8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
338 lines
11 KiB
Python
338 lines
11 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Face Processor Benchmark Runner
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测试不同 Face processor 版本的性能和质量
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测试版本:
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A. face_processor.py (InsightFace CPU)
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B. face_processor_mps.py (MediaPipe MPS)
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C. face_processor_optimized.py (OpenCV)
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D. face_processor_contract_v1.py (Contract)
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测试指标:
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- 处理时间
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- 内存峰值 (MB)
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- 检测人脸数
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- 输出文件大小 (KB)
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- 是否有 embedding
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- 是否有 landmarks
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"""
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import os
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import sys
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import json
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import time
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import subprocess
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import shutil
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from pathlib import Path
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from datetime import datetime
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SCRIPTS_DIR = Path(__file__).parent
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OUTPUT_DIR = SCRIPTS_DIR.parent / "output" / "benchmark" / "face_processor"
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def get_video_info(video_path):
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"""获取视频基本信息"""
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cmd = [
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"ffprobe",
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"-v", "quiet",
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"-print_format", "json",
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"-show_format",
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"-show_streams",
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video_path
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]
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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data = json.loads(result.stdout)
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video_stream = next((s for s in data["streams"] if s["codec_type"] == "video"), None)
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return {
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"duration": float(data["format"].get("duration", 0)),
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"size_mb": int(data["format"].get("size", 0)) / 1024 / 1024,
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"width": video_stream.get("width", 0) if video_stream else 0,
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"height": video_stream.get("height", 0) if video_stream else 0,
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"fps": video_stream.get("r_frame_rate", "0/1") if video_stream else "0/1",
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"total_frames": int(video_stream.get("nb_frames", 0)) if video_stream else 0
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}
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except Exception as e:
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print(f"获取视频信息失败: {e}")
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return {}
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def get_memory_peak(pid):
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"""获取进程内存峰值"""
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try:
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cmd = ["ps", "-p", str(pid), "-o", "rss="]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode == 0:
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return int(result.stdout.strip()) / 1024
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except:
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pass
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return 0
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def run_processor(script_name, video_path, output_path, uuid="", sample_interval=30):
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"""运行指定 Face processor"""
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script_path = SCRIPTS_DIR / script_name
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if not script_path.exists():
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print(f"❌ 脚本不存在: {script_path}")
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return None
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# 不同处理器使用不同的参数格式
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if script_name == "face_processor_mps.py":
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cmd = [
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sys.executable, str(script_path),
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"--video", video_path,
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"--output", output_path,
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"--sample-interval", str(sample_interval)
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]
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else:
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cmd = [sys.executable, str(script_path), video_path, output_path]
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if uuid:
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cmd.extend(["--uuid", uuid])
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if script_name in ["face_processor.py", "face_processor_optimized.py"]:
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cmd.extend(["--sample-interval", str(sample_interval)])
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print(f"\n执行: {script_name}")
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print(f"命令: {' '.join(cmd)}")
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start_time = time.time()
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process = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True
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)
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peak_memory = 0
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while process.poll() is None:
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mem = get_memory_peak(process.pid)
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if mem > peak_memory:
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peak_memory = mem
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time.sleep(0.5)
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stdout, stderr = process.communicate()
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elapsed_time = time.time() - start_time
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if process.returncode != 0:
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print(f"❌ 处理失败: {stderr}")
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return None
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if os.path.exists(output_path):
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with open(output_path) as f:
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result = json.load(f)
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frames_data = result.get("frames", {})
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# 处理两种格式：字典或列表
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if isinstance(frames_data, dict):
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total_faces = sum(len(f.get("faces", [])) for f in frames_data.values() if isinstance(f, dict))
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has_embedding = False
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has_landmarks = False
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for frame_data in frames_data.values():
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if isinstance(frame_data, dict):
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for face in frame_data.get("faces", []):
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if "embedding" in face:
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has_embedding = True
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if "landmarks" in face:
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has_landmarks = True
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elif isinstance(frames_data, list):
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total_faces = sum(len(f.get("faces", [])) for f in frames_data if isinstance(f, dict))
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has_embedding = False
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has_landmarks = False
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for frame_data in frames_data:
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if isinstance(frame_data, dict):
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for face in frame_data.get("faces", []):
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if "embedding" in face:
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has_embedding = True
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if "landmarks" in face:
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has_landmarks = True
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else:
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total_faces = 0
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has_embedding = False
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has_landmarks = False
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file_size_kb = os.path.getsize(output_path) / 1024
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return {
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"elapsed_time": elapsed_time,
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"peak_memory_mb": peak_memory,
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"total_frames": len(frames_data),
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"total_faces": total_faces,
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"file_size_kb": file_size_kb,
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"has_embedding": has_embedding,
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"has_landmarks": has_landmarks,
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"stdout": stdout,
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"stderr": stderr
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}
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return None
