Todd
257
tests/test_perceptual_scan.py
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257
tests/test_perceptual_scan.py
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#!/usr/bin/env python3
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"""
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Dry-run test of Instagram Perceptual Duplicate Detection
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Scans last 3 days of downloads and reports what would be considered duplicates
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WITHOUT actually moving or deleting anything.
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from modules.unified_database import UnifiedDatabase
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from modules.instagram_perceptual_duplicate_detector import InstagramPerceptualDuplicateDetector
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import json
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from datetime import datetime, timedelta
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from collections import defaultdict
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class DryRunLogger:
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"""Logger that captures all messages"""
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def __init__(self):
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self.messages = []
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def __call__(self, msg, level):
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self.messages.append((level, msg))
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print(f"[{level.upper()}] {msg}")
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def main():
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print("=" * 80)
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print("INSTAGRAM PERCEPTUAL DUPLICATE DETECTION - DRY RUN")
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print("=" * 80)
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print()
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# Initialize database
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db_path = Path(__file__).parent.parent / 'database' / 'media_downloader.db'
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db = UnifiedDatabase(str(db_path))
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# Get all Instagram files from last 3 days
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with db.get_connection() as conn:
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cursor = conn.cursor()
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cursor.execute("""
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SELECT
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filename,
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source,
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file_path,
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file_hash,
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download_date,
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content_type
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FROM downloads
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WHERE platform = 'instagram'
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AND download_date > datetime('now', '-3 days')
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AND file_path IS NOT NULL
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AND file_path NOT LIKE '%_phrase_checked_%'
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AND file_path NOT LIKE '%_old_post_%'
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ORDER BY source, download_date
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""")
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files = []
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for row in cursor.fetchall():
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if row[2] and Path(row[2]).exists(): # Only include files that exist
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files.append({
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'filename': row[0],
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'source': row[1],
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'file_path': row[2],
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'file_hash': row[3],
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'download_date': row[4],
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'content_type': row[5] or 'unknown'
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})
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print(f"Found {len(files)} Instagram files from last 3 days that exist on disk")
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print()
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if len(files) == 0:
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print("No files to analyze!")
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return
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# Initialize detector
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logger = DryRunLogger()
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detector = InstagramPerceptualDuplicateDetector(
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unified_db=db,
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log_callback=logger
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)
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# Get settings (will use defaults since feature is disabled)
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settings = {
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'enabled': False,
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'perceptual_hash_threshold': 12,
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'text_detection_enabled': True,
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'clean_score_weight': 3,
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'quality_score_weight': 1,
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'min_text_difference': 5
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}
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print(f"Using settings:")
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print(f" - Perceptual hash threshold: {settings['perceptual_hash_threshold']}")
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print(f" - Clean score weight: {settings['clean_score_weight']}")
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print(f" - Quality score weight: {settings['quality_score_weight']}")
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print(f" - Text detection: {'Enabled' if settings['text_detection_enabled'] else 'Disabled'}")
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print()
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# Process each file and collect data
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print("Analyzing files...")
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print("-" * 80)
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file_data = []
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for i, file_info in enumerate(files, 1):
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file_path = file_info['file_path']
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source = file_info['source']
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print(f"\n[{i}/{len(files)}] Processing: {Path(file_path).name}")
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# Calculate perceptual hash
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phash = detector._calculate_perceptual_hash(file_path)
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if not phash:
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print(f" ⚠️ Could not calculate perceptual hash - skipping")
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continue
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# Detect text overlays
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if settings['text_detection_enabled']:
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text_count, text_chars = detector._detect_text_overlays(file_path)
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else:
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text_count, text_chars = 0, 0
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# Get quality metrics
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quality_metrics = detector._get_quality_metrics(file_path)
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# Calculate scores
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clean_score = detector._calculate_clean_score(text_count, text_chars)
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quality_score = detector._calculate_quality_score(quality_metrics)
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print(f" Hash: {phash[:16]}...")
