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ninghongbin
2025-09-05 17:23:50 +08:00
parent 30bf7c9fcb
commit 9b3d20511a
3 changed files with 482 additions and 85 deletions
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147
core/all.py
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@ -1,45 +1,136 @@
from core.ocr import load_model as ocrLoadModel, detect as ocrDetect
from core.face import load_model as faceLoadModel, detect as faceDetect
from core.yolo import load_model as yoloLoadModel, detect as yoloDetect
from concurrent.futures import ThreadPoolExecutor, Future
import threading
import cv2
import numpy as np
# 添加一个标记变量，用于监控load_model是否已被调用
_model_loaded = False
# -------------------------- 核心配置参数 --------------------------
MAX_WORKERS = 6 # 线程池最大线程数
DETECTION_ORDER = ["yolo", "face", "ocr"] # 检测优先级顺序
TIMEOUT = 30 # 检测超时时间(秒)
# -------------------------- 全局状态管理 --------------------------
_executor = None # 线程池实例
_model_loaded = False # 模型加载状态标记
_model_lock = threading.Lock() # 模型加载线程锁
_executor_lock = threading.Lock() # 线程池初始化锁
_task_counter = 0 # 任务计数器
_task_counter_lock = threading.Lock() # 任务计数锁
# -------------------------- 工具函数 --------------------------
def _get_next_task_id():
"""获取唯一任务ID，用于日志追踪"""
global _task_counter
with _task_counter_lock:
_task_counter += 1
return _task_counter
# -------------------------- 模型加载 --------------------------
def load_model():
"""加载所有检测模型并初始化线程池（仅执行一次）"""
global _model_loaded
if not _model_loaded:
with _model_lock:
if not _model_loaded:
print("=== 开始加载检测模型 ===")
# 如果已经调用过，直接忽略
if _model_loaded:
return
# 首次调用时加载模型
ocrLoadModel()
faceLoadModel()
# 按顺序加载模型
print("加载YOLO模型...")
yoloLoadModel()
# 标记为已调用
print("加载人脸检测模型...")
faceLoadModel()
print("加载OCR模型...")
ocrLoadModel()
_model_loaded = True
print("=== 所有模型加载完成 ===")
# 初始化线程池
_init_thread_pool()
def detect(frame):
# 先进行YOLO检测
yolo_flag, yolo_result = yoloDetect(frame)
print("YOLO检测结果：", yolo_result)
if yolo_flag:
return (True, yolo_result, "yolo")
# -------------------------- 线程池管理 --------------------------
def _init_thread_pool():
"""初始化线程池（仅内部调用）"""
global _executor
with _executor_lock:
if _executor is None:
_executor = ThreadPoolExecutor(
max_workers=MAX_WORKERS,
thread_name_prefix="DetectionThread"
)
print(f"=== 线程池初始化完成，最大线程数: {MAX_WORKERS} ===")
# YOLO未检测到，进行人脸检测
face_flag, face_result = faceDetect(frame)
print("人脸检测结果：", face_result)
if face_flag:
return (True, face_result, "face")
# 人脸未检测到，进行OCR检测
ocr_flag, ocr_result = ocrDetect(frame)
print("OCR检测结果：", ocr_result)
if ocr_flag:
return (True, ocr_result, "ocr")
def shutdown():
"""关闭线程池，释放资源"""
global _executor
with _executor_lock:
if _executor is not None:
_executor.shutdown(wait=True)
_executor = None
print("=== 线程池已安全关闭 ===")
# -------------------------- 检测逻辑实现 --------------------------
def _detect_in_thread(frame: np.ndarray, task_id: int) -> tuple:
"""在子线程中执行检测逻辑"""
thread_name = threading.current_thread().name
print(f"任务[{task_id}] 开始执行，线程: {thread_name}")
try:
# 按照优先级执行检测
for detector in DETECTION_ORDER:
if detector == "yolo":
flag, result = yoloDetect(frame)
elif detector == "face":
flag, result = faceDetect(frame)
