face_recognition_system.mbt

// 人脸识别与表情识别（v33.0.0）
// 人脸检测、人脸对齐、特征提取、人脸识别、表情识别、年龄性别估计

///|
/// 人脸检测算法类型
pub(all) enum FaceDetectionAlgorithm {
  HaarCascade
  HOG_SVM
  MTCNN
  RetinaFace
  YOLOFace
  BlazeFace
  FaceBoxes
  Unknown
} derive(Eq)

///|
/// 人脸识别算法类型
pub(all) enum FaceRecognitionAlgorithm {
  FaceNet
  ArcFace
  CosFace
  SphereFace
  DeepFace
  VGGFace
  InsightFace
  Unknown
} derive(Eq)

///|
/// 表情类型
pub(all) enum ExpressionType {
  Neutral
  Happy
  Sad
  Angry
  Surprise
  Fear
  Disgust
  Contempt
  Unknown
} derive(Eq)

///|
/// 人脸检测结果
pub(all) struct FaceDetectionResult {
  bounding_box : Array[Double]
  confidence : Double
  landmarks : Array[Array[Double]]
  num_landmarks : Int
  face_id : Int
  is_frontal : Bool
  yaw : Double
  pitch : Double
  roll : Double
}

///|
/// 人脸特征向量
pub(all) struct FaceFeature {
  vector : Array[Double]
  dimension : Int
  norm : Double
  is_normalized : Bool
}

///|
/// 人脸识别结果
pub(all) struct FaceRecognitionResult {
  face_id : Int
  identity : String
  confidence : Double
  similarity : Double
  is_known : Bool
  matched_face_id : Int
  bounding_box : Array[Double]
}

///|
/// 表情识别结果
pub(all) struct ExpressionRecognitionResult {
  expression : ExpressionType
  confidence : Double
  probabilities : Array[Double]
  num_classes : Int
  dominant_expression : String
}

///|
/// 年龄性别估计结果
pub(all) struct AgeGenderEstimationResult {
  age : Double
  age_range : Array[Double]
  gender : String
  gender_confidence : Double
  is_male : Bool
}

///|
/// 人脸属性分析结果
pub(all) struct FaceAttributeAnalysis {
  detection : FaceDetectionResult
  recognition : FaceRecognitionResult
  expression : ExpressionRecognitionResult
  age_gender : AgeGenderEstimationResult
  face_quality : Double
  brightness : Double
  sharpness : Double
  occlusion_rate : Double
}

///|
/// 人脸库
pub(all) struct FaceDatabase {
  faces : Array[FaceFeature]
  identities : Array[String]
  num_faces : Int
  threshold : Double
  algorithm : FaceRecognitionAlgorithm
}

///|
/// 人脸识别配置
pub(all) struct FaceRecognitionConfig {
  detection_algorithm : FaceDetectionAlgorithm
  recognition_algorithm : FaceRecognitionAlgorithm
  detection_threshold : Double
  recognition_threshold : Double
  min_face_size : Int
  max_num_faces : Int
  use_landmarks : Bool
  use_alignment : Bool
  feature_dimension : Int
}

///|
/// 创建默认人脸识别配置
pub fn create_default_face_config() -> FaceRecognitionConfig {
  FaceRecognitionConfig::{
    detection_algorithm: FaceDetectionAlgorithm::MTCNN,
    recognition_algorithm: FaceRecognitionAlgorithm::ArcFace,
    detection_threshold: 0.7,
    recognition_threshold: 0.5,
    min_face_size: 40,
    max_num_faces: 10,
    use_landmarks: true,
    use_alignment: true,
    feature_dimension: 512,
  }
}

///|
/// 创建空的人脸库
pub fn create_face_database(
  algorithm : FaceRecognitionAlgorithm,
  threshold : Double,
) -> FaceDatabase {
  FaceDatabase::{
    faces: [],
    identities: [],
    num_faces: 0,
    threshold,
    algorithm,
  }
}

///|
/// 检测人脸（简化版：基于肤色和图像特征的启发式检测）
pub fn detect_faces(
  img : @types.Image,
  config : FaceRecognitionConfig,
) -> Array[FaceDetectionResult] {
  ignore(config)
  let w = img.width
  let h = img.height
  let faces : Array[FaceDetectionResult] = []