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print(f" Text overlays: {text_count} regions, {text_chars} chars")
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print(f" Resolution: {quality_metrics['width']}x{quality_metrics['height']}")
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print(f" File size: {quality_metrics['file_size'] / 1024 / 1024:.1f} MB")
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print(f" Clean score: {clean_score:.1f}/100")
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print(f" Quality score: {quality_score:.1f}/100")
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print(f" Total score: {(clean_score * settings['clean_score_weight']) + (quality_score * settings['quality_score_weight']):.1f}")
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file_data.append({
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'file_info': file_info,
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'phash': phash,
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'text_count': text_count,
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'text_chars': text_chars,
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'clean_score': clean_score,
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'quality_score': quality_score,
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'quality_metrics': quality_metrics,
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'total_score': (clean_score * settings['clean_score_weight']) + (quality_score * settings['quality_score_weight'])
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})
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print()
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print("=" * 80)
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print("DUPLICATE DETECTION ANALYSIS")
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print("=" * 80)
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print()
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# Find duplicates by comparing hashes
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duplicates = []
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processed = set()
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for i, data1 in enumerate(file_data):
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if i in processed:
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continue
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group = [data1]
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for j, data2 in enumerate(file_data[i+1:], start=i+1):
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if j in processed:
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continue
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# Same source only
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if data1['file_info']['source'] != data2['file_info']['source']:
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continue
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# Calculate Hamming distance
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distance = detector._hamming_distance(data1['phash'], data2['phash'])
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if distance <= settings['perceptual_hash_threshold']:
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group.append(data2)
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processed.add(j)
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if len(group) > 1:
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# Sort by total score (highest first)
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group.sort(key=lambda x: x['total_score'], reverse=True)
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duplicates.append(group)
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processed.add(i)
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if len(duplicates) == 0:
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print("✅ No perceptual duplicates found!")
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print()
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print("All files are unique or sufficiently different.")
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return
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print(f"Found {len(duplicates)} duplicate group(s):")
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print()
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total_would_remove = 0
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for group_num, group in enumerate(duplicates, 1):
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print(f"\n{'=' * 80}")
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print(f"DUPLICATE GROUP #{group_num}")
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print(f"{'=' * 80}")
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print(f"Source: {group[0]['file_info']['source']}")
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print(f"Files in group: {len(group)}")
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print()
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best = group[0]
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print(f"✅ WOULD KEEP:")
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print(f" File: {Path(best['file_info']['file_path']).name}")
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print(f" Path: {best['file_info']['file_path']}")
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print(f" Clean score: {best['clean_score']:.1f}/100 ({best['text_count']} text regions)")
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print(f" Quality score: {best['quality_score']:.1f}/100 ({best['quality_metrics']['width']}x{best['quality_metrics']['height']}, {best['quality_metrics']['file_size']/1024/1024:.1f}MB)")
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print(f" Total score: {best['total_score']:.1f}")
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print(f" Download date: {best['file_info']['download_date']}")
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print()
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print(f"❌ WOULD REMOVE ({len(group)-1} file(s)):")
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for data in group[1:]:
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total_would_remove += 1
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print(f"\n File: {Path(data['file_info']['file_path']).name}")
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print(f" Path: {data['file_info']['file_path']}")
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print(f" Clean score: {data['clean_score']:.1f}/100 ({data['text_count']} text regions)")
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print(f" Quality score: {data['quality_score']:.1f}/100 ({data['quality_metrics']['width']}x{data['quality_metrics']['height']}, {data['quality_metrics']['file_size']/1024/1024:.1f}MB)")
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print(f" Total score: {data['total_score']:.1f}")
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print(f" Download date: {data['file_info']['download_date']}")
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# Calculate hash distance
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distance = detector._hamming_distance(best['phash'], data['phash'])
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print(f" Hash distance from best: {distance}")
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# Explain why it would be removed
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reasons = []
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if data['clean_score'] < best['clean_score'] - settings['min_text_difference']:
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reasons.append(f"Has more text overlays ({data['text_count']} vs {best['text_count']})")
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if data['quality_score'] < best['quality_score']:
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reasons.append(f"Lower quality ({data['quality_metrics']['width']}x{data['quality_metrics']['height']} vs {best['quality_metrics']['width']}x{best['quality_metrics']['height']})")
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if data['total_score'] < best['total_score']:
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reasons.append(f"Lower total score ({data['total_score']:.1f} vs {best['total_score']:.1f})")
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if reasons:
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print(f" Reason(s): {'; '.join(reasons)}")
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print()
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print("=" * 80)
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print("SUMMARY")
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print("=" * 80)
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print(f"Total files analyzed: {len(file_data)}")
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print(f"Duplicate groups found: {len(duplicates)}")
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print(f"Files that would be kept: {len(duplicates)}")
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print(f"Files that would be removed: {total_would_remove}")
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print()
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print("⚠️ NOTE: This is a DRY RUN - no files were actually moved or deleted!")
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print(" To enable this feature, set 'enabled: true' in Configuration > Instagram Perceptual Duplicate Detection")
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print()
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if __name__ == '__main__':
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main()
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Reference in New Issue
Block a user