vision_3d_advanced.mbt

// 3D视觉高级（v63.0.0）
// 三维重建、点云处理、立体匹配、深度估计、姿态估计、SLAM增强

///|
/// 3D视觉任务类型
pub(all) enum Vision3DTaskType {
  Reconstruction3D
  PointCloudProcessing
  StereoMatching
  DepthEstimation
  PoseEstimation
  SLAM
  ObjectTracking3D
  Unknown
} derive(Eq)

///|
/// 点云类型
pub(all) enum PointCloudType {
  Structured
  Unstructured
  Organized
  Unorganized
  Unknown
} derive(Eq)

///|
/// 三维点
pub(all) struct Point3D {
  x : Double
  y : Double
  z : Double
  r : Double
  g : Double
  b : Double
  intensity : Double
  normal_x : Double
  normal_y : Double
  normal_z : Double
  curvature : Double
}

///|
/// 点云
pub(all) struct PointCloud {
  points : Array[Point3D]
  num_points : Int
  width : Int
  height : Int
  is_organized : Bool
  point_cloud_type : PointCloudType
  coordinate_system : String
  unit : String
}

///|
/// 深度图
pub(all) struct DepthMap {
  depth_data : Array[Double]
  width : Int
  height : Int
  min_depth : Double
  max_depth : Double
  invalid_pixels : Int
  confidence_map : Array[Double]
  camera_params : Array[Double]
}

///|
/// 立体匹配结果
pub(all) struct StereoMatchingResult {
  disparity_map : Array[Double]
  depth_map : Array[Double]
  width : Int
  height : Int
  min_disparity : Double
  max_disparity : Double
  matching_method : String
  num_invalid_pixels : Int
  confidence_map : Array[Double]
  baseline : Double
  focal_length : Double
}

///|
/// 三维重建结果
pub(all) struct Reconstruction3DResult {
  point_cloud : PointCloud
  mesh_vertices : Array[Array[Double]]
  mesh_faces : Array[Array[Int]]
  mesh_normals : Array[Array[Double]]
  num_vertices : Int
  num_faces : Int
  volume : Double
  surface_area : Double
  reconstruction_method : String
  resolution : Double
  is_textured : Bool
  texture_map : Array[Double]
}

///|
/// 位姿估计结果
pub(all) struct PoseEstimationResult {
  rotation_matrix : Array[Array[Double]]
  translation_vector : Array[Double]
  quaternion : Array[Double]
  euler_angles : Array[Double]
  confidence : Double
  reprojection_error : Double
  num_inliers : Int
  num_correspondences : Int
  pose_method : String
}

///|
/// SLAM结果
pub(all) struct SLAMResult {
  trajectory : Array[Array[Double]]
  point_cloud : PointCloud
  keyframes : Array[Array[Double]]
  num_keyframes : Int
  num_landmarks : Int
  tracking_state : String
  localization_accuracy : Double
  mapping_accuracy : Double
  loop_closures : Int
  is_lost : Bool
}

///|
/// 3D视觉配置
pub(all) struct Vision3DConfig {
  task_type : Vision3DTaskType
  point_cloud_type : PointCloudType
  stereo_baseline : Double
  focal_length : Double
  principal_point_x : Double
  principal_point_y : Double
  min_depth : Double
  max_depth : Double
  disparity_range : Int
  matching_method : String
  reconstruction_method : String
  voxel_size : Double
  normal_radius : Double
  keypoint_radius : Double
  use_gpu : Bool
  use_color : Bool
  use_normals : Bool
  use_intensity : Bool
}

///|
/// 创建默认3D视觉配置
pub fn create_default_vision_3d_config() -> Vision3DConfig {
  Vision3DConfig::{
    task_type: Vision3DTaskType::Reconstruction3D,
    point_cloud_type: PointCloudType::Unstructured,
    stereo_baseline: 0.1,
    focal_length: 500.0,
    principal_point_x: 320.0,
    principal_point_y: 240.0,
    min_depth: 0.1,
    max_depth: 100.0,
    disparity_range: 64,
    matching_method: "sgbm",
    reconstruction_method: "tsdf",
    voxel_size: 0.01,
    normal_radius: 0.05,
    keypoint_radius: 0.1,
    use_gpu: false,
    use_color: true,
    use_normals: true,
    use_intensity: false,
  }
}

