Live Code, Satellite Example Part A Here we provide you with a numpy notebook for basic image processing using numpy. If you're looking for the notebook, you need to go to your victory folder and find the satellite image analysis notebook. Please locate the WIFIRE folder with the data in there as well. We picked satellite imagery because satellite imagery is very interesting image data to exercise this. Let's begin by gathering our tools together. Like our other notebooks, we'll start by importing numpy as np, scipy, and matplotlib in this particular case. We go ahead and run that using shift and enter. The imread function will then be used to read our satellite image as an ndarray. Python gives us access to this function. We'll use the imread function to import a data package in this case, which is the WIFIRE folder containing the three-layer image you already have. If you look at the type of this data, we'll see that it's an ndarray. As we talked about before, this is an RGB image and the type is ndarray because it will be turned into an ndarray with three dimensions. We use the type function to display that type that it shows us ndarray of course. Let's now plot the image in our notebook to see it before we do anything with it. Before set up a frame size of 15 15. You can change that frame size the way you like if your notebook is high resolution and things like that. We'll use the imshow function in ski image to display it. We see that the image shows up. That's great. We see our colorful satellite image now. But what are the dimensions of the ndarray? Like I said, it's a three-dimensional one, and beneath the user shape function, type function won't be enough for that. Let's run photo data which is the name we gave to this array and shape as a function. We see that 3725, 300 700 25 is the height and width is 4797 and it has three layers for RGB. Those are our three layers. There's something interesting about this image. Like many other visualizations, the colors in each RGB layer mean something. For example, the intensity of the red will be an indication of altitude of the geographical data point in the pixel. The intensity of blue will indicate a measure of aspect and the green will indicate slope. To a trained eye, to a geo-scientist, or a field modeler, these colors will help to communicate this information in a quicker and more effective way rather than showing numbers. So let's go to the n4 line and uncomment that print function if we needed to display the numbers, for instance. We would observe a variety of figures. If you didn't have to use it, it doesn't really mean much. It would be challenging to simply comprehend the statistics. A skilled eye can already determine the altitude, slope, and aspect of this vivid image by simply glancing at it. The objective is to give these hues additional significance to denote a more scientific meaning. Once we load the image, we can start exploring with it. Let's check its size. We use the size function of the ndarray for that. Like many other things we do, we'll check the maximum and minimum of the data values and maybe the average pixel value. Here, we use ndarray's minimum function and maximum function. These values are important to verify since the eight-bit color intensity is, cannot be outside of the 0 to 255 range. Let's now discuss some fundamental NumPy image processing methods. NumPy gives us the tools we need to manipulate our image in a number of ways. Scaling of images is one of the procedures. Picture sizing is advantageous.