#2025/7/3 #新增qwen_detect_batch.py 用于生成批量图片的启动程序，目前是测试版本 #新增qwen_thread_batc...

shidong

2025-07-03 e461346ea50a71e8beb59569ed1d1713b5d0e012

#2025/7/3
#新增qwen_detect_batch.py 用于生成批量图片的启动程序，目前是测试版本
#新增qwen_thread_batch.py 用于生成批量图片的多线程处理，目前是测试版本

 qwen_detect_batch.py

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from operator import itemgetter
import threading
import time as time_sel
from typing import Dict
from qwen_thread_batch import qwen_thread_batch
import requests
import os
import logging
from pymilvus import connections, Collection
from logging.handlers import RotatingFileHandler
import get_mem

class ThreadPool:
    def __init__(self):
        #读取配置文件
        self.config = {}
        with open('./conf.txt', 'r', encoding='utf-8') as file:
            for line in file:
                # 去除每行的首尾空白字符（包括换行符）
                line = line.strip()
                # 跳过空行
                if not line:
                    continue
                # 分割键和值
                if '=' in line:
                    key, value = line.split('=', 1)
                    # 去除键和值的首尾空白字符
                    key = key.strip()
                    value = value.strip()
                    # 将键值对添加到字典中
                    self.config[key] = value
        # 配置日志
        # 确保日志目录存在
        log_dir = "logs"
        os.makedirs(log_dir, exist_ok=True)
        self.threads: Dict[str, threading.Thread] = {}
        self.lock = threading.Lock()

        # 初始化Milvus集合
        connections.connect("default", host=self.config.get("milvusurl"), port=self.config.get("milvusport"))
        # 加载集合
        self.collection = Collection(name="smartobject")
        self.collection.load()
        self.pool = qwen_thread_batch(int(self.config.get("threadnum")), self.config,"/home/debian/Qwen2.5-VL-3B-Instruct-GPTQ-Int4")
        #是否更新
        self._isupdate = False

        # 初始化共享内存
        get_mem.smem_init()

        # 配置日志
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(filename)s:%(lineno)d - %(funcName)s() - %(levelname)s: %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=[
                # 按大小轮转的日志文件（最大10MB，保留3个备份）
                RotatingFileHandler(
                    filename=os.path.join(log_dir, 'start_log.log'),
                    maxBytes=10 * 1024 * 1024,  # 10MB
                    backupCount=3,
                    encoding='utf-8'
                ),
                # 同时输出到控制台
                logging.StreamHandler()
            ]
        )

    #启动线程
    def safe_start(self, target_func, camera_id):
        """线程安全启动方法"""
        def wrapped():
            thread_name = threading.current_thread().name
            try:
                target_func(camera_id)
            except Exception as e:
                logging.error(f"线程异常: {str(e)}", exc_info=True)

        with self.lock:  # 确保线程安全创建
            t = threading.Thread(
                target=wrapped,
                daemon=True  # 设置为守护线程
            )
            t.start()
            self.threads[camera_id] = t
            return t

    # 启动线程任务
    def worker(self, camera_id):
        while True:
            try:
                res_a = self.collection.query(
                    expr=f"is_desc == 0 and video_point_id=={camera_id}",
                    output_fields=["id", "zh_desc_class", "text_vector", "bounding_box", "video_point_name", "task_id",
                                   "task_name", "event_level_id", "event_level_name",
                                   "video_point_id", "detect_num", "is_waning", "is_desc", "waning_value", "rule_id",
                                   "detect_id",
                                   "detect_time", "image_path", "image_desc_path", "video_path"],
                    consistency_level="Strong",
                    order_by_field="id",  # 按id字段排序
                    order_by_type="desc"  # 降序排列
                )

