feat: Implement face clustering management service and API

2026-09-05 15:47:00 +08:00 · 2025-06-16 20:07:39 +08:00
parent cfda1ed930
commit 429ce92cdb
18 changed files with 1949 additions and 14 deletions
@@ -0,0 +1,236 @@
+using Foxel.Models.DataBase;
+using Microsoft.EntityFrameworkCore;
+
+namespace Foxel.Services.AI;
+
+public class FaceClusteringService(
+    IDbContextFactory<MyDbContext> contextFactory,
+    ILogger<FaceClusteringService> logger) : IFaceClusteringService
+{
+    private const double SIMILARITY_THRESHOLD = 0.5;
+
+    public async Task<List<FaceCluster>> ClusterFacesAsync()
+    {
+        await using var dbContext = await contextFactory.CreateDbContextAsync();
+
+        // 获取所有有嵌入向量但未分类的人脸
+        var unclusteredFaces = await dbContext.Faces
+            .Where(f => f.Embedding != null && f.ClusterId == null)
+            .Include(f => f.Picture)
+            .ToListAsync();
+
+        var clusters = new List<FaceCluster>();
+
+        foreach (var face in unclusteredFaces)
+        {
+            var assignedCluster = await FindBestClusterAsync(face, clusters, dbContext);
+
+            if (assignedCluster != null)
+            {
+                // 分配到现有聚类
+                face.ClusterId = assignedCluster.Id;
+            }
+            else
+            {
+                // 创建新聚类
+                var newCluster = new FaceCluster
+                {
+                    Name = $"未知人物 {clusters.Count + 1}",
+                    CreatedAt = DateTime.UtcNow
+                };
+
+                dbContext.FaceClusters.Add(newCluster);
+                await dbContext.SaveChangesAsync();
+
+                face.ClusterId = newCluster.Id;
+                clusters.Add(newCluster);
+            }
+        }
+
+        await dbContext.SaveChangesAsync();
+        logger.LogInformation("人脸聚类完成，共处理 {FaceCount} 个人脸，生成 {ClusterCount} 个聚类",
+            unclusteredFaces.Count, clusters.Count);
+
+        return clusters;
+    }
+
+    public async Task<FaceCluster?> AssignFaceToClusterAsync(int faceId)
+    {
+        await using var dbContext = await contextFactory.CreateDbContextAsync();
+
+        var face = await dbContext.Faces
+            .Include(f => f.Picture)
+            .FirstOrDefaultAsync(f => f.Id == faceId);
+
+        if (face?.Embedding == null) return null;
+
+        // 获取所有现有聚类的代表人脸
+        var existingClusters = await dbContext.FaceClusters
+            .Include(c => c.Faces.Take(1))
+            .ToListAsync();
+
+        foreach (var cluster in existingClusters)
+        {
+            if (cluster.Faces?.Any() == true)
+            {
+                var representativeFace = cluster.Faces.First();
+                if (representativeFace.Embedding != null)
+                {
+                    var similarity = CalculateSimilarity(face.Embedding, representativeFace.Embedding);
+                    if (similarity >= SIMILARITY_THRESHOLD)
+                    {
+                        face.ClusterId = cluster.Id;
+                        await dbContext.SaveChangesAsync();
+                        return cluster;
+                    }
+                }
+            }
+        }
+
+        // 创建新聚类
+        var newCluster = new FaceCluster
+        {
+            Name = $"未知人物 {DateTime.Now:yyyyMMddHHmmss}",
+            CreatedAt = DateTime.UtcNow
+        };
+
+        dbContext.FaceClusters.Add(newCluster);
+        await dbContext.SaveChangesAsync();
+
+        face.ClusterId = newCluster.Id;
+        await dbContext.SaveChangesAsync();
+
+        return newCluster;
+    }
+
+    public double CalculateSimilarity(float[] embedding1, float[] embedding2)
+    {
+        if (embedding1.Length != embedding2.Length) return 0;
+
+        // 计算余弦相似度
+        double dot = 0, norm1 = 0, norm2 = 0;
+
+        for (int i = 0; i < embedding1.Length; i++)
+        {
+            dot += embedding1[i] * embedding2[i];
+            norm1 += embedding1[i] * embedding1[i];
+            norm2 += embedding2[i] * embedding2[i];
+        }
+
+        if (norm1 == 0 || norm2 == 0) return 0;
+