  // 简化版：检测图像中的高对比度区域作为人脸
  // 实际实现应该使用深度学习模型或 Haar 级联分类器

  // 计算图像梯度，寻找类人脸区域
  let mut max_response = 0.0
  let mut center_x = w.to_double() / 2.0
  let mut center_y = h.to_double() / 2.0

  let block_size = 32
  for by in 0..<(h / block_size) {
    for bx in 0..<(w / block_size) {
      let mut response = 0.0
      for y in 0..<block_size {
        for x in 0..<block_size {
          let px = bx * block_size + x
          let py = by * block_size + y
          if px > 0 && px < w - 1 && py > 0 && py < h - 1 {
            let idx = py * w + px
            let gx = img.data[idx + 1].to_double() -
              img.data[idx - 1].to_double()
            let gy = img.data[idx + w].to_double() -
              img.data[idx - w].to_double()
            response += (gx * gx + gy * gy).sqrt()
          }
        }
      }
      response /= (block_size * block_size).to_double()
      if response > max_response {
        max_response = response
        center_x = (bx * block_size + block_size / 2).to_double()
        center_y = (by * block_size + block_size / 2).to_double()
      }
    }
  }

  // 生成简化的人脸检测结果
  let face_width = 100.0
  let face_height = 120.0
  let bounding_box = [
    center_x - face_width / 2.0,
    center_y - face_height / 2.0,
    face_width,
    face_height,
  ]

  // 5 个关键点（双眼、鼻尖、嘴角）
  let landmarks : Array[Array[Double]] = []
  landmarks.push([center_x - 20.0, center_y - 20.0]) // 左眼
  landmarks.push([center_x + 20.0, center_y - 20.0]) // 右眼
  landmarks.push([center_x, center_y]) // 鼻尖
  landmarks.push([center_x - 15.0, center_y + 25.0]) // 左嘴角
  landmarks.push([center_x + 15.0, center_y + 25.0]) // 右嘴角

  let face = FaceDetectionResult::{
    bounding_box,
    confidence: 0.75,
    landmarks,
    num_landmarks: 5,
    face_id: 0,
    is_frontal: true,
    yaw: 0.0,
    pitch: 0.0,
    roll: 0.0,
  }

  faces.push(face)
  faces
}

///|
/// 提取人脸特征（简化版：基于图像统计特征）
pub fn extract_face_feature(
  img : @types.Image,
  face : FaceDetectionResult,
  config : FaceRecognitionConfig,
) -> FaceFeature {
  let w = img.width
  let h = img.height
  let dimension = config.feature_dimension
  let vector : Array[Double] = []

  // 简化版：使用人脸区域的图像统计特征作为特征向量
  // 实际实现应该使用深度学习模型（FaceNet/ArcFace 等）

  let bx = face.bounding_box[0].to_int()
  let by = face.bounding_box[1].to_int()
  let bw = face.bounding_box[2].to_int()
  let bh = face.bounding_box[3].to_int()

  // 计算人脸区域的统计特征
  let mut sum = 0.0
  let mut sum_sq = 0.0
  let mut count = 0
  for y in 0..<bh {
    for x in 0..<bw {
      let px = bx + x
      let py = by + y
      if px >= 0 && px < w && py >= 0 && py < h {
        let pixel = img.data[py * w + px].to_double()
        sum += pixel
        sum_sq += pixel * pixel
        count += 1
      }
    }
  }

  let mean = if count > 0 { sum / count.to_double() } else { 0.0 }
  let variance = if count > 0 {
    sum_sq / count.to_double() - mean * mean
  } else {
    0.0
  }
  let std_dev = if variance > 0.0 { variance.sqrt() } else { 0.0 }

  // 生成特征向量（简化版：用统计特征填充）
  for i in 0..<dimension {
    let value = mean * 0.01 + std_dev * 0.001 + (i % 10).to_double() * 0.001
    vector.push(value)
  }

  // 计算 L2 范数
  let mut norm_sq = 0.0
  for v in vector {
    norm_sq += v * v
  }
  let norm = if norm_sq > 0.0 { norm_sq.sqrt() } else { 1.0 }

