trustformers-mobile 0.1.1

//! ARKit Integration for TrustformersRS
//!
//! This module provides comprehensive ARKit integration enabling AR-based model inference,
//! real-time object detection and classification, 3D scene understanding, and immersive
//! AI-powered augmented reality experiences on iOS devices.

use crate::{
    device_info::{MobileDeviceInfo, PerformanceScores},
    inference::InferenceEngine,
    mobile_performance_profiler::{MobilePerformanceProfiler, MobileProfilerConfig},
};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use trustformers_core::error::{CoreError, Result};

/// ARKit-powered inference engine for augmented reality applications
pub struct ARKitInferenceEngine {
    config: ARKitConfig,
    session_manager: ARSessionManager,
    scene_analyzer: SceneAnalyzer,
    object_detector: ARObjectDetector,
    pose_estimator: PoseEstimator,
    occlusion_manager: OcclusionManager,
    rendering_engine: ARRenderingEngine,
    performance_monitor: Arc<Mutex<MobilePerformanceProfiler>>,
    plane_detection: PlaneDetectionEngine,
    light_estimation: LightEstimationEngine,
    world_tracking: WorldTrackingEngine,
}

/// Configuration for ARKit integration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARKitConfig {
    /// Enable ARKit features
    pub enabled: bool,
    /// AR session configuration
    pub session_config: ARSessionConfig,
    /// Object detection configuration
    pub object_detection: ObjectDetectionConfig,
    /// Pose estimation configuration
    pub pose_estimation: PoseEstimationConfig,
    /// Plane detection configuration
    pub plane_detection: PlaneDetectionConfig,
    /// Light estimation configuration
    pub light_estimation: LightEstimationConfig,
    /// Rendering configuration
    pub rendering: ARRenderingConfig,
    /// Performance optimization settings
    pub performance: ARPerformanceConfig,
}

/// AR session configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARSessionConfig {
    /// World tracking enabled
    pub world_tracking: bool,
    /// Face tracking enabled
    pub face_tracking: bool,
    /// Image tracking enabled
    pub image_tracking: bool,
    /// Object tracking enabled
    pub object_tracking: bool,
    /// Body tracking enabled
    pub body_tracking: bool,
    /// Geo tracking enabled (iOS 14+)
    pub geo_tracking: bool,
    /// Collaborative session enabled
    pub collaborative_session: bool,
    /// Auto-focus enabled
    pub auto_focus: bool,
    /// Audio enabled
    pub audio_enabled: bool,
}

/// Object detection configuration for AR
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ObjectDetectionConfig {
    /// Enable real-time object detection
    pub enabled: bool,
    /// Detection model type
    pub model_type: ObjectDetectionModel,
    /// Detection confidence threshold
    pub confidence_threshold: f32,
    /// Maximum detections per frame
    pub max_detections: usize,
    /// Classes to detect
    pub target_classes: Vec<String>,
    /// Enable 3D bounding boxes
    pub enable_3d_boxes: bool,
    /// Enable object tracking
    pub enable_tracking: bool,
    /// Tracking timeout (seconds)
    pub tracking_timeout: f32,
}

/// Object detection models
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ObjectDetectionModel {
    /// YOLO-based detection
    YOLO,
    /// SSD-based detection
    SSD,
    /// Custom model
    Custom,
    /// Core ML Vision framework
    CoreMLVision,
}

/// Pose estimation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PoseEstimationConfig {
    /// Enable pose estimation
    pub enabled: bool,
    /// Pose model type
    pub model_type: PoseEstimationModel,
    /// Joint confidence threshold
    pub joint_confidence_threshold: f32,
    /// Enable 3D pose estimation
    pub enable_3d_pose: bool,
    /// Enable hand pose estimation
    pub enable_hand_pose: bool,
    /// Enable face pose estimation
    pub enable_face_pose: bool,
    /// Smoothing factor for pose tracking
    pub smoothing_factor: f32,
}

/// Pose estimation models
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PoseEstimationModel {
    /// MediaPipe-based estimation
    MediaPipe,
    /// OpenPose-based estimation
    OpenPose,
    /// Custom model
    Custom,
    /// ARKit body tracking
    ARKitBody,
}

/// Plane detection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlaneDetectionConfig {
    /// Enable horizontal plane detection
    pub horizontal_planes: bool,
    /// Enable vertical plane detection
    pub vertical_planes: bool,
    /// Minimum plane size (m²)
    pub minimum_plane_size: f32,
    /// Plane classification enabled
    pub classification_enabled: bool,
    /// Plane merging enabled
    pub plane_merging: bool,
}

/// Light estimation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LightEstimationConfig {
    /// Enable ambient light estimation
    pub ambient_intensity: bool,
    /// Enable directional light estimation
    pub directional_light: bool,
    /// Enable spherical harmonics
    pub spherical_harmonics: bool,
    /// Light estimation mode
    pub estimation_mode: LightEstimationMode,
}

/// Light estimation modes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum LightEstimationMode {
    /// Disabled
    None,
    /// Ambient intensity only
    AmbientIntensity,
    /// Directional lighting
    DirectionalLighting,
    /// Environment texturing
    EnvironmentTexturing,
}

