📋 Implementation Details & Technical Specifications¶

class AfterEffectsMetadataExtractor:
    def extract_animation_metadata(self, ae_project_path):
        """Extract comprehensive metadata from After Effects project"""

        metadata = {
            "keyframes": [],
            "easing_curves": [],
            "timing_relationships": [],
            "layer_hierarchies": [],
            "composition_settings": {}
        }

        # Extract keyframe data
        keyframes = self.extract_keyframes(ae_project_path)
        metadata["keyframes"] = keyframes

        # Extract easing curves
        easing_curves = self.extract_easing_curves(ae_project_path)
        metadata["easing_curves"] = easing_curves

        # Extract timing relationships
        timing_data = self.extract_timing_relationships(ae_project_path)
        metadata["timing_relationships"] = timing_data

        return metadata

    def extract_keyframes(self, ae_project):
        """Extract keyframe data with precise timing"""
        keyframes = []

        for layer in ae_project.layers:
            for property in layer.properties:
                if property.numKeys > 0:
                    for i in range(1, property.numKeys + 1):
                        keyframe = {
                            "layer_id": layer.id,
                            "property": property.name,
                            "time": property.keyTime(i),
                            "value": property.keyValue(i),
                            "easing_in": property.keyInTemporalEase(i),
                            "easing_out": property.keyOutTemporalEase(i),
                            "spatial_interpolation": property.keySpatialTangent(i)
                        }
                        keyframes.append(keyframe)

        return keyframes

class WFCPatternExtractor:
    def extract_spatial_temporal_patterns(self, ground_truth_data):
        """Extract patterns for Wave Function Collapse system"""

        patterns = {
            "spatial_patterns": [],
            "temporal_patterns": [],
            "transition_patterns": [],
            "easing_libraries": []
        }

        # Extract spatial patterns
        for frame_data in ground_truth_data:
            spatial_pattern = self.extract_spatial_pattern(frame_data)
            patterns["spatial_patterns"].append(spatial_pattern)

        # Extract temporal patterns
        temporal_patterns = self.extract_temporal_patterns(ground_truth_data)
        patterns["temporal_patterns"] = temporal_patterns

        # Extract transition patterns
        transition_patterns = self.extract_transition_patterns(ground_truth_data)
        patterns["transition_patterns"] = transition_patterns

        # Build easing curve library
        easing_library = self.build_easing_library(ground_truth_data)
        patterns["easing_libraries"] = easing_library

        return patterns

    def extract_spatial_pattern(self, frame_data):
        """Extract spatial arrangement pattern from frame"""
        pattern = {
            "element_positions": {},
            "spatial_relationships": [],
            "layout_constraints": []
        }

        for element in frame_data["elements"]:
            pattern["element_positions"][element["type"]] = {
                "x": element["position"]["x"],
                "y": element["position"]["y"],
                "width": element["size"]["width"],
                "height": element["size"]["height"]
            }

        # Analyze spatial relationships
        relationships = self.analyze_spatial_relationships(frame_data["elements"])
        pattern["spatial_relationships"] = relationships

        return pattern

class AdvancedNatronProjectBuilder:
    def __init__(self):
        self.plugin_catalog = self.load_plugin_catalog()
        self.node_templates = self.load_node_templates()
        self.animation_library = self.load_animation_library()

    def create_advanced_project(self, motion_timeline, output_specs):
        """Create advanced Natron project with full feature set"""

        # Create project with advanced settings
        project = self.create_project_with_settings(output_specs)

        # Build complex node graph
        node_graph = self.build_advanced_node_graph(motion_timeline)

        # Apply advanced effects
        enhanced_graph = self.apply_advanced_effects(node_graph, motion_timeline)

        # Configure render settings
        render_settings = self.configure_advanced_render_settings(output_specs)

        # Generate optimized .ntp file
        natron_project = self.generate_optimized_ntp_file(
            project, enhanced_graph, render_settings
        )

        return natron_project

    def apply_advanced_effects(self, node_graph, timeline):
        """Apply advanced visual effects based on timeline analysis"""

        enhanced_graph = node_graph.copy()

