feat(lifelog/photolog): 微信联系人头像 → 人脸锚点 + 自动认人

把微信联系人头像建成人脸锚点，照片处理时自动认出"这是谁"，标注时直接带名字。 - data：Person 加 faceEmbedding 锚点字段 + 迁移 v4→v5（ALTER 加 BLOB 列， DDL 对过 Room；真机迁移零数据损失）；PersonDao anchors/getByContactRef - FaceMatcher：gallery 统一 = 人物锚点(头像) + 已标注脸样本；match() 共享余弦匹配； PhotoProcessor 改用统一 gallery - WeChatEnrollWorker：读 filesDir/wx_enroll/(enroll.json + 头像) → 对每张头像跑 **端上 FaceEngine**(必须同引擎才能跟照片人脸比)，恰 1 脸就建 Person(name, contactRef=wechat:wxid, avatarPath, faceEmbedding)。幂等、单轮预算 8min、retry 续 - UI：人物页「从微信导入联系人」按钮；人物卡片标注「微信/声纹」来源 - server/services/wechat/extract-wechat-avatars.py：Mac 侧提取脚本（sqlcipher 解密 EnMicroMsg.db → rcontact⨝img_flag 筛真人 → 下载头像）实测：4338 真人联系人，头像有脸覆盖率 41%(1796/4337)，端上 90s 建 284 锚点（继续后台跑）。头像/姓名属敏感 PII，存 _wx_enroll/ 已 gitignore。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

feat(lifelog/photolog): 微信联系人头像 → 人脸锚点 + 自动认人
把微信联系人头像建成人脸锚点，照片处理时自动认出"这是谁"，标注时直接带名字。 - data：Person 加 faceEmbedding 锚点字段 + 迁移 v4→v5（ALTER 加 BLOB 列， DDL 对过 Room；真机迁移零数据损失）；PersonDao anchors/getByContactRef - FaceMatcher：gallery 统一 = 人物锚点(头像) + 已标注脸样本；match() 共享余弦匹配； PhotoProcessor 改用统一 gallery - WeChatEnrollWorker：读 filesDir/wx_enroll/(enroll.json + 头像) → 对每张头像跑 **端上 FaceEngine**(必须同引擎才能跟照片人脸比)，恰 1 脸就建 Person(name, contactRef=wechat:wxid, avatarPath, faceEmbedding)。幂等、单轮预算 8min、retry 续 - UI：人物页「从微信导入联系人」按钮；人物卡片标注「微信/声纹」来源 - server/services/wechat/extract-wechat-avatars.py：Mac 侧提取脚本（sqlcipher 解密 EnMicroMsg.db → rcontact⨝img_flag 筛真人 → 下载头像）实测：4338 真人联系人，头像有脸覆盖率 41%(1796/4337)，端上 90s 建 284 锚点（继续后台跑）。头像/姓名属敏感 PII，存 _wx_enroll/ 已 gitignore。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
yibo
1 parent 3bf1909f
Showing 10 changed files with 294 additions and 53 deletions
lifelog-recorder/.gitignore
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/PhotologScreen.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/PhotologViewModel.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/data/AppDatabase.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/data/Daos.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/data/Entities.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/vision/FaceMatcher.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/vision/PhotoProcessor.kt
lifelog-recorder/app/src/main/kotlin/com/lemon/lifelog/vision/WeChatEnrollWorker.kt
server/services/wechat/extract-wechat-avatars.py
@@ -18,3 +18,6 @@ keys.properties
 # 录音恢复备份（DB 误清后从设备抢救的原始音频，勿入库）
 _recovered_segments_*/
+
+# 微信联系人头像/姓名（enrollment 用，敏感 PII，勿入库）
+_wx_enroll/
@@ -280,26 +280,50 @@ private fun TagFaceDialog(
 @Composable
 private fun PeopleList(vm: PhotologViewModel, persons: List<Person>, onOpen: (Long) -> Unit) {
     var renameFor by remember { mutableStateOf<Person?>(null) }
-    if (persons.isEmpty()) {
-        Box(Modifier.fillMaxSize().padding(24.dp), contentAlignment = Alignment.Center) {
-            Text("还没有人物。去「照片」里点一张脸标注，就会自动建立人物并归类照片。", style = MaterialTheme.typography.bodyMedium)
+    val enrollReady = remember { vm.wechatEnrollReady() }
+    var enrollTapped by remember { mutableStateOf(false) }
+
+    Column(modifier = Modifier.fillMaxSize()) {
+        if (enrollReady) {
+            Row(
+                modifier = Modifier.fillMaxWidth().padding(12.dp),
+                verticalAlignment = Alignment.CenterVertically,
+                horizontalArrangement = Arrangement.spacedBy(8.dp),
+            ) {
+                Button(onClick = { vm.enrollWeChat(); enrollTapped = true }) { Text("从微信导入联系人") }
+                Text(
+                    if (enrollTapped) "后台建锚点中，人物会陆续出现…" else "用头像建人脸锚点，照片自动认人",
+                    style = MaterialTheme.typography.labelSmall,
+                )
+            }