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def analyze_output(output_path):
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"""分析输出 JSON 质量"""
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if not os.path.exists(output_path):
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return None
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with open(output_path) as f:
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data = json.load(f)
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frames = data.get("frames", {})
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if not frames:
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return {"error": "no frames"}
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# 处理两种格式
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if isinstance(frames, dict):
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first_frame_key = list(frames.keys())[0]
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first_frame = frames[first_frame_key]
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elif isinstance(frames, list):
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first_frame = frames[0] if frames else {}
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else:
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return {"error": "unknown frames format"}
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faces = first_frame.get("faces", [])
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if not faces:
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return {"error": "no faces in first frame"}
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first_face = faces[0]
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return {
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"has_bbox": "bbox" in first_face,
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"has_confidence": "confidence" in first_face,
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"has_embedding": "embedding" in first_face,
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"embedding_dim": len(first_face.get("embedding", [])),
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"has_landmarks": "landmarks" in first_face,
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"landmarks_count": len(first_face.get("landmarks", [])),
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"has_age": "age" in first_face,
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"has_gender": "gender" in first_face
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}
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def main():
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print("=" * 80)
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print("Face Processor Benchmark 测试")
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print("=" * 80)
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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video_uuid = "ac625815183a21e1"
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video_path = "/Users/accusys/momentry/var/sftpgo/data/demo/Gamma Carry Saves the World..mp4"
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if not os.path.exists(video_path):
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print(f"❌ 测试视频不存在: {video_path}")
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sys.exit(1)
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video_info = get_video_info(video_path)
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print(f"\n测试视频:")
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print(f" UUID: {video_uuid}")
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print(f" 文件: {video_info.get('size_mb', 0):.1f} MB")
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print(f" 时长: {video_info.get('duration', 0):.1f} 秒")
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print(f" 分辨率: {video_info.get('width', 0)}x{video_info.get('height', 0)}")
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print(f" FPS: {video_info.get('fps', 'unknown')}")
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print(f" 总帧数: {video_info.get('total_frames', 0)}")
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processors = [
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("A", "face_processor.py", "InsightFace CPU"),
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("B", "face_processor_mps.py", "MediaPipe MPS"),
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("C", "face_processor_optimized.py", "OpenCV Optimized"),
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("D", "face_processor_contract_v1.py", "Contract v1"),
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]
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results = []
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for scheme_id, script_name, description in processors:
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print(f"\n{'=' * 80}")
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print(f"方案 {scheme_id}: {description}")
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print(f"{'=' * 80}")
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output_path = OUTPUT_DIR / f"scheme_{scheme_id}_{script_name.replace('.py', '.json')}"
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if os.path.exists(output_path):
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os.remove(output_path)
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result = run_processor(
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script_name,
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video_path,
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str(output_path),
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uuid=f"face_bench_{scheme_id}",
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sample_interval=30
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)
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if result:
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quality = analyze_output(str(output_path))
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duration = video_info.get("duration", 0)
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speed = duration / result["elapsed_time"] if result["elapsed_time"] > 0 else 0
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results.append({
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"scheme": scheme_id,
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"script": script_name,
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"description": description,
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"elapsed_time": result["elapsed_time"],
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"peak_memory_mb": result["peak_memory_mb"],
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"total_frames": result["total_frames"],
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"total_faces": result["total_faces"],
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"file_size_kb": result["file_size_kb"],
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"speed_ratio": speed,
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"quality": quality,
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"has_embedding": result["has_embedding"],
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"has_landmarks": result["has_landmarks"]
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})
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print(f"\n✅ 处理完成:")
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print(f" 时间: {result['elapsed_time']:.2f}秒")
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print(f" 速度: {speed:.2f}x 实时倍速")
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print(f" 内存峰值: {result['peak_memory_mb']:.1f} MB")
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print(f" 处理帧数: {result['total_frames']}")
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print(f" 检测人脸: {result['total_faces']}")
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print(f" 输出大小: {result['file_size_kb']:.1f} KB")
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print(f" Embedding: {'有' if result['has_embedding'] else '无'}")
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print(f" Landmarks: {'有' if result['has_landmarks'] else '无'}")
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if quality:
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print(f" 质量: {json.dumps(quality, indent=4)}")
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else:
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print(f"❌ 方案 {scheme_id} 处理失败")
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results.append({
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"scheme": scheme_id,
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"script": script_name,
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"description": description,
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"error": "processing failed"
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})
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report = {
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"test_date": datetime.now().isoformat(),
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"video_info": video_info,
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"video_uuid": video_uuid,
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"results": results
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}
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report_path = OUTPUT_DIR / "FACE_BENCHMARK_REPORT.json"
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with open(report_path, "w") as f:
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json.dump(report, f, indent=2, ensure_ascii=False)
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print(f"\n{'=' * 80}")
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print("测试报告已保存:")
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print(f" {report_path}")
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print(f"{'=' * 80}")
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print("\n【对比总结】")
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print(f"\n| 方案 | 脚本 | 时间(秒) | 速度 | 内存(MB) | 人脸数 | Embedding |")
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print("|------|------|---------|------|---------|--------|-----------|")
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for r in results:
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if "error" not in r:
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print(f"| {r['scheme']} | {r['script']} | {r['elapsed_time']:.2f} | {r['speed_ratio']:.2f}x | {r['peak_memory_mb']:.1f} | {r['total_faces']} | {'✅' if r['has_embedding'] else '❌'} |")
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else:
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print(f"| {r['scheme']} | {r['script']} | - | - | - | - | ❌ |")
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if __name__ == "__main__":
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main() |