Style Transfer in Computer Vision: What It Is and How It Works

Style transfer in computer vision is a technique that applies the artistic style of one image onto the content of another using neural networks. It blends the look of a painting or texture with a photo, creating a new image that keeps the original shapes but adopts the style's colors and patterns.

How It Works

Style transfer works by separating an image into two parts: the content (the shapes and objects) and the style (the colors, textures, and brush strokes). Imagine you have a photo of your friend and a painting by Van Gogh. Style transfer takes the shapes from your friend's photo but paints them using Van Gogh's style.

This is done using a special type of neural network called a convolutional neural network (CNN). The network extracts features from both images at different layers. Lower layers capture simple shapes (content), while higher layers capture textures and colors (style). The algorithm then adjusts a new image to match the content features of the photo and the style features of the painting, blending them together.

Think of it like mixing two paints: one paint holds the form, and the other adds the color and texture. The result is a new image that looks like your photo painted in the style of the artwork.

Example

This example uses TensorFlow and Keras to perform style transfer on two images: a content photo and a style painting. It creates a new image that combines the content of the photo with the style of the painting.

python

import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import PIL.Image

# Load content and style images
content_path = tf.keras.utils.get_file('content.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')
style_path = tf.keras.utils.get_file('style.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')

def load_img(path_to_img):
    max_dim = 512
    img = PIL.Image.open(path_to_img)
    long = max(img.size)
    scale = max_dim / long
    img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), PIL.Image.LANCZOS)
    img = np.array(img)
    img = img[tf.newaxis, :]
    return img

content_image = load_img(content_path)
style_image = load_img(style_path)

# Load TensorFlow Hub module for style transfer
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')

# Stylize image
stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]

# Convert tensor to image
def tensor_to_image(tensor):
    tensor = tensor*255
    tensor = np.array(tensor, dtype=np.uint8)
    if np.ndim(tensor)>3:
        assert tensor.shape[0] == 1
        tensor = tensor[0]
    return PIL.Image.fromarray(tensor)

result_image = tensor_to_image(stylized_image)
result_image.save('stylized_result.jpg')
print('Style transfer completed and image saved as stylized_result.jpg')

Output

Style transfer completed and image saved as stylized_result.jpg

When to Use

Style transfer is useful when you want to create artistic images by combining the content of one image with the style of another. It is popular in creative fields like digital art, photo editing, and design.

For example, you can turn your vacation photos into paintings that look like famous artists' work, or create unique textures for video games and animations. It can also be used in advertising to create eye-catching visuals or in social media apps to offer fun filters.

Key Points

• Style transfer blends the content of one image with the style of another using neural networks.
• It uses convolutional neural networks to separate and recombine content and style features.
• Commonly used for artistic image creation and creative applications.
• Requires a content image, a style image, and a model to perform the transfer.

Key Takeaways

Style transfer uses neural networks to combine the content of one image with the style of another.

It works by separating content and style features and blending them into a new image.

This technique is popular for creating artistic and creative images.

TensorFlow Hub provides easy-to-use models for style transfer.

Style transfer is ideal for digital art, photo editing, and creative media.