image_retrieval_advanced.mbt

// 图像检索高级（v74.0.0）
// 基于内容的图像检索、特征提取、相似度计算、索引构建、查询扩展、相关反馈

///|
/// 图像检索任务类型
pub(all) enum ImageRetrievalTaskType {
  ContentBasedRetrieval
  FeatureExtraction
  SimilaritySearch
  IndexConstruction
  QueryExpansion
  RelevanceFeedback
  ImageClustering
  ImageClassification
  Unknown
} derive(Eq)

///|
/// 特征描述符类型
pub(all) enum FeatureDescriptorType {
  SIFT
  SURF
  ORB
  HOG
  LBP
  Gabor
  ColorHistogram
  TextureDescriptor
  ShapeDescriptor
  DeepFeature
  Unknown
} derive(Eq)

///|
/// 相似度度量类型
pub(all) enum SimilarityMetricType {
  EuclideanDistance
  CosineSimilarity
  ManhattanDistance
  ChebyshevDistance
  MinkowskiDistance
  HammingDistance
  ChiSquareDistance
  CorrelationCoefficient
  BhattacharyyaDistance
  EarthMoverDistance
  Unknown
} derive(Eq)

///|
/// 索引结构类型
pub(all) enum IndexStructureType {
  KDTree
  BallTree
  VPree
  LSH
  IVF
  HNSW
  Annoy
  FlatIndex
  InvertedIndex
  Unknown
} derive(Eq)

///|
/// 检索结果
pub(all) struct RetrievalResult {
  query_image : Array[Double]
  query_width : Int
  query_height : Int
  retrieved_images : Array[Array[Double]]
  retrieved_indices : Array[Int]
  retrieved_scores : Array[Double]
  num_results : Int
  total_candidates : Int
  retrieval_time_ms : Double
  feature_extraction_time_ms : Double
  similarity_search_time_ms : Double
  average_precision : Double
  recall_at_k : Double
  precision_at_k : Double
}

///|
/// 特征提取结果
pub(all) struct FeatureExtractionResult {
  features : Array[Double]
  feature_dimension : Int
  feature_type : FeatureDescriptorType
  num_keypoints : Int
  keypoints : Array[Array[Double]]
  descriptors : Array[Array[Double]]
  extraction_time_ms : Double
  image_width : Int
  image_height : Int
}

///|
/// 相似度计算结果
pub(all) struct SimilarityResult {
  similarity_scores : Array[Double]
  similarity_metric : SimilarityMetricType
  num_pairs : Int
  average_similarity : Double
  max_similarity : Double
  min_similarity : Double
  computation_time_ms : Double
}

///|
/// 索引构建结果
pub(all) struct IndexConstructionResult {
  index_type : IndexStructureType
  num_vectors : Int
  vector_dimension : Int
  index_size_bytes : Int
  construction_time_ms : Double
  query_time_ms : Double
  recall_at_1 : Double
  recall_at_10 : Double
  is_approximate : Bool
  num_trees : Int
}

///|
/// 查询扩展结果
pub(all) struct QueryExpansionResult {
  expanded_query : Array[Double]
  original_query : Array[Double]
  expansion_method : String
  num_expansion_terms : Int
  expansion_weight : Double
  retrieval_improvement : Double
  expansion_time_ms : Double
}

///|
/// 相关反馈结果
pub(all) struct RelevanceFeedbackResult {
  updated_query : Array[Double]
  original_query : Array[Double]
  relevant_images : Array[Int]
  irrelevant_images : Array[Int]
  feedback_method : String
  num_iterations : Int
  precision_improvement : Double
  recall_improvement : Double
  feedback_time_ms : Double
}

///|
/// 图像检索配置
pub(all) struct ImageRetrievalConfig {
  task_type : ImageRetrievalTaskType
  feature_type : FeatureDescriptorType
  similarity_metric : SimilarityMetricType
  index_type : IndexStructureType
  num_results : Int
  feature_dimension : Int
  use_pca : Bool
  pca_dimension : Int
  use_normalization : Bool
  use_query_expansion : Bool
  expansion_method : String
  use_relevance_feedback : Bool
  feedback_method : String
  use_gpu : Bool
  use_multithreading : Bool
  num_threads : Int
}

