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.
- Getting started
- Neural Style Transfer for images
- Results for Neural Style Transfer for images
- Neural Style Transfer for videos
- Real time neural style transfer
- License
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 (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.
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.
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.
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