This chapter uses Python. Being a high-level, interpreted language with great third-party libraries for numeric and scientific computing, Python lets us focus on the functionality of the system rather than implementing subsystem details. For our first project, such a high-level perspective is precisely what we need.
Let's look at an overview of Luxocator's functionality and our choice of Python libraries that support this functionality. Like many computer vision applications, Luxocator has six basic steps:
- Acquire a static set of reference images: For Luxocator, we (the developers) choose certain images that we deem to be luxury indoor scenes, other images that we consider Stalinist indoor scenes, and so on. We load these images into memory.
- Train a model based on the reference images: For Luxocator, our model describes each image in terms...