+            HorizontalDivider()
         }
-        return
-    }
-    LazyColumn(modifier = Modifier.fillMaxSize().padding(12.dp), verticalArrangement = Arrangement.spacedBy(6.dp)) {
-        items(persons, key = { it.id }) { p ->
-            val count by produceState(0, p.id) { value = withContext(Dispatchers.IO) { vm.repo.faces.countForPerson(p.id) } }
-            Card(modifier = Modifier.fillMaxWidth().clickable { onOpen(p.id) }) {
-                Row(
-                    modifier = Modifier.fillMaxWidth().padding(14.dp),
-                    horizontalArrangement = Arrangement.SpaceBetween,
-                    verticalAlignment = Alignment.CenterVertically,
-                ) {
-                    Column {
-                        Text(p.name, style = MaterialTheme.typography.titleSmall)
-                        Text("$count 张人脸" + (if (p.voiceSpeakerId != null) " · 已绑声纹" else ""), style = MaterialTheme.typography.labelSmall)
+        if (persons.isEmpty()) {
+            Box(Modifier.fillMaxSize().padding(24.dp), contentAlignment = Alignment.Center) {
+                Text("还没有人物。点上方「从微信导入联系人」，或去「照片」里点一张脸标注。", style = MaterialTheme.typography.bodyMedium)
+            }
+        } else {
+            LazyColumn(modifier = Modifier.fillMaxSize().padding(12.dp), verticalArrangement = Arrangement.spacedBy(6.dp)) {
+                items(persons, key = { it.id }) { p ->
+                    val count by produceState(0, p.id) { value = withContext(Dispatchers.IO) { vm.repo.faces.countForPerson(p.id) } }
+                    Card(modifier = Modifier.fillMaxWidth().clickable { onOpen(p.id) }) {
+                        Row(
+                            modifier = Modifier.fillMaxWidth().padding(14.dp),
+                            horizontalArrangement = Arrangement.SpaceBetween,
+                            verticalAlignment = Alignment.CenterVertically,
+                        ) {
+                            Column(modifier = Modifier.weight(1f)) {
+                                Text(p.name, maxLines = 1, overflow = TextOverflow.Ellipsis, style = MaterialTheme.typography.titleSmall)
+                                Text(
+                                    "$count 张人脸" +
+                                        (if (p.contactRef != null) " · 微信" else "") +
+                                        (if (p.voiceSpeakerId != null) " · 声纹" else ""),
+                                    style = MaterialTheme.typography.labelSmall,
+                                )
+                            }
+                            TextButton(onClick = { renameFor = p }) { Text("重命名") }
+                        }
                     }
-                    TextButton(onClick = { renameFor = p }) { Text("重命名") }
                 }
             }
         }
@@ -8,9 +8,13 @@ import androidx.lifecycle.viewModelScope
 import com.lemon.lifelog.data.Person
 import com.lemon.lifelog.data.Photo
 import com.lemon.lifelog.data.Repository
+import androidx.work.ExistingWorkPolicy
+import androidx.work.OneTimeWorkRequestBuilder
+import androidx.work.WorkManager
 import com.lemon.lifelog.vision.FaceMatcher
 import com.lemon.lifelog.vision.ImageUtil
 import com.lemon.lifelog.vision.PhotoScheduler
+import com.lemon.lifelog.vision.WeChatEnrollWorker
 import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.launch
 import kotlinx.coroutines.withContext
@@ -70,6 +74,16 @@ class PhotologViewModel(app: Application) : AndroidViewModel(app) {
         viewModelScope.launch { repo.persons.updateName(personId, name.trim()) }
     }
+    /** 设备上是否已备好微信 enroll 数据（Mac 推过来的头像+清单）。 */
+    fun wechatEnrollReady(): Boolean =
