e75c4d6f07
- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
258 lines
9.3 KiB
Python
258 lines
9.3 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Face Detection Count Comparison
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对比四个版本在同一帧上的检测数量差异
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"""
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import json
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from pathlib import Path
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def load_results(filepath):
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"""加载检测结果"""
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with open(filepath) as f:
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data = json.load(f)
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results = {}
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# 处理不同的帧格式
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frames = data.get('frames', {})
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if isinstance(frames, dict):
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# InsightFace, MediaPipe格式: {"750": {...}, "900": {...}}
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for frame_num, frame_data in frames.items():
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if isinstance(frame_data, dict):
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faces = frame_data.get('faces', [])
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results[int(frame_num)] = {
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'count': len(faces),
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'faces': faces,
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'timestamp': frame_data.get('timestamp', frame_data.get('time_seconds', 0))
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}
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elif isinstance(frames, list):
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# OpenCV格式: [{...}, {...}]
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for frame_data in frames:
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if isinstance(frame_data, dict):
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frame_num = frame_data.get('frame', 0)
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faces = frame_data.get('faces', [])
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results[frame_num] = {
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'count': len(faces),
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'faces': faces,
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'timestamp': frame_data.get('timestamp', 0)
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}
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return results
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def compare_frames(results_a, results_b, results_c):
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"""对比同一帧的检测结果"""
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# 找出所有检测到的帧号
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all_frames = set()
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all_frames.update(results_a.keys())
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all_frames.update(results_b.keys())
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all_frames.update(results_c.keys())
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comparison = []
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for frame_num in sorted(all_frames):
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a_count = results_a.get(frame_num, {}).get('count', 0)
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b_count = results_b.get(frame_num, {}).get('count', 0)
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c_count = results_c.get(frame_num, {}).get('count', 0)
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# 只对比检测数量不同的帧
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if not (a_count == b_count == c_count):
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comparison.append({
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'frame': frame_num,
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'timestamp': results_a.get(frame_num, {}).get('timestamp',
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results_b.get(frame_num, {}).get('timestamp',
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results_c.get(frame_num, {}).get('timestamp', 0))),
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'insightface': a_count,
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'mediapipe': b_count,
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'opencv': c_count,
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'max': max(a_count, b_count, c_count),
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'min': min(a_count, b_count, c_count),
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'diff': max(a_count, b_count, c_count) - min(a_count, b_count, c_count)
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})
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return comparison
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def analyze_detection_distribution(results_a, results_b, results_c):
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"""分析检测分布"""
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stats = {
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'insightface': {
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'total_faces': sum(r['count'] for r in results_a.values()),
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'total_frames': len(results_a),
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'avg_per_frame': 0,
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'frames_with_faces': len([r for r in results_a.values() if r['count'] > 0]),
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'frames_no_faces': len([r for r in results_a.values() if r['count'] == 0]),
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'max_faces': max(r['count'] for r in results_a.values()) if results_a else 0,
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},
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'mediapipe': {
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'total_faces': sum(r['count'] for r in results_b.values()),
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'total_frames': len(results_b),
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'avg_per_frame': 0,
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'frames_with_faces': len([r for r in results_b.values() if r['count'] > 0]),
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'frames_no_faces': len([r for r in results_b.values() if r['count'] == 0]),
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'max_faces': max(r['count'] for r in results_b.values()) if results_b else 0,
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},
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'opencv': {
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'total_faces': sum(r['count'] for r in results_c.values()),
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'total_frames': len(results_c),
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'avg_per_frame': 0,
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'frames_with_faces': len([r for r in results_c.values() if r['count'] > 0]),
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'frames_no_faces': len([r for r in results_c.values() if r['count'] == 0]),
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'max_faces': max(r['count'] for r in results_c.values()) if results_c else 0,
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}
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}
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for key in stats:
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if stats[key]['total_frames'] > 0:
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stats[key]['avg_per_frame'] = stats[key]['total_faces'] / stats[key]['frames_with_faces']
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return stats
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def find_missed_frames(results_a, results_b, results_c):
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"""找出被漏检的帧"""
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all_frames = set()
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all_frames.update(results_a.keys())
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all_frames.update(results_b.keys())
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all_frames.update(results_c.keys())
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missed = []
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for frame_num in sorted(all_frames):
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a = results_a.get(frame_num, {}).get('count', 0)
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b = results_b.get(frame_num, {}).get('count', 0)