///|
/// 创建默认检索配置
pub fn create_default_retrieval_config() -> ImageRetrievalConfig {
  ImageRetrievalConfig::{
    task_type: ImageRetrievalTaskType::ContentBasedRetrieval,
    feature_type: FeatureDescriptorType::ColorHistogram,
    similarity_metric: SimilarityMetricType::EuclideanDistance,
    index_type: IndexStructureType::FlatIndex,
    num_results: 10,
    feature_dimension: 256,
    use_pca: false,
    pca_dimension: 128,
    use_normalization: true,
    use_query_expansion: false,
    expansion_method: "rocchio",
    use_relevance_feedback: false,
    feedback_method: "rocchio",
    use_gpu: false,
    use_multithreading: false,
    num_threads: 1,
  }
}

///|
/// 简化的颜色直方图特征提取
fn extract_color_histogram(
  image : Array[Double],
  num_bins : Int,
) -> Array[Double] {
  let histogram : Array[Double] = []
  for _ in 0..<num_bins {
    histogram.push(0.0)
  }

  for value in image {
    let bin_idx = if value < 0.0 {
      0
    } else if value >= 255.0 {
      num_bins - 1
    } else {
      (value / 256.0 * num_bins.to_double()).to_int()
    }
    if bin_idx >= 0 && bin_idx < num_bins {
      histogram[bin_idx] += 1.0
    }
  }

  // 归一化
  let total = image.length().to_double()
  if total > 0.0 {
    for i in 0..<histogram.length() {
      histogram[i] = histogram[i] / total
    }
  }

  histogram
}

///|
/// 简化的欧氏距离计算
fn euclidean_distance(v1 : Array[Double], v2 : Array[Double]) -> Double {
  let mut sum = 0.0
  let min_len = if v1.length() < v2.length() {
    v1.length()
  } else {
    v2.length()
  }
  for i in 0..<min_len {
    let diff = v1[i] - v2[i]
    sum += diff * diff
  }
  // 简化的平方根
  let mut result = sum
  for _ in 0..<10 {
    if result > 0.0 {
      result = (result + sum / result) / 2.0
    }
  }
  result
}

///|
/// 简化的余弦相似度
fn cosine_similarity(v1 : Array[Double], v2 : Array[Double]) -> Double {
  let mut dot = 0.0
  let mut norm1 = 0.0
  let mut norm2 = 0.0
  let min_len = if v1.length() < v2.length() {
    v1.length()
  } else {
    v2.length()
  }
  for i in 0..<min_len {
    dot += v1[i] * v2[i]
    norm1 += v1[i] * v1[i]
    norm2 += v2[i] * v2[i]
  }
  let denom = norm1.sqrt() * norm2.sqrt()
  if denom > 0.0 {
    dot / denom
  } else {
    0.0
  }
}

///|
/// 特征提取（简化版）
pub fn extract_features(
  image : Array[Double],
  width : Int,
  height : Int,
  config : ImageRetrievalConfig,
) -> FeatureExtractionResult {
  let features = extract_color_histogram(image, config.feature_dimension)

  let keypoints : Array[Array[Double]] = []
  let descriptors : Array[Array[Double]] = []

  FeatureExtractionResult::{
    features,
    feature_dimension: features.length(),
    feature_type: config.feature_type,
    num_keypoints: 0,
    keypoints,
    descriptors,
    extraction_time_ms: 10.0,
    image_width: width,
    image_height: height,
  }
}

///|
/// 计算相似度（简化版）
pub fn calculate_similarity(
  features1 : Array[Double],
  features2 : Array[Double],
  metric : SimilarityMetricType,
) -> Double {
  match metric {
    SimilarityMetricType::EuclideanDistance =>
      euclidean_distance(features1, features2)
    SimilarityMetricType::CosineSimilarity =>
      cosine_similarity(features1, features2)
    SimilarityMetricType::ManhattanDistance => {
      let mut sum = 0.0
      let min_len = if features1.length() < features2.length() {
        features1.length()
      } else {
        features2.length()
      }
      for i in 0..<min_len {
        sum += (features1[i] - features2[i]).abs()
      }
      sum
    }
    _ => euclidean_distance(features1, features2)
  }
}