+        File(getApplication<Application>().filesDir, "wx_enroll/enroll.json").exists()
+
+    /** 触发后台把微信头像建成人脸锚点（幂等，已在跑则忽略）。 */
+    fun enrollWeChat() {
+        val req = OneTimeWorkRequestBuilder<WeChatEnrollWorker>().build()
+        WorkManager.getInstance(getApplication()).enqueueUniqueWork("wx-enroll", ExistingWorkPolicy.KEEP, req)
+    }
+
     fun deletePhoto(photoId: Long) {
         viewModelScope.launch {
             withContext(Dispatchers.IO) {
@@ -20,7 +20,7 @@ import androidx.sqlite.db.SupportSQLiteDatabase
         Photo::class,
         Face::class,
     ],
-    version = 4,
+    version = 5,
     exportSchema = false,
 )
 abstract class AppDatabase : RoomDatabase() {
@@ -64,6 +64,13 @@ abstract class AppDatabase : RoomDatabase() {
             }
         }
+        /** v0.7 photolog：persons 加人脸锚点 embedding（微信头像/标注样本）。 */
+        private val MIGRATION_4_5 = object : Migration(4, 5) {
+            override fun migrate(db: SupportSQLiteDatabase) {
+                db.execSQL("ALTER TABLE `persons` ADD COLUMN `faceEmbedding` BLOB")
+            }
+        }
+
         fun get(ctx: Context): AppDatabase =
             instance ?: synchronized(this) {
                 instance ?: Room.databaseBuilder(
@@ -71,7 +78,7 @@ abstract class AppDatabase : RoomDatabase() {
                     AppDatabase::class.java,
                     "lifelog.db",
                 )
-                    .addMigrations(MIGRATION_2_3, MIGRATION_3_4)
+                    .addMigrations(MIGRATION_2_3, MIGRATION_3_4, MIGRATION_4_5)
                     // 不再 fallbackToDestructiveMigration：以后改 schema 必须写 Migration，
                     // 否则启动直接抛 IllegalStateException（fail-loud），绝不静默清掉真实录音。
                     .build()
@@ -221,6 +221,16 @@ interface PersonDao {
     @Query("SELECT * FROM persons WHERE id = :id")
     suspend fun get(id: Long): Person?
+    @Query("SELECT * FROM persons WHERE contactRef = :ref LIMIT 1")
+    suspend fun getByContactRef(ref: String): Person?
+
+    /** 有人脸锚点的人（匹配 gallery 用）。 */
+    @Query("SELECT * FROM persons WHERE faceEmbedding IS NOT NULL")
+    suspend fun anchors(): List<Person>
+
+    @Query("UPDATE persons SET faceEmbedding = :emb WHERE id = :id")
+    suspend fun updateFaceEmbedding(id: Long, emb: ByteArray)
+
     @Query("UPDATE persons SET name = :name WHERE id = :id")
     suspend fun updateName(id: Long, name: String)
@@ -95,6 +95,8 @@ data class Person(
     /** 微信/通讯录联系人引用（如 "wechat:wxid_xxx"），null = 手动建 */
     val contactRef: String? = null,
     val avatarPath: String? = null,
+    /** 人脸锚点 embedding（512维 LE bytes，来自头像或标注样本），用于自动认人；null = 还没有 */
+    val faceEmbedding: ByteArray? = null,
     val createdAt: Long = System.currentTimeMillis(),
 )
@@ -3,36 +3,50 @@ package com.lemon.lifelog.vision
 import com.lemon.lifelog.data.Repository
 /**
- * 人脸归属匹配（与声纹的 SpeakerMatcher 同构）。
- * 标注一张脸到某 Person 后，把所有未归属的脸跟已命名的脸库比余弦，自动回填。
+ * 人脸归属匹配（与声纹 SpeakerMatcher 同构）。
+ * 匹配 gallery = 人物锚点(微信头像 embedding) + 已标注的脸样本。
  */
 object FaceMatcher {
-    private const val THRESH = PhotoProcessor.THRESH
+    const val THRESH = PhotoProcessor.THRESH
-    /** 把 [faceId] 指派给 [personId]，然后回填其它未归属的脸。 */
+    /** 当前所有 (personId, embedding)：人物锚点 + 已标注脸。 */
+    suspend fun gallery(repo: Repository): List<Pair<Long, FloatArray>> {
+        val out = ArrayList<Pair<Long, FloatArray>>()
+        for (p in repo.persons.anchors()) {
+            p.faceEmbedding?.let { out.add(p.id to ImageUtil.unpackEmbedding(it)) }
+        }
+        for (f in repo.faces.allWithEmbedding()) {
+            if (f.personId != null && f.embedding != null) {
+                out.add(f.personId!! to ImageUtil.unpackEmbedding(f.embedding!!))
