A TensorFlow convolutional neural network (CNN) for fast style transfer of an image’s style to another image.
Clone this repository. Then run (you may need to sudo docker...):
# Build the docker image
$ docker build -t fast-style-transfer .
# List the docker image to check that it’s been built
$ docker images
# Run the image container
$ docker run -ti fast-style-transfer
# Run the image in the background
$ docker run -d -p 5000:5000 fast-style-transfer
# To attach to that running process, run $ docker ps to get the name.
$ docker attach container_name
# To create a new shell into that container
$ docker exec -ti container_name bash
# Setup the environment (if you're using Cuda)
$ source env/bin/activate
# Example style transfer
$ python evaluate.py --checkpoint path/to/style/model.ckpt \
--in-path dir/of/test/imgs/ \
--out-path dir/for/results/
# With the test image and scream model
$ python evaluate.py --checkpoint udlf_fst_checkpoints/scream.ckpt \
--in-path in/ \
--out-path out/
# In a new terminal window (don’t exit from the Docker container), get the name of the running container
$ docker ps
# Then copy the output folder to your computer.
$ docker cp container_name:/root/fast-style-transfer/out .
Get the Docker image running locally.Generate some sample images.- Train the network with our own source style.
Setup a simple server to handle processing the image and sending it back.
Time so far: ~12 hours.
Released under an MIT License.
Copyright (c) 2017 MOD.
Copyright (c) 2016 Logan Engstrom.
Thanks to Thom Miano for the original Dockerfile.