{"id":1599,"date":"2022-03-01T18:59:51","date_gmt":"2022-03-01T18:59:51","guid":{"rendered":"https:\/\/jenniferkwentoh.com\/?p=1599"},"modified":"2022-03-03T12:37:22","modified_gmt":"2022-03-03T12:37:22","slug":"neural-style-transfer-create-mardi-gras-art-with-python-tf-hub","status":"publish","type":"post","link":"https:\/\/jenniferkwentoh.com\/neural-style-transfer-create-mardi-gras-art-with-python-tf-hub\/","title":{"rendered":"Neural Style Transfer Create Mardi Gras\u00a0Art with Python TF Hub"},"content":{"rendered":"\n

Neural style transfer is referred to as an artistic algorithm that takes two images (in this case, a content image and a style reference image) and blends them to produce an image that looks like the content with attributes that take after the styling of the reference.<\/p>\n\n\n\n

In this tutorial, we would use a pre-trained deep learning (Convolutional Neural Network) model to create an image in the style of another one. <\/p>\n\n\n\n

TensorFlow Hub is a centralized repository of pre-trained machine learning<\/a> models. It provides a one-stop shop for developers who want to use machine learning in their applications without building and training their models from scratch.<\/p>\n\n\n\n

Mardi Gras<\/a> is a celebration of the last day before Lent. It’s a festival of food, music, and dance.<\/p>\n\n\n\n

It is one of the most famous festivals in the world and it takes place in New Orleans.<\/p>\n\n\n\n

We will use these Madia Gras<\/strong> and Paintings <\/strong>stock photos for the content and reference images<\/strong>. <\/p>\n\n\n\n

\n
\"neural
Neural style transfer content image
<\/figcaption><\/figure>\n\n\n\n
\"neural
Neural style transfer Reference Image
<\/figcaption><\/figure>\n\n\n\n
\"\"
Neural style transfer content image <\/figcaption><\/figure>\n\n\n\n
\"\"
Neural style transfer Reference Image<\/figcaption><\/figure>\n\n\n\n
\"\"
Neural style transfer content image <\/figcaption><\/figure>\n\n\n\n
\"\"
Neural style transfer Reference Image<\/figcaption><\/figure>\n
Photo credit <\/a>on Unsplash<\/a><\/em><\/figcaption><\/figure>\n\n\n\n

<\/p>\n\n\n\n

Style Transfer Environment Setup with TensorFlow on Colab<\/h2>\n\n\n\n

We would use google colab<\/a> environment to train and run our deep learning model. <\/p>\n\n\n\n

<\/p>\n\n\n\n

Code reproduced from Tensorflow.org<\/a>\nDownload Jupyter Notebook <\/a><\/pre>\n\n\n\n
Create a new notebook<\/strong><\/h2>\n\n\n\n
setup<\/p>\n\n\n\n
import os\nimport tensorflow as tf\n# Load compressed models from tensorflow_hub\nos.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n\n\n\n
import numpy as np\nimport IPython.display as display\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nmpl.rcParams['figure.figsize'] = (12, 12)\nmpl.rcParams['axes.grid'] = False\n\n\nimport PIL.Image\nimport time\nimport functools<\/code><\/pre>\n\n\n\n
Create a function to <\/p>\n\n\n\n
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)<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
Import the Madia Gras and Painting Images to Colab<\/p>\n\n\n\n
content_path_1 = 'content.jpg'\ncontent_path_2 = 'content2.jpg'\ncontent_path_3 = 'content3.jpg'\nstyle_path_1 = 'reference.jpg'\nstyle_path_2 = 'reference_style.jpg'\nstyle_path_3 = 'reference_style3.jpg'<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
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<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
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)<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
content_image = load_img(content_path_1)\nstyle_image = load_img(style_path_1)\n\nplt.subplot(1, 2, 1)\nimshow(content_image, 'Content Image')\n\nplt.subplot(1, 2, 2)\nimshow(style_image, 'Style Image')<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
Import TensorFlow Hub to use a pre-trained model<\/strong><\/p>\n\n\n\n
<\/p>\n\n\n\n
import tensorflow_hub as hub\nhub_model = hub.load('https:\/\/tfhub.dev\/google\/magenta\/arbitrary-image-stylization-v1-256\/2')\nstylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]\ntensor_to_image(stylized_image)<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
content_image = load_img(content_path_2)\nstyle_image = load_img(style_path_2)\n\nplt.subplot(1, 2, 1)\nimshow(content_image, 'Content Image')\n\nplt.subplot(1, 2, 2)\nimshow(style_image, 'Style Image')<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
import tensorflow_hub as hub\nhub_model = hub.load('https:\/\/tfhub.dev\/google\/magenta\/arbitrary-image-stylization-v1-256\/2')\nstylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]\ntensor_to_image(stylized_image)<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
content_image = load_img(content_path_3)\nstyle_image = load_img(style_path_3)\n\nplt.subplot(1, 2, 1)\nimshow(content_image, 'Content Image')\n\nplt.subplot(1, 2, 2)\nimshow(style_image, 'Style Image')<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
import tensorflow_hub as hub\nhub_model = hub.load('https:\/\/tfhub.dev\/google\/magenta\/arbitrary-image-stylization-v1-256\/2')\nstylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]\ntensor_to_image(stylized_image)<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
Final Images – A Masterpiece<\/h2>\n\n\n\n
\n
\"neural<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n
Images generated by Style transfer<\/figcaption><\/figure>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Neural style transfer is referred to as an artistic algorithm that takes two images (in this case, a content image and a style reference image) and blends them to produce an image that looks like the content with attributes that take after the styling of the reference. In this tutorial, we would use a pre-trained […]<\/p>\n","protected":false},"author":1,"featured_media":1613,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"tdm_status":"","tdm_grid_status":"","footnotes":""},"categories":[2370,86],"tags":[2371,2372,2374,2373],"yoast_head":"\nNeural Style Transfer Create Mardi Gras\u00a0Art with Python TF Hub<\/title>\n<meta name=\"description\" content=\"Neural style transfer is a technique that takes two images (content and reference image) and blends them to produce a new image.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jenniferkwentoh.com\/neural-style-transfer-create-mardi-gras-art-with-python-tf-hub\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Neural Style Transfer 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