import asyncio
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import base64
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import io
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import json
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import os
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import re
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import requests
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import torch
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import logging
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from PIL import Image
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from logging.handlers import RotatingFileHandler
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from transformers import AutoModelForVision2Seq, AutoProcessor
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class detect_tasks():
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def __init__(self):
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# 线程名称
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self._thread_name = ''
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# 初始化Milvus集合
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self.collection = None
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self.model = AutoModelForVision2Seq.from_pretrained(
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"./Qwen2.5-VL-3B-Instruct-GPTQ-Int4",
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device_map="auto"
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)
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self.processor = AutoProcessor.from_pretrained(
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"./Qwen2.5-VL-3B-Instruct-GPTQ-Int4",
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trust_remote_code=True,
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use_fast=True # 强制启用快速处理器
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)
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def remove_duplicate_lines(self, text):
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seen = set()
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result = []
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for line in text.split('。'): # 按句号分割
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if line.strip() and line not in seen:
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seen.add(line)
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result.append(line)
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return '。'.join(result)
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def remove_duplicate_lines_d(self, text):
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seen = set()
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result = []
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for line in text.split(','): # 按句号分割
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if line.strip() and line not in seen:
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seen.add(line)
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result.append(line)
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return '。'.join(result)
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def remove_duplicate_lines_n(self, text):
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seen = set()
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result = []
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for line in text.split('\n'): # 按句号分割
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if line.strip() and line not in seen:
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seen.add(line)
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result.append(line)
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return '。'.join(result)
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def init_logging(self, logname):
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# 创建实例专属logger
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self.logger = logging.getLogger(f"{self.__class__}_{id(self)}")
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self.logger.setLevel(logging.INFO)
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# 避免重复添加handler
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if not self.logger.handlers:
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handler = RotatingFileHandler(
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filename=os.path.join("logs", logname + '_log.log'),
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maxBytes=10 * 1024 * 1024,
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backupCount=3,
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encoding='utf-8'
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)
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formatter = logging.Formatter(
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'%(asctime)s - %(filename)s:%(lineno)d - %(funcName)s() - %(levelname)s: %(message)s'
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)
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handler.setFormatter(formatter)
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self.logger.addHandler(handler)
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def image_desc(self, image_path, ollama_url, ollama_mode="qwen2.5vl:3b"):
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image = Image.open(image_path).convert("RGB") # 替换为您的图片路径
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image = image.resize((600, 600), Image.Resampling.LANCZOS) # 高质量缩放
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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},
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{"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"},
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],
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}
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]
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# Preparation for inference
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text = self.processor.apply_chat_template(
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messages, add_generation_prompt=True
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)
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inputs = self.processor(
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text=[text],
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images=[image],
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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with torch.no_grad():
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outputs = self.model.generate(**inputs,
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max_new_tokens=300,
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do_sample=True,
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temperature=0.7,
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renormalize_logits=True
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)
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generated_ids = outputs[:, len(inputs.input_ids[0]):]
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image_text = self.processor.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)
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image_des = (image_text[0]).strip()
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return image_des
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def get_rule(self, ragurl):
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try:
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rule_text = None
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search_data = {
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"collection_name": "smart_rule",
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"query_text": "",
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"search_mode": "hybrid",
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"limit": 100,
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"weight_dense": 0.7,
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"weight_sparse": 0.3,
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"filter_expr": "",
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"output_fields": ["text"]
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}
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response = requests.post(ragurl + "/search", json=search_data)
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results = response.json().get('results')
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rule_text = ""
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ruleid = 1
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for rule in results:
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if rule['score'] >= 0:
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rule_text = rule_text + str(ruleid) + ". " + rule['entity'].get('text') + ";\n"
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ruleid = ruleid + 1
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# self.logger.info(len(rule_text))
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else:
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self.logger.info(f"{self._thread_name}线程：执行获取规则时出错:{response}")
