sync: Auto-sync from ACG-M-L5090 at 2026-02-09

Synced files:
- ai-misconceptions-reading-list.md (radio show research)
- ai-misconceptions-radio-segments.md (distilled radio segments)
- extract_license_plate.py
- review_best_plates.py

Machine: ACG-M-L5090
Timestamp: 2026-02-09

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

review_best_plates.py

@@ -0,0 +1,145 @@
+"""
+Identify the best license plate candidates from extraction results
+Filter by ideal aspect ratio (2-5) and larger area
+"""
+
+import re
+from pathlib import Path
+
+def parse_summary(summary_path):
+    """Parse summary.txt to find best candidates"""
+    candidates = []
+
+    with open(summary_path, 'r') as f:
+        content = f.read()
+
+    # Parse each candidate line
+    pattern = r'Time: ([\d.]+)s \| Candidate #(\d+) \| Aspect Ratio: ([\d.]+) \| Area: (\d+)'
+
+    for match in re.finditer(pattern, content):
+        timestamp = float(match.group(1))
+        candidate_num = int(match.group(2))
+        aspect_ratio = float(match.group(3))
+        area = int(match.group(4))
+
+        # Score candidates based on ideal license plate characteristics
+        # Ideal aspect ratio: 3-4.5 (most US license plates)
+        # Prefer larger areas (closer to camera)
+        ar_score = 0
+        if 2.5 <= aspect_ratio <= 5.0:
+            # Best score for aspect ratio between 3-4.5
+            if 3.0 <= aspect_ratio <= 4.5:
+                ar_score = 100
+            else:
+                ar_score = 50
+
+        # Area score (normalize to 0-100)
+        area_score = min(area / 500, 100)  # Scale area
+
+        # Combined score
+        total_score = (ar_score * 0.6) + (area_score * 0.4)
+
+        candidates.append({
+            'timestamp': timestamp,
+            'candidate': candidate_num,
+            'aspect_ratio': aspect_ratio,
+            'area': area,
+            'score': total_score
+        })
+
+    return candidates
+
+def main():
+    summary_path = Path("D:/Scratchpad/pickup_truck_25-30s/summary.txt")
+    output_dir = Path("D:/Scratchpad/pickup_truck_25-30s")
+
+    print("[INFO] Analyzing license plate candidates...")
+    candidates = parse_summary(summary_path)
+
+    # Sort by score
+    candidates.sort(key=lambda x: x['score'], reverse=True)
+
+    # Show top 20 candidates
+    print("\n" + "=" * 80)
+    print("TOP 20 LICENSE PLATE CANDIDATES")
+    print("=" * 80)
+    print(f"{'Rank':<6} {'Time':<10} {'Cand':<6} {'AR':<8} {'Area':<10} {'Score':<8} {'Files'}")
+    print("-" * 80)
+
+    for idx, candidate in enumerate(candidates[:20], 1):
+        timestamp = candidate['timestamp']
+        cand_num = candidate['candidate']
+        ar = candidate['aspect_ratio']
+        area = candidate['area']
+        score = candidate['score']
+
+        # Check which files exist for this candidate
+        frame_name = f"frame_{timestamp:.2f}s"
+        base_pattern = f"{frame_name}_plate_{cand_num}_"
+
+        # Count enhancement files
+        enhancement_files = list(output_dir.glob(f"{base_pattern}*.jpg"))
+        enhancement_count = len([f for f in enhancement_files if '_raw' not in f.name])
+
+        print(f"{idx:<6} {timestamp:<10.2f} {cand_num:<6} {ar:<8.2f} {area:<10} {score:<8.1f} {enhancement_count} enhanced")
+
+    # Create recommendation file
+    recommendation_path = output_dir / "RECOMMENDATIONS.txt"
+    with open(recommendation_path, 'w') as f:
+        f.write("LICENSE PLATE EXTRACTION - TOP CANDIDATES\n")
+        f.write("=" * 80 + "\n\n")
+        f.write("These are the top 20 most likely license plate candidates based on:\n")
+        f.write("- Aspect ratio (ideal: 3.0-4.5 for US plates)\n")
+        f.write("- Area size (larger = closer to camera)\n\n")
+        f.write("REVIEW THESE FILES FIRST:\n")
+        f.write("-" * 80 + "\n\n")
+
+        for idx, candidate in enumerate(candidates[:20], 1):
+            timestamp = candidate['timestamp']
+            cand_num = candidate['candidate']
+            ar = candidate['aspect_ratio']
+            area = candidate['area']
+            score = candidate['score']
+
+            f.write(f"RANK {idx}: Time {timestamp:.2f}s - Candidate #{cand_num}\n")
+            f.write(f"  Aspect Ratio: {ar:.2f} | Area: {area} | Score: {score:.1f}\n")
+            f.write(f"  Files to review:\n")
+
+            frame_name = f"frame_{timestamp:.2f}s"
+
+            # List specific enhancement files to check
+            enhancements = [
+                f"{frame_name}_detection_{cand_num}.jpg  (shows detection box on frame)",
+                f"{frame_name}_plate_{cand_num}_high_contrast.jpg  (best for dark plates)",
+                f"{frame_name}_plate_{cand_num}_extreme_sharp.jpg  (best for clarity)",
+                f"{frame_name}_plate_{cand_num}_adaptive_thresh.jpg  (best for OCR)",
+                f"{frame_name}_plate_{cand_num}_bilateral_sharp.jpg  (balanced enhancement)",
+            ]
+
+            for enhancement in enhancements:
+                f.write(f"    - {enhancement}\n")
+
+            f.write("\n")
+
+        f.write("\n" + "=" * 80 + "\n")
+        f.write("ENHANCEMENT TYPES EXPLAINED:\n")
+        f.write("-" * 80 + "\n")
+        f.write("- detection_X.jpg: Shows where the plate was detected on the frame\n")
+        f.write("- high_contrast.jpg: Best for dark/low-contrast plates\n")
+        f.write("- extreme_sharp.jpg: Best for overall clarity and readability\n")
+        f.write("- adaptive_thresh.jpg: Black/white threshold - best for OCR\n")
+        f.write("- bilateral_sharp.jpg: Noise reduction + sharpening\n")
+        f.write("- unsharp_mask.jpg: Professional-grade sharpening\n")
+        f.write("- bright_contrast.jpg: Brightness + contrast boost\n")
+
+    print("\n[SUCCESS] Analysis complete!")
+    print(f"[INFO] Recommendations saved to: {recommendation_path}")
+    print("\n[NEXT STEPS]")
+    print("1. Open the output directory in File Explorer:")
+    print(f"   {output_dir}")
+    print("2. Read RECOMMENDATIONS.txt for the best candidates")
+    print("3. Start with Rank 1, review the enhancement files listed")
+    print("4. The 'extreme_sharp' and 'adaptive_thresh' versions usually work best")
+
+if __name__ == "__main__":
+    main()