youpaishidifu
4418a46460
1. 放宽时间戳过滤：窗口30s→60s，消息长度4字→20字，包含中文即放行 2. 添加系统进程名过滤：200+个Windows/第三方进程名模式，防止混入OCR文本 3. EasyOCR崩溃修复：检测内存不足时设置CUDA_VISIBLE_DEVICES=''禁用CUDA，避免import torch加载CUDA库导致崩溃
329 lines
14 KiB
Python
329 lines
14 KiB
Python
#!/usr/bin/env python3
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"""EasyOCR 子进程 worker。
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通过 stdin 读 base64 图片、stdout 写 JSON 结果。GPU crash 只杀此进程，不冻主进程。
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协议：
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stdin: {"img":"<base64_png_bytes>"}
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stdout: {"ok":true,"text":"识别结果"}
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stdout: {"ok":false,"error":"错误描述"}
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初始化慢（首次 3-10s），初始化完成后常驻处理。
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"""
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import json
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import sys
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import base64
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import io
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import os
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import traceback
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from datetime import datetime
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# ── 崩溃日志文件：写入源码目录根（easyocr_crash.log），统一管理、便于父进程读取 ──
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def _get_virtual_memory_info() -> dict:
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"""获取虚拟内存（页面文件）状态，用于诊断 WinError 1455。"""
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try:
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import ctypes
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class MEMORYSTATUSEX(ctypes.Structure):
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_fields_ = [
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("dwLength", ctypes.c_ulong),
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("dwMemoryLoad", ctypes.c_ulong),
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("ullTotalPhys", ctypes.c_ulonglong),
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("ullAvailPhys", ctypes.c_ulonglong),
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("ullTotalPageFile", ctypes.c_ulonglong),
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("ullAvailPageFile", ctypes.c_ulonglong),
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("ullTotalVirtual", ctypes.c_ulonglong),
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("ullAvailVirtual", ctypes.c_ulonglong),
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("ullAvailExtendedVirtual", ctypes.c_ulonglong),
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]
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mem = MEMORYSTATUSEX()
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mem.dwLength = ctypes.sizeof(MEMORYSTATUSEX)
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ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(mem))
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return {
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"total_phys_gb": round(mem.ullTotalPhys / (1024**3), 1),
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"avail_phys_gb": round(mem.ullAvailPhys / (1024**3), 1),
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"total_pagefile_gb": round(mem.ullTotalPageFile / (1024**3), 1),
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"avail_pagefile_gb": round(mem.ullAvailPageFile / (1024**3), 1),
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"commit_pct": round((1 - mem.ullAvailPageFile / max(mem.ullTotalPageFile, 1)) * 100, 1),
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}
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except Exception:
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return {}
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def _get_crash_log_path() -> str:
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try:
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base = os.path.dirname(os.path.abspath(__file__))
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except Exception:
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base = "."
