Swift Accelerate and vImage: Getting Started

Learn how to process images using Accelerate and vImage in a SwiftUI application. By Bill Morefield.

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The most valuable benefit of modern computing might be the ability to do complicated math quickly and accurately. Early computers existed almost exclusively to automate tedious and error-prone calculations previously done by hand.

Today, a phone can do calculations in a fraction of a second that once would have taken a team of people weeks or even months. This increased ability invites programmers to add more complicated calculations to these devices, increasing the utility of a phone.

The Accelerate framework gives app developers an efficient, high-speed library for large-scale mathematical or image-based calculations. It uses the vector-processing capabilities on modern purpose-built CPUs to perform calculations quickly while maintaining efficient energy usage.

Getting Started

You need to first understand a bit about what Accelerate is and the components that you will use in this tutorial. So before diving into code, take a look at the components of Accelerate.

Accelerate comprises several related libraries, each of which performs a dedicated type of mathematical process. The libraries are:

  • BNNS: for training and running neural networks
  • vImage: an image processing library
  • vDSP: a library of digital signal processing functions
  • vForce: to perform arithmetic and transcendental calculations on large sets of numbers
  • Sparse Solvers, BLAS and LAPACK: for linear algebra calculations

Apple also uses these libraries as building blocks of other frameworks. For example, CoreML builds on top of BNNS. The archive and compression frameworks also build on top of Accelerate. Because Apple uses these frameworks extensively, you’ll also find support on all current Apple platforms.

In this tutorial, you’ll explore the Accelerate framework using the vImage library. All the libraries work similarly, and the vImage library provides clear visual examples that will be easier to understand than more complex tasks like digital signal processing.

Introducing vImage and vImage_Buffer

vImage gives you a way to manipulate large images using the CPU’s vector instruction set. These instructions let you write apps that can do complex image calculations quickly while placing less stress on mobile devices’ batteries than if you were to use general purpose instructions. vImage works well when you need to perform very complex calculations or process real-time video, or you require high accuracy.

Accelerate is a bit unique in older Apple frameworks in that it’s partially updated to Swift compatibility. Unlike many older frameworks, you’ll find functionality that works as you’d expect a native Swift library. But you can’t ignore the older origin of the framework because many calls still expect and use pre-Swift idioms and patterns. You’ll see how to manage those later in this tutorial.

For an image to work with vImage, you must first convert it to the native format for vImage — a vImage_Buffer. This buffer represents raw image data, and vImage functions treat it more as a set of numbers than as image data, keeping with the vector processing paradigm of Accelerate.

Creating vImage Buffers

Time to start coding!

Download the starter project by clicking the Download Materials button at the top or bottom of this tutorial. Open the starter project, then build and run.

Waterfall converted from image to buffer and vice versa

You’ll see an app that allows you to select a photo from the camera roll. It then displays the selected image. In UIKit and SwiftUI, you usually work with a UIImage. Because vImage does not understand this format, you’ll first convert this UIImage to something it can use.

Create a new Swift file named VImageWrapper.swift. Replace the contents of the file with:

import UIKit
import Accelerate

struct VImageWrapper {
  // 1
  let vNoFlags = vImage_Flags(kvImageNoFlags)

extension VImageWrapper {
  // 2
  func printVImageError(error: vImage_Error) {
    let errDescription = vImage.Error(vImageError: error).localizedDescription
    print("vImage Error: \(errDescription)")

This file contains the start of a Swift wrapper for several vImage processes you’ll create in this tutorial. Begin by importing UIKit for access to UIImage along with the Accelerate framework. The rest of the code will be useful later:

  1. Most Accelerate functions expect a flags parameter used to restrict or provide context to the function. For this tutorial, you won’t need to provide this, and vNoFlags = vImage_Flags(kvImageNoFlags) provides a handy constant for the value reflecting that.
  2. Even after the better integration with Swift, many methods still return an Objective C style value to indicate the method’s status. This method converts the returned vImage_Error value to a Swift-friendly vImage.Error. It then prints the description to the console for debugging. You’ll use this method to handle errors in this tutorial.

Converting UIImage to vImage

Next, add the following code to the VImageWrapper struct:

var uiImage: UIImage

init(uiImage: UIImage) {
  self.uiImage = uiImage

This code creates a UIImage property along with a custom initializer accepting a UIImage.

Next, add the following method to the structure.

func createVImage(image: UIImage) -> vImage_Buffer? {
    // 1
    let cgImage = uiImage.cgImage,
    // 2
    let imageBuffer = try? vImage_Buffer(cgImage: cgImage)
  else {
    // 3
    return nil
  // 4
  return imageBuffer

Notice the use of the guard statement to ensure that each step works. If any steps fail, then the else returns nil to represent something went wrong. You’ll use this logic often in this tutorial.

  1. Accelerate doesn’t provide a direct conversion from a UIImage to a vImage_Buffer. It does support converting a CGImage to a vImage_Buffer. Because a UIImage (usually) contains the CGImage in the cgImage property, you attempt to access the underlying CGImage of the UIImage.
  2. With a CGImage, you attempt to create a vImage_Buffer from it. The creation of this buffer can throw an error, so you use the try? operator to get a nil if an error occurs.
  3. If either of these steps fails, then you return nil.
  4. Otherwise, you return the vImage_Buffer.

That concludes the setup necessary for converting your images into a buffer that vImage can handle. You’ll use this extensively throughout the tutorial.

You’ll also need a way to convert back from a vImage_Buffer into a UIImage. So, code that now.

Converting vImage to UIImage

Add the following method at the end of the VImageWrapper struct:

func convertToUIImage(buffer: vImage_Buffer) -> UIImage? {
    // 1
    let originalCgImage = uiImage.cgImage,
    // 2
    let format = vImage_CGImageFormat(cgImage: originalCgImage),
    // 3
    let cgImage = try? buffer.createCGImage(format: format)
  else {
    return nil
  let image = UIImage(cgImage: cgImage)
  return image

You can see it takes a bit more work to go from the vImage_Buffer back to the UIImage:

  1. To begin, you’ll get the CGImage for the UIImage as earlier.
  2. As mentioned earlier, the vImage_Buffer contains only the image data. It has no information about what the buffer represents, and it needs to know the order and number of bits used for the image data. You can obtain this information from the original image, and that’s what you do here. You create a vImage_CGImageFormat object which contains information needed to interpret the image data of the original image.
  3. You call the createCGImage(format:) on the buffer to convert the image data into an image using the format determined in the last step.

Now, to see your work, add the following optional property to the end of the list of properties of the VImageWrapper struct:

var processedImage: UIImage?

Then, add the following code at the end of the init(uiImage:):

if let buffer = createVImage(image: uiImage), 
  let converted = convertToUIImage(buffer: buffer) {
    processedImage = converted

The block of code above attempts to convert the image to a buffer and then back to a UIImage.

Next, open ContentView.swift and, after the existing ImageView, add the following code:

if let image = processedImage {
    title: "Processed",
    image: image)

When a processed image exists, the app will display it using the ImageView defined in the starter project. You need to make one more change before you can see your work.