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The most important class in the Python Imaging Library is the Image class, defined in the module with the same name. You can create instances of this class in several ways; either by loading images from files, processing other images, or creating images from scratch. If successful, this function returns an Image object. The format attribute identifies the source of an image. If the image was not read from a file, it is set to None. The size attribute is a 2-tuple containing width and height (in pixels). The mode attribute defines the number and names of the bands in the image, and also the pixel type and depth. Common modes are "L" (luminance) for grayscale images, "RGB" for true color images, and "CMYK" for pre-press images. If the file cannot be opened, an OSError exception is raised. Once you have an instance of the Image class, you can use the methods defined by this class to process and manipulate the image.



(Image: [[https://image.shutterstock.com/image-photo/bedtime-woman-sleeping-bed-home-260nw-2490857527.jpg|https://image.shutterstock.com/image-photo/bedtime-woman-sleeping-bed-home-260nw-2490857527.jpg]])The standard version of show() is not very efficient, since it saves the image to a temporary file and calls a utility to display the image. If you don’t have an appropriate utility installed, it won’t even work. When it does work though, it is very handy for debugging and tests. The following sections provide an overview of the different functions provided in this library. The Python Imaging Library supports a wide variety of image file formats. To read files from disk, use the open() function in the Image module. You don’t have to know the file format to open a file. The library automatically determines the format based on the contents of the file. To save a file, use the save() method of the Image class. When saving files, the name becomes important. Unless you specify the format, the library uses the filename extension to discover which file storage format to use. A second argument can be supplied to the save() method which explicitly specifies a file format.



It is important to note that the library doesn’t decode or load the raster data unless it really has to. When you open a file, the file header is read to determine the file format and extract things like mode, size, and other properties required to decode the file, but the rest of the file is not processed until later. This means that opening an image file is a fast operation, which is independent of the file size and compression type. The Image class contains methods allowing you to manipulate regions within an image. To extract a sub-rectangle from an image, use the crop() method. The region is defined by a 4-tuple, where coordinates are (left, upper, right, lower). The Python Imaging Library uses a coordinate system with (0, 0) in the upper left corner. Also note that coordinates refer to positions between the pixels, so the region in the above example is exactly 64x64 pixels.



The region could now be processed in a certain manner and pasted back. When pasting regions back,  [[http://20.198.113.167:3000/eunicekinchela/derilapillow2996derila-pillow/wiki/The+Shocking+Citrus+Hack+Energizing+Millions+-+Sleep+Experts+HATE+This%2521|ergonomic sleep support]] the size of the region must match the given region exactly. In addition, the region cannot extend outside the image. However, the modes of the original image and the region do not need to match. If they don’t, the region is automatically converted before being pasted (see the section on Color transforms below for details). For more advanced tricks, the paste method can also take a transparency mask as an optional argument. In this mask, the value 255 indicates that the pasted image is opaque in that position (that is, the pasted image should be used as is). The value 0 means that the pasted image is completely transparent. Values in-between indicate different levels of transparency. For example, pasting an RGBA image and also using it as the mask would paste the opaque portion of the image but not its transparent background.



(Image: [[https://media.istockphoto.com/id/1218637048/photo/young-girl-embracing-pillow-covering-her-face-while-lying-in-bed-in-white-t-shirt-having-fun.jpg?s=612x612&w=0&k=20&c=9JvND6Lgj4jEPJAVsDsuuUmm5ynelo_CbjEaCfgmo-k=|https://media.istockphoto.com/id/1218637048/photo/young-girl-embracing-pillow-covering-her-face-while-lying-in-bed-in-white-t-shirt-having-fun.jpg?s=612x612&w=0&k=20&c=9JvND6Lgj4jEPJAVsDsuuUmm5ynelo_CbjEaCfgmo-k=]])The Python Imaging Library also allows you to work with the individual bands of an multi-band image, such as an RGB image. The split method creates a set of new images, each containing one band from the original multi-band image. The merge function takes a mode and a tuple of images, and combines them into a new image. Note that for a single-band image, split() returns the image itself. To work with individual color bands, you may want to convert the image to "RGB" first. The PIL.Image.Image class contains methods to resize() and rotate() an image. The former takes a tuple giving the new size, the latter the angle in degrees counter-clockwise. To rotate the image in 90 degree steps,  [[https://fossservice.net/board_guNo81/263319|ergonomic sleep support]] you can either use the rotate() method or the transpose() method. The latter can also be used to flip an image around its horizontal or vertical axis. Instead of calculating the size of the new image when resizing, you can also choose to resize relative to a given size.