It's interesting that if you google some of the language in this post, you find that it is just paraphrased form more popular tutorials: A crucial class in the Python Imaging Library is the Image class. It is defined in the Image module and provides a PIL image on which manipulation operations can be carried out. An instance of this class can be created in several ways: by loading images from a file, creating images from scratch or as a result of processing other images. We'll see all these in use. To load an image from a file, we use the open() function in the Image module passing it the path to the image. from PIL import Image image = Image.open('unsplash_01.jpg') If successful, the above returns an Image object. If there was a problem opening the file, an IOError exception will be raised. After obtaining an Image object, you can now use the methods and attributes defined by the class to process and manipulate it. Let's start by displaying the image. You can do this by calling the show() method on it. This displays the image on an external viewer (usually xv on Unix, and the Paint program on Windows). image.show() You can get some details about the image using the object's attributes. # The file format of the source file. print(image.format) # Output: JPEG # The pixel format used by the image. Typical values are “1”, “L”, “RGB”, or “CMYK.” print(image.mode) # Output: RGB # Image size, in pixels. The size is given as a 2-tuple (width, height). print(image.size) # Output: (1200, 776) vs 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. To load an image from a file, use the open function in the Image module. import Image im = Image.open("lena.ppm") If successful, this function returns an Image object. You can now use instance attributes to examine the file contents. print im.format, im.size, im.mode PPM (512, 512) RGB ... If the file cannot be opened, an IOError exception is raised. http://effbot.org/imagingbook/introduction.htm More on reddit.com

The images are probably already optimized and compressed. Why wouldn't they be? You may get a few percent better if you use a program specifically designed to optimize compression, which PIL is not. More on reddit.com

As a fellow newbie, I don’t have a specific recommendation. I do have two recommendations that I think will help: Look to the standard library first! In this case, Go has image and it’s sub-packages. If the standard library does not meet needs, look for pure Go packages. The awesome-go/images page contains many packages like bild and gift and others that are pure Go. While still a newbie, I have run into multiple Go packages containing CGo wrappers to great C libraries which have limitations either due to the limitations of CGo or the wrapper implementation of CGo. In my experience, the most common limitation I have encountered concerns shared memory that interferes with concurrency performance in goroutines. I find I need to use mutexes to insure that only one goroutine is using it at a time. Go’s concurrency is one of the main draws to it for me. The mutex in effect eliminates any performance increase due to concurrency. And lastly, a word of warning on imagick . I have been using ImageMagick and integrations of it into other languages for over two decades. It has tremendous capability and is a PITA to compile and install and has quirks in its interface even in C. I do not use it when I have other choices. YMMV Good Luck! I would love to see a future post about what you choose ands why! lbe More on reddit.com

For fast transformations of row data turning your pillow images into a numpy array is probably the way to go. Numpy can be crazy fast and moving between pillow and numpy is relatively quick and very easy. If you want to get fancy you can even directly modify a pillow memory buffer from numpy.

More on reddit.com