Implement perceptual hashing with BK-tree indexing for O(log n) similarity search

[Copilot]

Problem

Need perceptual hash (phash) support for image similarity detection. Naive implementation requires O(n) enumeration of all phashes for similarity queries, which doesn't scale beyond 10k images.

Solution

Implement BK-tree (Burkhard-Keller tree) indexing structure in KVRocks using Hamming distance as the metric. Tree pruning via triangle inequality reduces similarity search from O(n) to O(log n).

Tree structure:

phash:bktree:root                      → first phash inserted
phash:bktree:{phash}:children          → hash map: hamming_distance → child_phash

Search pruning:

# Only explore child if: |distance(node, query) - distance(node, child)| ≤ threshold
# Skips ~90%+ of tree for typical queries

Implementation

Core Objects

• Phashs.py: Phash object class, BK-tree operations (add, search, rebuild), hamming distance calculation
• Uses imagehash library for DCT-based 64-bit phash computation

Processing Pipeline

Image → ImagePhash module → calculates phash → creates Phash object + Image correlation
                          → PhashCorrelation queue → BK-tree search → creates Phash↔Phash correlations

Integration Points

• correlations_engine.py: Added phash: [image, phash] correlation types
• ail_objects.py: Registered Phash in OBJECTS_CLASS
• ail_core.py: Added phash to AIL_OBJECTS sets
• modules.cfg: ImagePhash subscribes to Image queue, publishes to PhashCorrelation queue

Configuration

[Images]
phash_max_hamming_distance = 8  # Hamming distance threshold for similarity (0-64 range)

UI

• Flask blueprint at /objects/phashes with daterange filtering
• Template follows DomHash/HHHash patterns

Maintenance

• rebuild_phash_index.py: Rebuilds BK-tree from all existing phash objects (for migrations or corruption recovery)
• Graceful fallback to linear search if BK-tree operations fail

Dependencies

• imagehash>=4.3.0 added to requirements.txt

Testing

27 tests covering phash object operations, hamming distance edge cases, BK-tree insertion/search with various thresholds, and index rebuilding.

Original prompt

Phash Implementation with Efficient BK-Tree Indexing

Overview

This PR implements perceptual hashing (phash) functionality for AIL with an efficient BK-tree indexing structure to enable fast similarity detection without full enumeration. This builds upon the work in PR #318 by @cavedave and addresses the performance concerns about enumerating all phash objects.

Key Improvements Over PR #318

1. BK-Tree Indexing for Efficient Search

• Implements a Burkhard-Keller tree structure in KVRocks for O(log n) similarity searches
• Avoids O(n) full enumeration of all phash objects
• Uses triangle inequality to prune search branches that cannot contain matches
• Provides 100-10000x performance improvement for large datasets (10k-100k images)

2. All Original Features from PR #318

• Perceptual hashing using imagehash library (64-bit DCT-based phash)
• Automatic correlation: Phash ↔ Image and Phash ↔ Phash
• Similarity detection using Hamming distance (configurable threshold, default: 8)
• UI integration: Phash object browser and correlation graph visualization
• Comprehensive testing: 100% coverage for Phash objects, 92%+ for modules

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Implementation Details

BK-Tree Index Structure

The BK-tree is stored in KVRocks using the following keys:

phash:bktree:root                           # Root node (first phash)
phash:bktree:{phash_value}:children        # Hash map: distance -> child_phash

How it works:

1. Each phash is inserted into the tree based on Hamming distance from parent nodes
2. Children are stored at keys representing their distance from parent
3. Search uses triangle inequality: if |d(node, query) - d(node, target)| > max_distance, skip that subtree
4. This prunes most of the search space without checking every phash

Performance:

• Current (PR Add perceptual hashing (phash) support for image similarity detection #318): O(n) - checks every phash
• With BK-tree: O(log n) average case
• For 10,000 images: ~100-1000x faster
• For 100,000 images: ~1000-10000x faster

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Files to Create/Modify

New Files (from PR #318)

1. bin/lib/objects/Phashs.py - Enhanced with BK-tree functions:

   • Phash class: Represents a perceptual hash value
   • Phashs collection class: Manages Phash objects
   • add_to_bktree_index(): Insert phash into BK-tree
   • search_bktree_index(): Fast similarity search using BK-tree
   • hamming_distance(): Calculate Hamming distance between phashes
   • rebuild_bktree_index(): Rebuild index from all existing phashes

2. bin/modules/ImagePhash.py (71 lines)

   • Processes images from Image queue
   • Calculates phash, stores in Image metadata, creates Phash objects
   • Creates Phash ↔ Image correlations

3. bin/modules/PhashCorrelation.py - Enhanced with BK-tree search:

   • Uses search_bktree_index() for efficient similarity search
   • Falls back to linear search if BK-tree fails (graceful degradation)
   • Creates Phash ↔ Phash correlations

4. bin/tools/rebuild_phash_index.py (NEW - not in original PR)

   • Maintenance script to rebuild BK-tree index
   • Useful after importing old data or index corruption

5. var/www/blueprints/objects_phash.py (74 lines)

   • Flask blueprint for Phash object routes
   • /objects/phashes - List view
   • /objects/phash/post - Form handling
   • /objects/phash/range/json - Chart data

6. var/www/templates/objects/phash/PhashDaterange.html (164 lines)

   • Jinja2 template for Phash object browser UI
   • Date range filtering and object listing

7. tests/test_objects_phashes.py (339 lines) - Enhanced with BK-tree tests:

   • Original 27 tests for Phash objects
   • Additional tests for BK-tree indexing functions
   • Tests for hamming_distance calculation
   • Tests for search_bktree_index with various thresholds
   • 100% code coverage

Modified Files (from PR #318)

8. bin/lib/objects/Images.py

   • Move phash calculation functions to Phashs.py (as suggested in PR review)
   • Keep references/wrappers for backward compatibility

9. bin/lib/objects/Screenshots.py

   • Move phash calculation functions to Phashs.py (as suggested in PR review)
   • Keep references/wrappers for backward compatibility

10. bin/lib/correlations_engine.py

    • Add "phash": ["image", "phash"] to CORRELATION_TYPES_BY_OBJ
    • Remove incorrect image↔image and screenshot↔image correlations (per PR review feedback)

11. bin/lib/objects/ail_objects.py

    • Register Phash in OBJECTS_CLASS dictionary

12. bin/lib/ail_core.py

    • Add 'phash' to AIL_OBJECTS set
    • Add 'phash' to AIL_OBJECTS_CORRELATIONS_DEFAULT set

13. configs/modules.cfg

    • Add [ImagePhash] section with queue configuration
    • Add [PhashCorrelation] section with queue configuration

14. bin/LAUNCH.sh

    • Add ImagePhash module to launch sequence
    • Add PhashCorrelation module to launch sequence

15. configs/core.cfg.sample

    • Add `phash_max_hamming_distance = 8...

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