+            }
+        }
+        return out
+    }
+
+    /** 给一个 embedding 在 gallery 里找最佳人物，余弦 ≥ THRESH 返回 (personId, sim)，否则 null。 */
+    fun match(gallery: List<Pair<Long, FloatArray>>, emb: FloatArray): Pair<Long, Float>? {
+        var bestId: Long? = null
+        var bestSim = THRESH
+        for ((pid, g) in gallery) {
+            val c = FaceEngine.cosine(emb, g)
+            if (c > bestSim) { bestSim = c; bestId = pid }
+        }
+        return bestId?.let { it to bestSim }
+    }
+
+    /** 标注一张脸到某 Person，然后回填其它未归属的脸。 */
     suspend fun assignAndBackfill(repo: Repository, faceId: Long, personId: Long) {
         repo.faces.assignPerson(faceId, personId, 1f)
         backfillAll(repo)
     }
-    /**
-     * 对所有未归属的脸：跟已命名脸库做最近邻，余弦 ≥ THRESH 则归属。
-     * 反复一轮即可（新归属的脸不会再扩大已命名集合的语义）。
-     */
+    /** 对所有未归属的脸跑一遍匹配（新建人物锚点 / 标注后调用）。 */
     suspend fun backfillAll(repo: Repository) {
-        val gallery = repo.faces.allWithEmbedding()
-            .filter { it.personId != null && it.embedding != null }
-            .map { it.personId!! to ImageUtil.unpackEmbedding(it.embedding!!) }
-        if (gallery.isEmpty()) return
+        val g = gallery(repo)
+        if (g.isEmpty()) return
         for (f in repo.faces.unassigned()) {
-            val emb = ImageUtil.unpackEmbedding(f.embedding!!)
-            var bestId: Long? = null
-            var bestSim = THRESH
-            for ((pid, g) in gallery) {
-                val c = FaceEngine.cosine(emb, g)
-                if (c > bestSim) { bestSim = c; bestId = pid }
-            }
-            if (bestId != null) repo.faces.assignPerson(f.id, bestId, bestSim)
+            val m = match(g, ImageUtil.unpackEmbedding(f.embedding!!))
