Abstract_art_conversion_using_Neural_Style_Transfer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM8dZXQyoa73I8VLP53/lHQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rithikafjohnson/AI-GENERATED-ART/blob/main/Abstract_art_conversion_using_Neural_Style_Transfer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uZ5azKxWu_o1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import tensorflow as tf\n",
        "# Load compressed models from tensorflow_hub\n",
        "os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'\n",
        "import tensorflow_hub as hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import IPython.display as display\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "mpl.rcParams['figure.figsize'] = (12, 12)\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "\n",
        "import numpy as np\n",
        "import PIL.Image\n",
        "import time\n",
        "import functools"
      ],
      "metadata": {
        "id": "AdXl8WpovWdJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tensor_to_image(tensor):\n",
        "  tensor = tensor*255\n",
        "  tensor = np.array(tensor, dtype=np.uint8)\n",
        "  if np.ndim(tensor)>3:\n",
        "    assert tensor.shape[0] == 1\n",
        "    tensor = tensor[0]\n",
        "  return PIL.Image.fromarray(tensor)"
      ],
      "metadata": {
        "id": "jAmneQC0vYg8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#style_path = tf.keras.utils.get_file('kandinsky5.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')\n",
        "#style_path = tf.keras.utils.get_file('shutterstock_1181534704.jpg','https://www.shutterstock.com/blog/wp-content/uploads/sites/5/2020/07/shutterstock_1181534704.jpg?w=750')\n",
        "style_path = tf.keras.utils.get_file('images.jpg','https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQskerveJy-siKWmPyoEh5KI9wjxVTmbrmaV-9uowQBtx_YekQbIzgBwdO5SGwx_VNvzf0&usqp=CAU')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjlTQONdvcJf",
        "outputId": "addc7cea-670b-4fcc-dcee-8d815bb8d193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQskerveJy-siKWmPyoEh5KI9wjxVTmbrmaV-9uowQBtx_YekQbIzgBwdO5SGwx_VNvzf0&usqp=CAU\n",
            "24576/22205 [=================================] - 0s 0us/step\n",
            "32768/22205 [============================================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_img(path_to_img):\n",
        "  max_dim = 512\n",
        "  img = tf.io.read_file(path_to_img)\n",
        "  img = tf.image.decode_image(img, channels=3)\n",
        "  img = tf.image.convert_image_dtype(img, tf.float32)\n",
        "\n",
        "  shape = tf.cast(tf.shape(img)[:-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",
        "  img = tf.image.resize(img, new_shape)\n",
        "  img = img[tf.newaxis, :]\n",
        "  return img"
      ],
      "metadata": {
        "id": "Hy2hJAr7veWP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imshow(image, title=None):\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)"
      ],
      "metadata": {
        "id": "5mR7JTVfvhTk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "implementation for conversion "
      ],
      "metadata": {
        "id": "kz8apPXKwyTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9YlHMfnv709",
        "outputId": "bb12dfda-c8c8-42fd-e97b-f09fdfda64ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path='/content/drive/MyDrive/abstract  base pictures'"
      ],
      "metadata": {
        "id": "6AL01sIXKerj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in os.listdir(path):\n",
        "  print(i)\n",
        "  content_path =path+'/'+i\n",
        "  style_image = load_img(style_path)\n",
        "  content_image = load_img(content_path)\n",
        "  hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')\n",
        "  stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]\n",
        "  tensor_to_image(stylized_image).save('/content/drive/MyDrive/abstract art converted folder/pattern3/'+i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8XmgFOK1PlN",
        "outputId": "184c891d-c6d5-4f60-ce2e-aa0878aadee3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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