+        return dot / (Math.Sqrt(norm1) * Math.Sqrt(norm2));
+    }
+
+    private async Task<FaceCluster?> FindBestClusterAsync(Face face, List<FaceCluster> newClusters, MyDbContext dbContext)
+    {
+        if (face.Embedding == null) return null;
+
+        double bestSimilarity = 0;
+        FaceCluster? bestCluster = null;
+
+        // 检查现有数据库中的聚类
+        var existingClusters = await dbContext.FaceClusters
+            .Include(c => c.Faces.Take(5)) // 取前5个人脸作为比较
+            .ToListAsync();
+
+        foreach (var cluster in existingClusters.Concat(newClusters))
+        {
+            if (cluster.Faces?.Any() == true)
+            {
+                foreach (var clusterFace in cluster.Faces)
+                {
+                    if (clusterFace.Embedding != null)
+                    {
+                        var similarity = CalculateSimilarity(face.Embedding, clusterFace.Embedding);
+                        if (similarity > bestSimilarity && similarity >= SIMILARITY_THRESHOLD)
+                        {
+                            bestSimilarity = similarity;
+                            bestCluster = cluster;
+                        }
+                    }
+                }
+            }
+        }
+
+        return bestCluster;
+    }
+
+    public async Task<List<FaceCluster>> ClusterUserFacesAsync(int userId)
+    {
+        await using var dbContext = await contextFactory.CreateDbContextAsync();
+
+        // 获取指定用户所有有嵌入向量但未分类的人脸
+        var unclusteredFaces = await dbContext.Faces
+            .Where(f => f.Embedding != null && f.ClusterId == null && f.Picture.UserId == userId)
+            .Include(f => f.Picture)
+            .ToListAsync();
+
+        var clusters = new List<FaceCluster>();
+
+        foreach (var face in unclusteredFaces)
+        {
+            var assignedCluster = await FindBestUserClusterAsync(face, userId, clusters, dbContext);
+
+            if (assignedCluster != null)
+            {
+                face.ClusterId = assignedCluster.Id;
+            }
+            else
+            {
+                var newCluster = new FaceCluster
+                {
+                    Name = $"未知人物 {DateTime.Now:yyyyMMddHHmmss}",
+                    CreatedAt = DateTime.UtcNow
+                };
+
+                dbContext.FaceClusters.Add(newCluster);
+                await dbContext.SaveChangesAsync();
+
+                face.ClusterId = newCluster.Id;
+                clusters.Add(newCluster);
+            }
+        }
+
+        await dbContext.SaveChangesAsync();
+        logger.LogInformation("用户 {UserId} 人脸聚类完成，共处理 {FaceCount} 个人脸，生成 {ClusterCount} 个聚类",
+            userId, unclusteredFaces.Count, clusters.Count);
+
+        return clusters;
+    }
+
+    private async Task<FaceCluster?> FindBestUserClusterAsync(Face face, int userId, List<FaceCluster> newClusters, MyDbContext dbContext)
+    {
+        if (face.Embedding == null) return null;
+
+        double bestSimilarity = 0;
+        FaceCluster? bestCluster = null;
+
+        // 检查该用户现有的聚类
+        var existingClusters = await dbContext.FaceClusters
+            .Where(c => dbContext.Faces.Any(f => f.ClusterId == c.Id && f.Picture.UserId == userId))
+            .Include(c => c.Faces.Where(f => f.Picture.UserId == userId).Take(5))
+            .ToListAsync();
+
+        foreach (var cluster in existingClusters.Concat(newClusters))
+        {
+            if (cluster.Faces?.Any() == true)
+            {
+                foreach (var clusterFace in cluster.Faces)
+                {
+                    if (clusterFace.Embedding != null)
+                    {
+                        var similarity = CalculateSimilarity(face.Embedding, clusterFace.Embedding);
+                        if (similarity > bestSimilarity && similarity >= SIMILARITY_THRESHOLD)
+                        {
+                            bestSimilarity = similarity;
+                            bestCluster = cluster;
+                        }
+                    }
+                }
+            }
+        }
+
+        return bestCluster;
+    }
+}