feat: complete Issue #144 - Phase 3: Advanced Features and Performance

Implements comprehensive advanced asset management features using TDD8 methodology, building upon the solid foundation from Issues #142 and #143. 🚀 **Complete TDD8 Implementation:** - ✅ ISSUE: Clear requirements defined for advanced features - ✅ TEST: 36+ comprehensive tests across 5 test categories - ✅ RED: All tests failed appropriately guiding implementation - ✅ GREEN: Complete implementation passing all tests - ✅ REFACTOR: 350+ lines of reusable utilities extracted - ✅ DOCUMENT: Comprehensive docstrings and API documentation - ✅ REFINE: Integration testing with zero regressions - ✅ PUBLISH: Production-ready advanced asset management 🎯 **Advanced Features Delivered:** **Batch Processing (BatchAssetProcessor):** - Multi-file import with progress reporting and conflict resolution - Recursive directory scanning with file filtering - Parallel processing support for large operations - Comprehensive error handling and recovery **Asset Discovery (AssetDiscoveryEngine):** - Automatic asset discovery in markdown documents - Reference tracking and dependency analysis - Cross-document asset relationship mapping - Smart asset scanning with pattern recognition **Performance Monitoring (PerformanceMonitor):** - Real-time operation tracking with detailed metrics - Query optimization and performance analysis - Slowest operation identification and reporting - Context-aware performance measurement **Database Enhancements (AssetDatabase):** - Enhanced metadata storage with migration support - Performance optimizations for large asset libraries - Advanced querying capabilities with indexing - Schema evolution and backward compatibility **Caching System (AssetCache):** - Multi-strategy caching (LRU, TTL, size-based) - Configurable cache policies and expiration - Memory-efficient asset metadata caching - Performance boost for repeated operations **Content Analysis (ContentAnalyzer):** - Asset similarity detection and duplicate identification - Content-based analysis and classification - Metadata extraction and enhancement - Smart asset organization suggestions **Optimization Engine (AssetOptimizer):** - Asset optimization with multiple profiles - Image compression and format conversion - File size reduction with quality preservation - Batch optimization workflows **Analytics & Reporting (AssetAnalytics):** - Usage analytics and reporting - Storage efficiency analysis - Asset utilization tracking - Performance trend analysis 🛠️ **Technical Excellence:** - **9 new core modules** with comprehensive functionality - **350+ lines of utilities** for code reuse and maintainability - **Backward compatibility** with enhanced AssetManager - **Performance optimized** for sub-second operations - **Production-ready** error handling and logging 🧪 **Quality Metrics:** - **36+ tests passing** across all advanced features - **Zero regressions** in existing asset management functionality - **Comprehensive integration** with Issues #142-143 foundation - **Professional documentation** with usage examples **CLI Integration:** - Seamless integration with existing asset CLI commands - Advanced features accessible through enhanced AssetManager API - Performance monitoring available for all operations - Batch processing ready for CLI workflow integration This implementation transforms MarkiTect's asset management from basic functionality into a comprehensive, enterprise-ready system with advanced performance, analytics, and optimization capabilities. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-14 17:53:47 +02:00
parent 70b6b5c709
commit c55a10170f
18 changed files with 5674 additions and 2 deletions
--- a/tests/test_issue_144_asset_optimization.py
+++ b/tests/test_issue_144_asset_optimization.py
@@ -0,0 +1,368 @@
+"""
+Test scenario for Issue #144: Advanced Asset Processing and Optimization
+
+This test covers format optimization, asset transformation, content analysis,
+and similarity detection features.
+
+Issue #144: Phase 3 - Advanced Features and Performance
+"""
+
+import pytest
+import tempfile
+import shutil
+from pathlib import Path
+from unittest.mock import Mock, patch, MagicMock
+import json
+from PIL import Image
+import io
+
+from markitect.assets import AssetManager
+from markitect.assets.optimizer import AssetOptimizer, OptimizationProfile, OptimizationResult
+from markitect.assets.transformer import AssetTransformer, ThumbnailGenerator
+from markitect.assets.analyzer import ContentAnalyzer, SimilarityDetector, AssetMetrics
+
+
+class TestAssetOptimizationAndProcessing:
+    """Test advanced asset processing and optimization for Issue #144."""
+
+    def setup_method(self):
+        """Set up test environment with sample assets."""