        # Analyze timeline for effect opportunities
        effect_opportunities = self.analyze_effect_opportunities(timeline)

        for opportunity in effect_opportunities:
            if opportunity.type == "motion_blur":
                enhanced_graph = self.add_motion_blur(enhanced_graph, opportunity)
            elif opportunity.type == "color_grading":
                enhanced_graph = self.add_color_grading(enhanced_graph, opportunity)
            elif opportunity.type == "particle_effects":
                enhanced_graph = self.add_particle_effects(enhanced_graph, opportunity)

        return enhanced_graph

    def add_motion_blur(self, graph, opportunity):
        """Add motion blur effect to specific elements"""
        motion_blur_node = self.create_node("fr.inria.built-in.Transform")
        motion_blur_node.getParam("motionBlur").setValue(opportunity.intensity)
        motion_blur_node.getParam("shutter").setValue(opportunity.shutter_angle)

        # Connect to appropriate elements
        for element_id in opportunity.affected_elements:
            element_node = graph.get_node(element_id)
            graph.connect_nodes(element_node, motion_blur_node)

        return graph

class TwoStepRenderer:
    def __init__(self):
        self.project_builder = AdvancedNatronProjectBuilder()
        self.render_manager = RenderManager()

    def render_with_two_step_method(self, motion_timeline, output_specs):
        """Use two-step methodology for reliable rendering"""

        # Step 1: Create and save project
        project_path = self.create_and_save_project(motion_timeline, output_specs)

        # Step 2: Load and render project
        output_path = self.load_and_render_project(project_path, output_specs)

        return output_path

    def create_and_save_project(self, timeline, specs):
        """Step 1: Create project and save to disk"""

        # Create project
        project = self.project_builder.create_advanced_project(timeline, specs)

        # Save project to disk
        project_path = f"/tmp/project_{uuid.uuid4()}.ntp"
        project.save(project_path)

        return project_path

    def load_and_render_project(self, project_path, specs):
        """Step 2: Load project and render"""

        # Load project
        app.loadProject(project_path)

        # Get render node
        render_node = app.getNode("RenderFinal")

        # Render with optimized settings
        render_settings = self.optimize_render_settings(specs)
        output_path = self.render_manager.render_with_settings(
            render_node, render_settings
        )

        return output_path

class EnhancedJSONSchemaGenerator:
    def __init__(self):
        self.schema_validator = AdvancedPydanticValidator()
        self.template_engine = AdvancedTemplateEngine()
        self.brand_manager = AdvancedBrandManager()
        self.ai_enhancer = AIEnhancementEngine()

    def generate_enhanced_schema(self, ai_content, project_config):
        """Generate enhanced JSON schema with AI improvements"""

        # Initial schema generation
        base_schema = self.generate_base_schema(ai_content, project_config)

        # AI enhancement
        enhanced_schema = self.ai_enhancer.enhance_schema(base_schema, project_config)

        # Brand compliance enhancement
        brand_enhanced_schema = self.brand_manager.apply_advanced_brand_guidelines(
            enhanced_schema, project_config.brand_id
        )

        # Quality optimization
        optimized_schema = self.optimize_schema_quality(brand_enhanced_schema)

        return optimized_schema

    def optimize_schema_quality(self, schema):
        """Optimize schema for quality and performance"""

        # Optimize animations
        schema.animations = self.optimize_animations(schema.animations)

        # Optimize layouts
        schema.layers = self.optimize_layouts(schema.layers)

        # Optimize assets
        schema.assets = self.optimize_assets(schema.assets)

        # Add performance hints
        schema.performance_hints = self.generate_performance_hints(schema)

        return schema

class AdvancedConstraintSolver:
    def __init__(self):
        self.spatial_solver = AdvancedSpatialSolver()
        self.temporal_solver = AdvancedTemporalSolver()
        self.multi_objective_optimizer = AdvancedMultiObjectiveOptimizer()
        self.performance_predictor = PerformancePredictor()

    def solve_advanced_constraints(self, states, transitions, constraints):
        """Solve advanced constraint problems with performance prediction"""