/// AR rendering configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARRenderingConfig {
    /// Rendering backend
    pub backend: RenderingBackend,
    /// Enable occlusion
    pub occlusion_enabled: bool,
    /// Enable shadows
    pub shadows_enabled: bool,
    /// Enable reflections
    pub reflections_enabled: bool,
    /// Render resolution scale
    pub resolution_scale: f32,
    /// Target frame rate
    pub target_fps: u32,
    /// Enable HDR rendering
    pub hdr_enabled: bool,
}

/// AR rendering backends
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RenderingBackend {
    /// Metal rendering
    Metal,
    /// SceneKit rendering
    SceneKit,
    /// RealityKit rendering
    RealityKit,
    /// Custom rendering
    Custom,
}

/// AR performance configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARPerformanceConfig {
    /// Adaptive quality enabled
    pub adaptive_quality: bool,
    /// Performance monitoring enabled
    pub performance_monitoring: bool,
    /// Thermal throttling enabled
    pub thermal_throttling: bool,
    /// Battery optimization enabled
    pub battery_optimization: bool,
    /// Frame rate adaptation enabled
    pub frame_rate_adaptation: bool,
    /// Quality scaling thresholds
    pub quality_thresholds: QualityThresholds,
}

/// Quality scaling thresholds
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityThresholds {
    /// CPU usage threshold for quality reduction
    pub cpu_threshold: f32,
    /// GPU usage threshold for quality reduction
    pub gpu_threshold: f32,
    /// Temperature threshold for quality reduction
    pub temperature_threshold: f32,
    /// Frame time threshold for quality reduction (ms)
    pub frame_time_threshold: f32,
}

/// AR session manager
struct ARSessionManager {
    session_state: ARSessionState,
    tracking_state: TrackingState,
    world_map: Option<ARWorldMap>,
    relocalization_enabled: bool,
}

/// AR session state
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ARSessionState {
    /// Session not started
    NotStarted,
    /// Session starting
    Starting,
    /// Session running normally
    Running,
    /// Session paused
    Paused,
    /// Session interrupted
    Interrupted,
    /// Session failed
    Failed,
}

/// Tracking state
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TrackingState {
    /// Tracking not available
    NotAvailable,
    /// Tracking limited
    Limited,
    /// Tracking normal
    Normal,
    /// Tracking lost
    Lost,
}

/// AR world map for session persistence
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARWorldMap {
    /// Map data
    pub data: Vec<u8>,
    /// Creation timestamp
    pub timestamp: u64,
    /// Map quality score
    pub quality_score: f32,
    /// Associated metadata
    pub metadata: HashMap<String, String>,
}

/// Scene analyzer for 3D understanding
struct SceneAnalyzer {
    semantic_segmentation: SemanticSegmentationEngine,
    depth_estimation: DepthEstimationEngine,
    surface_reconstruction: SurfaceReconstructionEngine,
    spatial_mapping: SpatialMappingEngine,
}

/// AR object detector
struct ARObjectDetector {
    detection_model: Box<dyn DetectionModel>,
    tracking_engine: ObjectTrackingEngine,
    detection_history: Vec<Detection>,
    confidence_threshold: f32,
}

/// Detection model trait
trait DetectionModel {
    fn detect(&self, frame: &ARFrame) -> Result<Vec<Detection>>;
    fn get_supported_classes(&self) -> Vec<String>;
    fn set_confidence_threshold(&mut self, threshold: f32);
}

/// Object detection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Detection {
    /// Object class
    pub class_name: String,
    /// Detection confidence
    pub confidence: f32,
    /// 2D bounding box
    pub bbox_2d: BoundingBox2D,
    /// 3D bounding box (if available)
    pub bbox_3d: Option<BoundingBox3D>,
    /// Object position in world space
    pub world_position: Option<Vec3>,
    /// Tracking ID
    pub tracking_id: Option<u32>,
    /// Detection timestamp
    pub timestamp: u64,
}

/// 2D bounding box
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BoundingBox2D {
    pub x: f32,
    pub y: f32,
    pub width: f32,
    pub height: f32,
}

/// 3D bounding box
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BoundingBox3D {
    pub center: Vec3,
    pub size: Vec3,
    pub rotation: Quaternion,
}

/// 3D vector
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct Vec3 {
    pub x: f32,
    pub y: f32,
    pub z: f32,
}

/// Quaternion for rotation
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct Quaternion {
    pub x: f32,
    pub y: f32,
    pub z: f32,
    pub w: f32,
}

/// Pose estimator
struct PoseEstimator {
    pose_model: Box<dyn PoseModel>,
    tracking_enabled: bool,
    smoothing_filter: KalmanFilter,
}

/// Pose model trait
trait PoseModel {
    fn estimate_pose(&self, frame: &ARFrame) -> Result<Pose>;
    fn estimate_hand_pose(&self, frame: &ARFrame) -> Result<Vec<HandPose>>;
    fn estimate_face_pose(&self, frame: &ARFrame) -> Result<FacePose>;
}

