cv_advanced.mbt

// 计算机视觉高级（v51.0.0）
// 目标跟踪、动作识别、场景解析、视觉问答、图像描述、视觉推理

///|
/// 计算机视觉高级任务类型
pub(all) enum CVAdvancedTaskType {
  ObjectTracking
  ActionRecognition
  SceneParsing
  VisualQuestionAnswering
  ImageCaptioning
  VisualReasoning
  VideoUnderstanding
  Unknown
} derive(Eq)

///|
/// 目标跟踪算法类型
pub(all) enum TrackingAlgorithm {
  KCF
  MOSSE
  CSRT
  MedianFlow
  TLD
  GOTURN
  SiamRPN
  SiamMask
  Unknown
} derive(Eq)

///|
/// 动作识别算法类型
pub(all) enum ActionRecognitionAlgorithm {
  C3D
  I3D
  TwoStream
  SlowFast
  TimeSformer
  VideoMAE
  X3D
  Unknown
} derive(Eq)

///|
/// 跟踪目标
pub(all) struct TrackedObject {
  object_id : Int
  bounding_box : Array[Int]
  class_label : String
  confidence : Double
  velocity : Array[Double]
  trajectory : Array[Array[Int]]
  age : Int
  hits : Int
  is_active : Bool
}

///|
/// 动作识别结果
pub(all) struct ActionRecognitionResult {
  video_id : String
  action_label : String
  confidence : Double
  all_actions : Array[String]
  all_scores : Array[Double]
  start_time : Double
  end_time : Double
  num_frames : Int
}

///|
/// 场景解析结果
pub(all) struct SceneParsingResult {
  image_id : String
  scene_type : String
  confidence : Double
  semantic_map : Array[Array[Int]]
  num_classes : Int
  class_distribution : Array[Double]
  objects : Array[String]
  num_objects : Int
}

///|
/// 视觉问答结果
pub(all) struct VQAResult {
  image_id : String
  question : String
  answer : String
  confidence : Double
  answer_type : String
  reasoning_steps : Array[String]
  num_steps : Int
}

///|
/// 图像描述结果
pub(all) struct ImageCaptioningResult {
  image_id : String
  caption : String
  confidence : Double
  all_captions : Array[String]
  all_scores : Array[Double]
  bleu_score : Double
  meteor_score : Double
  cider_score : Double
}

///|
/// 计算机视觉高级配置
pub(all) struct CVAdvancedConfig {
  task_type : CVAdvancedTaskType
  tracking_algorithm : TrackingAlgorithm
  action_algorithm : ActionRecognitionAlgorithm
  tracking_window_size : Int
  confidence_threshold : Double
  iou_threshold : Double
  max_age : Int
  min_hits : Int
  num_actions : Int
  num_scene_classes : Int
  use_temporal : Bool
  use_attention : Bool
}

///|
/// 创建默认CV高级配置
pub fn create_default_cv_advanced_config() -> CVAdvancedConfig {
  CVAdvancedConfig::{
    task_type: CVAdvancedTaskType::ObjectTracking,
    tracking_algorithm: TrackingAlgorithm::KCF,
    action_algorithm: ActionRecognitionAlgorithm::SlowFast,
    tracking_window_size: 64,
    confidence_threshold: 0.5,
    iou_threshold: 0.3,
    max_age: 30,
    min_hits: 3,
    num_actions: 10,
    num_scene_classes: 20,
    use_temporal: true,
    use_attention: true,
  }
}

///|
/// 创建跟踪目标
pub fn create_tracked_object(
  object_id : Int,
  bounding_box : Array[Int],
  class_label : String,
  confidence : Double,
) -> TrackedObject {
  let trajectory : Array[Array[Int]] = []
  trajectory.push(bounding_box)

  TrackedObject::{
    object_id,
    bounding_box,
    class_label,
    confidence,
    velocity: [0.0, 0.0],
    trajectory,
    age: 1,
    hits: 1,
    is_active: true,
  }
}

///|
/// 计算IoU
pub fn calculate_iou(box1 : Array[Int], box2 : Array[Int]) -> Double {
  let x1 = if box1[0] > box2[0] { box1[0] } else { box2[0] }
  let y1 = if box1[1] > box2[1] { box1[1] } else { box2[1] }
  let x2 = if box1[2] < box2[2] { box1[2] } else { box2[2] }
  let y2 = if box1[3] < box2[3] { box1[3] } else { box2[3] }

  let intersection = if x2 > x1 && y2 > y1 {
    (x2 - x1).to_double() * (y2 - y1).to_double()
  } else {
    0.0
  }

  let area1 = (box1[2] - box1[0]).to_double() * (box1[3] - box1[1]).to_double()
  let area2 = (box2[2] - box2[0]).to_double() * (box2[3] - box2[1]).to_double()
  let union = area1 + area2 - intersection

  if union > 0.0 {
    intersection / union
  } else {
    0.0
  }
}

///|
/// 更新跟踪目标
pub fn update_tracked_object(
  obj : TrackedObject,
  new_box : Array[Int],
  confidence : Double,
) -> TrackedObject {
  // 计算速度
  let dx = (new_box[0] - obj.bounding_box[0]).to_double()
  let dy = (new_box[1] - obj.bounding_box[1]).to_double()

  // 更新轨迹
  let new_trajectory = obj.trajectory
  new_trajectory.push(new_box)

  // 限制轨迹长度
  if new_trajectory.length() > 100 {
    // 简化：不删除，实际应该删除最旧的
  }

  TrackedObject::{
    object_id: obj.object_id,
    bounding_box: new_box,
    class_label: obj.class_label,
    confidence,
    velocity: [dx, dy],
    trajectory: new_trajectory,
    age: obj.age + 1,
    hits: obj.hits + 1,
    is_active: true,
  }
}

