Batch processing of fast-style-transfer for Udacity Deep Learning Nanodegree

batch-fast-style-transfer.sh

# This is a bash script for batch images processing using https://github.com/lengstrom/fast-style-transfer code.
# Used in udacity Deep Learning Nanodegree https://www.udacity.com/course/deep-learning-nanodegree--nd101
# Step 1. Configure environment
#   conda create -n style-transfer python=3
#   activate style-transfer
#   conda install tensorflow scipy pillow
#   pip install moviepy
#   python -c "import imageio; imageio.plugins.ffmpeg.download()"
#
# Step 2. Close source code and have fun
#   git clone https://github.com/lengstrom/fast-style-transfer.git
# 
# Create the following subfolders:
#   ./checkpoints - folder with style checkpoints
#   ./inputs - folder with images in *.jpg format
#   ./outputs - folder with results. Resulting filenames: <original_image_name>_<style_name>.jpg
#
# Step 3. My experience
# In case you experiencing OMP related problems visit: https://github.com/dmlc/xgboost/issues/1715
# 
# Step 4. Questions or comments? oleg.shpynov[at]gmail.com

for IMG in $(find $(pwd)/inputs -name "*.jpg"); do 
	echo "IMG: ${IMG}"
	for STYLE in $(find $(pwd)/checkpoints -name "*.ckpt"); do 
		echo "STYLE: ${STYLE}"
		NAME=$(echo ${STYLE} | sed -e 's#.*/##g' | sed 's#.ckpt##g')
		RESULT="$(echo ${IMG} | sed 's#.jpg##g' | sed 's#inputs#outputs#g')_${NAME}.jpg"
		echo "RESULT: ${RESULT}"
		if [[ ! -f ${RESULT} ]]; then 
			python evaluate.py --checkpoint ${STYLE} --in-path ${IMG} --out-path ${RESULT}
		fi
	done 
done

# The following python code can be used to create animated GIF results
# import glob
# import imageio as io
# import re
#
# for img in glob.glob('inputs/*.jpg'):
#      print('Processing {}'.format(img))
#      name = re.sub('inputs/|\.jpg', '', img)
#     images = [ img ] + glob.glob('outputs/{}*.jpg'.format(name))
#     gif = 'outputs/{}.gif'.format(name)
#     io.mimsave(gif, [ io.imread(i) for i in images ], duration=0.5)
#     print('Saved {}'.format(gif))