                # 读取共享内存中的图片
                # image_id = get_mem.smem_read_frame_qianwen(camera_id)
                if len(res_a) > 0:
                    sorted_results = sorted(res_a, key=itemgetter("id"), reverse=True)
                    # 查询前N个最大的ID
                    res_a = sorted_results[:int(self.config.get("detectnum"))-1]
                    res_data = []
                    for res in res_a:
                        data = {
                            "id": res['id'],
                            "event_level_id": res['event_level_id'],  # event_level_id
                            "event_level_name": res['event_level_name'],  # event_level_id
                            "rule_id": res["rule_id"],
                            "video_point_id": res['video_point_id'],  # video_point_id
                            "video_point_name": res['video_point_name'],
                            "is_waning": 0,
                            "is_desc": 1,
                            "zh_desc_class": res['zh_desc_class'],  # text_vector
                            "bounding_box": res['bounding_box'],  # bounding_box
                            "task_id": res['task_id'],  # task_id
                            "task_name": res['task_name'],  # task_id
                            "detect_id": res['detect_id'],  # detect_id
                            "detect_time": res['detect_time'],  # detect_time
                            "detect_num": res['detect_num'],
                            "waning_value": res['waning_value'],
                            "image_path": res['image_path'],  # image_path
                            "image_desc_path": res['image_desc_path'],  # image_desc_path
                            "video_path": res['video_path'],
                            "text_vector": res['text_vector']
                        }
                        # logging.info(f"读取图像成功: {res['id']}")
                        # 保存到milvus
                        image_id = self.collection.upsert(data).primary_keys
                        res['id'] = image_id[0]
                        res_data.append(res)
                        # logging.info(f"读取图像成功: {image_id}")
                    self.pool.submit(res_data)
                        # image_id = pool.tark_do(image_id,self.config.get("ragurl"),self.config.get("ragmode"),self.config.get("max_tokens"))
                        # logging.info(f"处理图像成功: {image_id}")
                    sorted_results = None
            except Exception as e:
                logging.info(f"{camera_id}线程错误:{e}")

    #调用是否需要更新
    def isUpdate(self):
        try:
            # 定义请求的 URL
            url = self.config.get("isupdateurl")
            # 发送 GET 请求
            response = requests.get(url)

            # 检查响应状态码
            if response.status_code == 200:
                data = response.json().get("data")
                if data.get("isChange") == 1:
                    return True
                else:
                    return False
        except Exception as e:
            logging.info(f"调用是否需要更新时出错:URL:{self.config.get('isupdateurl')}:{e}")
            return False

    #修改是否更新状态
    def update_status(self):
        try:
            # 更新状态
            url = self.config.get("updatestatusurl")
            # 发送 GET 请求
            response = requests.post(url)
            # 检查响应状态码
            if response.status_code == 200:
                return True
            else:
                return False
        except Exception as e:
            logging.info(f"修改是否更新状态时出错:URL:{self.config.get('updatestatusurl')}:{e}")
            return False

    def shutdown_all(self) -> None:
        """清理所有线程"""
        with self.lock:
            for camera_id, thread in list(self.threads.items()):
                if thread.is_alive():
                    thread.join(timeout=1)
                del self.threads[camera_id]

    #获取任务
    def getTaskconf(self,isupdate):
        try:
            # 定义请求的 URL
            url = self.config.get("gettaskconfurl")
            # 发送 GET 请求
            response = requests.get(url)
            # 检查响应状态码
            if response.status_code == 200:
                data = response.json()
                if isupdate:
                    # 更新状态
                    self.update_status()
                return data.get("data")
            else:
                return []
        except Exception as e:
            logging.info(f"调用获取任务时出错:URL:{self.config.get('gettaskconfurl')}:{e}")
            return []

