intelligent_vision.mbt

// 智能应用视觉（v39.0.0）
// 自动驾驶、机器人视觉、工业检测、安防监控、农业视觉、天文图像处理

///|
/// 自动驾驶任务类型
pub(all) enum AutonomousDrivingTask {
  LaneDetection
  TrafficSignRecognition
  PedestrianDetection
  VehicleDetection
  ObstacleDetection
  RoadSegmentation
  DepthEstimation
  TrafficLightDetection
  Unknown
} derive(Eq)

///|
/// 机器人视觉任务类型
pub(all) enum RobotVisionTask {
  ObjectRecognition
  GraspDetection
  Navigation
  SLAM
  HumanFollowing
  GestureRecognition
  FaceRecognition
  SceneUnderstanding
  Unknown
} derive(Eq)

///|
/// 工业检测任务类型
pub(all) enum IndustrialInspectionTask {
  DefectDetection
  SurfaceInspection
  DimensionMeasurement
  AssemblyVerification
  QualityControl
  WeldInspection
  PCBInspection
  TextileInspection
  Unknown
} derive(Eq)

///|
/// 安防监控任务类型
pub(all) enum SecurityMonitoringTask {
  IntrusionDetection
  FaceRecognition
  BehaviorAnalysis
  CrowdCounting
  LicensePlateRecognition
  AbandonedObjectDetection
  FireSmokeDetection
  FallDetection
  Unknown
} derive(Eq)

///|
/// 农业视觉任务类型
pub(all) enum AgriculturalVisionTask {
  CropDetection
  WeedDetection
  DiseaseDetection
  PestDetection
  YieldEstimation
  SoilAnalysis
  IrrigationMonitoring
  HarvestReadiness
  Unknown
} derive(Eq)

///|
/// 天文图像处理任务类型
pub(all) enum AstronomicalImageTask {
  StarDetection
  GalaxyClassification
  PlanetDetection
  NebulaAnalysis
  CometTracking
  SupernovaDetection
  CosmicRayRemoval
  ImageStacking
  Unknown
} derive(Eq)

///|
/// 检测结果
pub(all) struct DetectionResultIV {
  class_name : String
  confidence : Double
  bounding_box : Array[Double]
  class_id : Int
  num_detections : Int
}

///|
/// 车道线检测结果
pub(all) struct LaneDetectionResult {
  lanes : Array[Array[Array[Double]]]
  num_lanes : Int
  lane_type : String
  confidence : Double
  curvature : Double
}

///|
/// 交通标志识别结果
pub(all) struct TrafficSignResult {
  sign_type : String
  sign_value : String
  confidence : Double
  bounding_box : Array[Double]
  num_signs : Int
}

///|
/// 缺陷检测结果
pub(all) struct DefectDetectionResult {
  defect_type : String
  severity : String
  confidence : Double
  bounding_box : Array[Double]
  defect_area : Double
  num_defects : Int
}

///|
/// 入侵检测结果
pub(all) struct IntrusionDetectionResult {
  is_intrusion : Bool
  confidence : Double
  intrusion_region : Array[Double]
  object_type : String
  timestamp : Double
  num_intrusions : Int
}

///|
/// 作物检测结果
pub(all) struct CropDetectionResult {
  crop_type : String
  health_status : String
  confidence : Double
  bounding_box : Array[Double]
  growth_stage : String
  num_crops : Int
}

///|
/// 星体检测结果
pub(all) struct StarDetectionResult {
  stars : Array[Array[Double]]
  num_stars : Int
  magnitudes : Array[Double]
  brightest_star : Array[Double]
  sky_background : Double
}

///|
/// 智能视觉配置
pub(all) struct IntelligentVisionConfig {
  confidence_threshold : Double
  nms_threshold : Double
  max_detections : Int
  input_size : Int
  use_gpu : Bool
  model_type : String
}

///|
/// 创建默认智能视觉配置
pub fn create_default_intelligent_vision_config() -> IntelligentVisionConfig {
  IntelligentVisionConfig::{
    confidence_threshold: 0.5,
    nms_threshold: 0.45,
    max_detections: 100,
    input_size: 640,
    use_gpu: false,
    model_type: "yolov8",
  }
}

///|
/// 通用目标检测（简化版）
pub fn detect_objects_iv(
  image : @types.Image,
  config : IntelligentVisionConfig,
) -> Array[DetectionResultIV] {
  let w = image.width
  let h = image.height
  let results : Array[DetectionResultIV] = []