elif detector == "ocr":
flag, result = ocrDetect(frame)
else:
flag, result = False, None
print(f"任务[{task_id}] {detector}检测结果: {'成功' if flag else '失败'}")
if flag:
print(f"任务[{task_id}] 完成检测，使用检测器: {detector}")
return (True, result, detector, task_id)
# 所有检测器均未检测到结果
print(f"任务[{task_id}] 所有检测器均未检测到内容")
return (False, "未检测到任何内容", "none", task_id)
except Exception as e:
print(f"任务[{task_id}] 检测过程发生错误: {str(e)}")
return (False, f"检测错误: {str(e)}", "error", task_id)
# -------------------------- 外部调用接口 --------------------------
def detect(frame: np.ndarray) -> Future:
"""
提交检测任务到线程池
参数:
frame: 待检测图像(ndarray格式，cv2.imdecode生成)
返回:
Future对象，通过result()方法获取检测结果
"""
# 确保模型已加载
if not _model_loaded:
print("警告: 模型尚未加载，将自动加载")
load_model()
# 生成任务ID
task_id = _get_next_task_id()
# 提交任务到线程池
future = _executor.submit(_detect_in_thread, frame, task_id)
print(f"任务[{task_id}]： 已提交到线程池")
return future
# 所有检测都未检测到
return (False, "未检测到任何内容", "none")

220
core/face.py
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@ -1,27 +1,183 @@
import os
import numpy as np
import cv2
from PIL import Image # 确保正确导入Image类
import gc
import time
import threading
from PIL import Image
from insightface.app import FaceAnalysis
# 导入获取人脸信息的服务
from service.face_service import get_all_face_name_with_eigenvalue
# 用于检查GPU状态
try:
import pynvml
pynvml.nvmlInit()
_nvml_available = True
except ImportError:
print("警告: pynvml库未安装，无法检测GPU状态，将默认使用0号GPU")
_nvml_available = False
# 全局变量
_face_app = None
_known_faces_embeddings = {} # 存储姓名到特征值的映射
_known_faces_names = [] # 存储所有已知姓名
_using_gpu = False # 标记是否使用GPU
_used_gpu_id = -1 # 记录当前使用的GPU ID
# 资源管理变量
_ref_count = 0
_last_used_time = 0
_lock = threading.Lock()
_release_timeout = 8 # 5秒无使用则释放
_is_releasing = False # 标记是否正在释放
# 调试用计数器
_debug_counter = {
"created": 0,
"released": 0,
"detected": 0
}
def load_model():
"""加载人脸识别模型及已知人脸特征库"""
global _face_app, _known_faces_embeddings, _known_faces_names
def check_gpu_availability(gpu_id, threshold=0.7):
"""检查指定GPU是否可用（内存使用率低于阈值）"""
if not _nvml_available:
return True # 无法检测时默认认为可用
try:
handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
usage = mem_info.used / mem_info.total
# 内存使用率低于阈值则认为可用
return usage < threshold
except Exception as e:
print(f"检查GPU {gpu_id} 状态时出错: {e}")
return False
def select_best_gpu(preferred_gpus=[0, 1]):
"""选择最佳可用GPU，严格按照首选列表顺序检查，优先使用0号GPU"""
# 首先检查首选GPU列表
for gpu_id in preferred_gpus:
try:
# 检查GPU是否存在
if _nvml_available:
pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
# 检查GPU是否可用
if check_gpu_availability(gpu_id):
print(f"GPU {gpu_id} 可用，将使用该GPU")
return gpu_id
else:
if gpu_id == 0:
print(f"GPU 0 内存使用率过高（繁忙），尝试切换到其他GPU")
except Exception as e:
print(f"GPU {gpu_id} 不存在或无法访问: {e}")
continue
# 如果所有首选GPU都不可用，返回-1表示使用CPU
print("所有指定的GPU都不可用，将使用CPU进行计算")
return -1
def _release_engine():
"""释放人脸识别引擎资源"""
global _face_app, _is_releasing, _known_faces_embeddings, _known_faces_names
if not _face_app or _is_releasing:
return
try:
_is_releasing = True
# 释放InsightFace资源
if hasattr(_face_app, 'model'):
# 清除模型资源
_face_app.model = None
_face_app = None
# 清空人脸数据
_known_faces_embeddings.clear()
_known_faces_names.clear()
