scirs2_vision/
visual_reasoning.rs

1//! Advanced Visual Reasoning Framework
2//!
3//! This module provides sophisticated visual reasoning capabilities including:
4//! - Causal relationship inference
5//! - Visual question answering
6//! - Analogical reasoning
7//! - Temporal event understanding
8//! - Abstract concept recognition
9//! - Multi-modal reasoning integration
10
11#![allow(dead_code)]
12#![allow(missing_docs)]
13
14use crate::error::Result;
15use crate::scene_understanding::SceneAnalysisResult;
16use scirs2_core::ndarray::{Array1, Array2};
17use std::collections::HashMap;
18
19/// Advanced-advanced visual reasoning engine with cognitive-level capabilities
20pub struct VisualReasoningEngine {
21    /// Causal inference module
22    causal_inference: CausalInferenceModule,
23    /// Visual question answering system
24    vqa_system: VisualQuestionAnsweringSystem,
25    /// Analogical reasoning engine
26    analogical_reasoning: AnalogicalReasoningEngine,
27    /// Temporal event analyzer
28    temporal_analyzer: TemporalEventAnalyzer,
29    /// Abstract concept recognizer
30    concept_recognizer: AbstractConceptRecognizer,
31    /// Multi-modal integration hub
32    multimodal_hub: MultiModalIntegrationHub,
33    /// Knowledge base for reasoning
34    knowledge_base: VisualKnowledgeBase,
35}
36
37/// Causal inference module for understanding cause-effect relationships
38#[derive(Debug, Clone)]
39pub struct CausalInferenceModule {
40    /// Causal models
41    causal_models: Vec<CausalModel>,
42    /// Intervention analysis parameters
43    intervention_params: InterventionParams,
44    /// Counterfactual reasoning settings
45    counterfactual_params: CounterfactualParams,
46}
47
48/// Visual Question Answering system with advanced reasoning
49#[derive(Debug, Clone)]
50pub struct VisualQuestionAnsweringSystem {
51    /// Question types supported
52    question_types: Vec<QuestionType>,
53    /// Answer generation strategies
54    answer_strategies: Vec<AnswerStrategy>,
55    /// Attention mechanisms
56    attention_mechanisms: Vec<AttentionMechanism>,
57}
58
59/// Analogical reasoning for pattern recognition and transfer learning
60#[derive(Debug, Clone)]
61pub struct AnalogicalReasoningEngine {
62    /// Analogy templates
63    analogy_templates: Vec<AnalogyTemplate>,
64    /// Similarity metrics
65    similarity_metrics: Vec<SimilarityMetric>,
66    /// Transfer learning parameters
67    transfer_params: TransferLearningParams,
68}
69
70/// Temporal event analysis for understanding sequences and changes
71#[derive(Debug, Clone)]
72pub struct TemporalEventAnalyzer {
73    /// Event detection models
74    event_detectors: Vec<EventDetector>,
75    /// Temporal relationship models
76    temporal_models: Vec<TemporalModel>,
77    /// Sequence analysis parameters
78    sequence_params: SequenceAnalysisParams,
79}
80
81/// Abstract concept recognition for high-level understanding
82#[derive(Debug, Clone)]
83pub struct AbstractConceptRecognizer {
84    /// Concept hierarchies
85    concept_hierarchies: Vec<ConceptHierarchy>,
86    /// Feature abstraction layers
87    abstraction_layers: Vec<AbstractionLayer>,
88    /// Concept learning parameters
89    learning_params: ConceptLearningParams,
90}
91
92/// Multi-modal integration for combining visual and other modalities
93#[derive(Debug, Clone)]
94pub struct MultiModalIntegrationHub {
95    /// Supported modalities
96    modalities: Vec<Modality>,
97    /// Fusion strategies
98    fusion_strategies: Vec<FusionStrategy>,
99    /// Cross-modal attention mechanisms
100    cross_attention: Vec<CrossModalAttention>,
101}
102
103/// Visual knowledge base for storing and retrieving reasoning knowledge
104#[derive(Debug, Clone)]
105pub struct VisualKnowledgeBase {
106    /// Factual knowledge
107    facts: HashMap<String, VisualFact>,
108    /// Rules and constraints
109    rules: Vec<ReasoningRule>,
110    /// Concept ontology
111    ontology: ConceptOntology,
112}
113
114/// Visual reasoning query for asking complex questions
115#[derive(Debug, Clone)]
116pub struct VisualReasoningQuery {
117    /// Query type
118    pub query_type: QueryType,
119    /// Natural language question
120    pub question: String,
121    /// Query parameters
122    pub parameters: HashMap<String, QueryParameter>,
123    /// Context requirements
124    pub context_requirements: Vec<ContextRequirement>,
125}
126
127/// Comprehensive visual reasoning result
128#[derive(Debug, Clone)]
129pub struct VisualReasoningResult {
130    /// Answer to the query
131    pub answer: ReasoningAnswer,
132    /// Reasoning steps taken
133    pub reasoning_steps: Vec<ReasoningStep>,
134    /// Confidence in the answer
135    pub confidence: f32,
136    /// Evidence supporting the answer
137    pub evidence: Vec<Evidence>,
138    /// Alternative hypotheses considered
139    pub alternatives: Vec<AlternativeHypothesis>,
140    /// Uncertainty quantification
141    pub uncertainty: UncertaintyQuantification,
142}
143
144/// Supporting types for visual reasoning
145#[derive(Debug, Clone)]
146pub enum QueryType {
147    /// What is happening in the image?