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return rule_text
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except Exception as e:
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self.logger.info(f"{self._thread_name}线程：执行获取规则时出错:{e}")
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return None
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def image_rule_chat(self, image_des, rule_text, ollama_url, ollama_mode="qwen2.5vl:3b"):
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try:
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is_waning = 0
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# 请求内容
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content = (
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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[]")
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# API调用
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response = requests.post(
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# ollama地址
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url=f"{ollama_url}/api/chat",
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json={
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# 默认模型
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"model": ollama_mode,
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"messages": [
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{"role": "user", "content": content}
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],
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"stream": False # 关闭流式输出
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}
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)
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# 结果处理
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ret = response.json()["message"]["content"]
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if len(ret) > 2:
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is_waning = 1
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# self.logger.info(f"{self._thread_name}线程：执行规则匹配时出错:{image_des, rule_text,ret, response, ollama_url, ollama_mode}")
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return is_waning
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except Exception as e:
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self.logger.info(
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f"{self._thread_name}线程：执行规则匹配时出错:{image_des, rule_text, ollama_url, ollama_mode, e}")
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return None
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# 隐患描述
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def image_rule_chat_with_detail(self,filedata, rule_text, ollama_url, ollama_mode="qwen2.5vl:3b"):
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# API调用
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response = requests.post(
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# ollama地址
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url=f"{ollama_url}/chat",
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json={
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"prompt":"",
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# 请求内容
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"messages": [
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{
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"role": "user",
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"content": f"请根据规章制度[{filedata}]\n查找[{rule_text}]的安全隐患描述，不进行推理和think。返回信息小于800字"
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}
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],
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# 指定模型
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"llm_name": "qwen3:8b",
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"stream": False, # 关闭流式输出
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"gen_conf": {
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"temperature": 0.7, # 控制生成随机性
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"max_tokens": 800 # 最大输出长度
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}
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}
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)
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# 从json提取data字段内容
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ret = response.json()["data"]
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#result = response.json()
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#ret = result.get("data") or result.get("message", {}).get("content", "")
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# 移除<think>标签和内容
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ret = re.sub(r'<think>.*?</think>', '', ret, flags=re.DOTALL)
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# 字符串清理,移除空格,制表符,换行符,星号
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ret = ret.replace(" ", "").replace("\t", "").replace("\n", "").replace("**","")
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print(ret)
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return ret
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#处理建议
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def image_rule_chat_suggestion(self, rule_text, ollama_url, ollama_mode="qwen2.5vl:3b"):
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self.logger.info("----------------------------------------------------------------")
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# 请求内容
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content = (
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f"请根据违规内容[{rule_text}]\n进行返回处理违规建议，不进行推理和think。返回精准信息")
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response = requests.post(
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# ollama地址
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url=f"{ollama_url}/chat",
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json={
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# 指定模型
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"llm_name": "qwen3:8b",
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"messages": [
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{"role": "user", "content": content}
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],
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"stream": False # 关闭流式输出
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}
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)
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# 从json提取data字段内容
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ret = response.json()["data"]
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#result = response.json()
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#ret = result.get("data") or result.get("message", {}).get("content", "")
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# 移除<think>标签和内容
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ret = re.sub(r'<think>.*?</think>', '', ret, flags=re.DOTALL)
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# 字符串清理,移除空格,制表符,换行符,星号
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ret = ret.replace(" ", "").replace("\t", "").replace("\n", "").replace("**","")
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print(ret)
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return ret
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# RAG服务发送请求,获取知识库内容
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def get_filedata(self, searchtext, ragurl):
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search_data = {
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# 知识库集合
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"collection_name": "smart_knowledge",
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# 查询文本
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"query_text": searchtext,
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# 搜索模式
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"search_mode": "hybrid",
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# 最多返回结果
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"limit": 100,
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# 调密向量搜索权重
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"weight_dense": 0.7,
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# 稀疏向量搜索权重
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"weight_sparse": 0.3,
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# 空字符串
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"filter_expr": "",
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# 只返回 text 字段
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"output_fields": ["text"]
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}
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# 向 ragurl + "/search" 端点发送POST请求
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response = requests.post(ragurl + "/search", json=search_data)
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# 从响应中获取'results'字段
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results = response.json().get('results')
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# 初始化 text
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text = ""
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# 遍历所有结果规则(rule)，将每条规则的'entity'中的'text'字段取出.
