fixed a bug

app/src/main/java/com/example/studentfaceregistry/ImageUtils.kt

@@ -8,10 +8,10 @@ import androidx.camera.core.ImageProxy
 
 @ExperimentalGetImage
 object ImageUtils {
-    fun imageProxyToBitmap(imageProxy: ImageProxy): Bitmap {
+    fun imageProxyToBitmap(imageProxy: ImageProxy, applyRotation: Boolean = true): Bitmap {
         val image = imageProxy.image ?: error("ImageProxy does not contain an image.")
         val bitmap = yuv420ToBitmap(image)
-        if (imageProxy.imageInfo.rotationDegrees == 0) {
+        if (!applyRotation || imageProxy.imageInfo.rotationDegrees == 0) {
             return bitmap
         }
         val matrix = Matrix().apply {

app/src/main/java/com/example/studentfaceregistry/MainActivity.kt

@@ -42,6 +42,7 @@ import com.example.studentfaceregistry.face.MatchType
 import com.example.studentfaceregistry.face.RecognitionResult
 import com.google.mlkit.vision.common.InputImage
 import com.google.mlkit.vision.face.Face
+import com.google.mlkit.vision.face.FaceLandmark
 import com.example.studentfaceregistry.ui.DetectionUi
 import com.example.studentfaceregistry.ui.OverlayView
 import com.example.studentfaceregistry.upload.UploadServer
@@ -115,6 +116,20 @@ class MainActivity : AppCompatActivity() {
             repository.students.collectLatest { list ->
                 students = list
                 countText.text = getString(R.string.registered_student_count, list.size)
+                if (list.isNotEmpty()) {
+                    val first = list.first()
+                    val earliest = list.minOfOrNull { it.createdAt } ?: 0L
+                    val latest = list.maxOfOrNull { it.createdAt } ?: 0L
+                    Log.i(
+                        TAG,
+                        "已加载学生 ${list.size} 人，" +
+                            "特征维度=${first.embedding.size}，" +
+                            "注册时间范围=${fmtTime(earliest)} ~ ${fmtTime(latest)}，" +
+                            "特征范数=${String.format(java.util.Locale.US, "%.3f", embeddingNorm(first.embedding))}"
+                    )
+                } else {
+                    Log.i(TAG, "已加载学生 0 人（请先注册）")
+                }
             }
         }
 
@@ -589,8 +604,18 @@ class MainActivity : AppCompatActivity() {
                 if (facesNeedingRefresh.isNotEmpty()) {
                     val refreshStart = SystemClock.elapsedRealtime()
                     val bitmap = ImageUtils.imageProxyToBitmap(image)
+                    var unrotatedBitmap: Bitmap? = null
                     facesNeedingRefresh.forEach { (index, face) ->
-                        val refreshed = refreshRecognition(faceProcessor, bitmap, face)
+                        var refreshed = refreshRecognition(faceProcessor, bitmap, face)
+                        if (refreshed.matchType == MatchType.NO_MATCH && image.imageInfo.rotationDegrees != 0) {
+                            val fallbackBitmap = unrotatedBitmap ?: ImageUtils
+                                .imageProxyToBitmap(image, applyRotation = false)
+                                .also { unrotatedBitmap = it }
+                            val fallback = refreshRecognition(faceProcessor, fallbackBitmap, face)
+                            if (recognitionScore(fallback) > recognitionScore(refreshed)) {
+                                refreshed = fallback
+                            }
+                        }
                         refreshedResults[index] = refreshed
                     }
                     val refreshMs = SystemClock.elapsedRealtime() - refreshStart
@@ -941,6 +966,13 @@ class MainActivity : AppCompatActivity() {
         bitmap: Bitmap,
         face: Face
     ): RecognitionResult {
+        val landmarkCount = listOf(
+            face.getLandmark(FaceLandmark.LEFT_EYE),
+            face.getLandmark(FaceLandmark.RIGHT_EYE),
+            face.getLandmark(FaceLandmark.NOSE_BASE),
+            face.getLandmark(FaceLandmark.MOUTH_LEFT),
+            face.getLandmark(FaceLandmark.MOUTH_RIGHT)
+        ).count { it != null }
         val primary = matcher.findNearest(faceProcessor.embedFace(bitmap, face), students)
         val cached = face.trackingId?.let { recognitionCache[it] }
         // 未匹配时只做单次推理；仅在疑似匹配或人脸明显移动时才用多裁剪升级，
@@ -950,6 +982,7 @@ class MainActivity : AppCompatActivity() {
             primary.matchType != MatchType.NO_MATCH
 
         if (!needsEscalation) {
+            logRecognitionDebug("仅主候选", landmarkCount, primary)
             return primary
         }
 