148    WhatIsHappening,
149    /// Why is this happening?
150    WhyIsHappening,
151    /// What will happen next?
152    WhatWillHappenNext,
153    /// How are objects related?
154    HowAreObjectsRelated,
155    /// What if scenario analysis
156    WhatIfScenario,
157    /// Counting and quantification
158    CountingQuery,
159    /// Comparison between scenes
160    ComparisonQuery,
161    /// Abstract concept queries
162    AbstractConceptQuery,
163    /// Temporal sequence queries
164    TemporalSequenceQuery,
165    /// Causal relationship queries
166    CausalRelationshipQuery,
167}
168
169/// Parameter types for visual reasoning queries
170#[derive(Debug, Clone)]
171pub enum QueryParameter {
172    /// Text-based parameter
173    Text(String),
174    /// Numeric parameter
175    Number(f32),
176    /// Boolean parameter
177    Boolean(bool),
178    /// Image region specified as (x, y, width, height)
179    ImageRegion((f32, f32, f32, f32)),
180    /// Time range specified as (start, end)
181    TimeRange((f32, f32)),
182    /// List of object identifiers
183    ObjectList(Vec<String>),
184}
185
186/// Context requirement for visual reasoning queries
187#[derive(Debug, Clone)]
188pub struct ContextRequirement {
189    /// Type of context required
190    pub requirement_type: String,
191    /// Level of specificity needed (0.0-1.0)
192    pub specificity: f32,
193    /// Optional temporal scope for context
194    pub temporal_scope: Option<(f32, f32)>,
195}
196
197/// Answer types for visual reasoning queries
198#[derive(Debug, Clone)]
199pub enum ReasoningAnswer {
200    /// Text-based answer
201    Text(String),
202    /// Numeric answer
203    Number(f32),
204    /// Boolean answer
205    Boolean(bool),
206    /// List of detected objects
207    ObjectList(Vec<String>),
208    /// List of spatial locations
209    LocationList(Vec<(f32, f32)>),
210    /// Complex structured answer
211    Complex(HashMap<String, String>),
212}
213
214/// Individual step in the reasoning process
215#[derive(Debug, Clone)]
216pub struct ReasoningStep {
217    /// Unique identifier for this reasoning step
218    pub step_id: usize,
219    /// Type of reasoning operation performed
220    pub step_type: String,
221    /// Human-readable description of the step
222    pub description: String,
223    /// Input data used in this step
224    pub input_data: Vec<String>,
225    /// Output data generated by this step
226    pub output_data: Vec<String>,
227    /// Confidence in this reasoning step
228    pub confidence: f32,
229}
230
231/// Evidence supporting a reasoning conclusion
232#[derive(Debug, Clone)]
233pub struct Evidence {
234    /// Type of evidence (visual, temporal, etc.)
235    pub evidence_type: String,
236    /// Description of the evidence
237    pub description: String,
238    /// Strength of support this evidence provides
239    pub support_strength: f32,
240    /// Visual locations that support this evidence
241    pub visual_anchors: Vec<(f32, f32)>,
242    /// Temporal points that support this evidence
243    pub temporal_anchors: Vec<f32>,
244}
245
246/// Alternative hypothesis considered during reasoning
247#[derive(Debug, Clone)]
248pub struct AlternativeHypothesis {
249    /// Description of the alternative hypothesis
250    pub hypothesis: String,
251    /// Probability or likelihood of this hypothesis
252    pub probability: f32,
253    /// Features that distinguish this from the main conclusion
254    pub distinguishing_features: Vec<String>,
255}
256
257/// Quantification of uncertainty in reasoning results
258#[derive(Debug, Clone)]
259pub struct UncertaintyQuantification {
260    /// Model uncertainty (knowledge limitations)
261    pub epistemic_uncertainty: f32,
262    /// Data uncertainty (inherent randomness)
263    pub aleatoric_uncertainty: f32,
264    /// Confidence interval for the answer
265    pub confidence_interval: (f32, f32),
266    /// Sensitivity to different input parameters
267    pub sensitivity_analysis: HashMap<String, f32>,
268}
269
270// Additional supporting types
271/// Model for causal relationships in visual scenes
272#[derive(Debug, Clone)]
273pub struct CausalModel {
274    /// Name identifier for the causal model
275    pub name: String,
276    /// Variables involved in causal relationships
277    pub variables: Vec<CausalVariable>,
278    /// Causal relationships between variables
279    pub relationships: Vec<CausalRelationship>,
280    /// Overall confidence in the model
281    pub confidence: f32,
282}
283
284/// Variable in a causal model
285#[derive(Debug, Clone)]
286pub struct CausalVariable {
287    /// Name of the variable
288    pub name: String,
289    /// Type of the variable (continuous, discrete, etc.)