# 使用示例
if __name__ == "__main__":
    pool = ThreadPool()
    is_init = True
    camera_data = pool.getTaskconf(False)
    while True:
        try:
            pool._isupdate = False  # 是否更新数据
            # 是否需要更新任务数据
            if pool.isUpdate():
                # 获取摄像机任务
                camera_data = pool.getTaskconf(True)
                pool._isupdate = True  # 更新数据

            if is_init:
                if camera_data:
                    for camera in camera_data:
                        thread = pool.threads.get(camera.get("camera_id"))
                        if not thread:
                            logging.info(f"开始创建{camera.get('camera_id')}线程")
                            pool.safe_start(pool.worker, camera.get('camera_id'))
                            logging.info(f"{camera.get('camera_id')}线程创建完毕")

            if pool._isupdate:
                logging.info(f"更新线程开始")
                pool.shutdown_all()
                if camera_data:
                    for camera in camera_data:
                        thread = pool.threads.get(camera.get("camera_id"))
                        if not thread:
                            logging.info(f"开始创建{camera.get('camera_id')}线程")
                            pool.safe_start(pool.worker, camera.get('camera_id'))
                            logging.info(f"{camera.get('camera_id')}线程创建完毕")

                logging.info(f"更新线程结束")

            is_init = False
            time_sel.sleep(1)
        except Exception as e:
            logging.info(f"主线程未知错误:{e}")

 qwen_thread_batch.py

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import time
from concurrent.futures import ThreadPoolExecutor
import threading

import torch
from PIL import Image
from pymilvus import connections, Collection
from datetime import datetime
import os
import requests
import asyncio
import logging
import re
from logging.handlers import RotatingFileHandler

from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig


class qwen_thread_batch:
    def __init__(self, max_workers,config,model_path):
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
        self.semaphore = threading.Semaphore(max_workers)
        self.max_workers = max_workers
        # 初始化Milvus集合
        connections.connect("default", host=config.get("milvusurl"), port=config.get("milvusport"))
        # 加载集合
        self.collection = Collection(name="smartobject")
        self.collection.load()

        self.config = config
        self.model_pool = []
        self.lock_pool = [threading.Lock() for _ in range(max_workers)]
        quant_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_use_double_quant=True
        )
        for i in range(max_workers):
            model = AutoModelForVision2Seq.from_pretrained(
                model_path,
                device_map="cuda:1",
                trust_remote_code=True,
                quantization_config=quant_config,
                use_flash_attention_2=True,

            ).eval()
            self.model_pool.append(model)

        # 共享的处理器 (线程安全)
        self.processor = AutoProcessor.from_pretrained(model_path,use_fast=True)


        # 创建实例专属logger
        self.logger = logging.getLogger(f"{self.__class__}_{id(self)}")
        self.logger.setLevel(logging.INFO)
        # 避免重复添加handler
        if not self.logger.handlers:
            handler = RotatingFileHandler(
                filename=os.path.join("logs", 'thread_log.log'),
                maxBytes=10 * 1024 * 1024,
                backupCount=3,
                encoding='utf-8'
            )
            formatter = logging.Formatter(
                '%(asctime)s - %(filename)s:%(lineno)d - %(funcName)s() - %(levelname)s: %(message)s'
            )
            handler.setFormatter(formatter)
            self.logger.addHandler(handler)

    def submit(self,res_a):
        # 尝试获取信号量（非阻塞）
        acquired = self.semaphore.acquire(blocking=False)

        if not acquired:
            #self.logger.info(f"线程池已满，等待空闲线程... (当前活跃: {self.max_workers - self.semaphore._value}/{self.max_workers})")
            # 阻塞等待直到有可用线程
            self.semaphore.acquire(blocking=True)

        future = self.executor.submit(self._wrap_task, res_a)
        future.add_done_callback(self._release_semaphore)
        return future

    def _wrap_task(self, res_a):
        try:
            self.tark_do(res_a, self.config.get("ragurl"), self.config.get("ragmode"), self.config.get("max_tokens"))
        except Exception as e:
            self.logger.info(f"处理出错: {e}")
            raise