@@ -960,9 +993,39 @@ class MainActivity : AppCompatActivity() {
                 best = result
             }
         }
+        logRecognitionDebug("含升级候选", landmarkCount, primary, best)
         return best
     }
 
+    private fun logRecognitionDebug(
+        stage: String,
+        landmarkCount: Int,
+        primary: RecognitionResult,
+        best: RecognitionResult = primary
+    ) {
+        val primaryName = primary.student?.let { "${it.name}(${it.studentNo})" } ?: "无"
+        val bestName = best.student?.let { "${it.name}(${it.studentNo})" } ?: "无"
+        Log.i(
+            TAG,
+            String.format(
+                java.util.Locale.US,
+                "识别 debug: 阶段=%s 关键点=%d/5 主候选=%s(余弦=%.3f 欧氏=%.3f %s) " +
+                    "最优=%s(余弦=%.3f 欧氏=%.3f %s) 学生=%d",
+                stage,
+                landmarkCount,
+                primaryName,
+                primary.cosineSimilarity,
+                primary.distance,
+                primary.matchType,
+                bestName,
+                best.cosineSimilarity,
+                best.distance,
+                best.matchType,
+                students.size
+            )
+        )
+    }
+
     private fun resolveRecognitionResult(
         face: Face,
         refreshedResult: RecognitionResult?,
@@ -1020,7 +1083,21 @@ class MainActivity : AppCompatActivity() {
         }
     }
 
+    private fun embeddingNorm(embedding: FloatArray): Double {
+        var sum = 0.0
+        for (value in embedding) {
+            sum += value.toDouble() * value.toDouble()
+        }
+        return kotlin.math.sqrt(sum)
+    }
+
+    private fun fmtTime(timestamp: Long): String {
+        return java.text.SimpleDateFormat("yyyy-MM-dd HH:mm", java.util.Locale.US)
+            .format(java.util.Date(timestamp))
+    }
+
     companion object {
+        private const val TAG = "MainActivity"
         private const val MATCHER_PREFS_NAME = "face_matcher_config"
         private const val KEY_EUCLIDEAN_THRESHOLD = "euclidean_threshold"
         private const val KEY_COSINE_THRESHOLD = "cosine_threshold"

app/src/main/java/com/example/studentfaceregistry/data/StudentRepository.kt

@@ -13,7 +13,15 @@ class StudentRepository(context: Context) {
     val students: StateFlow<List<Student>> = _students
 
     fun add(student: Student) {
-        val next = _students.value.toMutableList().apply { add(student) }
+        val studentToStore = if (student.id == 0L) {
+            student.copy(id = stableId(student.studentNo))
+        } else {
+            student
+        }
+        val next = _students.value
+            .filterNot { it.studentNo == studentToStore.studentNo }
+            .toMutableList()
+            .apply { add(studentToStore) }
             .sortedByDescending { it.createdAt }
         _students.value = next
         save(next)
@@ -32,10 +40,11 @@ class StudentRepository(context: Context) {
         return buildList {
             for (index in 0 until array.length()) {
                 val obj = array.getJSONObject(index)
+                val studentNo = obj.getString("studentNo")
                 add(
                     Student(
-                        id = obj.optLong("id", index.toLong()),
+                        id = obj.optLong("id").takeIf { it != 0L } ?: stableId(studentNo),
-                        studentNo = obj.getString("studentNo"),
+                        studentNo = studentNo,
                         name = obj.getString("name"),
                         photoUri = obj.getString("photoUri"),
                         embedding = decodeEmbedding(obj.getJSONArray("embedding")),
@@ -80,4 +89,8 @@ class StudentRepository(context: Context) {
             }
         }
     }
+
+    private fun stableId(studentNo: String): Long {
+        return (studentNo.hashCode().toLong() and 0xffffffffL).coerceAtLeast(1L)
+    }
 }

app/src/main/java/com/example/studentfaceregistry/face/FaceAligner.kt

@@ -1,11 +1,10 @@
 package com.example.studentfaceregistry.face
 
 import android.graphics.Bitmap
+import android.graphics.Canvas
 import android.graphics.Matrix
+import android.graphics.Paint
 import kotlin.math.absoluteValue
-import kotlin.math.cos
-import kotlin.math.sin
-import kotlin.math.sqrt
 