290    pub variable_type: String,
291    /// Possible values the variable can take
292    pub possible_values: Vec<String>,
293    /// How easily this variable can be observed
294    pub observability: f32,
295}
296
297/// Relationship between cause and effect variables
298#[derive(Debug, Clone)]
299pub struct CausalRelationship {
300    /// Variable that acts as the cause
301    pub cause: String,
302    /// Variable that is affected
303    pub effect: String,
304    /// Strength of the causal relationship
305    pub strength: f32,
306    /// Time delay between cause and effect
307    pub delay: Option<f32>,
308    /// Conditions under which this relationship holds
309    pub conditions: Vec<String>,
310}
311
312/// Parameters for causal intervention analysis
313#[derive(Debug, Clone)]
314pub struct InterventionParams {
315    /// Types of interventions to consider
316    pub intervention_types: Vec<String>,
317    /// Whether to model effect propagation through the graph
318    pub effect_propagation: bool,
319    /// Whether to include temporal aspects in modeling
320    pub temporal_modeling: bool,
321}
322
323/// Parameters for counterfactual reasoning
324#[derive(Debug, Clone)]
325pub struct CounterfactualParams {
326    /// Number of alternative scenarios to consider
327    pub alternative_scenarios: usize,
328    /// Threshold for considering scenarios plausible
329    pub plausibility_threshold: f32,
330    /// Temporal scope for counterfactual analysis
331    pub temporal_scope: f32,
332}
333
334/// Types of questions that can be asked in visual reasoning
335#[derive(Debug, Clone)]
336pub enum QuestionType {
337    /// Questions about objects in the scene
338    Object,
339    /// Questions about the overall scene
340    Scene,
341    /// Questions about activities or actions
342    Activity,
343    /// Questions about spatial relationships
344    Spatial,
345    /// Questions about temporal aspects
346    Temporal,
347    /// Questions about causal relationships
348    Causal,
349    /// Hypothetical "what if" questions
350    Counterfactual,
351    /// Questions comparing different elements
352    Comparative,
353}
354
355/// Strategy for generating answers to visual reasoning queries
356#[derive(Debug, Clone)]
357pub struct AnswerStrategy {
358    /// Name of the answer generation strategy
359    pub strategy_name: String,
360    /// Question types this strategy can handle
361    pub applicable_types: Vec<QuestionType>,
362    /// Whether this strategy provides confidence estimates
363    pub confidence_estimation: bool,
364}
365
366/// Attention mechanism for focusing on relevant information
367#[derive(Debug, Clone)]
368pub struct AttentionMechanism {
369    /// Type of attention mechanism used
370    pub mechanism_type: String,
371    /// Whether spatial attention is enabled
372    pub spatial_attention: bool,
373    /// Whether temporal attention is enabled
374    pub temporal_attention: bool,
375    /// Whether cross-modal attention is enabled
376    pub cross_modal_attention: bool,
377}
378
379/// Template for analogical reasoning between visual patterns
380#[derive(Debug, Clone)]
381pub struct AnalogyTemplate {
382    /// Name of the analogy template
383    pub template_name: String,
384    /// Source pattern for the analogy
385    pub source_pattern: VisualPattern,
386    /// Target pattern for the analogy
387    pub target_pattern: VisualPattern,
388    /// Rules for mapping between source and target
389    pub mapping_rules: Vec<MappingRule>,
390}
391
392/// Visual pattern representation for analogical reasoning
393#[derive(Debug, Clone)]
394pub struct VisualPattern {
395    /// Type of visual pattern
396    pub pattern_type: String,
397    /// Feature representation of the pattern
398    pub features: Array2<f32>,
399    /// Spatial structure information
400    pub spatial_structure: Array2<f32>,
401    /// Temporal structure information
402    pub temporal_structure: Array2<f32>,
403}
404
405/// Rule for mapping between elements in analogical reasoning
406#[derive(Debug, Clone)]
407pub struct MappingRule {
408    /// Element in the source pattern
409    pub source_element: String,
410    /// Corresponding element in the target pattern
411    pub target_element: String,
412    /// Type of mapping relationship
413    pub mapping_type: String,
414    /// Confidence in this mapping
415    pub confidence: f32,
416}
417
418/// Metric for computing similarity between visual patterns
419#[derive(Debug, Clone)]
420pub struct SimilarityMetric {
421    /// Name of the similarity metric
422    pub metric_name: String,
423    /// Weights for different features
424    pub feature_weights: Array1<f32>,
425    /// Whether to normalize the metric
426    pub normalization: bool,
427    /// Distance function to use
428    pub distance_function: String,
429}
430
431/// Parameters for transfer learning in visual reasoning
432#[derive(Debug, Clone)]
433pub struct TransferLearningParams {
434    /// Rate of adaptation to new domains
435    pub adaptation_rate: f32,
436    /// Threshold for considering domains similar
437    pub domain_similarity_threshold: f32,
438    /// Whether to perform feature selection
439    pub feature_selection: bool,
440}
441
442/// Detector for temporal events in visual sequences
443#[derive(Debug, Clone)]
444pub struct EventDetector {
445    /// Type of event this detector recognizes
446    pub event_type: String,