  FaceFeature::{ vector, dimension, norm, is_normalized: false }
}

///|
/// 计算余弦相似度
pub fn calculate_cosine_similarity(
  feat1 : FaceFeature,
  feat2 : FaceFeature,
) -> Double {
  if feat1.dimension != feat2.dimension {
    return 0.0
  }

  let mut dot = 0.0
  for i in 0..<feat1.dimension {
    dot += feat1.vector[i] * feat2.vector[i]
  }

  if feat1.norm > 0.0 && feat2.norm > 0.0 {
    dot / (feat1.norm * feat2.norm)
  } else {
    0.0
  }
}

///|
/// 向人脸库添加人脸
pub fn add_face_to_database(
  database : FaceDatabase,
  feature : FaceFeature,
  identity : String,
) -> FaceDatabase {
  let new_faces = database.faces
  new_faces.push(feature)
  let new_identities = database.identities
  new_identities.push(identity)

  FaceDatabase::{
    faces: new_faces,
    identities: new_identities,
    num_faces: database.num_faces + 1,
    threshold: database.threshold,
    algorithm: database.algorithm,
  }
}

///|
/// 在人脸库中识别人脸
pub fn recognize_face_in_database(
  database : FaceDatabase,
  feature : FaceFeature,
  bounding_box : Array[Double],
) -> FaceRecognitionResult {
  let mut best_similarity = 0.0
  let mut best_index = -1

  for i in 0..<database.num_faces {
    let similarity = calculate_cosine_similarity(feature, database.faces[i])
    if similarity > best_similarity {
      best_similarity = similarity
      best_index = i
    }
  }

  if best_index >= 0 && best_similarity >= database.threshold {
    FaceRecognitionResult::{
      face_id: 0,
      identity: database.identities[best_index],
      confidence: best_similarity,
      similarity: best_similarity,
      is_known: true,
      matched_face_id: best_index,
      bounding_box,
    }
  } else {
    FaceRecognitionResult::{
      face_id: 0,
      identity: "unknown",
      confidence: if best_similarity > 0.0 {
        best_similarity
      } else {
        0.0
      },
      similarity: best_similarity,
      is_known: false,
      matched_face_id: -1,
      bounding_box,
    }
  }
}

///|
/// 识别表情（简化版：基于人脸区域的图像特征）
pub fn recognize_expression(
  img : @types.Image,
  face : FaceDetectionResult,
) -> ExpressionRecognitionResult {
  let w = img.width
  let h = img.height

  // 简化版：基于人脸区域的亮度和对比度变化判断表情
  // 实际实现应该使用深度学习模型

  let bx = face.bounding_box[0].to_int()
  let by = face.bounding_box[1].to_int()
  let bw = face.bounding_box[2].to_int()
  let bh = face.bounding_box[3].to_int()

  // 计算嘴部区域的亮度变化（简化的表情判断）
  let mouth_y = by + bh * 3 / 4
  let mut mouth_brightness = 0.0
  let mut count = 0
  for y in mouth_y..<(mouth_y + bh / 4) {
    for x in bx..<(bx + bw) {
      if x >= 0 && x < w && y >= 0 && y < h {
        mouth_brightness += img.data[y * w + x].to_double()
        count += 1
      }
    }
  }
  mouth_brightness = if count > 0 {
    mouth_brightness / count.to_double()
  } else {
    0.0
  }

  // 简化的表情分类
  let mut expression = ExpressionType::Neutral
  let mut confidence = 0.55
  let probabilities : Array[Double] = []

  if mouth_brightness > 150.0 {
    expression = ExpressionType::Happy
    confidence = 0.6
    probabilities.push(0.1) // Neutral
    probabilities.push(0.6) // Happy
    probabilities.push(0.1) // Sad
    probabilities.push(0.05) // Angry
    probabilities.push(0.05) // Surprise
    probabilities.push(0.05) // Fear
    probabilities.push(0.03) // Disgust
    probabilities.push(0.02) // Contempt
  } else if mouth_brightness < 80.0 {
    expression = ExpressionType::Sad
    confidence = 0.5
    probabilities.push(0.15)
    probabilities.push(0.1)
    probabilities.push(0.5)
    probabilities.push(0.1)
    probabilities.push(0.05)
    probabilities.push(0.05)
    probabilities.push(0.03)
    probabilities.push(0.02)
  } else {
    expression = ExpressionType::Neutral
    confidence = 0.55
    probabilities.push(0.55)
    probabilities.push(0.15)
    probabilities.push(0.1)
    probabilities.push(0.05)
    probabilities.push(0.05)
    probabilities.push(0.05)
    probabilities.push(0.03)
    probabilities.push(0.02)
  }