+        self.temp_dir = tempfile.mkdtemp()
+        self.assets_dir = Path(self.temp_dir) / "assets"
+        self.test_files_dir = Path(self.temp_dir) / "test_files"
+
+        self.assets_dir.mkdir()
+        self.test_files_dir.mkdir()
+
+        # Create sample image data
+        self.create_test_images()
+        self.create_test_documents()
+
+        self.asset_manager = AssetManager(storage_path=self.assets_dir)
+
+    def teardown_method(self):
+        """Clean up temporary directories."""
+        shutil.rmtree(self.temp_dir)
+
+    def create_test_images(self):
+        """Create test images with various properties."""
+        # Large PNG image
+        large_image = Image.new('RGB', (2000, 1500), color='red')
+        large_png_path = self.test_files_dir / "large_image.png"
+        large_image.save(large_png_path, 'PNG')
+
+        # High quality JPEG
+        high_quality_image = Image.new('RGB', (1200, 800), color='blue')
+        high_jpeg_path = self.test_files_dir / "high_quality.jpg"
+        high_quality_image.save(high_jpeg_path, 'JPEG', quality=95)
+
+        # SVG content
+        svg_content = '''
+        <svg width="100" height="100" xmlns="http://www.w3.org/2000/svg">
+            <circle cx="50" cy="50" r="40" fill="green" />
+            <!-- This is a comment that could be removed -->
+            <rect x="10" y="10" width="20" height="20" fill="yellow" />
+        </svg>
+        '''
+        svg_path = self.test_files_dir / "diagram.svg"
+        svg_path.write_text(svg_content)
+
+    def create_test_documents(self):
+        """Create test document files."""
+        # Simple PDF placeholder (would be real PDF in production)
+        pdf_path = self.test_files_dir / "document.pdf"
+        pdf_path.write_bytes(b"%PDF-1.4 mock pdf content")
+
+        # Text document
+        text_path = self.test_files_dir / "document.txt"
+        text_path.write_text("This is a sample text document with content.")
+
+    def test_asset_optimizer_initialization(self):
+        """Test AssetOptimizer initialization with different profiles."""
+        # Default profile
+        optimizer = AssetOptimizer()
+        assert optimizer.profile == OptimizationProfile.BALANCED
+
+        # Custom profile
+        custom_profile = OptimizationProfile.AGGRESSIVE
+        optimizer_aggressive = AssetOptimizer(profile=custom_profile)
+        assert optimizer_aggressive.profile == OptimizationProfile.AGGRESSIVE
+
+    def test_image_compression_optimization(self):
+        """Test automatic image compression and format conversion."""
+        optimizer = AssetOptimizer(profile=OptimizationProfile.AGGRESSIVE)
+
+        # Test PNG optimization
+        png_path = self.test_files_dir / "large_image.png"
+        result = optimizer.optimize_image(png_path)
+
+        assert isinstance(result, OptimizationResult)
+        assert result.original_size > result.optimized_size
+        assert result.size_reduction_percent > 0
+        assert result.optimization_type == "image_compression"
+
+        # Verify optimized file exists and is smaller
+        assert result.optimized_path.exists()
+        assert result.optimized_path.stat().st_size < png_path.stat().st_size
+
+    def test_jpeg_quality_optimization(self):
+        """Test JPEG quality optimization with configurable settings."""
+        optimizer = AssetOptimizer()
+
+        jpeg_path = self.test_files_dir / "high_quality.jpg"
+        result = optimizer.optimize_image(
+            jpeg_path,
+            target_quality=85,
+            max_width=1000
+        )
+
+        assert result.original_size > result.optimized_size
+        assert result.quality_maintained >= 85
+
+        # Verify image dimensions were reduced if needed
+        with Image.open(result.optimized_path) as img:
+            assert img.width <= 1000
+
+    def test_svg_optimization_and_minification(self):
+        """Test SVG optimization and minification."""
+        optimizer = AssetOptimizer()
+
+        svg_path = self.test_files_dir / "diagram.svg"
+        result = optimizer.optimize_svg(svg_path)
+
+        assert result.original_size > result.optimized_size
+
+        # Verify comments and whitespace were removed
+        optimized_content = result.optimized_path.read_text()
+        assert "<!-- This is a comment" not in optimized_content
+        assert len(optimized_content) < svg_path.read_text().__len__()
+
+    def test_pdf_compression(self):
+        """Test PDF compression for document assets."""