 /**
  * 人脸对齐器
@@ -34,10 +33,18 @@ class FaceAligner {
         faceTop: Int
     ): Bitmap {
         // 计算眼睛在裁剪后图片中的相对位置
-        val leftEyeRelX = leftEyeX - faceLeft
+        var leftEyeRelX = leftEyeX - faceLeft
-        val leftEyeRelY = leftEyeY - faceTop
+        var leftEyeRelY = leftEyeY - faceTop
-        val rightEyeRelX = rightEyeX - faceLeft
+        var rightEyeRelX = rightEyeX - faceLeft
-        val rightEyeRelY = rightEyeY - faceTop
+        var rightEyeRelY = rightEyeY - faceTop
+        if (leftEyeRelX > rightEyeRelX) {
+            val oldLeftX = leftEyeRelX
+            val oldLeftY = leftEyeRelY
+            leftEyeRelX = rightEyeRelX
+            leftEyeRelY = rightEyeRelY
+            rightEyeRelX = oldLeftX
+            rightEyeRelY = oldLeftY
+        }
 
         // 计算两眼连线的旋转角度（目标是让连线水平）
         val angle = calculateRotationAngle(leftEyeRelX, leftEyeRelY, rightEyeRelX, rightEyeRelY)
@@ -58,6 +65,40 @@ class FaceAligner {
         return Bitmap.createBitmap(bitmap, 0, 0, bitmap.width, bitmap.height, matrix, true)
     }
 
+    fun alignFivePoint(
+        bitmap: Bitmap,
+        leftEyeX: Float,
+        leftEyeY: Float,
+        rightEyeX: Float,
+        rightEyeY: Float,
+        noseX: Float,
+        noseY: Float,
+        leftMouthX: Float,
+        leftMouthY: Float,
+        rightMouthX: Float,
+        rightMouthY: Float,
+        outputSize: Int = ARC_FACE_SIZE
+    ): Bitmap {
+        val (imageLeftEye, imageRightEye) = sortedByX(leftEyeX, leftEyeY, rightEyeX, rightEyeY)
+        val (imageLeftMouth, imageRightMouth) = sortedByX(leftMouthX, leftMouthY, rightMouthX, rightMouthY)
+        val src = floatArrayOf(
+            imageLeftEye.x, imageLeftEye.y,
+            imageRightEye.x, imageRightEye.y,
+            noseX, noseY,
+            imageLeftMouth.x, imageLeftMouth.y,
+            imageRightMouth.x, imageRightMouth.y
+        )
+        val dst = arcFaceTemplate(outputSize)
+        val matrix = similarityTransform(src, dst)
+        val result = Bitmap.createBitmap(outputSize, outputSize, Bitmap.Config.ARGB_8888)
+        Canvas(result).drawBitmap(
+            bitmap,
+            matrix,
+            Paint(Paint.ANTI_ALIAS_FLAG or Paint.FILTER_BITMAP_FLAG)
+        )
+        return result
+    }
+
     /**
      * 计算两眼连线的旋转角度
      */
@@ -67,6 +108,63 @@ class FaceAligner {
         return Math.atan2(dy.toDouble(), dx.toDouble()).toFloat() * 180f / Math.PI.toFloat()
     }
 
+    private fun sortedByX(x1: Float, y1: Float, x2: Float, y2: Float): Pair<Point, Point> {
+        val first = Point(x1, y1)
+        val second = Point(x2, y2)
+        return if (x1 <= x2) first to second else second to first
+    }
+
+    private fun arcFaceTemplate(outputSize: Int): FloatArray {
+        val scale = outputSize / ARC_FACE_SIZE.toFloat()
+        return FloatArray(ARC_FACE_TEMPLATE.size) { index -> ARC_FACE_TEMPLATE[index] * scale }
+    }
+
+    private fun similarityTransform(src: FloatArray, dst: FloatArray): Matrix {
+        val count = src.size / 2
+        var srcCenterX = 0f
+        var srcCenterY = 0f
+        var dstCenterX = 0f
+        var dstCenterY = 0f
+        for (index in 0 until count) {
+            srcCenterX += src[index * 2]
+            srcCenterY += src[index * 2 + 1]
+            dstCenterX += dst[index * 2]
+            dstCenterY += dst[index * 2 + 1]
+        }
+        srcCenterX /= count
+        srcCenterY /= count
+        dstCenterX /= count
+        dstCenterY /= count
+
+        var numeratorA = 0f
+        var numeratorB = 0f
+        var denominator = 0f
+        for (index in 0 until count) {
+            val srcX = src[index * 2] - srcCenterX
+            val srcY = src[index * 2 + 1] - srcCenterY
+            val dstX = dst[index * 2] - dstCenterX
+            val dstY = dst[index * 2 + 1] - dstCenterY
+            numeratorA += srcX * dstX + srcY * dstY
+            numeratorB += srcX * dstY - srcY * dstX
+            denominator += srcX * srcX + srcY * srcY
+        }
+
+        val a = numeratorA / denominator.coerceAtLeast(1e-6f)
+        val b = numeratorB / denominator.coerceAtLeast(1e-6f)
+        val translateX = dstCenterX - a * srcCenterX + b * srcCenterY
+        val translateY = dstCenterY - b * srcCenterX - a * srcCenterY
+
+        return Matrix().apply {
+            setValues(
+                floatArrayOf(
+                    a, -b, translateX,
+                    b, a, translateY,
+                    0f, 0f, 1f
+                )
+            )
+        }
+    }
+
     /**
      * 简单版本：仅根据估计的眼睛位置进行对齐
      * 适用于没有启用关键点检测的场景
@@ -94,4 +192,17 @@ class FaceAligner {
             align(bitmap, leftEyeX, eyeY, rightEyeX, eyeY, faceLeft, faceTop)
         }
     }
+
+    private data class Point(val x: Float, val y: Float)
+
+    companion object {
+        private const val ARC_FACE_SIZE = 112
+        private val ARC_FACE_TEMPLATE = floatArrayOf(
+            38.2946f, 51.6963f,
+            73.5318f, 51.5014f,
+            56.0252f, 71.7366f,
+            41.5493f, 92.3655f,
+            70.7299f, 92.2041f
+        )
+    }
 }

