优化相似度判断

PhoneManager/Class/Tool/Class/PhotoAndVideoMananger/PhotoAndVideoMananger.swift

@@ -55,6 +55,9 @@ class PhotoAndVideoMananger {
     
     var ids:[String] = []
     
+    // 定义
+    private let hashDistance = 100
+    
     func setAssets() {
         
         let fetchOptions = PHFetchOptions()
@@ -1009,39 +1012,54 @@ class PhotoAndVideoMananger {
     
     // 计算两个哈希值的汉明距离
     func hammingDistance(_ hash1: String, _ hash2: String) -> Int {
-        var distance = 0
-        for (char1, char2) in zip(hash1, hash2) {
-            let int1 = Int(String(char1), radix: 16)!
-            let int2 = Int(String(char2), radix: 16)!
-            let xor = int1 ^ int2
-            distance += String(xor, radix: 2).filter { $0 == "1" }.count
-        }
-        return distance
+//        var distance = 0
+//        for (char1, char2) in zip(hash1, hash2) {
+//            let int1 = Int(String(char1), radix: 16)!
+//            let int2 = Int(String(char2), radix: 16)!
+//            let xor = int1 ^ int2
+//            distance += String(xor, radix: 2).filter { $0 == "1" }.count
+//        }
+//        return distance
+        
+        guard hash1.count == hash2.count else { return Int.max }
+
+        return zip(hash1, hash2).filter { $0 != $1 }.count
     }
     
+  
     func groupSimilarImages(assets: [PHAsset], progressCompletion: @escaping ([[AssetModel]]) -> Void, completion: @escaping ([[AssetModel]]) -> Void) {
+        print("开始处理相似图片")
         DispatchQueue.global().async {
+            print("进入异步任务处理相似图片")
+            
             var assetModels: [AssetModel] = []
             var hashes: [String: AssetModel] = [:]
             var groupedImages: [[AssetModel]] = []
+            let dispatchGroup = DispatchGroup()
             
             for asset in assets {
-                _ = asset.pixelWidth * asset.pixelHeight
+                // 创建 AssetModel
                 let createDate = asset.creationDate ?? Date()
                 let model = AssetModel(localIdentifier: asset.localIdentifier, assetSize: self.findAssetSize(asset: asset), createDate: createDate)
                 assetModels.append(model)
 
-                let manager = PHImageManager.default()
-                manager.requestImage(for: asset, targetSize: CGSize(width: 32, height: 32), contentMode: .aspectFit, options: nil) { (image, _) in
+                // 请求图像
+                dispatchGroup.enter()
+                PHImageManager.default().requestImage(for: asset, targetSize: CGSize(width: 32, height: 32), contentMode: .aspectFit, options: nil) { (image, _) in
                     if let image = image {
-                        let hash = self.pHash(for: image)
-                        if hash != "" {
+                        let hash = self.calculateImageHashUsingCoreImage(image)
+                        if !hash.isEmpty {
                             hashes[hash] = model
                         }
                     }
+                    dispatchGroup.leave()
                 }
             }
             
+            // 等待所有请求完成后进行比较
+            dispatchGroup.notify(queue: .global()) {
+                print("获取到全部hash值")
+
                 var usedHashes: Set<String> = []
                 for (hash1, model1) in hashes {
                     if usedHashes.contains(hash1) { continue }
@@ -1051,7 +1069,7 @@ class PhotoAndVideoMananger {
                     for (hash2, model2) in hashes {
                         if usedHashes.contains(hash2) { continue }
                         let distance = self.hammingDistance(hash1, hash2)
-                    if distance < 4 { // 汉明距离小于 5 认为相似
+                        if distance < self.hashDistance { // 可以根据需求调整阈值
                             similarModels.append(model2)
                             usedHashes.insert(hash2)
                         }
@@ -1060,6 +1078,7 @@ class PhotoAndVideoMananger {
                     if similarModels.count >= 2 {
                         groupedImages.append(similarModels)
                         // 每次找到新的相似组，通过 progressCompletion 回调返回当前已处理好的分组数据
+                        print("判断相似", similarModels)
                         DispatchQueue.main.async {
                             progressCompletion(groupedImages)
                         }
@@ -1071,6 +1090,7 @@ class PhotoAndVideoMananger {
                 }
             }
         }
+    }
 }
 
 
@@ -1148,3 +1168,69 @@ extension Array {
         }
     }
 }
+
+
+extension PhotoAndVideoMananger{
+    
+    // 计算图片的感知哈希值
+    func calculateImageHashUsingCoreImage(_ image: UIImage) -> String {
+        guard let cgImage = image.cgImage else { return "" }
+        
+        // 生成CIImage
+        let ciImage = CIImage(cgImage: cgImage)
+
+        // 创建滤镜：灰度化图像
+        let filter = CIFilter(name: "CIPhotoEffectNoir")!
+        filter.setValue(ciImage, forKey: kCIInputImageKey)
+
+        guard let outputImage = filter.outputImage else {
+            return ""
+        }
+        
+        // 将输出图像缩放到32x32
+        let context = CIContext()
+        let targetSize = CGSize(width: 32, height: 32)
+        let scaledImage = context.createCGImage(outputImage, from: outputImage.extent)!
+        let resizedImage = UIImage(cgImage: scaledImage, scale: 1.0, orientation: image.imageOrientation)
+
+        // 获取像素数据
+        guard let pixelData = resizedImage.cgImage?.dataProvider?.data,
+              let data = CFDataGetBytePtr(pixelData) else {
+            return ""
+        }
+        
+        // 计算灰度像素平均值
+        var pixels: [UInt8] = Array(repeating: 0, count: 1024) // 32 * 32
+        for i in 0..<32 {
+            for j in 0..<32 {
+                let pixelIndex = (i * 32 + j) * 4 // RGBA
+                let r = data[pixelIndex]
+                let g = data[pixelIndex + 1]
+                let b = data[pixelIndex + 2]
+                // 使用灰度公式转化
+                let gray = UInt8(0.299 * Double(r) + 0.587 * Double(g) + 0.114 * Double(b))
+                pixels[i * 32 + j] = gray
+            }
+        }
+        
+        // 计算平均值
+        let sum = pixels.reduce(0) { UInt32($0) + UInt32($1) } // 使用 UInt32 来避免溢出
+        let average = UInt8(sum / UInt32(pixels.count)) // 确保将 sum 转换为 UInt32
+
+        // 生成哈希值
+        var hash = ""
+        for pixel in pixels {
+            hash += pixel > average ? "1" : "0"
+        }
+        
+        return hash
+    }
+
+    // 计算汉明距离
+    func calculateHammingDistance(_ hash1: String, _ hash2: String) -> Int {
+        guard hash1.count == hash2.count else { return Int.max }
+
+        return zip(hash1, hash2).filter { $0 != $1 }.count
+    }
+    
+}