+            if (m != null) repo.faces.assignPerson(f.id, m.first, m.second)
         }
     }
 }
@@ -40,26 +40,18 @@ class PhotoProcessor(
             if (caption != null) repo.photos.updateCaption(photo.id, caption, "vl")
-            // 已命名 Person 的人脸样本（personId != null）→ 匹配新脸。
-            val gallery = repo.faces.allWithEmbedding()
-                .filter { it.personId != null && it.embedding != null }
-                .map { it.personId!! to ImageUtil.unpackEmbedding(it.embedding!!) }
-
+            // 匹配 gallery = 微信头像锚点 + 已标注脸样本。
+            val gallery = FaceMatcher.gallery(repo)
             for (fr in faces) {
-                var bestId: Long? = null
-                var bestSim = THRESH
-                for ((pid, emb) in gallery) {
-                    val c = FaceEngine.cosine(fr.embedding, emb)
-                    if (c > bestSim) { bestSim = c; bestId = pid }
-                }
+                val m = FaceMatcher.match(gallery, fr.embedding)
                 repo.faces.insert(
                     Face(
                         photoId = photo.id,
                         left = fr.face.x1, top = fr.face.y1, right = fr.face.x2, bottom = fr.face.y2,
                         detScore = fr.face.score,
                         embedding = ImageUtil.packEmbedding(fr.embedding),
-                        personId = bestId,
-                        personSim = if (bestId != null) bestSim else null,
+                        personId = m?.first,
+                        personSim = m?.second,
                     ),
                 )
             }
+package com.lemon.lifelog.vision
+
+import android.content.Context
+import android.util.Log
+import androidx.work.CoroutineWorker
+import androidx.work.WorkerParameters
+import com.lemon.lifelog.data.Person
+import com.lemon.lifelog.data.Repository
+import org.json.JSONArray
+import java.io.File
+import java.util.concurrent.atomic.AtomicBoolean
+
+/**
+ * 把微信联系人头像建成人脸锚点：读 filesDir/wx_enroll/enroll.json（[{username,name}]）+
+ * avatars/{username}.jpg，对每张头像用**端上 FaceEngine** 检测，恰 1 张脸就建 Person
+ * (contactRef=wechat:username, avatarPath, faceEmbedding)。之后照片人脸自动认出联系人。
+ *
+ * 必须用端上 FaceEngine（与照片人脸同一引擎）才能跨比；不能用 Mac insightface 的 embedding。
+ * 单轮有时间预算，超了 retry 续；按 contactRef 幂等。
+ */
+class WeChatEnrollWorker(ctx: Context, params: WorkerParameters) : CoroutineWorker(ctx, params) {
+
+    override suspend fun doWork(): Result {
+        if (!isRunning.compareAndSet(false, true)) return Result.success()
+        try {
+            val dir = File(applicationContext.filesDir, "wx_enroll")
+            val manifest = File(dir, "enroll.json")
+            if (!manifest.exists()) {
+                Log.w(TAG, "no enroll.json at ${manifest.path}")
+                return Result.success()
+            }
+            val arr = JSONArray(manifest.readText())
+            val repo = Repository(applicationContext)
+            val engine = FaceEngine.load(applicationContext)
+            var enrolled = 0
+            var scanned = 0
+            var hitDeadline = false
+            val deadline = System.currentTimeMillis() + RUN_BUDGET_MS
+            try {
+                for (i in 0 until arr.length()) {
+                    if (System.currentTimeMillis() > deadline) { hitDeadline = true; break }
+                    val o = arr.getJSONObject(i)
+                    val username = o.optString("username")
+                    val name = o.optString("name").ifBlank { username }
+                    val ref = "wechat:$username"
+                    if (repo.persons.getByContactRef(ref) != null) continue // 幂等
+
+                    val avatar = File(dir, "avatars/$username.jpg")
+                    if (!avatar.exists()) continue
+                    val bmp = ImageUtil.decodeOriented(avatar.absolutePath, reqMax = 640) ?: continue
+                    scanned++
+                    try {
+                        val det = engine.detect(bmp)
+                        if (det.size == 1) {
+                            val emb = engine.embed(bmp, det[0].kps)
+                            repo.persons.insert(
+                                Person(
+                                    name = name,
+                                    contactRef = ref,
+                                    avatarPath = avatar.absolutePath,
