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paddleocr

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This commit is contained in:
ninghongbin
2025-09-09 09:42:23 +08:00
parent 2571da3c2d
commit 0fe49bf829
1 changed files with 84 additions and 29 deletions
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113
core/ocr.py
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@ -3,14 +3,21 @@ import cv2
import gc
import time
import threading
from rapidocr import RapidOCR
import numpy as np
from paddleocr import PaddleOCR
from service.sensitive_service import get_all_sensitive_words
# 解决NumPy 1.20+版本中np.int已移除的兼容性问题
try:
if not hasattr(np, 'int'):
np.int = int
except Exception as e:
print(f"处理NumPy兼容性时出错: {e}")
# 全局变量
_ocr_engine = None
_forbidden_words = set()
_conf_threshold = 0.5
ocr_config_path = os.path.join(os.path.dirname(__file__), "config", "config.yaml")
# 资源管理变量
_ref_count = 0
@ -19,6 +26,9 @@ _lock = threading.Lock()
_release_timeout = 5 # 30秒无使用则释放
_is_releasing = False # 标记是否正在释放
# 并行处理配置
_max_workers = 4 # 并行处理的线程数
# 调试用计数器
_debug_counter = {
"created": 0,
@ -35,9 +45,6 @@ def _release_engine():
try:
_is_releasing = True
# 如果有释放方法则调用
if hasattr(_ocr_engine, 'release'):
_ocr_engine.release()
_ocr_engine = None
_debug_counter["released"] += 1
print(f"OCR engine released. Stats: {_debug_counter}")
@ -52,8 +59,9 @@ def _release_engine():
except ImportError:
pass
try:
import tensorflow as tf
tf.keras.backend.clear_session()
import paddle
if paddle.is_compiled_with_cuda():
paddle.device.cuda.empty_cache()
except ImportError:
pass
finally:
@ -61,12 +69,11 @@ def _release_engine():
def _monitor_thread():
"""监控线程、优化检查逻辑"""
"""监控线程，优化检查逻辑"""
global _ref_count, _last_used_time, _ocr_engine
while True:
time.sleep(5) # 每5秒检查一次
with _lock:
# 只有当引擎存在、没有引用且超时、才释放
if _ocr_engine and _ref_count == 0 and not _is_releasing:
elapsed = time.time() - _last_used_time
if elapsed > _release_timeout:
@ -91,25 +98,18 @@ def load_model():
print(f"Forbidden words load error: {e}")
return False
# 验证配置文件
if not os.path.exists(ocr_config_path):
print(f"OCR config not found: {ocr_config_path}")
return False
return True
def detect(frame):
"""OCR检测、优化引用计数管理"""
global _ocr_engine, _forbidden_words, _conf_threshold, _ref_count, _last_used_time
"""OCR检测，支持并行处理"""
global _ocr_engine, _forbidden_words, _conf_threshold, _ref_count, _last_used_time, _max_workers
# 验证前置条件
if not _forbidden_words:
return (False, "违禁词未初始化")
if frame is None or frame.size == 0:
return (False, "无效帧数据")
if not os.path.exists(ocr_config_path):
return (False, f"OCR配置文件不存在: {ocr_config_path}")
# 增加引用计数并获取引擎实例
engine = None
@ -121,15 +121,22 @@ def detect(frame):
# 初始化引擎（如果未初始化且不在释放中）
if not _ocr_engine and not _is_releasing:
try:
_ocr_engine = RapidOCR(config_path=ocr_config_path)
# 初始化PaddleOCR，设置并行处理参数
_ocr_engine = PaddleOCR(
use_angle_cls=True,
lang="ch",
show_log=False,
use_gpu=True,
max_text_length=1024,
threads=_max_workers
)
_debug_counter["created"] += 1
print(f"OCR engine initialized. Stats: {_debug_counter}")
print(f"PaddleOCR engine initialized with {_max_workers} workers. Stats: {_debug_counter}")
except Exception as e:
print(f"OCR model load failed: {e}")
_ref_count -= 1 # 恢复引用计数
_ref_count -= 1
return (False, f"引擎初始化失败: {str(e)}")
# 获取当前引擎引用
engine = _ocr_engine
# 检查引擎是否可用
@ -140,15 +147,56 @@ def detect(frame):
try:
# 执行OCR检测
ocr_res = engine(frame)
ocr_res = engine.ocr(frame, cls=True)
# 验证OCR结果格式
if not ocr_res or not hasattr(ocr_res, 'txts') or not hasattr(ocr_res, 'scores'):
if not ocr_res or not isinstance(ocr_res, list):
return (False, "无OCR结果")
# 处理OCR结果
texts = [t.strip() for t in ocr_res.txts if t and isinstance(t, str)]
confs = [c for c in ocr_res.scores if c and isinstance(c, (int, float))]
# 处理OCR结果 - 兼容多种格式
texts = []
confs = []
for line in ocr_res:
if line is None:
continue
# 处理line可能是列表或直接是文本信息的情况
if isinstance(line, list):
items_to_process = line
else:
items_to_process = [line]
for item in items_to_process:
# 跳过纯数字列表（可能是坐标信息）
if isinstance(item, list) and all(isinstance(x, (int, float)) for x in item):
continue
# 处理元组形式的文本和置信度 (text, confidence)
if isinstance(item, tuple) and len(item) == 2:
text, conf = item
if isinstance(text, str) and isinstance(conf, (int, float)):
texts.append(text.strip())
confs.append(float(conf))
continue
# 处理列表形式的[坐标信息, (text, confidence)]
if isinstance(item, list) and len(item) >= 2:
# 尝试从列表中提取文本和置信度
text_data = item[1]
if isinstance(text_data, tuple) and len(text_data) == 2:
text, conf = text_data
if isinstance(text, str) and isinstance(conf, (int, float)):
texts.append(text.strip())
confs.append(float(conf))
continue
elif isinstance(text_data, str):
# 只有文本没有置信度的情况
texts.append(text_data.strip())
confs.append(1.0) # 默认最高置信度
continue
# 无法识别的格式，记录日志
print(f"无法解析的OCR结果格式: {item}")
if len(texts) != len(confs):
return (False, "OCR结果格式异常")
@ -178,9 +226,16 @@ def detect(frame):
return (False, f"检测错误: {str(e)}")
finally:
# 减少引用计数、确保线程安全
# 减少引用计数，确保线程安全
with _lock:
_ref_count = max(0, _ref_count - 1)
# 持续使用时更新最后使用时间
if _ref_count > 0:
_last_used_time = time.time()
def batch_detect(frames):
"""批量检测接口，充分利用并行能力"""
results = []
for frame in frames:
results.append(detect(frame))
return results