In this blog post, we will explore how to apply style transfer using the fastai library in Python. Fastai is a high-level deep learning library built on top of PyTorch that makes it easy to implement state-of-the-art models and techniques.

What is Style Transfer?

Style transfer is a technique in deep learning that allows us to combine the content of one image with the style of another image. It involves extracting the content features and style features from the respective images, and then merging them to create a new image that preserves the content of the original image but adopts the style of the reference image.

Installing the Required Libraries

Before we start, let’s make sure we have all the necessary libraries installed. You can install the required libraries by running the following command:

!pip install fastai

Loading the Images

To begin with, we need to load the content and style images that we want to use for style transfer. Make sure you have these images available in your local directory.

from PIL import Image

content_image = Image.open("content.jpg")
style_image = Image.open("style.jpg")

Here, content.jpg and style.jpg are the filenames of the content and style images, respectively.

Applying Style Transfer

Now, let’s apply style transfer using the fastai library. The first step is to create a fastai Learner object by specifying the content and style images.

from fastai.vision import *

learner = cnn_learner(dls, resnet34)

Here, we create a cnn_learner object using the resnet34 architecture. The dls argument should be a DataLoaders object that contains the content and style images.

Next, we can apply style transfer to the content image by calling the learner.stylize method.

stylized_image = learner.stylize(content_image)

The stylize method takes a content image as input and returns the stylized image.

Visualizing the Results

Finally, let’s visualize the results by displaying the content image, style image, and the stylized image side by side.

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, figsize=(15, 5))
axes[0].imshow(content_image)
axes[0].set_title("Content Image")

axes[1].imshow(style_image)
axes[1].set_title("Style Image")

axes[2].imshow(stylized_image)
axes[2].set_title("Stylized Image")

for ax in axes:
    ax.axis("off")

plt.show()

In the above code, we use matplotlib to create subplots and display the images. The content image is displayed in the first subplot, the style image in the second subplot, and the stylized image in the third subplot.

Conclusion

In this blog post, we explored how to apply style transfer using the fastai library in Python. Style transfer is a powerful technique that allows us to create artistic and visually appealing images by combining the content of one image with the style of another. With the fastai library, implementing style transfer becomes straightforward and accessible to a wider range of users.

Note: Make sure to experiment with different content and style images to see how the results vary. Also, feel free to explore other advanced techniques and architectures to further enhance the style transfer process.