71 lines
1.9 KiB
Swift
71 lines
1.9 KiB
Swift
import Foundation
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import Vision
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import AppKit
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// Swift 5.9+ 可以用 CommandLine.arguments，兼容旧版本用下面的方式
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let args = ProcessInfo.processInfo.arguments
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guard args.count >= 2 else {
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print("Usage: swift macvision_swift.swift <image_path>")
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exit(1)
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}
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let imagePath = args[1]
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// 1. 加载图片
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guard let image = NSImage(contentsOfFile: imagePath) else {
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print("❌ 无法加载图片: \(imagePath)")
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exit(1)
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}
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// 2. 转换为 CGImage（Vision 需要）
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guard let tiffData = image.tiffRepresentation,
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let cgImage = NSBitmapImageRep(data: tiffData)?.cgImage else {
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print("❌ 无法转换图片")
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exit(1)
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}
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// 3. 设置信号量等待异步完成
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let semaphore = DispatchSemaphore(value: 0)
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var results: [(text: String, confidence: Float)] = []
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// 4. 创建 OCR 请求
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let request = VNRecognizeTextRequest { request, error in
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defer { semaphore.signal() } // 关键：完成后释放信号量
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if let error = error {
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print("❌ 识别错误：\(error)")
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return
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}
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guard let observations = request.results as? [VNRecognizedTextObservation] else {
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print("❌ 无结果")
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return
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}
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for observation in observations {
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guard let candidate = observation.topCandidates(1).first else { continue }
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results.append((candidate.string, candidate.confidence))
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}
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}
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// 5. 配置识别参数
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request.recognitionLanguages = ["zh-Hans", "zh-Hant", "en-US"] // 支持中文
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request.usesLanguageCorrection = true // 启用语言校正
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// 6. 执行识别
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let handler = VNImageRequestHandler(cgImage: cgImage, options: [:])
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do {
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try handler.perform([request])
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_ = semaphore.wait(timeout: .now() + 30) // 等待最多 30 秒
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// 输出结果
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for (text, conf) in results {
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print("文本：\(text)")
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print("置信度：\(String(format: "%.2f", conf * 100))%")
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}
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} catch {
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print("❌ 执行错误：\(error)")
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}
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