/// Human pose representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Pose {
    /// Joint positions
    pub joints: Vec<Joint>,
    /// Pose confidence
    pub confidence: f32,
    /// 3D pose data
    pub pose_3d: Option<Vec<Vec3>>,
    /// Person ID for tracking
    pub person_id: Option<u32>,
}

/// Joint information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Joint {
    /// Joint type
    pub joint_type: JointType,
    /// 2D position
    pub position_2d: Vec2,
    /// 3D position (if available)
    pub position_3d: Option<Vec3>,
    /// Confidence score
    pub confidence: f32,
}

/// 2D vector
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct Vec2 {
    pub x: f32,
    pub y: f32,
}

/// Joint types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum JointType {
    Head,
    Neck,
    Nose,
    LeftEye,
    RightEye,
    LeftEar,
    RightEar,
    LeftShoulder,
    RightShoulder,
    LeftElbow,
    RightElbow,
    LeftWrist,
    RightWrist,
    LeftHip,
    RightHip,
    LeftKnee,
    RightKnee,
    LeftAnkle,
    RightAnkle,
}

/// Hand pose representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HandPose {
    /// Hand landmarks
    pub landmarks: Vec<Vec3>,
    /// Hand chirality (left/right)
    pub chirality: HandChirality,
    /// Pose confidence
    pub confidence: f32,
}

/// Hand chirality
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum HandChirality {
    Left,
    Right,
    Unknown,
}

/// Face pose representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FacePose {
    /// Face landmarks
    pub landmarks: Vec<Vec3>,
    /// Face orientation
    pub orientation: Vec3,
    /// Expression coefficients
    pub expression: Option<Vec<f32>>,
    /// Confidence score
    pub confidence: f32,
}

/// Occlusion manager for realistic AR rendering
struct OcclusionManager {
    depth_buffer: Option<DepthBuffer>,
    occlusion_enabled: bool,
    people_occlusion: bool,
}

/// Depth buffer representation
#[derive(Debug, Clone)]
pub struct DepthBuffer {
    pub width: u32,
    pub height: u32,
    pub data: Vec<f32>,
    pub format: DepthFormat,
}

/// Depth buffer formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DepthFormat {
    Float32,
    Uint16,
    Uint32,
}

/// AR rendering engine
struct ARRenderingEngine {
    backend: RenderingBackend,
    render_targets: Vec<RenderTarget>,
    shader_manager: ShaderManager,
    texture_manager: TextureManager,
}

/// Render target
#[derive(Debug, Clone)]
pub struct RenderTarget {
    pub width: u32,
    pub height: u32,
    pub format: TextureFormat,
    pub samples: u32,
}

/// Texture formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TextureFormat {
    RGBA8,
    RGBA16F,
    RGBA32F,
    RGB10A2,
    Depth32F,
    Depth24Stencil8,
}

/// Plane detection engine
struct PlaneDetectionEngine {
    detected_planes: Vec<DetectedPlane>,
    classification_enabled: bool,
    merging_enabled: bool,
}

/// Detected plane information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedPlane {
    /// Plane identifier
    pub id: u32,
    /// Plane center position
    pub center: Vec3,
    /// Plane extent (width, height)
    pub extent: Vec2,
    /// Plane normal vector
    pub normal: Vec3,
    /// Plane orientation
    pub orientation: PlaneOrientation,
    /// Plane classification
    pub classification: PlaneClassification,
    /// Confidence score
    pub confidence: f32,
    /// Last update timestamp
    pub timestamp: u64,
}

/// Plane orientation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PlaneOrientation {
    Horizontal,
    Vertical,
    Unknown,
}

/// Plane classification
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PlaneClassification {
    None,
    Wall,
    Floor,
    Ceiling,
    Table,
    Seat,
    Window,
    Door,
}

/// Light estimation engine
struct LightEstimationEngine {
    ambient_intensity: f32,
    color_temperature: f32,
    directional_light: Option<DirectionalLight>,
    spherical_harmonics: Option<SphericalHarmonics>,
}

/// Directional light information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DirectionalLight {
    /// Light direction
    pub direction: Vec3,
    /// Light intensity
    pub intensity: f32,
    /// Light color
    pub color: Vec3,
}

/// Spherical harmonics for environment lighting
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SphericalHarmonics {
    /// SH coefficients
    pub coefficients: Vec<Vec3>,
}

/// World tracking engine
struct WorldTrackingEngine {
    camera_transform: Matrix4x4,
    world_origin: Vec3,
    tracking_quality: TrackingQuality,
}

/// 4x4 transformation matrix
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct Matrix4x4 {
    pub m: [[f32; 4]; 4],
}

/// Tracking quality
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TrackingQuality {
    Poor,
    Fair,
    Good,
    Excellent,
}

/// AR frame data
#[derive(Debug, Clone)]
pub struct ARFrame {
    /// Camera image
    pub camera_image: CameraImage,
    /// Depth data (if available)
    pub depth_data: Option<DepthBuffer>,
    /// Camera transform
    pub camera_transform: Matrix4x4,
    /// Timestamp
    pub timestamp: u64,
    /// Light estimate
    pub light_estimate: Option<LightEstimate>,
}

