This concludes our first chapter. We have come a long way! We began our discourse on image processing and computer vision by talking about images and how they are represented inside a computing device. We also began our journey into the world of OpenCV by discussing how the library handles image data in its programs, thereby introducing the Mat class. A significant portion of the chapter was devoted to learning about how to use the Mat class, instantiating objects, learning about its internal structure, and getting intimate with some memory management that takes place under the hood. I hope that, by now, handling images in your code has been demystified for you and you are comfortable dealing with the different forms in which Mat objects appear in the code samples scattered throughout the remainder of the book.
This chapter also served a first taste of some processing that we can perform on images using OpenCV. You learnt a couple of different methods to iterate through the image data stored inside a Mat object, discussing the pros and cons of each. We went on to establish a framework for writing code to help us in the pixel-wise traversal and processing of images. This very framework came to life when we implemented some common grayscale transformations such as negative, log, and exponential transforms. We witnessed what sort of changes these transformations bring forth in our images.
A very important theme that we touched upon briefly in this chapter and would be repeated in the chapters to come is that there are multiple ways to accomplish the same image processing task. We saw that here when we talked about implementing log transformations. One of the alternatives is to implement everything from first principles (reinvent the wheel) and the other is to rely on the functions and APIs provided to us by the OpenCV developers. In the subsequent chapters, we will be relying less on the former and more heavily on the latter. Our approach henceforth will be to explain the theoretical concepts from scratch using the basic principles but demonstrating the implementations using OpenCV functions. We believe that it will give you the best of both worlds.
Finally, as we close off the first chapter, here is what you can expect going forward. We discussed transforms, which were quite simplistic in the way that they operate. Each pixel in the output image was dependent on only a single pixel in the input image. We will discuss some more sophisticated forms of transformations in the next chapter, where the output at a particular pixel location depends not only on the corresponding pixel intensity at the input, but rather on a neighborhood of values. Also, we will learn about a fundamental manner in which such transformations are visualized-using a filter or a kernel. Such a filtering-based approach is extremely common in the image processing and computer vision world and will make a reappearance in more than one chapter! We will also get an opportunity to extend our arsenal of cool image manipulation techniques that we started building in this chapter.
