scene_understanding.mbt

// 场景理解与图像描述（v35.0.0）
// 场景分类、图像描述、视觉问答、图像翻译、场景图生成、目标关系推理

///|
/// 场景分类算法类型
pub(all) enum SceneClassificationAlgorithm {
  ResNet
  VGG
  Inception
  EfficientNet
  VisionTransformer
  SwinTransformer
  ConvNeXt
  DenseNet
  MobileNet
  Unknown
} derive(Eq)

///|
/// 图像描述算法类型
pub(all) enum ImageCaptioningAlgorithm {
  ShowAndTell
  ShowAttendAndTell
  UpDown
  TransformerCaption
  Oscar
  VinVL
  BLIP
  CLIPCaption
  Unknown
} derive(Eq)

///|
/// 视觉问答算法类型
pub(all) enum VisualQuestionAnsweringAlgorithm {
  VQA
  VisualBERT
  LXMERT
  UNITER
  ViLBERT
  CLIP_VQA
  BLIP_VQA
  GPT4V
  Unknown
} derive(Eq)

///|
/// 场景分类结果
pub(all) struct SceneClassificationResult {
  scene_type : String
  confidence : Double
  top_predictions : Array[String]
  top_confidences : Array[Double]
  num_classes : Int
  scene_category : String
}

///|
/// 图像描述结果
pub(all) struct ImageCaptioningResult {
  caption : String
  confidence : Double
  alternative_captions : Array[String]
  num_captions : Int
  bleu_score : Double
  cider_score : Double
  generation_time_ms : Double
}

///|
/// 视觉问答结果
pub(all) struct VisualQuestionAnsweringResult {
  answer : String
  confidence : Double
  alternative_answers : Array[String]
  question_type : String
  answer_type : String
  reasoning_steps : Array[String]
}

///|
/// 场景图节点
pub(all) struct SceneGraphNode {
  object_id : Int
  object_name : String
  bounding_box : Array[Double]
  attributes : Array[String]
  confidence : Double
}

///|
/// 场景图关系
pub(all) struct SceneGraphRelation {
  subject_id : Int
  object_id : Int
  relation : String
  confidence : Double
}

///|
/// 场景图
pub(all) struct SceneGraph {
  nodes : Array[SceneGraphNode]
  relations : Array[SceneGraphRelation]
  num_objects : Int
  num_relations : Int
}

///|
/// 场景理解配置
pub(all) struct SceneUnderstandingConfig {
  classification_algorithm : SceneClassificationAlgorithm
  captioning_algorithm : ImageCaptioningAlgorithm
  vqa_algorithm : VisualQuestionAnsweringAlgorithm
  classification_threshold : Double
  caption_temperature : Double
  max_caption_length : Int
  use_attention : Bool
  use_scene_graph : Bool
}

///|
/// 创建默认场景理解配置
pub fn create_default_scene_config() -> SceneUnderstandingConfig {
  SceneUnderstandingConfig::{
    classification_algorithm: SceneClassificationAlgorithm::VisionTransformer,
    captioning_algorithm: ImageCaptioningAlgorithm::BLIP,
    vqa_algorithm: VisualQuestionAnsweringAlgorithm::BLIP_VQA,
    classification_threshold: 0.5,
    caption_temperature: 1.0,
    max_caption_length: 50,
    use_attention: true,
    use_scene_graph: true,
  }
}

///|
/// 常见场景类型
pub fn get_common_scene_types() -> Array[String] {
  [
    "indoor", "outdoor", "urban", "rural", "natural", "office", "home", "restaurant",
    "store", "street", "park", "forest", "mountain", "beach", "sky", "night", "daytime",
    "sunset", "sunrise", "cloudy", "rainy", "snowy", "foggy", "sunny", "windy", "crowded",
    "empty", "modern", "traditional", "industrial",
  ]
}

///|
/// 场景分类（简化版：基于图像统计特征的启发式分类）
pub fn classify_scene(
  img : @types.Image,
  config : SceneUnderstandingConfig,
) -> SceneClassificationResult {
  ignore(config)
  let w = img.width
  let h = img.height
  let total = w * h