app/src/main/java/com/example/studentfaceregistry/face/FaceEmbedder.kt

@@ -30,6 +30,7 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
     private val accelerator: Accelerator
     private val inputPixels: IntArray
     private val inputValues: FloatArray
+    private var debugEmbedCount = 0
 
     // ArcFace 标准化参数 (RGB)
     private val mean = floatArrayOf(127.5f, 127.5f, 127.5f)
@@ -85,12 +86,57 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
             }
         }
 
+        debugEmbedCount++
+        val logInput = debugEmbedCount <= 30 || debugEmbedCount % 100 == 0
+        if (logInput) {
+            var sum = 0f
+            var sumSq = 0f
+            for (value in inputValues) {
+                sum += value
+                sumSq += value * value
+            }
+            val mean = sum / inputValues.size
+            val variance = (sumSq / inputValues.size - mean * mean).coerceAtLeast(0f)
+            Log.i(
+                TAG,
+                String.format(
+                    java.util.Locale.US,
+                    "embed debug #%d 输入均值=%.3f 输入标准差=%.3f",
+                    debugEmbedCount,
+                    mean,
+                    sqrt(variance)
+                )
+            )
+        }
+
         // 执行推理
         inputBuffer.writeFloat(inputValues)
         model.run(listOf(inputBuffer), listOf(outputBuffer))
 
+        val rawOutput = outputBuffer.readFloat()
+        if (logInput) {
+            var sum = 0f
+            var sumSq = 0f
+            for (value in rawOutput) {
+                sum += value
+                sumSq += value * value
+            }
+            val mean = sum / rawOutput.size
+            val variance = (sumSq / rawOutput.size - mean * mean).coerceAtLeast(0f)
+            Log.i(
+                TAG,
+                String.format(
+                    java.util.Locale.US,
+                    "embed debug #%d 输出均值=%.3f 输出标准差=%.3f",
+                    debugEmbedCount,
+                    mean,
+                    sqrt(variance)
+                )
+            )
+        }
+
         // L2 归一化输出特征
-        return l2Normalize(outputBuffer.readFloat())
+        return l2Normalize(rawOutput)
     }
 
     fun warmUp() {
@@ -138,19 +184,10 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
     }
 
     private fun createModel(context: Context): Pair<CompiledModel, Accelerator> {
-        return runCatching {
+        return CompiledModel.create(
-            CompiledModel.create(
+            context.assets,
-                context.assets,
+            MODEL_ASSET_NAME,
-                MODEL_ASSET_NAME,
+            CompiledModel.Options(setOf(Accelerator.CPU))
-                CompiledModel.Options(setOf(Accelerator.GPU))
+        ) to Accelerator.CPU
-            ) to Accelerator.GPU
-        }.getOrElse { gpuError ->
-            Log.w(TAG, "GPU initialization failed, falling back to CPU", gpuError)
-            CompiledModel.create(
-                context.assets,
-                MODEL_ASSET_NAME,
-                CompiledModel.Options(setOf(Accelerator.CPU))
-            ) to Accelerator.CPU
-        }
     }
 }

app/src/main/java/com/example/studentfaceregistry/face/FaceProcessor.kt

@@ -57,17 +57,19 @@ class FaceProcessor(context: Context) : AutoCloseable {
             box.width() * box.height()
         }
 