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for rule in results:
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text = text + rule['entity'].get('text') + ";\n"
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return text
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# 向RAG系统插入json格式数据
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async def insert_json_data(self, ragurl, data):
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try:
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# data 要插入的数据
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data = {'collection_name': "smartrag", "data": data, "description": ""}
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# 服务的基地址
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requests.post(ragurl + "/insert_json_data", json=data, timeout=(0.3, 0.3))
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# self.logger.info(f"调用录像服务:{ragurl, data}")
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except Exception as e:
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# self.logger.info(f"{self._thread_name}线程：调用录像时出错:地址：{ragurl}：{e}")
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return
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def tark_do(self, res, ollamaurl, ragurl, ollamamode, ragmode):
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try:
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# 1. 从集合A获取向量和元数据
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is_waning = 0
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is_desc = 2
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# 生成图片描述
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image_des = self.image_desc(res['image_desc_path'], ollamaurl, ollamamode)
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risk_description = ""
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suggestion = ""
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# 图片描述生成成功
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if image_des:
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is_desc = 2
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# 调用规则匹配方法,判断是否预警
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is_waning = self.image_rule_chat(image_des, res['waning_value'], ollamaurl, ollamamode)
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# 如果预警,则生成隐患描述和处理建议
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if is_waning == 1:
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# 获取规章制度数据
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filedata = self.get_filedata(res['waning_value'], ragurl)
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# 生成隐患描述
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risk_description = self.image_rule_chat_with_detail(filedata,res['waning_value'], ragurl, ragmode)
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# 生成处理建议
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suggestion = self.image_rule_chat_suggestion(res['waning_value'], ragurl, ragmode)
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else:
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is_desc = 3
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# 数据组
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data = {
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"id": res['id'],
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"event_level_id": res['event_level_id'], # event_level_id
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"event_level_name": res['event_level_name'], # event_level_id
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"rule_id": res["rule_id"],
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"video_point_id": res['video_point_id'], # video_point_id
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"video_point_name": res['video_point_name'],
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"is_waning": is_waning,
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"is_desc": is_desc,
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"zh_desc_class": image_des, # text_vector
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"bounding_box": res['bounding_box'], # bounding_box
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"task_id": res['task_id'], # task_id
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"task_name": res['task_name'], # task_id
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"detect_id": res['detect_id'], # detect_id
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"detect_time": res['detect_time'], # detect_time
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"detect_num": res['detect_num'],
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"waning_value": res['waning_value'],
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"image_path": res['image_path'], # image_path
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"image_desc_path": res['image_desc_path'], # image_desc_path
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"video_path": res['video_path'],
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"text_vector": res['text_vector'],
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"risk_description": risk_description,
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"suggestion": suggestion,
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"knowledge_id": res['knowledge_id']
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}
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# 保存到milvus
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image_id = self.collection.upsert(data).primary_keys
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logging.info(image_id)
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data = {
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"id": str(image_id[0]),
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"video_point_id": res['video_point_id'],
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"video_path": res["video_point_name"],
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"zh_desc_class": image_des,
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"detect_time": res['detect_time'],
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"image_path": f"{res['image_path']}",
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"task_name": res["task_name"],
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"event_level_name": res["event_level_name"],
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"rtsp_address": f"{res['video_path']}"
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}
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# 调用rag
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asyncio.run(self.insert_json_data(ragurl, data))
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return image_id
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except Exception as e:
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self.logger.info(f"{self._thread_name}线程：执行模型解析时出错:任务：{res['id']} :{e}")
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return 0
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