  // 计算图像统计特征
  let mut sum = 0.0
  let mut sum_sq = 0.0
  let mut edge_count = 0
  let mut _color_variation = 0.0

  for y in 0..<h {
    for x in 0..<w {
      let idx = y * w + x
      let pixel = img.data[idx].to_double()
      sum += pixel
      sum_sq += pixel * pixel

      if x > 0 && x < w - 1 && y > 0 && y < h - 1 {
        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()
        if (gx * gx + gy * gy).sqrt() > 20.0 {
          edge_count += 1
        }
      }
    }
  }

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

  // 简化的场景分类
  let mut scene_type = "indoor"
  let mut scene_category = "environment"
  let mut confidence = 0.45

  if mean < 60.0 {
    scene_type = "night"
    scene_category = "time_of_day"
    confidence = 0.7
  } else if mean > 200.0 {
    scene_type = "daytime"
    scene_category = "time_of_day"
    confidence = 0.65
  } else if edge_density > 0.3 {
    scene_type = "urban"
    scene_category = "environment"
    confidence = 0.6
  } else if edge_density < 0.1 {
    scene_type = "natural"
    scene_category = "environment"
    confidence = 0.55
  } else if std_dev > 60.0 {
    scene_type = "outdoor"
    scene_category = "environment"
    confidence = 0.5
  } else {
    scene_type = "indoor"
    scene_category = "environment"
    confidence = 0.45
  }

  // Top 预测
  let top_predictions : Array[String] = []
  let top_confidences : Array[Double] = []
  top_predictions.push(scene_type)
  top_confidences.push(confidence)
  top_predictions.push("indoor")
  top_confidences.push(0.3)
  top_predictions.push("outdoor")
  top_confidences.push(0.25)

  SceneClassificationResult::{
    scene_type,
    confidence,
    top_predictions,
    top_confidences,
    num_classes: 30,
    scene_category,
  }
}

///|
/// 生成图像描述（简化版：基于场景分类和图像特征的模板化描述）
pub fn generate_caption(
  img : @types.Image,
  config : SceneUnderstandingConfig,
) -> ImageCaptioningResult {
  let classification = classify_scene(img, config)
  let w = img.width
  let h = img.height
  let total = w * h

  // 计算更多图像特征
  let mut sum = 0.0
  for i in 0..<total {
    sum += img.data[i].to_double()
  }
  let mean = sum / total.to_double()

  // 简化的描述生成
  let mut caption = ""
  let brightness_desc = if mean < 60.0 {
    "dark"
  } else if mean < 120.0 {
    "dimly lit"
  } else if mean < 180.0 {
    "well-lit"
  } else {
    "bright"
  }

  caption = "A " + brightness_desc + " " + classification.scene_type + " scene."

  let alternative_captions : Array[String] = []
  alternative_captions.push(
    "This appears to be a " + classification.scene_type + " environment.",
  )
  alternative_captions.push(
    "An image showing a " + classification.scene_type + " setting.",
  )
  alternative_captions.push(
    "A photograph of a " +
    brightness_desc +
    " " +
    classification.scene_type +
    ".",
  )

  ImageCaptioningResult::{
    caption,
    confidence: classification.confidence * 0.8,
    alternative_captions,
    num_captions: 4,
    bleu_score: 0.3,
    cider_score: 0.5,
    generation_time_ms: 0.0,
  }
}

///|
/// 视觉问答（简化版：基于场景分类的规则化问答）
pub fn answer_visual_question(
  img : @types.Image,
  question : String,
  config : SceneUnderstandingConfig,
) -> VisualQuestionAnsweringResult {
  let classification = classify_scene(img, config)
  let w = img.width
  let h = img.height
  let total = w * h

  // 计算亮度
  let mut sum = 0.0
  for i in 0..<total {
    sum += img.data[i].to_double()
  }
  let mean = sum / total.to_double()

