单目3D初始代码
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zhao.zhu
313
eval_tools/heading_analysis/extract_bad_heading_cases.py
Executable file
313
eval_tools/heading_analysis/extract_bad_heading_cases.py
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"""
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Extract bad heading error cases from detailed_3d_matches.json
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This tool extracts and analyzes cases with large heading errors for visualization.
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Usage:
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python eval_tools/extract_bad_heading_cases.py \
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--input eval_results_common_match_comparison/yolov5s-300w/20260203_210259/detailed_3d_matches.json \
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--threshold 1.5 \
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--top-k 100 \
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--output bad_heading_cases.json
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"""
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import argparse
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import json
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import sys
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from pathlib import Path
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from collections import defaultdict
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import numpy as np
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# Allow importing class_config from the eval_tools root
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from class_config import CLASSES_3D as _CLASSES_3D, CLASS_NAMES as _CLASS_NAMES
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_CLASSES_3D_NAMES = set(_CLASS_NAMES[i] for i in _CLASSES_3D)
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def parse_args():
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parser = argparse.ArgumentParser(description='Extract bad heading error cases')
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parser.add_argument('--input', type=str, required=True,
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help='Path to detailed_3d_matches.json')
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parser.add_argument('--threshold', type=float, default=1.5,
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help='Heading error threshold in radians (default: 1.5 ≈ 85°)')
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parser.add_argument('--top-k', type=int, default=None,
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help='Only extract top K worst cases (default: all)')
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parser.add_argument('--classes', nargs='+', default=None,
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help='Filter by classes (e.g., vehicle pedestrian bicycle rider)')
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parser.add_argument('--min-distance', type=float, default=None,
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help='Minimum distance in meters')
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parser.add_argument('--max-distance', type=float, default=None,
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help='Maximum distance in meters')
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parser.add_argument('--min-confidence', type=float, default=None,
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help='Minimum detection confidence')
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parser.add_argument('--reversal-only', action='store_true',
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help='Only extract reversal errors (error > π - 0.1)')
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parser.add_argument('--output', type=str, default='bad_heading_cases.json',
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help='Output JSON file path')
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parser.add_argument('--stats', action='store_true',
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help='Print statistics')
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return parser.parse_args()
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def calculate_distance_3d(x, y, z):
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"""Calculate 3D Euclidean distance from ego vehicle."""
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return np.sqrt(x**2 + y**2 + z**2)
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def is_reversal_error(error, threshold=0.1):
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"""Check if error is a reversal error (≈ π or 180°)."""
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return error > (np.pi - threshold)
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def extract_bad_cases(data, args):
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"""Extract bad heading error cases based on criteria.
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Args:
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data: Loaded JSON data from detailed_3d_matches.json
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args: Command line arguments
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Returns:
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List of bad cases with metadata
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"""
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bad_cases = []
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stats = defaultdict(lambda: {'count': 0, 'reversal_count': 0, 'total_error': 0})
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total_processed = 0
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# Iterate through all matches
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for case_id, case_data in data.items():
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for frame_id, frame_data in case_data.items():
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if not isinstance(frame_data, dict):
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continue
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for class_name, class_data in frame_data.items():
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if class_name not in _CLASSES_3D_NAMES:
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continue
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# Filter by class if specified
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if args.classes and class_name not in args.classes:
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continue
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# class_data is already the list of matches
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matches = class_data if isinstance(class_data, list) else []
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for match in matches:
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total_processed += 1
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# Extract key information
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# heading_error is in errors['heading'], not directly in match
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errors = match.get('errors', {})
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heading_error = errors.get('heading', 0) if isinstance(errors, dict) else 0
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lateral_error = errors.get('lateral', 0) if isinstance(errors, dict) else 0