-        // 裁剪人脸
+        val alignedFace = alignFaceFromOriginal(bitmap, largestFace) ?: run {
-        val croppedFace = Bitmap.createBitmap(
+            // 裁剪人脸
-            bitmap,
+            val croppedFace = Bitmap.createBitmap(
-            faceBox.left,
+                bitmap,
-            faceBox.top,
+                faceBox.left,
-            faceBox.width(),
+                faceBox.top,
-            faceBox.height()
+                faceBox.width(),
-        )
+                faceBox.height()
+            )
 
-        // 使用关键点进行对齐
+            // 使用关键点进行对齐
-        val alignedFace = alignFaceWithLandmarks(croppedFace, largestFace, faceBox.left, faceBox.top)
+            alignFaceWithLandmarks(croppedFace, largestFace, faceBox.left, faceBox.top)
+        }
 
         // 图像预处理
         val preprocessedFace = preprocessor.preprocess(alignedFace)
@@ -127,6 +129,12 @@ class FaceProcessor(context: Context) : AutoCloseable {
      * 提取指定人脸的特征
      */
     fun embedFace(bitmap: Bitmap, face: Face): FloatArray {
+        val alignedFromLandmarks = alignFaceFromOriginal(bitmap, face)
+        if (alignedFromLandmarks != null) {
+            val preprocessed = preprocessor.preprocessRealtime(alignedFromLandmarks)
+            return embedder.embed(preprocessed)
+        }
+
         val crop = requireNotNull(safeCropRect(bitmap, face.boundingBox)) { "人脸区域超出图片边界。" }
         val cropped = Bitmap.createBitmap(bitmap, crop.left, crop.top, crop.width(), crop.height())
         val aligned = alignFaceForRecognition(cropped, face, crop.left, crop.top)
@@ -136,6 +144,9 @@ class FaceProcessor(context: Context) : AutoCloseable {
 
     fun embedFaceCandidates(bitmap: Bitmap, face: Face): List<FloatArray> {
         val embeddings = mutableListOf<FloatArray>()
+        alignFaceFromOriginal(bitmap, face)?.let { aligned ->
+            embeddings.add(embedder.embed(preprocessor.preprocessRealtime(aligned)))
+        }
         for (paddingRatio in FACE_PADDING_RATIOS) {
             val crop = safeCropRect(bitmap, face.boundingBox, paddingRatio) ?: continue
             val cropped = Bitmap.createBitmap(bitmap, crop.left, crop.top, crop.width(), crop.height())
@@ -146,6 +157,28 @@ class FaceProcessor(context: Context) : AutoCloseable {
         return embeddings
     }
 
+    private fun alignFaceFromOriginal(bitmap: Bitmap, face: Face): Bitmap? {
+        val leftEye = face.getLandmark(FaceLandmark.LEFT_EYE) ?: return null
+        val rightEye = face.getLandmark(FaceLandmark.RIGHT_EYE) ?: return null
+        val nose = face.getLandmark(FaceLandmark.NOSE_BASE) ?: return null
+        val leftMouth = face.getLandmark(FaceLandmark.MOUTH_LEFT) ?: return null
+        val rightMouth = face.getLandmark(FaceLandmark.MOUTH_RIGHT) ?: return null
+
+        return aligner.alignFivePoint(
+            bitmap = bitmap,
+            leftEyeX = leftEye.position.x,
+            leftEyeY = leftEye.position.y,
+            rightEyeX = rightEye.position.x,
+            rightEyeY = rightEye.position.y,
+            noseX = nose.position.x,
+            noseY = nose.position.y,
+            leftMouthX = leftMouth.position.x,
+            leftMouthY = leftMouth.position.y,
+            rightMouthX = rightMouth.position.x,
+            rightMouthY = rightMouth.position.y
+        )
+    }
+
     fun warmUp() {
         embedder.warmUp()
     }

app/src/main/java/com/example/studentfaceregistry/face/SmartEnrollment.kt

@@ -101,20 +101,23 @@ class SmartEnrollment(context: Context) : AutoCloseable {
                 return EnrollmentResult.Failure("照片模糊，请换更清晰的照片。")
             }
 
