This is Python 3 rewrite of "Fast Style Transfer in [TensorFlow]"(https://github.com/tensorflow/tensorflow)
On Windows Tensorflow no longer supports Python 2, so in order to make the project run on Windows, a number of python files were rewritten in Python 3 syntax.
This project allows you to transfer the style of an image or a video sequence based on the style image provided.
The implementation is based off of a combination of Gatys' A Neural Algorithm of Artistic Style, Johnson's Perceptual Losses for Real-Time Style Transfer and Super-Resolution, and Ulyanov's Instance Normalization.
Here we transformed every frame in a video, then combined the results.
We added styles from various paintings to a photo of Chicago. Click on thumbnails to see full applied style images.
Our implementation uses TensorFlow to train a fast style transfer network. We use roughly the same transformation network as described in Johnson, except that batch normalization is replaced with Ulyanov's instance normalization, and the scaling/offset of the output tanh layer is slightly different. We use a loss function close to the one described in Gatys, using VGG19 instead of VGG16 and typically using "shallower" layers than in Johnson's implementation (e.g. we use relu1_1 rather than relu1_2). Empirically, this results in larger scale style features in transformations.
Training Style Transfer Networks
Use style.py to train a new style transfer network. Run python style.py to view all the possible parameters. Training takes 4-6 hours on a Maxwell Titan X. More detailed documentation here. Before you run this, you should run setup.sh. Example usage:
python style.py --style path/to/style/img.jpg
--checkpoint-dir checkpoint/path
--test path/to/test/img.jpg
--test-dir path/to/test/dir
--content-weight 1.5e1
--checkpoint-iterations 1000
--batch-size 20
Evaluating Style Transfer Networks
Use evaluate.py to evaluate a style transfer network. Run python evaluate.py to view all the possible parameters. Evaluation takes 100 ms per frame (when batch size is 1) on a Maxwell Titan X. More detailed documentation here. Takes several seconds per frame on a CPU. Models for evaluation are located here. Example usage:
python evaluate.py --checkpoint path/to/style/model.ckpt
--in-path dir/of/test/imgs/
--out-path dir/for/results/
Stylizing Video
Use transform_video.py to transfer style into a video. Run python transform_video.py to view all the possible parameters. Requires ffmpeg. More detailed documentation here. Example usage:
python transform_video.py --in-path path/to/input/vid.mp4
--checkpoint path/to/style/model.ckpt
--out-path out/video.mp4
--device /gpu:0
--batch-size 4
Input Video
https://github.com/trendmaster1/FastVideoStyleTransfer/tree/master/examples/results/view.mp4
Output Video
https://github.com/trendmaster1/FastVideoStyleTransfer/tree/master/examples/results/view_out.mp4
Input Video
https://github.com/trendmaster1/FastVideoStyleTransfer/tree/master/examples/results/pool.mp4
Output Video
https://github.com/trendmaster1/FastVideoStyleTransfer/tree/master/examples/results/pool_output.mp4
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