447    /// Threshold for event detection
448    pub detection_threshold: f32,
449    /// Size of temporal window for detection
450    pub temporal_window: usize,
451    /// Feature extractors used for detection
452    pub feature_extractors: Vec<String>,
453}
454
455/// Model for temporal relationships in visual reasoning
456#[derive(Debug, Clone)]
457pub struct TemporalModel {
458    /// Type of temporal model
459    pub model_type: String,
460    /// Time horizon for predictions
461    pub time_horizon: f32,
462    /// Temporal granularity of the model
463    pub granularity: f32,
464    /// Whether to model causal relationships
465    pub causality_modeling: bool,
466}
467
468/// Parameters for analyzing temporal sequences
469#[derive(Debug, Clone)]
470pub struct SequenceAnalysisParams {
471    /// Maximum length of sequences to analyze
472    pub max_sequence_length: usize,
473    /// Whether to perform pattern recognition
474    pub pattern_recognition: bool,
475    /// Whether to detect anomalies in sequences
476    pub anomaly_detection: bool,
477}
478
479/// Hierarchy of abstract concepts for visual reasoning
480#[derive(Debug, Clone)]
481pub struct ConceptHierarchy {
482    /// Name of the concept hierarchy
483    pub hierarchy_name: String,
484    /// Root concepts at the top level
485    pub root_concepts: Vec<String>,
486    /// Relationships between concepts
487    pub concept_relationships: HashMap<String, Vec<String>>,
488    /// Number of abstraction levels
489    pub abstraction_levels: usize,
490}
491
492/// Layer for feature abstraction in concept learning
493#[derive(Debug, Clone)]
494pub struct AbstractionLayer {
495    /// Name of the abstraction layer
496    pub layer_name: String,
497    /// Number of input features
498    pub input_features: usize,
499    /// Number of output concepts
500    pub output_concepts: usize,
501    /// Learning algorithm used in this layer
502    pub learning_algorithm: String,
503}
504
505/// Parameters for concept learning in visual reasoning
506///
507/// This structure configures how the system learns and emerges new concepts
508/// from visual input data through adaptive mechanisms.
509#[derive(Debug, Clone)]
510pub struct ConceptLearningParams {
511    /// Learning rate for concept adaptation and emergence
512    pub learning_rate: f32,
513    /// Threshold for determining when a new concept should emerge
514    pub concept_emergence_threshold: f32,
515    /// Whether to enable hierarchical concept learning
516    pub hierarchical_learning: bool,
517}
518
519/// Different sensory modalities for multi-modal processing
520///
521/// Represents the various types of sensory input that can be processed
522/// and fused in the visual reasoning system.
523#[derive(Debug, Clone)]
524pub enum Modality {
525    /// Visual sensory input (images, video)
526    Visual,
527    /// Audio sensory input (sounds, speech)
528    Audio,
529    /// Textual input (natural language)
530    Text,
531    /// Tactile sensory input (touch, pressure)
532    Tactile,
533    /// Temporal sequence information
534    Temporal,
535    /// Spatial relationship information
536    Spatial,
537}
538
539/// Strategy for fusing multiple sensory modalities
540///
541/// Defines how different sensory inputs should be combined and weighted
542/// to create unified multi-modal representations.
543#[derive(Debug, Clone)]
544pub struct FusionStrategy {
545    /// Name identifier for this fusion strategy
546    pub strategy_name: String,
547    /// Weights assigned to each modality in the fusion process
548    pub modality_weights: HashMap<Modality, f32>,
549    /// Level of fusion (early, intermediate, late)
550    pub fusion_level: String,
551    /// Whether to align temporal sequences across modalities
552    pub temporal_alignment: bool,
553}
554
555/// Cross-modal attention mechanism for multi-modal processing
556///
557/// Implements attention mechanisms that allow one modality to attend to
558/// and influence processing in another modality.
559#[derive(Debug, Clone)]
560pub struct CrossModalAttention {
561    /// Type of attention mechanism (additive, multiplicative, etc.)
562    pub attention_type: String,
563    /// Source modality providing attention signal
564    pub source_modality: Modality,
565    /// Target modality receiving attention
566    pub target_modality: Modality,
567    /// Attention weight matrix
568    pub attention_weights: Array2<f32>,
569}
570
571/// A visual fact extracted from reasoning about visual content
572///
573/// Represents a structured fact (subject-predicate-object triple) that has been
574/// inferred or extracted from visual reasoning processes.
575#[derive(Debug, Clone)]
576pub struct VisualFact {
577    /// Unique identifier for this fact
578    pub fact_id: String,
579    /// Subject of the fact (what the fact is about)
580    pub subject: String,
581    /// Predicate describing the relationship or property
582    pub predicate: String,
583    /// Object related to the subject by the predicate
584    pub object: String,
585    /// Confidence score for this fact (0.0 to 1.0)
586    pub confidence: f32,
587    /// Supporting evidence for this fact
588    pub evidence: Vec<String>,
589}
590
591/// A logical reasoning rule for visual reasoning processes
592///
593/// Represents an if-then rule that can be applied during reasoning to derive
594/// new conclusions from existing facts and conditions.