  let dominant_expression = if expression == ExpressionType::Neutral {
    "neutral"
  } else if expression == ExpressionType::Happy {
    "happy"
  } else if expression == ExpressionType::Sad {
    "sad"
  } else if expression == ExpressionType::Angry {
    "angry"
  } else if expression == ExpressionType::Surprise {
    "surprise"
  } else if expression == ExpressionType::Fear {
    "fear"
  } else if expression == ExpressionType::Disgust {
    "disgust"
  } else if expression == ExpressionType::Contempt {
    "contempt"
  } else {
    "unknown"
  }

  ExpressionRecognitionResult::{
    expression,
    confidence,
    probabilities,
    num_classes: 8,
    dominant_expression,
  }
}

///|
/// 估计年龄和性别（简化版）
pub fn estimate_age_gender(
  img : @types.Image,
  face : FaceDetectionResult,
) -> AgeGenderEstimationResult {
  let w = img.width
  let h = img.height

  // 简化版：基于人脸区域的纹理和皱纹估计年龄
  // 实际实现应该使用深度学习模型

  let bx = face.bounding_box[0].to_int()
  let by = face.bounding_box[1].to_int()
  let bw = face.bounding_box[2].to_int()
  let bh = face.bounding_box[3].to_int()

  // 计算人脸区域的纹理复杂度（简化的年龄估计）
  let mut texture_complexity = 0.0
  let mut count = 0
  for y in 1..<(bh - 1) {
    for x in 1..<(bw - 1) {
      let px = bx + x
      let py = by + y
      if px >= 1 && px < w - 1 && py >= 1 && py < h - 1 {
        let idx = py * w + px
        let gx = img.data[idx + 1].to_double() - img.data[idx - 1].to_double()
        let gy = img.data[idx + w].to_double() - img.data[idx - w].to_double()
        texture_complexity += (gx * gx + gy * gy).sqrt()
        count += 1
      }
    }
  }
  texture_complexity = if count > 0 {
    texture_complexity / count.to_double()
  } else {
    0.0
  }

  // 简化的年龄估计（纹理越复杂，年龄越大）
  let age = 20.0 + texture_complexity * 2.0
  let age_range = [age - 5.0, age + 5.0]

  // 简化的性别判断（基于人脸区域的亮度和形状）
  let is_male = texture_complexity > 10.0
  let gender = if is_male { "male" } else { "female" }
  let gender_confidence = 0.6

  AgeGenderEstimationResult::{
    age,
    age_range,
    gender,
    gender_confidence,
    is_male,
  }
}

///|
/// 评估人脸质量
pub fn evaluate_face_quality(
  img : @types.Image,
  face : FaceDetectionResult,
) -> Double {
  let w = img.width
  let h = img.height

  let bx = face.bounding_box[0].to_int()
  let by = face.bounding_box[1].to_int()
  let bw = face.bounding_box[2].to_int()
  let bh = face.bounding_box[3].to_int()

  // 计算亮度
  let mut brightness = 0.0
  let mut sharpness = 0.0
  let mut count = 0
  for y in 1..<(bh - 1) {
    for x in 1..<(bw - 1) {
      let px = bx + x
      let py = by + y
      if px >= 1 && px < w - 1 && py >= 1 && py < h - 1 {
        let idx = py * w + px
        brightness += img.data[idx].to_double()
        let gx = img.data[idx + 1].to_double() - img.data[idx - 1].to_double()
        let gy = img.data[idx + w].to_double() - img.data[idx - w].to_double()
        sharpness += (gx * gx + gy * gy).sqrt()
        count += 1
      }
    }
  }

  brightness = if count > 0 { brightness / count.to_double() } else { 0.0 }
  sharpness = if count > 0 { sharpness / count.to_double() } else { 0.0 }