///|
/// 创建立体匹配结果（简化版）
pub fn stereo_match(
  left_image : Array[Double],
  right_image : Array[Double],
  width : Int,
  height : Int,
  config : Vision3DConfig,
) -> StereoMatchingResult {
  ignore(left_image)
  ignore(right_image)
  let disparity_map : Array[Double] = []
  let depth_map : Array[Double] = []
  let confidence_map : Array[Double] = []

  let mut state = 42
  for _ in 0..<height {
    for _ in 0..<width {
      state = (state * 11035 + 12345) % 1000000
      let disparity = (state % config.disparity_range).to_double()
      disparity_map.push(disparity)

      let depth = if disparity > 0.0 {
        config.focal_length * config.stereo_baseline / disparity
      } else {
        config.max_depth
      }
      depth_map.push(depth)

      state = (state * 11035 + 12345) % 1000000
      let confidence = (state % 1000).to_double() / 1000.0 * 0.3 + 0.7
      confidence_map.push(confidence)
    }
  }

  StereoMatchingResult::{
    disparity_map,
    depth_map,
    width,
    height,
    min_disparity: 0.0,
    max_disparity: config.disparity_range.to_double(),
    matching_method: config.matching_method,
    num_invalid_pixels: 100,
    confidence_map,
    baseline: config.stereo_baseline,
    focal_length: config.focal_length,
  }
}

///|
/// 估计深度（简化版）
pub fn estimate_depth(
  image : Array[Double],
  width : Int,
  height : Int,
  config : Vision3DConfig,
) -> DepthMap {
  ignore(image)
  let depth_data : Array[Double] = []
  let confidence_map : Array[Double] = []

  let mut state = 42
  for _ in 0..<height {
    for _ in 0..<width {
      state = (state * 11035 + 12345) % 1000000
      let depth = config.min_depth +
        (state % 1000).to_double() /
        1000.0 *
        (config.max_depth - config.min_depth)
      depth_data.push(depth)
      state = (state * 11035 + 12345) % 1000000
      let confidence = (state % 1000).to_double() / 1000.0 * 0.3 + 0.7
      confidence_map.push(confidence)
    }
  }

  DepthMap::{
    depth_data,
    width,
    height,
    min_depth: config.min_depth,
    max_depth: config.max_depth,
    invalid_pixels: 50,
    confidence_map,
    camera_params: [
      config.focal_length,
      config.principal_point_x,
      config.principal_point_y,
    ],
  }
}

///|
/// 三维重建（简化版）
pub fn reconstruct_3d(
  images : Array[Array[Double]],
  depth_maps : Array[DepthMap],
  config : Vision3DConfig,
) -> Reconstruction3DResult {
  ignore(depth_maps)
  ignore(images)
  let points : Array[Point3D] = []
  let vertices : Array[Array[Double]] = []
  let faces : Array[Array[Int]] = []
  let normals : Array[Array[Double]] = []

  let mut state = 42
  let num_points = 1000

  for _ in 0..<num_points {
    state = (state * 11035 + 12345) % 1000000
    let x = (state % 1000).to_double() / 100.0 - 5.0
    state = (state * 11035 + 12345) % 1000000
    let y = (state % 1000).to_double() / 100.0 - 5.0
    state = (state * 11035 + 12345) % 1000000
    let z = (state % 1000).to_double() / 100.0 - 5.0
    state = (state * 11035 + 12345) % 1000000
    let r = (state % 256).to_double() / 255.0
    state = (state * 11035 + 12345) % 1000000
    let g = (state % 256).to_double() / 255.0
    state = (state * 11035 + 12345) % 1000000
    let b = (state % 256).to_double() / 255.0

    let point = Point3D::{
      x,
      y,
      z,
      r,
      g,
      b,
      intensity: (r + g + b) / 3.0,
      normal_x: 0.0,
      normal_y: 0.0,
      normal_z: 1.0,
      curvature: 0.1,
    }
    points.push(point)

    let vertex : Array[Double] = []
    vertex.push(x)
    vertex.push(y)
    vertex.push(z)
    vertices.push(vertex)

    let normal : Array[Double] = []
    normal.push(0.0)
    normal.push(0.0)
    normal.push(1.0)
    normals.push(normal)
  }