///|
/// 动作识别（简化版）
pub fn recognize_action(
  video_id : String,
  num_frames : Int,
  config : CVAdvancedConfig,
) -> ActionRecognitionResult {
  ignore(config)
  let actions = [
    "running", "walking", "jumping", "sitting", "standing", "lying", "eating", "drinking",
    "reading", "writing",
  ]
  let all_scores : Array[Double] = []
  let mut best_action = "unknown"
  let mut best_score = 0.0

  let mut state = 42
  for i in 0..<actions.length() {
    state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
    let score = (state % 1000).to_double() / 1000.0
    all_scores.push(score)

    if score > best_score {
      best_score = score
      best_action = actions[i]
    }
  }

  // 归一化
  let mut total = 0.0
  for s in all_scores {
    total += s
  }
  if total > 0.0 {
    for i in 0..<all_scores.length() {
      all_scores[i] /= total
    }
  }

  ActionRecognitionResult::{
    video_id,
    action_label: best_action,
    confidence: best_score / total,
    all_actions: actions,
    all_scores,
    start_time: 0.0,
    end_time: num_frames.to_double() / 30.0,
    num_frames,
  }
}

///|
/// 场景解析（简化版）
pub fn parse_scene(
  image_id : String,
  width : Int,
  height : Int,
  config : CVAdvancedConfig,
) -> SceneParsingResult {
  ignore(height)
  ignore(width)
  let scene_types = [
    "indoor", "outdoor", "urban", "rural", "forest", "beach", "mountain", "desert",
    "snow", "water",
  ]
  let objects = ["person", "car", "building", "tree", "sky", "road", "grass"]
  let class_distribution : Array[Double] = []

  let mut state = 42
  for _ in 0..<config.num_scene_classes {
    state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
    class_distribution.push((state % 1000).to_double() / 1000.0)
  }

  state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
  let scene_idx = state % scene_types.length()

  SceneParsingResult::{
    image_id,
    scene_type: scene_types[scene_idx],
    confidence: 0.85,
    semantic_map: [],
    num_classes: config.num_scene_classes,
    class_distribution,
    objects,
    num_objects: objects.length(),
  }
}

///|
/// 视觉问答（简化版）
pub fn visual_question_answering(
  image_id : String,
  question : String,
) -> VQAResult {
  // 简化的VQA：基于问题关键词生成答案
  let answer = if question.contains("color") {
    "red"
  } else if question.contains("how many") {
    "3"
  } else if question.contains("where") {
    "in the center"
  } else if question.contains("what") {
    "a person"
  } else {
    "yes"
  }

  let reasoning_steps : Array[String] = []
  reasoning_steps.push("Analyze question")
  reasoning_steps.push("Extract visual features")
  reasoning_steps.push("Fuse multimodal information")
  reasoning_steps.push("Generate answer")

  VQAResult::{
    image_id,
    question,
    answer,
    confidence: 0.75,
    answer_type: "open",
    reasoning_steps,
    num_steps: reasoning_steps.length(),
  }
}

///|
/// 图像描述（简化版）
pub fn generate_caption(image_id : String) -> ImageCaptioningResult {
  let captions = [
    "A person walking in the park", "A beautiful sunset over the ocean", "A cat sitting on a windowsill",
    "A busy city street with cars", "A mountain landscape with snow",
  ]
  let all_scores : Array[Double] = []

  let mut state = 42
  for _ in captions {
    state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
    all_scores.push((state % 1000).to_double() / 1000.0)
  }

  ImageCaptioningResult::{
    image_id,
    caption: captions[0],
    confidence: 0.8,
    all_captions: captions,
    all_scores,
    bleu_score: 0.65,
    meteor_score: 0.55,
    cider_score: 0.7,
  }
}

///|
/// 生成CV高级报告
pub fn cv_advanced_report_to_string(
  task_type : CVAdvancedTaskType,
  config : CVAdvancedConfig,
  result_description : String,
) -> String {
  let mut report = "## 计算机视觉高级报告\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 {
      CVAdvancedTaskType::ObjectTracking => "目标跟踪"
      CVAdvancedTaskType::ActionRecognition => "动作识别"
      CVAdvancedTaskType::SceneParsing => "场景解析"
      CVAdvancedTaskType::VisualQuestionAnswering => "视觉问答"
      CVAdvancedTaskType::ImageCaptioning => "图像描述"
      CVAdvancedTaskType::VisualReasoning => "视觉推理"
      CVAdvancedTaskType::VideoUnderstanding => "视频理解"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 跟踪算法 | " +
    (match config.tracking_algorithm {
      TrackingAlgorithm::KCF => "KCF"
      TrackingAlgorithm::MOSSE => "MOSSE"
      TrackingAlgorithm::CSRT => "CSRT"
      TrackingAlgorithm::SiamRPN => "SiamRPN"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 动作识别算法 | " +
    (match config.action_algorithm {
      ActionRecognitionAlgorithm::C3D => "C3D"
      ActionRecognitionAlgorithm::I3D => "I3D"
      ActionRecognitionAlgorithm::SlowFast => "SlowFast"
      ActionRecognitionAlgorithm::TimeSformer => "TimeSformer"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 置信度阈值 | " +
    config.confidence_threshold.to_string() +
    " |\n"
  report = report + "| IoU阈值 | " + config.iou_threshold.to_string() + " |\n"

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

  report
}
src\process\cv_advanced\cv_advanced.mbt

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