Find bike image in Swift4.1 using CoreML & Vision

In this Post Inception V3 mlModel was used to find car image
Download and Drag InceptionV3 mlModel file and Drop to Xcode project.

 

 

integrating CoreML and Vision functions and find car image

import UIKit
import CoreML
import Vision

@available(iOS 11.0, *)
class ViewController: UIViewController {
    
    @IBOutlet weak var informationLabel: UILabel!
    @IBOutlet weak var imageView: UIImageView!
    
    override func viewDidLoad() {
        super.viewDidLoad()
    }
    
    override func didReceiveMemoryWarning() {
        super.didReceiveMemoryWarning()
        // Dispose of any resources that can be recreated.
    }
    
    // MARK : - ML
    /// - Tag: MLModelSetup
    lazy var classificationRequest: VNCoreMLRequest = {
        do {
            /*
             Use the Swift class `Inceptionv3` Core ML generates from the model.
             To use a different Core ML classifier model, add it to the project
             and replace `MobileNet` with that model's generated Swift class.
             */
            let model = try VNCoreMLModel(for: Inceptionv3().model)
            
            let request = VNCoreMLRequest(model: model, completionHandler: { [weak self] request, error in
                self?.processClassifications(for: request, error: error)
            })
            request.imageCropAndScaleOption = .centerCrop
            return request
        } catch {
            fatalError("Failed to load Vision ML model: \(error)")
        }
    }()
    
    func updateclassification(for image: UIImage) {
        
        informationLabel.text = "Classifying.."
        
        let orientation = CGImagePropertyOrientation(image.imageOrientation)
        guard let ciImage = CIImage(image: image) else { fatalError("Unable to create \(CIImage.self) from \(image).") }
        
        DispatchQueue.global(qos: .userInitiated).async {
            
            let handler = VNImageRequestHandler(ciImage: ciImage, orientation: orientation, options: [:])
            
            do {
                try handler.perform([self.classificationRequest])
            } catch let error {
                print(error.localizedDescription)
            }
            
        }
    }
    
    /// Updates the UI with the results of the classification.
    /// - Tag: ProcessClassifications
    func processClassifications(for request: VNRequest, error: Error?) {
       
        DispatchQueue.main.async {
            guard let results = request.results else {
                self.informationLabel.text = "Unable to classify image.\n\(error!.localizedDescription)"
                return
            }
            
            // The `results` will always be `VNClassificationObservation`s, as specified by the Core ML model in this project.
            let classifications = results as! [VNClassificationObservation]
            
            if classifications.isEmpty {
                self.informationLabel.text = "Nothing recognized."
            } else {
                // Display top classifications ranked by confidence in the UI.
                let topClassifications = classifications.prefix(2)
                
                let identifer = topClassifications.map({$0.identifier})
                
                identifer.forEach({ (id) in
                    let iDs = id.components(separatedBy: ", ")
                    let filtered = iDs.filter({$0 == "bike"})
                    
                    if filtered.count > 0 {
                       print("This is bike")
                    }
                })
                
                let descriptions = topClassifications.map { classification in
                    // Formats the classification for display; e.g. "(0.37) cliff, drop, drop-off".
                    return String(format: "  (%.2f) %@", classification.confidence, classification.identifier)
                }
                
                self.informationLabel.text = "Classification:\n" + descriptions.joined(separator: "\n")
            }
        }
    }
    
    // MARK: - Button action
    func presentPhotoPicker(sourceType: UIImagePickerControllerSourceType) {
        let picker = UIImagePickerController()
        picker.delegate = self
        picker.sourceType = sourceType
        present(picker, animated: true)
    }
    
    @IBAction func cameraButtonClicked(_ sender: Any) {
        // Show options for the source picker only if the camera is available.
        guard UIImagePickerController.isSourceTypeAvailable(.camera) else {
            presentPhotoPicker(sourceType: .photoLibrary)
            return
        }
        self.presentPhotoPicker(sourceType: .camera)
    }
    
    @IBAction func galleryButtonClicked(_ sender: Any) {
        self.presentPhotoPicker(sourceType: .photoLibrary)
    }
    
    
}

@available(iOS 11.0, *)
extension ViewController: UIImagePickerControllerDelegate, UINavigationControllerDelegate {
    // MARK: - Handling Image Picker Selection
    
    func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [String: Any]) {
        picker.dismiss(animated: true)
        
        // We always expect `imagePickerController(:didFinishPickingMediaWithInfo:)` to supply the original image.
        let image = info[UIImagePickerControllerOriginalImage] as! UIImage
        imageView.image = image
        updateclassification(for: image)
    }
}

import ImageIO

extension CGImagePropertyOrientation {
    /**
     Converts a `UIImageOrientation` to a corresponding
     `CGImagePropertyOrientation`. The cases for each
     orientation are represented by different raw values.
     
     - Tag: ConvertOrientation
     */
    init(_ orientation: UIImageOrientation) {
        switch orientation {
        case .up: self = .up
        case .upMirrored: self = .upMirrored
        case .down: self = .down
        case .downMirrored: self = .downMirrored
        case .left: self = .left
        case .leftMirrored: self = .leftMirrored
        case .right: self = .right
        case .rightMirrored: self = .rightMirrored
        }
    }
}

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ktrkathir