-            val embeddings = mutableListOf<FloatArray>()
+            val alignedFromLandmarks = alignFaceFromOriginal(bitmap, largestFace.face)
-            for (paddingRatio in IMAGE_PADDING_RATIOS) {
+            val cropEmbeddings = cropEmbeddingCandidates(bitmap, largestFace.face)
-                val cropRect = safeCropRect(bitmap, largestFace.face.boundingBox, paddingRatio) ?: continue
+            if (alignedFromLandmarks != null) {
-                val croppedFace = cropFace(bitmap, cropRect)
+                val preprocessedFace = preprocessor.preprocess(alignedFromLandmarks)
-                val alignedFace = alignFace(croppedFace, largestFace.face, cropRect)
+                val embedding = embedder.embed(preprocessedFace)
-                val preprocessedFace = preprocessor.preprocess(alignedFace)
+                return EnrollmentResult.Success(
-                embeddings.add(embedder.embed(preprocessedFace))
+                    embedding,
+                    EnrollmentSourceType.IMAGE,
+                    extraEmbeddings = cropEmbeddings
+                )
             }
 
-            if (embeddings.isEmpty()) {
+            if (cropEmbeddings.isEmpty()) {
                 return EnrollmentResult.Failure("未检测到可用人脸，请换更清晰或更正面的照片。")
             }
 
-            val embedding = fuseEmbeddings(embeddings)
+            val embedding = fuseEmbeddings(cropEmbeddings)
 
             EnrollmentResult.Success(embedding, EnrollmentSourceType.IMAGE)
         } catch (e: Exception) {
@@ -355,12 +358,30 @@ class SmartEnrollment(context: Context) : AutoCloseable {
      * 对单帧提取特征（裁剪 + 对齐 + 预处理 + 推理）
      */
     private fun embedFrame(frameInfo: FaceFrameInfo): FloatArray {
+        val alignedFromLandmarks = alignFaceFromOriginal(frameInfo.bitmap, frameInfo.face)
+        if (alignedFromLandmarks != null) {
+            val preprocessed = preprocessor.preprocess(alignedFromLandmarks)
+            return embedder.embed(preprocessed)
+        }
+
         val cropped = cropFace(frameInfo.bitmap, frameInfo.cropRect)
         val aligned = alignFace(cropped, frameInfo.face, frameInfo.cropRect)
         val preprocessed = preprocessor.preprocess(aligned)
         return embedder.embed(preprocessed)
     }
 
+    private fun cropEmbeddingCandidates(bitmap: Bitmap, face: Face): List<FloatArray> {
+        return buildList {
+            for (paddingRatio in IMAGE_PADDING_RATIOS) {
+                val cropRect = safeCropRect(bitmap, face.boundingBox, paddingRatio) ?: continue
+                val croppedFace = cropFace(bitmap, cropRect)
+                val alignedFace = alignFace(croppedFace, face, cropRect)
+                val preprocessedFace = preprocessor.preprocess(alignedFace)
+                add(embedder.embed(preprocessedFace))
+            }
+        }
+    }
+
     /**
      * 按偏航角将帧特征分组：正脸（主特征）、左右侧脸（补充特征）
      */
@@ -471,6 +492,28 @@ class SmartEnrollment(context: Context) : AutoCloseable {
         )
     }
 
+    private fun alignFaceFromOriginal(bitmap: Bitmap, face: Face): Bitmap? {
+        val leftEye = face.getLandmark(FaceLandmark.LEFT_EYE) ?: return null
+        val rightEye = face.getLandmark(FaceLandmark.RIGHT_EYE) ?: return null
+        val nose = face.getLandmark(FaceLandmark.NOSE_BASE) ?: return null
+        val leftMouth = face.getLandmark(FaceLandmark.MOUTH_LEFT) ?: return null
+        val rightMouth = face.getLandmark(FaceLandmark.MOUTH_RIGHT) ?: return null
+
+        return aligner.alignFivePoint(
+            bitmap = bitmap,
+            leftEyeX = leftEye.position.x,
+            leftEyeY = leftEye.position.y,
+            rightEyeX = rightEye.position.x,
+            rightEyeY = rightEye.position.y,
+            noseX = nose.position.x,
+            noseY = nose.position.y,
+            leftMouthX = leftMouth.position.x,
+            leftMouthY = leftMouth.position.y,
+            rightMouthX = rightMouth.position.x,
+            rightMouthY = rightMouth.position.y
+        )
+    }
+
     /**
      * 解码 Bitmap
      */
@@ -522,7 +565,7 @@ class SmartEnrollment(context: Context) : AutoCloseable {
         private val IMAGE_PADDING_RATIOS = floatArrayOf(0.18f, 0.25f, 0.34f)
         private const val MIN_ENROLL_FACE_PX = 96
         private const val MAX_ENROLL_YAW = 30f
-        private const val MAX_ENROLL_PITCH = 15f
+        private const val MAX_ENROLL_PITCH = 25f
         private const val BLUR_VARIANCE_THRESHOLD = 40.0
     }
 }