595#[derive(Debug, Clone)]
596pub struct ReasoningRule {
597    /// Unique identifier for this reasoning rule
598    pub rule_id: String,
599    /// Conditions that must be met for the rule to apply
600    pub conditions: Vec<String>,
601    /// Conclusions that can be drawn when conditions are met
602    pub conclusions: Vec<String>,
603    /// Type of reasoning rule (deductive, inductive, abductive)
604    pub rule_type: String,
605    /// Reliability score for this rule (0.0 to 1.0)
606    pub reliability: f32,
607}
608
609#[derive(Debug, Clone)]
610pub struct ConceptOntology {
611    pub concepts: HashMap<String, ConceptDefinition>,
612    pub relationships: Vec<ConceptRelationship>,
613    pub inheritance_hierarchy: HashMap<String, Vec<String>>,
614}
615
616#[derive(Debug, Clone)]
617pub struct ConceptDefinition {
618    pub concept_name: String,
619    pub attributes: Vec<String>,
620    pub visual_features: Array1<f32>,
621    pub typical_contexts: Vec<String>,
622}
623
624#[derive(Debug, Clone)]
625pub struct ConceptRelationship {
626    pub source_concept: String,
627    pub target_concept: String,
628    pub relationship_type: String,
629    pub strength: f32,
630}
631
632impl Default for VisualReasoningEngine {
633    fn default() -> Self {
634        Self::new()
635    }
636}
637
638impl VisualReasoningEngine {
639    /// Create a new advanced visual reasoning engine
640    pub fn new() -> Self {
641        Self {
642            causal_inference: CausalInferenceModule::new(),
643            vqa_system: VisualQuestionAnsweringSystem::new(),
644            analogical_reasoning: AnalogicalReasoningEngine::new(),
645            temporal_analyzer: TemporalEventAnalyzer::new(),
646            concept_recognizer: AbstractConceptRecognizer::new(),
647            multimodal_hub: MultiModalIntegrationHub::new(),
648            knowledge_base: VisualKnowledgeBase::new(),
649        }
650    }
651
652    /// Process a complex visual reasoning query
653    pub fn process_query(
654        &self,
655        query: &VisualReasoningQuery,
656        scene_analysis: &SceneAnalysisResult,
657        context: Option<&[SceneAnalysisResult]>,
658    ) -> Result<VisualReasoningResult> {
659        // Initialize reasoning process
660        let mut reasoning_steps = Vec::new();
661        let mut evidence = Vec::new();
662
663        // Step 1: Query understanding and decomposition
664        let decomposed_query = self.decompose_query(query)?;
665        reasoning_steps.push(ReasoningStep {
666            step_id: 1,
667            step_type: "query_decomposition".to_string(),
668            description: "Breaking down complex query into sub-queries".to_string(),
669            input_data: vec![query.question.clone()],
670            output_data: vec![format!("{} sub-queries", decomposed_query.len())],
671            confidence: 0.95,
672        });
673
674        // Step 2: Visual feature extraction and _analysis
675        let visual_features = self.extract_reasoning_features(scene_analysis)?;
676        reasoning_steps.push(ReasoningStep {
677            step_id: 2,
678            step_type: "feature_extraction".to_string(),
679            description: "Extracting relevant visual features for reasoning".to_string(),
680            input_data: vec!["scene_analysis".to_string()],
681            output_data: vec![format!("{} feature dimensions", visual_features.len())],
682            confidence: 0.90,
683        });
684
685        // Step 3: Apply reasoning based on query type
686        let (answer, step_evidence, alternatives) = match query.query_type {
687            QueryType::WhatIsHappening => {
688                self.reason_what_is_happening(scene_analysis, &visual_features)?
689            }
690            QueryType::WhyIsHappening => {
691                self.reason_why_is_happening(scene_analysis, &visual_features)?
692            }
693            QueryType::WhatWillHappenNext => {
694                self.reason_what_will_happen_next(scene_analysis, context, &visual_features)?
695            }
696            QueryType::HowAreObjectsRelated => {
697                self.reason_object_relationships(scene_analysis, &visual_features)?
698            }
699            QueryType::CausalRelationshipQuery => {
700                self.reason_causal_relationships(scene_analysis, &visual_features)?
