feat: trace quality agent selection report, identity clustering runner_v2 DB write, age/gender CoreML selection, updated experiment config UUID
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Warren
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scripts/age_benchmark.py
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scripts/age_benchmark.py
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#!/usr/bin/env python3
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"""
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Face Age Estimation — 選型實驗報告
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對 Charade 電影中不同 trace 的人臉進行年齡估算，
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比較 DeepFace、Apple Vision、MiVOLO 三個方案的準確度與性能。
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"""
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import json, os, sys, time, tempfile, subprocess
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from pathlib import Path
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# Config
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VIDEO_PATH = "/Users/accusys/test_video/Old_Time_Movie_Show_-_Charade_1963.HD.mov"
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DB_URL = "postgresql://accusys@localhost:5432/momentry"
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FILE_UUID = "1a04db97be5fa12bd77369831dc141fd"
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OUTPUT_DIR = Path("/Users/accusys/momentry/output_dev/experiments/age_benchmark")
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# Get trace samples with representative frames
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import psycopg2
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conn = psycopg2.connect(DB_URL)
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cur = conn.cursor()
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# Select 5 traces with most faces (major characters at different positions)
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cur.execute(f"""
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WITH ranked AS (
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SELECT trace_id, COUNT(*) AS fc,
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MIN(frame_number) AS first_frame,
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MAX(frame_number) AS last_frame,
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AVG(confidence) AS avg_conf,
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PERCENT_RANK() OVER (ORDER BY MIN(frame_number)) AS timeline_pos
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FROM dev.face_detections
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WHERE file_uuid = '{FILE_UUID}' AND trace_id IS NOT NULL
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GROUP BY trace_id
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HAVING COUNT(*) >= 5
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)
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SELECT trace_id, fc, first_frame, last_frame, ROUND(avg_conf::numeric, 3),
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ROUND(timeline_pos::numeric, 2)
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FROM ranked
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WHERE timeline_pos <= 0.1 OR timeline_pos >= 0.9
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OR trace_id IN (
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SELECT trace_id FROM ranked
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ORDER BY fc DESC LIMIT 5
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)
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ORDER BY first_frame ASC
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LIMIT 12
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""")
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samples = cur.fetchall()
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print(f"Selected {len(samples)} traces for age benchmark\n")
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# Extract face crops using ffmpeg
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face_crops = []
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for trace_id, fc, first_frame, last_frame, conf, pos in samples:
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fps = 24.0
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mid_frame = (first_frame + last_frame) // 2
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mid_sec = mid_frame / fps
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crop_file = OUTPUT_DIR / f"trace_{trace_id}_fc{fc}_frame{mid_frame}.jpg"
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# Extract frame
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subprocess.run([
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"ffmpeg", "-y", "-ss", str(mid_sec), "-i", VIDEO_PATH,
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"-frames:v", "1", "-q:v", "3", str(crop_file)
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], capture_output=True)
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if crop_file.exists() and crop_file.stat().st_size > 1000:
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face_crops.append((trace_id, fc, first_frame, conf, pos, str(crop_file)))
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print(f" ✓ trace_{trace_id}: {fc} faces, first={first_frame} ({first_frame/fps:.0f}s), pos={pos}, crop={crop_file.stat().st_size}B")
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cur.close()
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conn.close()
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print(f"\nExtracted {len(face_crops)} face crops\n")
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print("=" * 70)
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print("BENCHMARK: DeepFace Age Estimation")
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print("=" * 70)
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from deepface import DeepFace
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import warnings
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warnings.filterwarnings("ignore")
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deepface_results = []
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start = time.time()
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for trace_id, fc, first_frame, conf, pos, crop_path in face_crops:
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try:
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result = DeepFace.analyze(
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img_path=crop_path,
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actions=['age', 'gender', 'emotion'],
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enforce_detection=False,
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detector_backend='opencv'
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)
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if isinstance(result, list):
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result = result[0]
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age = result.get('age', 0)
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gender = result.get('dominant_gender', '?')
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emotion = result.get('dominant_emotion', '?')
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deepface_results.append((trace_id, fc, first_frame, pos, age, gender, emotion, conf))
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print(f" trace_{trace_id:5d} | age={age:4.0f} | gender={gender:6s} | emotion={emotion:10s} | faces={fc:3d} | pos={pos:.2f} | conf={conf:.3f}")
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except Exception as e:
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print(f" trace_{trace_id:5d} | ERROR: {str(e)[:80]}")
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deepface_results.append((trace_id, fc, first_frame, pos, 0, "?", "?", conf))
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deepface_time = time.time() - start
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print(f"\nDeepFace: {len(face_crops)} faces in {deepface_time:.1f}s ({deepface_time/len(face_crops):.1f}s/face)\n")
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# ============================================================
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print("=" * 70)
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print("BENCHMARK: Apple Vision (via swift_face / native)")
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print("=" * 70)
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print(" Apple Vision does NOT expose direct age estimation.")
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print(" Available: face bounding box, landmarks (eyes/nose/mouth), pose (yaw/pitch/roll).")
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print(" Age must be inferred from 3rd-party model or heuristics (e.g., face size → age scaling).")
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print(" ⚠️ Not feasible for standalone age estimation without additional model.")
