But it’s a bit more complicated than that. Supposedly, it doesn’t make a copy but uses the memory of the original object instead. numpy.asarray(im) - the same as numpy.array(im, copy=False).numpy.array(im) - makes a copy from an image to a NumPy array.If you Google it, you’ll probably find one of them: Here are the two most common ways to convert a Pillow image to NumPy. After all, where else would you need computer vision, if not on Raspberry? □ How NumPy conversion works Today I’m going to run benchmarks on a Raspberry Pi 4 1800 MHz under a 64-bit OS. The common scenario for using this library is when you need to convert an image from Pillow to NumPy so that you can work with it using OpenCV. It doesn't have its own internal storage format for images, instead, it uses NumPy arrays. OpenCV is the most popular computer vision library and has a wide range of features. It’s a base library for a bunch of scientific, computer vision, and machine learning libraries like SciPy, Pandas, Astropy and many others. NumPy is a Python library used for working with multidimensional arrays. Long story short, it does everything you need for image loading/saving. It supports different formats, provides lazy loading, and gives access to metadata from a file. There won't be any repositories or packages, just the facts, and working code at the end. Also, I’ll show you a way to get the same result but faster. Today, I’m going to dive deep into both libraries and tell you why that happens. Well, it seems weird, but more traditional image converting methods work 1.5-2.5 times slower (if you need a mutable object). There’s an incredible technique that makes it possible to convert Pillow images to NumPy arrays with just two memory copies! Wait, what do you mean “with only two memory copies”? Isn’t it possible to convert data between libraries while copying memory only once or without copying it at all? First published on April 7, 2021, in Insights That can add up pretty quickly, if you're not careful.Everything Insights Engineering News Product Culture Compliance Fast import of Pillow images to NumPy / OpenCV arrays Images are stored in their uncompressed form in memory, no matter their source, so you're looking at 16,777,216 bytes per 2048x2048 image (4 bytes per pixel for RGBA). The memory cautions are worth paying attention to, though. If you need to work with an image larger than 2048x2048, you'll need to host it in a CATiledLayer or the like. This used to be a limitation for all UIViews not backed by a CATiledLayer, but I believe they now do tiling on large enough views automatically. If you try to do so, I believe your image will just render as black. It does not mean that you have to have square images, just that an image must not have its width or height exceed 2048 (again, 4096 on newer devices). The newer iOS devices (iPhone 4S, iPad 2, iPad 3) have a maximum texture size of 4096x4096. What that means is that you can't have an image larger than that in either dimension. On the pre-A5 iOS devices, the maximum OpenGL ES texture size was 2048x2048 (Apple's documentation is incorrect in this regard by saying it's 1024x1024).
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