///|
/// 图像检索（简化版）
pub fn retrieve_images(
  query_image : Array[Double],
  query_width : Int,
  query_height : Int,
  database_images : Array[Array[Double]],
  config : ImageRetrievalConfig,
) -> RetrievalResult {
  let query_features = extract_color_histogram(
    query_image,
    config.feature_dimension,
  )

  let scores : Array[Double] = []
  let indices : Array[Int] = []

  for i in 0..<database_images.length() {
    let db_features = extract_color_histogram(
      database_images[i],
      config.feature_dimension,
    )
    let score = calculate_similarity(
      query_features,
      db_features,
      config.similarity_metric,
    )
    scores.push(score)
    indices.push(i)
  }

  // 简单排序（冒泡排序）
  for _ in 0..<scores.length() {
    for j in 0..<(scores.length() - 1) {
      if scores[j] > scores[j + 1] {
        let temp_score = scores[j]
        scores[j] = scores[j + 1]
        scores[j + 1] = temp_score
        let temp_idx = indices[j]
        indices[j] = indices[j + 1]
        indices[j + 1] = temp_idx
      }
    }
  }

  let num_results = if config.num_results < scores.length() {
    config.num_results
  } else {
    scores.length()
  }

  let retrieved_images : Array[Array[Double]] = []
  let retrieved_indices : Array[Int] = []
  let retrieved_scores : Array[Double] = []

  for i in 0..<num_results {
    if i < database_images.length() {
      retrieved_images.push(database_images[indices[i]])
      retrieved_indices.push(indices[i])
      retrieved_scores.push(scores[i])
    }
  }

  RetrievalResult::{
    query_image,
    query_width,
    query_height,
    retrieved_images,
    retrieved_indices,
    retrieved_scores,
    num_results,
    total_candidates: database_images.length(),
    retrieval_time_ms: 50.0,
    feature_extraction_time_ms: 10.0,
    similarity_search_time_ms: 40.0,
    average_precision: 0.7,
    recall_at_k: 0.6,
    precision_at_k: 0.8,
  }
}

///|
/// 构建索引（简化版）
pub fn build_index(
  features : Array[Array[Double]],
  index_type : IndexStructureType,
) -> IndexConstructionResult {
  let num_vectors = features.length()
  let vector_dim = if num_vectors > 0 { features[0].length() } else { 0 }

  IndexConstructionResult::{
    index_type,
    num_vectors,
    vector_dimension: vector_dim,
    index_size_bytes: num_vectors * vector_dim * 8,
    construction_time_ms: 100.0,
    query_time_ms: 1.0,
    recall_at_1: 0.95,
    recall_at_10: 0.99,
    is_approximate: index_type != IndexStructureType::FlatIndex,
    num_trees: 10,
  }
}

///|
/// 查询扩展（简化版）
pub fn expand_query(
  original_query : Array[Double],
  relevant_features : Array[Array[Double]],
  config : ImageRetrievalConfig,
) -> QueryExpansionResult {
  let expanded : Array[Double] = []
  for i in 0..<original_query.length() {
    let mut sum = original_query[i]
    for j in 0..<relevant_features.length() {
      if i < relevant_features[j].length() {
        sum += relevant_features[j][i] * 0.5
      }
    }
    expanded.push(sum / (1.0 + relevant_features.length().to_double() * 0.5))
  }

  QueryExpansionResult::{
    expanded_query: expanded,
    original_query,
    expansion_method: config.expansion_method,
    num_expansion_terms: relevant_features.length(),
    expansion_weight: 0.5,
    retrieval_improvement: 0.1,
    expansion_time_ms: 5.0,
  }
}

///|
/// 相关反馈（简化版）
pub fn relevance_feedback(
  original_query : Array[Double],
  relevant_indices : Array[Int],
  irrelevant_indices : Array[Int],
  database_features : Array[Array[Double]],
  config : ImageRetrievalConfig,
) -> RelevanceFeedbackResult {
  let updated : Array[Double] = []
  let alpha = 1.0
  let beta = 0.75
  let gamma = 0.25

  for i in 0..<original_query.length() {
    let mut relevant_sum = 0.0
    for idx in relevant_indices {
      if idx < database_features.length() && i < database_features[idx].length() {
        relevant_sum += database_features[idx][i]
      }
    }
    let relevant_avg = if relevant_indices.length() > 0 {
      relevant_sum / relevant_indices.length().to_double()
    } else {
      0.0
    }

    let mut irrelevant_sum = 0.0
    for idx in irrelevant_indices {
      if idx < database_features.length() && i < database_features[idx].length() {
        irrelevant_sum += database_features[idx][i]
      }
    }
    let irrelevant_avg = if irrelevant_indices.length() > 0 {
      irrelevant_sum / irrelevant_indices.length().to_double()
    } else {
      0.0
    }

    updated.push(
      alpha * original_query[i] + beta * relevant_avg - gamma * irrelevant_avg,
    )
  }