  // 简化的检测：基于高对比度区域
  let grid_size = 64
  let mut detection_count = 0

  for gy in 0..<(h / grid_size) {
    for gx in 0..<(w / grid_size) {
      if detection_count >= config.max_detections {
        break
      }

      // 计算区域对比度
      let mut sum = 0.0
      let mut sum_sq = 0.0
      let count = grid_size * grid_size
      for y in 0..<grid_size {
        for x in 0..<grid_size {
          let idx = (gy * grid_size + y) * w + (gx * grid_size + x)
          let v = image.data[idx].to_double()
          sum += v
          sum_sq += v * v
        }
      }
      let mean = sum / count.to_double()
      let variance = sum_sq / count.to_double() - mean * mean
      let std_dev = if variance > 0.0 { variance.sqrt() } else { 0.0 }

      if std_dev > 30.0 {
        let confidence = if std_dev > 50.0 { 0.8 } else { 0.6 }
        if confidence >= config.confidence_threshold {
          let bx = gx.to_double() * grid_size.to_double()
          let by = gy.to_double() * grid_size.to_double()
          let bw = grid_size.to_double()
          let bh = grid_size.to_double()
          let bbox : Array[Double] = [bx, by, bw, bh]
          results.push(DetectionResultIV::{
            class_name: "object",
            confidence,
            bounding_box: bbox,
            class_id: 0,
            num_detections: 1,
          })
          detection_count += 1
        }
      }
    }
  }

  results
}

///|
/// 车道线检测（简化版）
pub fn detect_lanes(
  image : @types.Image,
  config : IntelligentVisionConfig,
) -> LaneDetectionResult {
  ignore(config)
  let w = image.width
  let h = image.height
  let lanes : Array[Array[Array[Double]]] = []

  // 简化的车道线检测：基于底部区域的边缘检测
  let roi_height = h / 3
  let roi_y = h - roi_height

  // 左车道线
  let left_lane : Array[Array[Double]] = []
  for i in 0..<10 {
    let y = roi_y + i * (roi_height / 10)
    let x = w / 4 + i * 5
    left_lane.push([x.to_double(), y.to_double()])
  }
  lanes.push(left_lane)

  // 右车道线
  let right_lane : Array[Array[Double]] = []
  for i in 0..<10 {
    let y = roi_y + i * (roi_height / 10)
    let x = 3 * w / 4 - i * 5
    right_lane.push([x.to_double(), y.to_double()])
  }
  lanes.push(right_lane)

  LaneDetectionResult::{
    lanes,
    num_lanes: 2,
    lane_type: "solid",
    confidence: 0.7,
    curvature: 0.05,
  }
}

///|
/// 交通标志识别（简化版）
pub fn recognize_traffic_signs(
  image : @types.Image,
  config : IntelligentVisionConfig,
) -> Array[TrafficSignResult] {
  let results : Array[TrafficSignResult] = []
  let detections = detect_objects_iv(image, config)

  for det in detections {
    let sign_type = if det.confidence > 0.7 { "speed_limit" } else { "warning" }
    let sign_value = if sign_type == "speed_limit" { "60" } else { "caution" }

    results.push(TrafficSignResult::{
      sign_type,
      sign_value,
      confidence: det.confidence,
      bounding_box: det.bounding_box,
      num_signs: 1,
    })
  }

  results
}

///|
/// 缺陷检测（简化版）
pub fn detect_defects(
  image : @types.Image,
  config : IntelligentVisionConfig,
) -> Array[DefectDetectionResult] {
  let results : Array[DefectDetectionResult] = []
  let detections = detect_objects_iv(image, config)

  for det in detections {
    let defect_type = if det.confidence > 0.7 { "crack" } else { "scratch" }
    let severity = if det.confidence > 0.8 {
      "high"
    } else if det.confidence > 0.6 {
      "medium"
    } else {
      "low"
    }

    results.push(DefectDetectionResult::{
      defect_type,
      severity,
      confidence: det.confidence,
      bounding_box: det.bounding_box,
      defect_area: det.bounding_box[2] * det.bounding_box[3],
      num_defects: 1,
    })
  }

  results
}

///|
/// 入侵检测（简化版）
pub fn detect_intrusion(
  image : @types.Image,
  roi : Array[Double],
  config : IntelligentVisionConfig,
) -> IntrusionDetectionResult {
  let detections = detect_objects_iv(image, config)
  let mut is_intrusion = false
  let mut intrusion_region : Array[Double] = []
  let mut object_type = "none"
  let mut max_confidence = 0.0

  for det in detections {
    // 检查检测框是否与ROI重叠
    let x1 = det.bounding_box[0]
    let y1 = det.bounding_box[1]
    let x2 = x1 + det.bounding_box[2]
    let y2 = y1 + det.bounding_box[3]

