)\n",
        "\n",
        "  return image\n",
        "\n",
        "\n",
        "def load_images(content_path, style_path):\n",
        "  '''loads the content and path images as tensors'''\n",
        "  content_image = load_img(\"{}\".format(content_path))\n",
        "  style_image = load_img(\"{}\".format(style_path))\n",
        "\n",
        "  return content_image, style_image\n",
        "\n",
        "\n",
        "def imshow(image, title=None):\n",
        "  '''displays an image with a corresponding title'''\n",
        "  if len(image.shape) > 3:\n",
        "    image = tf.squeeze(image, axis=0)\n",
        "\n",
        "  plt.imshow(image)\n",
        "  if title:\n",
        "    plt.title(title)\n",
        "    \n",
        "    \n",
        "def show_images_with_objects(images, titles=[]):\n",
        "  '''displays a row of images with corresponding titles'''\n",
        "  if len(images) != len(titles):\n",
        "    return\n",
        "\n",
        "  plt.figure(figsize=(20, 12))\n",
        "  for idx, (image, title) in enumerate(zip(images, titles)):\n",
        "    plt.subplot(1, len(images), idx + 1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    imshow(image, title)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HztDrMKr_UqP"
      },
      "source": [
        "## Download the images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6oK104fPEjd"
      },
      "source": [
        "IMAGE_DIR = 'images'\n",
        "\n",
        "# create directory\n",
        "!mkdir {IMAGE_DIR}\n",
        "\n",
        "# download images to the directory you just created\n",
        "!wget -q -O ./images/cafe.jpg https://cdn.pixabay.com/photo/2018/07/14/15/27/cafe-3537801_1280.jpg\n",
        "!wget -q -O ./images/swan.jpg https://cdn.pixabay.com/photo/2017/02/28/23/00/swan-2107052_1280.jpg\n",
        "!wget -q -O ./images/tnj.jpg https://i.dawn.com/large/2019/10/5db6a03a4c7e3.jpg\n",
        "!wget -q -O ./images/rudolph.jpg https://cdn.pixabay.com/photo/2015/09/22/12/21/rudolph-951494_1280.jpg\n",
        "!wget -q -O ./images/dynamite.jpg https://cdn.pixabay.com/photo/2015/10/13/02/59/animals-985500_1280.jpg\n",
        "!wget -q -O ./images/painting.jpg https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg\n",
        "\n",
        "print(\"image files you can choose from: \")\n",
        "!ls images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOc6MutBVDm0"
      },
      "source": [
        "# set default images\n",
        "content_path = f'{IMAGE_DIR}/swan.jpg'\n",
        "style_path = f'{IMAGE_DIR}/painting.jpg'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3bBdTScVJ2p"
      },
      "source": [
        "# display the content and style image\n",
        "content_image, style_image = load_images(content_path, style_path)\n",
        "show_images_with_objects([content_image, style_image], \n",
        "                         titles=[f'content image: {content_path}',\n",
        "                                 f'style image: {style_path}'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VdZ7Jss_cVU"
      },
      "source": [
        "## Download the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKDdv4j_O2lj"
      },
      "source": [
        "# this will take a few minutes to load\n",
        "hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qgCFIWR_hyx"
      },
      "source": [
        "## Stylize the content image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlvkoZ-2_Nqo"
      },
      "source": [
        "# stylize the image using the model you just downloaded\n",
        "stylized_image = hub_module(tf.image.convert_image_dtype(content_image, tf.float32), \n",
        "                            tf.image.convert_image_dtype(style_image, tf.float32))[0]\n",
        "\n",
        "# convert the tensor to image\n",
        "tensor_to_image(stylized_image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-ogd5m2tXnxU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}