    def tark_do(self,res_a,ragurl,rag_mode,max_tokens):
        try :
            current_time = datetime.now()
            # 1. 从集合A获取向量和元数据
            is_waning = 0
            desc_list = self.image_desc(res_a)
            if desc_list:
                for desc,res in zip(desc_list,res_a):
                    if desc:
                        # rule_text = self.get_rule(ragurl)
                        is_waning = self.image_rule_chat(desc,res['waning_value'],ragurl,rag_mode,max_tokens)
                        is_desc = 2
                    else:
                        is_waning = 0
                        is_desc = 3
                    data = {
                        "id": res['id'],
                        "event_level_id": res['event_level_id'],  # event_level_id
                        "event_level_name": res['event_level_name'],  # event_level_id
                        "rule_id": res["rule_id"],
                        "video_point_id": res['video_point_id'],  # video_point_id
                        "video_point_name": res['video_point_name'],
                        "is_waning": is_waning,
                        "is_desc": is_desc,
                        "zh_desc_class": desc,  # text_vector
                        "bounding_box": res['bounding_box'],  # bounding_box
                        "task_id": res['task_id'],  # task_id
                        "task_name": res['task_name'],  # task_id
                        "detect_id": res['detect_id'],  # detect_id
                        "detect_time": res['detect_time'],  # detect_time
                        "detect_num": res['detect_num'],
                        "waning_value": res['waning_value'],
                        "image_path": res['image_path'],  # image_path
                        "image_desc_path": res['image_desc_path'],  # image_desc_path
                        "video_path": res['video_path'],
                        "text_vector": res['text_vector']
                    }
                    # 保存到milvus
                    image_id = self.collection.upsert(data).primary_keys
                    #self.logger.info(f"{res['id']}--{image_id}:{desc}")
                    if is_desc == 2:
                        data = {
                            "id": str(image_id[0]),
                            "video_point_id": res['video_point_id'],
                            "video_path": res["video_point_name"],
                            "zh_desc_class": desc,
                            "detect_time": res['detect_time'],
                            "image_path": f"{res['image_path']}",
                            "task_name": res["task_name"],
                            "event_level_name": res["event_level_name"],
                            "rtsp_address": f"{res['video_path']}"
                        }
                        # 调用rag
                        asyncio.run(self.insert_json_data(ragurl, data))
            self.logger.info(f"处理完毕:{datetime.now() - current_time}:{len(res_a)}")
        except Exception as e:
            self.logger.info(f"线程：执行模型解析时出错:任务：{e}")
            return 0

    def image_desc(self, res_data):
        try:
            model, lock = self._acquire_model()
            image_data = []
            for res in res_data:
                # 2. 处理图像
                image = Image.open(f"{res['image_desc_path']}").convert("RGB")  # 替换为您的图片路径
                image = image.resize((448, 448), Image.Resampling.LANCZOS)  # 高质量缩放
                image_data.append(image)

            messages = [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image",
                        },
                        {"type": "text", "text": "请详细描述图片中的目标信息及特征。返回格式为整段文字描述"},
                    ],
                }
            ]
            # Preparation for inference
            text = self.processor.apply_chat_template(
                messages, add_generation_prompt=True
            )
            inputs = self.processor(
                text=[text] * len(image_data),
                images=[image_data],
                padding=True,
                return_tensors="pt",
            )
            inputs = inputs.to("cuda:1")
            current_time = datetime.now()
            with torch.inference_mode():
                outputs = model.generate(**inputs,
                                                   max_new_tokens=50,
                                                   do_sample=False,
                                                   temperature=0.7,
                                                   top_k=40,
                                                   num_beams=1,
                                                   repetition_penalty= 1.1
                                                   )
            generated_ids = outputs[:, len(inputs.input_ids[0]):]
            image_text = self.processor.batch_decode(
                generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
            )
            image_des = []
            for text in image_text:
                image_des.append(text)
            return image_des
        except Exception as e:
            self.logger.info(f"线程：执行图片描述时出错:{e}")
        finally:
            # 4. 释放模型
            self._release_model(model)
            torch.cuda.empty_cache()