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return os.path.join(base, "easyocr_crash.log")
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CRASH_LOG = _get_crash_log_path()
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def _write_crash_log(msg: str):
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try:
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with open(CRASH_LOG, "a", encoding="utf-8") as f:
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f.write(msg + "\n")
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f.flush()
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except Exception:
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pass
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# ── 顶层异常捕获：确保崩溃原因写入 stderr + 落盘（否则父进程看到空 stderr） ──
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def _flush_exit(msg, code=1):
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full = f"[easyocr_worker] FATAL: {msg}"
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sys.stderr.write(full + "\n")
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sys.stderr.flush()
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_write_crash_log(f"{datetime.now()} {full}")
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sys.exit(code)
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# ── 全局未捕获异常钩子：任何漏网的 Python 异常都落盘 ──
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def _global_excepthook(exc_type, exc_val, exc_tb):
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msg = "".join(traceback.format_exception(exc_type, exc_val, exc_tb))
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sys.stderr.write(f"[easyocr_worker] 未捕获异常:\n{msg}")
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sys.stderr.flush()
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_write_crash_log(f"{datetime.now()} [easyocr_worker] 未捕获异常:\n{msg}")
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sys.excepthook = _global_excepthook
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# ── faulthandler：捕获原生层崩溃（segfault / CUDA 初始化竞态），Python try/except 抓不到 ──
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# 必须在重型 import 之前启用，连 import 阶段的原生崩溃也能 dump 出来。
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try:
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import faulthandler
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_fh = open(CRASH_LOG, "a", encoding="utf-8")
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faulthandler.enable(file=_fh, all_threads=True)
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except Exception:
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pass
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try:
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# ── 线程/进程限制：避免 MKL/OpenMP 在子进程多线程冲突 ──
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os.environ.setdefault("OMP_NUM_THREADS", "1")
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os.environ.setdefault("MKL_NUM_THREADS", "1")
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# ── 预检：物理内存+虚拟内存是否足够加载 CUDA 库 ──
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_vm = _get_virtual_memory_info()
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# ⭐ 统一使用 GlobalMemoryStatusEx（与 monitor.py / Task Manager 一致）
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# psutil.virtual_memory().available 在 Windows 上可比 Task Manager 少报 ~1.7GB，
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# 导致 4.2GB 可用时误判为 2.5GB 触发降级。
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if _vm:
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try:
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_avail_phys_gb = _vm.get("avail_phys_gb", 0)
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_total_phys_gb = _vm.get("total_phys_gb", 0)
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_used_phys_gb = _total_phys_gb - _avail_phys_gb
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_pagefile_pct = _vm.get("commit_pct", 0)
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_pagefile_used_gb = _vm.get("total_pagefile_gb", 0) - _vm.get("avail_pagefile_gb", 0)
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_commit_gb = _used_phys_gb + _pagefile_used_gb
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_commit_total_gb = _total_phys_gb + _vm.get("total_pagefile_gb", 0)
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_commit_pct = (_commit_gb / max(_commit_total_gb, 1)) * 100
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if _commit_pct > 90.0:
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_msg = (
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f"系统内存压力过高: 物理剩余{_avail_phys_gb:.1f}GB，"
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f"提交量{_commit_pct:.1f}% ({_commit_gb:.1f}GB/{_commit_total_gb:.1f}GB)"
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)
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_write_crash_log(f"{datetime.now()} [easyocr_worker] 系统内存压力预检失败: {_msg}")
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os.environ["EASYOCR_FORCE_CPU"] = "1"
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os.environ["CUDA_VISIBLE_DEVICES"] = "" # 禁用CUDA，防止加载CUDA库导致崩溃
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sys.stderr.write(f"[easyocr_worker] ⚠️ {_msg}，降级CPU模式\n")
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sys.stderr.flush()
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elif _avail_phys_gb < 3.0:
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_msg = (
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f"物理内存不足: 剩余{_avail_phys_gb:.1f}GB"
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f"(总计{_total_phys_gb:.1f}GB)"
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f"，CUDA加载需要≥3GB物理内存"
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)
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_write_crash_log(f"{datetime.now()} [easyocr_worker] 物理内存预检失败: {_msg}")
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os.environ["EASYOCR_FORCE_CPU"] = "1"
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os.environ["CUDA_VISIBLE_DEVICES"] = "" # 禁用CUDA，防止加载CUDA库导致崩溃
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sys.stderr.write(f"[easyocr_worker] ⚠️ {_msg}，降级CPU模式\n")
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sys.stderr.flush()
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except Exception:
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pass # GlobalMemoryStatusEx 失败时不阻断
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# ⭐ 检查页面文件：百分比>95% 或 剩余<2GB 时退出（防冻结）
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if _vm and (_vm.get("commit_pct", 0) > 95.0 or _vm.get("avail_pagefile_gb", 99) < 2.0):
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_msg = (
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f"页面文件不足: 已用{_vm.get('commit_pct', '?')}%"