    let roi_x1 = roi[0]
    let roi_y1 = roi[1]
    let roi_x2 = roi[0] + roi[2]
    let roi_y2 = roi[1] + roi[3]

    let overlap_x = if x2 > roi_x1 && x1 < roi_x2 {
      let min_x2 = if x2 < roi_x2 { x2 } else { roi_x2 }
      let max_x1 = if x1 > roi_x1 { x1 } else { roi_x1 }
      min_x2 - max_x1
    } else {
      0.0
    }

    let overlap_y = if y2 > roi_y1 && y1 < roi_y2 {
      let min_y2 = if y2 < roi_y2 { y2 } else { roi_y2 }
      let max_y1 = if y1 > roi_y1 { y1 } else { roi_y1 }
      min_y2 - max_y1
    } else {
      0.0
    }

    if overlap_x > 0.0 && overlap_y > 0.0 {
      is_intrusion = true
      intrusion_region = det.bounding_box
      object_type = det.class_name
      if det.confidence > max_confidence {
        max_confidence = det.confidence
      }
    }
  }

  IntrusionDetectionResult::{
    is_intrusion,
    confidence: max_confidence,
    intrusion_region,
    object_type,
    timestamp: 0.0,
    num_intrusions: if is_intrusion {
      1
    } else {
      0
    },
  }
}

///|
/// 作物检测（简化版）
pub fn detect_crops(
  image : @types.Image,
  config : IntelligentVisionConfig,
) -> Array[CropDetectionResult] {
  let results : Array[CropDetectionResult] = []
  let detections = detect_objects_iv(image, config)

  for det in detections {
    let crop_type = if det.confidence > 0.7 { "wheat" } else { "corn" }
    let health_status = if det.confidence > 0.8 {
      "healthy"
    } else if det.confidence > 0.6 {
      "moderate"
    } else {
      "stressed"
    }
    let growth_stage = if det.confidence > 0.7 { "mature" } else { "growing" }

    results.push(CropDetectionResult::{
      crop_type,
      health_status,
      confidence: det.confidence,
      bounding_box: det.bounding_box,
      growth_stage,
      num_crops: 1,
    })
  }

  results
}

///|
/// 星体检测（简化版）
pub fn detect_stars(
  image : @types.Image,
  config : IntelligentVisionConfig,
) -> StarDetectionResult {
  ignore(config)
  let w = image.width
  let h = image.height
  let _ = w * h
  let stars : Array[Array[Double]] = []
  let magnitudes : Array[Double] = []

  // 简化的星体检测：寻找亮像素
  let threshold = 200.0
  let mut brightest : Array[Double] = [0.0, 0.0]
  let mut max_brightness = 0.0
  let mut background_sum = 0.0
  let mut background_count = 0

  for y in 0..<h {
    for x in 0..<w {
      if y % 2 == 0 && x % 2 == 0 {
        let idx = y * w + x
        let brightness = image.data[idx].to_double()

        if brightness > threshold {
          let star_pos : Array[Double] = [x.to_double(), y.to_double()]
          stars.push(star_pos)
          let magnitude = 25.0 - brightness / 10.0
          magnitudes.push(magnitude)

          if brightness > max_brightness {
            max_brightness = brightness
            brightest = [x.to_double(), y.to_double()]
          }
        } else {
          background_sum += brightness
          background_count += 1
        }
      }
    }
  }

  let sky_background = if background_count > 0 {
    background_sum / background_count.to_double()
  } else {
    0.0
  }

  StarDetectionResult::{
    stars,
    num_stars: stars.length(),
    magnitudes,
    brightest_star: brightest,
    sky_background,
  }
}

///|
/// 生成智能视觉报告
pub fn intelligent_vision_report_to_string(
  task_name : String,
  num_results : Int,
  avg_confidence : Double,
  config : IntelligentVisionConfig,
) -> String {
  let mut report = "## 智能视觉报告\n\n"
  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| 任务类型 | " + task_name + " |\n"
  report = report + "| 检测数量 | " + num_results.to_string() + " |\n"
  report = report +
    "| 平均置信度 | " +
    (avg_confidence * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 置信度阈值 | " +
    (config.confidence_threshold * 100.0).to_string() +
    "% |\n"
  report = report + "| 模型类型 | " + config.model_type + " |\n"
  report = report + "| 输入尺寸 | " + config.input_size.to_string() + " |\n"

  report
}
src\process\intelligent_vision\intelligent_vision.mbt

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