A histogram is a count of how many times each color occurs in an image. Typically, we do not count all possible colors separately; rather, we group similar colors together into bins. With a smaller number of bins, the histogram occupies less memory and offers a coarser basis of comparison. Typically, we want to choose some middle ground between very many bins (as the histograms tend to be highly dissimilar) and very few bins (as the histograms tend to be highly similar). For BeanCounter, let's start with 32 bins per channel (or 32^3=32678 bins in total), but you may change this value in the code to experiment with its effect.

A comparison of histograms can tell us whether two images contain similar colors. This kind of similarity alone does not necessarily mean that the images contain matching objects. For example, a silver fork and silver coin could have similar histograms. OpenCV supports several popular comparison algorithms. We will use the Alternative...