Is there an easier way to setup a pixel buffer for CoreML? [duplicate] - swift

Question

I am trying to get Apple's sample Core ML Models that were demoed at the 2017 WWDC to function correctly. I am using the GoogLeNet to try and classify images (see the Apple Machine Learning Page). The model takes a CVPixelBuffer as an input. I have an image called imageSample.jpg that I'm using for this demo. My code is below:
var sample = UIImage(named: "imageSample")?.cgImage
let bufferThree = getCVPixelBuffer(sample!)
let model = GoogLeNetPlaces()
guard let output = try? model.prediction(input: GoogLeNetPlacesInput.init(sceneImage: bufferThree!)) else {
fatalError("Unexpected runtime error.")
}
print(output.sceneLabel)
I am always getting the unexpected runtime error in the output rather than an image classification. My code to convert the image is below:
func getCVPixelBuffer(_ image: CGImage) -> CVPixelBuffer? {
let imageWidth = Int(image.width)
let imageHeight = Int(image.height)
let attributes : [NSObject:AnyObject] = [
kCVPixelBufferCGImageCompatibilityKey : true as AnyObject,
kCVPixelBufferCGBitmapContextCompatibilityKey : true as AnyObject
]
var pxbuffer: CVPixelBuffer? = nil
CVPixelBufferCreate(kCFAllocatorDefault,
imageWidth,
imageHeight,
kCVPixelFormatType_32ARGB,
attributes as CFDictionary?,
&pxbuffer)
if let _pxbuffer = pxbuffer {
let flags = CVPixelBufferLockFlags(rawValue: 0)
CVPixelBufferLockBaseAddress(_pxbuffer, flags)
let pxdata = CVPixelBufferGetBaseAddress(_pxbuffer)
let rgbColorSpace = CGColorSpaceCreateDeviceRGB();
let context = CGContext(data: pxdata,
width: imageWidth,
height: imageHeight,
bitsPerComponent: 8,
bytesPerRow: CVPixelBufferGetBytesPerRow(_pxbuffer),
space: rgbColorSpace,
bitmapInfo: CGImageAlphaInfo.premultipliedFirst.rawValue)
if let _context = context {
_context.draw(image, in: CGRect.init(x: 0, y: 0, width: imageWidth, height: imageHeight))
}
else {
CVPixelBufferUnlockBaseAddress(_pxbuffer, flags);
return nil
}
CVPixelBufferUnlockBaseAddress(_pxbuffer, flags);
return _pxbuffer;
}
return nil
}
I got this code from a previous StackOverflow post (last answer here). I recognize that the code may not be correct, but I have no idea of how to do this myself. I believe that this is the section that contains the error. The model calls for the following type of input: Image<RGB,224,224>

Answer 1

You don't need to do a bunch of image mangling yourself to use a Core ML model with an image — the new Vision framework can do that for you.
import Vision
import CoreML
let model = try VNCoreMLModel(for: MyCoreMLGeneratedModelClass().model)
let request = VNCoreMLRequest(model: model, completionHandler: myResultsMethod)
let handler = VNImageRequestHandler(url: myImageURL)
handler.perform([request])
func myResultsMethod(request: VNRequest, error: Error?) {
guard let results = request.results as? [VNClassificationObservation]
else { fatalError("huh") }
for classification in results {
print(classification.identifier, // the scene label
classification.confidence)
}
}
The WWDC17 session on Vision should have a bit more info — it's tomorrow afternoon.

Answer 2

You can use a pure CoreML, but you should resize an image to (224,224)
DispatchQueue.global(qos: .userInitiated).async {
// Resnet50 expects an image 224 x 224, so we should resize and crop the source image
let inputImageSize: CGFloat = 224.0
let minLen = min(image.size.width, image.size.height)
let resizedImage = image.resize(to: CGSize(width: inputImageSize * image.size.width / minLen, height: inputImageSize * image.size.height / minLen))
let cropedToSquareImage = resizedImage.cropToSquare()
guard let pixelBuffer = cropedToSquareImage?.pixelBuffer() else {
fatalError()
}
guard let classifierOutput = try? self.classifier.prediction(image: pixelBuffer) else {
fatalError()
}
DispatchQueue.main.async {
self.title = classifierOutput.classLabel
}
}
// ...
extension UIImage {
func resize(to newSize: CGSize) -> UIImage {
UIGraphicsBeginImageContextWithOptions(CGSize(width: newSize.width, height: newSize.height), true, 1.0)
self.draw(in: CGRect(x: 0, y: 0, width: newSize.width, height: newSize.height))
let resizedImage = UIGraphicsGetImageFromCurrentImageContext()!
UIGraphicsEndImageContext()
return resizedImage
}
func cropToSquare() -> UIImage? {
guard let cgImage = self.cgImage else {
return nil
}
var imageHeight = self.size.height
var imageWidth = self.size.width
if imageHeight > imageWidth {
imageHeight = imageWidth
}
else {
imageWidth = imageHeight
}
let size = CGSize(width: imageWidth, height: imageHeight)
let x = ((CGFloat(cgImage.width) - size.width) / 2).rounded()
let y = ((CGFloat(cgImage.height) - size.height) / 2).rounded()
let cropRect = CGRect(x: x, y: y, width: size.height, height: size.width)
if let croppedCgImage = cgImage.cropping(to: cropRect) {
return UIImage(cgImage: croppedCgImage, scale: 0, orientation: self.imageOrientation)
}
return nil
}
func pixelBuffer() -> CVPixelBuffer? {
let width = self.size.width
let height = self.size.height
let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue,
kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue] as CFDictionary
var pixelBuffer: CVPixelBuffer?
let status = CVPixelBufferCreate(kCFAllocatorDefault,
Int(width),
Int(height),
kCVPixelFormatType_32ARGB,
attrs,
&pixelBuffer)
guard let resultPixelBuffer = pixelBuffer, status == kCVReturnSuccess else {
return nil
}
CVPixelBufferLockBaseAddress(resultPixelBuffer, CVPixelBufferLockFlags(rawValue: 0))
let pixelData = CVPixelBufferGetBaseAddress(resultPixelBuffer)
let rgbColorSpace = CGColorSpaceCreateDeviceRGB()
guard let context = CGContext(data: pixelData,
width: Int(width),
height: Int(height),
bitsPerComponent: 8,
bytesPerRow: CVPixelBufferGetBytesPerRow(resultPixelBuffer),
space: rgbColorSpace,
bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue) else {
return nil
}
context.translateBy(x: 0, y: height)
context.scaleBy(x: 1.0, y: -1.0)
UIGraphicsPushContext(context)
self.draw(in: CGRect(x: 0, y: 0, width: width, height: height))
UIGraphicsPopContext()
CVPixelBufferUnlockBaseAddress(resultPixelBuffer, CVPixelBufferLockFlags(rawValue: 0))
return resultPixelBuffer
}
}
The expected image size for inputs you can find in the mimodel file:
A demo project that uses both pure CoreML and Vision variants you can find here: https://github.com/handsomecode/iOS11-Demos/tree/coreml_vision/CoreML/CoreMLDemo

Answer 3

If the input is UIImage, rather than an URL, and you want to use VNImageRequestHandler, you can use CIImage.
func updateClassifications(for image: UIImage) {
let orientation = CGImagePropertyOrientation(image.imageOrientation)
guard let ciImage = CIImage(image: image) else { return }
let handler = VNImageRequestHandler(ciImage: ciImage, orientation: orientation)
}
From Classifying Images with Vision and Core ML