The next class that we will go through is a data set pre processor class. So this class is also found in this library under green heritage, called pre processing and recipe processor class. And this is the code for access small code. So preprocessor is nothing but there's a certain action that is it takes on an image, right? So in this particular case, this preprocessor takes in width, height, and the inner area of the image and then assign to the individual variable. And then we call the pre process method.

All it does is it just resizes the image to those specified size, okay? And it says it's in interval interpolation. Don't have to worry about this at this point. So are these proc preprocessor does at this point is just read sizes in each image that we get into 32 by 32. And we will set those parameters inside our data set loader class, right. So you can be 32 by 32 times 25 or 25. whatever size for machine learning problem, it's very important that your images could be in various sizes, but before we use for pre processing, we must resize them into a common size and that's exactly what the speed processor class does.

Now, we will go back again and see how this pre processor was used. So, I will show you that data set loader class again. So this was the class eight is owning. So this is what it does, right? So as so our Data Loader class We'll enumerate through each image file. And here, it applies the preprocessor code to each file.

So the process is nothing but what it does is resizes the image at this point, and it initializes. The pre processor initialize is also done in the main code. And we'll take a look at that later. So this is how a data set loaded with pre processor class was used to do certain pre processing of the image. These are very common libraries and could be used in a lot of different applications. That's why these have been created as a separate modules, and you can reuse them in any computer vision application that you would like to do.