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longitudinal_error = errors.get('longitudinal', 0) if isinstance(errors, dict) else 0
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gt_rotation = match.get('gt_rotation', 0)
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det_rotation = match.get('det_rotation', 0)
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# Get 3D center coordinates
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gt_center = match.get('gt_center_3d', [0, 0, 0])
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det_center = match.get('det_center_3d', [0, 0, 0])
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# Calculate distance
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distance = calculate_distance_3d(*gt_center)
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# Get other metadata
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confidence = match.get('confidence', 0)
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iou = match.get('iou', 0)
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# Apply filters
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if heading_error < args.threshold:
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continue
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if args.min_distance and distance < args.min_distance:
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continue
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if args.max_distance and distance > args.max_distance:
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continue
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if args.min_confidence and confidence < args.min_confidence:
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continue
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if args.reversal_only and not is_reversal_error(heading_error):
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continue
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# Update statistics
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stats[class_name]['count'] += 1
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stats[class_name]['total_error'] += heading_error
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if is_reversal_error(heading_error):
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stats[class_name]['reversal_count'] += 1
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# Create case entry
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case_entry = {
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'case_id': case_id,
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'frame_id': frame_id,
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'class': class_name,
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'heading_error': float(heading_error),
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'heading_error_deg': float(np.degrees(heading_error)),
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'gt_rotation': float(gt_rotation),
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'gt_rotation_deg': float(np.degrees(gt_rotation)),
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'det_rotation': float(det_rotation),
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'det_rotation_deg': float(np.degrees(det_rotation)),
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'is_reversal': is_reversal_error(heading_error),
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'distance': float(distance),
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'confidence': float(confidence),
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'iou': float(iou),
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'gt_center': [float(x) for x in gt_center],
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'det_center': [float(x) for x in det_center],
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'lateral_error': float(lateral_error),
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'longitudinal_error': float(longitudinal_error),
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'gt_bbox_2d': match.get('gt_bbox', [0, 0, 0, 0]),
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'det_bbox_2d': match.get('det_bbox', [0, 0, 0, 0]),
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}
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bad_cases.append(case_entry)
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# Sort by heading error (descending)
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bad_cases.sort(key=lambda x: x['heading_error'], reverse=True)
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# Limit to top-k if specified
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if args.top_k:
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bad_cases = bad_cases[:args.top_k]
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return bad_cases, stats, total_processed
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def print_statistics(bad_cases, stats, total_processed, args):
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"""Print detailed statistics about extracted cases."""
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print("\n" + "="*80)
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print("BAD HEADING ERROR CASES EXTRACTION SUMMARY")
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print("="*80)
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print(f"\nInput file: {args.input}")
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print(f"Threshold: {args.threshold:.2f} rad ({np.degrees(args.threshold):.1f}°)")
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print(f"Total processed: {total_processed:,}")
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print(f"Bad cases found: {len(bad_cases):,} ({100*len(bad_cases)/total_processed:.2f}%)")
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if args.top_k:
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print(f"Output limited to: Top {args.top_k}")
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print("\n" + "-"*80)
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print("STATISTICS BY CLASS")
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print("-"*80)
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print(f"{'Class':<15} {'Count':<10} {'Reversal':<12} {'Rev %':<10} {'Avg Error':<12}")
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print("-"*80)
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for class_name in sorted(stats.keys()):
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stat = stats[class_name]
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count = stat['count']
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rev_count = stat['reversal_count']
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avg_error = stat['total_error'] / count if count > 0 else 0
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rev_pct = 100 * rev_count / count if count > 0 else 0
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print(f"{class_name:<15} {count:<10,} {rev_count:<12,} {rev_pct:<10.1f} {avg_error:<12.3f}")
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print("-"*80)
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# Error distribution
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if bad_cases:
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errors = [c['heading_error'] for c in bad_cases]
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print(f"\nERROR DISTRIBUTION:")
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print(f" Min: {min(errors):.3f} rad ({np.degrees(min(errors)):.1f}°)")
