Data pre-processing basically refers to the task of gathering all the data (which is collected from various resources or a single resource) into a common format or into uniform datasets (depending on the type of data). Since real-world data is never ideal, there is a possibility that the data would have missing cells, errors, outliers, discrepancies in columns, and much more. Sometimes, images may not be correctly aligned, or may not be clear or may have a very large size. The goal of pre-processing is to remove these discrepancies and errors.
To get the resolution of an image, a built-in function named ‘shape’ is used. After the image is read, the pixel values are stored in the form of an array. This array is nothing but a Numpy array. Once the image is read and converted into an array, the shape function can be called on this image to understand its resolution.
Let us take an example of uploading an image and getting the resolution of the image on console using scikit-learn library −
from skimage import io path = "path to puppy.PNG" img = io.imread(path) print("Image being read") io.imshow(img) print("Image printed on console") print("The image resolution is ") print(img.shape)
Image being read Image printed on console The image resolution is (397, 558, 4)