Note: Docker is the preferred way to run the program, since it doesn't require you to manually download requirements.

The source code of my implementation can be found in the src/ directory. The algorithm is written in Cython (.pyx) which allows performance critical parts of the model (e.g. nested for loops) to be written in C.

The img/in/ directory is where the algorithm looks for input images and writes output images. Be sure to select an image that is in this directory, otherwise the program will fail. You can put your own images in here and run the algorithm on them.

The output images are written to img/out/<id>.jpg where <id> is the hexadecimal id printed to the console once the program has finished.

The purpose of the id is to allow you to quickly identify the generated image, and to prevent the overwriting of other images in that directory.

• Unzip the project
• Navigate to the project root
• run docker-compose run py

• Unzip the project
• Navigate to the project root
• Create a virtual environment by running python3 -m venv venv
• Activate the virtual environment with source ./venv/bin/activate
• Install the project requirements pip3 install -r requirements.txt
• Compile Cython and run the algorithm with ./run.sh

All commands should be executed from the project root.

In this case do one of the following, remembering to prepend docker-compose run py to the command if using docker:

• either run chmod +x ./run.sh
• or run python3 setup.py build_ext --inplace followed by python3 .

Make sure that there is an out/ folder in the img/ directory. If not then run mkdir img/out

Make sure the src/ dir only contains

• __init__.py
• model.pyx

and no other files (e.g. .c or .so files). Then try running the algorithm again.