app/src/main/java/com/example/studentfaceregistry/upload/UploadServer.kt

@@ -2,10 +2,12 @@ package com.example.studentfaceregistry.upload
 
 import android.content.Context
 import android.util.Base64
+import android.util.Log
 import com.example.studentfaceregistry.data.Student
 import com.example.studentfaceregistry.data.StudentRepository
 import com.example.studentfaceregistry.face.SmartEnrollment
 import kotlinx.coroutines.runBlocking
+import kotlinx.coroutines.withTimeoutOrNull
 import org.json.JSONObject
 import java.io.BufferedInputStream
 import java.io.ByteArrayOutputStream
@@ -58,13 +60,18 @@ class UploadServer(
     private fun handle(socket: Socket) {
         thread(name = "upload-request", isDaemon = true) {
             socket.use { client ->
-                val input = BufferedInputStream(client.getInputStream())
+                client.soTimeout = SOCKET_READ_TIMEOUT_MS
-                val output = client.getOutputStream()
+                runCatching {
-                val request = readHttpRequest(input)
+                    val input = BufferedInputStream(client.getInputStream())
-                when {
+                    val output = client.getOutputStream()
-                    request.method == "GET" && request.path == "/" -> respondHtml(output)
+                    val request = readHttpRequest(input)
-                    request.method == "POST" && request.path == "/upload" -> handleUpload(request, output)
+                    when {
-                    else -> respondText(output, 404, "Not found", "text/plain; charset=utf-8")
+                        request.method == "GET" && request.path == "/" -> respondHtml(output)
+                        request.method == "POST" && request.path == "/upload" -> handleUpload(request, output)
+                        else -> respondText(output, 404, "Not found", "text/plain; charset=utf-8")
+                    }
+                }.onFailure {
+                    Log.w(TAG, "Upload request failed", it)
                 }
             }
         }
@@ -133,33 +140,78 @@ class UploadServer(
               <pre id="result"></pre>
               <script>
                 const result = document.getElementById('result');
-                document.getElementById('uploadFolderBtn').onclick = async () => {
+                const uploadButton = document.getElementById('uploadFolderBtn');
+                const IMAGE_REQUEST_TIMEOUT_MS = 90000;
+                const VIDEO_REQUEST_TIMEOUT_MS = 210000;
+                uploadButton.onclick = async () => {
                   result.textContent = '上传中...';
                   const files = document.getElementById('folderFiles').files;
+                  if (!files.length) {
+                    result.textContent = '请选择文件后再上传。';
+                    return;
+                  }
                   let ok = 0;
                   let fail = 0;
                   const logs = [];
-                  for (let i = 0; i < files.length; i++) {
+                  uploadButton.disabled = true;
-                    const file = files[i];
+                  try {
-                    result.textContent = `上传中 ${'$'}{i + 1}/${'$'}{files.length}: ${'$'}{file.name}`;
+                    for (let i = 0; i < files.length; i++) {
-                    const base64 = await toBase64(file);
+                      const file = files[i];
-                    const resp = await fetch('/upload', {
+                      result.textContent = progressText('上传中', i + 1, files.length, file.name, ok, fail, logs);
-                      method: 'POST',
+                      try {
-                      headers: { 'Content-Type': 'application/json' },
+                        const base64 = await toBase64(file);
-                      body: JSON.stringify({ items: [{ fileName: file.name, base64, mimeType: file.type }] })
+                        const resp = await fetchWithTimeout('/upload', {
-                    });
+                          method: 'POST',
-                    const text = await resp.text();
+                          headers: { 'Content-Type': 'application/json' },
-                    logs.push(text);
+                          body: JSON.stringify({ items: [{ fileName: file.name, base64, mimeType: file.type }] })
-                    try {
+                        }, uploadTimeoutMs(file));
-                      const parsed = JSON.parse(text);
+                        const text = await resp.text();
-                      ok += parsed.accepted || 0;
+                        logs.push(text);
-                      fail += parsed.rejected || 0;
+                        try {
-                    } catch (e) {
+                          const parsed = JSON.parse(text);
-                      fail += 1;
+                          ok += parsed.accepted || 0;
+                          fail += parsed.rejected || 0;
+                        } catch (e) {
+                          fail += 1;
+                        }
+                      } catch (e) {
+                        fail += 1;
+                        const reason = e.name === 'AbortError'
+                          ? '处理超时，已跳过该文件。'
+                          : (e.message || String(e));
+                        logs.push(JSON.stringify({
+                          accepted: 0,
+                          rejected: 1,
+                          message: 'FAIL ' + file.name + ': ' + reason
+                        }));
+                      }
+                      result.textContent = progressText('上传中', i + 1, files.length, file.name, ok, fail, logs);
                     }
+                  } finally {
+                    uploadButton.disabled = false;
                   }
                   result.textContent = `完成：成功 ${'$'}{ok}，失败 ${'$'}{fail}\n\n` + logs.join('\n');
                 };
+                function progressText(state, current, total, fileName, ok, fail, logs) {
+                  const recent = logs.slice(-8).join('\n');
+                  const summary = `${'$'}{state} ${'$'}{current}/${'$'}{total}: ${'$'}{fileName}\n成功 ${'$'}{ok}，失败 ${'$'}{fail}`;
+                  return recent ? summary + '\n\n最近结果：\n' + recent : summary;
+                }
+                async function fetchWithTimeout(url, options, timeoutMs) {
+                  const controller = new AbortController();
+                  const timer = setTimeout(() => controller.abort(), timeoutMs);
+                  try {
+                    return await fetch(url, { ...options, signal: controller.signal });
+                  } finally {
+                    clearTimeout(timer);
+                  }
+                }
+                function uploadTimeoutMs(file) {
+                  return isVideo(file) ? VIDEO_REQUEST_TIMEOUT_MS : IMAGE_REQUEST_TIMEOUT_MS;
+                }
+                function isVideo(file) {
+                  return file.type.startsWith('video/') || /\.(mp4|avi|mov|wmv|flv|mkv|webm|3gp)$/i.test(file.name);
+                }
                 function toBase64(file) {
                   return new Promise((resolve, reject) => {
                     const reader = new FileReader();
@@ -213,10 +265,17 @@ class UploadServer(
         enrollment: SmartEnrollment,
         report: (ok: Boolean, name: String, error: String?) -> Unit
     ) {
+        val startedAt = System.currentTimeMillis()
+        val timeoutMs = enrollmentTimeoutMs(mimeType, fileName)
+        Log.i(TAG, "Enrollment started: $fileName (${data.size} bytes)")
         runCatching {
             val result = runBlocking {
-                enrollment.enroll(data, mimeType, fileName)
+                withTimeoutOrNull(timeoutMs) {
-            }
+                    enrollment.enroll(data, mimeType, fileName)
+                }
+            } ?: com.example.studentfaceregistry.face.EnrollmentResult.Failure(
+                "处理超时（超过 ${timeoutMs / 1000} 秒），已跳过该文件。"
+            )
 