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f"(剩余{_vm.get('avail_pagefile_gb', '?')}GB/{_vm.get('total_pagefile_gb', '?')}GB)"
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f"，CUDA库需要充足的虚拟地址空间"
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)
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_write_crash_log(f"{datetime.now()} [easyocr_worker] 页面文件预检失败: {_msg}")
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_flush_exit(f"页面文件不足: {_msg}", code=2)
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from PIL import Image
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import numpy as np
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import easyocr
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except Exception as e:
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_vm = _get_virtual_memory_info()
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_vm_hint = ""
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if _vm:
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_vm_hint = (
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f"\n[virtual memory] 物理内存: {_vm.get('avail_phys_gb',0)}/{_vm.get('total_phys_gb',0)} GB"
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f" | 页面文件: {_vm.get('avail_pagefile_gb',0)}/{_vm.get('total_pagefile_gb',0)} GB"
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f" | 提交使用率: {_vm.get('commit_pct',0)}%"
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)
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# WinError 1455 = 页面文件太小 → 标记为永久失败，不重试
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_is_pagefile_error = "1455" in str(e) or "页面文件" in str(e)
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_exit_code = 2 if _is_pagefile_error else 1 # exit(2) = 虚拟内存不足
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_write_crash_log(
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f"{datetime.now()} [easyocr_worker] import {_exit_code}: "
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f"{'页面文件不足' if _is_pagefile_error else 'import失败'}: {e}{_vm_hint}"
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)
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_flush_exit(f"import 失败{'(页面文件不足)' if _is_pagefile_error else ''}: {e}{_vm_hint}", code=_exit_code)
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def _init_reader():
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"""初始化 EasyOCR reader（GPU 优先，父进程可强制 CPU）。"""
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# 先检查环境变量是否强制CPU
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_force_cpu = os.environ.get("EASYOCR_FORCE_CPU") == "1"
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# 再次检查物理内存：加载torch+CUDA需要大量内存，内存不足时强制CPU
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if not _force_cpu:
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try:
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import psutil
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_mem = psutil.virtual_memory()
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_avail_phys_gb = _mem.available / (1024**3)
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if _avail_phys_gb < 3.0:
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_force_cpu = True
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os.environ["CUDA_VISIBLE_DEVICES"] = "" # 禁用CUDA，防止加载CUDA库导致崩溃
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sys.stderr.write(
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f"[easyocr_worker] ⚠️ 物理内存不足(剩余{_avail_phys_gb:.1f}GB)，"
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f"强制CPU模式（避免CUDA加载导致冻结）\n"
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)
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sys.stderr.flush()
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except Exception:
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pass # psutil不可用时不阻断
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try:
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import torch
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# 父进程 EASYOCR_FORCE_CPU=1 → 强制 CPU（显存反复 OOM 时降级，防冻结）
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use_gpu = (not _force_cpu) and torch.cuda.is_available()
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if use_gpu:
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# CUDA 健康检查：尝试创建一个小张量，避免驱动/显存问题导致后续崩溃
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try:
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_test_tensor = torch.zeros(1, device="cuda")
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del _test_tensor
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torch.cuda.empty_cache()
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sys.stderr.write(f"[easyocr_worker] CUDA 健康检查通过 (设备: {torch.cuda.get_device_name(0)})\n")
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sys.stderr.flush()
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except Exception as cuda_err:
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sys.stderr.write(f"[easyocr_worker] CUDA 健康检查失败({cuda_err})，降级 CPU\n")
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sys.stderr.flush()
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use_gpu = False
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os.environ["CUDA_VISIBLE_DEVICES"] = "" # 禁用CUDA，防止后续崩溃
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except Exception:
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use_gpu = False
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try:
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reader = easyocr.Reader(["ch_sim", "en"], gpu=use_gpu)
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mode = "GPU" if use_gpu else "CPU"
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sys.stderr.write(f"[easyocr_worker] Reader 就绪（{mode}模式）\n")
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sys.stderr.flush()
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return reader
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except Exception as e:
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# GPU 不可用 → CPU 兜底
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sys.stderr.write(f"[easyocr_worker] GPU 初始化失败({e})，尝试 CPU\n")
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sys.stderr.flush()
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reader = easyocr.Reader(["ch_sim", "en"], gpu=False)
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sys.stderr.write("[easyocr_worker] Reader 就绪（CPU 模式）\n")
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sys.stderr.flush()
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return reader
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def _process_image(img_b64: str, reader) -> str:
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"""解码 base64 图片 → EasyOCR 识别 → 返回空格拼接文本。"""