/// Camera image data
#[derive(Debug, Clone)]
pub struct CameraImage {
    pub width: u32,
    pub height: u32,
    pub format: ImageFormat,
    pub data: Vec<u8>,
}

/// Image formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ImageFormat {
    RGB8,
    RGBA8,
    YUV420,
    BGRA8,
}

/// Light estimation data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LightEstimate {
    pub ambient_intensity: f32,
    pub ambient_color_temperature: f32,
    pub directional_light: Option<DirectionalLight>,
}

// Placeholder implementations for compilation
struct SemanticSegmentationEngine;
struct DepthEstimationEngine;
struct SurfaceReconstructionEngine;
struct SpatialMappingEngine;
struct ObjectTrackingEngine;
struct KalmanFilter;
struct ShaderManager;
struct TextureManager;

// Default implementations

impl Default for ARKitConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            session_config: ARSessionConfig::default(),
            object_detection: ObjectDetectionConfig::default(),
            pose_estimation: PoseEstimationConfig::default(),
            plane_detection: PlaneDetectionConfig::default(),
            light_estimation: LightEstimationConfig::default(),
            rendering: ARRenderingConfig::default(),
            performance: ARPerformanceConfig::default(),
        }
    }
}

impl Default for ARSessionConfig {
    fn default() -> Self {
        Self {
            world_tracking: true,
            face_tracking: false,
            image_tracking: false,
            object_tracking: false,
            body_tracking: false,
            geo_tracking: false,
            collaborative_session: false,
            auto_focus: true,
            audio_enabled: false,
        }
    }
}

impl Default for ObjectDetectionConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            model_type: ObjectDetectionModel::CoreMLVision,
            confidence_threshold: 0.5,
            max_detections: 10,
            target_classes: vec![
                "person".to_string(),
                "car".to_string(),
                "chair".to_string(),
                "table".to_string(),
            ],
            enable_3d_boxes: true,
            enable_tracking: true,
            tracking_timeout: 3.0,
        }
    }
}

impl Default for PoseEstimationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            model_type: PoseEstimationModel::ARKitBody,
            joint_confidence_threshold: 0.3,
            enable_3d_pose: true,
            enable_hand_pose: true,
            enable_face_pose: false,
            smoothing_factor: 0.8,
        }
    }
}

impl Default for PlaneDetectionConfig {
    fn default() -> Self {
        Self {
            horizontal_planes: true,
            vertical_planes: true,
            minimum_plane_size: 0.1,
            classification_enabled: true,
            plane_merging: true,
        }
    }
}

impl Default for LightEstimationConfig {
    fn default() -> Self {
        Self {
            ambient_intensity: true,
            directional_light: true,
            spherical_harmonics: false,
            estimation_mode: LightEstimationMode::DirectionalLighting,
        }
    }
}

impl Default for ARRenderingConfig {
    fn default() -> Self {
        Self {
            backend: RenderingBackend::RealityKit,
            occlusion_enabled: true,
            shadows_enabled: true,
            reflections_enabled: false,
            resolution_scale: 1.0,
            target_fps: 60,
            hdr_enabled: false,
        }
    }
}

impl Default for ARPerformanceConfig {
    fn default() -> Self {
        Self {
            adaptive_quality: true,
            performance_monitoring: true,
            thermal_throttling: true,
            battery_optimization: true,
            frame_rate_adaptation: true,
            quality_thresholds: QualityThresholds {
                cpu_threshold: 80.0,
                gpu_threshold: 85.0,
                temperature_threshold: 42.0,
                frame_time_threshold: 20.0,
            },
        }
    }
}

// Main implementation

impl ARKitInferenceEngine {
    /// Create new ARKit inference engine
    pub fn new(config: ARKitConfig) -> Result<Self> {
        let device_info = crate::device_info::MobileDeviceDetector::detect()?;

        // Verify ARKit availability
        if !Self::is_arkit_available(&device_info) {
            return Err(TrustformersError::UnsupportedOperation(
                "ARKit not available on this device".into(),
            )
            .into());
        }

        let profiler_config = MobileProfilerConfig::default();
        let performance_monitor =
            Arc::new(Mutex::new(MobilePerformanceProfiler::new(profiler_config)?));

        let session_manager = ARSessionManager::new(config.session_config.clone())?;
        let scene_analyzer = SceneAnalyzer::new()?;
        let object_detector = ARObjectDetector::new(config.object_detection.clone())?;
        let pose_estimator = PoseEstimator::new(config.pose_estimation.clone())?;
        let occlusion_manager = OcclusionManager::new(config.rendering.occlusion_enabled)?;
        let rendering_engine = ARRenderingEngine::new(config.rendering.clone())?;
        let plane_detection = PlaneDetectionEngine::new(config.plane_detection.clone())?;
        let light_estimation = LightEstimationEngine::new(config.light_estimation.clone())?;
        let world_tracking = WorldTrackingEngine::new()?;

        Ok(Self {
            config,
            session_manager,
            scene_analyzer,
            object_detector,
            pose_estimator,
            occlusion_manager,
            rendering_engine,
            performance_monitor,
            plane_detection,
            light_estimation,
            world_tracking,
        })
    }

    /// Check if ARKit is available on the device
    fn is_arkit_available(device_info: &MobileDeviceInfo) -> bool {
        // ARKit requires iOS 11+ and A9 processor or newer
        device_info.platform.contains("iOS")
            && device_info.performance_tier != crate::device_info::PerformanceTier::Low
    }

    /// Start AR session
    pub fn start_session(&mut self) -> Result<()> {
        tracing::info!("Starting ARKit session");

        self.session_manager.start()?;
        self.performance_monitor
            .lock()
            .expect("performance_monitor lock should not be poisoned")
            .start_session()?;

        tracing::info!("ARKit session started successfully");
        Ok(())
    }

    /// Stop AR session
    pub fn stop_session(&mut self) -> Result<()> {
        tracing::info!("Stopping ARKit session");

        self.session_manager.stop()?;
        self.performance_monitor
            .lock()
            .expect("performance_monitor lock should not be poisoned")
            .stop_session()?;

        tracing::info!("ARKit session stopped");
        Ok(())
    }

    /// Process AR frame
    pub fn process_frame(&mut self, frame: ARFrame) -> Result<ARProcessingResult> {
        let start_time = Instant::now();

        // Update world tracking
        self.world_tracking.update_camera_transform(frame.camera_transform);

        // Detect planes
        let planes = if self.config.plane_detection.horizontal_planes
            || self.config.plane_detection.vertical_planes
        {
            self.plane_detection.detect_planes(&frame)?
        } else {
            Vec::new()
        };

        // Perform object detection
        let detections = if self.config.object_detection.enabled {
            self.object_detector.detect_objects(&frame)?
        } else {
            Vec::new()
        };

        // Estimate poses
        let poses = if self.config.pose_estimation.enabled {
            self.pose_estimator.estimate_poses(&frame)?
        } else {
            Vec::new()
        };

        // Update light estimation
        let light_estimate = if self.config.light_estimation.ambient_intensity {
            Some(self.light_estimation.estimate_lighting(&frame)?)
        } else {
            None
        };

        // Analyze scene
        let scene_analysis = self.scene_analyzer.analyze_scene(&frame)?;

        let processing_time = start_time.elapsed();

        Ok(ARProcessingResult {
            frame_timestamp: frame.timestamp,
            detections,
            poses,
            planes,
            light_estimate,
            scene_analysis,
            camera_transform: frame.camera_transform,
            processing_time_ms: processing_time.as_millis() as f32,
            tracking_state: self.session_manager.get_tracking_state(),
        })
    }

    /// Render AR scene
    pub fn render_scene(&mut self, result: &ARProcessingResult) -> Result<()> {
        self.rendering_engine.render_frame(result, &self.config.rendering)
    }

    /// Save AR world map for session persistence
    pub fn save_world_map(&self) -> Result<ARWorldMap> {
        self.session_manager.save_world_map()
    }

    /// Load AR world map for session restoration
    pub fn load_world_map(&mut self, world_map: ARWorldMap) -> Result<()> {
        self.session_manager.load_world_map(world_map)
    }

    /// Get current session statistics
    pub fn get_session_stats(&self) -> Result<ARSessionStats> {
        Ok(ARSessionStats {
            session_duration: self.session_manager.get_session_duration(),
            frames_processed: self.session_manager.get_frames_processed(),
            tracking_quality: self.world_tracking.get_tracking_quality(),
            detected_planes_count: self.plane_detection.get_plane_count(),
            active_detections_count: self.object_detector.get_active_detections_count(),
            average_processing_time_ms: self.get_average_processing_time(),
        })
    }

    fn get_average_processing_time(&self) -> f32 {
        // Calculate average processing time from performance monitor
        15.5 // Placeholder
    }
}

/// AR processing result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARProcessingResult {
    /// Frame timestamp
    pub frame_timestamp: u64,
    /// Detected objects
    pub detections: Vec<Detection>,
    /// Estimated poses
    pub poses: Vec<Pose>,
    /// Detected planes
    pub planes: Vec<DetectedPlane>,
    /// Light estimation
    pub light_estimate: Option<LightEstimate>,
    /// Scene analysis results
    pub scene_analysis: SceneAnalysis,
    /// Camera transformation matrix
    pub camera_transform: Matrix4x4,
    /// Processing time in milliseconds
    pub processing_time_ms: f32,
    /// Current tracking state
    pub tracking_state: TrackingState,
}

/// Scene analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SceneAnalysis {
    /// Semantic segmentation mask
    pub segmentation_mask: Option<Vec<u8>>,
    /// Depth map
    pub depth_map: Option<DepthBuffer>,
    /// Scene complexity score
    pub complexity_score: f32,
    /// Detected surfaces
    pub surfaces: Vec<Surface>,
}

/// Surface information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Surface {
    /// Surface vertices
    pub vertices: Vec<Vec3>,
    /// Surface normal
    pub normal: Vec3,
    /// Surface material classification
    pub material: SurfaceMaterial,
}

/// Surface materials
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum SurfaceMaterial {
    Unknown,
    Wood,
    Metal,
    Plastic,
    Glass,
    Fabric,
    Concrete,
    Paper,
}

/// AR session statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ARSessionStats {
    /// Total session duration
    pub session_duration: Duration,
    /// Total frames processed
    pub frames_processed: u64,
    /// Current tracking quality
    pub tracking_quality: TrackingQuality,
    /// Number of detected planes
    pub detected_planes_count: usize,
    /// Number of active object detections
    pub active_detections_count: usize,
    /// Average frame processing time
    pub average_processing_time_ms: f32,
}

// Implementation stubs for helper structs

impl ARSessionManager {
    fn new(_config: ARSessionConfig) -> Result<Self> {
        Ok(Self {
            session_state: ARSessionState::NotStarted,
            tracking_state: TrackingState::NotAvailable,
            world_map: None,
            relocalization_enabled: false,
        })
    }

    fn start(&mut self) -> Result<()> {
        self.session_state = ARSessionState::Running;
        self.tracking_state = TrackingState::Normal;
        Ok(())
    }

    fn stop(&mut self) -> Result<()> {
        self.session_state = ARSessionState::NotStarted;
        self.tracking_state = TrackingState::NotAvailable;
        Ok(())
    }

    fn get_tracking_state(&self) -> TrackingState {
        self.tracking_state
    }

    fn save_world_map(&self) -> Result<ARWorldMap> {
        Ok(ARWorldMap {
            data: vec![1, 2, 3, 4], // Placeholder
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("SystemTime should be after UNIX_EPOCH")
                .as_secs(),
            quality_score: 0.85,
            metadata: HashMap::new(),
        })
    }

    fn load_world_map(&mut self, world_map: ARWorldMap) -> Result<()> {
        self.world_map = Some(world_map);
        self.relocalization_enabled = true;
        Ok(())
    }

    fn get_session_duration(&self) -> Duration {
        Duration::from_secs(120) // Placeholder
    }

    fn get_frames_processed(&self) -> u64 {
        7200 // Placeholder
    }
}

impl SceneAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            semantic_segmentation: SemanticSegmentationEngine,
            depth_estimation: DepthEstimationEngine,
            surface_reconstruction: SurfaceReconstructionEngine,
            spatial_mapping: SpatialMappingEngine,
        })
    }

    fn analyze_scene(&self, _frame: &ARFrame) -> Result<SceneAnalysis> {
        Ok(SceneAnalysis {
            segmentation_mask: None,
            depth_map: None,
            complexity_score: 0.7,
            surfaces: Vec::new(),
        })
    }
}

impl ARObjectDetector {
    fn new(_config: ObjectDetectionConfig) -> Result<Self> {
        Ok(Self {
            detection_model: Box::new(YOLODetectionModel),
            tracking_engine: ObjectTrackingEngine,
            detection_history: Vec::new(),
            confidence_threshold: 0.5,
        })
    }

    fn detect_objects(&mut self, frame: &ARFrame) -> Result<Vec<Detection>> {
        self.detection_model.detect(frame)
    }

    fn get_active_detections_count(&self) -> usize {
        self.detection_history.len()
    }
}

impl PoseEstimator {
    fn new(_config: PoseEstimationConfig) -> Result<Self> {
        Ok(Self {
            pose_model: Box::new(ARKitPoseModel),
            tracking_enabled: true,
            smoothing_filter: KalmanFilter,
        })
    }

    fn estimate_poses(&self, frame: &ARFrame) -> Result<Vec<Pose>> {
        self.pose_model.estimate_pose(frame).map(|pose| vec![pose])
    }
}

impl OcclusionManager {
    fn new(_enabled: bool) -> Result<Self> {
        Ok(Self {
            depth_buffer: None,
            occlusion_enabled: _enabled,
            people_occlusion: true,
        })
    }
}

impl ARRenderingEngine {
    fn new(_config: ARRenderingConfig) -> Result<Self> {
        Ok(Self {
            backend: RenderingBackend::RealityKit,
            render_targets: Vec::new(),
            shader_manager: ShaderManager,
            texture_manager: TextureManager,
        })
    }

    fn render_frame(
        &self,
        _result: &ARProcessingResult,
        _config: &ARRenderingConfig,
    ) -> Result<()> {
        Ok(())
    }
}

impl PlaneDetectionEngine {
    fn new(_config: PlaneDetectionConfig) -> Result<Self> {
        Ok(Self {
            detected_planes: Vec::new(),
            classification_enabled: true,
            merging_enabled: true,
        })
    }

    fn detect_planes(&mut self, _frame: &ARFrame) -> Result<Vec<DetectedPlane>> {
        Ok(Vec::new())
    }

    fn get_plane_count(&self) -> usize {
        self.detected_planes.len()
    }
}

impl LightEstimationEngine {
    fn new(_config: LightEstimationConfig) -> Result<Self> {
        Ok(Self {
            ambient_intensity: 1000.0,
            color_temperature: 6500.0,
            directional_light: None,
            spherical_harmonics: None,
        })
    }

    fn estimate_lighting(&self, _frame: &ARFrame) -> Result<LightEstimate> {
        Ok(LightEstimate {
            ambient_intensity: self.ambient_intensity,
            ambient_color_temperature: self.color_temperature,
            directional_light: self.directional_light.clone(),
        })
    }
}

impl WorldTrackingEngine {
    fn new() -> Result<Self> {
        Ok(Self {
            camera_transform: Matrix4x4 { m: [[0.0; 4]; 4] },
            world_origin: Vec3 {
                x: 0.0,
                y: 0.0,
                z: 0.0,
            },
            tracking_quality: TrackingQuality::Good,
        })
    }

    fn update_camera_transform(&mut self, transform: Matrix4x4) {
        self.camera_transform = transform;
    }

    fn get_tracking_quality(&self) -> TrackingQuality {
        self.tracking_quality
    }
}

// Stub implementations for detection models
struct YOLODetectionModel;
struct ARKitPoseModel;

impl DetectionModel for YOLODetectionModel {
    fn detect(&self, _frame: &ARFrame) -> Result<Vec<Detection>> {
        Ok(Vec::new())
    }

    fn get_supported_classes(&self) -> Vec<String> {
        vec!["person".to_string(), "car".to_string()]
    }

    fn set_confidence_threshold(&mut self, _threshold: f32) {}
}

impl PoseModel for ARKitPoseModel {
    fn estimate_pose(&self, _frame: &ARFrame) -> Result<Pose> {
        Ok(Pose {
            joints: Vec::new(),
            confidence: 0.8,
            pose_3d: None,
            person_id: Some(1),
        })
    }

    fn estimate_hand_pose(&self, _frame: &ARFrame) -> Result<Vec<HandPose>> {
        Ok(Vec::new())
    }

    fn estimate_face_pose(&self, _frame: &ARFrame) -> Result<FacePose> {
        Ok(FacePose {
            landmarks: Vec::new(),
            orientation: Vec3 {
                x: 0.0,
                y: 0.0,
                z: 0.0,
            },
            expression: None,
            confidence: 0.8,
        })
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_arkit_config_creation() {
        let config = ARKitConfig::default();
        assert!(config.enabled);
        assert!(config.session_config.world_tracking);
        assert!(config.object_detection.enabled);
    }

    #[test]
    fn test_detection_bbox() {
        let bbox = BoundingBox2D {
            x: 10.0,
            y: 20.0,
            width: 100.0,
            height: 150.0,
        };
        assert_eq!(bbox.x, 10.0);
        assert_eq!(bbox.width, 100.0);
    }

    #[test]
    fn test_vec3_operations() {
        let v1 = Vec3 {
            x: 1.0,
            y: 2.0,
            z: 3.0,
        };
        let v2 = Vec3 {
            x: 4.0,
            y: 5.0,
            z: 6.0,
        };

        // These would be actual vector operations in a real implementation
        assert_eq!(v1.x + v2.x, 5.0);
        assert_eq!(v1.y + v2.y, 7.0);
        assert_eq!(v1.z + v2.z, 9.0);
    }

    #[test]
    fn test_arkit_session_config_default() {
        let config = ARSessionConfig::default();
        assert!(config.world_tracking);
        assert!(!config.face_tracking);
        assert!(!config.body_tracking);
        assert!(config.auto_focus);
    }

    #[test]
    fn test_object_detection_config_default() {
        let config = ObjectDetectionConfig::default();
        assert!(config.enabled);
        assert!(config.confidence_threshold > 0.0);
        assert!(config.max_detections > 0);
    }

    #[test]
    fn test_pose_estimation_config_default() {
        let config = PoseEstimationConfig::default();
        assert!(config.confidence_threshold > 0.0);
    }

    #[test]
    fn test_plane_detection_config_default() {
        let config = PlaneDetectionConfig::default();
        assert!(config.enabled);
    }

    #[test]
    fn test_light_estimation_config_default() {
        let config = LightEstimationConfig::default();
        assert!(config.enabled);
    }

    #[test]
    fn test_ar_rendering_config_default() {
        let config = ARRenderingConfig::default();
        assert!(config.enable_occlusion);
    }

    #[test]
    fn test_ar_performance_config_default() {
        let config = ARPerformanceConfig::default();
        assert!(config.target_fps > 0);
    }

    #[test]
    fn test_bounding_box_3d_creation() {
        let bbox = BoundingBox3D {
            center: Vec3 {
                x: 0.0,
                y: 0.0,
                z: 0.0,
            },
            extent: Vec3 {
                x: 1.0,
                y: 1.0,
                z: 1.0,
            },
        };
        assert_eq!(bbox.extent.x, 1.0);
        assert_eq!(bbox.center.x, 0.0);
    }

    #[test]
    fn test_quaternion_identity() {
        let q = Quaternion {
            x: 0.0,
            y: 0.0,
            z: 0.0,
            w: 1.0,
        };
        assert_eq!(q.w, 1.0);
        let norm_sq = q.x * q.x + q.y * q.y + q.z * q.z + q.w * q.w;
        assert!((norm_sq - 1.0).abs() < 1e-6);
    }

    #[test]
    fn test_pose_creation() {
        let pose = Pose {
            position: Vec3 {
                x: 1.0,
                y: 2.0,
                z: 3.0,
            },
            orientation: Quaternion {
                x: 0.0,
                y: 0.0,
                z: 0.0,
                w: 1.0,
            },
            confidence: 0.95,
        };
        assert_eq!(pose.confidence, 0.95);
        assert_eq!(pose.position.x, 1.0);
    }

    #[test]
    fn test_joint_creation() {
        let joint = Joint {
            name: "left_wrist".to_string(),
            position: Vec3 {
                x: 0.5,
                y: 0.3,
                z: 0.1,
            },
            confidence: 0.8,
            parent: Some("left_elbow".to_string()),
        };
        assert_eq!(joint.name, "left_wrist");
        assert!(joint.parent.is_some());
    }

    #[test]
    fn test_vec2_creation() {
        let v = Vec2 { x: 3.0, y: 4.0 };
        let magnitude = (v.x * v.x + v.y * v.y).sqrt();
        assert!((magnitude - 5.0).abs() < 1e-6);
    }

    #[test]
    fn test_detection_creation() {
        let detection = Detection {
            class_name: "person".to_string(),
            class_id: 0,
            confidence: 0.92,
            bounding_box_2d: BoundingBox2D {
                x: 10.0,
                y: 20.0,
                width: 50.0,
                height: 100.0,
            },
            bounding_box_3d: None,
            tracking_id: Some("track_1".to_string()),
            timestamp: 0.0,
        };
        assert_eq!(detection.class_name, "person");
        assert!(detection.bounding_box_3d.is_none());
        assert!(detection.tracking_id.is_some());
    }

    #[test]
    fn test_depth_buffer_creation() {
        let buffer = DepthBuffer {
            data: vec![1.0, 2.0, 3.0, 4.0],
            width: 2,
            height: 2,
            min_depth: 0.5,
            max_depth: 5.0,
            confidence: vec![0.9, 0.8, 0.7, 0.6],
        };
        assert_eq!(buffer.data.len(), buffer.width * buffer.height);
        assert_eq!(buffer.confidence.len(), buffer.data.len());
        assert!(buffer.min_depth < buffer.max_depth);
    }

    #[test]
    fn test_ar_world_map_creation() {
        let world_map = ARWorldMap {
            anchors: Vec::new(),
            feature_points: Vec::new(),
            planes: Vec::new(),
            center: Vec3 {
                x: 0.0,
                y: 0.0,
                z: 0.0,
            },
            extent: Vec3 {
                x: 10.0,
                y: 10.0,
                z: 10.0,
            },
            raw_feature_points_count: 0,
        };
        assert!(world_map.anchors.is_empty());
        assert_eq!(world_map.raw_feature_points_count, 0);
    }

    #[test]
    fn test_matrix4x4_identity() {
        let m = Matrix4x4 {
            data: [
                1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0,
            ],
        };
        assert_eq!(m.data[0], 1.0);
        assert_eq!(m.data[5], 1.0);
        assert_eq!(m.data[10], 1.0);
        assert_eq!(m.data[15], 1.0);
    }

    #[test]
    fn test_directional_light_creation() {
        let light = DirectionalLight {
            intensity: 1000.0,
            color_temperature: 6500.0,
            direction: Vec3 {
                x: 0.0,
                y: -1.0,
                z: 0.0,
            },
        };
        assert_eq!(light.intensity, 1000.0);
        assert_eq!(light.color_temperature, 6500.0);
    }

    #[test]
    fn test_arkit_engine_creation() {
        let config = ARKitConfig::default();
        let result = ARKitInferenceEngine::new(config);
        assert!(result.is_ok());
    }

    #[test]
    fn test_quality_thresholds_default() {
        let config = ARPerformanceConfig::default();
        assert!(config.quality_thresholds.min_detection_confidence > 0.0);
    }

    #[test]
    fn test_bounding_box_area() {
        let bbox = BoundingBox2D {
            x: 0.0,
            y: 0.0,
            width: 100.0,
            height: 200.0,
        };
        let area = bbox.width * bbox.height;
        assert_eq!(area, 20000.0);
    }

    #[test]
    fn test_vec3_dot_product() {
        let v1 = Vec3 {
            x: 1.0,
            y: 0.0,
            z: 0.0,
        };
        let v2 = Vec3 {
            x: 0.0,
            y: 1.0,
            z: 0.0,
        };
        let dot = v1.x * v2.x + v1.y * v2.y + v1.z * v2.z;
        assert_eq!(dot, 0.0); // Orthogonal vectors
    }

    #[test]
    fn test_vec3_magnitude() {
        let v = Vec3 {
            x: 3.0,
            y: 4.0,
            z: 0.0,
        };
        let mag = (v.x * v.x + v.y * v.y + v.z * v.z).sqrt();
        assert!((mag - 5.0).abs() < 1e-6);
    }
}