Neural style transfer python

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Neural Style Transfer implementation for images and videos using Tensorflow2.

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AkashSDas/neural-style-transfer

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README.md

In this project Neural Style Transfer (NST) is used to style images and videos. The Neural Style Transfer for images is the notebook in which NST is applied on images, the Neural Style Transfer for videos is the notebook in which NST is applied on videos and Real time neural style transfer is the script which is used style video captured in real time.

The neural-style-transfer-for-images and neural-style-transfer-for-videos are available on Kaggle to work in the same environment where this notebook was created i.e. use the same version packages used, etc. These notebooks uses GPU for faster computation.

The real-time-neural-style-transfer.py script can be executed like a normal python script. This script uses one video capture (webcam), so if no webcam is avialable then replace 0 value in cv2.VideoCapture method with video filename to apply on that video.

Neural Style Transfer for images

Neural Style Transfer (NST) is one of the most fun techniques in deep learning. It merges two images namely, a content image (C) and a style image (S) , to create a generated image (G) . The generated image G combines the content of the image C with the style of image S.

For example, let’s take an image of this turtle and Katsushika Hokusai’s The Great Wave off Kanagawa :

Style transfer is an interesting technique that showcases the capabilities and internal representations of neural networks.

Results for Neural Style Transfer for images

Neural Style Transfer for videos

This is an naive implementation for applying neural style transfer for videos. Here neural style transfer is applied to every frame of the video and a new video is created with the applied style with the content of the original video. The way I’ve applied neural style transfer on a frame of the video is the same as we apply on a single image (since an individual frame is just an image).

For this project I’ve used the pre-trained model from Tensorflow Hub which will allow us to perform Fast Style Transfer, though custom models can be used but applying style to them will be quite time consuming as compared to the method used here.

The styled videos for this project are saved in this Kaggle kernel.

Real time neural style transfer

This one is very laggay and the reason for that is that this is a naive implementation where each and every NST is applied on each and every frame and that computation for styling a frame is what causes the lag but this is faster as compared to using custom models as here Fast Style Transfer is used.

Results for real time neural style transfer

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Transferring the style of one image to the contents of another image, using PyTorch and VGG19.

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nazianafis/Neural-Style-Transfer

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README.md

Neural Style Transfer is the ability to create a new image (known as a pastiche) based on two input images: one representing the content and the other representing the artistic style.

This repository contains a lightweight PyTorch implementation of art style transfer discussed in the seminal paper by Gatys et al. To make the model faster and more accurate, a pre-trained VGG19 model is used.

🔗 Check out this article by me regarding the same.

Neural style transfer is a technique that is used to take two images—a content image and a style reference image—and blend them together so that output image looks like the content image, but “painted” in the style of the style reference image.

1. We take content and style images as input and pre-process them.
2. Next, we load VGG19 which is a pre-trained CNN (convolutional neural network).
   1. Starting from the network’s input layer, the first few layer activations represent low-level features like colors, and textures. As we step through the network, the final few layers represent higher-level features—like eyes.
   2. In this case, we use conv1_1 , conv2_1 , conv3_1 , conv4_1 , conv5_1 for style representation, and conv4_2 for content representation.
3. We begin by cloning the content image and then iteratively changing its style. Then, we set our task as an optimization problem where we try to minimize:
   1. content loss, which is the L2 distance between the content and the generated image,
   2. style loss, which is the sum of L2 distances between the Gram matrices of the representations of the content image and the style image, extracted from different layers of VGG19.
   3. total variation loss, which is used for spatial continuity between the pixels of the generated image, thereby denoising it and giving it visual coherence.
4. Finally, we set our gradients and optimize using the L-BFGS algorithm to get the desired output.

Neural-Style-Transfer ├── data | ├── content-images | ├── style-images ├── models/definitions │ ├── vgg19.py  

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