We have discussed the formation and representation of digital images and the concept of color spaces and color channels at length. Having laid a firm foundation on the basic principles of image processing, we now turn to the OpenCV library and take a look at what it has got to offer us in terms of storing digital images! Just like pixels are the building blocks of digital images, the Mat class is the cornerstone of OpenCV programming. Any instantiation of the Mat class is called the Mat object.
Before we embark on a description of the Mat object, I would urge you to think, keeping in mind whatever we have discussed regarding the structure and representation of digital images, about how you would go about designing a data structure in C++ that could store images for efficient processing. One obvious solution that comes to mind is using a two-dimensional array or a vector of vectors. What about the data types? An unsigned char should be sufficient since we would rarely need to store values beyond the range of 0 to 255. How would you go about implementing channels? Perhaps we could have an array of two-dimensional grids to represent color images (getting a little complicated now, isn't it?).
The Mat object is capable of doing all of the preceding things that were described (and much more) in the most efficient manner possible! It lets you handle multiple color channels and different color spaces without you (the programmer) having to worry about the internal implementation details. Since the library is written in C++, it also lifts the burden of memory management from the hands of the user. So, all you've got to worry about is building your cool application and you can trust Mat (and OpenCV) to take care of the rest!
According to OpenCV's official documentation:
"The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array."
We have already witnessed that digital images are two-dimensional arrays of pixels, where each pixel is associated with a numerical value from a predefined color space. This makes the Mat object a very obvious choice for representing images inside the world of OpenCV. And indeed, it does enable you to load, process, and store images and image data in your program. Most of the computer vision applications that you will be developing (as part of this book or otherwise) would involve abundant usage of images. These images would typically enter your system from the outside (user input), your application would apply several image processing algorithms on them, and finally produce an output, which may be written to disk. All these operations involve storing images inside your program and passing them around different modules of your code. This is precisely where the Mat object lends its utility.
Mat objects have two parts: a header and the actual matrix of pixel values. The header contains information, such as the size of the matrix, the memory address where it is stored (a pointer), and other pieces of information pertaining to the internal workings of Mat and OpenCV. The other part of the Mat object is where the actual pixel values are stored. The header for every Mat object is constant in size, but the size of the matrix of pixel values depends on the size of your image.
As this book progresses, you will realize that a Mat object is not always synonymous with images. You will work with certain instantiations of the Mat class, which do not represent a meaningful image as such. In such cases, it is more convenient to think of the Mat object as a data structure that helps us to operate on (possibly multidimensional) numerical arrays (as the official document suggests). But irrespective of whether we use Mat as an image store or a generic multidimensional array, you will soon realize the immense power that the creators of the library have placed in your hands through the Mat class. As mentioned earlier, its scope goes beyond merely storing images. It can act as a data structure and can provide the users with tools to use the most common linear algebra routines--matrix multiplication, inverse, eigen-values, PCA, norms, SVD, and even DFT and DCT--the list goes on.