_debug_counter["released"] += 1
print(f"Face recognition engine released. Stats: {_debug_counter}")
# 清理GPU缓存
gc.collect()
try:
import torch
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
except ImportError:
pass
try:
import tensorflow as tf
tf.keras.backend.clear_session()
except ImportError:
pass
finally:
_is_releasing = False
def _monitor_thread():
"""监控线程，检查并释放超时未使用的资源"""
global _ref_count, _last_used_time, _face_app
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 _face_app and _ref_count == 0 and not _is_releasing:
elapsed = time.time() - _last_used_time
if elapsed > _release_timeout:
print(f"Idle timeout ({elapsed:.1f}s > {_release_timeout}s), releasing face engine")
_release_engine()
def load_model(prefer_gpu=True, preferred_gpus=[0, 1]):
"""加载人脸识别模型及已知人脸特征库，默认优先使用0号GPU"""
global _face_app, _known_faces_embeddings, _known_faces_names, _using_gpu, _used_gpu_id
# 确保监控线程只启动一次
if not any(t.name == "FaceMonitor" for t in threading.enumerate()):
threading.Thread(target=_monitor_thread, daemon=True, name="FaceMonitor").start()
print("Face monitor thread started")
# 如果正在释放中，等待释放完成
while _is_releasing:
time.sleep(0.1)
# 如果已经初始化，直接返回
if _face_app:
return True
# 初始化InsightFace模型
try:
_face_app = FaceAnalysis(name='buffalo_l', root=os.path.expanduser('~/.insightface'))
_face_app.prepare(ctx_id=0, det_size=(640, 640))
# 初始化InsightFace
print("正在初始化InsightFace人脸识别引擎...")
_face_app = FaceAnalysis(name='buffalo_l', root='~/.insightface')
# 选择合适的GPU，默认优先使用0号
ctx_id = 0
if prefer_gpu:
ctx_id = select_best_gpu(preferred_gpus)
_using_gpu = ctx_id != -1
_used_gpu_id = ctx_id if _using_gpu else -1
if _using_gpu:
print(f"成功初始化，使用GPU {ctx_id} 进行计算")
else:
print("成功初始化，使用CPU进行计算")
# 准备模型
_face_app.prepare(ctx_id=ctx_id, det_size=(640, 640))
print("InsightFace人脸识别引擎初始化成功。")
_debug_counter["created"] += 1
print(f"Face engine initialized. Stats: {_debug_counter}")
except Exception as e:
print(f"Face model load failed: {e}")
print(f"初始化失败: {e}")
return False
# 从服务获取所有人脸姓名和特征值
@ -62,19 +218,52 @@ def load_model():
def detect(frame, threshold=0.4):
"""检测并识别人脸，返回结果元组(是否匹配到已知人脸, 结果字符串)"""
global _face_app, _known_faces_embeddings, _known_faces_names
global _face_app, _known_faces_embeddings, _known_faces_names, _using_gpu, _used_gpu_id
global _ref_count, _last_used_time
if not _face_app or not _known_faces_names or frame is None:
return (False, "未初始化或无效帧")
# 验证前置条件
if frame is None or frame.size == 0:
return (False, "无效帧数据")
# 增加引用计数并获取引擎实例
engine = None
with _lock:
_ref_count += 1
_last_used_time = time.time()
_debug_counter["detected"] += 1
# 初始化引擎（如果未初始化且不在释放中）
if not _face_app and not _is_releasing:
if not load_model(prefer_gpu=True):
_ref_count -= 1 # 恢复引用计数
return (False, "引擎初始化失败")
# 获取当前引擎引用
engine = _face_app
# 检查引擎是否可用
if not engine or not _known_faces_names:
with _lock:
_ref_count = max(0, _ref_count - 1)
return (False, "人脸识别引擎不可用或未初始化")
try:
# 如果使用GPU，确保输入帧在处理前是连续的数组
if _using_gpu and not frame.flags.contiguous:
frame = np.ascontiguousarray(frame)
faces = _face_app.get(frame)
except Exception as e:
print(f"Face detect error: {e}")
# 检测到错误时尝试重新选择GPU并重新初始化
print("尝试重新选择GPU并重新初始化...")
