给一个具体的样例，包括模型的加载，初始化、读取本地jpg图片，进行推理得到骨架的坐标，并画在原图上，最后释放模型资源-Go服务器开发

下面是一个使用Core ML在iOS应用中加载YOLOv8模型，读取本地JPG图片，并进行推理以获取骨架坐标的具体示例。这将包括如何绘制结果并释放模型资源。

1. 准备工作

确保你已经将训练好的YOLOv8模型转换为Core ML格式（.mlmodel）并添加到你的Xcode项目中。这里我们假设你已经有了一个名为 YOLOv8.mlmodel 的文件。

2. 创建Swift文件

创建一个新的Swift文件，比如 ObjectDetectionViewController.swift：

import UIKit
import CoreML
import Vision

class ObjectDetectionViewController: UIViewController {
    
    // MARK: - Properties
    private var model: YOLOv8!
    
    // MARK: - UI Components
    private let imageView = UIImageView()
    
    override func viewDidLoad() {
        super.viewDidLoad()
        setupUI()
        loadModel()
        performInference()
    }
    
    // MARK: - Setup UI
    private func setupUI() {
        view.backgroundColor = .white
        imageView.frame = view.bounds
        imageView.contentMode = .scaleAspectFit
        view.addSubview(imageView)
    }
    
    // MARK: - Load Model
    private func loadModel() {
        do {
            let configuration = MLModelConfiguration()
            model = try YOLOv8(configuration: configuration)
        } catch {
            print("Error loading model: \(error)")
        }
    }
    
    // MARK: - Perform Inference
    private func performInference() {
        
        guard let imagePath = Bundle.main.path(forResource: "example", ofType: "jpg"),
              let inputImage = UIImage(contentsOfFile: imagePath),
              let ciImage = CIImage(image: inputImage) else {
            print("Failed to load image.")
            return 
        }

        // Prepare the request to run inference using Vision framework.
        guard let resizedImage = resizeImage(image: ciImage, targetSize: CGSize(width: 640, height: 640)) else { return }

        do {
            let predictionInput = YOLOv8Input(image: resizedImage)
            let output = try model.prediction(input: predictionInput)

            drawBoundingBoxes(onImage: inputImage, outputData: output)

        } catch {
            print("Prediction failed with error \(error).")
        }
        
         releaseModelResources()
     }
     
     // Resize Image Function (for Core ML Input Size Requirement)
     private func resizeImage(image: CIImage, targetSize size: CGSize) -> CIImage? {
         return image.transformed(by:
             CGAffineTransform(scaleX:size.width / image.extent.width,
                               y:size.height / image.extent.height))
     }

     // Draw bounding boxes on the original UIImage and display it in the UIImageView.
     private func drawBoundingBoxes(onImage originalImage: UIImage, outputData results : YOLOv8Output) {

         UIGraphicsBeginImageContext(originalImage.size)

         originalImage.draw(at: CGPoint.zero)

         for bbox in results.boundingBoxes { 
             let rect = CGRect(x:bbox.origin.x * originalImage.size.width,
                               y:bbox.origin.y * originalImage.size.height,
                               width:bbox.width * originalImage.size.width,
                               height:bbox.height * originalImage.size.height)

             UIColor.red.setStroke()
             UIRectFrame(rect)
         }

         let drawn_image = UIGraphicsGetImageFromCurrentImageContext()
         UIGraphicsEndImageContext()

         if let drawn_image = drawn_image {
             DispatchQueue.main.async { [weak self] in 
                 self?.imageView.image = drawn_image 
             }
         }
     }

     // Release Model Resources After Use.
     private func releaseModelResources() {
         model = nil 
     }
}