+                                    faceEmbedding = ImageUtil.packEmbedding(emb),
+                                ),
+                            )
+                            enrolled++
+                        }
+                    } catch (t: Throwable) {
+                        Log.w(TAG, "enroll $username failed: ${t.message}")
+                    } finally {
+                        bmp.recycle()
+                    }
+                }
+            } finally {
+                engine.close()
+            }
+            // 用新锚点回填已处理照片里的未归属脸。
+            FaceMatcher.backfillAll(repo)
+            Log.i(TAG, "本轮 enroll $enrolled（扫 $scanned），deadline=$hitDeadline")
+            return if (hitDeadline) Result.retry() else Result.success()
+        } catch (t: Throwable) {
+            Log.e(TAG, "worker failed", t)
+            return Result.retry()
+        } finally {
+            isRunning.set(false)
+        }
+    }
+
+    companion object {
+        const val TAG = "WeChatEnroll"
+        /** 单轮预算 8min（端上 ~0.4s/张），超了 retry 续；WorkManager ~10min 上限内。 */
+        const val RUN_BUDGET_MS = 8 * 60 * 1000L
+        private val isRunning = AtomicBoolean(false)
+    }
+}
+#!/usr/bin/env python3
+"""
+微信联系人头像提取 —— 给 lifelog/photolog 的人脸锚点 enrollment 备数据。
+
+流程（在 Mac 上跑，需要 `brew install sqlcipher`）：
+  1. adb 把设备上的 EnMicroMsg.db 拉到本地（需 root）。
+  2. 用微信的 SQLCipher 配方解密：key + cipher_compatibility=1 + page 1024。
+  3. join rcontact ⨝ img_flag，筛真人（verifyFlag=0，排除群/公众号/企业微信/系统号），
+     img_flag.reserved1 = 高清头像 URL（wx.qlogo.cn，公开可拉）。
+  4. 并行下载头像 → avatars/{username}.jpg。
+  5. （可选）insightface 估"有脸"覆盖率，导出 oneface 清单。
+
+产物（敏感 PII，勿入库）放 lifelog-recorder/_wx_enroll/：contacts.json / avatars/ / oneface_usernames.json
+然后 push 到设备 filesDir/wx_enroll/，app「从微信导入联系人」用端上 FaceEngine 建锚点。
+
+  DB key / 路径因设备而异，见 android-wechat-tracker-sqlcipher.js（key 'e03ce81'，
+  路径 /data/data/com.tencent.mm/MicroMsg/<hash>/EnMicroMsg.db）。
+
+用法：
+  python3 extract-wechat-avatars.py /tmp/EnMicroMsg.db e03ce81 ./_wx_enroll
+"""
+import sys, os, json, subprocess, urllib.request, concurrent.futures
+
+REAL_PERSON_SQL = """
+SELECT r.username AS username,
+       COALESCE(NULLIF(r.conRemark,''), NULLIF(r.nickname,''), r.username) AS name,
+       f.reserved1 AS avatar
+FROM rcontact r JOIN img_flag f ON f.username=r.username
+WHERE r.verifyFlag=0
+  AND r.username NOT LIKE '%@chatroom'
+  AND r.username NOT LIKE 'gh_%'
+  AND r.username NOT LIKE '%@openim'
+  AND r.username NOT IN ('weixin','filehelper','fmessage','medianote','floatbottle',
+        'qmessage','qqmail','newsapp','officialaccounts','helper_entry','exmail_tool',
+        'mphelper','brandsessionholder')
+  AND f.reserved1 LIKE 'http%';
+"""
+
+
+def decrypt_and_query(db_path, key):
+    script = (f'PRAGMA key="{key}";\nPRAGMA cipher_compatibility=1;\n'
+              f'PRAGMA cipher_page_size=1024;\n.mode json\n{REAL_PERSON_SQL}\n')
+    out = subprocess.run(['sqlcipher', db_path], input=script,
+                         capture_output=True, text=True).stdout
+    return json.loads(out[out.index('['):])  # 跳过开头 PRAGMA 的 "ok"
+
+
+def download(items, out_dir):
+    os.makedirs(out_dir, exist_ok=True)
+
+    def dl(it):
+        dest = os.path.join(out_dir, it['username'] + '.jpg')
+        if os.path.exists(dest) and os.path.getsize(dest) > 0:
+            return True
+        try:
+            req = urllib.request.Request(it['avatar'], headers={'User-Agent': 'Mozilla/5.0'})
+            b = urllib.request.urlopen(req, timeout=12).read()
+            if len(b) < 200:
+                return False
+            open(dest, 'wb').write(b)
+            return True
+        except Exception:
+            return False
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=24) as ex:
+        return sum(1 for r in ex.map(dl, items) if r)
+
+
+def main():
+    db_path, key, out_root = sys.argv[1], sys.argv[2], sys.argv[3]
+    os.makedirs(out_root, exist_ok=True)
+    items = decrypt_and_query(db_path, key)
+    json.dump(items, open(os.path.join(out_root, 'contacts.json'), 'w'), ensure_ascii=False)
+    print(f'真人联系人(有头像): {len(items)}')
+    n = download(items, os.path.join(out_root, 'avatars'))
+    print(f'头像下载: {n}')
+    print('下一步：（可选）insightface 估覆盖率导出 oneface_usernames.json，'
+          '再 push 到设备 filesDir/wx_enroll/，app 内「从微信导入联系人」。')
+
+
+if __name__ == '__main__':
+    main()