        # Build advanced constraint graph
        constraint_graph = self.build_advanced_constraint_graph(states, transitions)

        # Apply advanced constraints
        for constraint in constraints:
            constraint_graph = self.apply_advanced_constraint(constraint_graph, constraint)

        # Multi-objective optimization with performance prediction
        objectives = [
            self.objective_visual_coherence,
            self.objective_communication_effectiveness,
            self.objective_technical_constraints,
            self.objective_historical_performance,
            self.objective_rendering_performance
        ]

        optimized_solution = self.multi_objective_optimizer.optimize_with_prediction(
            constraint_graph, objectives, self.performance_predictor
        )

        return optimized_solution

    def objective_rendering_performance(self, solution):
        """Optimize for rendering performance"""

        # Predict rendering time
        predicted_time = self.performance_predictor.predict_rendering_time(solution)

        # Optimize for target performance
        target_time = 30.0  # seconds
        performance_score = max(0, 1 - (predicted_time / target_time))

        return performance_score

class PerformancePredictor:
    def __init__(self):
        self.historical_data = HistoricalDataManager()
        self.ml_predictor = MLPerformancePredictor()
        self.rule_based_predictor = RuleBasedPredictor()

    def predict_rendering_time(self, solution):
        """Predict rendering time for a given solution"""

        # Extract features
        features = self.extract_performance_features(solution)

        # Use ML predictor
        ml_prediction = self.ml_predictor.predict(features)

        # Use rule-based predictor
        rule_prediction = self.rule_based_predictor.predict(features)

        # Combine predictions
        combined_prediction = self.combine_predictions(ml_prediction, rule_prediction)

        return combined_prediction

    def extract_performance_features(self, solution):
        """Extract features that affect rendering performance"""

        features = {
            "total_layers": len(solution.layers),
            "total_animations": len(solution.animations),
            "complexity_score": self.calculate_complexity_score(solution),
            "effect_count": self.count_effects(solution),
            "resolution": solution.composition.resolution,
            "duration": solution.composition.duration,
            "frame_rate": solution.composition.frame_rate
        }

        return features

class AdvancedQualityValidator:
    def __init__(self):
        self.brand_compliance = AdvancedBrandComplianceChecker()
        self.technical_validator = AdvancedTechnicalValidator()
        self.accessibility_checker = AdvancedAccessibilityChecker()
        self.performance_validator = PerformanceValidator()

    def validate_advanced_quality(self, motion_graphics):
        """Comprehensive quality validation"""

        validation_results = {
            'overall_score': 0,
            'detailed_scores': {},
            'recommendations': [],
            'performance_metrics': {},
            'accessibility_report': {}
        }

        # Brand compliance validation
        brand_results = self.brand_compliance.validate_advanced_compliance(motion_graphics)
        validation_results['detailed_scores']['brand_compliance'] = brand_results

        # Technical quality validation
        technical_results = self.technical_validator.validate_advanced_technical_quality(motion_graphics)
        validation_results['detailed_scores']['technical_quality'] = technical_results

        # Accessibility validation
        accessibility_results = self.accessibility_checker.validate_accessibility(motion_graphics)
        validation_results['accessibility_report'] = accessibility_results

        # Performance validation
        performance_results = self.performance_validator.validate_performance(motion_graphics)
        validation_results['performance_metrics'] = performance_results

        # Calculate overall score
        validation_results['overall_score'] = self.calculate_advanced_overall_score(
            validation_results['detailed_scores']
        )

        # Generate recommendations
        validation_results['recommendations'] = self.generate_advanced_recommendations(
            validation_results
        )

        return validation_results