Question

我已经训练了一个模型来区分恶性和良性皮肤病变，以潜在地检测患者是否患有皮肤癌，并将我的keras模型转换为coreML。现在，我试图使用我完全没有经验（仍然通过反复试验学习）的swift（通过Xcode）将模型应用于ios应用。

当前，我正在尝试通过一个简单的应用程序使模型工作，该应用程序仅从手机的摄像头获取图像以获取预测的标签作为输出，但是我非常想让摄像头实际工作以实现此目的。

import UIKit
import CoreML
import Vision
import Social

@UIApplicationMain
class ViewControl: UIViewController, UIImagePickerControllerDelegate, UINavigationControllerDelegate, UIApplicationDelegate {

    @IBOutlet weak var imageView: UIImageView!
    var classificationResults : [VNClassificationObservation] = []

    let imagePicker = UIImagePickerController()

    override func viewDidLoad() {
        super.viewDidLoad()

        imagePicker.delegate = self

    }

    func detect(image: CIImage) {

        // Load the ML model through its generated class
        guard let model = try? VNCoreMLModel(for: weights_skin_cancer().model) else {
            fatalError("can't load ML model")
        }

        let request = VNCoreMLRequest(model: model) { request, error in
            guard let results = request.results as? [VNClassificationObservation],
                let topResult = results.first
                else {
                    fatalError("unexpected result type from VNCoreMLRequest")
                }

                if topResult.identifier.contains("malignant") {
                    DispatchQueue.main.async {
                        self.navigationItem.title = "mal!"
                        self.navigationController?.navigationBar.barTintColor = UIColor.green
                        self.navigationController?.navigationBar.isTranslucent = false


                    }
                }
                else {
                    DispatchQueue.main.async {
                        self.navigationItem.title = "benign!"
                        self.navigationController?.navigationBar.barTintColor = UIColor.red
                        self.navigationController?.navigationBar.isTranslucent = false

                    }
                }


        }

        let handler = VNImageRequestHandler(ciImage: image)

        do { try handler.perform([request]) }
        catch { print(error) }



    }


    func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [UIImagePickerController.InfoKey : Any]) {


        if let image = info[UIImagePickerController.InfoKey.originalImage] as? UIImage {

            imageView.image = image

            imagePicker.dismiss(animated: true, completion: nil)


            guard let ciImage = CIImage(image: image) else {
                fatalError("couldn't convert uiimage to CIImage")
            }

            detect(image: ciImage)

        }
    }


    @IBAction func cameraTapped(_ sender: Any) {

        imagePicker.sourceType = .camera
        imagePicker.allowsEditing = false

        present(imagePicker, animated: true, completion: nil)
    }

}

这也是用于将我的模型转换为coreML以供参考的代码：

import coremltools

output_labels = ['benign', 'malignant']
scale = 1/255.
coreml_model = coremltools.converters.keras.convert('/Users/Grampun/Desktop/ISIC-Archive-Downloader-master/trained_models/lr_0.00006-400_DS-20_epochs/weights.best.from_scratch.6.hdf5',
                                                    input_names='image',
                                                    image_input_names='image',
                                                    output_names='output',
                                                    class_labels=output_labels,
                                                    image_scale=scale)

coreml_model.author = 'Jack Bugeja'
coreml_model.short_description = 'Model used to identify between benign and malignant skin lesions'

coreml_model.input_description['image'] = 'Dermascopic image of skin lesion to evaluate'
coreml_model.input_description['output'] = 'Malignant/Benign'

coreml_model.save(
    '/Users/Grampun/Desktop/ISIC-Archive-Downloader-master/trained_models/model_for_ios/lr_0.00006-400_DS-20_epochs/weights_skin_cancer.mlmodel')

一般而言，任何帮助将不胜感激。谢谢！

Answer 1

打开相机：

@IBAction func cameraTapped(_ sender: Any) {
    let controller = UIImagePickerController()
    controller.sourceType = .camera
    controller.mediaTypes = ["public.image"]
    controller.allowsEditing = false
    controller.delegate = self
    present(controller, animated: true)
}

将YourModel.mlmodel添加到您的项目中。

在didFinishPickingMediaWithInfo中添加以下代码：

if let imageURL = info[.imageURL] as? URL {
    if let image = UIImage(contentsOfFile: imageURL.absoluteString) {
        self.getPrediction(image)
    }
}

添加它以获得预测：

func getPrediction(_ image: UIImage) {
    let model = YourModel()

    guard let pixelBuffer = buffer(from: image) else { return }
    guard let prediction = try? model.prediction(image: pixelBuffer) else { return }

    print(prediction.classLabel) // Most likely image category as string value
}

使用此辅助功能从您的UIImage中提取一个CVPixelBuffer，您需要在getPrediction()中使用它

func buffer(from image: UIImage) -> CVPixelBuffer? {
    let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue, kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue] as CFDictionary
    var pixelBuffer : CVPixelBuffer?
    let status = CVPixelBufferCreate(kCFAllocatorDefault, Int(image.size.width), Int(image.size.height), kCVPixelFormatType_32ARGB, attrs, &pixelBuffer)
    guard (status == kCVReturnSuccess) else {
        return nil
    }

    CVPixelBufferLockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))
    let pixelData = CVPixelBufferGetBaseAddress(pixelBuffer!)

    let rgbColorSpace = CGColorSpaceCreateDeviceRGB()
    let context = CGContext(data: pixelData, width: Int(image.size.width), height: Int(image.size.height), bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(pixelBuffer!), space: rgbColorSpace, bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue)

    context?.translateBy(x: 0, y: image.size.height)
    context?.scaleBy(x: 1.0, y: -1.0)

    UIGraphicsPushContext(context!)
    image.draw(in: CGRect(x: 0, y: 0, width: image.size.width, height: image.size.height))
    UIGraphicsPopContext()
    CVPixelBufferUnlockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))

    return pixelBuffer
}

在Swift-图像分类器中设置CoreML模型

1 个答案: