//! Advanced Scene Understanding Framework
//!
//! This module provides advanced scene understanding capabilities including:
//! - Semantic scene segmentation and classification
//! - Object relationship reasoning
//! - Spatial layout understanding
//! - Temporal scene analysis
//! - Multi-modal scene representation

#![allow(dead_code)]

use crate::error::Result;
use scirs2_core::ndarray::{Array2, Array3, ArrayView3};
use std::collections::HashMap;

/// Advanced-advanced scene understanding engine with multi-level reasoning
pub struct SceneUnderstandingEngine {
    /// Semantic segmentation models
    segmentation_models: Vec<SemanticSegmentationModel>,
    /// Object detection and classification
    object_detector: ObjectDetector,
    /// Spatial relationship analyzer
    spatial_analyzer: SpatialRelationshipAnalyzer,
    /// Temporal scene tracker
    temporal_tracker: TemporalSceneTracker,
    /// Scene graph builder
    scene_graph_builder: SceneGraphBuilder,
    /// Context-aware reasoning engine
    reasoning_engine: ContextualReasoningEngine,
}

/// Semantic segmentation model with advanced-high accuracy
#[derive(Debug, Clone)]
pub struct SemanticSegmentationModel {
    /// Model type identifier
    model_type: String,
    /// Class labels
    class_labels: Vec<String>,
    /// Model confidence threshold
    confidence_threshold: f32,
    /// Multi-scale analysis parameters
    scale_factors: Vec<f32>,
}

/// Advanced object detector with relationship understanding
#[derive(Debug, Clone)]
pub struct ObjectDetector {
    /// Detection confidence threshold
    confidence_threshold: f32,
    /// Non-maximum suppression threshold
    nms_threshold: f32,
    /// Supported object classes
    object_classes: Vec<String>,
    /// Feature extraction layers
    feature_layers: Vec<String>,
}

/// Spatial relationship analyzer for scene understanding
#[derive(Debug, Clone)]
pub struct SpatialRelationshipAnalyzer {
    /// Relationship types
    relationship_types: Vec<SpatialRelationType>,
    /// Distance thresholds for relationships
    distance_thresholds: HashMap<String, f32>,
    /// Directional analysis parameters
    directional_params: DirectionalParams,
}

/// Temporal scene tracking for video understanding
#[derive(Debug, Clone)]
pub struct TemporalSceneTracker {
    /// Frame buffer size
    buffer_size: usize,
    /// Motion detection threshold
    motion_threshold: f32,
    /// Object tracking parameters
    tracking_params: TrackingParams,
    /// Scene change detection
    change_detection: ChangeDetectionParams,
}

/// Scene graph construction for relationship modeling
#[derive(Debug, Clone)]
pub struct SceneGraphBuilder {
    /// Maximum number of nodes
    max_nodes: usize,
    /// Edge confidence threshold
    edge_threshold: f32,
    /// Graph simplification parameters
    simplification_params: GraphSimplificationParams,
}

/// Contextual reasoning engine for high-level understanding
#[derive(Debug, Clone)]
pub struct ContextualReasoningEngine {
    /// Reasoning rules
    rules: Vec<ReasoningRule>,
    /// Context windows
    context_windows: Vec<ContextWindow>,
    /// Inference parameters
    inference_params: InferenceParams,
}

/// Detected object with rich metadata
#[derive(Debug, Clone)]
pub struct DetectedObject {
    /// Object class
    pub class: String,
    /// Bounding box (x, y, width, height)
    pub bbox: (f32, f32, f32, f32),
    /// Detection confidence
    pub confidence: f32,
    /// Object features
    pub features: Array2<f32>,
    /// Object mask (if available)
    pub mask: Option<Array2<bool>>,
    /// Object attributes
    pub attributes: HashMap<String, f32>,
}

/// Spatial relationship between objects
#[derive(Debug, Clone)]
pub struct SpatialRelation {
    /// Source object ID
    pub source_id: usize,
    /// Target object ID
    pub target_id: usize,
    /// Relationship type
    pub relation_type: SpatialRelationType,
    /// Relationship confidence
    pub confidence: f32,
    /// Spatial parameters
    pub parameters: HashMap<String, f32>,
}

/// Scene understanding result with comprehensive analysis
#[derive(Debug, Clone)]
pub struct SceneAnalysisResult {
    /// Detected objects
    pub objects: Vec<DetectedObject>,
    /// Spatial relationships
    pub relationships: Vec<SpatialRelation>,
    /// Scene classification
    pub scene_class: String,
    /// Scene confidence
    pub scene_confidence: f32,
    /// Semantic segmentation map
    pub segmentation_map: Array2<u32>,
    /// Scene graph representation
    pub scene_graph: SceneGraph,
    /// Temporal information (if applicable)
    pub temporal_info: Option<TemporalInfo>,
    /// Reasoning results
    pub reasoning_results: Vec<ReasoningResult>,
}

/// Supporting types for scene understanding
#[derive(Debug, Clone)]
pub enum SpatialRelationType {
    /// Object A is on top of object B
    OnTop,
    /// Object A is inside object B
    Inside,
    /// Object A is next to object B
    NextTo,
    /// Object A is in front of object B
    InFrontOf,
    /// Object A is behind object B
    Behind,
    /// Object A is above object B
    Above,
    /// Object A is below object B
    Below,
    /// Object A is to the left of object B
    LeftOf,
    /// Object A is to the right of object B
    RightOf,
    /// Object A contains object B
    Contains,
    /// Object A supports object B
    Supports,
    /// Object A is connected to object B
    ConnectedTo,
    /// Custom relationship
    Custom(String),
}

/// Parameters for directional spatial relationship analysis
#[derive(Debug, Clone)]
pub struct DirectionalParams {
    /// Angular tolerance for directional relationships (in radians)
    pub angular_tolerance: f32,
    /// Whether to normalize distances for scale invariance
    pub distance_normalization: bool,
    /// Whether to apply perspective correction
    pub perspective_correction: bool,
}

/// Parameters for temporal object tracking
#[derive(Debug, Clone)]
pub struct TrackingParams {
    /// Maximum frames an object can disappear before being lost
    pub max_disappearance_frames: usize,
    /// Tracking algorithm identifier
    pub tracking_algorithm: String,
    /// Threshold for feature matching in tracking
    pub feature_matching_threshold: f32,
}

/// Parameters for scene change detection
#[derive(Debug, Clone)]
pub struct ChangeDetectionParams {
    /// Sensitivity level for change detection (0.0-1.0)
    pub sensitivity: f32,
    /// Size of temporal window for change analysis
    pub temporal_window: usize,
    /// Background model type identifier
    pub background_model: String,
}

/// Parameters for scene graph simplification
#[derive(Debug, Clone)]
pub struct GraphSimplificationParams {
    /// Minimum edge weight to retain in graph
    pub min_edge_weight: f32,
    /// Whether to remove redundant edges
    pub redundancy_removal: bool,
    /// Whether to apply hierarchical clustering
    pub hierarchical_clustering: bool,
}

/// Rule for contextual reasoning about scenes
#[derive(Debug, Clone)]
pub struct ReasoningRule {
    /// Name identifier for the reasoning rule
    pub name: String,
    /// Conditions that must be met for rule to apply
    pub conditions: Vec<String>,
    /// Conclusions drawn when conditions are met
    pub conclusions: Vec<String>,
    /// Confidence level of the rule (0.0-1.0)
    pub confidence: f32,
}

/// Context window for reasoning about scene elements
#[derive(Debug, Clone)]
pub struct ContextWindow {
    /// Number of frames to consider for temporal context
    pub temporal_span: usize,
    /// Spatial extent (width, height) for context
    pub spatial_extent: (f32, f32),
    /// Threshold for relevance filtering
    pub relevance_threshold: f32,
}

/// Parameters for reasoning inference process
#[derive(Debug, Clone)]
pub struct InferenceParams {
    /// Maximum iterations for inference convergence
    pub max_iterations: usize,
    /// Threshold for determining convergence
    pub convergence_threshold: f32,
    /// Method for handling uncertainty in inference
    pub uncertainty_handling: String,
}

/// Graph representation of scene structure and relationships
#[derive(Debug, Clone)]
pub struct SceneGraph {
    /// Nodes representing objects and regions in the scene
    pub nodes: Vec<SceneGraphNode>,
    /// Edges representing relationships between objects
    pub edges: Vec<SceneGraphEdge>,
    /// Global scene properties and metadata
    pub global_properties: HashMap<String, f32>,
}

/// Node in scene graph representing an object or region
#[derive(Debug, Clone)]
pub struct SceneGraphNode {
    /// Unique identifier for the node
    pub id: usize,
    /// Type or class of the object
    pub object_type: String,
    /// Properties and attributes of the object
    pub properties: HashMap<String, f32>,
    /// Spatial location (x, y) in the scene
    pub spatial_location: (f32, f32),
}

/// Edge in scene graph representing a relationship
#[derive(Debug, Clone)]
pub struct SceneGraphEdge {
    /// Source node ID
    pub source: usize,
    /// Target node ID
    pub target: usize,
    /// Type of relationship
    pub relation_type: String,
    /// Strength or confidence of the relationship
    pub weight: f32,
    /// Additional properties of the relationship
    pub properties: HashMap<String, f32>,
}

/// Temporal information for video scene understanding
#[derive(Debug, Clone)]
pub struct TemporalInfo {
    /// Index of the current frame
    pub frame_index: usize,
    /// Timestamp of the frame
    pub timestamp: f64,
    /// Motion vectors for temporal analysis
    pub motion_vectors: Array3<f32>,
    /// Detected changes in the scene
    pub scene_changes: Vec<SceneChange>,
}

/// Information about a detected change in the scene
#[derive(Debug, Clone)]
pub struct SceneChange {
    /// Type of change that occurred
    pub change_type: String,
    /// Location (x, y) where change occurred
    pub location: (f32, f32),
    /// Magnitude of the change
    pub magnitude: f32,
    /// Confidence in the change detection
    pub confidence: f32,
}

/// Result of contextual reasoning about the scene
#[derive(Debug, Clone)]
pub struct ReasoningResult {
    /// Name of the rule that generated this result
    pub rule_name: String,
    /// Conclusion reached by the reasoning process
    pub conclusion: String,
    /// Confidence in the reasoning result
    pub confidence: f32,
    /// Evidence supporting the conclusion
    pub evidence: Vec<String>,
}

impl Default for SceneUnderstandingEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl SceneUnderstandingEngine {
    /// Create a new advanced scene understanding engine
    pub fn new() -> Self {
        Self {
            segmentation_models: Vec::new(),
            object_detector: ObjectDetector::new(),
            spatial_analyzer: SpatialRelationshipAnalyzer::new(),
            temporal_tracker: TemporalSceneTracker::new(),
            scene_graph_builder: SceneGraphBuilder::new(),
            reasoning_engine: ContextualReasoningEngine::new(),
        }
    }

    /// Analyze a single image with comprehensive scene understanding
    pub fn analyze_scene(&self, image: &ArrayView3<f32>) -> Result<SceneAnalysisResult> {
        // Multi-scale semantic segmentation
        let segmentation_map = self.perform_semantic_segmentation(image)?;

        // Object detection and feature extraction
        let objects = self.detect_objects(image)?;

        // Spatial relationship analysis
        let relationships = self.analyze_spatial_relationships(&objects)?;

        // Scene classification
        let (scene_class, scene_confidence) = self.classify_scene(image, &objects)?;

        // Scene graph construction
        let scene_graph = self.build_scene_graph(&objects, &relationships)?;

        // Contextual reasoning
        let reasoning_results = self.perform_reasoning(&objects, &relationships, &scene_class)?;

        Ok(SceneAnalysisResult {
            objects,
            relationships,
            scene_class,
            scene_confidence,
            segmentation_map,
            scene_graph,
            temporal_info: None,
            reasoning_results,
        })
    }

    /// Analyze video sequence with temporal understanding
    pub fn analyze_video_sequence(
        &mut self,
        frames: &[ArrayView3<f32>],
    ) -> Result<Vec<SceneAnalysisResult>> {
        let mut results = Vec::new();

        for (frame_idx, frame) in frames.iter().enumerate() {
            // Analyze individual frame
            let mut frame_result = self.analyze_scene(frame)?;

            // Add temporal analysis
            if frame_idx > 0 {
                let temporal_info =
                    self.analyze_temporal_changes(frame, &frames[..frame_idx], frame_idx)?;
                frame_result.temporal_info = Some(temporal_info);
            }

            results.push(frame_result);
        }

        // Post-process for temporal consistency
        self.enforce_temporal_consistency(&mut results)?;

        Ok(results)
    }

    /// Perform advanced-accurate semantic segmentation
    fn perform_semantic_segmentation(&self, image: &ArrayView3<f32>) -> Result<Array2<u32>> {
        let (height, width, _channels) = image.dim();
        let mut segmentation_map = Array2::zeros((height, width));

        // Multi-scale segmentation for enhanced accuracy
        for scale_factor in &[0.5, 1.0, 1.5, 2.0] {
            let scaled_result = self.segment_at_scale(image, *scale_factor)?;
            self.merge_segmentation_results(&mut segmentation_map, &scaled_result)?;
        }

        // Post-processing for spatial consistency
        self.enforce_spatial_consistency(&mut segmentation_map)?;

        Ok(segmentation_map)
    }

    /// Detect objects with rich feature extraction
    fn detect_objects(&self, image: &ArrayView3<f32>) -> Result<Vec<DetectedObject>> {
        let mut objects = Vec::new();

        // Multi-scale object detection
        let detection_results = self.object_detector.detect_multi_scale(image)?;

        for detection in detection_results {
            // Extract rich features for each object
            let features = self.extract_object_features(image, &detection.bbox)?;

            // Compute object mask
            let mask = self.compute_object_mask(image, &detection)?;

            // Analyze object attributes
            let attributes = self.analyze_object_attributes(image, &detection, &features)?;

            objects.push(DetectedObject {
                class: detection.class,
                bbox: detection.bbox,
                confidence: detection.confidence,
                features,
                mask: Some(mask),
                attributes,
            });
        }

        Ok(objects)
    }

    /// Analyze spatial relationships between objects
    fn analyze_spatial_relationships(
        &self,
        objects: &[DetectedObject],
    ) -> Result<Vec<SpatialRelation>> {
        let mut relationships = Vec::new();

        for (i, obj1) in objects.iter().enumerate() {
            for (j, obj2) in objects.iter().enumerate() {
                if i != j {
                    let relations = self.spatial_analyzer.analyze_pair(obj1, obj2, i, j)?;
                    relationships.extend(relations);
                }
            }
        }

        // Filter relationships based on confidence
        relationships.retain(|r| r.confidence > 0.5);

        Ok(relationships)
    }

    /// Classify the overall scene
    fn classify_scene(
        &self,
        image: &ArrayView3<f32>,
        objects: &[DetectedObject],
    ) -> Result<(String, f32)> {
        // Extract global scene features
        let global_features = self.extract_global_features(image)?;

        // Analyze object composition
        let object_composition = self.analyze_object_composition(objects)?;

        // Combine features for scene classification
        let scene_features = self.combine_scene_features(&global_features, &object_composition)?;

        // Perform classification
        let (scene_class, confidence) = self.classify_from_features(&scene_features)?;

        Ok((scene_class, confidence))
    }

    /// Build comprehensive scene graph
    fn build_scene_graph(
        &self,
        objects: &[DetectedObject],
        relationships: &[SpatialRelation],
    ) -> Result<SceneGraph> {
        let nodes = objects
            .iter()
            .enumerate()
            .map(|(i, obj)| SceneGraphNode {
                id: i,
                object_type: obj.class.clone(),
                properties: obj.attributes.clone(),
                spatial_location: (obj.bbox.0 + obj.bbox.2 / 2.0, obj.bbox.1 + obj.bbox.3 / 2.0),
            })
            .collect();

        let edges = relationships
            .iter()
            .map(|rel| SceneGraphEdge {
                source: rel.source_id,
                target: rel.target_id,
                relation_type: format!("{:?}", rel.relation_type),
                weight: rel.confidence,
                properties: rel.parameters.clone(),
            })
            .collect();

        let global_properties = HashMap::new(); // TODO: Implement global scene properties

        Ok(SceneGraph {
            nodes,
            edges,
            global_properties,
        })
    }

    /// Perform contextual reasoning on scene understanding results
    fn perform_reasoning(
        &self,
        objects: &[DetectedObject],
        relationships: &[SpatialRelation],
        scene_class: &str,
    ) -> Result<Vec<ReasoningResult>> {
        let mut results = Vec::new();

        // Apply reasoning rules
        for rule in &self.reasoning_engine.rules {
            if let Some(result) =
                self.apply_reasoning_rule(rule, objects, relationships, scene_class, scene_class)?
            {
                results.push(result);
            }
        }

        Ok(results)
    }

    /// Helper methods (placeholder implementations for compilation)
    fn segment_at_scale(&self, image: &ArrayView3<f32>, scale: f32) -> Result<Array2<u32>> {
        Ok(Array2::zeros((100, 100))) // Placeholder
    }

    fn merge_segmentation_results(&self, base: &mut Array2<u32>, new: &Array2<u32>) -> Result<()> {
        Ok(()) // Placeholder
    }

    fn enforce_spatial_consistency(&self, segmentation: &mut Array2<u32>) -> Result<()> {
        Ok(()) // Placeholder
    }

    fn extract_object_features(
        &self,
        image: &ArrayView3<f32>,
        _bbox: &(f32, f32, f32, f32),
    ) -> Result<Array2<f32>> {
        Ok(Array2::zeros((1, 256))) // Placeholder
    }

    fn compute_object_mask(
        &self,
        image: &ArrayView3<f32>,
        _detection: &DetectionResult,
    ) -> Result<Array2<bool>> {
        Ok(Array2::from_elem((50, 50), false)) // Placeholder
    }

    fn analyze_object_attributes(
        &self,
        image: &ArrayView3<f32>,
        _detection: &DetectionResult,
        features: &Array2<f32>,
    ) -> Result<HashMap<String, f32>> {
        Ok(HashMap::new()) // Placeholder
    }

    fn extract_global_features(&self, image: &ArrayView3<f32>) -> Result<Array2<f32>> {
        Ok(Array2::zeros((1, 512))) // Placeholder
    }

    fn analyze_object_composition(&self, objects: &[DetectedObject]) -> Result<Array2<f32>> {
        Ok(Array2::zeros((1, 128))) // Placeholder
    }

    fn combine_scene_features(
        &self,
        global: &Array2<f32>,
        _composition: &Array2<f32>,
    ) -> Result<Array2<f32>> {
        Ok(Array2::zeros((1, 640))) // Placeholder
    }

    fn classify_from_features(&self, features: &Array2<f32>) -> Result<(String, f32)> {
        Ok(("indoor_scene".to_string(), 0.85)) // Placeholder
    }

    fn apply_reasoning_rule(
        &self,
        _rule: &ReasoningRule,
        _objects: &[DetectedObject],
        _relationships: &[SpatialRelation],
        _scene: &str,
        _class: &str,
    ) -> Result<Option<ReasoningResult>> {
        Ok(None) // Placeholder
    }

    fn analyze_temporal_changes(
        &self,
        _current_frame: &ArrayView3<f32>,
        _previous_frames: &[ArrayView3<f32>],
        _frame_idx: usize,
    ) -> Result<TemporalInfo> {
        Ok(TemporalInfo {
            frame_index: _frame_idx,
            timestamp: _frame_idx as f64 / 30.0, // Assuming 30 FPS
            motion_vectors: Array3::zeros((100, 100, 2)),
            scene_changes: Vec::new(),
        })
    }

    fn enforce_temporal_consistency(&mut self, results: &mut [SceneAnalysisResult]) -> Result<()> {
        Ok(()) // Placeholder
    }
}

// Placeholder detection result structure
#[derive(Debug, Clone)]
struct DetectionResult {
    class: String,
    bbox: (f32, f32, f32, f32),
    confidence: f32,
}

// Implementation stubs for associated types
impl ObjectDetector {
    fn new() -> Self {
        Self {
            confidence_threshold: 0.5,
            nms_threshold: 0.4,
            object_classes: vec!["person".to_string(), "car".to_string(), "chair".to_string()],
            feature_layers: vec!["conv5".to_string(), "fc7".to_string()],
        }
    }

    fn detect_multi_scale(&self, image: &ArrayView3<f32>) -> Result<Vec<DetectionResult>> {
        Ok(Vec::new()) // Placeholder
    }
}

impl SpatialRelationshipAnalyzer {
    fn new() -> Self {
        Self {
            relationship_types: vec![SpatialRelationType::OnTop, SpatialRelationType::NextTo],
            distance_thresholds: HashMap::new(),
            directional_params: DirectionalParams {
                angular_tolerance: 15.0,
                distance_normalization: true,
                perspective_correction: true,
            },
        }
    }

    fn analyze_pair(
        &self,
        obj1: &DetectedObject,
        _obj2: &DetectedObject,
        id1: usize,
        _id2: usize,
    ) -> Result<Vec<SpatialRelation>> {
        Ok(Vec::new()) // Placeholder
    }
}

impl TemporalSceneTracker {
    fn new() -> Self {
        Self {
            buffer_size: 30,
            motion_threshold: 0.1,
            tracking_params: TrackingParams {
                max_disappearance_frames: 10,
                tracking_algorithm: "kalman".to_string(),
                feature_matching_threshold: 0.8,
            },
            change_detection: ChangeDetectionParams {
                sensitivity: 0.5,
                temporal_window: 5,
                background_model: "gaussian_mixture".to_string(),
            },
        }
    }
}

impl SceneGraphBuilder {
    fn new() -> Self {
        Self {
            max_nodes: 100,
            edge_threshold: 0.3,
            simplification_params: GraphSimplificationParams {
                min_edge_weight: 0.1,
                redundancy_removal: true,
                hierarchical_clustering: true,
            },
        }
    }
}

impl ContextualReasoningEngine {
    fn new() -> Self {
        Self {
            rules: Vec::new(),
            context_windows: Vec::new(),
            inference_params: InferenceParams {
                max_iterations: 100,
                convergence_threshold: 0.01,
                uncertainty_handling: "bayesian".to_string(),
            },
        }
    }
}

/// Advanced-advanced scene understanding with cognitive-level reasoning
#[allow(dead_code)]
pub fn analyze_scene_with_reasoning(
    image: &ArrayView3<f32>,
    context: Option<&SceneAnalysisResult>,
) -> Result<SceneAnalysisResult> {
    let engine = SceneUnderstandingEngine::new();
    let mut result = engine.analyze_scene(image)?;

    // Apply contextual reasoning if previous context is available
    if let Some(prev_context) = context {
        result = apply_contextual_enhancement(&result, prev_context)?;
    }

    Ok(result)
}

/// Apply contextual enhancement based on previous scene understanding
#[allow(dead_code)]
fn apply_contextual_enhancement(
    current: &SceneAnalysisResult,
    previous: &SceneAnalysisResult,
) -> Result<SceneAnalysisResult> {
    // Placeholder for contextual enhancement logic
    Ok(current.clone())
}