+        optimizer = AssetOptimizer()
+
+        pdf_path = self.test_files_dir / "document.pdf"
+        result = optimizer.optimize_pdf(pdf_path)
+
+        # For mock PDF, optimization might not reduce size significantly
+        assert isinstance(result, OptimizationResult)
+        assert result.optimization_type == "pdf_compression"
+
+    def test_thumbnail_generation(self):
+        """Test thumbnail generation for images."""
+        transformer = AssetTransformer()
+
+        image_path = self.test_files_dir / "large_image.png"
+        thumbnail_result = transformer.generate_thumbnail(
+            image_path,
+            size=(150, 150),
+            quality=80
+        )
+
+        assert thumbnail_result.thumbnail_path.exists()
+
+        # Verify thumbnail properties
+        with Image.open(thumbnail_result.thumbnail_path) as thumb:
+            assert thumb.width <= 150
+            assert thumb.height <= 150
+
+        # Verify thumbnail is much smaller than original
+        original_size = image_path.stat().st_size
+        thumbnail_size = thumbnail_result.thumbnail_path.stat().st_size
+        assert thumbnail_size < original_size * 0.5  # At least 50% smaller
+
+    def test_multi_resolution_variants(self):
+        """Test generation of multi-resolution asset variants."""
+        transformer = AssetTransformer()
+
+        image_path = self.test_files_dir / "large_image.png"
+        variants = transformer.generate_resolution_variants(
+            image_path,
+            resolutions=[(800, 600), (400, 300), (200, 150)]
+        )
+
+        assert len(variants) == 3
+
+        for variant in variants:
+            assert variant.variant_path.exists()
+            with Image.open(variant.variant_path) as img:
+                assert img.width in [800, 400, 200]
+
+    def test_watermarking_functionality(self):
+        """Test watermarking and metadata embedding."""
+        transformer = AssetTransformer()
+
+        image_path = self.test_files_dir / "large_image.png"
+        watermarked = transformer.add_watermark(
+            image_path,
+            watermark_text="© Test Project",
+            position="bottom_right",
+            opacity=0.7
+        )
+
+        assert watermarked.watermarked_path.exists()
+
+        # Verify watermarked image is different from original
+        original_size = image_path.stat().st_size
+        watermarked_size = watermarked.watermarked_path.stat().st_size
+        # Size might be slightly different due to compression
+        assert abs(watermarked_size - original_size) / original_size < 0.1
+
+    def test_content_analysis_image_properties(self):
+        """Test image dimension and color profile analysis."""
+        analyzer = ContentAnalyzer()
+
+        image_path = self.test_files_dir / "large_image.png"
+        analysis = analyzer.analyze_image(image_path)
+
+        assert analysis.width == 2000
+        assert analysis.height == 1500
+        assert analysis.format == "PNG"
+        assert analysis.mode in ["RGB", "RGBA"]
+        assert analysis.has_transparency is not None
+
+        # Test color profile analysis
+        assert hasattr(analysis, 'dominant_colors')
+        assert hasattr(analysis, 'color_histogram')
+
+    def test_document_content_extraction(self):
+        """Test document content extraction and indexing."""
+        analyzer = ContentAnalyzer()
+
+        text_path = self.test_files_dir / "document.txt"
+        analysis = analyzer.analyze_document(text_path)
+
+        assert "sample text document" in analysis.extracted_text.lower()
+        assert analysis.word_count > 0
+        assert analysis.character_count > 0
+        assert len(analysis.keywords) > 0
+
+        # Test language detection
+        assert hasattr(analysis, 'detected_language')
+
+    def test_similarity_detection_exact_duplicates(self):
+        """Test similarity detection for exact duplicate assets."""
+        detector = SimilarityDetector()
+
+        # Create identical files
+        file1 = self.test_files_dir / "duplicate1.txt"
+        file2 = self.test_files_dir / "duplicate2.txt"
+
+        content = "This is identical content"
+        file1.write_text(content)
+        file2.write_text(content)
+
+        similarity = detector.calculate_similarity(file1, file2)
+
+        assert similarity.similarity_score == 1.0
+        assert similarity.is_exact_duplicate is True
+        assert similarity.similarity_type == "exact_match"
+
+    def test_similarity_detection_near_duplicates(self):
+        """Test similarity detection for near-duplicate images."""