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if reader is None:
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return ""
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raw = base64.b64decode(img_b64)
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img = Image.open(io.BytesIO(raw))
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# 保持与 monitor.py 一致：RGBA → numpy
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if img.mode != "RGBA":
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img = img.convert("RGBA")
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img_np = np.array(img)
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results = reader.readtext(img_np)
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return "\n".join([r[1] for r in results]).strip()
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def _main():
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_write_crash_log(f"{datetime.now()} [easyocr_worker] 启动 (pid={os.getpid()})")
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reader = None
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try:
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reader = _init_reader()
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except Exception as e:
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sys.stderr.write(f"[easyocr_worker] Reader 初始化失败: {e}\n")
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sys.stderr.flush()
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_write_crash_log(f"{datetime.now()} [easyocr_worker] Reader 初始化失败: {e}")
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# ⭐ 就绪握手：即使 Reader 失败也要发 ready，否则父进程永远等握手
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sys.stdout.write(json.dumps({"ready": True}, ensure_ascii=False) + "\n")
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sys.stdout.flush()
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sys.stderr.write("[easyocr_worker] 就绪，等待 stdin 输入\n")
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sys.stderr.flush()
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for line in sys.stdin:
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line = line.strip()
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if not line:
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continue
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try:
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payload = json.loads(line)
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img_b64 = payload.get("img", "")
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if not img_b64:
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raise ValueError("缺少 img 字段")
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sys.stderr.write(f"[easyocr_worker] 收到请求，处理中... (img长度={len(img_b64)})\n")
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sys.stderr.flush()
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text = _process_image(img_b64, reader)
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sys.stderr.write(f"[easyocr_worker] 处理完成，结果长度={len(text)}\n")
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sys.stderr.flush()
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out = json.dumps({"ok": True, "text": text}, ensure_ascii=False)
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sys.stdout.write(out + "\n")
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sys.stdout.flush()
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except Exception as e:
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estr = str(e)
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# ⭐ CUDA OOM / kernel error：GPU context 已损坏，继续跑只会反复卡死占显存
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# → 立即退出进程释放显存，父进程检测到 stdout 关闭后自动重启/降级 CPU
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if "cuda" in estr.lower() or "out of memory" in estr.lower():
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_write_crash_log(f"{datetime.now()} [easyocr_worker] CUDA 错误，主动退出释放显存: {estr[:200]}")
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_flush_exit(f"CUDA 错误，主动退出释放显存: {estr[:200]}")
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out = json.dumps({"ok": False, "error": str(e)}, ensure_ascii=False)
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sys.stdout.write(out + "\n")
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sys.stdout.flush()
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sys.stderr.write(f"[easyocr_worker] 处理失败: {e}\n")
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sys.stderr.flush()
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def _cleanup_gpu():
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"""退出时清理 CUDA/GPU 内存，防止子进程被 kill 后 GPU 内存泄漏到系统。"""
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try:
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import gc
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gc.collect()
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except Exception:
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pass
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try:
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import torch
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception:
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pass
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def _signal_handler(signum, frame):
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"""捕获 SIGTERM/SIGINT，先清理 GPU 再退出（proc.terminate() 触发此处理）。"""
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_cleanup_gpu()
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sys.exit(0)
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# 注册清理钩子：atexit（正常退出）+ signal（SIGTERM/SIGINT）
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import atexit
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atexit.register(_cleanup_gpu)
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try:
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import signal
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signal.signal(signal.SIGTERM, _signal_handler)
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signal.signal(signal.SIGINT, _signal_handler)
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except Exception:
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pass
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if __name__ == "__main__":
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try:
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_main()
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except Exception as e:
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_flush_exit(f"_main 崩溃: {e}\n{traceback.format_exc()}")
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finally:
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_cleanup_gpu()
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