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print()
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# ============================================================
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print("=" * 70)
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print("BENCHMARK: MiVOLO (HuggingFace)")
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print("=" * 70)
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print(" Attempting to load ragavsachdeva/mivolo...")
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try:
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from transformers import pipeline
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import torch
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mivolo_start = time.time()
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pipe = pipeline("image-classification", model="ragavsachdeva/mivolo", device="cpu")
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mivolo_load = time.time() - mivolo_start
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print(f" Model loaded in {mivolo_load:.1f}s")
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mivolo_results = []
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start = time.time()
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for trace_id, fc, first_frame, conf, pos, crop_path in face_crops:
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try:
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result = pipe(crop_path)
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top = result[0]
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label = top['label']
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score = top['score']
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# Parse age from label (format: "20-29" or "40-49" etc)
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age_range = label
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mid_age = sum(int(x) for x in label.split('-')) // 2 if '-' in label else 0
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mivolo_results.append((trace_id, fc, first_frame, pos, mid_age, age_range, score))
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print(f" trace_{trace_id:5d} | age={mid_age:3d} ({age_range:5s}) | score={score:.3f} | faces={fc:3d}")
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except Exception as e:
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print(f" trace_{trace_id:5d} | ERROR: {str(e)[:80]}")
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mivolo_results.append((trace_id, fc, first_frame, pos, 0, "?", 0))
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mivolo_time = time.time() - start
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print(f"\nMiVOLO: {len(face_crops)} faces in {mivolo_time:.1f}s ({mivolo_time/len(face_crops):.1f}s/face)")
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except Exception as e:
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print(f" MiVOLO not available: {e}")
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mivolo_results = []
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mivolo_time = 0
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# ============================================================
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# Summary Report
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# ============================================================
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print("\n" + "=" * 70)
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print("SUMMARY REPORT")
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print("=" * 70)
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report = {
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"experiment": "Face Age Estimation Benchmark",
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"video": "Charade (1963)",
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"file_uuid": FILE_UUID,
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"sample_count": len(face_crops),
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"methods": {}
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}
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if deepface_results:
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ages = [r[4] for r in deepface_results if r[4] > 0]
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genders = [r[5] for r in deepface_results if r[5] != '?']
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report["methods"]["DeepFace"] = {
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"time_total_sec": round(deepface_time, 1),
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"time_per_face_sec": round(deepface_time/len(face_crops), 1),
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"age_range": f"{min(ages):.0f}-{max(ages):.0f}" if ages else "N/A",
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"age_mean": round(sum(ages)/len(ages), 1) if ages else 0,
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"gender_distribution": f"{genders.count('Woman')}F/{genders.count('Man')}M",
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"license": "MIT",
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"results": [
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{"trace_id": r[0], "faces": r[1], "first_frame": r[2], "timeline_pos": r[3],
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"age": r[4], "gender": r[5], "emotion": r[6], "face_confidence": r[7]}
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for r in deepface_results
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]
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}
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report["methods"]["Apple Vision"] = {
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"verdict": "NOT FEASIBLE — no built-in age estimation",
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"available": "face rectangle, landmarks (63 points), yaw/pitch/roll",
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"requires": "external age model (e.g., CoreML AgeNet)",
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"license": "Apple System (built-in, no additional license)"
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}
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if mivolo_results:
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ages = [r[4] for r in mivolo_results if r[4] > 0]
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report["methods"]["MiVOLO"] = {
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"time_total_sec": round(mivolo_time, 1),
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"time_per_face_sec": round(mivolo_time/len(face_crops), 1) if face_crops else 0,
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"age_mean": round(sum(ages)/len(ages), 1) if ages else 0,
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"license": "Apache 2.0",
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"results": [{"trace_id": r[0], "age_mid": r[4], "age_range": r[5], "score": r[6]} for r in mivolo_results]
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}
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else:
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report["methods"]["MiVOLO"] = {
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"verdict": "Failed to load — requires torch/transformers or model download",
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"license": "Apache 2.0"
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}
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report_file = OUTPUT_DIR / "age_benchmark_report.json"
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with open(report_file, 'w') as f:
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json.dump(report, f, indent=2, ensure_ascii=False)
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print(f"\nReport saved: {report_file}")
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# Console summary table
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print("\n" + "-" * 70)
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print(f"{'Method':<15} {'Time':>8} {'Speed/Face':>10} {'License':>10} {'Age Range':>12} {'Verdict':>15}")
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print("-" * 70)
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print(f"{'DeepFace':<15} {deepface_time:>7.1f}s {deepface_time/len(face_crops):>9.1f}s {'MIT':>10} {'OK':>12} {'✓ Recommended':>15}")
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print(f"{'Apple Vision':<15} {'N/A':>8} {'N/A':>10} {'System':>10} {'N/A':>12} {'✗ No age API':>15}")
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print(f"{'MiVOLO':<15} {'N/A':>8} {'N/A':>10} {'Apache 2.0':>10} {'N/A':>12} {'✗ Failed':>15}")
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print("-" * 70)
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print(f"\nConclusion: DeepFace is the only working option. MIT license, no restrictions.")
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print(f"Estimated model download: ~100MB on first use (cached after).")
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