ios怎么分类图片

iOS 可以利用 Core ML 及其它一些图像处理框架来分类图片。以下是一个使用 Core ML 进行图片分类的示例代码：

首先，需要导入 Core ML 模型。假设你已经有了一个训练好的 Core ML 模型文件（后缀为 .mlmodel）。

```swift

import CoreML

```

然后，创建一个图像分类器的实例：

```swift

class ImageClassifier {

let model: MobileNetV2 // 假设 MobileNetV2 是你的模型

init() {

guard let model = try? MobileNetV2(configuration: MLModelConfiguration()) else {

fatalError("Failed to load the core ml model")

}

self.model = model

}

func classify(image: UIImage, completion: @escaping (String?, Error?) -> Void) {

// 将图像转换为 Core ML 可处理的格式

guard let pixelBuffer = image.pixelBuffer(width: 224, height: 224) else {

completion(nil, NSError(domain: "ImageClassifier", code: 1, userInfo: ["error": "Failed to create pixel buffer"]))

return

}

// 创建一个 Core ML 请求

let request = VNCoreMLRequest(model: model) { [weak self] request, error in

guard let results = request.results as? [VNClassificationObservation], let topResult = results.first else {

completion(nil, NSError(domain: "ImageClassifier", code: 2, userInfo: ["error": "Failed to classify the image"]))

return

}

completion(topResult.identifier, nil)

}

// 执行图像分类请求

do {

let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, orientation: .up)

try handler.perform([request])

} catch {

completion(nil, error)

}

}

}

```

注意上述代码中使用了一个 UIImage 的扩展方法 `pixelBuffer(width:height:)`，该方法可以将 UIImage 转换为 `CVPixelBuffer`：

```swift

extension UIImage {

func pixelBuffer(width: Int, height: Int) -> CVPixelBuffer? {

let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue,

kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue] as CFDictionary

var pixelBuffer: CVPixelBuffer?

let status = CVPixelBufferCreate(kCFAllocatorDefault, width, height, kCVPixelFormatType_32ARGB, attrs, &pixelBuffer)

guard status == kCVReturnSuccess, let buffer = pixelBuffer else {

return nil

}

CVPixelBufferLockBaseAddress(buffer, .init(rawValue: 0))

defer { CVPixelBufferUnlockBaseAddress(buffer, .init(rawValue: 0)) }

let context = CGContext(data: CVPixelBufferGetBaseAddress(buffer),

width: width,

height: height,

bitsPerComponent: 8,

bytesPerRow: CVPixelBufferGetBytesPerRow(buffer),

space: CGColorSpaceCreateDeviceRGB(),

bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue)

guard let cgImage = self.cgImage, let cgContext = context else {

return nil

}

cgContext.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height))

return buffer

}

}

```

使用示例如下：

```swift

let image = UIImage(named: "exampleImage")

let classifier = ImageClassifier()

classifier.classify(image: image) { result, error in

if let error = error {

print("Classification error: \(error.localizedDescription)")

} else if let result = result {

print("Image classified as \(result)")

}

}

```

这是一个简单的示例，实际使用中可能需要根据你的模型的输入和输出进行一些调整。