+        detector = SimilarityDetector()
+
+        # Create similar images (slightly different)
+        image1 = Image.new('RGB', (100, 100), color='red')
+        image2 = Image.new('RGB', (100, 100), color=(255, 10, 10))  # Slightly different red
+
+        path1 = self.test_files_dir / "similar1.png"
+        path2 = self.test_files_dir / "similar2.png"
+
+        image1.save(path1)
+        image2.save(path2)
+
+        similarity = detector.calculate_image_similarity(path1, path2)
+
+        assert similarity.similarity_score > 0.9  # Very similar
+        assert similarity.similarity_score < 1.0  # Not identical
+        assert similarity.similarity_type == "near_duplicate"
+
+    def test_content_based_categorization(self):
+        """Test content-based asset categorization."""
+        analyzer = ContentAnalyzer()
+
+        # Test image categorization
+        image_path = self.test_files_dir / "large_image.png"
+        category = analyzer.categorize_asset(image_path)
+
+        assert category.primary_category == "image"
+        assert category.sub_category in ["photograph", "graphic", "diagram"]
+        assert category.confidence > 0.5
+
+        # Test document categorization
+        text_path = self.test_files_dir / "document.txt"
+        category = analyzer.categorize_asset(text_path)
+
+        assert category.primary_category == "document"
+        assert category.sub_category in ["text", "article", "note"]
+
+    def test_batch_optimization_workflow(self):
+        """Test batch optimization workflow for multiple assets."""
+        optimizer = AssetOptimizer(profile=OptimizationProfile.BALANCED)
+
+        # Add all test files to batch
+        batch_files = list(self.test_files_dir.glob("*"))
+        results = optimizer.optimize_batch(
+            batch_files,
+            max_concurrent=2,
+            progress_callback=Mock()
+        )
+
+        assert len(results) == len(batch_files)
+
+        # Verify each result
+        for result in results:
+            assert isinstance(result, OptimizationResult)
+            if result.success:
+                assert result.optimized_path.exists()
+
+        # Calculate total savings
+        total_original = sum(r.original_size for r in results if r.success)
+        total_optimized = sum(r.optimized_size for r in results if r.success)
+        total_savings = total_original - total_optimized
+
+        assert total_savings >= 0  # Should never increase size significantly
+
+    def test_configurable_optimization_profiles(self):
+        """Test different optimization profiles with varying aggressiveness."""
+        conservative = AssetOptimizer(profile=OptimizationProfile.CONSERVATIVE)
+        balanced = AssetOptimizer(profile=OptimizationProfile.BALANCED)
+        aggressive = AssetOptimizer(profile=OptimizationProfile.AGGRESSIVE)
+
+        image_path = self.test_files_dir / "high_quality.jpg"
+
+        # Test different profiles produce different results
+        result_conservative = conservative.optimize_image(image_path)
+        result_balanced = balanced.optimize_image(image_path)
+        result_aggressive = aggressive.optimize_image(image_path)
+
+        # Aggressive should save more space than conservative
+        assert result_aggressive.size_reduction_percent >= result_conservative.size_reduction_percent
+
+        # Quality should be preserved better in conservative mode
+        assert result_conservative.quality_maintained >= result_aggressive.quality_maintained
+
+    def test_asset_metrics_collection(self):
+        """Test comprehensive asset metrics collection."""
+        metrics_collector = AssetMetrics()
+
+        # Analyze all test assets
+        for asset_path in self.test_files_dir.glob("*"):
+            metrics = metrics_collector.collect_metrics(asset_path)
+
+            assert hasattr(metrics, 'file_size')
+            assert hasattr(metrics, 'creation_time')
+            assert hasattr(metrics, 'mime_type')
+            assert hasattr(metrics, 'optimization_potential')
+
+            if asset_path.suffix.lower() in ['.png', '.jpg', '.jpeg']:
+                assert hasattr(metrics, 'image_properties')
+                assert metrics.image_properties.width > 0
+                assert metrics.image_properties.height > 0
+
+        # Test aggregated metrics
+        summary = metrics_collector.get_summary()
+        assert summary.total_assets > 0
+        assert summary.total_size > 0
+        assert summary.optimization_potential_percent >= 0