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c = results_c.get(frame_num, {}).get('count', 0)
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# 某个版本完全漏检（检测到0张）
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if a > 0 and b == 0:
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missed.append({
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'frame': frame_num,
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'missed_by': 'MediaPipe',
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'insightface_count': a,
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'opencv_count': c
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})
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if a > 0 and c == 0:
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missed.append({
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'frame': frame_num,
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'missed_by': 'OpenCV',
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'insightface_count': a,
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'mediapipe_count': b
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})
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if (a > 0 or c > 0) and b == 0:
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missed.append({
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'frame': frame_num,
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'missed_by': 'MediaPipe',
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'others_count': max(a, c)
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})
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return missed
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def main():
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benchmark_dir = Path('/Users/accusys/momentry_core_0.1/output/benchmark/face_processor')
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# 加载四个版本的结果
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print("=" * 80)
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print("Face Detection Count Comparison")
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print("=" * 80)
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print()
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results_a = load_results(benchmark_dir / 'scheme_A_fixed.json')
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results_b = load_results(benchmark_dir / 'scheme_B_mediapipe_fixed.json')
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results_c = load_results(benchmark_dir / 'scheme_C_face_processor_optimized.json')
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# results_d = load_results(benchmark_dir / 'scheme_D_face_processor_contract_v1.json')
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print("【检测结果统计】")
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print()
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stats = analyze_detection_distribution(results_a, results_b, results_c)
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print("| 版本 | 总人脸数 | 检测帧数 | 有人脸帧 | 无人脸帧 | 平均每帧 | 最多人脸 |")
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print("|------|---------|---------|---------|---------|---------|---------|")
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for name, s in stats.items():
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print(f"| {name} | {s['total_faces']} | {s['total_frames']} | {s['frames_with_faces']} | {s['frames_no_faces']} | {s['avg_per_frame']:.2f} | {s['max_faces']} |")
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print()
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print("【检测数量差异对比】")
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print()
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comparison = compare_frames(results_a, results_b, results_c)
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print(f"共有 {len(comparison)} 帧检测数量不同")
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print()
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print("| 帧号 | 时间(秒) | InsightFace | MediaPipe | OpenCV | 最大差异 |")
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print("|------|---------|------------|----------|--------|---------|")
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for item in comparison[:30]: # 只显示前30帧
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print(f"| {item['frame']} | {item['timestamp']:.2f} | {item['insightface']} | {item['mediapipe']} | {item['opencv']} | {item['diff']} |")
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if len(comparison) > 30:
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print("| ... | ... | ... | ... | ... | ... |")
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print(f"| 共 {len(comparison)} 帧有差异 |")
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print()
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print("【漏检分析】")
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print()
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missed = find_missed_frames(results_a, results_b, results_c)
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mediapipe_missed = [m for m in missed if m.get('missed_by') == 'MediaPipe']
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opencv_missed = [m for m in missed if m.get('missed_by') == 'OpenCV']
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print(f"MediaPipe漏检帧数: {len(mediapipe_missed)}")
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print(f"OpenCV漏检帧数: {len(opencv_missed)}")
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print()
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if mediapipe_missed:
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print("MediaPipe漏检详情（前10帧）:")
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print("| 帧号 | InsightFace检测 | OpenCV检测 |")
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print("|------|----------------|-----------|")
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for m in mediapipe_missed[:10]:
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print(f"| {m['frame']} | {m.get('insightface_count', m.get('others_count', '?'))} | {m.get('opencv_count', '?')} |")
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print()
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print("【检测率分析】")
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print()
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baseline = stats['insightface']['total_faces']
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print(f"以InsightFace为基准（{baseline}张人脸）:")
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print()
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print("| 版本 | 检测数 | 检测率 | 漏检数 |")
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print("|------|--------|--------|--------|")
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for name, s in stats.items():
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rate = s['total_faces'] / baseline * 100 if baseline > 0 else 0
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missed_count = baseline - s['total_faces']
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print(f"| {name} | {s['total_faces']} | {rate:.1f}% | {missed_count} |")
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print()
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print("=" * 80)
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print("对比完成")
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print("=" * 80)
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# 保存详细对比结果
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output = {
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'stats': stats,
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'comparison': comparison,
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'missed_frames': missed,
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'summary': {
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'baseline_faces': baseline,
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'mediapipe_detection_rate': stats['mediapipe']['total_faces'] / baseline * 100 if baseline > 0 else 0,
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'opencv_detection_rate': stats['opencv']['total_faces'] / baseline * 100 if baseline > 0 else 0,
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}
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}
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output_path = benchmark_dir / 'FACE_COUNT_COMPARISON.json'
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with open(output_path, 'w') as f:
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json.dump(output, f, indent=2, ensure_ascii=False)
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print(f"\n详细对比已保存: {output_path}")
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if __name__ == '__main__':
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main() |