http://www.microsoft.com/en-us/download/details.aspx?id=44266

Install the following dependencies using the command pip (remember to run as admin)

pip install numpy matplotlib

Note that numpy will take a while since it will use the C++ compiler.

http://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv

http://www.scipy.org/scipylib/download.html

This repository includes code for both the serial and parallel implementations of the Image Analogy algorithm described in the research paper "Image Analogies" by Hertzmann, et al. (http://sites.fas.harvard.edu/~cs278/papers/analogies-72dpi.pdf).

It includes the following files:

• image_analogies_serial.py

This is the serial implementation, which can be run with the following line where imageA is your original image, ImageAPrime is your final image A, image B is the image that you will process and output file is the name of the processed B file that you will save.

python image_analogies_serial.py [imageA_path] [imageA_prime_path] [imageB_path] [output_file]

• image_analogies_parallel.py

This is the parallel implementation, which utilizes Cheetah, CUDA, and MPI. It must be run on a cluster with GPU-equipped nodes, with the following command:

mpiexec -n [num_processes] python image_analogies_mpi.py [imageA_path] [imageA_prime_path] [imageB_path] [output_file]

• run.qsub

This is a script that can be submitted to the headnode of a cluster, to run image_analogies_parallel.py on more than one node (i.e. if you need > 2 GPUs on the SEAS Resonance cluster). Use the following command:

qsub run.qsub

To customize the input files and number of processes, you have to go into the qsub file and manually edit it.

• graph_maker.py, graph_maker2.py

Not relevant to the program execution, these are the scripts that we used to generate the graphs of our results.

We've also included sample image files that the code can be run on:

• artistic1_A1.jpg, artistic1_A2.jpg (artsy effects)
• embossA1.jpg, embossA2.jpg (emboss effects)
• blurA1.jpg, blurA2.jpg (blur effects)
• artistic1_B1.jpg, blurB1.jpg, fun.jpg (sample images to run the transformations on)