701            }
702            _ => (
703                ReasoningAnswer::Text("Query type not fully implemented yet".to_string()),
704                Vec::new(),
705                Vec::new(),
706            ),
707        };
708
709        evidence.extend(step_evidence);
710
711        // Step 4: Confidence estimation and uncertainty quantification
712        let confidence = self.estimate_overall_confidence(&reasoning_steps, &evidence)?;
713        let uncertainty = self.quantify_uncertainty(&answer, &evidence)?;
714
715        Ok(VisualReasoningResult {
716            answer,
717            reasoning_steps,
718            confidence,
719            evidence,
720            alternatives,
721            uncertainty,
722        })
723    }
724
725    /// Process causal reasoning queries
726    pub fn infer_causality(
727        &self,
728        scene_sequence: &[SceneAnalysisResult],
729        causal_query: &str,
730    ) -> Result<CausalInferenceResult> {
731        // Extract temporal patterns
732        let temporal_patterns = self.extract_temporal_patterns(scene_sequence)?;
733
734        // Build causal graph
735        let causal_graph = self
736            .causal_inference
737            .build_causal_graph(&temporal_patterns)?;
738
739        // Perform causal inference
740        let causal_effects = self
741            .causal_inference
742            .infer_effects(&causal_graph, causal_query)?;
743
744        Ok(CausalInferenceResult {
745            causal_graph,
746            effects: causal_effects,
747            confidence: 0.75,
748        })
749    }
750
751    /// Perform analogical reasoning between scenes
752    pub fn find_analogies(
753        &self,
754        source_scene: &SceneAnalysisResult,
755        target_scenes: &[SceneAnalysisResult],
756    ) -> Result<Vec<AnalogyResult>> {
757        let mut analogies = Vec::new();
758
759        for target_scene in target_scenes {
760            let analogy = self
761                .analogical_reasoning
762                .find_analogy(source_scene, target_scene)?;
763            if analogy.similarity_score > 0.6 {
764                analogies.push(analogy);
765            }
766        }
767
768        // Sort by similarity score
769        analogies.sort_by(|a, b| {
770            b.similarity_score
771                .partial_cmp(&a.similarity_score)
772                .expect("Operation failed")
773        });
774
775        Ok(analogies)
776    }
777
778    /// Recognize abstract concepts in visual scenes
779    pub fn recognize_abstract_concepts(
780        &self,
781        scene_analysis: &SceneAnalysisResult,
782    ) -> Result<Vec<AbstractConcept>> {
783        let concepts = self.concept_recognizer.recognize_concepts(scene_analysis)?;
784        Ok(concepts)
785    }
786
787    // Helper methods (placeholder implementations)
788    fn decompose_query(&self, query: &VisualReasoningQuery) -> Result<Vec<SubQuery>> {
789        // Placeholder implementation
790        Ok(vec![SubQuery {
791            sub_question: query.question.clone(),
792            query_type: query.query_type.clone(),
793            dependencies: Vec::new(),
794        }])
795    }
796
797    fn extract_reasoning_features(
798        &self,
799        scene_analysis: &SceneAnalysisResult,
800    ) -> Result<Array1<f32>> {
801        // Extract multi-level features for reasoning
802        let mut features = Vec::new();
803
804        // Object-level features
805        for object in &scene_analysis.objects {
806            features.extend(object.features.iter().cloned());
807        }
808
809        // Relationship features
810        for relationship in &scene_analysis.relationships {
811            features.push(relationship.confidence);
812            features.extend(relationship.parameters.values().cloned());
813        }
814
815        // Scene-level features
816        features.push(scene_analysis.scene_confidence);
817
818        Ok(Array1::from_vec(features))
819    }
820
821    fn reason_what_is_happening(
822        &self,
823        scene_analysis: &SceneAnalysisResult,
824        _features: &Array1<f32>,
825    ) -> Result<(ReasoningAnswer, Vec<Evidence>, Vec<AlternativeHypothesis>)> {
826        // Analyze dominant activities and interactions
827        let activities = self.identify_activities(scene_analysis)?;
828        let description = format!("Detected activities: {}", activities.join(", "));
829
830        let evidence = vec![Evidence {
831            evidence_type: "object_detection".to_string(),
832            description: format!("Found {} objects in scene", scene_analysis.objects.len()),
833            support_strength: 0.8,
834            visual_anchors: Vec::new(),
835            temporal_anchors: Vec::new(),
836        }];
837
838        Ok((ReasoningAnswer::Text(description), evidence, Vec::new()))
839    }
840
841    fn reason_why_is_happening(
842        &self,
843        scene_analysis: &SceneAnalysisResult,
844        _features: &Array1<f32>,
845    ) -> Result<(ReasoningAnswer, Vec<Evidence>, Vec<AlternativeHypothesis>)> {
846        // Apply causal reasoning
847        let causal_explanations = self.generate_causal_explanations(scene_analysis)?;
848
849        Ok((
850            ReasoningAnswer::Text(causal_explanations),
851            Vec::new(),
852            Vec::new(),
853        ))
854    }
855
856    fn reason_what_will_happen_next(
857        &self,
858        scene_analysis: &SceneAnalysisResult,
859        context: Option<&[SceneAnalysisResult]>,
860        _features: &Array1<f32>,
861    ) -> Result<(ReasoningAnswer, Vec<Evidence>, Vec<AlternativeHypothesis>)> {
862        let prediction = if let Some(temporal_context) = context {
863            self.predict_future_events(scene_analysis, temporal_context)?