with _lock:
_ref_count = max(0, _ref_count - 1)
load_model(prefer_gpu=True) # 重新初始化时保持默认GPU优先级
return (False, f"检测错误: {str(e)}")
result_parts = []
has_matched = False # 新增标记：是否有匹配到的已知人脸
has_matched = False # 标记是否有匹配到的已知人脸
for face in faces:
# 特征归一化
@ -109,5 +298,12 @@ def detect(frame, threshold=0.4):
else:
result_str = "; ".join(result_parts)
# 第一个返回值改为：是否匹配到已知人脸
# 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()
# 第一个返回值为：是否匹配到已知人脸
return (has_matched, result_str)

148
core/ocr.py
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@ -1,5 +1,8 @@
import os
import cv2
import gc
import time
import threading
from rapidocr import RapidOCR
from service.sensitive_service import get_all_sensitive_words
@ -7,51 +10,146 @@ from service.sensitive_service import get_all_sensitive_words
_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
_last_used_time = 0
_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
# 调试用计数器
_debug_counter = {
"created": 0,
"released": 0,
"detected": 0
}
def _release_engine():
"""释放OCR引擎资源"""
global _ocr_engine, _is_releasing
if not _ocr_engine or _is_releasing:
return
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}")
# 清理GPU缓存
gc.collect()
try:
import torch
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
except ImportError:
pass
try:
import tensorflow as tf
tf.keras.backend.clear_session()
except ImportError:
pass
finally:
_is_releasing = False
def _monitor_thread():
"""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:
print(f"Idle timeout ({elapsed:.1f}s > {_release_timeout}s), releasing engine")
_release_engine()
def load_model():
"""加载OCR引擎及违禁词列表"""
global _ocr_engine, _forbidden_words, _conf_threshold
"""加载违禁词列表和初始化监控线程"""
global _forbidden_words
# 确保监控线程只启动一次
if not any(t.name == "OCRMonitor" for t in threading.enumerate()):
threading.Thread(target=_monitor_thread, daemon=True, name="OCRMonitor").start()
print("OCR monitor thread started")
# 加载违禁词
try:
_forbidden_words = get_all_sensitive_words()
print(f"Loaded {len(_forbidden_words)} forbidden words")
except Exception as e:
print(f"Forbidden words load error: {e}")
return False
# 初始化OCR引擎
# 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
try:
_ocr_engine = RapidOCR(config_path=ocr_config_path)
except Exception as e:
print(f"OCR model load failed: {e}")
return False
return True if _ocr_engine else False
return True
def detect(frame):
"""OCR检测并筛选违禁词，返回(是否检测到违禁词, 结果字符串)"""
if not _ocr_engine or not _forbidden_words or frame is None or frame.size == 0:
return (False, "未初始化或无效帧")
"""OCR检测，优化引用计数管理"""
global _ocr_engine, _forbidden_words, _conf_threshold, _ref_count, _last_used_time
# 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
with _lock:
_ref_count += 1
_last_used_time = time.time()
_debug_counter["detected"] += 1
# 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)
_debug_counter["created"] += 1
print(f"OCR engine initialized. Stats: {_debug_counter}")
except Exception as e:
print(f"OCR model load failed: {e}")
_ref_count -= 1 # 恢复引用计数
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
if not engine:
with _lock:
_ref_count -= 1
return (False, "OCR引擎不可用")
try:
ocr_res = _ocr_engine(frame)
except Exception as e:
print(f"OCR detect error: {e}")
return (False, f"检测错误: {str(e)}")
# 执行OCR检测
ocr_res = engine(frame)
# 验证OCR结果格式
if not ocr_res or not hasattr(ocr_res, 'txts') or not hasattr(ocr_res, 'scores'):
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))]
if len(texts) != len(confs):
return (False, "OCR结果格式异常")
@ -64,7 +162,7 @@ def detect(frame):
if matched:
vio_info.append(f"文本: '{txt}' 包含违禁词: {', '.join(matched)} (置信度: {conf:.2f})")
# 构建结果字符串
# 构建结果
has_text = len(texts) > 0
has_violation = len(vio_info) > 0
@ -74,3 +172,15 @@ def detect(frame):
return (True, "; ".join(vio_info))
else:
return (False, "未检测到违禁词")
except Exception as e:
print(f"OCR detect error: {e}")
return (False, f"检测错误: {str(e)}")
finally:
# 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()