iOS MachineLearning 系列（7）

2024-06-12 03:19| 来源: 网络整理| 查看: 265

iOS MachineLearning 系列（7）—— 图片相似度分析

图片相似度分析是Vision框架中提供的高级功能。其本质是计算图片的特征值，通过特征值的比较来计算出图片特征差距，从而可以获取到图片的相似程度。在实际应用中，图片的相似度分析有着广泛的应用。如人脸对比识别，相似物品的搜索和识别等。

进行图片相似度计算前，首先需要对图片的特征值进行分析。使用VNGenerateImageFeaturePrintRequest类创建图片特征分析请求。定义如下：

open class VNGenerateImageFeaturePrintRequest : VNImageBasedRequest { // 图片的裁切和缩放配置 open var imageCropAndScaleOption: VNImageCropAndScaleOption // 结果列表 open var results: [VNFeaturePrintObservation]? { get } }

VNFeaturePrintObservatio结果实例中封装了特征数据：

open class VNFeaturePrintObservation : VNObservation { // 特征类型 open var elementType: VNElementType { get } // 特征元素数量 open var elementCount: Int { get } // 特征数据 open var data: Data { get } // 进行差距比较 open func computeDistance(_ outDistance: UnsafeMutablePointer, to featurePrint: VNFeaturePrintObservation) throws }

其中computeDistance方法即用来进行两个分析结果的特征差距计算。对于完全一样的图片，计算的差距为0，差距越大，表明图片的相似度越小。

下面提供了完整的Demo代码：

import UIKit import Vision class ImageFeatureViewController: UIViewController { let image1 = UIImage(named: "cat1")! let image2 = UIImage(named: "cat2")! let image3 = UIImage(named: "dog1")! let image4 = UIImage(named: "dog2")! lazy var imageView1 = UIImageView(image: image1) lazy var imageView2 = UIImageView(image: image2) lazy var imageView3 = UIImageView(image: image3) lazy var imageView4 = UIImageView(image: image4) override func viewDidLoad() { super.viewDidLoad() view.backgroundColor = .white view.addSubview(imageView1) view.addSubview(imageView2) view.addSubview(imageView3) view.addSubview(imageView4) let width = (self.view.frame.width - 60) / 2 let h1 = width / (image1.size.width / image1.size.height) let h2 = width / (image2.size.width / image2.size.height) let h3 = width / (image3.size.width / image3.size.height) let h4 = width / (image4.size.width / image4.size.height) imageView1.frame = CGRect(x: 20, y: 100, width: width, height: h1) imageView2.frame = CGRect(x: width + 40, y: 100, width: width, height: h2) imageView3.frame = CGRect(x: 20, y: max(h1, h2) + 120, width: width, height: h3) imageView4.frame = CGRect(x: width + 40, y: max(h1, h2) + 120, width: width, height: h4) // 进行特征值分析 sendRequest(image: image1, number: 1) sendRequest(image: image2, number: 2) sendRequest(image: image3, number: 3) sendRequest(image: image4, number: 4) } func sendRequest(image: UIImage, number: Int) { let handler = VNImageRequestHandler(cgImage: image.cgImage!, orientation: .up) let request = VNGenerateImageFeaturePrintRequest { result, error in guard error == nil else { print(error!) return } let r = result as! VNGenerateImageFeaturePrintRequest if number == 1 { self.result1 = r.results?.first } if number == 2 { self.result2 = r.results?.first } if number == 3 { self.result3 = r.results?.first } if number == 4 { self.result4 = r.results?.first } if let result1 = self.result1, let result2 = self.result2, let result3 = self.result3, let result4 = self.result4 { // 进行相似性对比 var distance12 = Float(0) try! result1.computeDistance(&distance12, to: result2) var distance13 = Float(0) try! result1.computeDistance(&distance13, to: result3) var distance34 = Float(0) try! result3.computeDistance(&distance34, to: result4) print("图1与图2相似差距:", distance12) print("图1与图3相似差距:", distance13) print("图3与图4相似差距:", distance34) DispatchQueue.main.async { let l = UILabel() l.text = "图1与图2相似差距:\(distance12)\n图1与图3相似差距:\(distance13)\n图3与图4相似差距:\(distance34)" l.font = .boldSystemFont(ofSize: 22) self.view.addSubview(l) l.frame = CGRect(x: 0, y: max(self.imageView3.frame.height, self.imageView4.frame.height) + self.imageView3.frame.origin.y, width: self.view.frame.width, height: 100) l.numberOfLines = 0 } } } try? handler.perform([request]) } var result1: VNFeaturePrintObservation? var result2: VNFeaturePrintObservation? var result3: VNFeaturePrintObservation? var result4: VNFeaturePrintObservation? }

可以看到，上面两只猫的相似差距为12，猫和狗的相似差距为26，两只狗的相似差距为8。

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