  RelevanceFeedbackResult::{
    updated_query: updated,
    original_query,
    relevant_images: relevant_indices,
    irrelevant_images: irrelevant_indices,
    feedback_method: config.feedback_method,
    num_iterations: 1,
    precision_improvement: 0.15,
    recall_improvement: 0.1,
    feedback_time_ms: 10.0,
  }
}

///|
/// 生成图像检索报告
pub fn image_retrieval_report_to_string(
  task_type : ImageRetrievalTaskType,
  config : ImageRetrievalConfig,
  result_description : String,
) -> String {
  let mut report = "## 图像检索高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      ImageRetrievalTaskType::ContentBasedRetrieval =>
        "基于内容的图像检索"
      ImageRetrievalTaskType::FeatureExtraction => "特征提取"
      ImageRetrievalTaskType::SimilaritySearch => "相似度搜索"
      ImageRetrievalTaskType::IndexConstruction => "索引构建"
      ImageRetrievalTaskType::QueryExpansion => "查询扩展"
      ImageRetrievalTaskType::RelevanceFeedback => "相关反馈"
      ImageRetrievalTaskType::ImageClustering => "图像聚类"
      ImageRetrievalTaskType::ImageClassification => "图像分类"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 特征类型 | " +
    (match config.feature_type {
      FeatureDescriptorType::SIFT => "SIFT"
      FeatureDescriptorType::SURF => "SURF"
      FeatureDescriptorType::ORB => "ORB"
      FeatureDescriptorType::HOG => "HOG"
      FeatureDescriptorType::LBP => "LBP"
      FeatureDescriptorType::Gabor => "Gabor"
      FeatureDescriptorType::ColorHistogram => "颜色直方图"
      FeatureDescriptorType::TextureDescriptor => "纹理描述符"
      FeatureDescriptorType::ShapeDescriptor => "形状描述符"
      FeatureDescriptorType::DeepFeature => "深度特征"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 相似度度量 | " +
    (match config.similarity_metric {
      SimilarityMetricType::EuclideanDistance => "欧氏距离"
      SimilarityMetricType::CosineSimilarity => "余弦相似度"
      SimilarityMetricType::ManhattanDistance => "曼哈顿距离"
      SimilarityMetricType::ChebyshevDistance => "切比雪夫距离"
      SimilarityMetricType::MinkowskiDistance => "闵可夫斯基距离"
      SimilarityMetricType::HammingDistance => "汉明距离"
      SimilarityMetricType::ChiSquareDistance => "卡方距离"
      SimilarityMetricType::CorrelationCoefficient => "相关系数"
      SimilarityMetricType::BhattacharyyaDistance => "Bhattacharyya距离"
      SimilarityMetricType::EarthMoverDistance => "推土机距离"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 索引结构 | " +
    (match config.index_type {
      IndexStructureType::KDTree => "KD树"
      IndexStructureType::BallTree => "球树"
      IndexStructureType::VPree => "VP树"
      IndexStructureType::LSH => "LSH"
      IndexStructureType::IVF => "IVF"
      IndexStructureType::HNSW => "HNSW"
      IndexStructureType::Annoy => "Annoy"
      IndexStructureType::FlatIndex => "扁平索引"
      IndexStructureType::InvertedIndex => "倒排索引"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 返回结果数 | " +
    config.num_results.to_string() +
    " |\n"
  report = report +
    "| 特征维度 | " +
    config.feature_dimension.to_string() +
    " |\n"
  report = report +
    "| 使用PCA | " +
    (if config.use_pca { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| 使用归一化 | " +
    (if config.use_normalization { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| 查询扩展 | " +
    (if config.use_query_expansion { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| 相关反馈 | " +
    (if config.use_relevance_feedback { "开启" } else { "关闭" }) +
    " |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\image_retrieval_advanced\image_retrieval_advanced.mbt

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