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print(f" Max: {max(errors):.3f} rad ({np.degrees(max(errors)):.1f}°)")
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print(f" Mean: {np.mean(errors):.3f} rad ({np.degrees(np.mean(errors)):.1f}°)")
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print(f" Median: {np.median(errors):.3f} rad ({np.degrees(np.median(errors)):.1f}°)")
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print(f" Std: {np.std(errors):.3f} rad ({np.degrees(np.std(errors)):.1f}°)")
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# Reversal statistics
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reversal_count = sum(1 for c in bad_cases if c['is_reversal'])
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print(f"\n Reversal errors (>3.04 rad): {reversal_count} ({100*reversal_count/len(bad_cases):.1f}%)")
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# Distance distribution
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distances = [c['distance'] for c in bad_cases]
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print(f"\nDISTANCE DISTRIBUTION:")
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print(f" Min: {min(distances):.1f} m")
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print(f" Max: {max(distances):.1f} m")
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print(f" Mean: {np.mean(distances):.1f} m")
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print(f" Median: {np.median(distances):.1f} m")
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# Confidence distribution
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confidences = [c['confidence'] for c in bad_cases]
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print(f"\nCONFIDENCE DISTRIBUTION:")
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print(f" Min: {min(confidences):.3f}")
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print(f" Max: {max(confidences):.3f}")
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print(f" Mean: {np.mean(confidences):.3f}")
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print(f" Median: {np.median(confidences):.3f}")
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print("\n" + "="*80)
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def main():
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args = parse_args()
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# Load input JSON
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input_path = Path(args.input)
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if not input_path.exists():
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print(f"Error: Input file not found: {input_path}")
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sys.exit(1)
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print(f"Loading data from {input_path}...")
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with open(input_path, 'r') as f:
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data = json.load(f)
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print(f"Loaded {len(data)} cases")
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# Extract bad cases
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print(f"\nExtracting bad cases with heading_error > {args.threshold:.2f} rad...")
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bad_cases, stats, total_processed = extract_bad_cases(data, args)
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# Print statistics
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if args.stats or len(bad_cases) > 0:
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print_statistics(bad_cases, stats, total_processed, args)
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# Save output
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output_path = Path(args.output)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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output_data = {
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'metadata': {
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'source': str(input_path),
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'threshold': args.threshold,
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'threshold_degrees': float(np.degrees(args.threshold)),
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'total_processed': total_processed,
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'total_extracted': len(bad_cases),
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'filters': {
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'classes': args.classes,
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'min_distance': args.min_distance,
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'max_distance': args.max_distance,
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'min_confidence': args.min_confidence,
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'reversal_only': args.reversal_only,
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'top_k': args.top_k
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}
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},
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'statistics': {
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class_name: {
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'count': stat['count'],
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'reversal_count': stat['reversal_count'],
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'reversal_percentage': 100 * stat['reversal_count'] / stat['count'] if stat['count'] > 0 else 0,
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'avg_error': stat['total_error'] / stat['count'] if stat['count'] > 0 else 0
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}
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for class_name, stat in stats.items()
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},
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'cases': bad_cases
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}
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with open(output_path, 'w') as f:
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json.dump(output_data, f, indent=2)
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print(f"\nExtracted {len(bad_cases)} bad cases")
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print(f"Output saved to: {output_path}")
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# Show top 5 worst cases
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if bad_cases:
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print(f"\nTop 5 Worst Cases:")
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print("-"*80)
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print(f"{'No':<5} {'Class':<12} {'Error (rad)':<12} {'Error (°)':<12} {'Distance':<12} {'Reversal':<10}")
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print("-"*80)
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for i, case in enumerate(bad_cases[:5], 1):
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print(f"{i:<5} {case['class']:<12} {case['heading_error']:<12.3f} "
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f"{case['heading_error_deg']:<12.1f} {case['distance']:<12.1f} "
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f"{'✓' if case['is_reversal'] else '':<10}")
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print("-"*80)
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if __name__ == '__main__':
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main()
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