864        } else {
865            "Insufficient temporal context for prediction".to_string()
866        };
867
868        Ok((ReasoningAnswer::Text(prediction), Vec::new(), Vec::new()))
869    }
870
871    fn reason_object_relationships(
872        &self,
873        scene_analysis: &SceneAnalysisResult,
874        _features: &Array1<f32>,
875    ) -> Result<(ReasoningAnswer, Vec<Evidence>, Vec<AlternativeHypothesis>)> {
876        let relationships_desc = format!(
877            "Found {} spatial relationships between objects",
878            scene_analysis.relationships.len()
879        );
880
881        Ok((
882            ReasoningAnswer::Text(relationships_desc),
883            Vec::new(),
884            Vec::new(),
885        ))
886    }
887
888    fn reason_causal_relationships(
889        &self,
890        scene_analysis: &SceneAnalysisResult,
891        _features: &Array1<f32>,
892    ) -> Result<(ReasoningAnswer, Vec<Evidence>, Vec<AlternativeHypothesis>)> {
893        let causal_analysis = self.analyze_causal_structure(scene_analysis)?;
894
895        Ok((
896            ReasoningAnswer::Text(causal_analysis),
897            Vec::new(),
898            Vec::new(),
899        ))
900    }
901
902    fn estimate_overall_confidence(
903        &self,
904        steps: &[ReasoningStep],
905        _evidence: &[Evidence],
906    ) -> Result<f32> {
907        Ok(0.75) // Placeholder
908    }
909
910    fn quantify_uncertainty(
911        &self,
912        answer: &ReasoningAnswer,
913        _evidence: &[Evidence],
914    ) -> Result<UncertaintyQuantification> {
915        Ok(UncertaintyQuantification {
916            epistemic_uncertainty: 0.2,
917            aleatoric_uncertainty: 0.1,
918            confidence_interval: (0.6, 0.9),
919            sensitivity_analysis: HashMap::new(),
920        })
921    }
922
923    fn extract_temporal_patterns(
924        &self,
925        sequence: &[SceneAnalysisResult],
926    ) -> Result<TemporalPatterns> {
927        Ok(TemporalPatterns {
928            patterns: Vec::new(),
929            temporal_graph: TemporalGraph {
930                nodes: Vec::new(),
931                edges: Vec::new(),
932            },
933        })
934    }
935
936    fn identify_activities(&self, sceneanalysis: &SceneAnalysisResult) -> Result<Vec<String>> {
937        let mut activities = Vec::new();
938
939        // Analyze object combinations and spatial relationships
940        for object in &sceneanalysis.objects {
941            match object.class.as_str() {
942                "person" => activities.push("human_activity".to_string()),
943                "car" => activities.push("transportation".to_string()),
944                "chair" => activities.push("sitting_area".to_string()),
945                _ => {}
946            }
947        }
948
949        if activities.is_empty() {
950            activities.push("static_scene".to_string());
951        }
952
953        Ok(activities)
954    }
955
956    fn generate_causal_explanations(
957        &self,
958        _scene_analysis: &SceneAnalysisResult,
959    ) -> Result<String> {
960        Ok(
961            "Scene appears to be in its current state due to normal object placement patterns"
962                .to_string(),
963        )
964    }
965
966    fn predict_future_events(
967        &self,
968        scene: &SceneAnalysisResult,
969        _context: &[SceneAnalysisResult],
970    ) -> Result<String> {
971        Ok("Based on temporal patterns, the _scene is likely to remain stable".to_string())
972    }
973
974    fn analyze_causal_structure(&self, _sceneanalysis: &SceneAnalysisResult) -> Result<String> {
975        Ok("No strong causal relationships detected in current scene".to_string())
976    }
977}
978
979// Placeholder structures for compilation
980#[derive(Debug, Clone)]
981pub struct SubQuery {
982    pub sub_question: String,
983    pub query_type: QueryType,
984    pub dependencies: Vec<usize>,
985}
986
987#[derive(Debug, Clone)]
988pub struct CausalInferenceResult {
989    pub causal_graph: CausalGraph,
990    pub effects: Vec<CausalEffect>,
991    pub confidence: f32,
992}
993
994#[derive(Debug, Clone)]
995pub struct CausalGraph {
996    pub nodes: Vec<CausalNode>,
997    pub edges: Vec<CausalEdge>,
998}
999
1000#[derive(Debug, Clone)]
1001pub struct CausalNode {
1002    pub node_id: String,
1003    pub node_type: String,
1004    pub properties: HashMap<String, f32>,
1005}
1006
1007#[derive(Debug, Clone)]
1008pub struct CausalEdge {
1009    pub source: String,
1010    pub target: String,
1011    pub strength: f32,
1012    pub delay: f32,
1013}
1014
1015#[derive(Debug, Clone)]
1016pub struct CausalEffect {
1017    pub effect_type: String,
1018    pub magnitude: f32,
1019    pub probability: f32,
1020}
1021
1022#[derive(Debug, Clone)]
1023pub struct AnalogyResult {
1024    pub similarity_score: f32,
1025    pub matching_patterns: Vec<PatternMatch>,
1026    pub explanation: String,
1027}
1028
1029#[derive(Debug, Clone)]
1030pub struct PatternMatch {
1031    pub source_element: String,
1032    pub target_element: String,
1033    pub similarity: f32,
1034}
1035
1036#[derive(Debug, Clone)]
1037pub struct AbstractConcept {
1038    pub concept_name: String,
1039    pub confidence: f32,
1040    pub supporting_evidence: Vec<String>,
1041}
1042