  // 简化的问答
  let mut answer = ""
  let mut question_type = ""
  let mut answer_type = ""
  let mut confidence = 0.0

  if question.contains("scene") ||
    question.contains("environment") ||
    question.contains("place") {
    answer = classification.scene_type
    question_type = "scene_recognition"
    answer_type = "open"
    confidence = classification.confidence
  } else if question.contains("bright") ||
    question.contains("dark") ||
    question.contains("light") {
    if mean < 60.0 {
      answer = "dark"
    } else if mean < 120.0 {
      answer = "dimly lit"
    } else if mean < 180.0 {
      answer = "well-lit"
    } else {
      answer = "bright"
    }
    question_type = "attribute_recognition"
    answer_type = "open"
    confidence = 0.7
  } else if question.contains("indoor") || question.contains("outdoor") {
    answer = classification.scene_type
    question_type = "binary"
    answer_type = "yes/no"
    confidence = classification.confidence
  } else if question.contains("color") || question.contains("colour") {
    if mean < 85.0 {
      answer = "dark colors"
    } else if mean < 170.0 {
      answer = "medium colors"
    } else {
      answer = "light colors"
    }
    question_type = "attribute_recognition"
    answer_type = "open"
    confidence = 0.6
  } else {
    answer = "I'm not sure about that."
    question_type = "unknown"
    answer_type = "open"
    confidence = 0.3
  }

  let alternative_answers : Array[String] = []
  alternative_answers.push(classification.scene_type)
  alternative_answers.push("indoor")
  alternative_answers.push("outdoor")

  let reasoning_steps : Array[String] = []
  reasoning_steps.push("Analyze image statistics")
  reasoning_steps.push("Classify scene type")
  reasoning_steps.push("Match question to answer type")
  reasoning_steps.push("Generate answer")

  VisualQuestionAnsweringResult::{
    answer,
    confidence,
    alternative_answers,
    question_type,
    answer_type,
    reasoning_steps,
  }
}

///|
/// 生成场景图（简化版：基于图像区域分割的场景图）
pub fn generate_scene_graph(
  img : @types.Image,
  config : SceneUnderstandingConfig,
) -> SceneGraph {
  ignore(config)
  let w = img.width
  let h = img.height

  // 简化版：将图像分为 4 个区域，每个区域作为一个对象
  let nodes : Array[SceneGraphNode] = []
  let relations : Array[SceneGraphRelation] = []

  let regions = [
    [0.0, 0.0, w.to_double() / 2.0, h.to_double() / 2.0],
    [w.to_double() / 2.0, 0.0, w.to_double() / 2.0, h.to_double() / 2.0],
    [0.0, h.to_double() / 2.0, w.to_double() / 2.0, h.to_double() / 2.0],
    [
      w.to_double() / 2.0,
      h.to_double() / 2.0,
      w.to_double() / 2.0,
      h.to_double() / 2.0,
    ],
  ]

  let object_names = [
    "top_left_object", "top_right_object", "bottom_left_object", "bottom_right_object",
  ]

  for i in 0..<4 {
    let attributes : Array[String] = []
    attributes.push("region_" + i.to_string())

    nodes.push(SceneGraphNode::{
      object_id: i,
      object_name: object_names[i],
      bounding_box: regions[i],
      attributes,
      confidence: 0.5,
    })
  }

  // 添加空间关系
  relations.push(SceneGraphRelation::{
    subject_id: 0,
    object_id: 1,
    relation: "next_to",
    confidence: 0.8,
  })
  relations.push(SceneGraphRelation::{
    subject_id: 0,
    object_id: 2,
    relation: "above",
    confidence: 0.8,
  })
  relations.push(SceneGraphRelation::{
    subject_id: 1,
    object_id: 3,
    relation: "above",
    confidence: 0.8,
  })
  relations.push(SceneGraphRelation::{
    subject_id: 2,
    object_id: 3,
    relation: "next_to",
    confidence: 0.8,
  })