             when (result) {
                 is com.example.studentfaceregistry.face.EnrollmentResult.Success -> {
@@ -240,8 +299,21 @@ class UploadServer(
                 }
             }
         }.onFailure {
+            Log.w(TAG, "Enrollment failed: $fileName", it)
             report(false, fileName, it.message)
         }
+        val elapsedMs = System.currentTimeMillis() - startedAt
+        Log.i(TAG, "Enrollment finished: $fileName in ${elapsedMs}ms")
+    }
+
+    private fun enrollmentTimeoutMs(mimeType: String, fileName: String): Long {
+        val lowerMimeType = mimeType.lowercase()
+        val lowerFileName = fileName.lowercase()
+        return if (lowerMimeType.startsWith("video/") || VIDEO_EXTENSIONS.any { lowerFileName.endsWith(it) }) {
+            VIDEO_ENROLLMENT_TIMEOUT_MS
+        } else {
+            IMAGE_ENROLLMENT_TIMEOUT_MS
+        }
     }
 
     @Synchronized
@@ -329,6 +401,7 @@ class UploadServer(
 
     private fun statusText(code: Int): String = when (code) {
         200 -> "OK"
+        400 -> "Bad Request"
         404 -> "Not Found"
         503 -> "Service Unavailable"
         else -> "OK"
@@ -343,6 +416,14 @@ class UploadServer(
     }
 
     private fun jsonString(value: String): String = JSONObject.quote(value)
+
+    companion object {
+        private const val TAG = "UploadServer"
+        private const val SOCKET_READ_TIMEOUT_MS = 120_000
+        private const val IMAGE_ENROLLMENT_TIMEOUT_MS = 45_000L
+        private const val VIDEO_ENROLLMENT_TIMEOUT_MS = 180_000L
+        private val VIDEO_EXTENSIONS = setOf(".mp4", ".avi", ".mov", ".wmv", ".flv", ".mkv", ".webm", ".3gp")
+    }
 }
 
 private data class HttpRequest(