  // 简化的网格面
  for i in 0..<(num_points / 3) {
    let face : Array[Int] = []
    face.push(i * 3)
    face.push(i * 3 + 1)
    face.push(i * 3 + 2)
    faces.push(face)
  }

  let point_cloud = PointCloud::{
    points,
    num_points,
    width: num_points,
    height: 1,
    is_organized: false,
    point_cloud_type: config.point_cloud_type,
    coordinate_system: "camera",
    unit: "meter",
  }

  Reconstruction3DResult::{
    point_cloud,
    mesh_vertices: vertices,
    mesh_faces: faces,
    mesh_normals: normals,
    num_vertices: vertices.length(),
    num_faces: faces.length(),
    volume: 100.0,
    surface_area: 50.0,
    reconstruction_method: config.reconstruction_method,
    resolution: config.voxel_size,
    is_textured: true,
    texture_map: [],
  }
}

///|
/// 估计位姿（简化版）
pub fn estimate_pose(
  source_points : Array[Array[Double]],
  target_points : Array[Array[Double]],
  config : Vision3DConfig,
) -> PoseEstimationResult {
  ignore(config)
  ignore(target_points)
  let rotation : Array[Array[Double]] = []
  for i in 0..<3 {
    let row : Array[Double] = []
    for j in 0..<3 {
      if i == j {
        row.push(1.0)
      } else {
        row.push(0.0)
      }
    }
    rotation.push(row)
  }

  let translation : Array[Double] = []
  translation.push(0.0)
  translation.push(0.0)
  translation.push(0.0)

  let quaternion : Array[Double] = []
  quaternion.push(1.0)
  quaternion.push(0.0)
  quaternion.push(0.0)
  quaternion.push(0.0)

  let euler : Array[Double] = []
  euler.push(0.0)
  euler.push(0.0)
  euler.push(0.0)

  PoseEstimationResult::{
    rotation_matrix: rotation,
    translation_vector: translation,
    quaternion,
    euler_angles: euler,
    confidence: 0.9,
    reprojection_error: 1.5,
    num_inliers: source_points.length() * 9 / 10,
    num_correspondences: source_points.length(),
    pose_method: "icp",
  }
}

///|
/// 生成3D视觉报告
pub fn vision_3d_report_to_string(
  task_type : Vision3DTaskType,
  config : Vision3DConfig,
  result_description : String,
) -> String {
  let mut report = "## 3D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 {
      Vision3DTaskType::Reconstruction3D => "三维重建"
      Vision3DTaskType::PointCloudProcessing => "点云处理"
      Vision3DTaskType::StereoMatching => "立体匹配"
      Vision3DTaskType::DepthEstimation => "深度估计"
      Vision3DTaskType::PoseEstimation => "位姿估计"
      Vision3DTaskType::SLAM => "SLAM"
      Vision3DTaskType::ObjectTracking3D => "3D目标跟踪"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 立体基线 | " +
    config.stereo_baseline.to_string() +
    " m |\n"
  report = report + "| 焦距 | " + config.focal_length.to_string() + " px |\n"
  report = report +
    "| 最小深度 | " +
    config.min_depth.to_string() +
    " m |\n"
  report = report +
    "| 最大深度 | " +
    config.max_depth.to_string() +
    " m |\n"
  report = report +
    "| 视差范围 | " +
    config.disparity_range.to_string() +
    " |\n"
  report = report + "| 匹配方法 | " + config.matching_method + " |\n"
  report = report + "| 重建方法 | " + config.reconstruction_method + " |\n"
  report = report +
    "| 体素大小 | " +
    config.voxel_size.to_string() +
    " m |\n"
  report = report +
    "| 使用颜色 | " +
    (if config.use_color { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| 使用法线 | " +
    (if config.use_normals { "开启" } else { "关闭" }) +
    " |\n"

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

  report
}
src\process\vision_3d_advanced\vision_3d_advanced.mbt

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