    def get_rule(self,ragurl):
        try:
            rule_text = None
            search_data = {
                "collection_name": "smart_rule",
                "query_text": "",
                "search_mode": "hybrid",
                "limit": 100,
                "weight_dense": 0.7,
                "weight_sparse": 0.3,
                "filter_expr": "",
                "output_fields": ["text"]
            }
            response = requests.post(ragurl + "/search", json=search_data)
            results = response.json().get('results')
            rule_text = ""
            ruleid = 1
            for rule in results:
                if rule['score'] >= 0:
                    rule_text = rule_text + str(ruleid) + ". " + rule['entity'].get('text') + ";\n"
                    ruleid = ruleid + 1
            # self.logger.info(len(rule_text))
            else:
                self.logger.info(f"线程：执行获取规则时出错:{response}")
            return rule_text
        except Exception as e:
            self.logger.info(f"线程：执行获取规则时出错:{e}")
            return None

    def image_rule_chat(self, image_des,rule_text, ragurl, rag_mode,max_tokens):
        try:
            content = (
                f"图片描述内容为：\n{image_des}\n规则内容：\n{rule_text}。\n请验证图片描述中是否有符合规则的内容，不进行推理和think。返回结果格式为[xxx符合的规则id]，如果没有返回[]")
            # self.logger.info(content)
            #self.logger.info(len(content))
            search_data = {
                "prompt": "",
                "messages": [
                    {
                        "role": "user",
                        "content": content
                    }
                ],
                "llm_name": rag_mode,
                "stream": False,
                "gen_conf": {
                    "temperature": 0.7,
                    "max_tokens": max_tokens
                }
            }
            response = requests.post(ragurl + "/chat", json=search_data)
            results = response.json().get('data')
            #self.logger.info(len(results))
            # self.logger.info(results)
            ret = re.sub(r'<think>.*?</think>', '', results, flags=re.DOTALL)
            ret = ret.replace(" ", "").replace("\t", "").replace("\n", "")
            is_waning = 0
            if len(ret) > 2:
                is_waning = 1
            return is_waning
        except Exception as e:
            self.logger.info(f"线程：执行规则匹配时出错:{image_des, rule_text, ragurl, rag_mode,e}")
            return None

    async def insert_json_data(self, ragurl, data):
        try:
            data = {'collection_name': "smartrag", "data": data, "description": ""}
            requests.post(ragurl + "/insert_json_data", json=data, timeout=(0.3, 0.3))
            #self.logger.info(f"调用录像服务:{ragurl, data}")
        except Exception as e:
            #self.logger.info(f"{self._thread_name}线程：调用录像时出错:地址：{ragurl}：{e}")
            return

    def _release_semaphore(self, future):
        self.semaphore.release()
        #self.logger.info(f"释放线程 (剩余空闲: {self.semaphore._value}/{self.max_workers})")

    def shutdown(self):
        """安全关闭"""
        self.executor.shutdown(wait=False)
        for model in self.model_pool:
            del model
        torch.cuda.empty_cache()

    def _acquire_model(self):
        """从池中获取一个空闲模型 (简单轮询)"""
        while True:
            for i, (model, lock) in enumerate(zip(self.model_pool, self.lock_pool)):
                if lock.acquire(blocking=False):
                    return model, lock
            time.sleep(0.1)  # 避免CPU空转

    def _release_model(self, model):
        """释放模型回池"""
        for i, m in enumerate(self.model_pool):
            if m == model:
                self.lock_pool[i].release()
                break


    def remove_duplicate_lines(self,text):
        seen = set()
        result = []
        for line in text.split('。'):  # 按句号分割
            if line.strip() and line not in seen:
                seen.add(line)
                result.append(line)
        return '。'.join(result)
    def remove_duplicate_lines_d(self,text):
        seen = set()
        result = []
        for line in text.split(','):  # 按句号分割
            if line.strip() and line not in seen:
                seen.add(line)
                result.append(line)
        return '。'.join(result)
    def remove_duplicate_lines_n(self,text):
        seen = set()
        result = []
        for line in text.split('\n'):  # 按句号分割
            if line.strip() and line not in seen:
                seen.add(line)
                result.append(line)
        return '。'.join(result)