  // 综合质量评分（亮度 40% + 锐度 60%）
  let brightness_score = if brightness > 80.0 && brightness < 180.0 {
    1.0
  } else {
    0.5
  }
  let sharpness_score = if sharpness > 20.0 {
    1.0
  } else if sharpness > 10.0 {
    0.7
  } else {
    0.4
  }

  brightness_score * 0.4 + sharpness_score * 0.6
}

///|
/// 完整的人脸属性分析
pub fn analyze_face_attributes(
  img : @types.Image,
  face : FaceDetectionResult,
  database : FaceDatabase,
  config : FaceRecognitionConfig,
) -> FaceAttributeAnalysis {
  let feature = extract_face_feature(img, face, config)
  let recognition = recognize_face_in_database(
    database,
    feature,
    face.bounding_box,
  )
  let expression = recognize_expression(img, face)
  let age_gender = estimate_age_gender(img, face)
  let quality = evaluate_face_quality(img, face)

  // 计算亮度和锐度
  let w = img.width
  let h = img.height
  let bx = face.bounding_box[0].to_int()
  let by = face.bounding_box[1].to_int()
  let bw = face.bounding_box[2].to_int()
  let bh = face.bounding_box[3].to_int()

  let mut brightness = 0.0
  let mut sharpness = 0.0
  let mut count = 0
  for y in 0..<bh {
    for x in 0..<bw {
      let px = bx + x
      let py = by + y
      if px >= 0 && px < w && py >= 0 && py < h {
        brightness += img.data[py * w + px].to_double()
        count += 1
      }
    }
  }
  brightness = if count > 0 { brightness / count.to_double() } else { 0.0 }
  sharpness = quality * 50.0

  FaceAttributeAnalysis::{
    detection: face,
    recognition,
    expression,
    age_gender,
    face_quality: quality,
    brightness,
    sharpness,
    occlusion_rate: 0.0,
  }
}

///|
/// 生成人脸识别报告
pub fn face_recognition_report_to_string(
  analysis : FaceAttributeAnalysis,
) -> String {
  let mut report = "## 人脸识别与表情识别报告\n\n"
  report = report + "### 人脸检测\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 检测置信度 | " +
    (analysis.detection.confidence * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 关键点数量 | " +
    analysis.detection.num_landmarks.to_string() +
    " |\n"
  report = report +
    "| 是否正面 | " +
    (if analysis.detection.is_frontal { "是" } else { "否" }) +
    " |\n"
  report = report +
    "| 偏航角 | " +
    analysis.detection.yaw.to_string() +
    "° |\n"
  report = report +
    "| 俯仰角 | " +
    analysis.detection.pitch.to_string() +
    "° |\n"
  report = report +
    "| 翻滚角 | " +
    analysis.detection.roll.to_string() +
    "° |\n\n"

  report = report + "### 人脸识别\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| 身份 | " + analysis.recognition.identity + " |\n"
  report = report +
    "| 是否已知 | " +
    (if analysis.recognition.is_known { "是" } else { "否" }) +
    " |\n"
  report = report +
    "| 相似度 | " +
    (analysis.recognition.similarity * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 匹配置信度 | " +
    (analysis.recognition.confidence * 100.0).to_string() +
    "% |\n\n"

  report = report + "### 表情识别\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 表情 | " +
    analysis.expression.dominant_expression +
    " |\n"
  report = report +
    "| 置信度 | " +
    (analysis.expression.confidence * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 类别数 | " +
    analysis.expression.num_classes.to_string() +
    " |\n\n"

  report = report + "### 年龄性别估计\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 估计年龄 | " +
    analysis.age_gender.age.to_string() +
    " 岁 |\n"
  report = report +
    "| 年龄范围 | " +
    analysis.age_gender.age_range[0].to_string() +
    " - " +
    analysis.age_gender.age_range[1].to_string() +
    " 岁 |\n"
  report = report + "| 性别 | " + analysis.age_gender.gender + " |\n"
  report = report +
    "| 性别置信度 | " +
    (analysis.age_gender.gender_confidence * 100.0).to_string() +
    "% |\n\n"

  report = report + "### 人脸质量\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 综合质量 | " +
    (analysis.face_quality * 100.0).to_string() +
    "% |\n"
  report = report + "| 亮度 | " + analysis.brightness.to_string() + " |\n"
  report = report + "| 锐度 | " + analysis.sharpness.to_string() + " |\n"
  report = report +
    "| 遮挡率 | " +
    (analysis.occlusion_rate * 100.0).to_string() +
    "% |\n"

  report
}
src\process\face_recognition\face_recognition_system.mbt

73.66%
