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 take an overview of Luxocator's functionality and our choice of Python libraries that support this functionality. Like many computer vision applications, Luxocator has 6 basic steps:
Acquire a static set of reference images: For Luxocator, we (the developers) will choose certain images that we will deem to be "Luxury, indoor" scenes, other images that we will consider as "Stalinist, indoor" scenes, and so on. We will load these images into memory.
Train a model based on the reference images: For Luxocator, our model will describe each image in terms of its normalized color histogram, that is, the distribution of colors across the image's pixels...