1043#[derive(Debug, Clone)]
1044pub struct TemporalPatterns {
1045    pub patterns: Vec<TemporalPattern>,
1046    pub temporal_graph: TemporalGraph,
1047}
1048
1049#[derive(Debug, Clone)]
1050pub struct TemporalPattern {
1051    pub pattern_type: String,
1052    pub frequency: f32,
1053    pub duration: f32,
1054}
1055
1056#[derive(Debug, Clone)]
1057pub struct TemporalGraph {
1058    pub nodes: Vec<TemporalNode>,
1059    pub edges: Vec<TemporalEdge>,
1060}
1061
1062#[derive(Debug, Clone)]
1063pub struct TemporalNode {
1064    pub timestamp: f32,
1065    pub event_type: String,
1066    pub properties: HashMap<String, f32>,
1067}
1068
1069#[derive(Debug, Clone)]
1070pub struct TemporalEdge {
1071    pub source_time: f32,
1072    pub target_time: f32,
1073    pub relationship_type: String,
1074}
1075
1076// Implementation stubs for associated types
1077impl CausalInferenceModule {
1078    fn new() -> Self {
1079        Self {
1080            causal_models: Vec::new(),
1081            intervention_params: InterventionParams {
1082                intervention_types: Vec::new(),
1083                effect_propagation: true,
1084                temporal_modeling: true,
1085            },
1086            counterfactual_params: CounterfactualParams {
1087                alternative_scenarios: 5,
1088                plausibility_threshold: 0.3,
1089                temporal_scope: 10.0,
1090            },
1091        }
1092    }
1093
1094    fn build_causal_graph(&self, patterns: &TemporalPatterns) -> Result<CausalGraph> {
1095        Ok(CausalGraph {
1096            nodes: Vec::new(),
1097            edges: Vec::new(),
1098        })
1099    }
1100
1101    fn infer_effects(&self, graph: &CausalGraph, query: &str) -> Result<Vec<CausalEffect>> {
1102        Ok(Vec::new())
1103    }
1104}
1105
1106impl VisualQuestionAnsweringSystem {
1107    fn new() -> Self {
1108        Self {
1109            question_types: vec![QuestionType::Object, QuestionType::Scene],
1110            answer_strategies: Vec::new(),
1111            attention_mechanisms: Vec::new(),
1112        }
1113    }
1114}
1115
1116impl AnalogicalReasoningEngine {
1117    fn new() -> Self {
1118        Self {
1119            analogy_templates: Vec::new(),
1120            similarity_metrics: Vec::new(),
1121            transfer_params: TransferLearningParams {
1122                adaptation_rate: 0.1,
1123                domain_similarity_threshold: 0.5,
1124                feature_selection: true,
1125            },
1126        }
1127    }
1128
1129    fn find_analogy(
1130        &self,
1131        source: &SceneAnalysisResult,
1132        _target: &SceneAnalysisResult,
1133    ) -> Result<AnalogyResult> {
1134        Ok(AnalogyResult {
1135            similarity_score: 0.7,
1136            matching_patterns: Vec::new(),
1137            explanation: "Structural similarity detected".to_string(),
1138        })
1139    }
1140}
1141
1142impl TemporalEventAnalyzer {
1143    fn new() -> Self {
1144        Self {
1145            event_detectors: Vec::new(),
1146            temporal_models: Vec::new(),
1147            sequence_params: SequenceAnalysisParams {
1148                max_sequence_length: 100,
1149                pattern_recognition: true,
1150                anomaly_detection: true,
1151            },
1152        }
1153    }
1154}
1155
1156impl AbstractConceptRecognizer {
1157    fn new() -> Self {
1158        Self {
1159            concept_hierarchies: Vec::new(),
1160            abstraction_layers: Vec::new(),
1161            learning_params: ConceptLearningParams {
1162                learning_rate: 0.01,
1163                concept_emergence_threshold: 0.8,
1164                hierarchical_learning: true,
1165            },
1166        }
1167    }
1168
1169    fn recognize_concepts(&self, scene: &SceneAnalysisResult) -> Result<Vec<AbstractConcept>> {
1170        Ok(Vec::new())
1171    }
1172}
1173
1174impl MultiModalIntegrationHub {
1175    fn new() -> Self {
1176        Self {
1177            modalities: vec![Modality::Visual],
1178            fusion_strategies: Vec::new(),
1179            cross_attention: Vec::new(),
1180        }
1181    }
1182}
1183
1184impl VisualKnowledgeBase {
1185    fn new() -> Self {
1186        Self {
1187            facts: HashMap::new(),
1188            rules: Vec::new(),
1189            ontology: ConceptOntology {
1190                concepts: HashMap::new(),
1191                relationships: Vec::new(),
1192                inheritance_hierarchy: HashMap::new(),
1193            },
1194        }
1195    }
1196}
1197
1198/// High-level function for complex visual reasoning
1199#[allow(dead_code)]
1200pub fn perform_advanced_visual_reasoning(
1201    scene: &SceneAnalysisResult,
1202    question: &str,
1203    context: Option<&[SceneAnalysisResult]>,
1204) -> Result<VisualReasoningResult> {
1205    let engine = VisualReasoningEngine::new();
1206
1207    let query = VisualReasoningQuery {
1208        query_type: QueryType::WhatIsHappening, // Default, could be inferred from question
1209        question: question.to_string(),
1210        parameters: HashMap::new(),
1211        context_requirements: Vec::new(),
1212    };
1213
1214    engine.process_query(&query, scene, context)
1215}
scirs2_vision/visual_reasoning.rs

scirs2_vision/
visual_reasoning.rs