  SceneGraph::{ nodes, relations, num_objects: 4, num_relations: 4 }
}

///|
/// 完整的场景理解
pub fn understand_scene(
  img : @types.Image,
  config : SceneUnderstandingConfig,
) -> (SceneClassificationResult, ImageCaptioningResult, SceneGraph) {
  let classification = classify_scene(img, config)
  let caption = generate_caption(img, config)
  let scene_graph = if config.use_scene_graph {
    generate_scene_graph(img, config)
  } else {
    SceneGraph::{ nodes: [], relations: [], num_objects: 0, num_relations: 0 }
  }

  (classification, caption, scene_graph)
}

///|
/// 生成场景理解报告
pub fn scene_understanding_report_to_string(
  classification : SceneClassificationResult,
  caption : ImageCaptioningResult,
  vqa : VisualQuestionAnsweringResult,
  scene_graph : SceneGraph,
) -> String {
  let mut report = "## 场景理解与图像描述报告\n\n"

  report = report + "### 场景分类\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| 场景类型 | " + classification.scene_type + " |\n"
  report = report + "| 场景类别 | " + classification.scene_category + " |\n"
  report = report +
    "| 置信度 | " +
    (classification.confidence * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 类别数 | " +
    classification.num_classes.to_string() +
    " |\n\n"

  report = report + "### Top 预测\n\n"
  report = report + "| 排名 | 场景 | 置信度 |\n|------|------|------|\n"
  for i in 0..<classification.top_predictions.length() {
    report = report +
      "| " +
      (i + 1).to_string() +
      " | " +
      classification.top_predictions[i] +
      " | " +
      (classification.top_confidences[i] * 100.0).to_string() +
      "% |\n"
  }

  report = report + "\n### 图像描述\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| 主描述 | " + caption.caption + " |\n"
  report = report +
    "| 置信度 | " +
    (caption.confidence * 100.0).to_string() +
    "% |\n"
  report = report + "| BLEU 分数 | " + caption.bleu_score.to_string() + " |\n"
  report = report +
    "| CIDEr 分数 | " +
    caption.cider_score.to_string() +
    " |\n"
  report = report +
    "| 备选描述数 | " +
    caption.num_captions.to_string() +
    " |\n\n"

  report = report + "### 备选描述\n\n"
  for i in 0..<caption.alternative_captions.length() {
    report = report +
      (i + 1).to_string() +
      ". " +
      caption.alternative_captions[i] +
      "\n"
  }

  report = report + "\n### 视觉问答\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| 答案 | " + vqa.answer + " |\n"
  report = report +
    "| 置信度 | " +
    (vqa.confidence * 100.0).to_string() +
    "% |\n"
  report = report + "| 问题类型 | " + vqa.question_type + " |\n"
  report = report + "| 答案类型 | " + vqa.answer_type + " |\n\n"

  report = report + "### 推理步骤\n\n"
  for i in 0..<vqa.reasoning_steps.length() {
    report = report + (i + 1).to_string() + ". " + vqa.reasoning_steps[i] + "\n"
  }

  report = report + "\n### 场景图\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 对象数 | " +
    scene_graph.num_objects.to_string() +
    " |\n"
  report = report +
    "| 关系数 | " +
    scene_graph.num_relations.to_string() +
    " |\n\n"

  report = report + "### 对象列表\n\n"
  report = report + "| ID | 名称 | 置信度 |\n|------|------|------|\n"
  for node in scene_graph.nodes {
    report = report +
      "| " +
      node.object_id.to_string() +
      " | " +
      node.object_name +
      " | " +
      (node.confidence * 100.0).to_string() +
      "% |\n"
  }

  report = report + "\n### 关系列表\n\n"
  report = report +
    "| 主体 | 关系 | 客体 | 置信度 |\n|------|------|------|------|\n"
  for rel in scene_graph.relations {
    report = report +
      "| " +
      rel.subject_id.to_string() +
      " | " +
      rel.relation +
      " | " +
      rel.object_id.to_string() +
      " | " +
      (rel.confidence * 100.0).to_string() +
      "% |\n"
  }

  report
}
src\process\scene_understanding\scene_understanding.mbt

84.62%
